Machine Learning for Neuromusculoskeletal Modelling (C03)
Acronym: SFB 1483 EmpkinS C03
Project leader: Björn Eskofier, Jörg Miehling, Sandro Wartzack, Jürgen Winkler
Project members: Sophie Fleischmann, Julian Shanbhag, Sven Wirsching, Alexander Wolf
Start date: 1. July 2021
End date: 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
Abstract
A novel postural control model of walking is explored to characterise the components of dynamic balance control. For this purpose, clinically annotated gait movements are used as input data and muscle actuated multi-body models are extended by a sensorimotor level. Neuromotor and control model parameters of (patho-)physiological movement are identified with the help of machine learning methods. Technical and clinical validation of the models will be performed. New EmpkinS measurement techniques are to be transferred to the developed models as soon as possible.
Publications
2023
- Fleischmann S., Shanbhag J., Miehling J., Wartzack S., Leyendecker S., Koelewijn A., Eskofier B.:
Time vs. Space: Comparing gait cycle normalization methods and their effect on foot placement control
28th Congress of the European Society of Biomechanics (Maastricht, 9. July 2023 - 12. July 2023)
BibTeX: Download - Shanbhag J., Fleischmann S., Eskofier B., Koelewijn A., Wartzack S., Miehling J.:
Towards postural control simulation using a sensorimotor enhanced musculoskeletal human model
ISPGR World Congress 2023 (Brisbane, 9. July 2023 - 13. July 2023)
BibTeX: Download