The award was presented at the IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN`22), held in Ioannina, Greece, September 27-30, 2022.
The submitted paper addresses the development of a modeling and simulation approach to synthesize body-worn inertial sensor data based on personalized human body surface and biomechanical models. This involved comparing simulated inertial sensor data on surface models and skeletal models with physical measurements made by inertial measurement units. The results of motion simulations in activities of daily living consistently show similar or lower errors of surface models compared to established skeletal models. Thus, compared to skeletal models, body surface models can provide a more realistic basis for simulation-based analysis and optimization of wearable inertial sensor systems. The findings are fundamental for the model-based development of sensor systems, e.g., medical wearables in motion therapy for post-stroke patients.