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 phys- ical parameters to infer missing data without the need for tedious opti- mization. Both a k-nearest-neighbour approach and asymmetrical prin- cipal 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 regres- sion increasingly outperforms the iterative optimization-based method.
['Petrissa Zell', 'Bodo Rosenhahn']
A Physics-Based Statistical Model for Human Gait Analysis