Gait analysis using optimality criteria imputed from human data

Abstract

This study proposes an efficient and automatic tool to understand and analyze the human natural and fast gait tasks. First, the gait tasks are modeled as a nonlinear optimal control problem along with a nonlinear model predictive control, usually used in the humanoid robots control. Second, under the assumption that the walking motions are the result of an optimization process, the identification of plausible optimality criteria weight values is achieved with an inverse optimal control (IOC) approach. Our IOC walking scheme is performed on ten subjects with ten trials for each gait task considered. Results show that the variability observed in the experimental data are exhibited by our proposed scheme. Finally, an easy distinguish can be made between the gait tasks only by checking out the exhibited criteria weight values.