{"id":2522,"date":"2024-03-18T14:36:52","date_gmt":"2024-03-18T13:36:52","guid":{"rendered":"https:\/\/www.asm.tf.fau.de\/?page_id=2522"},"modified":"2024-03-18T14:36:52","modified_gmt":"2024-03-18T13:36:52","slug":"machine-learning-for-personalisation-of-biomechanical-movement-simulations-c01","status":"publish","type":"page","link":"https:\/\/www.asm.tf.fau.de\/en\/startseite\/research\/biomac\/machine-learning-for-personalisation-of-biomechanical-movement-simulations-c01\/","title":{"rendered":"Machine Learning for Personalisation of Biomechanical Movement Simulations (C01)"},"content":{"rendered":"

\nAcronym:<\/b> SFB 1483 EmpkinS C01
\nProject leader:<\/b> Anne Koelewijn<\/span>
\nProject members:<\/b> Eva Dorschky<\/span>, Markus Gambietz<\/span>, Marlies Nitschke<\/span>
\nStart date:<\/b> 1. July 2021
\nEnd date:<\/b> 30. June 2025
\nFunding source:<\/b> DFG \/ Sonderforschungsbereich (SFB)<\/p>\n

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Abstract<\/h3>\n

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.
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Students<\/h3>\n
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