% Encoding: UTF-8
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@article{faucris.259213527,
abstract = {Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject’s knees, high beam energy ensures the visibility of internal structures but leads to overexposure in less dense border regions. In this work, we propose to use an additional low-dose scan to correct the saturation artifacts of AEC scans. Overexposed pixels are identified in the projection images of the AEC scan using histogram-based thresholding. The saturation-free pixels from the AEC scan are combined with the skin border pixels of the low-dose scan prior to volumetric reconstruction. To compensate for patient motion between the two scans, a 3D non-rigid alignment of the projection images in a backward-forward-projection process based on fiducial marker positions is proposed. On numerical simulations, the projection combination improved the structural similarity index measure from 0.883 to 0.999. Further evaluations were performed on two in vivo subject knee acquisitions, one without and one with motion between the AEC and low-dose scans. Saturation-free reference images were acquired using a beam attenuator. The proposed method could qualitatively restore the information of peripheral tissue structures. Applying the 3D non-rigid alignment made it possible to use the projection images with inter-scan subject motion for projection image combination. The increase in radiation exposure due to the additional low-dose scan was found to be negligibly low. The presented methods allow simple but effective correction of saturation artifacts.
A considerable number of wearable system applications necessitate early event detection (EED). EED is defined as the detection of an event with as much lead time as possible. Applications include physiological (e.g., epileptic seizure or heart stroke) or biomechanical (e.g., fall movement or sports event) monitoring systems. EED for wearable systems is under-investigated in literature. Therefore, we introduce a novel EED framework for wearable systems based on hybrid Hidden Markov Models. Our study specifically targets EED based on inertial measurement unit (IMU) signals in sports. We investigate the early detection of high intensive soccer kicks, with the possible pre-kick adaptation of a soccer shoe before the shoe-ball impact in mind. We conducted a study with ten subjects and recorded 226 kicks using a custom IMU placed in a soccer shoe cavity. We evaluated our framework in terms of EED accuracy and EED latency. In conclusion, our framework delivers the required accuracy and lead times for EED of soccer kicks and can be straightforwardly adapted to other wearable system applications that necessitate EED.},
author = {Dorschky, Eva and Schuldhaus, Dominik and Koerger, Harald and Eskofier, Björn},
booktitle = {Proceedings of the 2015 ACM International Symposium on Wearable Computers},
faupublication = {yes},
keywords = {Early Event Detection; Wearable Systems; Hybrid HMM},
pages = {109-112},
peerreviewed = {unknown},
title = {{A} framework for early event detection for wearable systems},
venue = {Osaka},
year = {2015}
}
@article{faucris.266052253,
abstract = {Background: Available smartphone-based interventions for depression predominantly use evidence-based strategies from cognitive-behavioral therapy (CBT), but patient engagement and reported effect sizes are small. Recently, studies have demonstrated that smartphone-based interventions combining CBT with gamified approach-avoidance bias modification training (AAMT) can foster patient engagement and reduce symptoms of several mental health problems.
Objective: Based on these findings, we developed a gamified smartphone-based intervention, mentalis Phoenix (MT-Phoenix), and hypothesized the program would both engage patients and produce preliminary evidence for the reduction of depressive symptoms.
Methods: To test this hypothesis, we evaluated MT-Phoenix in a randomized controlled pilot trial including 77 individuals with elevated depression scores (Patient Health Questionnaire-9 scores ≥5). Participants were either instructed to train for 14 days with MT-Phoenix or assigned to a waitlist control condition. Engagement with the intervention was measured by assessing usage data. The primary outcome was reduction in depressive symptom severity at postassessment.
Results: Data from this pilot trial shows that participants in the intervention group used the smartphone-based intervention for 46% of all days (6.4/14) and reported a significantly greater reduction of depressive symptoms than did participants in the control condition (F1,74=19.34; P=.001), with a large effect size (d=1.02). Effects were sustained at a 3-month follow-up.
Conclusions: A gamified smartphone-based intervention combining CBT with AAMT may foster patient engagement and effectively target depressive symptoms. Future studies should evaluate the effectiveness of this intervention in a phase 3 trial using clinical samples. Moreover, the intervention should be compared to active control conditions.},
author = {Lukas, Christian and Eskofier, Björn and Berking, Matthias},
doi = {10.2196/16643},
faupublication = {yes},
journal = {JMIR Mental Health},
keywords = {smartphone technology; depression; cognitive behavioral therapy; approach/avoidance; gamification},
note = {CRIS-Team Scopus Importer:2021-08-06},
peerreviewed = {Yes},
title = {{A} gamified smartphone-based intervention for depression: {Randomized} {Controlled} {Pilot} {Trial}.},
volume = {8},
year = {2021}
}
@inproceedings{faucris.283169918,
abstract = {The human hand possesses a large number of degrees of freedom. Hand dexterity is encoded by the discharge times of spinal motor units (MUs). Most of our knowledge on the neural control of movement is based on the discharge times of MUs during isometric contractions. Here we designed a noninvasive framework to study spinal motor neurons during dynamic hand movements with the aim to understand the neural control of MUs during sinusoidal hand digit flexion and extension at different rates of force development. The framework included 320 high-density surface EMG electrodes placed on the forearm muscles, with markerless 3D hand kinematics extracted with deep learning, and a realistic virtual hand that displayed the motor tasks. The movements included flexion and extension of individual hand digits at two different speeds (0.5 Hz and 1.5 Hz) for 40 seconds. We found on average 4.7-1.7 MUs across participants and tasks. Most MUs showed a biphasic pattern closely mirroring the flexion and extension kinematics. Indeed, a factor analysis method (non-negative matrix factorization) was able to learn the two components (flexion/extension) with high accuracy at the individual MU level ({R}=0.87-0.12). Although most MUs were highly correlated with either flexion or extension movements, there was a smaller proportion of MUs that was not task-modulated and controlled by a different neural module (7.1% of all MUs with {R} < 0.3). This work shows a noninvasive visually guided framework to study motor neurons controlling the movement of the hand in human participants during dynamic hand digit movements.},
author = {Cakici, Andre L. and Oßwald, Marius and Souza de Oliveira, Daniela and Braun, Dominik and Simpetru, Raul C. and Kinfe, Thomas Mehari and Eskofier, Björn and Del Vecchio, Alessandro},
booktitle = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
date = {2022-07-11/2022-07-15},
doi = {10.1109/EMBC48229.2022.9870914},
faupublication = {yes},
isbn = {9781728127828},
note = {CRIS-Team Scopus Importer:2022-10-14},
pages = {4115-4118},
peerreviewed = {unknown},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{A} {Generalized} {Framework} for the {Study} of {Spinal} {Motor} {Neurons} {Controlling} the {Human} {Hand} {During} {Dynamic} {Movements}},
venue = {Glasgow, GBR},
volume = {2022-July},
year = {2022}
}
@inproceedings{faucris.307522189,
abstract = {The human hand possesses a large number of degrees of freedom. Hand
dexterity is encoded by the discharge times of spinal motor units (MUs).
Most of our knowledge on the neural control of movement is based on the
discharge times of MUs during isometric contractions. Here we designed a
noninvasive framework to study spinal motor neurons during dynamic hand
movements with the aim to understand the neural control of MUs during
sinusoidal hand digit flexion and extension at different rates of force
development. The framework included 320 high-density surface EMG
electrodes placed on the forearm muscles, with markerless 3D hand
kinematics extracted with deep learning, and a realistic virtual hand
that displayed the motor tasks. The movements included flexion and
extension of individual hand digits at two different speeds (0.5 Hz and
1.5 Hz) for 40 seconds. We found on average 4.7 +- 1.7 MUs across participants and tasks. Most MUs showed a biphasic pattern
closely mirroring the flexion and extension kinematics. Indeed, a factor
analysis method (non-negative matrix factorization) was able to learn
the two components (flexion/extension) with high accuracy at the
individual MU level (R = 0.87 +- 0.12). Although most MUs were highly correlated with either flexion or
extension movements, there was a smaller proportion of MUs that was not
task-modulated and controlled by a different neural module (7.1% of all
MUs with R < 0.3). This work shows a noninvasive visually guided framework to study
motor neurons controlling the movement of the hand in human participants
during dynamic hand digit movements.},
author = {Cakici, Andre and Oßwald, Marius and Souza de Oliveira, Daniela and Braun, Dominik and Simpetru, Raul and Kinfe, Thomas Mehari and Eskofier, Björn and Del Vecchio, Alessandro},
booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022},
date = {2022-07-11/2022-07-15},
doi = {10.1109/EMBC48229.2022.9870914},
faupublication = {yes},
isbn = {9781728127828},
pages = {4115-4118},
peerreviewed = {Yes},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{A} {Generalized} {Framework} for the {Study} of {Spinal} {Motor} {Neurons} {Controlling} the {Human} {Hand} {During} {Dynamic} {Movements}},
venue = {Scottish Event Campus, Glasgow},
year = {2022}
}
@incollection{faucris.123823524,
address = {Hamburg},
author = {Klamroth, Sarah and Steib, Simon and Gaßner, Heiko and Winkler, Jürgen and Eskofier, Björn and Klucken, Jochen and Pfeifer, Klaus},
booktitle = {Moving Minds - Crossing Boundaries in Sport Science},
editor = {Könecke, T., Preuß, H. & Schöllhorn, W.},
faupublication = {yes},
peerreviewed = {unknown},
publisher = {Czwalina},
title = {{Akute} {Anpassungen} des {Gangbildes} und {Gleichgewichts} an ein sensomotorisches {Laufbandtraining} bei {Patienten} mit {Morbus} {Parkinson}},
volume = {251},
year = {2015}
}
@article{faucris.295329613,
abstract = {Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders’ needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners’ nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients’ FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient–practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training.
0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.},
author = {Salis, Francesca and Bertuletti, Stefano and Bonci, Tecla and Caruso, Marco and Scott, Kirsty and Alcock, Lisa and Buckley, Ellen and Gazit, Eran and Hansen, Clint and Schwickert, Lars and Aminian, Kamiar and Becker, Clemens and Brown, Philip and Carsin, Anne Elie and Caulfield, Brian and Chiari, Lorenzo and D’Ascanio, Ilaria and Del Din, Silvia and Eskofier, Björn and Garcia-Aymerich, Judith and Hausdorff, Jeffrey M. and Hume, Emily C. and Kirk, Cameron and Kluge, Felix and Koch, Sarah and Küderle, Arne and Maetzler, Walter and Micó-Amigo, Encarna M. and Mueller, Arne and Neatrour, Isabel and Paraschiv-Ionescu, Anisoara and Palmerini, Luca and Yarnall, Alison J. and Rochester, Lynn and Sharrack, Basil and Singleton, David and Vereijken, Beatrix and Vogiatzis, Ioannis and Della Croce, Ugo and Mazzà, Claudia and Cereatti, Andrea},
doi = {10.3389/fbioe.2023.1143248},
faupublication = {yes},
journal = {Frontiers in Bioengineering and Biotechnology},
keywords = {distance sensors; ecological conditions; gait analysis; IMU; pressure insoles; spatial-temporal gait parameters; wearable sensors},
note = {CRIS-Team Scopus Importer:2023-06-02},
peerreviewed = {Yes},
title = {{A} multi-sensor wearable system for the assessment of diseased gait in real-world conditions},
volume = {11},
year = {2023}
}
@inproceedings{faucris.107969664,
address = {Heidelberg},
author = {Schuldhaus, Dominik and Dorn, Sabrina and Leutheuser, Heike and Tallner, Alexander and Klucken, Jochen and Eskofier, Björn},
booktitle = {The 15th International Conference on Biomedical Engineering},
date = {2013-12-04/2013-12-07},
editor = {Goh James},
faupublication = {yes},
pages = {124-127},
peerreviewed = {Yes},
publisher = {Springer},
title = {{An} {Adaptable} {Inertial} {Sensor} {Fusion}-{Based} {Approach} for {Energy} {Expenditure} {Estimation}.},
url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Schuldhaus14-AAI.pdf},
venue = {University Town, Singapore},
year = {2013}
}
@inproceedings{faucris.109689624,
abstract = {Flat-Panel C-arm Computed Tomography (CT) suffers from pixel saturation due to the detector’s limited dynamic range. We describe a novel approach to analog, non-linear tone mapping (TM) for preventing detector saturation. An analog TM operator (TMO) applies a non-linear transformation in a CMOS sensor and its inverse TMO based on 14-bit digital raw data. This is done in order to prevent overexposure and to enhance image quality to 32 bits. The method was applied to the cases of low-contrast head imaging and to that of imaging both knees. Cone-beam projection data with and without overexposure was simulated for a 200° short-scan of the knees and a 360° full-scan of a Forbild head phantom. The results show an increased correlation coefficient of 0.99 compared to 0.96 for overexposed knee data and a higher low-contrast visibility (CC=0.99) compared to linear quantization (CC=0.97},
author = {Shi, Lan and Berger, Martin and Bier, Bastian and Söll, Christopher and Röber, Jürgen and Fahrig, Rebecca and Eskofier, Björn and Maier, Andreas and Maier, Jennifer},
booktitle = {IEEE Medical Imaging Conference (MIC)},
faupublication = {yes},
keywords = {GRK-1773},
note = {elib2cris::1512001600,shi2016a},
peerreviewed = {Yes},
title = {{Analog} {Non}-{Linear} {Transformation}-{Based} {Tone} {Mapping} for {Image} {Enhancement} in {C}-arm {CT}},
venue = {Strasbourg, France},
year = {2016}
}
@article{faucris.122102684,
abstract = {In theory, kernel support vector machines (SVMs) can be reformulated to linear SVMs. This reformulation can speed up SVM classifications considerably, in particular, if the number of support vectors is high. For the widely-used Gaussian radial basis function (RBF) kernel, however, this theoretical fact is impracticable because the reproducing kernel Hilbert space (RKHS) of this kernel has infinite dimensionality. Therefore, we derive a finite-dimensional approximative feature map, based on an orthonormal basis of the kernel’s RKHS, to enable the reformulation of Gaussian RBF SVMs to linear SVMs. We show that the error of this approximative feature map decreases with factorial growth if the approximation quality is linearly increased. Experimental evaluations demonstrated that the approximative feature map achieves considerable speed-ups (about 18-fold on average), mostly without losing classification accuracy. Therefore, the proposed approximative feature map provides an efficient SVM evaluation method with minimal loss of precision.},
author = {Ring, Matthias and Eskofier, Björn},
doi = {10.1016/j.patrec.2016.08.013},
faupublication = {yes},
journal = {Pattern Recognition Letters},
pages = {107–113},
peerreviewed = {Yes},
title = {{An} approximation of the {Gaussian} {RBF} kernel for efficient classification with {SVMs}},
volume = {84},
year = {2016}
}
@article{faucris.117707964,
author = {Pasluosta, Cristian Federico and Gaßner, Heiko and Winkler, Jürgen and Klucken, Jochen and Eskofier, Björn},
doi = {10.1109/JBHI.2015.2461555},
faupublication = {yes},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1873-1881},
peerreviewed = {Yes},
title = {{An} {Emerging} {Era} in the {Management} of {Parkinson}'s disease: {Wearable} {Technologies} and the {Internet} of {Things}},
volume = {19},
year = {2015}
}
@article{faucris.117779244,
author = {Paulus, Jan and Hornegger, Joachim and Schmidt, Michael and Eskofier, Björn and Michelson, Georg},
doi = {10.1167/13.9.1171},
faupublication = {yes},
journal = {Journal of Vision},
pages = {1171},
peerreviewed = {Yes},
title = {{An} evaluation system for stereopsis of beach volleyball players measuring perception time as a function of disparity within a virtual environment},
volume = {13},
year = {2013}
}
@article{faucris.270680840,
abstract = {Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with proportional and simultaneous control schemes. Machine learning approaches and methods based on regression are often used to realize the desired functionality. Training procedures often include the tracking of visual stimuli on a screen or additional sensors, such as cameras or force sensors, to create labels for decoder calibration. In certain scenarios, where ground truth, such as additional sensor data, can not be measured, e.g., with people suffering from physical disabilities, these methods come with the challenge of generating appropriate labels. We introduce a new approach that uses the EMG-feature stream recorded during a simple training procedure to generate continuous labels. The method avoids synchronization mismatches in the labels and has no need for additional sensor data. Furthermore, we investigated the influence of the transient phase of the muscle contraction when using the new labeling approach. For this purpose, we performed a user study involving 10 subjects performing online 2D goal-reaching and tracking tasks on a screen. In total, five different labeling methods were tested, including three variations of the new approach as well as methods based on binary labels, which served as a baseline. Results of the evaluation showed that the introduced labeling approach in combination with the transient phase leads to a proportional command that is more accurate than using only binary labels. In summary, this work presents a new labeling approach for proportional EMG control without the need of a complex training procedure or additional sensors.},
author = {Hagengruber, Annette and Leipscher, Ulrike and Eskofier, Björn and Vogel, Jörn},
doi = {10.3390/s22041368},
faupublication = {yes},
journal = {Sensors},
keywords = {Electromyography; EMG-control schemes; Human machine interface; Robotcontrol},
note = {CRIS-Team Scopus Importer:2022-03-11},
peerreviewed = {Yes},
title = {{A} {New} {Labeling} {Approach} for {Proportional} {Electromyographic} {Control}},
volume = {22},
year = {2022}
}
@inproceedings{faucris.247957493,
abstract = {Strabismus is a visual disorder characterized by eye misalignment. The effect of Panum’s Fusional Area (PFA) compensates for small misalignments. However, prominent misalignments affect binocular vision and when present in childhood it may lead to amblyopia, a developmental disorder of the visual system. With the advent of Virtual Reality (VR) technology, possibilities for novel binocular treatments to amblyopia arise in which the measurement of strabismus is crucial to correctly compensate for it. Thus, VR yields great potential due to the ability of displaying content to each eye independently. Major research in VR addresses this topic using eye-tracking while there is a paucity of research on image-based assessment methods. In this work, we propose a VR application for measuring strabismus in nine lines of sight. We conducted a study with 14 healthy participants to evaluate the system under two conditions: no strabismus and an artificial deviation induced by prism lenses. Further, we evaluated the effect of PFA on the system by measuring its extent in horizontal and vertical lines of sight. Results show significant difference between the expected deviation induced by prism lenses and the measured deviation. The existing difference within the measurements can be explained with the recorded extent of the PFA.
Individuals with chronic ankle instability (CAI) demonstrate altered ankle kinematics during running compared to uninjured individuals; however, little is known about differences between individuals with CAI and those who recover successfully from an index sprain (copers).
Thirty-two young male athletes with prior ankle sprain were investigated, eighteen with CAI and fourteen copers. Instrumented running analysis was performed on a treadmill at two velocities: moderate (2.63 ± 0.20 m/s, rate of perceived of exertion = 14/20); and high velocity (3.83 ± 0.20 m/s). Mean ankle kinematics and stride-to-stride variability were analyzed applying the statistical parametric mapping method.
At both running velocities, no statistically significant differences in mean ankle kinematics were observed. At high running velocity, athletes with CAI demonstrated significantly increased frontal plane variability at 17-19% of the running gait cycle (p = 0.009). Additionally, large between-group effect sizes (Hedges’ g ≥ 0.8) may potentially indicate increased frontal plane variability during initial contact and terminal swing, as well as decreased variability in sagittal plane at 34-35% in CAI. A similar tendency existed at moderate velocity, with large effect sizes indicating decreased dorsiflexion at 75-89% in CAI, as well as an increased frontal plane variability at 16-25%, and 97-99%.
Compared to copers, individuals with CAI demonstrate increased variability of ankle kinematics - mainly in the frontal plane and particularly during stance phase - while mean ankle kinematics seems minimally affected. Increased ankle variability at high running velocity may best reflect persisting sensorimotor control deficits in athletes with chronically instable ankles.
The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate.
These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data.
The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.
}, author = {Kautz, Thomas and Eskofier, Björn}, faupublication = {yes}, journal = {Sensors}, keywords = {Kalman filter; fixed-lag smoothing; outlier detection; real-time filtering; non-uniform sampling; parameter estimation}, note = {UnivIS-Import:2015-04-14:Pub.2015.tech.IMMD.IMMD5.arobus}, pages = {4975 - 4995}, peerreviewed = {Yes}, title = {{A} {Robust} {Kalman} {Framework} with {Resampling} and {Optimal} {Smoothing}}, url = {https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2015/Kautz15-ARK.pdf}, volume = {15}, year = {2015} } @inproceedings{faucris.117767584, abstract = {Far too many people are dying from stroke or other heart related diseases each year. Early detection of abnormal heart rhythm could trigger the timely presentation to the emergency department or outpatient unit. Smartphones are an integral part of everyone;s life and they form the ideal basis for mobile monitoring and real-time analysis of signals related to the human heart. In this work, we investigated the performance of arrhythmia classification systems using only features calculated from the time instances of individual heart beats. We built a sinusoidal model using N (N = 10, 15, 20) consecutive RR intervals to predict the (N+1)th RR interval. The integration of the innovative sinusoidal regression feature, together with the amplitude and phase of the proposed sinusoidal model, led to an increase in the mean class-dependent classification accuracies. Best mean class-dependent classification accuracies of 90% were achieved using a Naïve Bayes classifier. Well-performing realtime analysis arrhythmia classification algorithms using only the time instances of individual heart beats could have a tremendous impact in reducing healthcare costs and reducing the high number of deaths related to cardiovascular diseases.}, author = {Leutheuser, Heike and Tobola, Andreas and Anneken, Lars and Gradl, Stefan and Lang, Nadine and Achenbach, Stephan and Eskofier, Björn and Arnold, Martin}, booktitle = {12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, date = {2015-06-09/2015-06-12}, doi = {10.1109/BSN.2015.7299371}, faupublication = {yes}, isbn = {9781467372015}, peerreviewed = {unknown}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, title = {{Arrhythmia} classification using {RR} intervals: {Improvement} with sinusoidal regression feature}, venue = {Cambridge, USA}, year = {2015} } @article{faucris.315806418, abstract = {Current research on trend detection in artificial intelligence (AI) mainly concerns academic data sources and industrial applications of AI. However, we argue that industrial trends are influenced by public perception and political decisions (e.g., through industry subsidies and grants) and should be reflected in political data sources. To investigate this hypothesis, we examine the AI trend development in German business and politics from 1998 to 2020. Therefore, we propose a web mining approach to collect a novel data set consisting of business and political data sources combining 1.07 million articles and documents. We identify 246 AI-related buzzwords extracted from various glossaries. We use them to conduct an extensive trend detection and analysis study on the collected data using machine learning-based approaches. This study successfully detects an AI trend and follows its evolution in business and political data sources over the past two decades. Moreover, we find a faster adoption of AI in business than in politics, with a considerable increase in policy discourse in recent years. Finally, we show that the collected data can be used for trend detection besides AI-related topics using topic clustering and the COVID-19 pandemic as examples.Our inner clock is responsible for creating a circadian rhythm that controls our sleep-wake cycle, and, hence, is also involved in the process of awakening from sleep. Awakening is accompanied by the cortisol awakening response (CAR). The purpose of the CAR is, presumably, to prepare our body for the upcoming challenges of the day. It is assumed that our inner clock is anticipating awakening, and thus, initiates the awakening process while we are still asleep, leading to the common phenomenon of waking up immediately before a known alarm. However, the role of the inner clock on the awakening process was only assessed in sleep laboratories and using invasive, blood-based biomarkers. For that reason, we investigated n = 117 participants by collecting cortisol data from saliva samples and IMU data from a wrist-worn inertial measurement unit (IMU) sensor node in a home environment over two nights. We compared cortisol data, characterizing the CAR, and IMU features, characterizing pre-awakening movement, between spontaneous awakening, awakening by a known alarm, and by an unknown alarm. We observed significant differences between the three study conditions in both cortisol and IMU data indicating higher cortisol reactivity and less movement if participants woke up by an unknown alarm. Our findings all support the assumption that our inner clock is anticipating our wake-up time. Utilizing our results, this work lays the foundation for the development of automatic classification models aimed at determining the ideal awakening time of individuals based on the analysis of pre-awakening movement.
Introduction
Lateral ankle sprains are the most common sport injury to the lower extremity, and at least one third of the affected athletes develop chronic ankle instability (CAI). Changes in ankle kinematics during running have been demonstrated in CAI compared to uninjured individuals (Moisan et al., 2017). However, little is known about differences between individuals with CAI and those who recovered successfully from an initial ankle sprain (coper). The aim of this study was to compare ankle kinematics and variability during running between athletes with CAI and copers.
Methods
Thirty-two male recreational athletes with prior ankle sprain were investigated, eighteen with CAI (age: 24.7±3.0 years, Cumberland Ankle Instability Tool (CAIT): 21.9±3.4) and fourteen copers (age: 25.5±3.7 years, CAIT: 28.1±1.7). Running analysis was performed on an instrumented treadmill at two individual running speeds: velocity 1 (2.63±0.20 m/s, pace at RPE=14); velocity 2 (3.83±0.20 m/s; first velocity plus 1.2 m/s). Mean ankle joint kinematics and variability were analyzed. Variability was calculated as intra-individual stride-to-stride standard deviation. The statistical parametric mapping (SPM) method, which adjusts for multiple testing, was applied and effect sizes were calculated to identify possible group differences.
Results
At both running velocities, no statistically significant differences in mean ankle kinematics were observed. At high running velocity, athletes with CAI demonstrated significantly increased frontal plane variability at 16-19% of the running cycle (p= 0.009). Additionally, large between-group effect sizes (Hedges’ g ≥ 0.8) may potentially indicate increased frontal plane variability during initial contact and terminal swing, as well as decreased variability in the sagittal plane at 34-35%. A similar tendency existed at moderate velocity, with large effect sizes indicating decreased dorsiflexion at 75-89% in CAI, as well as an increased frontal plane variability at 16-25%, and 97-99%.
Discussion
Compared to copers,
individuals with CAI demonstrated increased variability of ankle kinematics in
the frontal plane, while mean ankle kinematics seems minimally affected.
Increased ankle variability at high running velocity may best reflect persisting
sensorimotor control deficits in athletes with chronically instable ankles.
These findings may indicate altered sensorimotor control adaptations rather
than only mechanical insufficiencies in CAI that may explain feelings of
giving-way and present a potential re-injury risk factor.
References
Moisan, G., Descarreaux, M., & Cantin, V. (2017). Effects of chronic
ankle instability on kinetics, kinematics and muscle activity during walking
and running: A systematic review. Gait & Posture, 52, 381–399.
https://doi.org/10.1016/j.gaitpost.2016.11.037},
author = {Wanner, Philipp and Schmautz, Thomas and Kluge, Felix and Eskofier, Björn and Pfeifer, Klaus and Steib, Simon},
booktitle = {Abstractband zur 16. Jahrestagung der dvs-Sektion Sportmotorik},
date = {2019-01-16/2019-01-18},
doi = {10.7892/boris.123517},
editor = {Klostermann, A., Vater, C., & Hossner, E.-J.},
faupublication = {yes},
month = {Jan},
peerreviewed = {unknown},
title = {{Athletes} with chronic ankle instability demonstrate altered ankle angle variability during running compared to copers},
url = {https://boris.unibe.ch/123517/},
venue = {Bern},
year = {2019}
}
@inproceedings{faucris.123040764,
abstract = {Bioimpedance analysis (BIA) estimates the amount of total body water (TBW) in the human body. During sports, however, the increased skin temperature distorts bioimpedance measurements and, thus, prevents the application of BIA. In this paper, we propose a two-stage regression that includes temperature information in order to correct the temperature-distorted bioimpedance. In detail, the first regression stage corrects temperature-distored bioimpedance using information of skin and core temperature. The second regression stage estimates TBW loss on basis of the corrected bioimpedance. The two-stage regression was evaluated using data of an ongoing study. The results showed that estimations of TBW loss during sports can be considerably improved if temperature information is included. However, a remaining error was still observed. Therefore, additional measurements, e.g., skin blood flow, are discussed because they also influence bioimpedance and could further reduce the error.},
author = {Ring, Matthias and Lohmüller, Clemens and Rauh, Manfred and Eskofier, Björn},
booktitle = {Proceedings of the 2014 22nd International Conference on Pattern Recognition (ICPR)},
date = {2014-08-24/2014-08-28},
doi = {10.1109/ICPR.2014.773},
editor = {IEEE},
faupublication = {yes},
pages = {4519 - 4524},
peerreviewed = {Yes},
title = {{A} {Two}-{Stage} {Regression} {Using} {Bioimpedance} and {Temperature} for {Hydration} {Assessment} {During} {Sports}},
venue = {Stockholm},
year = {2014}
}
@inproceedings{faucris.123134704,
abstract = {Accurate position tracking is a crucial task in many applications ranging from car navigation over robot control to sports analysis. In order to improve the accuracy of position tracking, we introduce a novel method for constraining Kalman filters by incorporating prior knowledge in an augmented motion model. In contrast to previously reported methods, our approach does not require cumbersome tuning of additional filter parameters and causes less computational overhead. We demonstrate our method in the context of sports analysis in athletics. Using 34 data sets recorded during 400m and 800m runs, we compare our approach to unconstrained and pseudo-measurement filters. The presented augmented motion model in conjunction with an Extended Kalman Filter (EKF) reduced the root mean square error of the filtered output by 60% compared to unconstrained filtering and by 50% compared to a pseudo-measurement EKF.},
author = {Kautz, Thomas and Groh, Benjamin and Eskofier, Björn},
booktitle = {19th International Conference on Information Fusion (FUSION 2016)},
date = {2016-07-05/2016-07-08},
faupublication = {yes},
peerreviewed = {unknown},
title = {{Augmented} {Motion} {Models} for {Constrained} {Position} {Tracking} with {Kalman} {Filters}},
url = {https://www.mad.tf.fau.de/files/2017/06/2016-Kautz-FUSION-AMM.pdf},
venue = {Heidelberg, Germany},
year = {2016}
}
@article{faucris.312689367,
abstract = {Correction to: Scientific Reports, published online 13 August 2023 In the original version of this Article, Bettina Hohberger, Georg Michelson and Bjoern Eskofier were omitted as equally contributing authors. The original Article has been corrected.},
author = {Mehringer, Wolfgang and Stöve, Maike and Krauß, Daniel and Ring, Matthias and Steussloff, Fritz and Güttes, Moritz and Zott, Julia and Hohberger, Bettina and Michelson, Georg and Eskofier, Björn},
doi = {10.1038/s41598-023-43373-7},
faupublication = {yes},
journal = {Scientific Reports},
note = {CRIS-Team Scopus Importer:2023-10-13},
peerreviewed = {Yes},
title = {{Author} {Correction}: {Virtual} reality for assessing stereopsis performance and eye characteristics in {Post}-{COVID} ({Scientific} {Reports}, (2023), 13, 1, (13167), 10.1038/s41598-023-40263-w)},
volume = {13},
year = {2023}
}
@article{faucris.310114387,
abstract = {Background: Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. Methods: This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 × 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. Results: The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. Conclusion: Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.},
author = {Ollenschläger, Malte and Höfner, Patrick and Ullrich, Martin and Kluge, Felix and Greinwalder, Teresa and Loris, Evelyn and Regensburger, Martin and Eskofier, Björn and Winkler, Jürgen and Gaßner, Heiko},
doi = {10.1186/s13023-023-02854-8},
faupublication = {yes},
journal = {Orphanet Journal of Rare Diseases},
keywords = {Classification; Gait analysis; Motion capture; Muscle spasticity; Range of motion; Wearable sensors},
note = {CRIS-Team Scopus Importer:2023-09-08},
peerreviewed = {Yes},
title = {{Automated} assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning},
volume = {18},
year = {2023}
}
@article{faucris.119137964,
author = {Groh, Benjamin and Warschun, Frank and Deininger, Martin and Kautz, Thomas and Martindale, Christine and Eskofier, Björn},
doi = {10.1145/3130918},
faupublication = {yes},
journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
peerreviewed = {Yes},
title = {{Automated} {Ski} {Velocity} and {Jump} {Length} {Determination} in {Ski} {Jumping} {Based} on {Unobtrusive} and {Wearable} {Sensors}},
url = {https://www.mad.tf.fau.de/files/2017/10/2017-Groh-IMWUT-ASV.pdf},
volume = {1},
year = {2017}
}
@article{faucris.270735077,
abstract = {The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.
Several research tools and projects require groups of similar code changes as input. Examples are recommendation and bug finding tools that can provide valuable information to developers based on such data. With the help of similar code changes they can simplify the application of bug fixes and code changes to multiple locations in a project. But despite their benefit, the practical value of existing tools is limited, as users need to manually specify the input data, i.e., the groups of similar code changes.
To overcome this drawback, this paper presents and evaluates two syntactical similarity metrics, one of them is specifically designed to run fast, in combination with two carefully selected and self-tuning clustering algorithms to automatically detect groups of similar code changes.
We evaluate the combinations of metrics and clustering algorithms by applying them to several open source projects and also publish the detected groups of similar code changes online as a reference dataset. The automatically detected groups of similar code changes work well when used as input for LASE, a recommendation system for code changes.
}, author = {Kreutzer, Patrick and Dotzler, Georg and Ring, Matthias and Eskofier, Björn and Philippsen, Michael}, booktitle = {Proceedings of the 13th International Conference on Mining Software Repositories (MSR'16)}, date = {2016-05-14/2016-05-15}, doi = {10.1145/2901739.2901749}, faupublication = {yes}, isbn = {978-1-4503-4186-8}, pages = {61-72}, peerreviewed = {Yes}, title = {{Automatic} clustering of code changes}, url = {http://dl.acm.org/citation.cfm?id=2901749}, venue = {Austin, TX, USA}, year = {2016} } @inproceedings{faucris.119446184, author = {Maier, Jennifer and Reinfelder, Samuel and Barth, Jens and Klucken, Jochen and Eskofier, Björn}, booktitle = {BSA Conference 2013 - Biosignal Analysis}, faupublication = {yes}, note = {UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.automa{\_}27}, pages = {1-4}, peerreviewed = {unknown}, title = {{Automatic} detection of inertial sensor orientation for movement analysis in {Parkinson}s disease}, url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2013/Maier13-ADO.pdf}, venue = {Rio de Janeiro, Brazil}, year = {2013} } @inproceedings{faucris.118745704, author = {Leutheuser, Heike and Gottschalk, Tristan and Anneken, Lars and Struck, Matthias and Heuberger, Albert and Achenbach, Stephan and Eskofier, Björn and Arnold, Martin}, booktitle = {Proceedings of International Conference on Mobile and Information Technologies in Medicine and Health}, faupublication = {yes}, isbn = {978-80-01-05637-0}, note = {UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.automa{\_}72}, pages = {1-4}, title = {{Automatic} {ECG} {Arrhythmia} {Detection} in {Real}-{Time} on {Android}-based {Mobile} {Devices}}, venue = {Czech Technical University, Prague}, year = {2014} } @article{faucris.227311398, abstract = {To obtain CT images of the knee joint in a more lifelike position, data acquisition can be performed with patients in standing rather than in lying position. However, in that situation, people tend to show involuntary motion. One possibility to compensate for that motion is the use of Inertial Measurement Units, that capture the accelerations during the scan. For this purpose, their local coordinate system needs to be known. An estimation based on the SIFT algorithm was implemented and compared to an existing approach that uses the Fast Radial Symmetry transform and to expert labels for evaluation. The SIFT method showed to be superior to the existing approach as it could extract stable feature points from the projections that were used to estimate the three-dimensional coordinate system in a reliable manner. The final algorithm achieved a mean euclidean distance of 2.61 mm between the calculated position of the origin and the assumed ground truth by the expert labels.}, author = {Thies, Mareike and Maier, Jennifer and Eskofier, Björn and Maier, Andreas and Levenston, Marc and Gold, Garry and Fahrig, Rebecca}, doi = {10.1515/cdbme-2019-0050}, faupublication = {yes}, journal = {Current Directions in Biomedical Engineering}, keywords = {Computed Tomography; Feature Extraction; Motion Compensated Reconstruction; SIFT}, note = {CRIS-Team Scopus Importer:2019-10-01}, pages = {195-198}, peerreviewed = {Yes}, title = {{Automatic} orientation estimation of inertial sensors in {C}-{Arm} {CT} {Projections}}, volume = {5}, year = {2019} } @inproceedings{faucris.106575964, abstract = {Diagnosis and severity staging of Parkinsons disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls. EMG signals were recorded from 5 patients with PD and 5 younger healthy controls, while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier. Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for P}, author = {Kugler, Patrick and Jaremenko, Christian and Schlachetzki, Johannes and Winkler, Jürgen and Klucken, Jochen and Eskofier, Björn}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, date = {2013-07-03/2013-07-07}, doi = {10.1109/EMBC.2013.6610865}, editor = {IEEE Engineering in Medicine and Biology Society}, faupublication = {yes}, pages = {5781-5784}, peerreviewed = {unknown}, title = {{Automatic} {Recognition} of {Parkinson}s {Disease} {Using} {Surface} {Electromyography} {During} {Standardized} {Gait} {Tests}}, url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2013/Kugler13-ARO.pdf}, venue = {Osaka, Japan}, year = {2013} } @inproceedings{faucris.113217324, author = {Paulus, Jan and Hornegger, Joachim and Schmidt, Michael and Eskofier, Björn and Michelson, Georg}, booktitle = {Sportinformatik 2012}, date = {2012-09-12/2012-09-14}, editor = {Byshko R., Dahmen T., Gratkowski M., Gruber M., Quintana J., Saupe D., Vieten M., Woll A.}, faupublication = {yes}, pages = {102-105}, peerreviewed = {unknown}, title = {{A} {Virtual} {Environment} {Based} {Evaluating} {System} for {Goalkeepers} {Stereopsis} {Performance} in {Soccer}}, url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2012/Paulus12-AVE.pdf}, venue = {Konstanz}, year = {2012} } @inproceedings{faucris.222478463, abstract = {Visually impaired people struggle with detecting obstacles in their upper body region which cannot be noticed by a common white cane. Therefore, we introduce the sensaid backpack, an obstacle detection system for blind people which covers head-to-hip region. Detection is based on ultrasonic sensors and feedback is given by vibration and audio within a distance of 1.5m and 0.7m, respectively. In a technical test the functionality and reliability were proven. The detection rate was determined at 94.4% in a practical user test. A limited detection rate was found for objects not perpendicular to the sensing device.
Besides anti-inflammatory medication, physical exercise represents a cornerstone of modern treatment for patients with axial spondyloarthritis (AS). Digital health apps (DHAs) such as the yoga app YogiTherapy could remotely empower patients to autonomously and correctly perform exercises.
Objective:
This study aimed to design and develop a smartphone-based app, YogiTherapy, for patients with AS. To gain additional insights into the usability of the graphical user interface (GUI) for further development of the app, this study focused exclusively on evaluating users’ interaction with the GUI.
Methods:
The development of the app and the user experience study took place between October 2020 and March 2021. The DHA was designed by engineering students, rheumatologists, and patients with AS. After the initial development process, a pilot version of the app was evaluated by 5 patients and 5 rheumatologists. The participants had to interact with the app’s GUI and complete 5 navigation tasks within the app. Subsequently, the completion rate and experience questionnaire (attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty) were completed by the patients.
Results:
The results of the posttest questionnaires showed that most patients were already familiar with digital apps (4/5, 80%). The task completion rates of the usability test were 100% (5/5) for the tasks T1 and T2, which included selecting and starting a yoga lesson and navigating to an information page. Rheumatologists indicated that they were even more experienced with digital devices (2/5, 40% experts; 3/5, 60% intermediates). In this case, they scored task completion rates of 100% (5/5) for all 5 usability tasks T1 to T5. The mean results from the User Experience Questionnaire range from −3 (most negative) to +3 (most positive). According to rheumatologists’ evaluations, attractiveness (mean 2.267, SD 0.401) and stimulation (mean 2.250, SD 0.354) achieved the best mean results compared with dependability (mean 2.000, SD 0.395). Patients rated attractiveness at a mean of 2.167 (SD 0.565) and stimulation at a mean of 1.950 (SD 0.873). The lowest mean score was reported for perspicuity (mean 1.250, SD 1.425).
Conclusions:
The newly developed and tested DHA YogiTherapy demonstrated moderate usability among rheumatologists and patients with rheumatic diseases. The app can be used by patients with AS as a complementary treatment. The initial evaluation of the GUI identified significant usability problems that need to be addressed before the start of a clinical evaluation. Prospective trials are also needed in the second step to prove the clinical benefits of the ap},
author = {Truong, Minh Tam and Nwosu, Obioma Bertrand and Gaytan Torres, Maria Elena and Segura Vargas, Maria Paula and Seifer, Ann-Kristin and Nitschke, Marlies and Ibrahim, Alzhraa and Knitza, Johannes and Krusche, Martin and Eskofier, Björn and Schett, Georg and Morf, Harriet},
doi = {10.2196/34566},
faupublication = {yes},
journal = {JMIR Formative Research},
pages = {e34566},
peerreviewed = {Yes},
title = {{A} {Yoga} {Exercise} {App} {Designed} for {Patients} {With} {Axial} {Spondylarthritis}: {Development} and {User} {Experience} {Study}},
url = {https://formative.jmir.org/2022/6/e34566},
volume = {6},
year = {2022}
}
@article{faucris.221134348,
abstract = {Background: Personal autonomy in advanced age critically depends on mobility in the environment. Geriatric patients are often not able to walk safely with sufficient velocity. In many cases, multiple factors contribute to the deficit. Diagnostic identification of single components enables a specific treatment. Objective: This article describes the most common neurological causes of imbalance and impaired gait that are relevant for a pragmatic approach for the assessment of deficits in clinical and natural environments taking into account the physiology of balance and gait control, typical morbidities in older people and the potential of innovative assessment technologies. Material and methods: Expert opinion based on a narrative review of the literature and with reference to selected research topics. Results and discussion: Common neurological causes of impaired balance and mobility are sensory deficits (reduced vision, peripheral neuropathy, vestibulopathy), neurodegeneration in disorders with an impact on movement control and motoric functions (Parkinsonian syndromes, cerebellar ataxia, vascular encephalopathy) and functional (psychogenic) disorders, particularly a fear of falling. Clinical tests and scores in laboratory environments are complemented by the assessment in the natural environment. Wearable sensors, mobile smartphone-based assessment of symptoms and functions and adopted strategies for analysis are currently emerging. Use of these data enables a personalized treatment. Furthermore, sensor-based assessment ensures that effects are measured objectively.},
author = {Jahn, Klaus and Freiberger, Ellen and Eskofier, Björn and Bollheimer, Cornelius and Klucken, Jochen},
doi = {10.1007/s00391-019-01561-z},
faupublication = {yes},
journal = {Zeitschrift für Gerontologie und Geriatrie},
keywords = {Dizziness; Falls; Gait disorder; Postural control; Vertigo},
note = {CRIS-Team Scopus Importer:2019-06-21},
pages = {316-323},
peerreviewed = {Yes},
title = {{Balance} and mobility in geriatric patients},
volume = {52},
year = {2019}
}
@inproceedings{faucris.118054684,
abstract = {In this paper, we present an approach for a ball impact localization on table tennis rackets using piezo-electric sensors. Three sensors were used as vibration sensors at the outer edge of the racket. We analyzed ball impact vibration appearances and measured time differences of arriving wave fronts for each sensor pair. A study was conducted comprising six different racket-rubber combinations. Firstly, we calculated a time difference distribution model based on training sets of impact data constructed by a grid of measuring points. Secondly, this model was used to estimate the impact position of test data sets using three different methods. Best performance for each racket-rubber combination was achieved with a linear regression based method yielding a RMSE in x-direction (transversal racket direction) of 22.1 mm and a RMSE of 19.8 mm in y-direction (longitudinal racket direction). The measuring system is small enough to be completely invisibly integrated into table tennis rackets. This contribution is the first attempt to address a ball impact localization system for table tennis rackets that has the potential to be used in real-world training exercises and competitions to assist table tennis players during matches and to support the athletes' training progress.},
author = {Blank, Peter and Kautz, Thomas and Eskofier, Björn},
booktitle = {Proceedings of the 2016 ACM International Symposium on Wearable Computers},
date = {2016-09-12/2016-09-16},
doi = {10.1145/2971763.2971778},
faupublication = {yes},
isbn = {978-1-4503-4460-9},
keywords = {piezo-electric sensors; vibration measurement; localization; table tennis},
pages = {72-79},
peerreviewed = {unknown},
publisher = {Association for Computing Machinery, Inc},
title = {{Ball} impact localization on table tennis rackets using piezo-electric sensors},
venue = {Heidelberg, Deutschland},
year = {2016}
}
@inproceedings{faucris.123680744,
author = {Blank, Peter and Groh, Benjamin and Eskofier, Björn},
booktitle = {Proceedings of the 2017 ACM International Symposium on Wearable Computers},
date = {2017-09-11/2017-09-15},
doi = {10.1145/3123021.3123040},
faupublication = {yes},
peerreviewed = {unknown},
title = {{Ball} {Speed} and {Spin} {Estimation} in {Table} {Tennis} {Using} a {Racket}-mounted {Inertial} {Sensor}},
venue = {Maui, Hawaii, USA},
year = {2017}
}
@article{faucris.236871112,
author = {Bazo, Rodrigo and Reis, Eduardo and Adams Seewald, Lucas and Facco Rodrigues, Vinicius and Andre da Costa, Cristiano and Gonzaga, Luiz and Stoffel Antunes, Rodolfo and da Rosa Righi, Rodrigo and Maier, Andreas and Eskofier, Björn and Fahrig, Rebecca and Horz, Tim},
doi = {10.1016/j.inffus.2020.03.011},
faupublication = {yes},
journal = {Information Fusion},
peerreviewed = {Yes},
title = {{Baptizo}: a sensor fusion based model for tracking the identity of human poses},
year = {2020}
}
@inproceedings{faucris.249181813,
abstract = {Biomechanical analysis of human motion is applied in medicine, sports and product design. However, visualizations of biomechanical variables are still highly abstract and technical since the body is visualized with a skeleton and muscles are represented as lines. We propose a more intuitive and realistic visualization of kinematics and muscle activity to increase accessibility for non-experts like patients, athletes, or designers. To this end, the Biomechanical Animated Skinned Human (BASH) model is created and scaled to match the anthropometry defined by a musculoskeletal model in OpenSim file format. Motion is visualized with an accurate pose transformation of the BASH model using kinematic data as input. A statistical model contributes to a natural human appearance and realistic soft tissue deformations during the animation. Finally, muscle activity is highlighted on the model surface. The visualization pipeline is easily applicable since it requires only the musculoskeletal model, kinematics and muscle activation patterns as input. We demonstrate the capabilities for straight and curved running simulated with a full-body musculoskeletal model. We conclude that our visualization could be perceived as intuitive and better accessible for non-experts than conventional skeleton and line representations. However, this has to be confirmed in future usability and perception studies.
Insufficient sleep quality is directly linked to a series of physical and physiological diseases [1]. Therefore, reliable sleep monitoring is essential for the prevention, diagnosis, and treatment of such. As sleep laboratories are very cost- and resource-prohibitive, wearable sensors are a promising alternative for unobtrusive sleep monitoring at home. During sleep, body movements decrease compared to a wakeful state [2]. In addition, cardiac and respiratory activity changes during sleep [3]. Current systems are mostly based on wrist movement, typically assessed using actigraphy (ACT), for unobtrusive sleep/wake detection [4]. However, movement-based systems tend to overestimate sleep due to a lack of movement shortly before falling asleep or in short periods of wakefulness. Previous research showed promising improvements in sleep/wake detection by combining ACT with cardiac and respiratory information such as heart rate variability (HRV) and respiration rate variability (RRV) [5]. However, this was only evaluated on small cohorts and not in large-scale studies. For that reason, this work aims to systematically compare ACT-based sleep detection with multimodal approaches combining ACT and HRV by benchmarking different state-of-the-art machine- and deep learning algorithms on a large-scale dataset. In particular, we investigate whether the classification performance can be further improved by including respiratory information into machine learning models.
II. METHODS
The data used in this work were collected in a sleep study of 2,237 participants, which contains ACT and polysomnography (PSG). PSG was used as ground truth for sleep/wake phases as well as to extract HRV and RRV from electrocardiography and respiratory induction plethysmography respectively [7]. In total, 370 ACT features and 30 HRV features were extracted according to Zhai et al. [6]. In addition, 62 RRV features were extracted using the Neurokit2 library [8]. To find the best set of hyperparameters, a grid search with embedded 5-fold cross-validation was performed over a defined search space.
III. RESULTS & DISCUSSION
Our results show that including RRV features in the classification algorithms significantly improved the key metrics of assessing sleep/wake detection performance (Fig 1). The best-performing algorithm to discriminate between sleep and wake phases was a Multi-Layer Perceptron with an accuracy of 85.1±8.5%. In particular, specificity, which is a good marker for assessing the overprediction of sleep, showed a strong increase in performance after adding RRV (63.5±22.3% vs. 72.4±17.2%). Our findings underscore the potential of including respiratory information, which can also be extracted from wearable sensors, to improve sleep/wake detection algorithms and, thus, help to transfer sleep laboratories into a home monitoring environmen},
author = {Krauß, Daniel and Richer, Robert and Küderle, Arne and Beilner, Janina and Rohleder, Nicolas and Eskofier, Björn},
booktitle = {IEEE-EMBS International Conference on Biomedical and Health Informatics},
date = {2022-09-26/2022-09-30},
faupublication = {yes},
keywords = {Machine learning; Deep learning; Wearable sensors; Multimodal sensing; Neural networks},
peerreviewed = {unknown},
series = {IEEE-EMBS International Conference on Biomedical and Health Informatics},
title = {{Benchmarking} of {Sleep}/{Wake} {Detection} {Algorithms} on a {Large} {Cohort} using {Actigraphy}, {HRV}, and {Respiration} {Information}},
venue = {Ioaninna},
year = {2022}
}
@inproceedings{faucris.121165704,
abstract = {Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists. © 2011 IEEE.},
author = {Barth, Jens and Klucken, Jochen and Kugler, Patrick and Kammerer, Thomas and Steidl, Ralph and Winkler, Jürgen and Hornegger, Joachim and Eskofier, Björn},
booktitle = {Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE},
date = {2011-08-30/2011-09-03},
doi = {10.1109/IEMBS.2011.6090226},
faupublication = {yes},
pages = {868-871},
peerreviewed = {unknown},
title = {{Biometric} and {Mobile} {Gait} {Analysis} for {Early} {Detection} and {Therapy} {Monitoring} in {Parkinson}'s {Disease}},
venue = {Boston, MA},
volume = {null},
year = {2011}
}
@article{faucris.265133012,
abstract = {Biopsychology is a field of psychology that analyzes how biological processes interact with
behaviour, emotion, cognition, and other mental processes. Biopsychology covers, among
others, the topics of sensation and perception, emotion regulation, movement (and control of
such), sleep and biological rhythms, as well as acute and chronic stress.
To assess the interaction between biological and mental processes a variety of different modali-
ties are used in the field of biopsychology, such as electrophysiology, assessed, for instance, via
electrocardiography (ECG), electrodermal activity (EDA), or electroencephalography (EEG),
sleep, activity and movement, assessed via inertial measurement units (IMUs), neuroendocrine
and inflammatory biomarkers, assessed by saliva and blood samples, as well as self-reports,
assessed via psychological questionnaires.
These different modalities are collected either “in the lab,” during standardized laboratory
protocols, or “in the wild,” during unsupervised protocols in home environments. The collected
data are typically analyzed using statistical methods, or, more recently, using machine learning
methods.
While some software packages exist that allow for the analysis of single data modalities, such
as electrophysiological data, or sleep, activity and movement data, no packages are available
for the analysis of other modalities, such as neuroendocrine and inflammatory biomarker,
and self-reports. In order to fill this gap, and, simultaneously, to combine all required tools
analyzing biopsychological data from beginning to end into one single Python package, we
developed BioPsyKit.},
author = {Richer, Robert and Küderle, Arne and Ullrich, Martin and Rohleder, Nicolas and Eskofier, Björn},
doi = {10.21105/joss.03702},
faupublication = {yes},
journal = {Journal of Open Source Software},
pages = {3702},
peerreviewed = {Yes},
title = {{BioPsyKit}: {A} {Python} package for the analysis of biopsychological data},
url = {https://www.theoj.org/joss-papers/joss.03702/10.21105.joss.03702.pdf},
volume = {6},
year = {2021}
}
@article{faucris.290250450,
abstract = {Movement disorders can occur at every age. They restrict the affected persons' mobility and limit his or her quality of life. The most common neurodegenerative movement disorder in the elderly is Parkinson's disease. During disease progression movement impairments increase and become the main therapeutic target for patients and physicians. So far, the extent of motor impairments is based on the individual case history, which is influenced by the patients' emotions and cognitive abilities. An additional physical examination further helps the physician to estimate the degree of disability. However, the course of movement disorders throughout the day as well as over a period of several days or weeks is the main therapeutic criteria, which cannot be determined in single-phase surveys. This survey intends to present a variety of inpatient and mobile systems for sensorbased and automatic movement monitoring, which allow objective and quantitatively comparable evidence about motor impairments. We will focus on a study, which investigated a mobile gait analysis system allowing the monitoring of Parkinson-associated movement disorders via movement sensors attached to the patients' shoes. Via this system information about the extent of motor disorders and the diseases' stage can be collected independently and help the therapist with his or her assessment. In the future, these invisible sensor systems embedded in the patients' clothing may support diagnosis and therapy of other motor disorders as well and may be applied in prevention and therapy. © Hippocampus Verlag 2013.},
author = {Klucken, Jochen and Barth, J. and Eskofier, Björn and Winkler, Jürgen},
faupublication = {yes},
journal = {Neurologie und Rehabilitation},
keywords = {Biosensors; Gait analysis; Movement disorders; Parkinson's disease},
note = {CRIS-Team Scopus Importer:2023-03-07},
pages = {69-76},
peerreviewed = {No},
title = {{Biosensorische} {Bewegungserfassung} beim {Parkinson}-{Syndrom}},
volume = {19},
year = {2013}
}
@inproceedings{faucris.123672384,
author = {Saffoury, Rimon and Blank, Peter and Seßner, Julian and Groh, Benjamin and Martindale, Christine and Dorschky, Eva and Franke, Jörg and Eskofier, Björn},
booktitle = {International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW)},
date = {2016-12-01/2016-12-03},
doi = {10.1109/TISHW.2016.7847770},
editor = {IEEE},
faupublication = {yes},
pages = {1-7},
peerreviewed = {unknown},
title = {{Blind} {Path} {Obstacle} {Detector} using {Smartphone} {Camera} and {Line} {Laser} {Emitter}},
url = {https://www.mad.tf.fau.de/files/2017/06/2016-Saffoury-BPO.pdf},
venue = {Vila Real, Portugal},
year = {2016}
}
@inproceedings{faucris.106684424,
abstract = {The correct treatment of diabetes is vital to a patient’s health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body’s response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.},
author = {Reymann, Maximilian and Dorschky, Eva and Groh, Benjamin and Martindale, Christine and Blank, Peter and Eskofier, Björn},
booktitle = {38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
date = {2016-08-16/2016-08-19},
doi = {10.1109/EMBC.2016.7591358},
faupublication = {yes},
peerreviewed = {unknown},
title = {{Blood} glucose level prediction based on support vector regression using mobile platforms},
url = {https://www.mad.tf.fau.de/files/2017/06/2016-Reymann-EMBC-BGL.pdf},
venue = {Orlando, Florida, USA},
year = {2016}
}
@article{faucris.289746709,
abstract = {Background
Many studies investigating the cortisol awakening response (CAR) suffer from low adherence to the study protocol as well as from the lack of precise and objective methods for assessing the awakening and saliva sampling times which leads to measurement bias on CAR quantification.
Methods
To address this issue, we have developed “CARWatch”, a smartphone application that aims to enable low-cost and objective assessment of saliva sampling times as well as to concurrently increase protocol adherence. As proof-of-concept study, we assessed the CAR of N=117 healthy participants (24.2 ± 8.7 years, 79.5% female) on two consecutive days. During the study, we recorded awakening times (AW) using self-reports, the CARWatch application, and a wrist-worn sensor, and saliva sampling times (ST) using self-reports and the CARWatch application. Using combinations of different AW and ST modalities, we derived different reporting strategies and compared the reported time information to a Naive sampling strategy assuming an ideal sampling schedule. Additionally, we compared the AUCI, computed using information from different reporting strategies, against each other to demonstrate the effect of inaccurate sampling on the CAR.
Results
The use of CARWatch led to a more consistent sampling behavior and reduced sampling delay compared to self-reported saliva sampling times. Additionally, we observed that inaccurate saliva sampling times, as resulting from self-reports, were associated with an underestimation of CAR measures. Our findings also revealed potential error sources for inaccuracies in self-reported sampling times and showed that CARWatch can help in better identifying, and possibly excluding, sampling outliers that would remain undiscovered by self-reported sampling.
Conclusion
The results from our proof-of-concept study demonstrated that CARWatch can be used to objectively record saliva sampling times. Further, it suggests its potential of increasing protocol adherence and sampling accuracy in CAR studies and might help to reduce inconsistencies in CAR literature resulting from inaccurate saliva sampling. For that reason, we published CARWatch and all necessary tools under an open-source license, making it freely accessible to every researche},
author = {Richer, Robert and Abel, Luca and Küderle, Arne and Eskofier, Björn and Rohleder, Nicolas},
doi = {10.1016/j.psyneuen.2023.106073},
faupublication = {yes},
journal = {Psychoneuroendocrinology},
keywords = {CAR; Saliva; Adherence; Sampling accuracy; Smartphone; App},
pages = {106073},
peerreviewed = {Yes},
title = {{CARWatch} – {A} smartphone application for improving the accuracy of cortisol awakening response sampling},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0306453023000513},
volume = {151},
year = {2023}
}
@article{faucris.280977257,
abstract = {Optimal control simulations of musculoskeletal models can be used to
reconstruct motions measured with optical motion capture to estimate
joint and muscle kinematics and kinetics. These simulations are mutually
and dynamically consistent, in contrast to traditional inverse methods.
Commonly, optimal control simulations are generated by tracking
generalized coordinates in combination with ground reaction forces. The
generalized coordinates are estimated from marker positions using, for
example, inverse kinematics. Hence, inaccuracies in the estimated
coordinates are tracked in the simulation. We developed an approach to
reconstruct arbitrary motions, such as change of direction motions,
using optimal control simulations of 3D full-body musculoskeletal models
by directly tracking marker and ground reaction force data. For
evaluation, we recorded three trials each of straight running, curved
running, and a v-cut for 10 participants. We reconstructed the
recordings with marker tracking simulations, coordinate tracking
simulations, and inverse kinematics and dynamics. First, we analyzed the
convergence of the simulations and found that the wall time increased
three to four times when using marker tracking compared to coordinate
tracking. Then, we compared the marker trajectories, ground reaction
forces, pelvis translations, joint angles, and joint moments between the
three reconstruction methods. Root mean squared deviations between
measured and estimated marker positions were smallest for inverse
kinematics (e.g., 7.6 ± 5.1 mm for v-cut). However, measurement
noise and soft tissue artifacts are likely also tracked in inverse
kinematics, meaning that this approach does not reflect a gold standard.
Marker tracking simulations resulted in slightly higher root mean
squared marker deviations (e.g., 9.5 ± 6.2 mm for v-cut) than
inverse kinematics. In contrast, coordinate tracking resulted in
deviations that were nearly twice as high (e.g., 16.8 ± 10.5 mm
for v-cut). Joint angles from coordinate tracking followed the estimated
joint angles from inverse kinematics more closely than marker tracking (e.g., root mean squared deviation of 1.4 ± 1.8 deg vs.
3.5 ± 4.0 deg for v-cut). However, we did not have a gold standard
measurement of the joint angles, so it is unknown if this larger
deviation means the solution is less accurate. In conclusion, we showed
that optimal control simulations of change of direction running motions
can be created by tracking marker and ground reaction force data. Marker
tracking considerably improved marker accuracy compared to coordinate
tracking. Therefore, we recommend reconstructing movements by directly
tracking marker data in the optimal control simulation when precise
marker tracking is required.},
author = {Nitschke, Marlies and Marzilger, Robert and Leyendecker, Sigrid and Eskofier, Björn and Koelewijn, Anne},
doi = {10.7717/peerj.14852},
faupublication = {yes},
journal = {PeerJ},
peerreviewed = {Yes},
title = {{Change} the direction: {3D} optimal control simulation by directly tracking marker and ground reaction force data},
url = {https://peerj.com/articles/14852/},
year = {2023}
}
@article{faucris.248106201,
abstract = {Background Gait impairment is a pivotal feature of parkinsonian syndromes and increased gait variability is associated with postural instability and a higher risk of falls. Objectives We compared gait variability at different walking velocities between and within groups of patients with Parkinson-variant multiple system atrophy, idiopathic Parkinson's disease, and a control group of older adults. Methods Gait metrics were recorded in 11 multiple system atrophy, 12 Parkinson's disease patients, and 18 controls using sensor-based gait analysis. Gait variability was analyzed for stride, swing and stance time, stride length and gait velocity. Values were compared between and within the groups at self-paced comfortable, fast and slow walking speed. Results Multiple system atrophy patients displayed higher gait variability except for stride time at all velocities compared with controls, while Parkinson's patients did not. Compared with Parkinson's disease, multiple system atrophy patients displayed higher variability of swing time, stride length and gait velocity at comfortable speed and at slow speed for swing and stance time, stride length and gait velocity (all P < 0.05). Stride time variability was significantly higher in slow compared to comfortable walking in patients with multiple system atrophy (P = 0.014). Variability parameters significantly correlated with the postural instability/gait difficulty subscore in both disease groups. Conversely, significant correlations between variability parameters and MDS-UPDRS III score was observed only for multiple system atrophy patients. Conclusion This analysis suggests that gait variability parameters reflect the major axial impairment and postural instability displayed by multiple system atrophy patients compared with Parkinson's disease patients and controls.},
author = {Sidoroff, Victoria and Raccagni, Cecilia and Kaindlstorfer, Christine and Eschlboeck, Sabine and Fanciulli, Alessandra and Granata, Roberta and Eskofier, Björn and Seppi, Klaus and Poewe, Werner and Willeit, Johann and Kiechl, Stefan and Mahlknecht, Philipp and Stockner, Heike and Marini, Kathrin and Schorr, Oliver and Rungger, Gregorio and Klucken, Jochen and Wenning, Gregor and Gaßner, Heiko},
doi = {10.1007/s00415-020-10355-y},
faupublication = {yes},
journal = {Journal of Neurology},
note = {CRIS-Team WoS Importer:2021-01-22},
peerreviewed = {Yes},
title = {{Characterization} of gait variability in multiple system atrophy and {Parkinson}'s disease},
year = {2020}
}
@article{faucris.123693944,
abstract = {The application of wearables and customized signal processing methods offers new opportunities for motion analysis and visualization in skateboarding. In this work, we propose an automatic trick analysis and visualization application based on inertial-magnetic data. Skateboard tricks are detected and classified in real-time and visualized by means of an animated 3D-graphic. We achieved a trick detection recall of 96.4%, a classification accuracy of 89.1% (considering correctly performed tricks) and an error of the board orientation visualization of 2.2° ± 1.9°. The system is extendable in its application and can be incorporated as support for skateboard training and competitions.},
author = {Groh, Benjamin and Fleckenstein, Martin and Kautz, Thomas and Eskofier, Björn},
doi = {10.1016/j.pmcj.2017.05.007},
faupublication = {yes},
journal = {Pervasive and Mobile Computing},
pages = {42-55},
peerreviewed = {unknown},
title = {{Classification} and visualization of skateboard tricks using wearable sensors},
url = {https://authors.elsevier.com/a/1VDaj5bwSmo0qn},
volume = {40},
year = {2017}
}
@inproceedings{faucris.217911707,
abstract = {Modern machine learning techniques enable new possibilities for the analysis of psychological data. In the field of health psychology, it is of interest to explore the biological processes triggered by acute stress. This work introduces a method to automatically classify individuals into distinct stress responder groups based on these biological processes. Two important stress-sensitive markers were used: Salivary cortisol and Interleukin-6 (IL-6) in blood plasma. Controlled stress was induced using the Trier Social Stress Test on two consecutive days. Results show that Support Vector Machines performed best on the given dataset. We distinguished four different cortisol and three different IL-6 responder types with high mean accuracies (92.2%±9.7% and 91.2%±6.3%, respectively). Classification results were mainly limited by class imbalances and high intra-class standard deviations. Whereas promising as a first application of machine learning on such datasets, generalizability and real-world applicability of our results need to be proven by further research.
in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines. All approaches were applied to manual segmentations of tibia and lateral and medial tibial cartilage performed by experienced raters. The underlying data were contrast agent-enhanced cone-beam C-arm CT reconstructions of one healthy subject’s knee. The subject was scanned three times, once in supine position and two times in a standing weight-bearing position. A comparison of the resulting thickness maps shows similar distributions and high correlation coefficients between the approaches above 0.90. The nearest neighbor method results on average in the lowest cartilage thickness values, while the local thickness approach assigns the highest values. We showed that the different methods agree in their thickness distribution. The results will be used for a future evaluation of cartilage change under weight-bearing conditions.},
author = {Maier, Jennifer and Black, Marianne and Bonaretti, Serena and Bier, Bastian and Eskofier, Björn and Choi, Jang-Hwan and Levenston, Marc and Gold, Garry and Fahrig, Rebecca and Maier, Andreas},
doi = {10.1515/jib-2017-0015},
faupublication = {yes},
journal = {Journal of integrative bioinformatics},
keywords = {Cartilage strain; Cone-beam C-arm CT; Weight-bearing; Local thickness; Potential field lines},
peerreviewed = {Yes},
title = {{Comparison} of {Different} {Approaches} for {Measuring} {Tibial} {Cartilage} {Thickness}},
volume = {14},
year = {2017}
}
@article{faucris.110776644,
author = {Christian, Josef and Kluge, Felix and Eskofier, Björn and Schwameder, Hermann},
doi = {10.5430/jbei.v3n1p10},
faupublication = {yes},
journal = {Journal of Biomedical Engineering and Informatics},
keywords = {Marker set; Marker trajectory; Gait; Pattern classification; Machine learning; Principal component analysis},
pages = {10-17},
peerreviewed = {Yes},
title = {{Comparison} of different marker sets for marker trajectory and principal component analysis based classification of simulated gait impairments},
volume = {3},
year = {2017}
}
@inproceedings{faucris.236491271,
author = {Christian, Josef and Kluge, Felix and Eskofier, Björn and Schwameder, Hermann},
booktitle = {21st Annu. Congr. Eur. Coll. Sport Sci. 2016},
doi = {10.5430/jbei.v3n1p10},
faupublication = {yes},
peerreviewed = {unknown},
title = {{Comparison} of different marker sets for marker trajectory based classification of simulated gait impairments},
venue = {Wien},
year = {2016}
}
@inproceedings{faucris.122937584,
author = {Leutheuser, Heike and Gradl, Stefan and Kugler, Patrick and Anneken, Lars and Achenbach, Stephan and Eskofier, Björn and Arnold, Martin},
booktitle = {IEEE EMBC 2014},
faupublication = {yes},
pages = {2690-2693},
peerreviewed = {unknown},
title = {{Comparison} of {Real}-{Time} {Classification} {Systems} for {Arrhythmia} {Detection} on {Android}-based {Mobile} {Devices}},
venue = {Chicago, Illinois, USA},
year = {2014}
}
@inproceedings{faucris.120330364,
author = {Leutheuser, Heike and Gabsteiger, Florian and Hebenstreit, Felix and Reis, Pedro and Lochmann, Matthias and Eskofier, Björn},
booktitle = {IEEE EMBC 2013},
date = {2013-07-03/2013-07-07},
editor = {Engineering in Medicine and Biology Society},
faupublication = {yes},
pages = {6804-6807},
title = {{Comparison} of the {AMICA} and the {InfoMax} {Algorithm} for the {Reduction} of {Electromyogenic} {Artifacts} in {EEG} {Data}},
url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2013/Leutheuser13-COT.pdf},
venue = {Osaka},
year = {2013}
}
@inproceedings{faucris.281902109,
abstract = {
I. INTRODUCTION
According to estimations by the World Health Organization, more than 8.5 million people are diagnosed with Parkinson’s disease, making it the second most prevalent neurodegenerative disorder worldwide [1]. While the most commonly-known symptoms are motor symptoms, the disease is also characterized by sleep-related non-motor symptoms such as rapid- eye-movement sleep behavior disorder, insomnia, or daytime sleepiness. Therefore, reliable sleep monitoring is crucial for the diagnosis and treatment. As traditional sleep monitoring is obtrusive and hard to perform longitudinally, the contactless measurement of biosignals at home is a promising alternative for meaningful analysis of sleep [2].
Previous research has shown encouraging results in the extraction of relevant biosignals such as heart sounds or respi- ration waves from radar data [3]. However, there is insufficient research on assessing the robustness against different sleeping positions, as well as the combination of multiple radar sensors.
For that reason, this work aims to present a radar sens- ing system for nocturnal heart sound extraction using a bi- directional Long short-term memory network. In particular, we combine the information collected by four radar sensors placed horizontally at thorax height under the bed mattress. Furthermore, we compared the extracted heart rate with respect to different sleeping positions and upright sitting.
II. METHODS
The data used in this work were obtained from an overnight
sleep study of 17 participants (age: 27.6 ± 8.4; 52.9% female), resulting in a total of 99.5 hours of sleep data. The dataset includes polysomnography recordings and simultaneous sig- nals captured by four 61 GHz continuous wave doppler radars. For each radar sensor, heart sounds were extracted from the raw signals using a pre-trained bi-directional LSTM model. On the model output, we performed a threshold-based peak detection to identify individual heart beats. To fuse the output of the four sensors, we selected the sensor with the highest number of detected heart beats for every 30-second window. Subsequently, the selected beats were utilized to calculate the mean heart rate, which was then compared with the heart rate extracted from the electrocardiogram data for each respective 30-second window.
III. RESULTS & DISCUSSION
Our results show that the mean absolute error of the extracted heart rate is 1.53 ± 2.14 bpm (Table 1). This performance is consistent across right, left, supine, or prone pose. Only when participants were sitting in the bed, the extraction performance dropped noticeably. However, this posture is not relevant for the analysis of specific sleep patterns.
Our findings underline the potential of contactless vital sign monitoring for unobtrusive sleep analysis. This allows for longitudinal and more realistic measurements of sleep patterns. Thus, it is a promising step towards transferring sleep labora- tories into a home monitoring environment.
Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometers-based systems have been developed in the past few years , to tackle common issues in ultrasound, monitoring and monitoring, self-administered monitoring of fetal exercise during preg- nancy. HOWEVER, many questions remain unanswered to date on the optimal setup in terms of body-worn accelerometers as well as signal processing and machine learning techniques used to detect fetal movement. In this paper, we investigate the trade-offs between sensor and positioning, the presence of reference accelerometers. Using a dataset of 15 measurements collected Employing 6 Three- axial accelerometers we show did Including a reference ac celerometer on the back of the participant consistently Improves fetal movement detection performance Regardless of the number of sensors Utilized. We also have two accelerometers plus a reference accelerometer.
}, address = {Orlando, USA}, author = {Gradl, Stefan and Altini, Marco and Grieten, Lars and Eskofier, Björn and Geusens, Nele and Mullan, Patrick and Penders, Julien and Rooijakkers, Michiel}, booktitle = {Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2016)}, date = {2016-08-16/2016-08-20}, editor = {IEEE Engineering in Medicine and Biology Society}, faupublication = {yes}, pages = {5319-5322}, peerreviewed = {unknown}, title = {{Detection} of {Fetal} {Kicks} {Using} {Body}-{Worn} {Accelerometers} {During} {Pregnancy}: {Trade}-offs {Between} {Sensors} {Number} and {Positioning}}, venue = {Orlando, USA}, year = {2016} } @article{faucris.234895382, abstract = {Mobile gait analysis using wearable inertial measurement units (IMUs) provides valuable insights for the assessment of movement impairments in different neurological and musculoskeletal diseases, for example Parkinson's disease (PD). The increase in data volume due to arising long-term monitoring requires valid, robust and efficient analysis pipelines. In many studies an upstream detection of gait is therefore applied. However, current methods do not provide a robust way to successfully reject non-gait signals. Therefore, we developed a novel algorithm for the detection of gait from continuous inertial data of sensors worn at the feet. The algorithm is focused not only on a high sensitivity but also a high specificity for gait. Sliding windows of IMU signals recorded from the feet of PD patients were processed in the frequency domain. Gait was detected if the frequency spectrum contained specific patterns of harmonic frequencies. The approach was trained and evaluated on 150 clinical measurements containing standardized gait and cyclic movement tests. The detection reached as sensitivity of 0.98 and a specificity of 0.96 for the best sensor configuration (angular rate around the medio-lateral axis). On an independent validation data set including 203 unsupervised, semi-standardized gait tests, the algorithm achieved a sensitivity of 0.97. Our algorithm for the detection of gait from continuous IMU signals works reliably and showed promising results for the application in the context of free-living and non-standardized monitoring scenario}, author = {Ullrich, Martin and Küderle, Arne and Hannink, Julius and Del Din, Silvia and Gaßner, Heiko and Marxreiter, Franz and Klucken, Jochen and Eskofier, Björn and Kluge, Felix}, doi = {10.1109/JBHI.2020.2975361}, faupublication = {yes}, journal = {IEEE Journal of Biomedical and Health Informatics}, keywords = {Accelerometer, Fourier transform, Gyroscope, Parkinsons disease, Walking bouts}, pages = {1869 - 1878}, peerreviewed = {Yes}, title = {{Detection} of {Gait} {From} {Continuous} {Inertial} {Sensor} {Data} {Using} {Harmonic} {Frequencies}}, url = {https://www.mad.tf.fau.de/files/2020/11/2020{\_}ullrich{\_}gaitsequencedetection.pdf}, volume = {24}, year = {2020} } @inproceedings{faucris.228585757, address = {ROCKVILLE}, author = {Kara, David Delil and Ring, Matthias and Mehringer, Wolfgang and Eskofier, Björn and Hennig, Friedrich and Michelson, Georg}, booktitle = {INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE}, date = {2019-04-28/2019-05-02}, faupublication = {yes}, note = {CRIS-Team WoS Importer:2019-11-01}, peerreviewed = {unknown}, publisher = {ASSOC RESEARCH VISION OPHTHALMOLOGY INC}, title = {{Detection} of {Mild} {Traumatic} {Brain} {Injury} by a {Virtual} {Reality} {System}}, venue = {Vancouver, CANADA}, year = {2019} } @inproceedings{faucris.121346324, abstract = {The usage of tracking technology in sports became standard in most major team sports like soccer, basketball or American football. Official tracking in soccer is done by video tracking and is currently limited to physical assessments of the players. New high rate tracking technologies for players and balls could also enable a detailed technical assessment of athletes. This work investigates the detection of single ball contacts, distinguishing left and right foot, using a radio based real-time tracking system. Ball tracking is done at 2000 Hz and player tracking at 200 Hz. Miniaturized transmitters were attached to the shins, enabling the distinction between left and right contacts with the ball. Data acquisition was done in an indoor environment. Six small sided games (749 contacts) and 25 repetitions of training exercises (195 contacts) were recorded. The ground truth of 941 single ball contacts was assessed by video annotation. Detection in the training scenario shows a precision of 97-100 % and a recall of 87-100 %. For the game scenario, precision is 89 % and recall reaches 93 %. Detection errors mainly affect non crucial contacts during longer dribblings, which are not the main interest of many analyses. The high precision and recall rates indicate a solid base for higher level technical assessments in automatic match (statistics) and training analysis (skill evaluation).}, author = {Witt, Nicolas and Völker, Matthias and Eskofier, Björn}, booktitle = {icSports 2016}, date = {2016-11-07/2016-11-09}, editor = {SCITEPRESS}, faupublication = {yes}, keywords = {soccer;tracking data;ball contacts; actions}, pages = {35-35}, peerreviewed = {Yes}, title = {{Detection} of {Single} {Ball} {Contacts} using a {Radio}-based {Tracking} {System} - {A} {Basis} for {Technical} {Performance} {Analysis}}, venue = {Porto}, year = {2016} } @article{faucris.265159892, abstract = {Gait tests as part of home monitoring study protocols for patients with movement disorders may provide valuable standardized anchor-points for real-world gait analysis using inertial measurement units (IMUs). However, analyzing unsupervised gait tests relies on reliable test annotations by the patients requiring a potentially error-prone interaction with the recording system. To overcome this limitation, this work presents a novel algorithmic pipeline for the automated detection of unsupervised standardized gait tests from continuous real-world IMU data. In a study with twelve Parkinson’s disease patients, we recorded real-world gait data over two weeks using foot-worn IMUs. During continuous daily recordings, the participants performed series of three consecutive 4x10-Meters-Walking-Tests (4x10MWTs) at different walking speeds, besides their usual daily-living activities. The algorithm first detected these gait test series using a gait sequence detection algorithm, a peak enhancement pipeline, and subsequence Dynamic Time Warping and then decomposed them into single 4x10MWTs based on the walking speed. In the evaluation with 419 available gait test series, the detection reached an F1-score of 88.9% and the decomposition an F1-score of 94.0%. A concurrent validity evaluation revealed very good agreement between spatio-temporal gait parameters derived from manually labelled and automatically detected 4x10MWTs. Our algorithm allows to remove the burden of system interaction from the patients and reduces the time for manual data annotation for researchers. The study contributes to an improved automated processing of real-world IMU gait data and enables a simple integration of standardized tests into continuous long-term recordings. This will help to bridge the gap between supervised and unsupervised gait assessment.}, author = {Ullrich, Martin and Mücke, Annika and Küderle, Arne and Roth, Nils and Gladow, Till and Gaßner, Heiko and Marxreiter, Franz and Klucken, Jochen and Eskofier, Björn and Kluge, Felix}, doi = {10.1109/TNSRE.2021.3119390}, faupublication = {yes}, journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering}, keywords = {Machine learning; activity recognition; accelerometer; gyroscope; legged locomotion; protocol; diseases; pipelines; monitoring; hospitals; annotations}, pages = {2103-2111}, peerreviewed = {Yes}, title = {{Detection} of unsupervised standardized gait tests from real-world inertial sensor data in {Parkinson}’s disease}, url = {https://ieeexplore.ieee.org/abstract/document/9567680}, year = {2021} } @article{faucris.222105992, abstract = {BackgroundGait symptoms and balance impairment are characteristic indicators for the progression in Parkinson's disease (PD). Current gait assessments mostly focus on straight strides with assumed constant velocity, while acceleration/deceleration and turning strides are often ignored. This is either due to the set up of typical clinical assessments or technical limitations in capture volume. Wearable inertial measurement units are a promising and unobtrusive technology to overcome these limitations. Other gait phases such as initiation, termination, transitioning (between straight walking and turning) and turning might be relevant as well for the evaluation of gait and balance impairments in PD.MethodIn a cohort of 119 PD patients, we applied unsupervised algorithms to find different gait clusters which potentially include the clinically relevant information from distinct gait phases in the standardized 4x10 m gait test. To clinically validate our approach, we determined the discriminative power in each gait cluster to classify between impaired and unimpaired PD patients and compared it to baseline (analyzing all straight strides).ResultsAs a main result, analyzing only one of the gait clusters constant, non-constant or turning led in each case to a better classification performance in comparison to the baseline (increase of area under the curve (AUC) up to 19% relative to baseline). Furthermore, gait parameters (for turning, constant and non-constant gait) that best predict motor impairment in PD were identified.ConclusionsWe conclude that a more detailed analysis in terms of different gait clusters of standardized gait tests such as the 4x10 m walk may give more insights about the clinically relevant motor impairment in PD patients.}, author = {Nguyen, An and Roth, Nils and Haji Ghassemi, Nooshin and Hannink, Julius and Seel, Thomas and Klucken, Jochen and Gaßner, Heiko and Eskofier, Björn}, doi = {10.1186/s12984-019-0548-2}, faupublication = {yes}, journal = {Journal of neuroEngineering and rehabilitation}, note = {CRIS-Team WoS Importer:2019-07-12}, peerreviewed = {Yes}, title = {{Development} and clinical validation of inertial sensor-based gait-clustering methods in {Parkinson}'s disease}, volume = {16}, year = {2019} } @inproceedings{faucris.222794361, abstract = {A stable knee is of great importance as lack of stability can cause injuries like ligament ruptures and may result in the end of a professional athlete’s career. Several studies confirm the effectiveness of prevention trainings for knee stabilization and can reduce the risk for anterior cruciate ligament ruptures up to 90 % [1]. This paper describes a new approach to measure parameters of knee stabilizing exercises using a sensor equipped knee sleeve [2].
Based on literature research several risk factors for an unstable knee were identified (e.g. weak knee flexors, proprioception deficits) and a test battery with stabilizing exercises was composed. The test battery included balance, strength and jump tests. Based on those exercises, we developed algorithms to evaluate the knee stability using data from a knee sleeve equipped with two inertial measurement units (IMUs) located on the thigh and the shank. The algorithms using a subsequence dynamic time warping (sDTW) approach are capable of computing jump height using a flight-time-approach and the number of repetitions of strength exercises.
We conducted a study with 16 subjects to evaluate the accuracy of the developed algorithms on the test battery. As gold standard, we used a force plate for computing jump height and videos to count the number of repetitions. The jump height computed using the data obtained by the knee sleeve showed an error of 3.3 cm. The sensitivity of the sDTW algorithm used to extract the repetitions in the IMU data streams was 97.6 %.
The results show that the sensor equipped knee sleeve can accurately extract knee stability relevant
parameters. These parameters can be used to monitor and improve the knee stability using training plans, which can not only prevent ligament ruptures, but also help in recovery scenarios.
},
author = {Dörflinger, Luisa and Nissen, Michael and Jäger, Katharina and Wirth, Markus and Titzmann, Adriana and Pontones, Constanza and Fasching, Peter and Beckmann, Matthias and Gradl, Stefan and Eskofier, Björn},
booktitle = {Mensch und Computer 2021},
date = {2021-09-05/2021-09-08},
doi = {10.1145/3473856.3473994},
faupublication = {yes},
isbn = {978-1-4503-8645-6},
peerreviewed = {Yes},
title = {{Digital} {Maternity} {Records}: {Motivation}, {Acceptance}, {Requirements}, {Usability} and {Prototype} {Evaluation} of an {Interface} for {Physicians} and {Midwives}},
venue = {Ingolstadt},
year = {2021}
}
@inproceedings{faucris.246235509,
abstract = {Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a realtime handwriting recognition system to be used for writing on normal paper.
To overcome this problem we propose a simple yet effective modification to the gradient calculation of state-of-the-art first-order adversarial attacks.
Normally, the gradient update of an attack is directly calculated for the given data point. This approach is sensitive to noise and small local optima of the loss function. Inspired by gradient sampling techniques from non-convex optimization, we propose Dynamically Sampled Nonlocal Gradient Descent (DSNGD). DSNGD calculates the gradient direction of the adversarial attack as the weighted average over past gradients of the optimization history. Moreover, distribution hyperparameters that define the sampling operation are automatically learned during the optimization scheme. We empirically show that by incorporating this nonlocal gradient information, we are able to give a more accurate estimation of the global descent direction on noisy and non-convex loss surfaces. In addition, we show that DSNGD-based attacks are on average 35% faster while achieving 0.9% to 27.1% higher success rates compared to their gradient descent-based counterpart}, author = {Schwinn, Leo and Nguyen, An and Raab, René and Zanca, Dario and Eskofier, Björn and Tenbrinck, Daniel and Burger, Martin}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, date = {2021-07-18/2021-07-22}, doi = {10.1109/ijcnn52387.2021.9534190}, faupublication = {yes}, peerreviewed = {unknown}, title = {{Dynamically} {Sampled} {Nonlocal} {Gradients} for {Stronger} {Adversarial} {Attacks}}, venue = {Online}, year = {2021} } @article{faucris.119737904, abstract = {Background
Sway is a crucial gait characteristic tightly correlated with the risk of falling in patients with Parkinson´s disease (PD). So far, the swaying pattern during locomotion has not been investigated in rodent models using the analysis of dynamic footprint recording obtained from the CatWalk gait recording and analysis system.
New Methods
We present three methods for describing locomotion sway and apply them to footprint recordings taken from C57BL6/N wild-type mice and two different α-synuclein transgenic PD-relevant mouse models (α-synm-ko, α-synm-koxα-synh-tg). Individual locomotion data were subjected to three different signal processing analytical approaches: the first two methods are based on Fast Fourier Transform (FFT), while the third method uses Low Pass Filters (LPF). These methods use the information associated with the locomotion sway and generate sway-related parameters.
Results
The three proposed methods were successfully applied to the footprint recordings taken from all paws as well as from front/hind-paws separately. Nine resulting sway-related parameters were generated and successfully applied to differentiate between the mouse models under study. Namely, α-synucleinopathic mice revealed higher sway and sway itself was significantly higher in the α-synm-koxα-synh-tg mice compared to their wild-type littermates in eight of the nine sway-related parameters.
Comparison with Existing Method
Previous locomotion sway index computation is based on the estimated center of mass position of mice.
Conclusions
The methods presented in this study provide a sway-related gait characterization. Their application is straightforward and may lead to the identification of gait pattern derived biomarkers in rodent models of P}, author = {Timotius, Ivanna and Canneva, Fabio and Minakaki, Georgia and Pasluosta, Cristian Federico and Moceri, Sandra and Casadei, Nicolas and Riess, Olaf and Winkler, Jürgen and Klucken, Jochen and von Hörsten, Stephan and Eskofier, Björn}, doi = {10.1016/j.jneumeth.2017.12.004}, faupublication = {yes}, journal = {Journal of Neuroscience Methods}, keywords = {Locomotion; sway; gait analysis; CatWalk system; Parkinson's disease; alpha-synuclein transgenic}, note = {EVALuna2:32788}, pages = {1-11}, peerreviewed = {Yes}, title = {{Dynamic} footprint based locomotion sway assessment in α-synucleinopathic mice using {Fast} {Fourier} {Transform} and {Low} {Pass} {Filter}}, url = {http://www.sciencedirect.com/science/article/pii/S0165027017304211}, volume = {296}, year = {2018} } @article{faucris.119909504, abstract = {Characterizing gait is important in the study of movement disorders, also in clinical mouse models. Gait data are therefore necessary for the development of gait analysis methods and the study of diseases. This article presents gait data of two α-synucleinopathic transgenic mouse models and their non-transgenic littermate, backcrossed into the C57BL/6N genetic background. The animal gait was recorded using CatWalk system, which provides the information for each run about the paw positions, paw print sizes, and paw intensities as a function of time or video frame. A total of 90 run data files are provided in this article.}, author = {Timotius, Ivanna and Canneva, Fabio and Minakaki, Georgia and Pasluosta, Cristian Federico and Moceri, Sandra and Casadei, Nicolas and Riess, Olaf and Winkler, Jürgen and Klucken, Jochen and von Hörsten, Stephan and Eskofier, Björn}, doi = {10.1016/j.dib.2017.12.067}, faupublication = {yes}, journal = {Data in Brief}, keywords = {Data; gait analysis; CatWalk system; Parkinson's disease; alpha-synuclein transgenic.}, pages = {189-193}, peerreviewed = {unknown}, title = {{Dynamic} footprints of α-synucleinopathic mice recorded by {CatWalk} gait analysis}, url = {https://www.sciencedirect.com/science/article/pii/S2352340918300015}, volume = {17}, year = {2018} } @inproceedings{faucris.235499963, abstract = {
Einleitung
Sprunggelenksdistorsionen zählen zu den häufigsten Sportverletzungen und mindestens ein Drittel der betroffenen Sportler entwickelt eine chronische Gelenksinstabilität (CAI) [1]. Folgen sind Funktionsverlust und ein hohes Wiederverletzungsrisiko. Die Entwicklung einer CAI wird vor allem mit Defiziten in der sensomotorischen Kontrolle assoziiert [2]. Ungeklärt bleibt jedoch, wie sich diese potentiellen Veränderungen in der sensomotorischen Kontrolle auf das Laufmuster auswirken. Ziel der Studie war es, die dynamische Stabilität der Fußkinematik von Sportlern mit CAI bei unterschiedlichen Lauftempi und fortschreitender Ermüdung zu untersuchen, um potentielle Defizite in der sensomotorischen Kontrolle zu identifizieren.
Methode
27 männliche Sportler mit vorausgegangener Sprunggelenksverletzung wurden untersucht, 15 davon mit CAI (Alter: 24,5 ± 3,3; Cumberland Ankle Instability Tool (CAIT): 21,5 ± 3,1) und 12 ohne persistierende Instabilität (Coper; Alter: 25,9 ± 3,9; CAIT: 28,4 ± 1,7). Die Laufanalyse erfolgte auf einem Laufband bei drei Geschwindigkeiten: 1) moderat-intensiv (Borg = 14/20; 2,64 ± 0,19 m/s), 2 & 3) Steigerung der initialen Geschwindigkeit um jeweils 0,6 m/s. Die Aufzeichnung der Fußkinematik erfolgte mittels einem am Schuh fixierten Inertialsensor (Shimmer). Zusätzlich absolvierten 17 Probanden (CAI: 9; Coper: 8) die Laufanalyse erneut nach einem neuromuskulären Ermüdungsprotokoll bestehend aus verschiedenen Sprung- und Laufsequenzen, um potentielle Ermüdungseinflüsse auf die Stabilität des Fußes zu untersuchen. Zur Analyse potentieller Veränderungen in der sensomotorischen Kontrolle wurde die lokale dynamische Stabilität (LDS) des Fußes mittels des maximalen Lyapunov Exponenten (MLE) analysiert und Gruppenunterschiede sowie Veränderungen über die Zeit untersucht (ANOVA mit Messwiederholung).
Ergebnisse
Die Sportler mit CAI wiesen sowohl im unermüdeten (p = 0,027; ƞ2 = 0,180) als auch im ermüdeten (p = 0,028; ƞ2 = 0,283) Zustand eine signifikant höhere LDS (= geringerer MLE) im Vergleich zu Copern auf (Abb. 1). Die Laufgeschwindigkeit hatte keinen Einfluss auf die LDS (p = .981; ƞ2 = 0,000). Im ermüdeten Zustand konnte in beiden Gruppen eine statistisch nicht-signifikante (p = .059; ƞ2 = 0,026) Abnahme der LDS festgestellt werden.
Diskussion & Zusammenfassung
Entgegen unserer Annahmen wiesen Sportler mit CAI sowohl vor als auch nach Ermüdung eine höhere Stabilität der Fußkinematik beim Laufen auf. Dies könnte auf Kompensationsmechanismen im Sinne einer überhöhten Gelenkstabilisierung und eines rigideren Laufmusters bei Sprunggelenksinstabilität hindeuten. Die Abnahme der LDS im ermüdeten Zustand gilt es im Hinblick auf ein erhöhtes Verletzungsrisiko zu untersuchen.
Referenzen
[1] Gribble;2016;British Journal of Sports Medicine;50(24).
[2] Hertel;2019;Journal of Athletic Training;54(6}, author = {Wanner, Philipp and Hamacher, Daniel and Schmautz, Thomas and Eskofier, Björn and Pfeifer, Klaus and Steib, Simon}, booktitle = {Tagungsband 3. Gamma-Kongress}, date = {2020-03-06/2020-03-07}, faupublication = {yes}, pages = {77-78}, peerreviewed = {Yes}, title = {{Dynamische} {Laufstabilität} des {Fußes} bei {Sportlern} mit und ohne chronischer {Sprunggelenksinstabilität} nach {Sprunggelenksdistorsion} unter {Berücksichtigung} des {Lauftempos} und neuromuskulärer {Ermüdung}}, venue = {München}, year = {2020} } @article{faucris.307831824, abstract = {
Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user’s daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid’s integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.
Approach: The analysis was performed in 3 stages: feature extraction, preprocessing and
feature selection and classification. Twenty eight predictive features were calculated on
4s episode of the pre-shock VF signal. The preprocessing included instances
normalization and oversampling. Seven machine learning algorithms were employed for
selecting the best performing single feature and combination of features using wrapper
method: Logistic Regression (LR), Naïve-Bayes (NB), Decision tree (C4.5),
AdaBoost.M1 (AB), Support Vector Machine (SVM), Nearest Neighbour (NN) and
Random Forest (RF). Evaluation of the algorithms was performed by nested 10 fold
cross-validation procedure.
Main results: A total of 251 unbalanced first shocks (195 unsuccessful and 56 successful)
were oversampled to 195 instances in each class. Performance metric based on average
accuracy of feature combination has shown that LR and NB exhibit no improvement,
C4.5 and AB an improvement not greater than 1% and SVM, NN and RF an
improvement greater than 5% in predicting defibrillation outcome in comparison to the
best single feature.
Significance: By performing wrapper method to select best performing feature
combination the non-linear machine learning strategies (SVM, NN, RF) can improve
defibrillation prediction performance.
},
author = {Nitschke, Marlies and Dorschky, Eva and Heinrich, Dieter and Schlarb, Heiko and Eskofier, Björn and Koelewijn, Anne and van den Bogert, Antonie J.},
doi = {10.1038/s41598-020-73856-w},
faupublication = {yes},
journal = {Scientific Reports},
keywords = {Biomechanics; Musculoskeletal Simulation; Optimal Control; Motion Analysis},
peerreviewed = {Yes},
title = {{Efficient} trajectory optimization for curved running using a {3D} musculoskeletal model with implicit dynamics},
url = {https://www.nature.com/articles/s41598-020-73856-w},
year = {2020}
}
@inproceedings{faucris.231199615,
abstract = {
Einleitung
Sprunggelenksdistorsionen führen bei Sportlern häufig zu chronischen Instabilitäten (CAI), was vor allem mit sensomotorischen Defiziten und dadurch veränderten Bewegungsmustern assoziiert wird. Ziel der Studie war es, die dynamische Stabilität der Fußkinematik von Sportlern mit CAI bei unterschiedlichen Lauftempi und fortschreitender Ermüdung zu untersuchen.
Methode
27 männliche Sportler mit vorausgegangener Sprunggelenksverletzung wurden untersucht, 15 davon mit CAI und 12 ohne persistierende Instabilität (Coper). Die Laufanalyse erfolgte auf einem Laufband bei drei Geschwindigkeiten: 1) moderat (2,64±0,19 m/s), 2 & 3) jeweils Steigerung um 0,6 m/s. Die Aufzeichnung der Fußkinematik erfolgte mittels einem am Schuh fixierten Inertialsensor. Zusätzlich absolvierten 17 Probanden (CAI: 9; Coper: 8) die Laufanalyse erneut nach einem neuromuskulären Ermüdungsprotokoll. Die lokale dynamische Stabilität (LDS) des Fußes wurde analysiert und Gruppenunterschiede sowie Veränderungen über die Zeit untersucht (ANOVA mit Messwiederholung).
Ergebnisse
Ein signifikanter Gruppeneffekt (p = .027) deutete auf eine höhere LDS bei Sportlern mit CAI im Vergleich zu Copern hin, wobei keine Veränderung durch die Laufgeschwindigkeit festzustellen war (p = .981). Nach Ermüdung tendierten die Sportler unabhängig der Gruppe zu einer Abnahme der LDS (p = .059), bei signifikant höherer LDS der CAI-Gruppe (p = .028).
Diskussion
Sportler mit CAI wiesen sowohl vor als auch nach Ermüdung eine höhere Stabilität der Fußkinematik beim Laufen auf. Dies könnte auf eine verstärkte muskuläre Gelenkstabilisierung und ein rigideres Laufmuster bei Sportlern mit Sprunggelenksinstabilität hindeute}, address = {Hamburg}, author = {Wanner, Philipp and Hamacher, Daniel and Schmautz, Thomas and Eskofier, Björn and Pfeifer, Klaus and Steib, Simon}, booktitle = {Sport im öffentlichen Raum - Abstractband des 24. dvs-Hochschultags}, date = {2019-09-18/2019-09-20}, editor = {Arampatzis, A., Braun, S., Schmitt, K., Wolfarth, B}, faupublication = {yes}, isbn = {978-3-88020-679-3}, keywords = {Sprunggelenksdistorsion; Laufanalyse; lokale dynamische Stabilität}, pages = {239}, peerreviewed = {Yes}, publisher = {Feldhaus}, title = {{Einfluss} von {Lauftempo} und neuromuskulärer {Ermüdung} auf die dynamische {Laufstabilität} des {Fußes} bei {Sportlern} mit chronischer {Sprunggelenkinstabilität}}, venue = {Berlin}, year = {2019} } @article{faucris.251057047, abstract = {The cooperation between robots and astronauts will become a core element of future space missions. This is accompanied by the demand for suitable input devices. An interface based on electromyography (EMG) represents a small, light, and wearable device to generate a continuous three-dimensional (3D) control signal from voluntarily muscle activation of the operator’s arm. We analyzed the influence of microgravity on task performance during a two-dimensional (2D) task on a screen. Six subjects performed aiming and tracking tasks in parabolic flights. Three different levels of fixation—fixed feet using foot straps, semi-free by using a foot rail, and free-floating feet—are tested to investigate how much user fixation is required to operate via the interface. The user study showed that weightlessness affects the usage of the interface only to a small extent. Success rates between 89$\text{\%}$ and 96$\text{\%}$ are reached within all conditions during microgravity. A significant effect between 0 and 1G could not be identified for the test series of fixed and semi-free feet, while free-floating feet showed significantly worse results in fine and gross motion times in 0G compared to ground tests (with success rates of 92$\text{\%}$ for 0G and 99$\text{\%}$ for 1G). Further adaptation to the altered proprioception may be needed here. Hence, foot rails as already mounted in the International Space Station (ISS) would be sufficient to use the interface in weightlessness. Low impact of microgravity, high success rates, and an easy handling of the system, indicates a high potential of an EMG-based interface for teleoperation in space.}, author = {Hagengruber, Annette and Leipscher, Ulrike and Eskofier, Björn and Vogel, Jorn}, doi = {10.1109/THMS.2020.3047975}, faupublication = {yes}, journal = {IEEE Transactions on Human-Machine Systems}, keywords = {Aerospace electronics; Electromyography; Electromyography (EMG); human machine interfaces; Muscles; robotcontrol; Robots; space application; Space missions; Task analysis; teleoperation; Training; weightlessness}, note = {CRIS-Team Scopus Importer:2021-03-05}, peerreviewed = {Yes}, title = {{Electromyography} for {Teleoperated} {Tasks} in {Weightlessness}}, year = {2021} } @inproceedings{faucris.120335644, author = {Eskofier, Björn and Kornhuber, Johannes and Hornegger, Joachim}, booktitle = {Russian-Bavarian Conference on Biomedical Engineering}, date = {2008-07-08/2008-07-09}, editor = {Bauernschmitt Robert, Chaplygin Yuri, Feußner Hubertus, Gulyeav Yuri, Hornegger Joachim, Mayr Ernst, Navab Nassir, Schookin Sergey, Selishchev Sergey, Umnyashkin Sergei}, faupublication = {yes}, pages = {48-52}, peerreviewed = {unknown}, title = {{Embedded} {QRS} {Detection} for {Noisy} {ECG} {Sensor} {Data} {Using} a {Matched} {Filter} and {Directed} {Graph} {Search}}, url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2008/Eskofier08-EQD.pdf}, venue = {Moskow Institute of Technology, Zelenograd}, year = {2008} } @article{faucris.120197264, abstract = {In this presentation, we give a detailed analysis of the considerations needed for mapping the complete pattern classification chain to the restricted embedded system hardware environment. We describe the methodology of the design, realization and testing process that takes these hardware limitations into account. For this purpose, we consider a particular embedded application from the field of digital sports: a novel running shoe that is capable of sensing run-specific parameters and adapting the cushioning setting accordingly. Of utmost importance in this context is the classification of the current surface condition in order to enable optimal adaptation to the prevailing situation. Following our design approach, we provide a classification system with a runner-independent surface classification rate of more than 80%. This system is implemented in the current version of the aforementioned running shoe. The presented methodology is quite general as it makes no system-dependent assumptions and can thus be transferred to many other embedded classification applications. © 2009 Elsevier B.V. All rights reserved.}, author = {Eskofier, Björn and Oleson, M. and Dibenedetto, C. and Hornegger, Joachim}, doi = {10.1016/j.patrec.2009.08.004}, faupublication = {yes}, journal = {Pattern Recognition Letters}, pages = {1448-1456}, peerreviewed = {Yes}, title = {{Embedded} surface classification in digital sports}, volume = {30}, year = {2009} } @inproceedings{faucris.281542078, author = {Gmelch, Lena Marie and Böhme, Stephanie and Capito, Klara and Rupp, Lydia and Richer, Robert and Sadeghi, Misha and Eskofier, Björn and Berking, Matthias}, booktitle = {Deutscher Psychotherapiekongress (DPK)}, date = {2022-06-07/2022-06-11}, faupublication = {yes}, peerreviewed = {Yes}, title = {{EmpkinS} - {Empathokinästhetische} {Sensorik} für {Biofeedback} bei {Depression}}, url = {https://deutscher-psychotherapie-kongress.de/wp-content/uploads/2022/06/web{\_}Programm{\_}DPK2022{\_}SS{\_}09062022{\_}final.pdf}, venue = {Berlin}, year = {2022} } @inproceedings{faucris.242719216, abstract = {Fragestellung: Der Bedarf an spezialisierter ambulanter Palliativversorgung (SAPV) steigt bei abnehmenden Ressourcen. Um eine flächendeckende Versorgung zu gewährleisten, können digitale Systeme Patienten und Fachpersonal unterstützen. Wir erforschen eine Smartphone Applikation (App) und Web-Interface (WI) die gewährleisten sollen, dass Patienten und SAPV Zugriff auf biometrische und psychometrische Daten haben, um die Versorgung möglichst Ressourcen-effizient zu gestalten.
Studiendesign: Explorative Anwendungs- und Proof-of-Concept-Studie mit Probanden
Methodik: App und WI Entwurf nach klinisch-medizinischen Vorgaben von erfahrenen Palliativmedizinern integriert psychometrische (MIDOS{\_}2) und exemplarische biometrische Messung (Körpergewicht (KG), Körperflüssigkeitsanteil (KFA), Blutdruck (BD)) mit automatischer (Bluetooth) und manueller Eingabe; Evaluation des ersten Design-Modells der App in begleitender Beobachtung; offenes Interview mit n = 2 Probanden (männlich, Alter 70-85);Implementierung des Feedbacks; Pilotierung mit n = 6 Probanden (2 weiblich, Alter 23-30);
Ergebnis: Erster Entwurf nach klinisch-medizinischen Vorgaben: App: KG des Patienten wird automatisch und KF, BD, MIDOS{\_}2 manuell eingegeben. Die Daten werden in App und WI visuell individuell anpassbar dargestellt. App wurde nach begleitender Beobachtung hinsichtlich Akzeptanz und Nutzbarkeit überarbeitet: vereinfachte Nutzerschnittstelle. Pilotierung mit Probanden: Dateneingabe, -übertragung, -abruf und webbasierter Zugriff erfolgten in 100 % der Fälle fehlerfrei. Anwendungsdauer für KG, KFA, BD, MIDOS{\_}2 betrug im Durchschnitt 4 min.
Diskussion: Austausch von psychometrischen und biometrischen Daten zwischen Patient (App) und SAPV-Team (WI) werden ermöglicht und so können SAPV Teams bei ihrer Behandlungsplanung unterstützet werden.
Take Home Message für die Kongressbesucher: App und WI kann die SAPV effizienter gestalten. Weitere Untersuchungen mit Patienten sind notwendi}, author = {Sternemann, Ulla and Suchantke, Insa and Schmidt, Klaus-Günther and Höfner, Patrick and Wagner, Daniel and Ollenschläger, Malte and Heckel, Maria and Ostgathe, Christoph and Eskofier, Björn and Steigleder, Tobias}, booktitle = {Zeitschrift für Palliativmedizin 2020; 21(05)}, date = {2020-09-09/2020-09-12}, doi = {10.1055/s-0040-1714998}, faupublication = {yes}, keywords = {Palliativmedizin; SAPV; Smartphone}, peerreviewed = {unknown}, publisher = {Georg Thieme Verlag KG}, title = {{Entwicklung} einer {Applikation} zum {Home}-{Monitoring} des {Gesundheitszustandes} von {Palliativpatienten} - eine {Proof}-of-{Concept}-{Studie}}, url = {https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0040-1714998#info}, venue = {Wiesbaden}, year = {2020} } @inproceedings{faucris.123005784, author = {Eischer, Michael and Blank, Peter and Danzer, Alexander and Hauck, Adrian and Hoffmann, Markus and Reck, Benjamin and Eskofier, Björn}, booktitle = {Robot Soccer World Cup XIX Proceedings}, faupublication = {yes}, note = {UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.erforc}, pages = {-}, peerreviewed = {unknown}, title = {{ER}-{Force} {Extended} {Team} {Description} {Paper} for {Robocup} 2015}, url = {https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2015/Eischer15-EET.pdf}, venue = {Hefei, China}, year = {2015} } @article{faucris.223642185, abstract = {
Inertial sensing enables field studies of human movement and ambulant assessment of patients. However, the challenge is to obtain a comprehensive analysis from low-quality data and sparse measurements. In this paper, we present a method to estimate gait kinematics and kinetics directly from raw inertial sensor data performing a single dynamic optimization. We formulated an optimal control problem to track accelerometer and gyroscope data with a planar musculoskeletal model. In addition, we minimized muscular effort to ensure a unique solution and to prevent the model from tracking noisy measurements too closely. For evaluation, we recorded data of ten subjects walking and running at six different speeds using seven inertial measurement units (IMUs). Results were compared to a conventional analysis using optical motion capture and a force plate. High correlations were achieved for gait kinematics (ρ ≥ 0.93) and kinetics (ρ ≥ 0.90). In contrast to existing IMU processing methods, a dynamically consistent simulation was obtained and we were able to estimate running kinetics. Besides kinematics and kinetics, further metrics such as muscle activations and metabolic cost can be directly obtained from simulated model movements. In summary, the method is insensitive to sensor noise and drift and provides a detailed analysis solely based on inertial sensor data.
Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research.
Objective:
This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2.
Methods:
Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed.
Results:
A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm).
Conclusions:
Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirement},
author = {Nissen, Michael and Slim, Syrine and Jäger, Katharina and Flaucher, Madeleine and Hübner, Hanna and Danzberger, Nina and Fasching, Peter and Beckmann, Matthias and Gradl, Stefan and Eskofier, Björn},
doi = {10.2196/33635},
faupublication = {yes},
journal = {JMIR Formative Research},
keywords = {wearable validation; heart rate validation; Fitbit Charge 4; Samsung Galaxy Watch Active2; heart rate accuracy; fitness tracker accuracy; wearable accuracy; wearable (21); Fitbit (10); heart rate (14); fitness tracker (3); fitness (2); cardiovascular (8)},
pages = {e33635},
peerreviewed = {Yes},
title = {{Heart} {Rate} {Measurement} {Accuracy} of {Fitbit} {Charge} 4 and {Samsung} {Galaxy} {Watch} {Active2}: {Device} {Evaluation} {Study}},
volume = {6},
year = {2022}
}
@article{faucris.119979684,
abstract = {The normal oscillation of the heart rate is called Heart Rate Variability (HRV). HRV parameters change under different conditions like rest, physical exercise, mental stress, and body posture changes. However, results how HRV parameters adapt to physical exercise have been inconsistent. This study investigated how different HRV parameters changed during one hour of running. We used datasets of 295 athletes where each dataset had a total length of about 65 minutes. Data was divided in segments of five minutes and three HRV parameters and one kinematic parameter were calculated for each segment. We applied two different analysis of variance (ANOVA) models to analyze the differences in the means of each segment for every parameter. The two ANOVA models were univariate ANOV A with repeated measures and multivariate ANOV A with repeated measures. The obligatory post-hoc procedure consisted of multiple dependent t tests with Bonferroni correction. We investigated the last three segments of the parameters in more detail and detected a delay of one minute between varying running speed and measured heart rate. Hence, the circulatory system of our population needed one minute to adapt to a change in running speed. The method we provided can be used to further investigate more HRV parameters. },
author = {Leutheuser, Heike and Eskofier, Björn},
faupublication = {yes},
journal = {International Journal of Computer Science in Sport},
keywords = {ADAPTION OF HRV PARAMETERS; RUNNING; UNIVARIATE ANOVA WITH REPEATED-MEASURES; MULTIVARIATE ANOVA WITH REPEATED MEASURES},
pages = {61-68},
peerreviewed = {unknown},
title = {{Heart} {Rate} {Variability} {During} {Physical} {Exercise}},
volume = {12.0},
year = {2013}
}
@inproceedings{faucris.108082744,
author = {Leutheuser, Heike and Eskofier, Björn},
booktitle = {KOPS Institutional Repository University of Konstanz},
date = {2012-09-12/2012-09-14},
editor = {Byshko R., Dahmen T., Gratkowski M., Gruber M., Quintana J., Saupe D., Vieten M., Woll A.},
faupublication = {yes},
pages = {14-20},
title = {{Heart} {Rate} {Variability} {During} {Physical} {Exercise}},
url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2012/Leutheuser12-HRV.pdf},
venue = {Universität Konstanz},
year = {2012}
}
@article{faucris.262434391,
abstract = {Objective. Voluntary control of sensorimotor rhythms (SMRs, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g. in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. Therefore, it would be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme. Approach. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline of SMR-ERD control across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 min each. During the 2nd run, task difficulty was gradually increased. Main results. While control performance (p = .18) and HRV (p = .16) remained unchanged across participants during the 1st run, during the 2nd run, both measures declined over time at high correlation (performance: -0.61%/10 s, p = 0; HRV: -0.007 ms/10 s, p < .001). We found that HRV exhibited predictive characteristics with regard to within-session BCI control performance on an individual participant level (p < .001). Significance. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g. to individualize and optimize assistive BCI systems in stroke.},
author = {Nann, Marius and Haslacher, David and Colucci, Annalisa and Eskofier, Björn and Von Tscharner, Vinzenz and Soekadar, Surjo R.},
doi = {10.1088/1741-2552/ac1177},
faupublication = {yes},
journal = {Journal of Neural Engineering},
note = {CRIS-Team WoS Importer:2021-08-06},
peerreviewed = {Yes},
title = {{Heart} rate variability predicts decline in sensorimotor rhythm control},
volume = {18},
year = {2021}
}
@inproceedings{faucris.267145082,
abstract = {Strabismus is a visual disorder characterized by eye misalignment. The extent of ocular misalignment is denoted as the deviation angle. With the advent of Virtual Reality (VR) Head-Mounted-Displays (HMD) and eye tracking technology, new possibilities measuring strabismus arise. Major research addresses the novel field of VR strabismus assessment by replicating prism cover tests while there is a paucity of research on screen tests. In this work the Hess Screen Test was implemented in VR using a HMD with eye tracking for an objective measurement of the deviation angle. In a study, the functionality was tested and compared with a 2D monitor-based test. The results showed significant differences in the measured deviation angle between the methods. This can be attributed to the type of dissociation of the eyes.
50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion: The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.},
author = {Roth, Nils and Küderle, Arne and Ullrich, Martin and Gladow, Till and Marxreiter, Franz and Klucken, Jochen and Eskofier, Björn and Kluge, Felix},
doi = {10.1186/s12984-021-00883-7},
faupublication = {yes},
journal = {Journal of neuroEngineering and rehabilitation},
keywords = {HMM; IMU; Machine learning; Mobile gait analysis; Stride borders; Wearable sensors},
note = {CRIS-Team Scopus Importer:2021-06-11},
peerreviewed = {Yes},
title = {{Hidden} {Markov} {Model} based stride segmentation on unsupervised free-living gait data in {Parkinson}’s disease patients},
volume = {18},
year = {2021}
}
@article{faucris.108144564,
abstract = {Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. The assessment of DLAs with small and light-weight wearable sensors (e.g. inertial measurement units) provides a reliable and objective method. State-of-the-art human physical activity classification systems differ in e.g. the number and kind of sensors, the performed activities, and the sampling rate. Hence, it is difficult to compare newly proposed classification algorithms to existing approaches in literature and no commonly used dataset exists. We generated a publicly available benchmark dataset for the classification of DLAs. Inertial data were recorded with four sensor nodes, each consisting of a triaxial accelerometer and a triaxial gyroscope, placed on wrist, hip, chest, and ankle. Further, we developed a novel, hierarchical, multi-sensor based classification system for the distinction of a large set of DLAs. Our hierarchical classification system reached an overall mean classification rate of 89.6% and was diligently compared to existing state-of-the-art algorithms using our benchmark dataset. For future research, the dataset can be used in the evaluation process of new classification algorithms and could speed up the process of getting the best performing and most appropriate DLA classification system.},
author = {Leutheuser, Heike and Schuldhaus, Dominik and Eskofier, Björn},
doi = {10.1371/journal.pone.0075196},
faupublication = {yes},
journal = {Plos One},
pages = {e75196},
peerreviewed = {Yes},
title = {{Hierarchical}, {Multi}-{Sensor} {Based} {Classification} of {Daily} {Life} {Activities}: {Comparison} with {State}-of-the-{Art} {Algorithms} {Using} a {Benchmark} {Dataset}},
volume = {8.0},
year = {2013}
}
@inproceedings{faucris.313091181,
abstract = {This paper presents a solution in measuring the moisture content of soil more reliably and more reproducibly. The sensor can detect moisture levels in different depths of the soil probed and in a more homogeneous way than comparable sensors that are commonly used. The repeatability of the measurement is also increased intrinsically because a larger soil volume is being sampled.
Macular degeneration is the third leading cause of blindness worldwide and the leading cause of blindness in the developing world. The analysis of gait parameters can be used to assess the influence of macular degeneration on gait. This study examines the effect of macular degeneration on gait using inertial sensor based 3D spatio-temporal gait parameters. We acquired gait data from 21 young and healthy subjects during a 40m obstacle walk. All subjects had to perform the gait trial with and without macular degeneration simulation glasses. The order of starting with or without glasses alternated between each subject in order to test for training effects. Multiple 3D spatio-temporal gait parameters were calculated for the normal vision as well as the impaired vision groups. The parameters trial time, stride time, stride time coefficient of variation (CV), stance time, stance time CV, stride length, cadence, gait velocity and angle at toe off showed statistically significant differences between the two groups. Training effects were visible for the trials which started without vision impair- ment. Inter-group differences in the gait pattern occurred due to an increased sense of insecurity related with the loss of visual acuity from the simulation glasses. In summary, we showed that 3D spatio-temporal gait pa- rameters derived from inertial sensor data are viable to detect differences in the gait pattern of subjects with and without a macular degeneration simulation. We believe that this study provides the basis for an in-depth analysis regarding the impact of macular degeneration on gait.
}, author = {Kanzler, Christoph M. and Barth, Jens and Klucken, Jochen and Eskofier, Björn}, booktitle = {Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference}, date = {2016-08-16/2016-08-20}, faupublication = {yes}, note = {EVALuna2:33529}, pages = {4979-4982}, peerreviewed = {unknown}, title = {{Inertial} {Sensor} based {Gait} {Analysis} {Discriminates} {Subjects} with and without {Visual} {Impairment} {Caused} by {Simulated} {Macular} {Degeneration}}, venue = {Orlando, USA}, year = {2016} } @article{faucris.247291199, abstract = {Background: Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. Methods: Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. Results: Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. Conclusions: The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.}, author = {Ibrahim, Alzhraa and Küderle, Arne and Gaßner, Heiko and Klucken, Jochen and Eskofier, Björn and Kluge, Felix}, doi = {10.1186/s12984-020-00798-9}, faupublication = {yes}, journal = {Journal of neuroEngineering and rehabilitation}, keywords = {Accelerometer; Digital biomarker; Fatigue; Gait; IMU; Machine learning; MS}, note = {CRIS-Team Scopus Importer:2020-12-25}, peerreviewed = {Yes}, title = {{Inertial} sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis}, volume = {17}, year = {2020} } @inproceedings{faucris.285175201, abstract = {Cardiac parameters are important indicators for health assessment. Radar-based monitoring with microwave interferometric sensors (MIS) is a promising alternative to conventional measurement methods, as it enables completely contactless cardiac function diagnostics. In this study, we evaluated the effects of sensor positioning and movement on the accuracy of radar-based heart rate measurements with MIS. For this purpose, we recruited 29 participants which performed semi-standardized movements, a reading task, and a standardized laboratory stress test in a seated position. Furthermore, we compared three different sensor positions (dorsal, upper pectoral, and lower pectoral) to a gold standard 1-channel wearable ECG sensor node. The dorsal positioning achieved the best results with a mean error (ME) of 0.2±5.4 bpm and a mean absolute error (MAE) of 3.5±4.1 bpm for no movement and also turned out to be most robust against motion artifacts with an overall ME of 0.1±14.1 bpm (MAE: 9.5±10.4 bpm). No correlation was found between movement intensity and measurement error. Instead, movement type and direction were identified as primary impact factors. This study provides a valuable contribution towards the applicability of radar-based vital sign monitoring with MIS in real-world scenarios. However, further research is needed to sufficiently prevent and compensate for movement artifacts.
Migraine attacks can be accompanied by many different symptoms, some of them appearing within 24 hours before the onset of the headache. In previous work, reduced habituation to an electrical pain stimulus at the head was observed in the pre-ictal phase within 24 hours before the headache attack. Based on these results, this work presents an application to track influence factors on migraine attacks and an Arduino-based control unit which replaces the traditional approach of manual electrical stimulation. The usability of both components of the project was evaluated in separate user studies. Results of the usability study show a good acceptance of the systems with a mean SUS score of 92.4. Additionally, they indicate that the developed control unit may substitute the current manual electrical stimulation. Overall, the designed system allows standardized repeatable measurements and is a first step towards the home-use of a device for establishing a new method for migraine prediction.
}, author = {Richer, Robert and Maiwald, Tim and Pasluosta, Cristian Federico and Hensel, Bernhard and Eskofier, Björn}, booktitle = {2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, date = {2015-06-09/2015-06-12}, doi = {10.1109/BSN.2015.7299363}, editor = {IEEE}, faupublication = {yes}, isbn = {978-1-4673-7201-5}, peerreviewed = {unknown}, title = {{Novel} {Human} {Computer} {Interaction} {Principles} for {Cardiac} {Feedback} using {Google} {Glass} and {Android} {Wear}}, url = {https://www.mad.tf.fau.de/files/2018/04/2015-Richer-BSN-DailyHeart.pdf}, venue = {Cambridge, MA}, year = {2015} } @article{faucris.232025065, abstract = {Background: Gait deficits are common in multiple sclerosis (MS) and contribute to disability but may not be easily detected in the early stages of the disease. Objectives: We investigated whether sensor-based gait analysis is able to detect gait impairments in patients with MS (PwMS). Methods: A foot-worn sensor-based gait analysis system was used in 102 PwMS and 22 healthy controls (HC) that were asked to perform the 25-foot walking test (25FWT) two times in a self-selected speed (25FWT{\_}pref), followed by two times in a speed as fast as possible (25FWT{\_}fast). The Multiple Sclerosis Walking Scale (MSWS-12) was used as a subjective measure of patient mobility. Patients were divided into EDSS and functional system subgroups. Results: Datasets between two consecutive measurements (test-retest-reliability) were highly correlated in all analysed mean gait parameters (e.g. 25FWT{\_}fast: stride length r = 0.955, gait speed r = 0.969) Subgroup analysis between HC and PwMS with lower (EDSS≤3.5) and higher (EDSS 4.0–7.0) disability showed significant differences in mean stride length, gait speed, toe off angle, stance time and swing time (e.g. stride length of EDSS subgroups 25FWT{\_}fast p ≤ 0.001, 25FWT{\_}pref p = 0.003). The differences between EDSS subgroups were more pronounced in fast than in self-selected gait speed (e.g. stride length 25FWT{\_}fast 33.6 cm vs. 25FWT{\_}pref 16.3 cm). Stride length (25FWT{\_}fast) highly correlated to EDSS (r=-0.583) and MSWS-12 (r=-0.668). We observed significant differences between HC and PwMS with (FS 0–1) and without (FS≥2) pyramidal or cerebellar disability (e.g. gait speed of FS subgroups p ≤ 0.001). Conclusion: Sensor-based gait analysis objectively supports the clinical assessment of gait abnormalities even in the lower stages of MS, especially when walking with fast speed. Stride length and gait speed where identified as the most clinically relevant gait measures. Thus, it may be used to support the assessment of PwMS with gait impairment in the future, e.g. for more objective classification of disability. Its role in home-monitoring scenarios need to be evaluated in further studies.}, author = {Flachenecker, Felix and Gaßner, Heiko and Hannik, Julius and Lee, De-Hyung and Flachenecker, Peter and Winkler, Jürgen and Eskofier, Björn and Linker, Ralf A. and Klucken, Jochen}, doi = {10.1016/j.msard.2019.101903}, faupublication = {yes}, journal = {Multiple Sclerosis and Related Disorders}, keywords = {Ambulatory sensing; EDSS; Gait analysis; Gait impairment; Mobility disability, 25 foot walk; Wearable sensors}, note = {CRIS-Team Scopus Importer:2020-01-21}, peerreviewed = {Yes}, title = {{Objective} sensor-based gait measures reflect motor impairment in multiple sclerosis patients: {Reliability} and clinical validation of a wearable sensor device}, volume = {39}, year = {2020} } @inproceedings{faucris.243895062, abstract = {Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks promise decentralization benefits. P2P PHSs, such as decentralized personal health records or interoperable Covid-19 proximity trackers, can enhance data sovereignty and resilience to single points of failure, but the openness of P2P networks introduces new security issues. We propose a novel, simple, and secure mutual authentication protocol that supports offline access, leverages independent and stateless encryption services, and enables patients and medical professionals to establish secure connections when using P2P PHSs. Our protocol includes a virtual smart card (software-based) feature to ease integration of authentication features of emerging national health-IT infrastructures. The security evaluation shows that our protocol resists most online and offline threats while exhibiting performance comparable to traditional, albeit less secure, password-based authentication methods. Our protocol serves as foundation for the design and implementation of P2P PHSs that will make use of P2P PHSs more secure and trustworthy.
This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions with a 3D full-body musculoskeletal model by tracking marker and ground reaction force (GRF) data in optimal control simulations. We recorded in total 30 trials with optical motion capture. Using this data, we compared inverse methods (inverse kinematics and dynamics) to coordinate tracking simulations and marker tracking simulations.
<}, author = {Nitschke, Marlies and Marzilger, Robert and Leyendecker, Sigrid and Eskofier, Björn and Koelewijn, Anne}, doi = {10.5281/zenodo.6949012}, faupublication = {yes}, keywords = {Optical motion capture; Biomechanical simulation}, peerreviewed = {automatic}, title = {{Optical} motion capturing of change of direction motions reconstructed with inverse kinematics and dynamics and optimal control simulation}, year = {2022} } @inproceedings{faucris.204519023, abstract = {Movement analysis is widely used to gain insights into human health and motor control. Especially walking and running are examined in medical diagnostics, rehabilitation and performance assessment in sports. Inertial measurement units (IMUs) enables an ambulatory assessment of kinematics or kinetics. The challenge is to obtain a detailed analysis based on noisy data, sensor drift and a limited number of sensors. Current methods focus on kinematic parameters only or additional sensors had to be applied for kinetic analysis. Alternatively, ground reaction forces had to be estimated.[1] A.J. van den Bogert, M. Hupperets, H. Schlarb, and B. Krabbe,“Predictive musculoskeletal simulation using optimal control: effects of added limb mass on energy cost and kinematics of walking and running,” in Proc. Inst Mech Eng, Part P: J Sports Eng Technol, vol. 226, pp. 123-133, 2012.
[2] A.E. Minetti, C. Moia, G.S. Roi, D. Susta, and G. Ferretti, “Energy cost of walking and running at extreme uphill and downhill slopes,” J Appl Physiol, vol. 93, no. 3, pp. 1039-1046, 2002.
}, author = {Dorschky, Eva and van den Bogert, Antonie J. and Schlarb, Heiko and Eskofier, Björn}, booktitle = {Book of Abstracts of the 20th Annual Congress of the European College of Sport Science}, date = {2015-06-24/2015-06-27}, faupublication = {yes}, isbn = {978-91-7104-567-6}, note = {UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.predic{\_}8}, pages = {126}, peerreviewed = {unknown}, title = {{Predictive} {Musculoskeletal} {Simulation} of {Uphill} and {Downhill} {Running}}, venue = {Malmö - Schweden}, year = {2015} } @article{faucris.203794585, author = {Kluge, Felix and Hannink, Julius and Pasluosta, C. F. and Klucken, Jochen and Gaßner, Heiko and Gelse, Kolja and Eskofier, Björn and Krinner, Sebastian}, doi = {10.1016/j.gaitpost.2018.08.026}, faupublication = {yes}, journal = {Gait & Posture}, pages = {194-200}, peerreviewed = {Yes}, title = {{Pre}-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty}, url = {https://www.mad.tf.fau.de/files/2020/12/kluge{\_}2018{\_}gp{\_}proof.pdf}, volume = {66}, year = {2018} } @article{faucris.312950804, abstract = {During pregnancy, almost all women experience pregnancy-related symptoms. The relationship between symptoms and their association with pregnancy outcomes is not well understood. Many pregnancy apps allow pregnant women to track their symptoms. To date, the resulting data are primarily used from a commercial rather than a scientific perspective. In this work, we aim to examine symptom occurrence, course, and their correlation throughout pregnancy. Self-reported app data of a pregnancy symptom tracker is used. In this context, we present methods to handle noisy real-world app data from commercial applications to understand the trajectory of user and patient-reported data. We report real-world evidence from patient-reported outcomes that exceeds previous works: 1,549,186 tracked symptoms from 183,732 users of a smartphone pregnancy app symptom tracker are analyzed. The majority of users track symptoms on a single day. These data are generalizable to those users who use the tracker for at least 5 months. Week-by-week symptom report data are presented for each symptom. There are few or conflicting reports in the literature on the course of diarrhea, fatigue, headache, heartburn, and sleep problems. A peak in fatigue in the first trimester, a peak in headache reports around gestation week 15, and a steady increase in the reports of sleeping difficulty throughout pregnancy are found. Our work highlights the potential of secondary use of industry data. It reveals and clarifies several previously unknown or disputed symptom trajectories and relationships. Collaboration between academia and industry can help generate new scientific knowledge.}, author = {Nissen, Michael and Barrios Campo, Nuria and Flaucher, Madeleine and Jäger, Katharina and Titzmann, Adriana and Blunck, Dominik and Fasching, Peter and Engelhardt, Victoria and Eskofier, Björn and Leutheuser, Heike}, doi = {10.1038/s41746-023-00935-3}, faupublication = {yes}, journal = {npj Digital Medicine}, note = {CRIS-Team Scopus Importer:2023-10-20}, peerreviewed = {Yes}, title = {{Prevalence} and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data}, volume = {6}, year = {2023} } @inproceedings{faucris.124052984, author = {Morgner, Philipp and Müller, Christian and Ring, Matthias and Eskofier, Björn and Riess, Christian and Armknecht, Frederik and Benenson, Zinaida}, booktitle = {Proceedings of the 22nd European Symposium on Research in Computer Security (ESORICS 2017)}, doi = {10.1007/978-3-319-66399-9{\_}18}, faupublication = {yes}, pages = {324-343}, peerreviewed = {Yes}, title = {{Privacy} {Implications} of {Room} {Climate} {Data}}, url = {https://faui1-files.cs.fau.de/filepool/publications/esorics2017{\_}privacy{\_}room{\_}climate.pdf}, venue = {Oslo}, year = {2017} } @inproceedings{faucris.120097604, abstract = {
The ability to relax is sometimes challenging to achieve, nevertheless it is extremely important for mental and physical health, particularly to effectively manage stress and anxiety. We propose a virtual reality experience that integrates a wearable, low-cost EEG headband and an olfactory necklace that passively promotes relaxation. The physiological response was measured from the EEG signal. Relaxation scores were computed from EEG frequency bands associated with a relaxed mental state using an entropy-based signal processing approach. The subjective perception of relaxation was determined using a questionnaire. A user study involving 12 subjects showed that the subjective perception of relaxation increased by 26.1 % when using a VR headset with the olfactory necklace, compared to not being exposed to any stimulus. Similarly, the physiological response also increased by 25.0 %. The presented work is the first Virtual Reality Therapy system that uses scent in a wearable manner and proves its effectiveness to increase relaxation in everyday life situations.
}, author = {Amores, Judith and Richer, Robert and Zhao, Nan and Maes, Pattie and Eskofier, Björn}, booktitle = {2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, date = {2018-03-04/2018-03-07}, doi = {10.1109/BSN.2018.8329668}, editor = {IEEE}, faupublication = {yes}, isbn = {978-1-5386-1109-8}, peerreviewed = {Yes}, title = {{Promoting} {Relaxation} {Using} {Virtual} {Reality}, {Olfactory} {Interfaces} and {Wearable} {EEG}}, url = {https://www.mad.tf.fau.de/files/2018/04/2018-Amores-BSN-PRVR.pdf}, venue = {Las Vegas, NV}, year = {2018} } @inproceedings{faucris.122536524, author = {Pasluosta, Cristian Federico and Barth, Jens and Gaßner, Heiko and Klucken, Jochen and Eskofier, Björn}, booktitle = {37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC ’15}, faupublication = {yes}, peerreviewed = {unknown}, title = {{Pull} {Test} {Estimation} in {Parkinson}'s {Disease} {Patients} using {Wearable} {Sensor} {Technology}}, venue = {Milano}, year = {2015} } @inproceedings{faucris.319163787, abstract = {Popular metrics for clustering comparison, like the Adjusted Rand Index and the Adjusted Mutual Information, are type II biased. The Standardized Mutual Information removes this bias but suffers from counterintuitive non-monotonicity and poor computational efficiency. We introduce the p-value adjusted Rand Index (PMI2), the first cluster comparison method that is type II unbiased and provably monotonous. The PMI2 has fast approximations that outperform the Standardized Mutual information. We demonstrate its unbiased clustering selection, approximation quality, and runtime efficiency on synthetic benchmarks. In experiments on image and social network datasets, we show how the PMI2 can help practitioners choose better clustering and community detection algorithm}, author = {Klede, Kai and Altstidl, Thomas Robert and Zanca, Dario and Eskofier, Björn}, booktitle = {Advances in Neural Information Processing Systems 36}, date = {2023-12-10/2023-12-16}, editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine}, faupublication = {yes}, keywords = {Clustering; Mutual Information; Machine Learning; Artificial Intelligence}, pages = {27113–27128}, peerreviewed = {Yes}, publisher = {Curran Associates, Inc.}, series = {Advances in Neural Information Processing Systems}, title = {p-value {Adjustment} for {Monotonous}, {Unbiased}, and {Fast} {Clustering} {Comparison}}, url = {https://proceedings.neurips.cc/paper{\_}files/paper/2023/file/563d94819f68cb73d6a382809e587b54-Paper-Conference.pdf}, venue = {New Orleans}, volume = {36}, year = {2023} } @article{faucris.109562464, abstract = {Among elderly males, benign prostate syndrome (BPS) is the most common urinary disorder. Nocturia is one of the major symptoms of BPS and has a considerable influence on quality of life (QoL). For assessment of BPS (including nocturia), the International Prostate Symptom Score (IPSS) is widely used, but questionnaires are prone to bias. To date, there is no objective measurement system available for nocturia. In this study, we present an unobtrusive and non-stigmatizing device for objective measurement of nighttime micturition. In a preliminary study of 6 males diagnosed with BPS and nighttime micturition >= 2 times, we showed that the device is accurate, with an average misdetection rate of 0.32 events and a mean absolute deviation of 3.8% when comparing the average number of nighttime micturition occurrences. In this extended study, an additional 9 males were recorded and data from an occupancy sensor were also included. The results of the preliminary study were confirmed with an average misdetection rate of 0.33 events and a mean absolute deviation of 9.1%. The system can therefore be used to objectively measure nighttime micturition, and thereby provide the basis for treatment, e.g., medication efficacy assessment.}, author = {Huppert, Verena and Paulus, Jan and Paulsen, Ute and Burkart, Martin and Wullich, Bernd and Eskofier, Björn}, doi = {10.1109/JBHI.2015.2421487}, faupublication = {yes}, journal = {IEEE Journal of Biomedical and Health Informatics}, note = {EVALuna2:16321}, peerreviewed = {Yes}, title = {{Quantification} of {Nighttime} {Micturition} with an {Ambulatory} {Sensor}-{Based} {System}}, year = {2015} } @incollection{faucris.119960764, author = {Hannink, Julius and Kluge, Felix and Gaßner, Heiko and Klucken, Jochen and Eskofier, Björn}, booktitle = {2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, doi = {10.1109/BSN.2017.7936024}, faupublication = {yes}, pages = {129-132}, peerreviewed = {unknown}, title = {{Quantifying} postural instability in {Parkinsonian} gait from inertial sensor data during standardised clinical gait tests}, url = {http://ieeexplore.ieee.org/document/7936024/}, year = {2017} } @inproceedings{faucris.314919511, abstract = {The accurate detection and quantification of activities of daily life (ADL) are crucial for assessing the health status of palliative patients to allow an optimized treatment in the last phase of life. Current evaluation methods heavily rely on subjective self-reports or external observations by clinical staff, lacking objectivity. To address this limitation, we propose a radar-based approach for recognizing ADLs in a palliative care context. In our proof of concept study, we recorded five different ADLs relevant to palliative care, all occurring within a hospital bed, from N=14 healthy participants (57% women, aged 28.6 ± 5.3years). All movements were recorded using two frequency-modulated continuous wave radar systems measuring velocity, range, and angle. A convolutional neural network combined with long short-term memory achieved a classification accuracy of 99.8 ± 0.4% across five cross-validation folds. Furthermore, we compare our initial approach, which takes into account all dimensions of the available radar data, to a simplified version, where only velocity information over time is fed into the network. While these results demonstrate the high potential of radar-based sensing to automatically detect and quantify activities in a palliative care context, future work is still necessary to assess the applicability to real-world hospital scenarios.
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data.
We collected data from ten patients with idiopathic Parkinson’s disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.
}, author = {Eskofier, Björn and Lee, Sunghoon I. and Daneault, Jean-Francois and Golabchi, Fatemeh N. and Ferreira-Carvalho, Gabriela and Vergara-Diaz, Gloria and Sapienza, Stefano and Costante, Gianluca and Klucken, Jochen and Kautz, Thomas and Bonato, Paolo}, booktitle = {Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2016)}, date = {2016-08-16/2016-08-20}, faupublication = {yes}, pages = {655-658}, peerreviewed = {unknown}, title = {{Recent} {Machine} {Learning} {Advancements} in {Sensor}-{Based} {Mobility} {Analysis}: {Deep} {Learning} for {Parkinson}’s {Disease} {Assessment}}, venue = {Orlando, USA}, year = {2016} } @inproceedings{faucris.209891049, author = {Orlemann, Till and Zenker, Björn and Reljic, Dejan and Meyer, Julia and Oberlaender, Jana and Herrmann, Hans Joachim and Eskofier, Björn and Neurath, Markus and Zopf, Yurdagül}, faupublication = {yes}, note = {EVALuna2:35184}, pages = {S28-S29}, peerreviewed = {Yes}, title = {{Recording} and {Optimizing} the {Nutritional} {Behavior} of oncological {Patients} by using a {Smartphone} {App} ({OncoFood})}, volume = {59}, year = {2018} } @article{faucris.122083104, author = {Leutheuser, Heike and Heyde, Christian and Röcker, Kai and Gollhofer, Albert and Eskofier, Björn}, doi = {10.1109/TBME.2017.2675941}, faupublication = {yes}, journal = {IEEE Transactions on Biomedical Engineering}, keywords = {Respiration; treadmill running; least squares regression (LSQ); qualitative diagnostic calibration (QDC); adjustment algorithm}, peerreviewed = {Yes}, title = {{Reference}-{Free} {Adjustment} of {Respiratory} {Inductance} {Plethysmography} for {Measurements} during {Physical} {Exercise}}, year = {2017} } @article{faucris.318855135, abstract = {Background:
Mobile eHealth apps have been used as a complementary treatment to increase the quality of life of patients and provide new opportunities for the management of rheumatic diseases. Telemedicine, particularly in the areas of prevention, diagnostics, and therapy, has become an essential cornerstone in the care of patients with rheumatic diseases.
Objective:
This study aims to improve the design and technology of YogiTherapy and evaluate its usability and quality.
Methods:
We newly implemented the mobile eHealth app YogiTherapy with a modern design, the option to change language, and easy navigation to improve the app’s usability and quality for patients. After refinement, we evaluated the app by conducting a study with 16 patients with AS (4 female and 12 male; mean age 48.1, SD 16.8 y). We assessed the usability of YogiTherapy with a task performance test (TPT) with a think-aloud protocol and the quality with the German version of the Mobile App Rating Scale (MARS).
Results:
In the TPT, the participants had to solve 6 tasks that should be performed on the app. The overall task completion rate in the TPT was high (84/96, 88% completed tasks). Filtering for videos and navigating to perform an assessment test caused the largest issues during the TPT, while registering in the app and watching a yoga video were highly intuitive. Additionally, 12 (75%) of the 16 participants completed the German version of MARS. The quality of YogiTherapy was rated with an average MARS score of 3.79 (SD 0.51) from a maximum score of 5. Furthermore, results from the MARS questionnaire demonstrated a positive evaluation regarding functionality and aesthetics.
Conclusions:
The refined and tested YogiTherapy app showed promising results among most participants. In the future, the app could serve its function as a complementary treatment for patients with AS. For this purpose, surveys with a larger number of patients should still be conducted. As a substantial advancement, we made the app free and openly available on the iOS App and Google Play store},
author = {Nitschke, Marlies and Nwosu, Obioma Bertrand and Grube, Lara and Knitza, Johannes and Seifer, Ann-Kristin and Eskofier, Björn and Schett, Georg and Morf, Harriet},
doi = {10.2196/47426},
faupublication = {yes},
journal = {JMIR Formative Research},
keywords = {ankylosing spondylitis; axial spondylarthritis; DHA; digital health application; eHealth; self-assessment; Usability; Yoga; YogiTherapy},
pages = {e47426},
peerreviewed = {Yes},
title = {{Refinement} and {Usability} {Analysis} of an {eHealth} {App} for {Ankylosing} {Spondylitis} as a {Complementary} {Treatment} to {Physical} {Therapy}: {Development} and {Usability} {Study}},
volume = {7},
year = {2023}
}
@inproceedings{faucris.212473500,
author = {Flachenecker, F. and Gaßner, Heiko and Lee, De-Hyung and Eskofier, Björn and Klucken, Jochen and Flachenecker, P. and Linker, Ralf},
faupublication = {yes},
note = {EVALuna2:22575},
pages = {148-149},
peerreviewed = {Yes},
title = {{Reliability} and validity of a new, sensor-based system for gait analysis in patients with multiple sclerosis},
volume = {23 3},
year = {2017}
}
@inproceedings{faucris.106501604,
author = {Steib, Simon and Gaßner, Heiko and Eskofier, Björn and Winkler, Jürgen and Pfeifer, Klaus and Klucken, Jochen},
booktitle = {ISPGR World Congress},
date = {2015-06-28/2015-07-02},
faupublication = {yes},
peerreviewed = {Yes},
title = {{Reliability} of clinical and instrumented balance assessments in patients with {Parkinson}’s {Disease}},
venue = {Sevilla, Spain},
year = {2015}
}
@article{faucris.114008004,
abstract = {Introduction: The aim of this study was to provide a rationale for future validations of a priori calibrated respiratory inductance plethysmography (RIP) when used under exercise conditions. Therefore, the validity of a posteriori-adjusted gain factors and accuracy in resultant breath-by-breath RIP data recorded under resting and running conditions were examined. Methods: Healthy subjects, 98 men and 88 women (mean ± SD: height = 175.6 ± 8.9 cm, weight = 68.9 ± 11.1 kg, age = 27.1 ± 8.3 yr), underwent a standardized test protocol, including a period of standing still, an incremental running test on treadmill, and multiple periods of recovery. Least square regression was used to calculate gain factors, respectively, for complete individual data sets as well as several data subsets. In comparison with flowmeter data, the validity of RIP in breathing rate (fR) and inspiratory tidal volume (VTIN) were examined using coefficients of determination (R). Accuracy was estimated from equivalence statistics. Results: Calculated gains between different data subsets showed no equivalence. After gain adjustment for the complete individual data set, fR and VTIN between methods were highly correlated (R = 0.96 ± 0.04 and 0.91 ± 0.05, respectively) in all subjects. Under conditions of standing still, treadmill running, and recovery, 86%, 98%, and 94% (fR) and 78%, 97%, and 88% (VTIN), respectively, of all breaths were accurately measured within ±20% limits of equivalence. Conclusion: In case of the best possible gain adjustment, RIP confidentially estimates tidal volume accurately within ±20% under exercise conditions. Our results can be used as a rationale for future validations of a priori calibration procedures. © 2014 by the American College of Sports Medicine.},
author = {Heyde, Christian and Leutheuser, Heike and Eskofier, Björn and Röcker, Kai and Gollhofer, Albert},
doi = {10.1249/MSS.0000000000000130.},
faupublication = {yes},
journal = {Medicine and science in sports and exercise },
keywords = {VENTILATION; RUNNING EXERCISE; MONITORING; RESPIRATION; AMBULATORY MONITORING},
note = {UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.respir},
pages = {488-495},
peerreviewed = {Yes},
title = {{Respiratory} {Inductance} {Plethysmography} - {A} {Rationale} for {Validity} during {Exercise}},
volume = {46},
year = {2014}
}
@article{faucris.260659620,
abstract = {Objective: Finishing a marathon requires to prepare for a 42.2 km run. Current literature describes which training characteristics are related to marathon performance. However, which training is most effective in terms of a performance improvement remains unclear. Methods: We conducted a retrospective analysis of training responses during a 16 weeks training period prior to an absolved marathon. The analysis was performed on unsupervised fitness app data (Runtastic) from 6,771 marathon finishers. Differences in training volume and intensity between three response and three marathon performance groups were analyzed. Training response was quantified by the improvement of the velocity of 10 km runs Δv
Using the gait parameters assessed by CatWalk system [8–10], we examined the correlation between body-length/body-weight with stride length using CatWalk-video-derived body silhouette length. To examine the effect of growth and aging in rodents, we studied wild-type Sprague-Dawley male rats and C57BL/6N mice. Moreover, we examined the body size difference between genotypes in BACSCNA transgenic rats [11,12] and BACHD transgenic mice [13]. The CatWalk data were collected as the rodents walked freely on top of a glass-floored corridor. The rat gait was monitored at 4 different age points (10, 26, 55 and 62 weeks old), whereas the mice were monitored at 3 different age points (20, 32, and 47 weeks old). The rodent numbers included in the experiments were: (a) wild-type male rats (n=16-27/time point), (b) BACSCNA rats (n=19-32/time point), (c) wild-type mice (n=12-13/time point), and (d) BACHD mice (n=9-11/time point). The rodents were maintained under specific-pathogen-free condition. All research and animal care procedures were performed in compliance with international animal welfare standards and approved by the district governments of Lower Franconia, Würzburg, Bavaria, Germany (RegUFr#55.2-2532-2-218).
The correlations between front stride length (averaged from the left and right side) and the body-silhouette-length/body-weight are shown in Figure 1. Significant correlations are shown with both body silhouette lengths and body weight. The correlation between hind stride length and body size showed the similar relationship as the front stride length (data not shown). Besides body size differences due to growth, pathological body size differences were also observed as shown in Figure 2.
Thus, we demonstrated that stride length is highly correlated with body silhouette length (based on the CatWalk video) and body weight. In the near future, we need to subject gait parameters to a scaling process using body weight or silhouette length, raising the possibility of normalization for differences in size as a potential confounding of gait measure}, address = {UK}, author = {Timotius, Ivanna and Moceri, Sandra and Plank, Anne-Christine and Habermeyer, Johanna and Canneva, Fabio and Casadei, Nicolas and Riess, Olaf and Winkler, Jürgen and Klucken, Jochen and Eskofier, Björn and von Hörsten, Stephan}, booktitle = {Measuring Behavior 2018}, date = {2018-06-05/2018-06-08}, editor = {Robyn Grant, Tom Allen, Andrew Spink, Matthew Sullivan}, faupublication = {yes}, isbn = {978-1-910029-39-8}, keywords = {Stride Length; Body Size; Rodent; CatWalk}, peerreviewed = {Yes}, title = {{Rodent}’s {Stride} {Length} {Depends} on {Body} {Size}: {Implications} for {CatWalk} {Assay}}, url = {https://www.measuringbehavior.org/mb2018/Publications}, venue = {Manchester}, year = {2018} } @article{faucris.120323764, author = {Eskofier, Björn and Hornegger, Joachim and Oleson, Mark and Munson, Ian and DiBenedetto, Christian}, faupublication = {yes}, journal = {Künstliche Intelligenz}, pages = {45-48}, peerreviewed = {No}, title = {{Run} {Surface} {Classification}: a {Digital} {Sports} {Embedded} {Application}}, volume = {04/2008}, year = {2008} } @article{faucris.314917687, abstract = {Background:
A key vulnerability factor in mental health problems is chronic stress. There is a need for easy-to-disseminate and effective interventions to advance the prevention of stress-related illnesses. App-based stress management trainings can fulfill this need. As subjectively experienced stress may be influenced by dysfunctional beliefs, modifying their evaluations might reduce subjective stress. Approach-avoidance modification trainings (AAMT) can be used to modify stimulus evaluations and are promising candidates for a mobile stress intervention. As the standard training reactions of the AAMT (swiping and joystick motion) have little valence, emotions could be incorporated as approach and avoidance reactions to enhance the effectiveness of AAMTs.
Objective:
We aimed to evaluate the feasibility of a mobile emotion-enhanced AAMT that engages users to display sadness to move stress-enhancing beliefs away and display positive emotions to move stress-reducing beliefs toward themselves (emotion-based AAMT using sadness and positive emotions [eAAMT-SP]). We explored the clinical efficacy of this novel intervention.
Methods:
We allocated 30 adult individuals with elevated stress randomly to 1 of 3 conditions (eAAMT-SP, a swipe control condition, and an inactive control condition). We evaluated the feasibility of the intervention (technical problems, adherence, usability, and acceptability). To explore the clinical efficacy of the intervention, we compared pretest-posttest differences in perceived stress (primary clinical outcome) and 3 secondary clinical outcomes (agreement with and perceived helpfulness of dysfunctional beliefs, emotion regulation, and depressive symptoms) among the conditions.
Results:
The predetermined benchmarks of 50% for intervention completion and 75% for feasibility of the study design (completion of the study design) were met, whereas the cutoff for technical feasibility of the study design (95% of trials without technical errors) was not met. Effect sizes for usability and acceptability were in favor of the eAAMT-SP condition (compared with the swipe control condition; intelligibility of the instructions: g=−0.86, distancing from dysfunctional beliefs: g=0.22, and approaching functional beliefs: g=0.55). Regarding clinical efficacy, the pretest-posttest effect sizes for changes in perceived stress were g=0.80 for the comparison between the eAAMT-SP and inactive control conditions and g=0.76 for the comparison between the eAAMT-SP and swipe control conditions. Effect sizes for the secondary clinical outcomes indicated greater pretest-posttest changes in the eAAMT-SP condition than in the inactive control condition and comparable changes in the swipe control condition.
Conclusions:
The findings regarding the feasibility of the intervention were satisfactory except for the technical feasibility of the intervention, which should be improved. The effect sizes for the clinical outcomes provide preliminary evidence for the therapeutic potential of the intervention. The findings suggest that extending the AAMT paradigm through the use of emotions may increase its efficacy. Future research should evaluate the eAAMT-SP in sufficiently powered randomized controlled trial}, author = {Rupp, Lydia and Keinert, Marie and Böhme, Stephanie and Gmelch, Lena Marie and Eskofier, Björn and Schuller, Björn W. and Berking, Matthias}, doi = {10.2196/50324}, faupublication = {yes}, journal = {JMIR Formative Research}, keywords = {stress (262); emotion (46); eHealth (1701); approach-avoidance (1); mental health (1497); somatic health (1); chronic stress (3); intervention (495); stress-related illness (1); app-based (6); stress management (29); belief (10); training (138); mobile phone (2614)}, pages = {e50324}, peerreviewed = {Yes}, title = {{Sadness}-{Based} {Approach}-{Avoidance} {Modification} {Training} for {Subjective} {Stress} in {Adults}: {Pilot} {Randomized} {Controlled} {Trial}}, volume = {7}, year = {2023} } @article{faucris.119557504, author = {Ring, Matthias and Lohmueller, Clemens and Rauh, Manfred and Mester, Joachim and Eskofier, Björn}, doi = {10.1109/JBHI.2016.2598854}, faupublication = {yes}, journal = {IEEE Journal of Biomedical and Health Informatics}, pages = {1306-1314}, peerreviewed = {Yes}, title = {{Salivary} {Markers} for {Quantitative} {Dehydration} {Estimation} {During} {Physical} {Exercise}}, volume = {21}, year = {2017} } @inproceedings{faucris.121516164, author = {Tobola, Andreas and Streit, Franz-Josef and Espig, Chris and Korpok, Oliver and Leutheuser, Heike and Sauter, Christian and Lang, Nadine and Schmitz, Björn and Hofmann, Christian and Struck, Matthias and Weigand, Christian and Eskofier, Björn and Fischer, Georg}, booktitle = {12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, date = {2015-06-09/2015-06-12}, doi = {10.1109/BSN.2015.7299392}, faupublication = {yes}, isbn = {9781467372015}, note = {UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.sampli}, pages = {1-6}, peerreviewed = {Yes}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, title = {{Sampling} rate impact on energy consumption of biomedical signal processing systems}, venue = {Cambridge, USA}, year = {2015} } @inproceedings{faucris.107007824, abstract = {Everything in nature tries to reach the lowest possible energy level. Therefore any natural or artificial system must have the ability to adjust itself to the changing requirements of its surrounding environment. In this paper we address this issue by an ECG sensor designed to be adjustable during runtime, having the ability to reduce the power consumption at cost of the informational content. Accessible for everyone, standard ECG hardware and open source software has been used to realize an ECG processing system for wearable applications. The average power consumption has been measured for each mode of operation. Finally we take conclusion to conciser context-aware scaling as key feature to address the energy issue of wearable sensor system}, author = {Tobola, Andreas and Espig, Chris and Streit, Franz-Josef and Korpok, Oliver and Leutheuser, Heike and Schmitz, Björn and Hofmann, Christian and Struck, Matthias and Weigand, Christian and Eskofier, Björn and Fischer, Georg}, booktitle = {2015 IEEE International Symposium on Medical Measurements and Applications}, doi = {10.1109/MeMeA.2015.7145209}, faupublication = {yes}, isbn = {978-1-4799-6476-5}, note = {UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.scalab}, pages = {255-260}, peerreviewed = {unknown}, publisher = {IEEE}, title = {{Scalable} {ECG} {Hardware} and {Algorithms} for {Extended} {Runtime} of {Wearable} {Sensors}}, venue = {Torino}, year = {2015} } @inproceedings{faucris.121331144, address = {Düsseldorf}, author = {Traxdorf, Maximilian and Gradl, Stefan and Kugler, Patrick and Leutheuser, Heike and Lauten, Juliane and Eskofier, Björn and Angerer, Florian and Iro, Heinrich}, booktitle = {Proceedings of the 84th Annual Meeting of the German Society of Oto-Rhino-Laryngology, Head and Neck Surgery}, date = {2013-05-08/2013-05-12}, doi = {10.3205/13hnod723}, editor = {Deutsche Gesellschaft für Hals-Nasen-Ohren-Heilkunde Kopf- und Hals-Chirurgie}, faupublication = {yes}, pages = {158}, publisher = {German Medical Science GMS Publishing House}, title = {{Schlafstadienbestimmung} mit {Hilfe} bewegungsbasierter {Sensortechnologie}}, venue = {Nürnberg}, year = {2013} } @article{faucris.266234968, abstract = {Background:
Patient-centered health care information systems (PHSs) enable patients to take control and become knowledgeable about their own health, preferably in a secure environment. Current and emerging PHSs use either a centralized database, peer-to-peer (P2P) technology, or distributed ledger technology for PHS deployment. The evolving COVID-19 decentralized Bluetooth-based tracing systems are examples of disease-centric P2P PHSs. Although using P2P technology for the provision of PHSs can be flexible, scalable, resilient to a single point of failure, and inexpensive for patients, the use of health information on P2P networks poses major security issues as users must manage information security largely by themselves.
Objective:
This study aims to identify the inherent security issues for PHS deployment in P2P networks and how they can be overcome. In addition, this study reviews different P2P architectures and proposes a suitable architecture for P2P PHS deployment.
Methods:
A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Thematic analysis was used for data analysis. We searched the following databases: IEEE Digital Library, PubMed, Science Direct, ACM Digital Library, Scopus, and Semantic Scholar. The search was conducted on articles published between 2008 and 2020. The Common Vulnerability Scoring System was used as a guide for rating security issues.
Results:
Our findings are consolidated into 8 key security issues associated with PHS implementation and deployment on P2P networks and 7 factors promoting them. Moreover, we propose a suitable architecture for P2P PHSs and guidelines for the provision of PHSs while maintaining information security.
Conclusions:
Despite the clear advantages of P2P PHSs, the absence of centralized controls and inconsistent views of the network on some P2P systems have profound adverse impacts in terms of security. The security issues identified in this study need to be addressed to increase patients’ intention to use PHSs on P2P networks by making them safe to us}, author = {Abdullahi, Imrana and Dehling, Tobias and Kluge, Felix and Geck, Jurgen and Sunyaev, Ali and Eskofier, Björn}, doi = {10.2196/24460}, faupublication = {yes}, journal = {Journal of Medical Internet Research}, keywords = {patient-centered; health care; information infrastructures; decentralization; mobile health; peer-to-peer; COVID-19 proximity trackers; edge computing; security; vulnerabilities; attacks; threats; mobile phone}, pages = {e24460}, peerreviewed = {Yes}, title = {{Security} {Engineering} of {Patient}-{Centered} {Health} {Care} {Information} {Systems} in {Peer}-to-{Peer} {Environments}: {Systematic} {Review}}, url = {https://www.jmir.org/2021/11/e24460}, volume = {23}, year = {2021} } @article{faucris.275240789, abstract = {Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson's disease (PD) is missing. In this paper, we compare four prevalent gait segmentation methods in order to reveal their strengths and drawbacks in gait processing. We considered peak detection from event-based methods, two variations of dynamic time warping from template matching methods, and hierarchical hidden Markov models (hHMMs) from machine learning methods. To evaluate the methods, we included two supervised and instrumented gait tests that are widely used in the examination of Parkinsonian gait. In the first experiment, a sequence of strides from instructed straight walks was measured from 10 PD patients. In the second experiment, a more heterogeneous assessment paradigm was used from an additional 34 PD patients, including straight walks and turning strides as well as non-stride movements. The goal of the latter experiment was to evaluate the methods in challenging situations including turning strides and non-stride movements. Results showed no significant difference between the methods for the first scenario, in which all methods achieved an almost 100% accuracy in terms of F-score. Hence, we concluded that in the case of a predefined and homogeneous sequence of strides, all methods can be applied equally. However, in the second experiment the difference between methods became evident, with the hHMM obtaining a 96% F-score and significantly outperforming the other methods. The hHMM also proved promising in distinguishing between strides and non-stride movements, which is critical for clinical gait analysis. Our results indicate that both the instrumented test procedure and the required stride segmentation algorithm have to be selected adequately in order to support and complement classical clinical examination by sensor-based movement assessment.}, author = {Haji Ghassemi, Nooshin and Hannink, Julius and Martindale, Christine and Gaßner, Heiko and Müller, Meinard and Klucken, Jochen and Eskofier, Björn}, doi = {10.3390/s18010145}, faupublication = {yes}, journal = {Sensors}, month = {Jan}, note = {EVALuna2:213357}, peerreviewed = {Yes}, title = {{Segmentation} of {Gait} {Sequences} in {Sensor}-{Based} {Movement} {Analysis}: {A} {Comparison} of {Methods} in {Parkinson}'s {Disease}.}, volume = {18}, year = {2018} } @article{faucris.119908624, author = {Haji Ghassemi, Nooshin and Hannink, Julius and Martindale, Christine and Gaßner, Heiko and Müller, Meinard and Klucken, Jochen and Eskofier, Björn}, doi = {10.3390/s18010145}, faupublication = {yes}, journal = {Sensors}, keywords = {Parkinson’s Disease; gait analysis; inertial sensors; step segmentation; stride segmentation; accelerometer; gyroscope}, peerreviewed = {Yes}, title = {{Segmentation} of {Gait} {Sequences} in {Sensor}-{Based} {Movement} {Analysis}: {A} {Comparison} of {Methods} in {Parkinson}’s {Disease}}, url = {http://www.mdpi.com/1424-8220/18/1/145}, year = {2018} } @inproceedings{faucris.123602204, abstract = {Gait analysis is an important tool for diagnosis, monitoring and treatment of neurological diseases. Among these are hereditary spastic paraplegias (HSPs) whose main characteristic is heterogeneous gait disturbance. So far HSP gait has been analysed in a limited number of studies, and within a laboratory set up only. Although the rarity of orphan diseases often limits larger scale studies, the investigation of these diseases is still important, not only to the affect population, but also for other diseases which share gait characteristics.
In this paper we use foot-mounted inertial measurement units (IMU) as a mobile solution to measure the gait of 21 HSP patients while performing a 4 by 10 m walk at self-selected pace. Two algorithms common to other gait analysis solutions, the hidden Markov model (HMM) and dynamic time warping (DTW), were applied to these signals in order to investigate their effectiveness when faced with the heterogeneous nature and range of foot strike techniques of HSP gait, sometimes even lacking a heel strike. Using a nested cross validation for parameter choice and validation, the HMM was found to be superior for segmentation purposes with a mean segmentation error of 0.10 ± 0.05 s. Stride segmentation of such a diverse dataset is the first step towards creating a clinically relevant system which could assist physicians working with HSP patients by providing objective, automated gait parameters. To the best of the authors’ knowledge, this is the first paper to investigate solutions for mobile gait analysis of patients affected by HSPs. Ultimately, automated, mobile gait analysis of HSP patients would allow ongoing and long term monitoring, providing useful insights into this orphan diseas},
author = {Martindale, Christine and Strauss, Martin and Gaßner, Heiko and List, Julia and Müller, Meinard and Klucken, Jochen and Kohl, Zacharias and Eskofier, Björn},
booktitle = {Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE},
date = {2017-07-11/2017-07-15},
doi = {10.1109/EMBC.2017.8037062},
faupublication = {yes},
isbn = {978-1-5090-2809-2},
keywords = {Hidden Markov models; segmentation; gait analysis; hereditary spastic paraplegia},
peerreviewed = {Yes},
publisher = {IEEE},
title = {{Segmentation} of gait sequences using inertial sensor data in hereditary spastic paraplegia},
url = {https://www.mad.tf.fau.de/files/2018/09/embc2017{\_}martindale.pdf},
venue = {Jeju Island, South Korea},
year = {2017}
}
@article{faucris.209164693,
author = {Tobola, Andreas and Leutheuser, Heike and Pollak, M and Spies, Peter and Hofmann, Christian and Weigand, Christian and Eskofier, Björn and Fischer, Georg},
faupublication = {yes},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {15-22},
peerreviewed = {Yes},
title = {{Self}-powered {Multiparameter} {Health} {Sensor}},
volume = {22},
year = {2018}
}
@incollection{faucris.106928844,
author = {Klamroth, Sarah and Gaßner, Heiko and Winkler, Jürgen and Eskofier, Björn and Klucken, Jochen and Pfeifer, Klaus and Steib, Simon},
booktitle = {27. Rehabilitationswissenschaftliches Kolloquium: Rehabilitation bewegt!},
editor = {Deutsche Rentenversicherung Bund},
faupublication = {yes},
isbn = {978-3-9817814-8-9},
pages = {356-357},
peerreviewed = {Yes},
series = {DRV-Schriften},
title = {{Sensomotorisches} {Laufbandtraining} in der {Rehabilitation} von {Gangstörungen} bei {Patienten} mit {Morbus} {Parkinson}},
volume = {113},
year = {2018}
}
@inproceedings{faucris.235365593,
author = {Gladow, Till and Gaßner, Heiko and Ullrich, Martin and Hannink, Julius and Roth, Nils and Marxreiter, Franz and Küderle, Arne and Kohl, Zacharias and Winkler, Jürgen and Eskofier, Björn and Klucken, Jochen},
faupublication = {yes},
note = {EVALuna2:213285},
pages = {657-657},
peerreviewed = {Yes},
title = {{Sensor}-based gait analysis distinguishes fallers from non-fallers in {Parkinson}'s disease under clinical and real-life conditions},
volume = {126},
year = {2019}
}
@article{faucris.212612283,
abstract = {BACKGROUND AND OBJECTIVES: Gait impairment and reduced mobility are typical features of idiopathic Parkinson's disease (iPD) and atypical parkinsonian disorders (APD). Quantitative gait assessment may have value in the diagnostic workup of parkinsonian patients and as endpoint in clinical trials. The study aimed to identify quantitative gait parameter differences in iPD and APD patients using sensor-based gait analysis and to correlate gait parameters with clinical rating scales.
SUBJECTS AND METHODS: Patients with iPD and APD including Parkinson variant multiple system atrophy and progressive supranuclear palsy matched for age, gender, and Hoehn and Yahr (≤3) were recruited at two Movement Disorder Units and assessed using standardized clinical rating scales (MDS-UPDRS-3, UMSARS, PSP-RS). Gait analysis consisted of inertial sensor units laterally attached to shoes, generating as objective targets spatiotemporal gait parameters from 4 × 10 m walk tests.
RESULTS: Objective sensor-based gait analysis showed that gait speed and stride length were markedly reduced in APD compared to iPD patients. Moreover, clinical ratings significantly correlated with gait speed and stride length in APD patients.
CONCLUSION: Our findings suggest that patients with APD had more severely impaired gait parameters than iPD patients despite similar disease severity. Instrumented gait analysis provides complementary rater independent, quantitative parameters that can be exploited for clinical trials and care.
},
author = {Raccagni, Cecilia and Gaßner, Heiko and Eschlboeck, Sabine and Boesch, Sylvia and Krismer, Florian and Seppi, Klaus and Poewe, Werner and Eskofier, Björn and Winkler, Jürgen and Wenning, Gregor and Klucken, Jochen},
doi = {10.1002/brb3.977},
faupublication = {yes},
journal = {Brain and Behavior},
note = {EVALuna2:36378},
peerreviewed = {Yes},
title = {{Sensor}-based gait analysis in atypical parkinsonian disorders},
volume = {8},
year = {2018}
}
@article{faucris.212608849,
abstract = {Mobile, sensor-based gait analysis in Parkinson's disease (PD) facilitates the objective measurement of gait parameters in cross-sectional studies. Besides becoming outcome measures for clinical studies, the application of gait parameters in personalized clinical decision support is limited. Therefore, the aim of this study was to evaluate whether the individual response of PD patients to dopaminergic treatment may be measured by sensor-based gait analysis. 13 PD patients received apomorphine every 15 min to incrementally increase the bioavailable apomorphine dose. Motor performance (UPDRS III) was assessed 10 min after each apomorphine injection. Gait parameters were obtained after each UPDRS III rating from a 2 × 10 m gait sequence, providing 41.2 ± 9.2 strides per patient and injection. Gait parameters and UPDRS III ratings were compared cross-sectionally after apomorphine titration, and more importantly between consecutive injections for each patient individually. For the individual response, the effect size Cohen's d for gait parameter changes was calculated based on the stride variations of each gait sequence after each injection. Cross-sectionally, apomorphine improved stride speed, length, gait velocity, maximum toe clearance, and toe off angle. Between injections, the effect size for individual changes in stride speed, length, and maximum toe clearance correlated to the motor improvement in each patient. In addition, significant changes of stride length between injections were significantly associated with UPDRS III improvements. We therefore show, that sensor-based gait analysis provides objective gait parameters that support clinical assessment of individual PD patients during dopaminergic treatment. We propose clinically relevant instrumented gait parameters for treatment studies and especially clinical care.},
author = {Marxreiter, Franz and Gaßner, Heiko and Borozdina, Olga and Barth, Jens and Kohl, Zacharias and Schlachetzki, Johannes and Thun-Hohenstein, Caroline and Volc, Dieter and Eskofier, Björn and Winkler, Jürgen and Klucken, Jochen},
doi = {10.1007/s00415-018-9012-7},
faupublication = {yes},
journal = {Journal of Neurology},
note = {EVALuna2:36376},
pages = {2656-2665},
peerreviewed = {Yes},
title = {{Sensor}-based gait analysis of individualized improvement during apomorphine titration in {Parkinson}'s disease},
volume = {265},
year = {2018}
}
@inproceedings{faucris.235365823,
author = {Marxreiter, Franz and Gaßner, Heiko and Borozdina, Olga and Barth, Jens and Kohl, Zacharias and Schlachetzki, Johannes and Thun-Hohenstein, Caroline and Volc, Dieter and Eskofier, Björn and Winkler, Jürgen and Klucken, Jochen},
doi = {10.1007/s00415-018-9012-7},
faupublication = {yes},
note = {EVALuna2:213284},
pages = {652-652},
peerreviewed = {Yes},
title = {{Sensor}-based gait analysis of individualized improvement during apomorphine titration in {Parkinson}'s disease},
volume = {126},
year = {2019}
}
@article{faucris.107690264,
abstract = {Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of eight spatio-temporal stride parameters. To this end, two modelling approaches are compared: A combined network estimating all parameters of interest and an ensemble approach that spawns less complex networks for each parameter individually. The ensemble approach is outperforming the combined modelling in the current application. On a clinically relevant and publicly available benchmark dataset, we estimate stride length, width and medio-lateral change in foot angle up to -0.15 ±6.09 cm, -0.09 ±4.22 cm and 0.13 ±3.78 ° respectively. Stride, swing and stance time as well as heel and toe contact times are estimated up to ±0.07, ±0.05, ±0.07, ±0.07 and ±0.12 s respectively. This is comparable to and in parts outperforming or defining stateof- the-art. Our results further indicate that the proposed change in methodology could substitute assumption-driven doubleintegration methods and enable mobile assessment of spatiotemporal stride parameters in clinically critical situations as e.g. in the case of spastic gait impairments.},
author = {Hannink, Julius and Kautz, Thomas and Pasluosta, Cristian Federico and Gaßmann, Karl-Günter and Klucken, Jochen and Eskofier, Björn},
doi = {10.1109/JBHI.2016.2636456},
faupublication = {yes},
journal = {IEEE Journal of Biomedical and Health Informatics},
month = {Jan},
pages = {85--93},
peerreviewed = {Yes},
title = {{Sensor}-based {Gait} {Parameter} {Extraction} with {Deep} {Convolutional} {Neural} {Networks}.},
url = {https://www.mad.tf.fau.de/files/2017/06/Hannink-et-al.-2017-Sensor-Based-Gait-Parameter-Extraction-With-Deep-Convolutional-Neural-Networks.pdf},
volume = {21},
year = {2017}
}
@inproceedings{faucris.122405184,
author = {Blank, Peter and Hoßbach, Julian and Schuldhaus, Dominik and Eskofier, Björn},
booktitle = {Proceedings of the 2015 ACM International Symposium on Wearable Computers},
date = {2015-09-07/2015-09-11},
doi = {10.1145/2802083.2802087},
faupublication = {yes},
isbn = {9781450335782},
keywords = {Inertial sensors; Movement analysis; Stroke detection and classification; Table tennis},
note = {UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.sensor{\_}6},
pages = {93-100},
peerreviewed = {unknown},
publisher = {Association for Computing Machinery, Inc},
title = {{Sensor}-based stroke detection and stroke type classification in table tennis},
venue = {Osaka},
year = {2015}
}
@inproceedings{faucris.124118984,
abstract = {The use of wearable sensors for automatic recognition of human activities has pervaded both professional and recreational sports. While many activities involving only a single athlete can be classified robustly, the automatic classification of complex activities involving several athletes is still in its infancy. In this paper, we present a novel approach for the recognition of such multi-player activities in the context of game sports. Our method is based on the fusion of position measurements with inertial measurements in a set of interaction features. We demonstrate the efficacy of our method in the recognition of tackles and scrums in Rugby Sevens. The results of our current work suggest that the proposed features can be leveraged to achieve classification accuracies of more than 97%.},
author = {Kautz, Thomas and Groh, Benjamin and Eskofier, Björn},
booktitle = {Workshop on Large-Scale Sports Analytics (21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining)},
faupublication = {yes},
peerreviewed = {unknown},
title = {{Sensor} fusion for multi-player activity recognition in game-sports},
url = {https://www.mad.tf.fau.de/files/2017/06/2015-Kautz-KDD{\_}LSSA-SFM.pdf},
venue = {Sydney, Australia},
year = {2015}
}
@article{faucris.315832094,
abstract = {It has been reported that a distinct ‘old person smell’ can develop with advancing age, however, this odour has not yet been sufficiently described in previous research. Sensory evaluation by a trained panel might be useful to describe alterations with age in body odour (BO). To evaluate the alterations and achieve first insights into the ‘old person smell’, this pilot study determined the odour profiles of BO samples from both a younger and an older age group with a trained panel. In addition, we aimed to assess whether the panellists can recognize the age group based on the smell of the BO samples. Eight younger (20–28 years) and eight older (80–83 years) participants sampled their BO by wearing a cotton T-shirt for one night. The samples were sensorially evaluated by a trained panel, including ratings of total intensity and pleasantness. Additionally, an age labelling task was performed as a forced-choice decision. Results revealed that the odour profiles of the BO samples were very similar for both age groups. Nevertheless, trained panellists were able to predict the age group with significantly higher accuracy (p =.042) than expected by chance (61% mean accuracy over all panellists). Furthermore, a linear support vector machine (SVM) classifier achieved an average accuracy of 69%. This finding indicates that the age of a person affects the BO, though it is not reflected in significantly distinct odour profiles.},
author = {Owsienko, Diana and Schwinn, Leo and Eskofier, Björn and Kiesswetter, Eva and Loos, Helene},
doi = {10.1002/ffj.3762},
faupublication = {yes},
journal = {Flavour and Fragrance Journal},
keywords = {ageing; body odour; odour profile; old person smell},
month = {Jan},
note = {CRIS-Team Scopus Importer:2023-12-22},
pages = {3-9},
peerreviewed = {Yes},
title = {{Sensory} evaluation of axillary odour samples of younger and older adults by a trained panel},
volume = {39},
year = {2024}
}
@article{faucris.268441976,
abstract = {Background: Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system, affecting more than 2.3 million people worldwide. Fatigue is among the most common symptoms in MS, resulting in reduced mobility and quality of life. The six-minute walking test (6MWT) is commonly used as a measure of fatigability for the assessment of state fatigue throughout treatment or rehabilitation programs. This ‘gold standard’ test is time-consuming and can be difficult and exhausting for some patients with high levels of disability or high rates of fatigue. Research question: Can short inertial sensor-based gait tests assess perceived state fatigue in MS patients? Methods: Sixty-five MS patients equipped with one sensor on each foot performed the 6 min walk test (6MWT) and the 25-foot walk (25FW, at both preferred and fastest speed). Perceived state fatigue was measured after each minute of the 6MWT, using the Borg rating. The highest of these ratings served as a measure of overall perceived state fatigue. Stride-wise spatio-temporal gait parameters were extracted from the 25FW and from the first minute, first 2 min, and first 4 min of the 6MWT. Principal component analysis was performed. Perceived state fatigue was predicted in a regression analysis, using the principal components of gait parameters as predictors. Statistical tests evaluated differences in performance between the full 6MWT, the shortened 6MWT, and the 25FW. Results: A mean absolute error of less than 2 points on the Borg rating was obtained using the shortened 6MWT and the 25FW. There were no significant differences between the prediction accuracy of the full 6MWT and that of the shortened gait tests. Significance: It is possible to use shortened gait tests when evaluating perceived state fatigue in MS patients using inertial sensors. Substituting them for long gait tests may reduce the burden of the testing on both patients and clinicians. Further, the approach taken here may prompt future work to explore the use of short bouts of real-world walking with unobtrusive inertial sensors for state fatigue assessment.},
author = {Ibrahim, Alzhraa and Flachenecker, Felix and Gaßner, Heiko and Rothammer, Veit and Klucken, Jochen and Eskofier, Björn and Kluge, Felix},
doi = {10.1016/j.msard.2022.103519},
faupublication = {yes},
journal = {Multiple Sclerosis and Related Disorders},
keywords = {Accelerometer; Fatigue; IMU; MS; Short gait test},
note = {CRIS-Team Scopus Importer:2022-01-28},
peerreviewed = {Yes},
title = {{Short} inertial sensor-based gait tests reflect perceived state fatigue in multiple sclerosis},
volume = {58},
year = {2022}
}
@article{faucris.226021659,
abstract = {We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.
The determination of the orientation of the skis during ski jumping provides fundamental information for athletes, coaches and spectators. Athletes and coaches can improve the training and the jump performance. Spectators can obtain interesting facts and a more attractive way of jump visualization by an orientation and jump angle determination. Existing camera-based systems to determine jump angles require a complex setup and calibration procedure. In contrast, inertial sensor-based methods can provide similar information with a low-cost and easy maintainable sensor setup. In this paper, we describe the processing of inertial sensor data (3D accelerometer, 3D gyroscope) in order to obtain the 3D orientation of the skis of an athlete during the whole jump sequence. Our methods include a functional sensor calibration to deal with sensor misalignment and a quaternion-based processing of sensor data. Acceleration data are used to determine the start and end of the jump and specific periods for the functional calibration. Gyroscope data are used to obtain the current orientation of the skis in each step of the movement. The orientation determination is evaluated by comparing the IMU calculated angle of attack (pitch angle of moving system) with a high-speed camera system. Our results show a root mean square error of 2.0° for the right ski and 9.3° for the left ski. It can be assumed that this difference of accuracy is influenced by the simple 2D evaluation method and perspective-related errors. A 3D high-speed video system with an accurate 3D representation of the skis is discussed for further evaluation.
}, author = {Groh, Benjamin and Weeger, Nicolas and Warschun, Frank and Eskofier, Björn}, booktitle = {Proceedings on Inertial Sensors and Systems Symposium (ISS)}, doi = {10.1109/InertialSensors.2014.7049482}, faupublication = {yes}, note = {UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.simpli{\_}3}, pages = {1-11}, title = {{Simplified} {Orientation} {Determination} in {Ski} {Jumping} using {Inertial} {Sensor} {Data}}, url = {https://www.mad.tf.fau.de/files/2017/06/2014-Groh-ISS-SOD.pdf}, venue = {Karlsruhe}, year = {2014} } @article{faucris.241982201, abstract = {Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person’s movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person’s and beacons’ localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant’s localization error was 1.05 ± 0.44 m, and the average beacons’ localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons’ data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).}, author = {Ceron, Jesus D. and Kluge, Felix and Küderle, Arne and Eskofier, Björn and López, Diego M.}, doi = {10.3390/s20174742}, faupublication = {yes}, journal = {Sensors}, keywords = {Indoor localization; Indoor tracking; Particle filter; SLAM}, note = {CRIS-Team Scopus Importer:2020-08-28}, pages = {1-21}, peerreviewed = {Yes}, title = {{Simultaneous} indoor pedestrian localization and house mapping based on inertial measurement unit and bluetooth low-energy beacon data}, volume = {20}, year = {2020} } @inproceedings{faucris.107022784, abstract = {The knee flexion-extension angle is an important criterion for the rehabilitation process after a rupture of the anterior cruciate ligament (ACL), which is a typical sports injury. This paper describes the development and evaluation of a smart knee sleeve, which is capable of calculating the knee flexion-extension angle based on inertial sensors. The output is to be used to supervise and speed up rehabilitation by guiding patients through exercises and giving feedback about the execution. The accuracy of the knee angle calculation was evaluated in a study with twelve healthy subjects performing six different exercises with an optical system as reference. The effects of different functional alignment movements, knee sleeve material and recalibration on the final knee angle were evaluated. The mean absolute error (MAE) of all maxima and minima was calculated and averaged over all subjects and exercises. An MAE of 5.2° compared to the gold standard was achieved while the Pearson correlation was 0.96 for 30 minutes training without recalibration or restart of the system.}, author = {Maurer, Mathias and Zrenner, Markus and Reynolds, David and Dümler, Burkhard and Eskofier, Björn}, booktitle = {Proceedings of the 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, date = {2018-03-04/2018-03-07}, doi = {10.1109/bsn.2018.8329644}, editor = {IEEE}, faupublication = {yes}, keywords = {Rehabilitation; Inertial Sensors; Sleeve}, pages = {1-4}, peerreviewed = {unknown}, title = {{Sleeve} {Based} {Knee} {Angle} {Calculation} for {Rehabilitation}}, venue = {Las Vegas, Nevada}, year = {2018} } @article{faucris.107050064, abstract = {Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions. This paper presents a smart annotation method that reduces this cost of labeling for sensor-based data, which is applicable to data collected outside of strict laboratory conditions. The method uses semi-supervised learning of sections of cyclic data with a known cycle number. A hierarchical hidden Markov model (hHMM) is used, achieving a mean absolute error of 0.041 ± 0.020 s relative to a manually-annotated reference. The resulting model was also used to simultaneously segment and classify continuous, ‘in the wild’ data, demonstrating the applicability of using hHMM, trained on limited data sections, to label a complete dataset. This technique achieved comparable results to its fully-supervised equivalent. Our semi-supervised method has the significant advantage of reduced annotation cost. Furthermore, it reduces the opportunity for human error in the labeling process normally required for training of segmentation algorithms. It also lowers the annotation cost of training a model capable of continuous monitoring of cycle characteristics such as those employed to analyze the progress of movement disorders or analysis of running technique.}, author = {Martindale, Christine and Hönig, Florian Thomas and Strohrmann, Christina and Eskofier, Björn}, doi = {10.3390/s17102328}, faupublication = {yes}, journal = {Sensors}, keywords = {hierarchical hidden Markov models; segmentation; smart annotation; cyclic sensor data; semi-supervised learning; annotation cost; activity recognition; gait classification; inertial sensors; wearable sensors}, peerreviewed = {Yes}, title = {{Smart} {Annotation} of {Cyclic} {Data} {Using} {Hierarchical} {Hidden} {Markov} {Models}}, url = {http://www.mdpi.com/1424-8220/17/10/2328}, volume = {17}, year = {2017} } @inproceedings{faucris.203913853, abstract = {The monitoring of patients within a natural, home environment is important in order to close knowledge gaps in the treatment and care of neurodegenerative diseases, such as quantifying the daily fluctuation of Parkinson’s patients’ symptoms. The combination of machine learning algorithms and wearable sensors for gait analysis is becoming capable of achieving this. However, these algorithms require large, labelled, realistic datasets for training. Most systems used as a ground truth for labelling are restricted to the laboratory environment, as well as being large and expensive. We propose a study design for a realistic activity monitoring dataset, collected with inertial measurement units, pressure insoles and cameras. It is not restricted by a fixed location or capture volume and still enables the labelling of gait phases or, where non-gait movement such as jumping occur: on-the-ground, off-the-ground phases. Additionally, this paper proposes a smart annotation tool which reduces annotation cost by more than 80%. This smart annotation is based on edge detection within the pressure sensor signal. The tool also enables annotators to perform assisted correction of these labels in a post-processing step. This system enables the collection and labelling of large, fairly realistic datasets where 93% of the automatically generated labels are correct and only an additional 10% need to be inserted manually. Our tool and protocol, as a whole, will be useful for efficiently collecting the large datasets needed for training and validation of algorithms capable of cyclic human motion analysis in natural environment}, author = {Martindale, Christine and Roth, Nils and Hannink, Julius and Sprager, Sebastijan and Eskofier, Björn}, booktitle = {2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)}, date = {2018-03-19/2018-03-23}, doi = {10.1109/PERCOMW.2018.8480193}, faupublication = {yes}, peerreviewed = {Yes}, title = {{Smart} {Annotation} {Tool} for {Multi}-sensor gait based daily activity data}, url = {https://www.mad.tf.fau.de/files/2018/09/percom2018{\_}martindale.pdf}, venue = {Athens}, year = {2018} } @article{faucris.276892437, abstract = {Objective: Clinical urine tests are a key component of prenatal care. As of now, urine test strips are evaluated through a time consuming, often error-prone and operator-dependent visual color comparison of test strips and reference cards by medical staff. Methods and procedures: This work presents an automated pipeline for urinalysis with urine test strips using smartphone camera images in home environments, combining several image processing and color combination techniques. Our approach is applicable to off-the-shelf test strips in home conditions with no additional hardware required. For development and evaluation of our pipeline we collected image data from two sources: i) A user study (26 participants, 150 images) and ii) a lab study (135 images). Results: We trained a region-based convolutional neural network that is able to detect the urine test strip location and orientation in images with a wide variety of light conditions, backgrounds and perspectives with an accuracy of 85.5%. The reference card can be robustly detected through a feature matching approach in 98.6% of the images. Color comparison by Hue channel (0.81 F1-Score), Matching factor (0.80 F1-Score) and Euclidean distance (0.70 F1-Score) were evaluated to determine the urinalysis results. Conclusion: We show that an automated smartphone-based colorimetric analysis of urine test strips in a home environment is feasible. It facilitates examinations and provides the possibility to shift care into an at-home environment. Clinical impact: The findings demonstrate that routine urine examinations can be transferred into the home environment using a smartphone. Simultaneously, human error is avoided, accuracy is increased and medical staff is relieve}, author = {Flaucher, Madeleine and Nissen, Michael and Jäger, Katharina and Titzmann, Adriana and Pontones, Constanza and Hübner, Hanna and Fasching, Peter and Beckmann, Matthias and Gradl, Stefan and Eskofier, Björn}, doi = {10.1109/JTEHM.2022.3179147}, faupublication = {yes}, journal = {IEEE Journal of Translational Engineering in Health and Medicine}, pages = {1-9}, peerreviewed = {Yes}, title = {{Smartphone}-{Based} {Colorimetric} {Analysis} of {Urine} {Test} {Strips} for {At}-{Home} {Prenatal} {Care}}, year = {2022} } @inproceedings{faucris.209668277, address = {Aachen}, author = {Blank, Peter and Eskofier, Björn}, booktitle = {Technologien im Leistungssport 3}, date = {2018-05-14/2019-05-15}, editor = {Ina Fichtner, Institut für angewandte Trainingswissenschaft}, faupublication = {yes}, isbn = {978-3-8403-7628-3}, pages = {126-133}, peerreviewed = {unknown}, publisher = {Meyer & Meyer Verlag}, title = {{Smart} {Racket} – {Echtzeitfeedbacksystem} im {Tischtennis}}, venue = {Leipzig}, year = {2018} } @inproceedings{faucris.118681244, abstract = {In many sports and medical applications small and wearable sensors are used that capture motion and physiological signals. Yet, commonly such sensor are only capable of acquiring raw data, which is afterwards transmitted to a smartphone or other mobile computing device where processing and classification is carried out. This results in limited usability because another device has to be worn. Moreover, high power consumption due to continuous transmission is a main disadvantage. Therefore, we propose a smart sensor approach that can alleviate these problems by carrying out the processing on the sensor itself. To house the processing schemes in the sensor node we developed a new computer architecture utilizing an FPGA and an ASIC. To show the benefits of the in-sensor processing, we chose two representative biosensor applications: a movement and fall detection system for the elderly and a swimming style recognition system for professional athletes. Compared to conventional approaches the same classification rate can be achieved while saving space, power, weight and setup costs.}, address = {Cagliari, Italy}, author = {Pfundt, Benjamin and Reichenbach, Marc and Eskofier, Björn and Fey, Dietmar}, booktitle = {Proceedings of the 2013 Conference on Design & Architectures for Signal & Image Processing}, date = {2013-10-08/2013-10-10}, edition = {1}, faupublication = {yes}, isbn = {979-10-92279-02-3}, keywords = {biosignal processing; smart sensors; FPGA; ASIC}, note = {UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD3.smarts}, pages = {1-8}, publisher = {ECSI Media}, title = {{Smart} {Sensor} {Architectures} for {Embedded} {Biosignal} {Analysis}}, venue = {Cagliari}, volume = {1}, year = {2013} } @inproceedings{faucris.111222364, abstract = {In this paper we present a smart soccer shoe that uses textile pressure sensing matrices to detect and analyze the interaction between players' foot and the ball. We describe the sensor system that consists of two 3 × 4 and one 3 × 3 matrices sampled at over 500Hz with low power electronics that allows continuous operation (incl. wireless transmission) for 8 hours using a small 800mA/h Li-Po battery. We show how relevant parameters for shot analysis such as contact speed and contact angles can be reliably derived from the sensor signals. To ensure reliable ground truth we evaluated the system with a kick robot in the adidas testing facility, which is the standard approach used by adidas to systematically and quantitatively test new shoes and balls. The test encompasses 17 different types of shots and achieves a near 100% classification accuracy/F-score. The system endured extreme levels of impact resulting in over 100km/hr ball speed.}, author = {Zhou, Bo and Wirth, Markus and Koerger, Harald and Zwick, Constantin and Martindale, Christine and Cruz, Heber and Eskofier, Björn and Lukowicz, Paul}, booktitle = {The 20th International Conference on Symposium on Wearable Computers}, date = {2016-09-14/2016-09-16}, doi = {10.1145/2971763.2971784}, faupublication = {yes}, isbn = {978-1-4503-4460-9}, peerreviewed = {unknown}, publisher = {ACM}, title = {{Smart} soccer shoe: monitoring foot-ball interaction with shoe integrated textile pressure sensor matrix}, venue = {Heidelberg}, year = {2016} } @inproceedings{faucris.215002195, author = {Maier, Jennifer and Black, Marianne and Hall, Mary and Choi, Jang-Hwan and Levenston, Marc and Gold, Garry and Fahrig, Rebecca and Eskofier, Björn and Maier, Andreas}, booktitle = {Bildverarbeitung für die Medizin 2019}, date = {2019-03-17/2019-03-19}, doi = {10.1007/978-3-658-25326-4{\_}21}, faupublication = {yes}, pages = {86-91}, peerreviewed = {unknown}, publisher = {Springer Fachmedien Wiesbaden}, title = {{Smooth} {Ride}: {Low}-{Pass} {Filtering} of {Manual} {Segmentations} {Improves} {Consensus}}, url = {https://link.springer.com/chapter/10.1007/978-3-658-25326-4{\_}21}, venue = {Lübeck}, year = {2019} } @inproceedings{faucris.120173284, abstract = {Embedded microcontrollers are employed in an increasing number of applications as a target for the implementation of classification systems. This is true for example for the fields of sports, automotive and medical engineering. However, important challenges arise when implementing classification systems on embedded microcontrollers, which is mainly due to limited hardware resources. In this paper, we present a solution to the two main challenges, namely obtaining a classification system with low computational complexity and at the same time high classification accuracy. For the first challenge, we propose complexity measures on the mathematical operation and parameter level, because the abstraction level of the commonly used Landau notation is too high in the context of embedded system implementation. For the second challenge, we present a software toolbox that trains different classification systems, compares their classification rate, and finally analyzes the complexity of the trained system. To give an impression of the importance of such complexity measures when dealing with limited hardware resources, we present the example analysis of the popular Pima Indians Diabetes data set, where considerable complexity differences between classification systems were revealed.}, author = {Ring, Matthias and Jensen, Ulf and Kugler, Patrick and Eskofier, Björn}, booktitle = {Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR)}, date = {2012-11-11/2012-11-15}, editor = {IEEE}, faupublication = {yes}, pages = {2266-2269}, peerreviewed = {Yes}, title = {{Software}-based {Performance} and {Complexity} {Analysis} for the {Design} of {Embedded} {Classification} {Systems}}, venue = {Tsukuba}, year = {2012} } @inproceedings{faucris.117354204, abstract = {Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50%of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. © 2013 IEEE.}, author = {Gradl, Stefan and Leutheuser, Heike and Kugler, Patrick and Biermann, Teresa and Kreil, Sebastian and Kornhuber, Johannes and Bergner, Matthias and Eskofier, Björn}, booktitle = {IEEE EMBC 2013}, date = {2013-07-03/2013-07-07}, doi = {10.1109/EMBC.2013.6609717}, editor = {Engineering in Medicine and Biology Society}, faupublication = {yes}, isbn = {9781457702167}, pages = {1182-1185}, peerreviewed = {Yes}, title = {{Somnography} using unobtrusive motion sensors and {Android}-based mobile phones}, venue = {Osaka}, year = {2013} } @article{faucris.290189115, author = {Lee, Sunghoon I. and Eskofier, Björn}, doi = {10.3390/app8020167}, faupublication = {yes}, journal = {Applied Sciences}, month = {Jan}, note = {CRIS-Team Scopus Importer:2023-03-07}, peerreviewed = {Yes}, title = {{Special} issue on wearable computing and machine learning for applications in sports, health, and medical engineering}, volume = {8}, year = {2018} } @article{faucris.121896544, abstract = {Laterally wedged insoles have been shown to be effective for the reduction of the knee adduction moment and other biomechanical variables that are associated with the pathogenesis of knee osteoarthritis. However, inconclusive results such as adverse effects in individual subjects or even no group-wise wedge effects have been presented in different studies and it has been suggested to identify variables that potentially confound the wedge effect. The main objective of this study was the investigation of interaction effects of lateral wedges with walking speed, as different self-select speeds have mainly been used in previous studies.
Twenty-two healthy subjects completed gait analysis trials on an instrumented treadmill. They walked in different speed conditions (0.9, 1.1, 1.3, 1.5 m/s) with a neutral and a laterally wedged insole. Kinematics were acquired using infrared cinematography with reflective markers attached to the lower body. From the stance phase we extracted biomechanical parameters that are associated with knee joint loading and osteoarthritis severity.
No interaction effect of lateral wedges and speed was observed for most biomechanical parameters except for the ankle eversion range of motion. The main effects of wedges were reductions of the external knee adduction moment and of the knee adduction angular impulse. All biomechanical variables changed with increasing speed. Only the lateral offset of the center of pressure did not respond to wedge or to speed changes.
Our results suggest that different self-selected speeds do not confound the effect of laterally wedged insole}, author = {Kluge, Felix and Krinner, Sebastian and Lochmann, Matthias and Eskofier, Björn}, doi = {10.1016/j.gaitpost.2017.04.012}, faupublication = {yes}, journal = {Gait & Posture}, keywords = {Walking speed; Kinematics; Kinetics; Orthotic device; Joint loading; Knee osteoarthritis}, pages = {145-149}, peerreviewed = {Yes}, title = {{Speed} dependent effects of laterally wedged insoles on gait biomechanics in healthy subjects}, url = {https://www.mad.tf.fau.de/files/2020/12/kluge{\_}2017{\_}gp{\_}proof.pdf}, volume = {55}, year = {2017} } @inproceedings{faucris.121213664, author = {Hebenstreit, Felix and Leibold, Andreas and Krinner, Sebastian and Welsch, Götz and Lochmann, Matthias and Eskofier, Björn}, booktitle = {XXV Congress of the International Society of Biomechanics}, date = {2015-07-12/2015-07-16}, faupublication = {yes}, pages = {1835-1836}, peerreviewed = {unknown}, title = {{Speed} modelling of relative gait phase durations.}, venue = {Glasgow}, year = {2015} } @incollection{faucris.123580424, abstract = {
Einleitung: Distorsionen des oberen Sprunggelenks zählen zu den häufigsten Sportverletzungen der unteren Extremität, mindestens ein Drittel aller Betroffenen entwickelt chronische Gelenkinstabilitäten (CAI) (Gribble et al., 2016). Veränderungen der Sprunggelenkskinematik beim Laufen konnten im Vergleich zu unverletzten Probanden (Moisan et al, 2017), nicht aber zu Verletzten ohne resultierende Instabilität (Coper), nachgewiesen werden. Ziel der Studie war es, die Sprunggelenkskinematik bei moderaten und hohen Laufgeschwindigkeiten zwischen Sportlern mit CAI und Copern zu vergleichen.
Methode: Einundzwanzig männliche Sportler mit vorausgegangener Sprunggelenkverletzung wurden untersucht, elf davon mit CAI (Alter: 24,0 ± 3,6; Cumberland Ankle Instability Tool (CAIT): 21,0 ± 3,3) und zehn ohne persistierende Instabilität (Coper; Alter: 23,3 ± 1,8; CAIT: 28,3 ± 1,8). Die kinematische Analyse (Qualisys, Göteborg, Schweden) erfolgte auf einem Laufband bei zwei individuellen Laufgeschwindigkeiten für jeweils 60 Sekunden: 1) moderat-intensiv (Borg = 14; Geschwindigkeit: 2,60 m/s ± 0,19); 2) erste Geschwindigkeit plus 1,8 m/s. Unterschiede der Sprunggelenkskinematik zwischen den Gruppen wurden mittels des „Statistical Parametric Mapping“ Verfahren untersucht.
Ergebnisse: Bei beiden Laufgeschwindigkeiten zeigte sich kein signifikanter Unterschied in der Sprunggelenkskinematik zwischen den CAI Patienten und Copern. Die subjektive Funktionseinschränkung (Sport-Skala des Foot and Ankle Ability Measure) unterschied sich signifikant zwischen den Gruppen (CAI: 85,0% ± 10,4; Copern 98,6% ± 3,5; p < 0,001).
Diskussion: In der vorliegenden Studie konnten keine Unterschiede in der Laufkinematik zwischen Sportlern mit CAI und Copern festgestellt werden. Dies entspricht nicht den bisherigen Ergebnissen zwischen CAI und Unverletzten (Moisan et al, 2017), bestätigt jedoch erste Untersuchungen zwischen CAI und Copern (De Ridder et al., 2013). Coper stellen eine wichtige Vergleichsgruppe dar, um langfristige Verletzungsfolgen zu identifizieren.
Literatur:
De Ridder, R., Willems, T., Vanrenterghem, J. et al. (2013). Med Sci Sports Exerc, 45 (11), 2129-36.
Gribble, P., Bleakley, C., Caulfield, B., et al. (2016). British journal of sports medicine, 50 (24), 1496-1505.
Moisan, G., Descarreaux, M., Cantin, V. (2017). Gait & Posture, 52, 381-39},
address = {Hamburg},
author = {Wanner, Philipp and Schmautz, Thomas and Kluge, Felix and Eskofier, Björn and Pfeifer, Klaus and Steib, Simon},
booktitle = {Innovation & Technologie im Sport. Abstractband des 23. dvs-Hochschultags vom 13. bis 15. September 2017 in München},
editor = {Schwirtz, A., Mess, F., Demetriou, Y. & Senner, V. (Hrsg.)},
faupublication = {yes},
isbn = {978-3-88020-655-7},
keywords = {Chronische Sprunggelenksinstabilität, Coper, Laufanalyse, Kinematik},
pages = {206},
peerreviewed = {Yes},
publisher = {Feldhaus},
title = {{Sportler} mit {Sprunggelenksinstabilität} weisen im {Vergleich} zu {Copern} keine {Veränderungen} der {Sprunggelenkskinematik} beim {Laufen} auf},
year = {2017}
}
@inproceedings{faucris.120321124,
author = {Hebenstreit, Felix and Eskofier, Björn and Blanke, M and Lochmann, Matthias},
booktitle = {Proceeding of: Tagung Deutsche Vereinigung für Sportwissenschaft, Sektion Biomechanik},
date = {2013-03-13/2013-03-15},
editor = {Deutsche Vereinigung für Sportwissenschaft, Sektion Biomechanik},
faupublication = {yes},
pages = {7},
title = {{Statistische} {Analyse} {Ensemble}-gemittelter zyklischer {Bewegungsdaten}},
url = {http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2013/Hebenstreit13-SAE.pdf},
venue = {Technische Universität Chemnitz},
year = {2013}
}
@article{faucris.269961740,
author = {Mehringer, Wolfgang and Wirth, Markus and Roth, Daniel and Michelson, Georg and Eskofier, Björn},
doi = {10.1109/TVCG.2022.3150486},
faupublication = {yes},
journal = {IEEE Transactions on Visualization and Computer Graphics},
keywords = {Games; Headphones; Software verification and validation; Stereo image processing; Stereo vision; Task analysis; Training; Virtual reality; Vision defects; Visualization; Visualization design},
note = {CRIS-Team Scopus Importer:2022-02-25},
peerreviewed = {Yes},
title = {{Stereopsis} {Only}: {Validation} of a {Monocular} {Depth} {Cues} {Reduced} {Gamified} {Virtual} {Reality} with {Reaction} {Time} {Measurement}},
year = {2022}
}
@article{faucris.117708404,
author = {Barth, Jens and Oberndorfer, Cäcilia and Pasluosta, Cristian Federico and Schülein, Samuel and Gaßner, Heiko and Reinfelder, Samuel and Kugler, Patrick and Schuldhaus, Dominik and Winkler, Jürgen and Klucken, Jochen and Eskofier, Björn},
doi = {10.3390/s150306419},
faupublication = {yes},
journal = {Sensors},
keywords = {inertial sensors; stride segmentation; accelerometer; gyroscope; dynamic time warping; free walk; gait analysis; Parkinsons disease; geriatric patients; movement impairments},
note = {UnivIS-Import:2015-04-14:Pub.2015.tech.IMMD.IMMD5.stride},
pages = {6419-6440},
peerreviewed = {Yes},
title = {{Stride} {Segmentation} {During} {Free} {Walk} {Movements} {Using} {Multi}-dimensional {Subsequence} {Dynamic} {Time} {Warping} on {Inertial} {Sensor} {Data}},
volume = {15},
year = {2015}
}
@inproceedings{faucris.106900024,
author = {Drory, Ami and Groh, Benjamin and Eskofier, Björn},
booktitle = {Proceedings of the 26th Congress of the International Society of Biomechanics (ISB)},
date = {2017-07-23/2017-07-27},
faupublication = {yes},
peerreviewed = {unknown},
title = {{Supervised} {Learning} {Approach} to {Markerless} {Acquisition} of {Rower} {Kinematics}},
venue = {Brisbane, Australia},
year = {2017}
}
@article{faucris.278155585,
abstract = {Efficient handwriting trajectory reconstruction (TR) requires specific writing surfaces for detecting movements of digital pens. Although several motion-based solutions have been developed to remove the necessity of writing surfaces, most of them are based on classical sensor fusion methods limited, by sensor error accumulation over time, to tracing only single strokes. In this work, we present an approach to map the movements of an IMU-enhanced digital pen to relative displacement data. Training data is collected by means of a tablet. We propose several pre-processing and data-preparation methods to synchronize data between the pen and the tablet, which are of different sampling rates, and train a convolutional neural network (CNN) to reconstruct multiple strokes without the need of writing segmentation or post-processing correction of the predicted trajectory. The proposed system learns the relative displacement of the pen tip over time from the recorded raw sensor data, achieving a normalized error rate of 0.176 relative to unit-scaled tablet ground truth (GT) trajectory. To test the effectiveness of the approach, we train a neural network for character recognition from the reconstructed trajectories, which achieved a character error rate of 19.51%. Finally, a joint model is implemented that makes use of both the IMU data and the generated trajectories, which outperforms the sensor-only-based recognition approach by 0.75%.
® system as reference.
Gait events based on the insoles’ pressure sensors were manually
extracted to calculate temporal gait features such as double support
time and double support. Compared to the reference system a mean error
of 0.06 s ±0.06 s and 3.89 % ±2.61 % was achieved,
respectively. The proposed insoles proved their ability to acquire
synchronized gait parameters and address the requirements for
home-monitoring scenarios, pushing the boundaries of clinical gait
analysi},
author = {Roth, Nils and Martindale, Christine and Gaßner, Heiko and Kohl, Zacharias and Klucken, Jochen and Eskofier, Björn},
doi = {10.1515/cdbme-2018-0103},
faupublication = {yes},
keywords = {Double support; Home-monitoring; Gait; Synchronization; Insole},
pages = {433-437},
peerreviewed = {unknown},
publisher = {Walter de Gruyter GmbH},
title = {{Synchronized} sensor insoles for clinical gait analysis in home-monitoring applications},
volume = {4},
year = {2018}
}
@article{faucris.223524682,
abstract = {
Motor impairment appears as a characteristic symptom of several diseases and injuries. Therefore, tests for analyzing motor dysfunction are widely applied across preclinical models and disease stages. Among those, gait analysis tests are commonly used, but they generate a huge number of gait parameters. Thus, complications in data analysis and reporting raise, which often leads to premature parameter selection.
In order to avoid arbitrary parameter selection, we present here a systematic initial data analysis by utilizing heat-maps for data reporting. We exemplified this approach within an intervention study, as well as applied it to two longitudinal studies in rodent models related to Parkinson’s disease (PD) and Huntington disease (HD).
The systematic initial data analysis (IDA) is feasible for exploring gait parameters, both in experimental and longitudinal studies. The resulting heat maps provided a visualization of gait parameters within a single chart, highlighting important clusters of differences.
Often, premature parameter selection is practiced, lacking comprehensiveness. Researchers often use multiple separated graphs on distinct gait parameters for reporting. Additionally, negative results are often not reported.
Heat mapping utilized in initial data analysis is advantageous for reporting clustered gait parameter differences in one single chart and improves data mining.
•Gait impairment correlates with the fall in blood pressure upon standing.
•Wearable sensors detect the impact of orthostatic hypotension on gai},
author = {Raccagni, Cecilia and Sidoroff, Victoria and Goebel, Georg and Granata, Roberta and Leys, Fabian and Klucken, Jochen and Eskofier, Björn and Richer, Robert and Seppi, Klaus and Wenning, Gregor K. and Fanciulli, Alessandra},
doi = {10.1016/j.parkreldis.2020.06.029},
faupublication = {yes},
journal = {Parkinsonism & Related Disorders},
keywords = {Parkinson's disease; Multiple system atrophy; Orthostatic hypotension; Gait analysis},
peerreviewed = {Yes},
title = {{The} footprint of orthostatic hypotension in parkinsonian syndromes},
year = {2020}
}
@article{faucris.298196852,
abstract = {The purpose of our study was to identify the low-dimensional latent components, defined hereafter as motor unit modes, underlying the discharge rates of the motor units in two knee extensors (vastus medialis and lateralis, eight men) and two hand muscles (first dorsal interossei and thenars, seven men and one woman) during submaximal isometric contractions. Factor analysis identified two independent motor unit modes that captured most of the covariance of the motor unit discharge rates. We found divergent distributions of the motor unit modes for the hand and vastii muscles. On average, 75% of the motor units for the thenar muscles and first dorsal interosseus were strongly correlated with the module for the muscle in which they resided. In contrast, we found a continuous distribution of motor unit modes spanning the two vastii muscle modules. The proportion of the muscle-specific motor unit modes was 60% for vastus medialis and 45% for vastus lateralis. The other motor units were either correlated with both muscle modules (shared inputs) or belonged to the module for the other muscle (15% for vastus lateralis). Moreover, coherence of the discharge rates between motor unit pools was explained by the presence of shared synaptic inputs. In simulations with 480 integrate-and-fire neurons, we demonstrate that factor analysis identifies the motor unit modes with high levels of accuracy. Our results indicate that correlated discharge rates of motor units that comprise motor unit modes arise from at least two independent sources of common input among the motor neurons innervating synergistic muscles.SIGNIFICANCE STATEMENT It has been suggested that the nervous system controls synergistic muscles by projecting common synaptic inputs to the engaged motor neurons. In our study, we reduced the dimensionality of the output produced by pools of synergistic motor neurons innervating the hand and thigh muscles during isometric contractions. We found two neural modules, each representing a different common input, that were each specific for one of the muscles. In the vastii muscles, we found a continuous distribution of motor unit modes spanning the two synergistic muscles. Some of the motor units from the homonymous vastii muscle were controlled by the dominant neural module of the other synergistic muscle. In contrast, we found two distinct neural modules for the hand muscles.},
author = {Del Vecchio, Alessandro and Marconi Germer, Carina and Kinfe, Thomas Mehari and Nuccio, Stefano and Hug, François and Eskofier, Björn and Farina, Dario and Enoka, Roger M.},
doi = {10.1523/JNEUROSCI.1265-22.2023},
faupublication = {yes},
journal = {The Journal of Neuroscience},
keywords = {common synaptic input; motor neurons; motor unit; muscle synergies},
note = {CRIS-Team Scopus Importer:2023-04-28},
pages = {2860-2873},
peerreviewed = {Yes},
title = {{The} {Forces} {Generated} by {Agonist} {Muscles} during {Isometric} {Contractions} {Arise} from {Motor} {Unit} {Synergies}},
volume = {43},
year = {2023}
}
@inproceedings{faucris.259572719,
abstract = {Gait supervision plays an important role in the diagnosis, analysis and rehabilitation of motor impairments and neurodegenerative disorders. For example, in Parkinson's disease, gait assessment is used for progression observation and medication guidance. Previous work has presented the potential of virtual reality (VR) supported gait applications. While virtual environments and user representation strategies are used for gait applications, the influence of appearance and context cues on gait performance is not extensively researched. In this paper, we analyzed the influence of avatar appearance, environment awareness, and camera perspective on gait parameters relevant for clinical application. Four different avatar appearances, varying in abstraction, two environmental settings, as well as an egocentric and exocentric camera perspective were compared in three walking tasks on a treadmill. Our results show that variability, as an indicator for gait stability, is significantly impacted by VR exposure in comparison to a real world (in vivo) baseline. Further, our results revealed that walking tasks influence gait behavior significantly different in VR compared to in vivo. Overall, these findings suggest that particular care has to be taken when assessing gait characteristics acquired from subjects immersed in VR and that equivalence of results with in vivo may not be blindly assumed.},
author = {Wirth, Markus and Gradl, Stefan and Prosinger, Georg and Kluge, Felix and Roth, Daniel and Eskofier, Björn},
booktitle = {Proceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2021},
date = {2021-03-27/2021-04-03},
doi = {10.1109/VR50410.2021.00055},
faupublication = {yes},
isbn = {9780738125565},
keywords = {Computer graphics; Computing methodologies; Graphics systems and interfaces; Virtual reality},
note = {CRIS-Team Scopus Importer:2021-06-04},
pages = {326-335},
peerreviewed = {unknown},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{The} impact of avatar appearance, perspective and context on gait variability and user experience in virtual reality},
venue = {Virtual, Lisboa, PRT},
year = {2021}
}
@inproceedings{faucris.315822445,
abstract = {
Understanding an ice hockey player’s movements and the restrictions incurred by the protective equipment is crucial for improving the equipment and subsequently, player’s performance. The optimum design of the protective equiment is specially challenging given the complexity of the movements and manuevers in ice hockey. This complexity arises from the multitude of possible variables that describe player’s motion and therefore complex analysis methods are required to help direct the researcher’s attention toward the right variables. The purpose of this work was to utilize artificial neural network (ANN) and layer-wise relevance propagation (LRP) [1] to understand how complex drills in ice hockey were affected by the presence of protective equipment.
Seventeen male ice hockey players (age: 26.4 ± 7.0 years; mass: 86.4 ± 6.5 kg; height:183.8 ± 6.5 cm) skated the long axis of the ice rink as fast as possible. The sprint time over 30 meters was recorded (Brower TCi Timing system) and the movement data was captured by inertial measurements units integrated into the Xsens MVN Awinda System. The sprints were performed twice in either with (Equipment) or without protective equipment (No Equipment) conditions in a randomized order. Individual strides were defined from one ice contact to the subsequent ice contact of the same foot. A total of 24 strides were extracted for each of the participants. The trajectories of 12 joint angles (Figure 1) were extracted and normalized. All trajectories for one movement were concatenated into one single feature vector. A shallow ANN was trained to distinguish whether the sprint stride was performed in Equipment or No Equipment condition. Using LRP, the contribution of each variable to the classification result of the ANN was determined.
On average, the participants performed the sprint drill 1.64 % faster in No Equipment compared to Equipment condition (p = 0.0037). The model was trained 17 times while leaving one participant out each time for testing, reaching an average accuracy of 99.3 %. The average relevance scores that were derived from the ANN model are depicted in Figure 1. Each row corresponds to one rotational degree of freedom, while each column depicts one percent of the stride cycle. The histograms at the top and right part of the figure show the vertical, and horizontal summation of the heatmap respectively. The results show that the ANN can distinguish between the two conditions while observing differences in performance. Thus, it appears that protective equipment impairs performance. The presented data indicate that rotations around the medial-lateral axis (in sagittal plane) and movements associated to the shoulder, knee, and hip joint contributed the most to the classification result.
The proposed methodology based on ANN and LRP was able to highlight the variables and time points that differ between Equipment and No Equipment conditions. We were able to distinguish the important movements that the protective equipment restricts, and future design can leverage this information. In general, this approach allows researchers to investigate a question from a holistic point of view and therefore make informed decisions in complex, multivariate problems.
[1] Bach S et al. PLOS ONE 10(7), 201},
author = {Lennartz, Rebecca and Khassetarash, Arash and Spyrou, Evangelos and Hallihan, Aiden and Eskofier, Björn and Nigg, Benno},
booktitle = {ISB Program & Abstract Book},
date = {2023-07-30/2023-08-03},
faupublication = {yes},
keywords = {Explainable AI; Machine Learning; Biomechanics; Performance; Joint Angles},
pages = {665},
peerreviewed = {unknown},
title = {{The} {Influence} of {Protective} {Equipment} on {Performance} in {Ice} {Hockey}},
url = {https://263c08f5a9.clvaw-cdnwnd.com/8878d3cf4a2a08b86932691d02722bb8/200000434-f2bb0f2bb3/ISB{\_}JSB2023 Program - Abstract Book.pdf?ph=263c08f5a9},
venue = {Fukuoka},
year = {2023}
}
@article{faucris.242382169,
abstract = {This paper presents a handwriting recognition (HWR) system that deals with online character recognition in real-time. Our sensor-enhanced ballpoint pen delivers sensor data streams from triaxial acceleration, gyroscope, magnetometer and force signals at 100 Hz. As most existing datasets do not meet the requirements of online handwriting recognition and as they have been collected using specific equipment under constrained conditions, we propose a novel online handwriting dataset acquired from 119 writers consisting of 31,275 uppercase and lowercase English alphabet character recordings (52 classes) as part of the UbiComp 2020 Time Series Classification Challenge. Our novel OnHW-chars dataset allows for the evaluations of uppercase, lowercase and combined classification tasks, on both writer-dependent (WD) and writer-independent (WI) classes and we show that properly tuned machine learning pipelines as well as deep learning classifiers (such as CNNs, LSTMs, and BiLSTMs) yield accuracies up to 90 % for the WD task and 83 % for the WI task for uppercase characters. Our baseline implementations together with the rich and publicly available OnHW dataset serve as a baseline for future research in that area.
},
author = {Adams Seewald, Lucas and Facco Rodrigues, Vinicius and Ollenschläger, Malte and Stoffel Antunes, Rodolfo and Andre da Costa, Cristiano and da Rosa Righi, Rodrigo and da Silveira Junior, Luiz Gonzaga and Maier, Andreas and Eskofier, Björn and Fahrig, Rebecca},
doi = {10.1016/j.cviu.2018.09.010},
faupublication = {yes},
journal = {Computer Vision and Image Understanding},
keywords = {Computer vision; Interference; Multi-camera; Depth camera; Evaluation},
peerreviewed = {Yes},
title = {{Toward} analyzing mutual interference on infrared-enabled depth cameras},
year = {2019}
}
@inproceedings{faucris.264151900,
abstract = {Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition system for word recognition which is based on inertial measurement units (IMUs) for digitizing text written on paper. This is obtained by means of a sensor-equipped pen that provides acceleration, angular velocity, and magnetic forces streamed via Bluetooth. Our model combines convolutional and bidirectional LSTM networks, and is trained with the Connectionist Temporal Classification loss that allows the interpretation of raw sensor data into words without the need of sequence segmentation. We use a dataset of words collected using multiple sensor-enhanced pens and evaluate our model on distinct test sets of seen and unseen words achieving a character error rate of 17.97% and 17.08%, respectively, without the use of a dictionary or language model.},
author = {Wehbi, Mohamad and Hamann, Tim and Barth, Jens and Kaempf, Peter and Zanca, Dario and Eskofier, Björn},
booktitle = {International Conference on Document Analysis and Recognition ICDAR 2021},
date = {2021-09-05/2021-09-10},
doi = {10.1007/978-3-030-86334-0{\_}19},
faupublication = {yes},
keywords = {Online handwriting recognition; Digital pen; Inertial measurement unit; Time-series data},
pages = {289-303},
peerreviewed = {unknown},
publisher = {Springer Link},
title = {{Towards} an {IMU}-based {Pen} {Online} {Handwriting} {Recognizer}},
venue = {Lausanne},
year = {2021}
}
@inproceedings{faucris.119491064,
author = {Schuldhaus, Dominik and Leutheuser, Heike and Eskofier, Björn},
booktitle = {9th International Conference on Body Area Networks},
date = {2014-09-29/2014-10-01},
faupublication = {yes},
note = {UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.toward{\_}23},
pages = {97-103},
title = {{Towards} {Big} {Data} for {Activity} {Recognition}: {A} {Novel} {Database} {Fusion} {Strategy}},
url = {https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Schuldhaus14-TBD.pdf},
venue = {London},
year = {2014}
}
@article{faucris.244955015,
abstract = {When children suffer from cognitive disorders, school performance and social environment are affected. Measuring changes in cognitive progress is essential for assessing the clinical follow-up of the patient's cognitive abilities. This process is considered as a challenge in ambulatory settings, where follow-ups should be non-invasive and continuous. Psychophysiological measures are an objective and unobtrusive evaluation alternative for recognizing cognitive changes. This paper aims to validate the relationship between cognition and the changes in physiological signals of children suffering from Specic Learning Disorders (SLD). This validation was carried out in an eHealth rehabilitation context (with the HapHop-Physio game). Electrodermal activity (EDA) signals were collected, processed, and analyzed through a machine learning approach. Obtained results were: a dataset built from wearable physiological data and a supervised classication model. The classication model can identify the children's cognitive performance (class) from the features of the tonic component of the EDA signal (attributes) with an accuracy of 79.95%. The presented results evidence that psychophysiological measures could allow for a highly objective follow-up for patients. They can also lead to creating a basis for further improvement of rehabilitation environments and developing neurofeedback applications.
The best developer experience is usually achieved when the entire analysis can be represented with the tools provided by a single library. For example, when an entire machine learning pipeline is represented by a scikit-learn pipeline (Pedregosa et al., 2018), it is extremely easy to switch out and train algorithms. Furthermore, train/test leaks and other methodological errors at various stages in the analysis are automatically prevented – even if the user might not be aware of these issues.
However, if the performed analysis gets too complex, too specific to an application domain, or requires the use of tooling and algorithms from multiple frameworks, developers lose a lot of the benefits provided by individual libraries. In turn, the required skill level and the chance of methodological errors rise.
With tpcp we attempt to overcome the issue by providing higher-level tooling and structure for algorithm development and evaluation that is independent of the frameworks required for the algorithm implementatio},
author = {Küderle, Arne and Richer, Robert and Simpetru, Raul and Eskofier, Björn},
doi = {10.21105/joss.04953},
faupublication = {yes},
journal = {Journal of Open Source Software},
pages = {4953},
peerreviewed = {Yes},
title = {tpcp: {Tiny} {Pipelines} for {Complex} {Problems} - {A} set of framework independent helpers for algorithms development and evaluation},
url = {https://joss.theoj.org/papers/10.21105/joss.04953},
volume = {8},
year = {2023}
}
@misc{faucris.231099045,
abstract = {In a lab-in-the-field experiment, we investigate how using tracking and tracing technology impacts work performance, division of tasks, and stress levels of individuals measured by salivary biomarkers in team work. We focus on situations where individuals engage in teamwork and compare treatments, in which (i) teams are not subject to tracking technology and all team members receive equal payment depending on the team output; (ii) team interactions are tracked and analyzed and all team members receive equal payment depending on the team output; (iii) team interactions are tracked and analyzed to estimate individual productivity and each team member receives a payment that is proportional to estimated worker productivity. The tracking technology employed measures team interactions in the form of individual discussion contributions. The results do not indicate increased biological activation levels, but provide some evidence for increases in social stress when teams are tracked. The tracking does not affect the individual or the team output. Payment contingent on individual performance indicators significantly reduces how specialized team members work but does not affect the output. Our findings suggest that the effects of tracking and of individual incentives on stress levels and on productivity are modest. We provide some further analysis of diversification as insurance mechanism against the risk of low payment under individual incentives. As result of low power we find no sufficient evidence that risk preferences can explain the specialization difference between team and individual incentives.
curved running can be generated from a random initial guess in about two hours.
The possibilities for wearable health care technology to improve the quality of life for chronic disease patients has been increasing within recent years. For instance, unobtrusive cardiac monitoring can be applied to people suffering from a disorder of the autonomic nervous system (ANS) which show a significantly lower heart rate variability (HRV) than healthy people. Although recent work presented solutions to analyze this relationship, they did not perform it during daily life situations. For that reason, this work presents a system for a real-time analysis of the user's HRV on an Android-based mobile device throughout the day. The system was used for the detection of an orthostatic dysregulation which can be an indicator for a disorder of the ANS. Measures for HRV analysis were computed from acquired ECG data and compared before and after a posture change. For triggering the HRV analysis, an IMU-based algorithm which detects stand up events was developed. As a proof of concept for an automatic assessment of an orthostatic dysregulation, a classification based on the derived HRV measures was performed. The performance of the stand up detection was evaluated in the first part of this study. The second part was conducted for the evaluation of the derived HRV measures and involved healthy subjects as well as patients with idiopathic Parkinson's Disease. The results of the evaluation showed a recognition rate of 90.0% for the stand up detection algorithm. Furthermore, a clear difference in the change of HRV measures between the two groups before and after standing up was observed. The classification provided an accuracy of 96.0%, and a sensitivity of 93.3%. The results demonstrated the possibility of unobtrusive HRV monitoring during daily life situations.},
author = {Richer, Robert and Groh, Benjamin and Blank, Peter and Dorschky, Eva and Martindale, Christine and Klucken, Jochen and Eskofier, Björn},
booktitle = {2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)},
date = {2016-06-14/2016-06-17},
doi = {10.1109/BSN.2016.7516257},
editor = {IEEE},
faupublication = {yes},
isbn = {978-1-5090-3087-3},
peerreviewed = {Yes},
title = {{Unobtrusive} {Real}-time {Heart} {Rate} {Variability} {Analysis} for the {Detection} of {Orthostatic} {Dysregulation}},
url = {https://www.mad.tf.fau.de/files/2017/06/2016-Richer-BSN-URH.pdf},
venue = {San Francisco, CA},
year = {2016}
}
@inproceedings{faucris.223181799,
abstract = {Sensor-based gait analysis is a valuable tool in
diagnosis and assessment of Parkinson’s disease. Especially for
large data sets, efficient analysis pipelines are required. Presegmentation
of long time series into chunks of interest is a possible
approach to increase efficiency. Therefore, we developed an
unsupervised algorithm for the detection of gait sequences from
continuous sensor signals. In the proposed method, gyroscope
signals representing the angular rate of the feet are analyzed in
the frequency domain using moving windows. A gait sequence
was detected, if the frequency spectrum of a given window
contained harmonic frequencies. The approach was tested on
two data sets that differed in the ratio of clinical gait and cyclic
movement tests. Sensitivity in both data sets was higher than
99% in a stride-to-stride comparison with ground truth. The
specificity was measured with 76.1% (data set 1) and 94.5% (data
set 2) for tests against sequences of other cyclic movements. In
conclusion, the algorithm offers a reliable and efficient approach
for the detection of gait sequences in time series data and is
also promising for the application in long-term home-monitoring
scenarios.
90%. Moreover, the proposed method outperformed a state-of-the-art blind source separation approach in terms of the number of reliably detected motor units. In conclusion, we have proposed the first fully unsupervised neural network approach to the problem of neural decoding of intramuscular EMG time series by translating the available theoretical knowledge on the signal properties into an autoencoding architecture tailored to the decomposition problem.},
author = {Mayer, Kenneth and Del Vecchio, Alessandro and Eskofier, Björn and Farina, Dario},
doi = {10.1016/j.bspc.2023.105178},
faupublication = {yes},
journal = {Biomedical Signal Processing and Control},
keywords = {Autoencoder; Blind deconvolution; EMG signal decomposition; Sparse signals; Unsupervised learning},
note = {CRIS-Team Scopus Importer:2023-07-28},
peerreviewed = {Yes},
title = {{Unsupervised} neural decoding of signals recorded by thin-film electrode arrays implanted in muscles using autoencoding with a physiologically derived optimisation criterion},
volume = {86},
year = {2023}
}
@inproceedings{faucris.120024784,
abstract = {Einleitung: Sprunggelenksdistorsionen zählen zu den häufigsten Sportverletzungen, mindestens ein Drittel aller Betroffenen entwickelt eine chronische Sprunggelenksinstabilität (CAI) [1]. Veränderungen der Sprunggelenkskinematik beim Laufen konnten im Vergleich zu unverletzten Probanden [2], nicht aber zu Verletzten ohne resultierende Instabilität (Coper), nachgewiesen werden [3]. Ungeklärt bleibt jedoch, ob Personen mit CAI im Vergleich zu Copern eine veränderte Variabilität des Sprunggelenks aufweisen. Ziel der Studie war es, die Variabilität der Sprunggelenkskinematik bei moderaten und hohen Laufgeschwindigkeiten zwischen Sportlern mit CAI und Copern zu vergleichen.
Methode: Einundzwanzig männliche Sportler mit vorausgegangener Sprunggelenkverletzung wurden untersucht, elf davon mit CAI (Alter: 24,0 ± 3,6; Cumberland AnkleInstability Tool (CAIT): 21,0 ± 3,3) und zehn ohne persistierende Instabilität (Coper; Alter: 23,3 ± 1,8; CAIT: 28,3 ± 1,8). Die kinematische Analyse (Qualisys, Göteborg, Schweden) erfolgte auf einem Laufband bei zwei individuellen Laufgeschwindigkeiten für jeweils 60 Sekunden: 1) moderat-intensiv (Borg = 14; Geschwindigkeit: 2,60 m/s ± 0,19); 2) erste Geschwindigkeit plus 1,8 m/s. Unterschiede in der Variabilität der Sprunggelenkskinematik (intraindividuelle Standardabweichung aller Schritte) zwischen den Gruppen wurden mittels des „Statistical Parametric Mapping“ Verfahren untersucht.
Ergebnisse: Bei beiden Laufgeschwindigkeiten zeigte sich kein signifikanter Unterschied in der Variabilität der Sprunggelenkskinematik zwischen den CAI Patienten und Copern. Jedoch ließ sich zu mehreren Zeitpunkten des Gangzyklus eine Tendenz zu einer erhöhten Variabilität in Frontalebene der CAI Gruppe erkennen (d ≥ 0,8).
Diskussion & Zusammenfassung: In der vorliegenden Studie tendierten Sportler mit einer CAI zu einer erhöhten Variabilität der Sprunggelenkskinematik in Frontalebene im Vergleich zu Copern. Dies konnte bisher beim Vergleich der Sprunggelenkskinematik zwischen den Gruppen nicht festgestellt werden [3] und könnte einen Hinweis auf eine veränderte sensomotorische Kontrolle bei einer chronischen Instabilität geben. Coper stellen eine wichtige Vergleichsgruppe dar, um langfristige Verletzungsfolgen zu identifizieren.
Literatur
[1] Gribble, P., Bleakley, C., Caulfield, B., et al. (2016). British Journal of Sports Medicine, 50 (24), 1496-1505.
[2] Moisan, G., Descarreaux, M., Cantin, V. (2017). Gait & Posture, 52, 381-399.
[3] Ridder, R., Willems, T., Vanrenterghem, J. et al. (2013). Med Sci Sports Exerc, 45 (11), 2129-3},
author = {Wanner, Philipp and Schmautz, Thomas and Kluge, Felix and Eskofier, Björn and Pfeifer, Klaus and Steib, Simon},
booktitle = {2. GAMMA Kongress},
date = {2018-02-09/2018-02-10},
faupublication = {yes},
keywords = {Chronische Sprunggelenksinstabilität, Coper, Laufanalyse, Variabilität},
peerreviewed = {Yes},
title = {{Unterschiede} in der {Sprunggelenksvariabilität} beim {Laufen} zwischen {Sportlern} mit chronischer {Instabilität} und {Copern}},
venue = {Hamburg},
year = {2018}
}
@inproceedings{faucris.290193833,
abstract = {The dual task paradigm (DTP), where performance of a walking task co-occurs with a cognitive task to assess performance decrement, has been controversially mooted as a more suitable task to test safety from falls in outdoor and urban environments than simple walking in a hospital corridor. There are a variety of different cognitive tasks that have been used in the DTP, and we wanted to assess the use of a secondary task that requires mental tracking (the alternate letter alphabet task) against a more automatic working memory task (counting backward by ones). In this study we validated the x-io x-IMU wearable inertial sensors, used them to record healthy walking, and then used dynamic time warping to assess the elements of the gait cycle. In the timed 25 foot walk (T25FW) the alternate letter alphabet task lengthened the stride time significantly compared to ordinary walking, while counting backward did not. We conclude that adding a mental tracking task in a DTP will elicit performance decrement in healthy volunteers.},
author = {Witchel, Harry J. and Needham, Robert and Healy, Aoife and Guppy, Joseph H. and Bush, Jake and Oberndorfer, Cäcilia and Herberz, Chantal and Westling, Carina E.I. and Kim, Dawit and Roggen, Daniel and Barth, Jens and Eskofier, Björn and Rashid, Waqar and Chockalingam, Nachiappan and Klucken, Jochen},
booktitle = {ACM International Conference Proceeding Series},
date = {2017-09-20/2017-09-22},
doi = {10.1145/3121283.3121285},
faupublication = {yes},
isbn = {9781450352567},
keywords = {Accelerometer; Accelerometry; Ambulation; Ambulatory; Gyroscope; Inertial; MEMS; T25FW; X-io; XIMU; Xio},
note = {CRIS-Team Scopus Importer:2023-03-07},
pages = {150-157},
peerreviewed = {unknown},
publisher = {Association for Computing Machinery},
title = {{Using} wearable inertial sensors to compare different versions of the dual task paradigm during walking},
venue = {Umea},
volume = {Part F131193},
year = {2017}
}
@inproceedings{faucris.117694324,
abstract = {Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days. A wearable sensor system, e.g. a wristband, serving as diagnostic tool or event monitor, would allow unobtrusive ambulatory long-term monitoring while reducing costs. Previous studies showed that seizures with motor symptoms such as generalized tonic-clonic seizures can be detected by measuring the electrodermal activity (EDA) and motion measuring acceleration (ACC). In this study, EDA and ACC from 8 patients were analyzed. In extension to previous studies, different types of seizures, including seizures without motor activity, were taken into account. A hierarchical classification approach was implemented in order to detect different types of epileptic seizures using data from wearable sensors. Using a k-nearest neighbor (kNN) classifier an overall sensitivity of 89.1% and an overall specificity of 93.1% were achieved, for seizures without motor activity the sensitivity was 97.1% and the specificity was 92.9%. The presented method is a first step towards a reliable ambulatory monitoring system for epileptic seizures with and without motor activity.},
author = {Heldberg, Beeke and Kautz, Thomas and Leutheuser, Heike and Hopfengärtner, Rüdiger and Kasper, Burkhard and Eskofier, Björn},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference},
faupublication = {yes},
pages = {5593-5596},
peerreviewed = {Yes},
title = {{Using} wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy},
venue = {Milan, Italy},
year = {2015}
}
@article{faucris.286391832,
abstract = {Inertial measurement units (IMU) are used diagnostically in the movement analysis of Parkinson's disease (PD) patients, allowing an objective way to assess biomechanical motion and gait parameters. The Timed Up and Go (TUG) is a standardized clinical gait test widely used in the monitoring of patient fall risk and disease progression. Gait tests performed at home have been applied as part of movement monitoring protocols, enabling a link to clinical supervised reference assessments. However, unsupervised gait tests in a real-world data context present challenges, mainly regarding the interaction between participants and the recording system. Therefore, we developed and evaluated a novel algorithmic pipeline called unsupervised TUG (uTUG). Our contribution is the automatic detection and decomposition of TUG tests into their subphases, performed at home with no clinician supervision. In contrast to related studies, we used only foot-mounted IMU with no additional markers or manual annotations, allowing the detection of TUG test frames for subsequent classification by machine learning Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes Classifier (NBC) algorithms. The evaluation comprised 96 daily recordings of real-world gait data and 81 clinical visits accumulating 300 real TUG test samples processed from 32 PD patients. A prefiltering sensitivity of 98.6%, followed by the precision of 90.6%, recall of 88.5%, and Fl-score of 89.6% for TUG test detection were achieved using RF for the automatic classification in continuous real-world gait data. Thus, uTUG simplifies the test for patients and avoids manual annotations for clinicians, automatically detecting TUG tests.},
author = {da Rosa Tavares, João Elison and Ullrich, Martin and Roth, Nils and Kluge, Felix and Eskofier, Björn and Gaßner, Heiko and Klucken, Jochen and Gladow, Till and Marxreiter, Franz and da Costa, Cristiano André and da Rosa Righi, Rodrigo and Victória Barbosa, Jorge Luis},
doi = {10.1016/j.bspc.2022.104394},
faupublication = {yes},
journal = {Biomedical Signal Processing and Control},
keywords = {Gait analysis; Gait test; IMU; Machine learning; TUG; Wearable sensors},
note = {CRIS-Team Scopus Importer:2022-12-09},
peerreviewed = {Yes},
title = {{uTUG}: {An} unsupervised {Timed} {Up} and {Go} test for {Parkinson}'s disease},
volume = {81},
year = {2023}
}
@article{faucris.285509972,
abstract = {Chronic stress is linked to dysregulations of the two major stress pathways—the sympathetic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis, which could for example result from maladaptive responses to repeated acute stress. Improving recovery from acute stress could therefore help to prevent this dysregulation. One possibility of physiologically interfering with an acute stress reaction might be provided by applying a cold stimulus to the face (Cold Face Test, CFT) which activates the parasympathetic nervous system (PNS), leading to immediate heart rate decreases. Therefore, we investigated the use of the CFT protocol as an intervention to reduce acute stress responses. Twenty-eight healthy participants were exposed to acute psychosocial stress via the Montreal Imaging Stress Task (MIST) in a randomized between-subjects design while heart rate (HR), heart rate variability (HRV), and salivary cortisol were assessed. While both groups were equally stressed during the procedure, participants with CFT intervention showed better recovery, indicated by significant (p<0.05) differences in HR(V). We additionally found a significantly (p<0.05) lower cortisol response to the MIST and less overall cortisol secretion in the CFT condition. Both findings indicate that the CFT can successfully stimulate the PNS and inhibit the HPA axis. To the best of our knowledge, our experiment is the first to successfully use the CFT as a simple and easy-to-apply method to modify biological responses to acute stress.
0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.},
author = {Jakob, Verena and Küderle, Arne and Klucken, Jochen and Eskofier, Björn and Winkler, Jürgen and Winterholler, Martin and Gaßner, Heiko and Kluge, Felix},
doi = {10.3390/s21227680},
faupublication = {yes},
journal = {Sensors},
keywords = {Inertial sensors; Machine learning algorithm; Parkinson’s disease; Spatiotemporal gait parameters; Three-dimensional gait analysis; Wearables},
note = {CRIS-Team Scopus Importer:2021-11-26},
peerreviewed = {Yes},
title = {{Validation} of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson’s disease},
volume = {21},
year = {2021}
}
@unpublished{faucris.318032313,
abstract = {Fingerprint-based localization improves the positioning performance in challenging, non-line-of-sight (NLoS) dominated indoor environments. However, fingerprinting models require an expensive life-cycle management including recording and labeling of radio signals for the initial training and regularly at environmental changes. Alternatively, channel-charting avoids this labeling effort as it implicitly associates relative coordinates to the recorded radio signals. Then, with reference real-world coordinates (positions) we can use such charts for positioning tasks. However, current channel-charting approaches lag behind fingerprinting in their positioning accuracy and still require reference samples for localization, regular data recording and labeling to keep the models up to date. Hence, we propose a novel framework that does not require reference positions. We only require information from velocity information, e.g., from pedestrian dead reckoning or odometry to model the channel charts, and topological map information, e.g., a building floor plan, to transform the channel charts into real coordinates. We evaluate our approach on two different real-world datasets using 5G and distributed single-input/multiple-output system (SIMO) radio systems. Our experiments show that even with noisy velocity estimates and coarse map information, we achieve similar position accuracies
Wearables are becoming mainstream technology, however there is still room for improvement in the sports domain of this field. Monitoring performance and collecting large scale data are of high interest among athletes - amateurs and professionals alike. The current state-of-the art wearable solutions for sports analysis are able to provide individual statistics to the user, however they have shortcomings in certain aspects, such as isolating and visualizing important information for the user, beyond statistics. This workshop focuses on the application of wearable technology in sports. We will explore novel ideas and application scenarios of how sensors and actuators are capable of supporting athletes in monitoring and improving their performance. We will discuss the design space of the domain by bringing together experts from various communities and exchanging ideas from different perspectives on wearables for sports applications. Participants will collaboratively produce sports related prototype applications.},
author = {Martindale, Christine and Wirth, Markus and Schneegas, Stefan and Zrenner, Markus and Groh, Benjamin and Blank, Peter and Schuldhaus, Dominik and Kautz, Thomas and Eskofier, Björn},
booktitle = {2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
date = {2016-09-12/2016-09-16},
doi = {10.1145/2968219.2968583},
faupublication = {yes},
isbn = {978-1-4503-4462-3},
peerreviewed = {unknown},
title = {{Workshop} on wearables for sports},
venue = {Heidelberg},
year = {2016}
}
@article{faucris.285673904,
abstract = {With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis’ orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 (Formula presented.) for the generalization to new athletes and an MAE of 5.9 (Formula presented.) for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions’ accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor’s data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.},
author = {Link, Johannes and Schwinn, Leo and Pulsmeyer, Falk and Kautz, Thomas and Eskofier, Björn},
doi = {10.3390/s22218474},
faupublication = {yes},
journal = {Sensors},
keywords = {inertial measurement unit; performance analysis; performance prediction; sports analytics; ultra-wideband; wearable sensors},
note = {CRIS-Team Scopus Importer:2022-11-25},
peerreviewed = {Yes},
title = {{xLength}: {Predicting} {Expected} {Ski} {Jump} {Length} {Shortly} after {Take}-{Off} {Using} {Deep} {Learning}},
volume = {22},
year = {2022}
}
@inproceedings{faucris.310419926,
abstract = {The collection and use of personal data is increasing and new developments in Big Data Analytics allow for innovative applications. Recent developments in healthcare such as the proposal of the European Health Data Space point towards a more data-driven future of diagnostics and therapy. These developments lead to new challenges, especially in how to design interaction between individuals and their personal health data. With this proposed workshop we want to stimulate discussion about these challenges from the interaction perspective and critically ask, where our health data should lie in the future and who will be owning i},
author = {Flaucher, Madeleine and Zakreuskaya, Anastasiya and Jäger, Katharina and Richer, Robert and Smeddinck, Jan David and Kumar, Devender and Grimme, Sophie and Klein, Julia and Hrynyschyn, Robert and Eskofier, Björn and Leutheuser, Heike},
booktitle = {Workshopband},
date = {2023-09-03/2023-09-06},
doi = {10.18420/muc2023-mci-ws14-117},
faupublication = {yes},
peerreviewed = {unknown},
title = {{Your} {Health}, {Your} {Data}: {Combining} {Interdisciplinary} {Views}, {Concepts}, and {Practices} to {Empower} {Patients} in {Their} {Engagement} {With} {Personal} {Health} {Data}},
venue = {Rapperswil},
year = {2023}
}
@inproceedings{faucris.106721824,
abstract = {Manually browsing through high amount of sports videos, selecting interesting highlight scenes, and applying video effects are time-consuming and one major burden these days. Automatic approaches are preferred, but currently require high-quality TV broadcast material which is usually not available in recreational sports. Thus, the purpose of this paper was to develop a personal low-cost movie producer for highlight videos using wearables. The feasibility of the proposed approach was shown for soccer scenarios. The automatic highlight video generation included three contributions: (i) sensor-based full-instep kick detection and extraction of corresponding video segments, (ii) sensor-based ball speed estimation for provision of highlight-related metadata shown in the final video, and (iii) sensor-driven video effect generation. The proposed system was evaluated on eleven subjects which were equipped with inertial sensors in the cavity of soccer shoes. A mean sensitivity of 95.6 % and a mean absolute error of 7.7 km/h were achieved for the full-instep kick classification and the ball speed estimation, respectively. This personal movie producer based on wearables is a novel idea to provide recreational athletes with attractive automatically generated highlight videos in sports.},
address = {New York, NY, USA},
author = {Schuldhaus, Dominik and Jakob, Carolin and Zwick, Constantin and Koerger, Harald and Eskofier, Björn},
booktitle = {Proceedings of the 2016 ACM International Symposium on Wearable Computers},
date = {2016-09-12/2016-09-16},
doi = {10.1145/2971763.2971772},
faupublication = {yes},
keywords = {Wearables; Inertial Sensors; Soccer; Data Mining; Highlight Videos},
pages = {80-83},
peerreviewed = {Yes},
publisher = {ACM},
title = {{Your} {Personal} {Movie} {Producer}: {Generating} {Highlight} {Videos} in {Soccer} {Using} {Wearables}},
venue = {Heidelberg},
year = {2016}
}