A Physics-Based Statistical Model for Human Gait Analysis
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.
- 1.Al-Naser, M., Söderström, U.: Reconstruction of occluded facial images using asymmetrical principal component analysis. Integr. Comput. Aided Eng. 19(3), 273–283 (2012)Google Scholar
- 2.Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: CVPR. IEEE Computer Society (2008)Google Scholar
- 3.Brubaker, M.A., Fleet, D.J.: The kneed walker for human pose tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
- 4.Brubaker, M.A., Sigal, L., Fleet, D.J.: Estimating contact dynamics. In: IEEE 12th International Conference on Computer Vision, ICCV 2009, September 27 - October 4 2009, Kyoto, Japan, pp. 2389–2396 (2009)Google Scholar
- 7.Jiang, Z., Lin, Z., Davis, L.S.: Learning a discriminative dictionary for sparse coding via label consistent K-SVD. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1697–1704 (2011)Google Scholar
- 13.Sok, K.W., Kim, M., Lee, J.: Simulating biped behaviours from human motion data. In: Proceedings of the ACM SIGGRAPH 2007, p. 107 (2007)Google Scholar
- 21.Yin, K., Loken, K., van de Panne, M.: Simbicon: simple biped locomotion control. ACM Trans. Graph. 26(3) (2007)Google Scholar
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.