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Physics-based human motion models

TNT members involved in this project:
Prof. Dr.-Ing. Bodo Rosenhahn
Petrissa Zell, M.Sc.

Many medical procedures rely on detailed analysis of the human motion apparatus. In this context correspondences between motion patterns and hidden physical parameters like joint torques are highly interesting. To gain accurate information about the correlation between patient specific body parameters, the internal forces acting on joints and a healthy movement, a framework is required, that combines all of these characteristics.

The project aims at a direct access to physical properties of an investigated motion without the necessity of tedious simulation using inverse or forward dynamics. By use of machine learning techniques the computational expense can be reduced and the need for expensive equipment like force plates is extinct.

Interior joint torques and forces are estimated fom motions by means of machine learning approaches. The necessary training data can be generated using predictive dynamics optimization based on a skeletal model of the human body.

Show recent publications only
  • Book Chapters
    • Petrissa Zell, Bastian Wandt, Bodo Rosenhahn
      Physics-based Models for Human Gait Analysis
      Handbook of Human Motion, Springer International Publishing, 2018, edited by Bertram Müller, Sebastian I. Wolf
  • Conference Contributions
    • Petrissa Zell, Bodo Rosenhahn
      Learning-Based Inverse Dynamics of Human Motion
      The IEEE International Conference on Computer Vision (ICCV) Workshops, pp. 842-850, October 2017
    • Petrissa Zell, Bastian Wandt, Bodo Rosenhahn
      Joint 3D Human Motion Capture and Physical Analysis from Monocular Videos
      The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017
    • Petrissa Zell and Bodo Rosenhahn
      A physics-based statistical model for human gait analysis
      German Conference on Pattern Recognition (GCPR), October 2015