Machine Learning for Personalization of Musculoskeletal Models, Movement Analysis, and Movement Predictions (C01)

• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 442419336

We aim to research if gait simulations can effectively be personalized using neural networks based on movement data. We investigate first the effect of body parameters on the simulations. An initial personalization network is trained on simulated movement data because a ground truth is known. Then we research gradient-free methods to further train the network with experimental movement data. The resulting network is validated with magnetic resonance imaging, electromyography, and internal body variables.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1483: Empathokinaesthetic Sensor Technology – Sensor Techniques and Data Analysis Methods for Empathokinaesthetic Modeling and Condition monitoring
• Applicant Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
• Project Head: Professorin Dr. Anne Koelewijn