Machine Learning for Personalisation of Biomechanical Movement Simulations (C01)

Acronym: SFB 1483 EmpkinS C01
Project leader:
Project members: , ,
Start date: 1. July 2021
End date: 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)



The extent to which a neural network can be used to effectively personalise gait simulations using motion data is explored. We first investigate the influence of body parameters on gait simulation. An initial version of the personalisation is trained with simulated motion data, since ground truth data is known for this purpose. We then explore gradient-free methods to fit the network for experimental motion data. The resulting network is validated with magnetic resonance imaging, electromyography and intra-body variables.


Current Students

Prajjwal Nag

Master's Thesis

Smartphone-Based Human Model Personalisation

Anne Dröge

Bachelor's Thesis

Optimal Control Radar Tracking

Past Students

Akat Altan

Master's Thesis

"In the wild" Movement Analysis Using Physics-Informed Neural Networks

Nico Weber

BioMAC Group

Linus Hötzel


Daniel Janischowsky

Master's Thesis

Predictive simulations of gait with ankle exoskeleton that alters energetics