A Physics-Based Statistical Model for Human Gait Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

Physics-based modeling is a powerful tool for human gait analysis and synthesis. Unfortunately, its application suffers from high computational cost regarding the solution of optimization problems and uncertainty in the choice of a suitable objective energy function and model parametrization. Our approach circumvents these problems by learning model parameters based on a training set of walking sequences. We propose a combined representation of motion parameters and physical parameters to infer missing data without the need for tedious optimization. Both a k-nearest-neighbour approach and asymmetrical principal component analysis are used to deduce ground reaction forces and joint torques directly from an input motion. We evaluate our methods by comparing with an iterative optimization-based method and demonstrate the robustness of our algorithm by reducing the input joint information. With decreasing input information the combined statistical model regression increasingly outperforms the iterative optimization-based method.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.Institut Für Informationsverarbeitung (TNT)Leibniz Universität HannoverHanoverGermany

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