A computational roadmap to electronic drugs - Rowald, Andreas; Amft, Oliver (Frontiers in Neurorobotics, 2022)
A growing number of complex neurostimulation strategies promise symptom relief and functional recovery for several neurological, psychiatric, and even multi-organ disorders. Although pharmacological interventions are currently the mainstay of treatment, neurostimulation offers a potentially effective and safe alternative, capable of providing rapid adjustment to short-term variation and long-term decline of physiological functions. However, rapid advances made by clinical studies have often preceded the fundamental understanding of mechanisms underlying the interactions between stimulation and the nervous system. In turn, therapy design and verification are largely driven by clinical-empirical evidence. Even with titanic efforts and budgets, it is infeasible to comprehensively explore the multi-dimensional optimization space of neurostimulation through empirical research alone, especially since anatomical structures and thus outcomes vary dramatically between patients. Instead, we believe that the future of neurostimulation strongly depends on personalizable computational tools, i.e. Digital Neuro Twins (DNTs) to efficiently identify effective and safe stimulation parameters. DNTs have the potential to accelerate scientific discovery and hypothesis-driven engineering, and aid as a critical regulatory and clinical decision support tool. We outline here how DNTs will pave the way toward effective, cost-, time-, and risk-limited electronic drugs with a broad application bandwidth.
CRIS-Team Scopus Importer:2022-12-02
Keywords: computational modeling; digital twins; neurological disorders; neuromodulation; neurostimulation; psychiatric disorders

AIM in Eating Disorders - Kopyto, David; Uhlenberg, Lena; Zhang, Rui; Stonawski, Valeska; Horndasch, Stefanie; Amft, Oliver (Artificial Intelligence in Medicine, 2022)
Over the past decades, the burden of eating disorders (ED) and comorbidities increased worldwide. Assisting diet monitoring with AI methods and Automated Dietary Monitoring (ADM) can support ED risk prediction, diagnosis, tracking associated symptoms, and medical guidance during a long-term behavior change process. This chapter gives an overview of important directions in AI in the field of EDs and obesity. State-of-the-art methods and technologies for ADM are summarized in connection to digital biomarkers that reflect diet-related behavior in general. Two sensorbased ADM examples are detailed: food type classification and eating timing estimation. On the example of anorexia nervosa (AN), dietrelated psychological parameters are detailed and AI-based approaches to support AN diagnosis and treatment are described.
CRIS-Team Scopus Importer:2024-12-16
Keywords: ADM; Ambient technology; Dietary behavior; Digital biomarkers; Embedded systems; Smart eyeglasses; Ubiquitous computing; Wearable computing

AIM in Wearable and Implantable Computing - Baronetto, Annalisa; Amft, Oliver (Springer International Publishing, 2022)
Wearable and implantable computing devices are becoming an integral component in healthcare thanks to their ability to measure multiple physical, physiological, and environmental variables in everyday life. The analysis of the large volume, velocity, and variety of data requires AI methods and context awareness. Body-worn computing devices can help clinicians by providing important information in decision-making, e.g., during diagnosis and therapy management, but similarly could help to implement interventions, e.g., to provide reminders and nudges. This chapter gives an overview on AI-empowered wearable and implantable computing systems and their use in medicine. First, context awareness and its implementation is introduced. AI application examples are detailed. Subsequently, AI methods for personalization and optimal system design are reviewed, including methods for fabrication planning and monitoring. The chapter is concluded by discussing procedures to validate medical wearable and implantable systems, from safety assessments to usability tests.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Edge computing; Edge intelligence; Embedded systems; Medical wearables; Smart clothing; Smart textiles; Ubiquitous computing; Wearable computing

A metric for upper extremity functional range of motion analysis in long-term stroke recovery using wearable motion sensors and posture cubics - Derungs, Adrian; Schuster-Amft, Corina; Amft, Oliver (2018 IEEE 15TH INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) AND THE WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN), 2018)
CRIS-Team WoS Importer:2019-11-08

Attention-based adaptive sampling for continuous EMG data streams - Schiboni, Giovanni; Suarez Mora, Juan Carlos; Zhang, Rui; Amft, Oliver (2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019)
This paper presents an online attention-based adaptive sampling approach for EMG data streams. Our sampling strategy is based on dynamically tuning the duty cycle of an EMG monitoring and recognition system. A response model was developed to adjust the EMG system's sampling rate depending on the signal pattern. The response model was implemented by two sampling rate states. We report a case study of an eyeglasses diet monitoring system that implements the adaptive sampling strategy to monitor the Temporalis muscle activity. We show that the adaptive sampling approach can reduce energy consumption in a free-living study dataset with ten participants. Compared to a static uniform sampling, our approach yields an energy saving on 70%, while recognition performance remained above 80%.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Automatic dietary monitoring; Energy saving; Pattern spotting

Audio-Based Onset Detection applied to Chewing Cycle Segmentation - Kopyto, David; Zhang, Rui; Amft, Oliver (IWSC'21: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS, 2021)
CRIS-Team WoS Importer:2022-04-01

Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems - Schiboni, Giovanni; Vicario, Celia Martin; Suarez, Juan Carlos; Cruciani, Federico; Amft, Oliver (IEEE Transactions on Mobile Computing, 2021)
This paper investigates a context-adaptive sample acquisition strategy at sub-Nyquist sampling rate for wearable embedded sensor devices. Our approach can be applied to compressive sensing frameworks to minimise sampling and transmission costs. We consider a context estimate to represent the local signal structure and a feed-forward response model to continuously tune signal acquisition of an online sampling and transmission system. To evaluate our approach, we analysed the performance in different pattern recognition scenarios. We report three case studies here: (1) eating monitoring based on electromyography measurements in smart eyeglasses, (2) human activity recognition based on waist-worn inertial sensor data, and (3) heartbeat detection and arrhythmia classification based on single-lead electrocardiogram readings. Compared to conventional sub-Nyquist sampling, our context-adaptive approach saves between 13% to 22% of energy, while achieving similar pattern recognition performance and reconstruction error.
CRIS-Team Scopus Importer:2021-06-11
Keywords: Biomedical monitoring; Context modeling; Information rates; Receivers; Sensors; Wearable computers; Wearable sensors

Deep 3D Body Landmarks Estimation for Smart Garments Design - Baronetto, Annalisa; Wassermann, Dominik; Amft, Oliver (2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021, 2021)
We propose a framework to automatically extract body landmarks and related measurements from 3D body scans and replace manual body shape estimation in fitting smart garments. Our framework comprises five steps: 3D scan acquisition and segmentation, 2D image conversion, extraction of body landmarks using a Convolutional Neural Network (CNN), back projection and mapping of extracted landmarks to 3D space, body measurements estimation and tailored garment generation. We trained and tested the algorithm on 3000 synthetic 3D body models and estimated body landmarks required for T-Shirt design. The results show that the algorithm can successfully extract 3D body landmarks of the upper front with a mean error of 1.01 cm and of the upper back with a mean error of 0.78 cm. We validated the framework the framework in automated tailoring of an electrocardiogram (ECG)-monitoring shirt based on the predicted landmarks. The ECG shirt can fit all evaluated body shapes with an average electrode-skin distance of 0.61 cm.
CRIS-Team Scopus Importer:2021-09-10
Keywords: 3D body landmarks; deep learning; garment design; wearable sensors

Dyndse: Automated multi-objective design space exploration for context-adaptivewearable IOT edge devices - Schiboni, Giovanni; Suarez Mora, Juan Carlos; Zhang, Rui; Amft, Oliver (Sensors, 2020)
We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.
CRIS-Team Scopus Importer:2020-11-13
Keywords: Automatic dietary monitoring; Embedded machine learning; Energy saving; Health monitoring; Pattern spotting; Physiological sensing

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis - Derungs, Adrian; Amft, Oliver (Scientific Reports, 2020)
We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.
CRIS-Team Scopus Importer:2020-07-24

Evaluation of HRV estimation algorithms from PPG data using neural networks - Koch, R.; Pfeiffer, N.; Lang, N.; Struck, M.; Amft, Oliver; Eskofier, Björn; Wittenberg, Thomas (Current Directions in Biomedical Engineering, 2020)
Heart rate variability (HRV) is a powerful measure to gain information on the activation of the central nervous system and is thus a strong indicator for the overall health and emotional state of a person. Currently, the gold standard for HRV analysis is the examination of R-peaks in electrocardiograms (ECG), which requires a placement of electrodes on the torso. This is often impracticable, especially for the use in daily routines or 24/7 measurements. Photoplethysmograms (PPG) are an alternative to ECG assessment and are easier to acquire, e.g. by using fitness trackers or smart watches. Nevertheless, PPG data is more susceptible to motion artifacts. Hence, goal of this work is to develop and evaluate an artificial neural network (ANN) approach to estimate the R-peak locations in complex PPG signals. Public data collections were used as benchmark to compare our ANN-based approach to state-of-the-art methods. Results show that ANNs can improve HRV estimation during motion. HRV estimations from baseline methods (decision-tree based and automatic multiscalebased peak detection) were compared with the best performing neural network (3L-GRU) using the TROIKA dataset with respect to reference parameters obtained from a manual selection of the peaks in ECG data. In most cases, the neural network based HRV estimation was closer to the reference HRV compared to baseline methods (lower μ and σ) Also, σ is smaller for the best performing ANN approach across most HRV parameters. Inclusion of another PPG or acceleration channel did not affect HRV estimation. Although, the neural network learning approach outperforms conventional methods, the examined PPG-based HRV estimation has still accuracy limitations. Nonetheless, the proposed estimation approach opens up new directions for further improvement.
CRIS-Team Scopus Importer:2020-12-18
Keywords: heart-rate variability; HRV; neural networks; photoplethysmography; PPG

Fabricating Pervasive Computing Systems Introduction - Amft, Oliver; Baker, Mary; Mankoff, Jennifer (IEEE Pervasive Computing, 2019)
CRIS-Team WoS Importer:2020-02-14

Feasibility and Acceptability of Wearable Sensor Placement for Measuring Screen Time of Children - Willis, Erik A.; Hales, Derek; Smith, Falon T.; Burney, Regan; El-Zaatari, Helal M.; Rzepka, Michelle C.; Amft, Oliver; Barr, Rachel; Evenson, Kelly R.; Kosorok, Michael R.; Ward, Dianne S. (Translational Journal of the American College of Sports Medicine, 2022)
Introduction/Purpose Wearables that include a color light sensor are a promising measure of electronic screen use in adults. However, to extend this approach to children, we need to understand feasibility of wear placement. The purpose of this study was to examine parent perceptions of children's acceptability of different sensor placements and feasibility of free-living 3- to 7-d wear protocols. Methods This study was conducted in three phases. In phase 1, caregivers (n = 161) of 3- to 8-yr-old children completed an online survey to rate aspects of fitting and likelihood of wear for seven methods (headband, eyeglasses, skin adhesive patch, shirt clip/badge, mask, necklace, and vest). In phase 2, children (n = 31) were recruited to wear one of the top five prototypes for 3 d (n = 6 per method). In phase 3, children (n = 23) were recruited to wear one of the top three prototypes from phase 2 (n = 8 per method) for 7 d. In phases 2 and 3, parents completed wear logs and surveys about their experiences. Parents scored each wearable on three domains (ease of use, likelihood of wear, and child enjoyment). Scores were averaged to compute an everyday "usability" score (0, worst, to 200, best). Results Phase 1 results suggested that the headband, eyeglasses, patch, clip/badge, and vest had the best potential for long-term wear. In phase 2, time spent wearing prototypes and usability scores were highest for the eyeglasses (10.4 h center dot d(-1); score, 155.4), clip/badge (9.8 h center dot d(-1); score, 145.8), and vest (7.1 h center dot d(-1); score, 141.7). In phase 3, wearing time and usability scores were higher for the clip/badge (9.4 h center dot d(-1); score, 169.6) and eyeglasses (6.5 h center dot d(-1); score, 145.3) compared with the vest (4.8 h center dot d(-1); score, 112.5). Conclusions Results indicate that wearable sensors clipped to a child's shirt or embedded into eyeglasses are feasible and acceptable wear methods in free-living settings. The next step is to assess the quality, validity, and reliability of data captured using these wear methods.
CRIS-Team WoS Importer:2022-10-28

Feasibility And Acceptability Of Wearable Sensor Placement For Young Children. - Willis, Erik A.; Hales, Derek; Smith, Falon; Burney, Regan; Rzepka, Michelle C.; Amft, Oliver; Barr, Rachel; Evenson, Kelly R.; Kosorok, Micheal R.; Ward, Dianne S. (MEDICINE & SCIENCE IN SPORTS & EXERCISE, 2022)
CRIS-Team WoS Importer:2022-12-23

GastroDigitalShirt: A smart shirt for digestion acoustics monitoring - Baronetto, Annalisa; Graf, Luisa; Fischer, Sören; Neurath, Markus; Amft, Oliver (Proceedings - International Symposium on Wearable Computers, ISWC, 2020)
We present the GastroDigitalShirt, a smart T-Shirt for capturing abdominal sounds produced during digestion. The garment prototype embeds an array of eight miniaturised microphones connected to a low-power wearable computer and is designed for long-term recording. We present the microphone integration and shirt wiring layout. With the GastroDigitalShirt we monitored the different digestion phases over six hours in four healthy participants with no prior gastro-intestinal diseases. The collected data were annotated by two independent raters to mark Bowel Sounds (BS) instances. The interrater agreement was substantial, with Cohen's Kappa of 0.7, confirming a consistent labeling approach. Overall 3046 BS instances were individually annotated. The extracted BS were structured by Hierarchical Agglomerative Clustering. The analysis highlighted the presence of 4 BS types. The results show that our prototype can capture the main BS types reported in literature.
CRIS-Team Scopus Importer:2020-11-13
Keywords: bowel sounds; smart garments; wearable sensors

Guest Editorial Special Section on Cloud Computing, Edge Computing, Internet of Things, and Big Data Analytics Applications for Healthcare Industry 4.0 - Celesti, Antonio; Amft, Oliver; Villari, Massimo (IEEE Transactions on Industrial Informatics, 2019)
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How Wearable Computing Is Shaping Digital Health - Amft, Oliver (IEEE Pervasive Computing, 2018)
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ISWC 2018: Towards Smarter Wearable Technologies That Blend With Our Bodies - Amft, Oliver; Van Laerhoven, Kristof (IEEE Pervasive Computing, 2019)
CRIS-Team WoS Importer:2019-08-30

Longitudinal Walking Analysis in Hemiparetic Patients Using Wearable Motion Sensors: Is There Convergence Between Body Sides? - Derungs, Adrian; Schuster-Amft, Carina; Amft, Oliver (Frontiers in Bioengineering and Biotechnology, 2018)
Background: Longitudinal movement parameter analysis of hemiparetic patients over several months could reveal potential recovery trends and help clinicians adapting therapy strategies to maximize recovery outcome. Wearable sensors offer potential for day-long movement recordings in realistic rehabilitation settings including activities of daily living, e.g., walking. The measurement of walking-related movement parameters of affected and non-affected body sides are of interest to determine mobility and investigate recovery trends. Methods: By comparing movement of both body sides, recovery trends across the rehabilitation duration were investigated. We derived and validated selected walking segments from free-living, day-long movement by using rules that do not require data-based training or data annotations. Automatic stride segmentation using peak detection was applied to walking segments. Movement parameters during walking were extracted, including stride count, stride duration, cadence, and sway. Finally, linear regression models over each movement parameter were derived to forecast the moment of convergence between body sides. Convergence points were expressed as duration and investigated in a patient observation study. Results: Convergence was analyzed in walking-related movement parameters in an outpatient study including totally 102 full-day recordings of inertial movement data from 11 hemiparetic patients. The recordings were performed over several months in a day-care centre. Validation of the walking extraction method from sensor data yielded sensitivities up to 80 % and specificity above 94 % on average. Comparison of automatically and manually derived movement parameters showed average relative errors below 6 % between affected and non-affected body sides. Movement parameter variability within and across patients was observed and confirmed by case reports, reflecting individual patient behavior. Conclusion: Convergence points were proposed as intuitive metric, which could facilitate training personalization for patients according to their individual needs. Our continuous movement parameter extraction and analysis, was feasible for realistic, day-long recordings without annotations. Visualizations of movement parameter trends and convergence points indicated that individual habits and patient therapies were reflected in walking and mobility. Context information of clinical case reports supported trend and convergence interpretation. Inconsistent convergence point estimation suggested individually varying deficiencies. Long-term recovery monitoring using convergence points could support patient-specific training strategies in future remote rehabilitation.
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Makers of Pervasive Systems and Crafts - Amft, Oliver; Baker, Mary (IEEE Pervasive Computing, 2019)
We recently had the pleasure of interviewing researchers in electronics and materials for pervasive systems, as well as researchers, practitioners, and digital tool builders in crafts such as music and food. We have separated the conversations into two interview digests. The first highlights important topics for future research in pervasive systems and provides guidance for new investigators and entrepreneurs interested in the area. The second focuses on the interaction between crafts and new pervasive technologies to discover the ways technology is amplifying tradition or helping to create new hybrid crafts-crafts that combine the physical and digital.
CRIS-Team WoS Importer:2020-02-14

Message from the program chairs - Chen, Chao; Amft, Oliver; Hanke, Sten; Roggen, Daniel (Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019, 2019)
CRIS-Team Scopus Importer:2021-04-20

Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study - Knitza, Johannes; Simon, David; Lambrecht, Antonia; Raab, Christina; Tascilar, Koray; Hagen, Melanie; Kleyer, Arnd; Bayat, Sara; Derungs, Adrian; Amft, Oliver; Schett, Georg; Hueber, Axel (JMIR mHealth and uHealth, 2020)
BACKGROUND: Mobile health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases. OBJECTIVE: The aim of the study was to explore mHealth usage, preferences, barriers, and eHealth literacy reported by German patients with rheumatic diseases. METHODS: Between December 2018 and January 2019, patients (recruited consecutively) with rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis were asked to complete a paper-based survey. The survey included questions on sociodemographics, health characteristics, mHealth usage, eHealth literacy using eHealth Literacy Scale (eHEALS), and communication and information preferences. RESULTS: Of the patients (N=193) who completed the survey, 176 patients (91.2%) regularly used a smartphone, and 89 patients (46.1%) regularly used social media. Patients (132/193, 68.4%) believed that using medical apps could be beneficial for their own health. Out of 193 patients, only 8 (4.1%) were currently using medical apps, and only 22 patients (11.4%) stated that they knew useful rheumatology websites/mobile apps. Nearly all patients (188/193, 97.4%) would agree to share their mobile app data for research purposes. Out of 193 patients, 129 (66.8%) would regularly enter data using an app, and 146 patients (75.6%) would welcome official mobile app recommendations from the national rheumatology society. The preferred duration for data entry was not more than 15 minutes (110/193, 57.0%), and the preferred frequency was weekly (59/193, 30.6%). Medication information was the most desired app feature (150/193, 77.7%). Internet was the most frequently utilized source of information (144/193, 74.6%). The mean eHealth literacy was low (26.3/40) and was positively correlated with younger age, app use, belief in benefit of using medical apps, and current internet use to obtain health information. CONCLUSIONS: Patients with rheumatic diseases are very eager to use mHealth technologies to better understand their chronic diseases. This open-mindedness is counterbalanced by low mHealth usage and competency. Personalized mHealth solutions and clear implementation recommendations are needed to realize the full potential of mHealth in rheumatology.
CRIS-Team Scopus Importer:2020-08-28
Keywords: eHEALS; eHealth; mHealth; mobile applications; rheumatology; telemedicine

Monitoring Chewing and Eating in Free-Living Using Smart Eyeglasses - Zhang, Rui; Amft, Oliver (IEEE Journal of Biomedical and Health Informatics, 2018)
We propose to 3-D-print personal fitted regular-look smart eyeglasses frames equipped with bilateral electromyography recording to monitor temporalis muscles' activity for automatic dietary monitoring. Personal fitting supported electrode-skin contacts are at temple ear bend and temple end positions. We evaluated the smart monitoring eyeglasses during in-lab and free-living studies of food chewing and eating event detection with ten participants. The in-lab study was designed to explore three natural food hardness levels and determine parameters of an energy-based chewing cycle detection. Our free-living study investigated whether chewing monitoring and eating event detection using smart eyeglasses is feasible in free-living. An eating event detection algorithm was developed to determine intake activities based on the estimated chewing rate. Results showed an average food hardness classification accuracy of 94% and chewing cycle detection precision and recall above 90% for the in-lab study and above 77% for the free-living study covering 122 hours of recordings. Eating detection revealed the 44 eating events with an average accuracy above 95%. We conclude that smart eyeglasses are suitable for monitoring chewing and eating events in free-living and even could provide further insights into the wearer's natural chewing patterns.
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Motion-Induced Imbalance of Contact Impedance in ECG Capture: Comparison of Electrode Materials in Capacitive Coupling - Pfeiffer, Sebastian; Meyer, Sebastian; Amft, Oliver; Anzai, Daisuke; Wang, Jianqing; Fischer, Georg; Kirchner, Jens (2019 IEEE SENSORS, 2019)
CRIS-Team WoS Importer:2020-07-17

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study. - Baronetto, Annalisa; Graf, Luisa; Fischer, Sarah; Neurath, Markus; Amft, Oliver (JMIR AI, 2024)
BACKGROUND: Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. OBJECTIVE: This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. METHODS: We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. RESULTS: Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. CONCLUSIONS: The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.
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Patients' Experience to MRI Examinations—A Systematic Qualitative Review With Meta-Synthesis - Nieto Alvarez, Isabel; Madl, Janika; Becker, Linda; Amft, Oliver (Journal of Magnetic Resonance Imaging, 2024)
Background: Patients often mention distress, anxiety, or claustrophobia related to MRI, resulting in no-shows, disturbances of the workflow, and lasting psychological effects. Patients' experience varies and is moderated by socio-demographic aspects alongside the clinical condition. While qualitative studies help understand individuals' experiences, to date a systematic review and aggregation of MRI individuals' experience is lacking. Purpose: To investigate how adult patients experience MRI, and the characterizing factors. Study Type: Systematic review with meta-aggregation and meta-synthesis. Population: 220 patients' reported experience of adults undergoing clinical MRI and 144 quotes from eight qualitative studies. Assessment: Systematic search in PubMed, Scopus, Web of Science, and PsycInfo databases according to the PRISMA guidelines. For quality appraisal, the Joanna Briggs Institute (JBI) tools were used. Convergent segregated approach was undertaken. Data Analysis: Participant recruitment, setting of exploration, type of interview, and analysis extracted through Joana Briggs Qualitative Assessment and Review Instrument (JBI QARI) tool. Meta-synthesis was supported by a concept map. For meta-aggregation, direct patient quotes were extracted, findings grouped, themes and characterizing factors at each stage abstracted, and categories coded in two cycles. Frequency of statements was quantified. Interviews' raw data unavailability impeded computer-aided analysis. Results: Eight articles out of 12,755 initial studies, 220 patients, were included. Meta-aggregation of 144 patient quotes answered: (1) experiences before, at the scanning table, during, and after an MRI, (2) differences based on clinical condition, and (3) characterizing factors, including coping strategies, look-and-feel of medical technology, interaction with professionals, and information. Seven publications lack participants' health literacy level, occupation, and eight studies lack developmental conditions, ethnicity, or country of origin. Six studies were conducted in university hospitals. Data Conclusion: Aggregation of patients' quotes provide a foundational description of adult patients' MRI experience across the stages of an MRI process. Insufficient raw data of individual quotes and limited socio-demographic diversity may constrain the understanding of individual experience and characterizing factors. Level of Evidence: 1. Technical Efficacy: Stage 5.
CRIS-Team Scopus Importer:2024-04-12
Keywords: anxiety; information; magnetic resonance imaging; patient experience; patient-centered care

Personalized Pervasive Health - Amft, Oliver; Favela, Jesus; Intille, Stephen; Musolesi, Micro; Kostakos, Vassilis (IEEE Pervasive Computing, 2020)
CRIS-Team WoS Importer:2020-08-21

Physical Activity Comparison Between Body Sides in Hemiparetic Patients Using Wearable Motion Sensors in Free-Living and Therapy: A Case Series - Derungs, Adrian; Schuster-Amft, Corina; Amft, Oliver (Frontiers in Bioengineering and Biotechnology, 2018)
Background: Physical activity (PA) is essential in stroke rehabilitation of hemiparetic patients to avoid health risks, and moderate to vigorous PA could promote patients' recovery. However, PA assessments are limited to clinical environments. Little is known about PA in unguided free-living. Wearable sensors could reveal patients' PA during rehabilitation, and day-long long-term measurements over several weeks might reveal recovery trends of affected and less-affected body sides. Methods: We investigated PA in an observation study during outpatient rehabilitation in a day-care center. PA of affected and less-affected body sides, including upper and lower limbs were derived using wearable motion sensors. In this analysis we focused on PA during free-living and clinician guided therapies, and investigated differences between body-sides. Linear regressions were used to estimate metabolic equivalents for each limb at comparable scale. Non-parametric statistics were derived to quantify PA differences between body sides. Results: We analyzed 102 full-day movement data recordings from eleven hemiparetic patients during individual rehabilitation periods up to 79 days. The comparison between free-living and clinician guided therapy showed on average 16.1% higher PA in the affected arm during therapy and 5.3% higher PA in the affected leg during therapy. Average differences between free-living and therapy in the less-affected side were below 4.5%. Conclusion: We analyzed PA of patients with a hemiparesis in two distinct rehabilitation settings, including free-living and clinician guided therapies over several weeks and compared MET values of affected and less-affected body sides. In particular, we investigated PA using individual regression models for each limb. We demonstrated that wearable motion sensors provide insights in patient's PA during rehabilitation. Although, no clear PA trends were found, our analysis showed patients' tendency to sedentary behavior, confirming previous lab study results. Our PA analysis approach could be used beyond clinical rehabilitation to devise personalized patient and limb-specific exercise recommendations in future remote rehabilitation.
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Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining - Haas, Oliver; Lopera Gonzalez, Luis Ignacio; Hofmann, Sonja; Ostgathe, Christoph; Maier, Andreas; Rothgang, Eva; Amft, Oliver; Steigleder, Tobias (Frontiers in Digital Health, 2021)
We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.
Keywords: anxiety; machine learning; association rule mining; palliative care; routine data; questionnaire

Printing Wearable Devices in 2D and 3D: An Overview on Mechanical and Electronic Digital Co-design - Tansaz, Samira; Baronetto, Annalisa; Zhang, Rui; Derungs, Adrian; Amft, Oliver (IEEE Pervasive Computing, 2019)
Multiprocess Additive Manufacturing (AM) offers system designers new, exciting computational tools to rapidly realise smart wearable sensing devices in two-dimensional (2D) and 3D shapes. We guide readers through the novel development and fabrication process based on a digital co-design framework and highlight AM techniques, functional materials, and assembly procedures for designing wearables as flexible and stretchable on-skin patches, e-textiles, and smart accessories for everyday use.
CRIS-Team Scopus Importer:2019-12-20
Keywords: Fabrication; Ink; Printing; Substrates; Three-dimensional displays; Tools; Two dimensional displays

Privacy Risk Awareness in Wearables and the Internet of Things - Psychoula, Ismini; Chen, Liming; Amft, Oliver (IEEE Pervasive Computing, 2020)
Day to day interactions with wearable and pervasive systems lead to collected data that capture various aspects of human behavior and enable machine learning algorithms to extract extensive information about users. We discuss privacy risk awareness, and ways to preserve privacy and integrate it in current frameworks.
CRIS-Team WoS Importer:2020-08-28

Retrieval and timing performance of chewing-based eating event detection in wearable sensors - Zhang, Rui; Amft, Oliver (Sensors, 2020)
We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottom-up approach. We evaluated the algorithm using electromyographic (EMG) recordings from diet-monitoring eyeglasses in free-living and compared the bottom-up approach against two top-down algorithms. We show that the F1 score was no longer the primary relevant evaluation metric when retrieval rates exceeded approx. 90%. Instead, detection timing errors provided more important insight into detection performance. In 122 hours of free-living EMG data from 10 participants, a total of 44 eating occasions were detected, with a maximum F1 score of 99.2%. Average detection timing errors of the bottom-up algorithm were 2.4 ± 0.4 s and 4.3 ± 0.4 s for the start and end of eating occasions, respectively. Our bottom-up algorithm has the potential to work with different wearable sensors that provide chewing cycle data. We suggest that the research community report timing errors (e.g., using the metrics described in this work).
CRIS-Team Scopus Importer:2020-02-04
Keywords: Automated dietary monitoring; Biomedical signal processing; Eating detection; Eating timing error analysis; Smart eyeglasses; Wearable health monitoring

Saving Energy on Wrist-Mounted Inertial Sensors by Motion-Adaptive Duty-Cycling in Free-Living - Schiboni, Giovanni; Amft, Oliver (2018 IEEE 15TH INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) AND THE WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN), 2018)
CRIS-Team WoS Importer:2019-11-08

Simulation of Garment-Embedded Contact Sensor Performance under Motion Dynamics - Baronetto, Annalisa; Uhlenberg, Lena; Wassermann, Dominik; Amft, Oliver (IWSC'21: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS, 2021)
We propose a simulation method to evaluate the performance of garment-embedded contact sensors while performing common Activities of Daily Living (ADL). Our method comprises four steps: dynamic 3D human body model generation, automated smart garment design, ADL simulation, dynamic sensor fitting and sensor displacement evaluation. We generated 100 3D human body models with varying body shapes and virtually dressed them with three differently fitted smart T-Shirts. We then analysed the sensor-body distance and sensor displacement while performing common ADLs. An Electrocardiogram (ECG) smart shirt was considered as an example application. Results show a decrease in sensor distance while BMI increases for both sexes. Compared to females, males show higher sensor displacement and displacement variance, whereas women show higher distance variance compared to men for all ADLs, especially in the region below the breast. Our method can be used to evaluate contact sensor performance for different body shapes, ADLs, and garment designs.<br
CRIS-Team WoS Importer:2022-04-01

Synthesising motion sensor data from biomechanical simulations to investigate motion sensor placement and orientation variations - Derungs, Adrian; Amft, Oliver (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019)
We propose a motion sensor data synthesis approach to investigate the performance effect of sensor placement and orientation variation on health marker estimation. Using OpenSim, we simulate walking motion of patients after stroke and synthesise inertial sensor data. We analyse 384 sensor positions with 192 sensors simulated at each leg's thigh. To demonstrate how synthesised sensor data could be used to analyse the performance of functional ability estimation, we estimated scores from the Lower-Extremity Fugl-Meyer-Assessment (LE-FMA) using regression methods. We evaluated our approach using a public dataset, including 8 stroke patients and showed that LE-FMA scores could be estimated with an error below 0.12 score points on average, compared to manually derived scores. We further show that sensors should be preferably placed at the thigh front. Our approach demonstrates the potential of combining biomechanical simulations and motion sensor data synthesis with algorithms for health marker estimation, thus providing rapid insight into sensor positioning and orientation variation.
CRIS-Team Scopus Importer:2020-02-21

The Psychological, Physiological, and Behavioral Responses of Patients to Magnetic Resonance Imaging (MRI): A Systematic Review and Meta-Analysis - Madl, Janika; Nieto Alvarez, Isabel; Amft, Oliver; Rohleder, Nicolas; Becker, Linda (Journal of Magnetic Resonance Imaging, 2023)
Background: MRI is generally well-tolerated although it may induce physiological stress responses and anxiety in patients. Purpose: Investigate the psychological, physiological, and behavioral responses of patients to MRI, their evolution over time, and influencing factors. Study Type: Systematic review with meta-analysis. Population: 181,371 adult patients from 44 studies undergoing clinical MRI. Assessment: Pubmed, PsycInfo, Web of Science, and Scopus were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quality appraisal was conducted with the Joanna Briggs Institute critical appraisal tools. Meta-analysis was conducted via Meta-Essentials workbooks when five studies were available for an outcome. Psychological and behavioral outcomes could be analyzed. Psychological outcomes were anxiety (State–Trait-Anxiety Inventory, STAI-S; 37) and willingness to undergo MRI again. Behavioral outcomes included unexpected behaviors: No shows, sedation, failed scans, and motion artifacts. Year of publication, sex, age, and positioning were examined as moderators. Statistical Tests: Meta-analysis, Hedge's g. A P value <0.05 was considered to indicate statistical significance. Results: Of 12,755 initial studies, 104 studies were included in methodological review and 44 (181,371 patients) in meta-analysis. Anxiety did not significantly reduce from pre- to post-MRI (Hedge's g = −0.20, P = 0.051). Pooled values of STAI-S (37) were 44.93 (pre-MRI) and 40.36 (post-MRI). Of all patients, 3.9% reported unwillingness to undergo MRI again. Pooled prevalence of unexpected patient behavior was 11.4%; rates for singular behaviors were: Failed scans, 2.1%; no-shows, 11.5%; sedation, 3.3%; motion artifacts, 12.2%. Year of publication was not a significant moderator (all P > 0.169); that is, the patients' response was not improved in recent vs. older studies. Meta-analysis of physiological responses was not feasible since preconditions were not met for any outcome. Data Conclusion: Advancements of MRI technology alone may not be sufficient to eliminate anxiety in patients undergoing MRI and related unexpected behaviors. Level of Evidence: 1. Technical Efficacy: Stage 5.
CRIS-Team Scopus Importer:2023-12-01
Keywords: anxiety; artifacts; magnetic resonance imaging; patient outcome assessment; prevalence; stress

Wearable motion sensors and digital biomarkers in stroke rehabilitation - Derungs, Adrian; Schuster-Amft, Corina; Amft, Oliver (Current Directions in Biomedical Engineering, 2020)
We propose three novel digital biomarkers for the longitudinal performance monitoring and movement evaluation of hemiparetic patients, e.g. after stroke. We devised convergence points (CP) for the bilateral walking analysis based on gait parameters, e.g. stride duration using regression- modelling to estimate similarity between body sides. The physical activity (PA) was devised to evaluate the energy expenditure of all extremities during training and free-living. The functional range of motion (fROM) is a digital biomarker to quantify the upper arm reaching ability, represented in 3D visualisations. In this work, we detail CP, PA, and fROM to derive rehabilitation insights for personalising therapies. We evaluated the proposed digital biomarkers in a clinical observation study with 11 patients after stroke during their rehabilitation including therapy and self-paced daily routines.
CRIS-Team Scopus Importer:2020-12-18
Keywords: digital twin; free-living; gait analysis; gait parameter; hemiparesis; Inertial measurement unit; stroke

Wearables to Fight COVID-19: From Symptom Tracking to Contact Tracing - Amft, Oliver; Lopera Gonzalez, Luis Ignacio; Lukowicz, Paul; Bian, Sizhen; Burggraf, Paul (IEEE Pervasive Computing, 2020)
CRIS-Team WoS Importer:2020-12-18

What Will We Wear After Smartphones? - Amft, Oliver; Van Laerhoven, Kristof (IEEE Pervasive Computing, 2017)
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