An Adaptable Inertial Sensor Fusion-Based Approach for Energy Expenditure Estimation. - Schuldhaus, Dominik; Dorn, Sabrina; Leutheuser, Heike; Tallner, Alexander; Klucken, Jochen; Eskofier, Björn (The 15th International Conference on Biomedical Engineering, 2013)

Arrhythmia classification using RR intervals: Improvement with sinusoidal regression feature - Leutheuser, Heike; Tobola, Andreas; Anneken, Lars; Gradl, Stefan; Lang, Nadine; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015)
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.

Automatic Classification of Sport Exercises for Training Support - Schuldhaus, Dominik; Leutheuser, Heike; Eskofier, Björn (Sportinformatik 2012, 2012)

Automatic ECG Arrhythmia Detection in Real-Time on Android-based Mobile Devices - Leutheuser, Heike; Gottschalk, Tristan; Anneken, Lars; Struck, Matthias; Heuberger, Albert; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (Proceedings of International Conference on Mobile and Information Technologies in Medicine and Health, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.automa_72

A wearable real-time activity tracker - Jensen, Ulf; Leutheuser, Heike; Hofmann, Steffen; Schüpferling, Benno; Suttner, Gerald; Seiler, Kristin; Kornhuber, Johannes; Eskofier, Björn (Biomedical Engineering Letters, 2015)
Keywords: activity tracking; wearable computing; MET levels; accelerometers; mental health

Battery Runtime Optimization Toolbox for Wearable Biomedical Sensors - Tobola, Andreas; Leutheuser, Heike; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; Fischer, Georg (13th International Conference on Wearable and Implantable Body Sensor Networks BSN, 2016)
Battery runtime is a critical concern for practical usage of wearable biomedical sensor systems. A long runtime requires an interdisciplinary low-power knowledge and appropriate design tools. We addressed this issue designing a toolbox in three parts: 1) Modular evaluation kit for development of wearable ultralow- power biomedical sensors; (2) Miniaturized, wearable, and code compatible sensor system with the same properties as the development kit; (3) Web-based battery runtime calculator for our sensor systems. The purpose of the development kit is optimization of the power consumption. Once optimization is finished, the same embedded software can be transferred to the miniaturized body worn sensor. The web-based application supports development quantifying the effects of use case and design decisions on battery runtime. A sensor developer can select sensor modules, configure sensor parameters, enter use case specific requirements, and select a battery to predict the battery runtime for a specific application. Our concept adds value to development of ultra-low-power biomedical wearable sensors. The concept is effective for professional work and educational purposes.
elib2cris::1512001600,tobola2016
Keywords: Batteries; Electrocardiography; Runtime; Power demand; Computational modeling; Temperature sensors; Biological system modeling

Blood glucose forecasting from temporal and static information in children with T1D - Marx, Alexander; Di Stefano, Francesco; Leutheuser, Heike; Chin-Cheong, Kieran; Pfister, Marc; Burckhardt, Marie Anne; Bachmann, Sara; Vogt, Julia E. (Frontiers in Pediatrics, 2023)
Background: The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods: In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data—(dilated) recurrent neural networks and a transformer—on our dataset for short-term ((Formula presented.) min) and long-term ((Formula presented.) h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results: Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of (Formula presented.) min (RMSE of (Formula presented.) mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of (Formula presented.) mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion: We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.
Created from Fastlane, Scopus look-up
Keywords: deep learning; diabetes; forecasting; machine learning; pediatrics; physical activity; T1D

Can HCI Lead the Way? Workshop on Exploring Conscious AI - Moradi, Hamid; Flaucher, Madeleine; Ott, Tabea; Ringgold, Veronika; Eskofier, Björn; Leutheuser, Heike (2024)
The discourse of consciousness in AI is as old as the field of artificial intelligence itself. With the substantial progress of AI in recent years and emerging models, the conversation about conscious AI is becoming even more crucial. Although theories try to define consciousness, there is no reliable common ground among scientists. In this workshop, we want to take a different approach by presenting the issue to the HCI community as the connecting point between human interaction and AI models. We want to collect information on how participants perceive a conscious AI, discuss methods to measure consciousness in these systems, and consider how to ethically interact with a conscious AI. We believe that this can be a starting point for opening up the discussion among people who are practically more involved with AI.<br
Keywords: Consciousness; Artificial Intelligence; Artificial General Intelligence; Strong AI; Ethics in AI

Classification of Daily Life Activities by Decision Level Fusion of Inertial Sensor Data - Schuldhaus, Dominik; Leutheuser, Heike; Eskofier, Björn (8th International Conference on Body Area Networks, 2013)

Comparative Study on Classifying Gait With a Single Trunk-Mounted Inertial-Magnetic Measurement Unit - Full, Katharina; Leutheuser, Heike; Schlessmann, Jason; Armitage, Roger; Eskofier, Björn (Wearable and Implantable Body Sensor Networks (BSN), 2015 IEEE 12th International Conference on, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.compar_7

Comparison of A Priori Calibration Models for Respiratory Inductance Plethysmography During Running - Leutheuser, Heike; Heyde, Christian; Gollhofer, Albert; Eskofier, Björn (IEEE EMBC 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.compar_3

Comparison of Real-Time Classification Systems for Arrhythmia Detection on Android-based Mobile Devices - Leutheuser, Heike; Gradl, Stefan; Kugler, Patrick; Anneken, Lars; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (IEEE EMBC 2014, 2014)

Comparison of the AMICA and the InfoMax Algorithm for the Reduction of Electromyogenic Artifacts in EEG Data - Leutheuser, Heike; Gabsteiger, Florian; Hebenstreit, Felix; Reis, Pedro; Lochmann, Matthias; Eskofier, Björn (IEEE EMBC 2013, 2013)

Contactless Heart Rate Estimation using a 61 GHz Continuous-Wave Radar - Krauß, Daniel; Richer, Robert; Albrecht, Nils Christian; Küderle, Arne; Abel, Luca; Leutheuser, Heike; Jukic, Jelena; German, Alexander; Kölpin, Alexander; Winkler, Jürgen; Eskofier, Björn (IEEE-EMBS International Conference on Body Sensor Networks: Sensor and Systems for Digital Health, 2023)
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.

Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review - Flaucher, Madeleine; Zakreuskaya, Anastasiya; Nissen, Michael; Mocker, Alexander; Fasching, Peter; Beckmann, Matthias; Eskofier, Björn; Leutheuser, Heike (Oncologist, 2023)

Examining the Association Between Self-Reported Estimates of Function and Objective Measures of Gait and Physical Capacity in Lumbar Stenosis - Odonkor, Charles; Taraben, Salam; Tomkins-Lane, Christy; Zhang, Wei; Muaremi, Amir; Leutheuser, Heike; Sun, Ruopeng; Smuck, Matthew (Archives of Rehabilitation Research and Clinical Translation, 2021)
Results: Higher self-reported physical function scores (SF-36 physical functioning) correlated with lower disability ratings, neurogenic claudication, and symptom severity ratings in patients with LSS (P<.05). Compared with controls without LSS, patients with LSS have lower scores on physical capacity measures (median total distance traveled on 6-minute walk test: controls 505 m vs LSS 316 m; median total distance traveled on self-paced walking test: controls 718 m vs LSS 174 m). Observed differences in IMU-derived gait features, physical capacity measures, disability ratings, and neurogenic claudication scores between populations with and without LSS were statistically significant. Conclusions: Further evaluation of the association of IMU-derived temporospatial gait with self-reported physical function, pain related-disability, neurogenic claudication, and spinal stenosis symptom severity score in LSS would help clarify their role in tracking LSS outcomes.
Keywords: Exercise; Gait; Gait analysis; Patient reported outcome measures; Rehabilitation; Spinal stenosis; Walking

Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning - Gabler, Elisabeth; Nissen, Michael; Altstidl, Thomas Robert; Titzmann, Adriana; Packhäuser, Kai; Maier, Andreas; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023)
Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL-PLANES-DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and explainability.Clinical relevance - To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.
Created from Fastlane, Scopus look-up
Keywords: Pregnancy; Deep learning; Visualization; Ultrasonic imaging; Fetus; Stability analysis; Biology

Filter and Processing Method to Improve R-Peak Detection for ECG Data with Motion Artefacts from Wearable Systems - Lang, Nadine; Brischwein, Matthias; Haßlmeyer, Erik; Tantinger, Daniel; Feilner, Sven; Heinrich, Axel; Leutheuser, Heike; Gradl, Stefan; Weigand, Christian; Eskofier, Björn; Struck, Matthias (Computing in Cardiology 2015, 2015)
The electrocardiogram (ECG) is one of the most reliable information sources for assessing cardiovascular health and training success. Since the early 1990s, the heart rate variability (HRV), namely the variation from beat to beat, has become the focus of investigations as it provides insight into the complex interplay of body circulation and the influence of the autonomic nervous system on heartbeats. However, HRV parameters during physical activity are poorly understood, mostly due to the challenging signal processing in the presence of motion artefacts. To derive HRV parameters in time (heart rate (HR)) and frequency domains (high frequency (HF), low frequency (LF)), it is crucial to reliably detect the exact position of the R-peaks. We introduce a full algorithm chain where a sophisticated filtering technique is combined with an enhanced R-peak detection that can cope with motion artefacts in ECG data originating from physical activity.

Gait features for discriminating between mobility-limiting musculoskeletal disorders: Lumbar spinal stenosis and knee osteoarthritis - Odonkor, Charles; Kuwabara, Anne; Tomkins-Lane, Christy; Zhang, Wei; Muaremi, Amir; Leutheuser, Heike; Sun, Ruopeng; Smuck, Matthew (Gait & Posture, 2020)
Background: Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications. Research question: In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls? Methods: We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test– 6MWT, self-paced walk test – SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group). Spatiotemporal gait characteristics were extracted and pairwise compared (Omega partial squared – ωp 2) between patients and controls. Results: We found that normal paced walking tests (6MWT, SPWT) are better suited for distinguishing gait characteristics between patients and controls. Among the sensor-based gait parameters, stance and double support phase timing were identified as the best gait characteristics for the OA population discrimination, whereas foot flat ratio, gait speed, stride length and cadence were identified as the best gait characteristics for the LSS population discrimination. Significance: These findings provide guidance on the selection of sensor-derived gait parameters and clinical walking tests to detect alterations in mobility for people with LSS and knee OA.
CRIS-Team Scopus Importer:2020-06-09
Keywords: Feature selection; Gait characteristics; Inertial measurement unit; Knee osteoarthritis; Lumbar spinal stenosis

Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors - Kushioka, Junichi; Sun, Ruopeng; Zhang, Wei; Muaremi, Amir; Leutheuser, Heike; Odonkor, Charles A.; Smuck, Matthew (Sensors, 2022)
Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)—neurogenic diseases, and knee osteoarthritis (KOA)—structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.
CRIS-Team Scopus Importer:2022-12-23
Keywords: gait impairment; gait variability; knee osteoarthritis; lumbar spinal stenosis; wearable IMU sensor

Heart Rate Variability During Physical Exercise - Leutheuser, Heike; Eskofier, Björn (International Journal of Computer Science in Sport, 2013)
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.
Keywords: ADAPTION OF HRV PARAMETERS; RUNNING; UNIVARIATE ANOVA WITH REPEATED-MEASURES; MULTIVARIATE ANOVA WITH REPEATED MEASURES

Heart Rate Variability During Physical Exercise - Leutheuser, Heike; Eskofier, Björn (KOPS Institutional Repository University of Konstanz, 2012)

Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset - Leutheuser, Heike; Schuldhaus, Dominik; Eskofier, Björn (Plos One, 2013)
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.

Human Authentication Implemented for Mobile Applications Based on ECG-Data Acquired from Sensorized Garments - Tantinger, Daniel; Zrenner, Markus; Lang, Nadine; Leutheuser, Heike; Eskofier, Björn; Weigand, Christian; Struck, Matthias (Computing in Cardiology 2015, 2015)

ICA-based Reduction of Electromyogenic Artifacts in EEG Data: Comparison With and Without EMG Data - Gabsteiger, Florian; Leutheuser, Heike; Reis, Pedro; Lochmann, Matthias; Eskofier, Björn (IEEE EMBC 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.icabas

Incorporating respiratory signals for machine learning-based multimodal sleep stage classification: a large-scale benchmark study with actigraphy and heart rate variability - Krauß, Daniel; Richer, Robert; Küderle, Arne; Jukic, Jelena; German, Alexander; Leutheuser, Heike; Regensburger, Martin; Winkler, Jürgen; Eskofier, Björn (Sleep, 2025)
Insufficient sleep quality is directly linked to various diseases, making reliable sleep monitoring crucial for prevention, diagnosis, and treatment. As sleep laboratories are cost- and resource-prohibitive, wearable sensors offer a promising alternative for long-term unobtrusive sleep monitoring at home. Current unobtrusive sleep detection systems are mostly based on actigraphy (ACT) that tend to overestimate sleep due to a lack of movement in short periods of wakefulness. Previous research established sleep stage classification by combining ACT with cardiac information but has not investigated the incorporation of respiration in large-scale studies. For that reason, this work aims to systematically compare ACT-based sleep-stage classification with multimodal approaches combining ACT, heart rate variability (HRV) as well as respiration rate variability (RRV) using state-of-the-art machine- and deep learning algorithms. The evaluation is performed on a publicly available sleep dataset including more than 1000 recordings. Respiratory information is introduced through ECG-derived respiration features, which are evaluated against traditional respiration belt data. Results show that including RRV features improves the Matthews Correlation Coefficient (MCC), with long short-term memory (LSTM) algorithms performing best. For sleep staging based on AASM standards, the LSTM achieved a median MCC of 0.51 (0.16 IQR). Respiratory information enhanced classification performance, particularly in detecting wake and rapid eye movement (REM) sleep epochs. Our findings underscore the potential of including respiratory information in sleep analysis to improve sleep detection algorithms and, thus, help to transfer sleep laboratories into a home monitoring environment. The code used in this work can be found online at https://github.com/mad-lab-fau/sleep_analysi
Keywords: machine learning; deep learning; wearable electronic devices; multimodal sensing; neural networks; sleep; sleep-stage

Instantaneous P-and T-wave detection: Assessment of three ECG fiducial points detection algorithms - Leutheuser, Heike; Gradl, Stefan; Anneken, Lars; Lang, Nadine; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016)

Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG - Jäger, Katharina; Nissen, Michael; Richer, Robert; Rahm, Simone; Titzmann, Adriana; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2022)
Preterm births account for more than 10 % of all newborns. An adverse fetal presentation is a risk factor for intrapartum and neonatal mortality. To date, no technology enables a longitudinal, ubiquitous, and unobtrusive monitoring of fetal presentation. This study presents a first approach to fetal orientation detection based on non-invasive fetal electrocardiography (NI-fECG) using the non-invasive multi-modal foetal ECG-Doppler data set for antenatal cardiology research. The data set contains 60 recordings from 39 pregnant women (21–27 weeks), including NI-fECG and ultrasound position ground truth. We evaluated both handcrafted and generic features for five different classifiers (k-Nearest-Neighbor, Decision Tree Classifier, Support Vector Classification, AdaBoost Classifier, and Multilayer Perceptron) using cross-validation on subject splits on a cleaned subset. Best results for the distinction between vertex (head down) and breech (head up) were achieved using an AdaBoost classifier with a balanced accuracy of 86.5 ± 15.0 %. With this work, we take a first step towards longitudinal fetal presentation monitoring, which contributes to a better understanding of reduced fetal movements and extends the potential applications of NI-fECG in prenatal care. In future work, we will expand our classification system to detect more detailed fetal presentations using a newly created data set.<br

Motivational Factors for Experienced Users of Mobile Health Applications in Heart Failure Management - Flaucher, Madeleine; Prümer, Florian; Jäger, Katharina; Rolny, Jana; Trissler, Patricia; Eckl, Sebastian; Eskofier, Björn; Leutheuser, Heike (NordiCHI '24 Adjunct: Adjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction, 2024)
Mobile health applications have a high potential to effectively support patients with heart failure throughout their therapy by offering continuous support and personalized care. Since these applications are intended for long-term use, an important factor for their effectiveness is sustaining patient motivation. While current research has identified relevant motivational factors such as perceived usefulness and ease of use, there remains a need to deepen the understanding of what sustains long-term engagement. To address this, we conducted semi-structured interviews with patients (n = 9) and medical experts (n = 5), all of whom have extensive experience with a medically approved mobile health application. This focus on experienced users allowed us to gather detailed feedback on the app’s impact over time. From a thematic analysis, we derived the five motivational factors: Personalisation, Support, Simplicity, Overview, and Habit. Participant statements also showed that it was important for patients to have a feeling of safety, control, and usefulness while interacting with mobile health applications. The results from this study give detailed insights into relevant factors in the everyday use of patients with heart failure and provide grounds for future steps regarding the motivational design of mobile health application

Performance Comparison of Two Step Segmentation Algorithms using Different Step Activities - Leutheuser, Heike; Dölfel, Sina; Schuldhaus, Dominik; Reinfelder, Samuel; Eskofier, Björn (Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on, 2014)
Insufficient physical activity is the 4th leading risk factor for mortality. The physical activity of a person is reflected in the walking behavior. Different methods for the calculation of the accurate step number exists and most of them are evaluated using different walking speeds measured on a treadmill or using a small sample size of overground walking. In this paper, we introduce the BaSA (Basic Step Activities) dataset consisting of four different step activities (walking, jogging, ascending, and descending stairs) that were performed under natural conditions. We further compare two step segmentation algorithms (a simple peak detection algorithm vs. subsequence Dynamic Time Warping (sDTW)). We calculated a multivariate Analysis of Variance (ANOVA) with repeated measures followed by multiple dependent t-tests with Bonferroni correction to test for significant differences in the two algorithms. sDTW performed equally good compared to the peak detection algorithm, but was not considerably better. In further analysis, continuous, real walking signals with transitions from one step activity to the other step activity should be considered to investigate the adaptability of these two step segmentation algorithms. © 2014 IEEE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.perfor_2

Power-MF: Robust fetal QRS detection from non-invasive fetal electrocardiogram recordings - Jäger, Katharina; Nissen, Michael; Rahm, Simone; Titzmann, Adriana; Fasching, Peter; Beilner, Janina; Eskofier, Björn; Leutheuser, Heike (Physiological Measurement, 2024)
Objective. Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts. Approach. In this work, we propose Power-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA). Main results. Our results show that Power-MF outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5 % ± 0.5 % F1-score, B2 Labour: 98.0 % ± 3.0 % F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise. Significance. Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.<br

Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes - Leutheuser, Heike; Bartholet, Marc; Marx, Alexander; Pfister, Marc; Burckhardt, Marie Anne; Bachmann, Sara; Vogt, Julia E. (Frontiers in Medicine, 2024)
Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learning approaches for nocturnal hypoglycemia prediction for different prediction horizons. Studies focusing on nocturnal hypoglycemia prediction in children are scarce, and none, to the best knowledge of the authors, investigate the effect of previous physical activity. The primary objective of this work was to assess the risk of hypoglycemia throughout the night (prediction horizon 9 h) associated with physical activity in children with T1D using data from a structured setting. Continuous glucose and physiological data from a sports day camp for children with T1D were input for logistic regression, random forest, and deep neural network models. Results were evaluated using the F2 score, adding more weight to misclassifications as false negatives. Data of 13 children (4 female, mean age 11.3 years) were analyzed. Nocturnal hypoglycemia occurred in 18 of a total included 66 nights. Random forest using only glucose data achieved a sensitivity of 71.1% and a specificity of 75.8% for nocturnal hypoglycemia prediction. Predicting the risk of nocturnal hypoglycemia for the upcoming night at bedtime is clinically highly relevant, as it allows appropriate actions to be taken—to lighten the burden for children with T1D and their families.
CRIS-Team Scopus Importer:2024-11-22
Keywords: biomedical signal processing; diabetes management; digital health; machine learning; supervised learning

Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data - Nissen, Michael; Barrios Campo, Nuria; Flaucher, Madeleine; Jäger, Katharina; Titzmann, Adriana; Blunck, Dominik; Fasching, Peter; Engelhardt, Victoria; Eskofier, Björn; Leutheuser, Heike (npj Digital Medicine, 2023)
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.
CRIS-Team Scopus Importer:2023-10-20

Quantitative analysis of the cardiorespiratory system during paced respiration - Leutheuser, Heike; Schaffer, Thorsten; Hensel, Bernhard; Weigand, Christian; Schüttler, Jürgen; Jeleazcov, Christian (7th Russian-Bavarian Conference on Biomedical Engineering (RBC), 2011)
The phase relationship between respiration and cardio- vascular system is investigated under the influence of paced respiration with respiratory rates from 4 to 25 breaths per minute. Particularly, the phase shift dependency on breathing rate as well as the correlation between respiratory sinus arrhythmia (RSA) and breathing frequency is examined. It can be shown that there is an increasing phase relationship between RSA and breathing frequency and that RSA diminishes with rising breathing frequenc
Keywords: respiratory sinus arrhythmia; paced respiration; cardiorespiratory system

Real-Time Respiratory Motion Analysis Using 4-D Shape Priors - Wasza, Jakob; Fischer, Peter; Leutheuser, Heike; Oefner, Tobias; Bert, Christoph; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Biomedical Engineering, 2016)
Respiratory motion analysis based on range imaging (RI) has emerged as a popular means of generating respiration surrogates to guide motion management strategies in computer-assisted interventions. However, existing approaches employ heuristics, require substantial manual interaction, or yield highly redundant information. In this paper, we propose a framework that uses preprocedurally obtained 4-D shape priors from patient-specific breathing patterns to drive intraprocedural RI-based real-time respiratory motion analysis. As the first contribution, we present a shape motion model enabling an unsupervised decomposition of respiration induced high-dimensional body surface displacement fields into a low-dimensional representation encoding thoracic and abdominal breathing. Second, we propose a method designed for GPU architectures to quickly and robustly align our models to high-coverage multiview RI body surface data. With our fully automatic method, we obtain respiration surrogates yielding a Pearson correlation coefficient (PCC) of 0.98 with conventional surrogates based on manually selected regions on RI body surface data. Compared to impedance pneumography as a respiration signal that measures the change of lung volume, we obtain a PCC of 0.96. Using off-the-shelf hardware, our framework enables high temporal resolution respiration analysis at 50 Hz.
Keywords: Graphics processing unit (GPU); radiation therapy; range imaging (RI); respiratory motion

Reference-Free Adjustment of Respiratory Inductance Plethysmography for Measurements during Physical Exercise - Leutheuser, Heike; Heyde, Christian; Röcker, Kai; Gollhofer, Albert; Eskofier, Björn (IEEE Transactions on Biomedical Engineering, 2017)
Keywords: Respiration; treadmill running; least squares regression (LSQ); qualitative diagnostic calibration (QDC); adjustment algorithm

Respiratory Inductance Plethysmography - A Rationale for Validity during Exercise - Heyde, Christian; Leutheuser, Heike; Eskofier, Björn; Röcker, Kai; Gollhofer, Albert (Medicine and science in sports and exercise, 2014)
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.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.respir
Keywords: VENTILATION; RUNNING EXERCISE; MONITORING; RESPIRATION; AMBULATORY MONITORING

Sampling rate impact on energy consumption of biomedical signal processing systems - Tobola, Andreas; Streit, Franz-Josef; Espig, Chris; Korpok, Oliver; Leutheuser, Heike; Sauter, Christian; Lang, Nadine; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; et al. (12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.sampli

Scalable ECG Hardware and Algorithms for Extended Runtime of Wearable Sensors - Tobola, Andreas; Espig, Chris; Streit, Franz-Josef; Korpok, Oliver; Leutheuser, Heike; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; Fischer, Georg (2015 IEEE International Symposium on Medical Measurements and Applications, 2015)
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
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.scalab

Schlafstadienbestimmung mit Hilfe bewegungsbasierter Sensortechnologie - Traxdorf, Maximilian; Gradl, Stefan; Kugler, Patrick; Leutheuser, Heike; Lauten, Juliane; Eskofier, Björn; Angerer, Florian; Iro, Heinrich (Proceedings of the 84th Annual Meeting of the German Society of Oto-Rhino-Laryngology, Head and Neck Surgery, 2013)

Self-powered Multiparameter Health Sensor - Tobola, Andreas; Leutheuser, Heike; Pollak, M; Spies, Peter; Hofmann, Christian; Weigand, Christian; Eskofier, Björn; Fischer, Georg (IEEE Journal of Biomedical and Health Informatics, 2018)

Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception - Nissen, Michael; Huang, Shih-Yuan; Jäger, Katharina; Flaucher, Madeleine; Titzmann, Adriana; Bleher, Hannah; Pontones, Constanza; Hübner, Hanna; Danzberger, Nina; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (BMC Pregnancy and Childbirth, 2024)
Background Over 50% of pregnant women use pregnancy applications (apps). Some app s lack credibility, information accuracy, and evidence-based clinical advice, containing potentially harmful functionality. Previous studies have only conducted a limited analysis of pregnancy app functionalities, expert involvement/evidence-based content, used commercialization techniques, and user perception. Methods We used the keyword “pregnancy” to scrape (automatically extract) apps and app information from Apple App Store and Google Play. Unique functionalities were derived from app descriptions and user reviews. App descriptions were screened for evidence-based content and expert involvement, and apps were subsequently analyzed in detail. Apps were opened and searched for used commercialization techniques, such as advertisements or affiliate marketing. Automated text analysis (natural language processing) was used on app reviews to assess users’ perception of evidence-based content/expert involvement and commercialization techniques. Results In total, 495 apps were scraped. 226 remained after applying exclusion criteria. Out of these, 36 represented 97%/88% of the total market share (Apple App Store/Google Play), and were thus considered for review. Overall, 49 distinct functionalities were identified, out of which 6 were previously unreported. Functionalities for fetal kick movement counting were found. All apps are commercial. Only 15 apps mention the involvement of medical experts. 10.3% of two-stars user reviews include commercial topics, and 0.6% of one-/two-/three-/five stars user reviews include references to scientific content accuracy. Conclusion Problematic features and inadequate advice continue to be present in pregnancy apps. App developers should adopt an evidence-based development approach and avoid implementing as many features as possible, potentially at the expense of their quality or over-complication (“feature creep”). Financial incentives, such as grant programs, could support adequate content quality. Caregivers play a key role in pregnant individuals’ decision-making, should be aware of potential dangers, and could guide them to trustworthy app

Somnography using unobtrusive motion sensors and Android-based mobile phones - Gradl, Stefan; Leutheuser, Heike; Kugler, Patrick; Biermann, Teresa; Kreil, Sebastian; Kornhuber, Johannes; Bergner, Matthias; Eskofier, Björn (IEEE EMBC 2013, 2013)
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.

SVM for Semi-automatic Selection of ICA Components of Electromyogenic Artifacts in EEG Data - Gabsteiger, Florian; Leutheuser, Heike; Reis, Pedro; Lochmann, Matthias; Eskofier, Björn (The 15th International Conference on Biomedical Engineering, 2013)

Systematic Comparison of ECG Delineation Algorithm Performance on Smartwatch Data - Jäger, Katharina; Nissen, Michael; Flaucher, Madeleine; Graf, Luisa; Joanidopoulos, Joana; Anneken, Lars; Hübner, Hanna; Goossens, Chloe; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Beckmann, Matthias; et al. (IEEE Access, 2024)
Cardiovascular diseases are the leading cause of global mortality, necessitating early detection and continuous monitoring for timely interventions. Smartwatches with electrocardiogram (ECG) recording capabilities enable real-time, at-home cardiac monitoring. Specific ECG characteristics can provide insights into cardiovascular diseases. The delineation of ECGs, which is the identification of fiducial points (such as onsets, offsets, and peaks), is a time-consuming task. Automated ECG delineation can enhance this process, but existing research comparing available algorithms is limited. Furthermore, to the best of our knowledge, none have addressed single-lead ECGs from smartwatches, which can be noisy and unfiltered. Thus, this study evaluates the best-performing open-source algorithm for single-lead and smartwatch ECG data. We used two public datasets (Lobachevsky University Database, QT Database) and two smartwatch datasets (SmartHeartWatch Dataset, SMART Start Dataset) including two devices (AppleWatch, Withings ScanWatch). Algorithms from three toolkits (NeuroKit, ECGKit, ECGdeli) were assessed based on the time deviation between algorithm outputs and reference annotations, sensitivity, true positives, and false negatives. Results were further evaluated against the Common Standards for Quantitative Electrocardiography (CSE) recommendations. ECGdeli outperformed the other algorithms. For QRS on- and offset, ECGkit shows comparable sensitivity, but otherwise lower scores. NeuroKit consistently shows lower sensitivity across all four data sets, however, the temporal deviation between detected point and reference was higher. Overall, sensitivity scores were higher for Apple Watch data compared to Withings ScanWatch data. This study demonstrates that segmentation algorithms are applicable to single-lead smartwatch ECG data, with ECGdeli being the most stable overall, and NeuroKit recommended for scenarios prioritizing the temporal accuracy of detected points.
CRIS-Team Scopus Importer:2024-11-15
Keywords: Cardiovascular Diseases; ECG Segmentation; Mobile Health; Wearable Devices

System Design Impacts on Battery Runtime of Wearable Medical Sensors - Tobola, Andreas; Korpok, Oliver; Leutheuser, Heike; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; Heuberger, Albert; Fischer, Georg (Mobile and Information Technologies in Medicine and Health, Proceedings (MobileMed), 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.system

Temporal correction of detected R-peaks in ECG signals: A crucial step to improve QRS detection algorithms - Gradl, Stefan; Leutheuser, Heike; Elgendi, Mohamed; Lang, Nadine; Eskofier, Björn (37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, 2015)
In the last decade the interest for heart rate variability analysis has increased tremendously. Related algorithms depend on accurate temporal localization of the heartbeat, e.g. the R-peak in electrocardiogram signals, especially in the presence of arrhythmia. This localization can be delivered by numerous solutions found in the literature which all lack an exact specification of their temporal precision. We implemented three different state-of-the-art algorithms and evaluated the precision of their R-peak localization. We suggest a method to estimate the overall R-peak temporal inaccuracy - dubbed beat slackness - of QRS detectors with respect to normal and abnormal beats. We also propose a simple algorithm that can complement existing detectors to reduce this slackness. Furthermore we define improvements to one of the three detectors allowing it to be used in real-time on mobile devices or embedded hardware. Across the entire MIT-BIH Arrhythmia Database, the average slackness of all the tested algorithms was 9ms for normal beats and 13ms for abnormal beats. Using our complementing algorithm this could be reduced to 4ms for normal beats and to 7ms for abnormal beats. The presented methods can be used to significantly improve the precision of R-peak detection and provide an additional measurement for QRS detector performance.

Textile Integrated Wearable Technologies for Sports and Medical Applications - Leutheuser, Heike; Lang, Nadine; Gradl-Trautvetter, Stefan; Struck, Matthias; Tobola, Andreas; Hofmann, Christian; Anneken, Lars; Eskofier, Björn (Smart Textiles, 2017)

Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy - Schuldhaus, Dominik; Leutheuser, Heike; Eskofier, Björn (9th International Conference on Body Area Networks, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.toward_23

Unobtrusive Heart Rate Estimation during Physical Exercise using Photoplethysmographic and Acceleration Data - Mullan, Patrick; Kanzler, Christoph; Lorch, Benedikt; Schroeder, Lea; Winkler, Ludwig; Larissa, Laich; Riedel, Frederik; Richer, Robert; Luckner, Christoph; Leutheuser, Heike; Eskofier, Björn; Pasluosta, Cristian Federico (37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC ’15, 2015)

Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study - Nissen, Michael; Perez, Carlos A.; Jäger, Katharina; Bleher, Hannah; Flaucher, Madeleine; Hübner, Hanna; Danzberger, Nina; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Beckmann, Matthias; Eskofier, Björn; et al. (JMIR Pediatrics and Parenting, 2023)
Background: Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study. Objective: The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy. Methods: A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective. Results: A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated “above average” in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes. Conclusions: Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.
Created from Fastlane, Scopus look-up
Keywords: app; application; applications; apps; attitude; attitudes; data sharing; develop; development; digital; electronic; electronic maternity record; experience; experiences; feature; features; health record; health records; information behavior; information behaviour; information seeking; log; logbook; maternal; maternity; maternity log; maternity logbook; maternity record; mHealth; mobile health; online search; opinion; opinions; paper hand-held record; perception; perceptions; personal health record; perspective; perspectives; PHR; pregnancy; pregnancy app; pregnancy record; pregnant; privacy; searching; smartphone; smartphones; usability; wearable; wearables

Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy - Heldberg, Beeke; Kautz, Thomas; Leutheuser, Heike; Hopfengärtner, Rüdiger; Kasper, Burkhard; Eskofier, Björn (Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference, 2015)
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.

Wearable computing applications in eHealth - Leutheuser, Heike (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Non-communicable diseases are the leading cause of death and disability worldwide. Almost two thirds of them are linked to the following risk factors: physical inactivity, unhealthy diets, tobacco use, and harmful use of alcohol. At the same time, everyone is surrounded increasingly with wearables, like smartphones and smartwatches, making wearable computing an integral part of everyday life. The combination of addressing non-communicable diseases with the help of wearables seems one potential solution. In this thesis, methods and algorithms for three wearable computing applications in eHealth are presented: I) mobile breathing analysis, II) mobile electrocardiogram (ECG) analysis, and III) inertial measurement unit (IMU)-based activity recognition. Respiratory inductance plethysmography (RIP) provides an unobtrusive and mobile method for measuring breathing characteristics, avoiding the measurement with flowmeters (FMs) as with them the natural breathing pattern is altered. The output of RIP devices needs to be adjusted to result in correct ventilatory tidal volumes. State of the art methods for adjusting RIP data can only be applied after the actual measurement as these require the simultaneous data acquisition of RIP and FM. In this thesis, novel adjustment algorithms were created enabling the usage of RIP solely, and by this outside the controlled laboratory environment. Respiratory diseases and infections are amongst the most leading deaths group. In future work, it has to be investigated how RIP devices can effectively be used for patients suffering from these conditions. The reasons for mobile ECG analysis were to provide real-time algorithms for two particular scopes. The first scope dealt with identifying arrhythmic beats using only time instances of successive heartbeats – the RR intervals. In the second scope, three high-accurate single-lead, instantaneous P- and T-wave detection algorithms were compared. These two applications could help in addressing cardiovascular diseases that are the number one cause of deaths worldwide. An effective and easy to handle arrhythmia classification algorithm could send people in risk early to a physician. High-accurate P- and T-wave detection algorithms requiring only a single lead are beneficial for all ECG monitoring fields, and at the same time, enhance the patient’s comfort. IMU-based activity recognition provides an objective method for classifying activities addressing the risk factor physical inactivity. Therefore, a common, publicly available dataset DaLiAc was created to enable the comparison of activity recognition algorithms (http://www.activitynet.org/). Using the benchmark dataset DaLiAc, a hierarchical classification system was created that outperformed six state of the art activity recognition algorithms. Recognizing single activities of daily living might increase the awareness of individuals to increase their physical activity as physical inactivity is one of the four leading risk factors for non-communicable diseases. In this thesis, three different wearable computing applications addressing different non-communicable diseases were presented. Wearables have been continuously used for health monitoring in recent years – with still increasing trend – as they bring certain benefits to the user in daily life. This will be enlarged in the future and fostered by ongoing inventions, accumulating knowledge, technological process, and progressive digitalization.<br
Keywords: Activity recognition; Adjustment algorithm; Biosignal processing; Machine learning; Mobile ECG analysis; Mobile breathing analysis; Wearable Computing; Wearable Computer; Smart Device; Gesundheitstelematik; Funktionsdiagnostik

Wearable Current-Based ECG Monitoring System with Non-Insulated Electrodes for Underwater Application - Gradl, Stefan; Cibis, Tobias; Lauber, Jasmine; Richer, Robert; Rybalko, Ruslan; Pfeiffer, Norman; Leutheuser, Heike; Wirth, Markus; von Tscharner, Vinzenz; Eskofier, Björn (Applied Sciences, 2017)

Wearable Real-Time ECG Monitoring with Emergency Alert System for Scuba Diving - Cibis, Tobias; Groh, Benjamin; Gatermann, Heike; Leutheuser, Heike; Eskofier, Björn (Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015)
Medical diagnosis is the first level for recognition and treatment of diseases. To realize fast diagnosis, we propose a concept of a basic framework for the underwater monitoring of a diver's ECG signal, including an alert system that warns the diver of predefined medical emergency situations. The framework contains QRS detection, heart rate calculation and an alert system. After performing a predefined study protocol, the algorithm's accuracy was evaluated with 10 subjects in a dry environment and with 5 subjects in an underwater environment. The results showed that, in 3 out of 5 dives as well as in dry environment, data transmission remained stable. In these cases, the subjects were able to trigger the alert system. The evaluated data showed a clear ECG signal with a QRS detection accuracy of 90 %. Thus, the proposed framework has the potential to detect and to warn of health risks. Further developments of this sample concept can imply an extension for monitoring different biomedical parameters.
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.wearab_5

WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography - Nissen, Michael; Flaucher, Madeleine; Jäger, Katharina; Hübner, Hanna; Danzberger, Nina; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023)
Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser's insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical practice.Clinical relevance - WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation
Created from Fastlane, Scopus look-up
Keywords: Heart rate; Limiting; Cameras; Photoplethysmography; Libraries; Recording; Usability

Your Health, Your Data: Combining Interdisciplinary Views, Concepts, and Practices to Empower Patients in Their Engagement With Personal Health Data - Flaucher, Madeleine; Zakreuskaya, Anastasiya; Jäger, Katharina; Richer, Robert; Smeddinck, Jan David; Kumar, Devender; Grimme, Sophie; Klein, Julia; Hrynyschyn, Robert; Eskofier, Björn; Leutheuser, Heike (Workshopband, 2023)
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