Physics-Based Models for Human Gait Analysis

  • Petrissa Zell
  • Bastian Wandt
  • Bodo Rosenhahn
Reference work entry


This chapter deals with fundamental methods as well as current research on physics-based human gait analysis. We present valuable concepts that allow efficient modeling of the kinematics and the dynamics of the human body. The resulting physical model can be included in an optimization-based framework. In this context, we show how forward dynamics optimization can be used to determine the producing forces of gait patterns.

To present a current subject of research, we provide a description of a 2D physics-based statistical model for human gait analysis that exploits parameter learning to estimate unobservable joint torques and external forces directly from motion input. The robustness of this algorithm with respect to occluded joint trajectories is shown in a short experiment. Furthermore, we present a method that uses the former techniques for video-based gait analysis by combining them with a nonrigid structure from motion approach. To examine the applicability of this method, a brief evaluation of the performance regarding joint torque and ground reaction force estimation is provided.


Computer vision Human motion analysis Physics-based simulation Forward dynamics optimization Data-driven regression 3D motion reconstruction Video-based force estimation 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institut für InformationsverarbeitungLeibniz Universität HannoverHannoverGermany

Section editors and affiliations

  • William Scott Selbie
    • 1
  1. 1.Has-Motion Inc.KingstonCanada

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