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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.

A Physics-Based Statistical Model for Human Gait Analysis