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

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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.