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

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

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