Human Gait Cost Function Varies With Walking Speed: An Inverse Optimal Control Study

This work investigates the optimal cost function composition for human gait at different walking speeds. Kinematic and kinetic data for walking at four walking speeds were collected from five individuals without any known disability. The data was then used to recover optimal cost functions in a predictive simulation environment with musculoskeletal models. Twenty inverse optimal control (IOC) problems were solved for cost function weight tuning using the previously developed and validated Adaptive Reference IOC (AR-IOC) algorithm. Given the walking speed range examined (0.6–1.5 m/s), the converged cost function weights suggest that the increase in walking speed attributes to a reduction of foot sliding penalty weight and weight increase for the center of mass (CoM) acceleration and stability as confirmed by several experiments. Furthermore, we did not observe any significant weight shift in effort reduction between the upper and the lower body with respect to walking speed. The obtained results from this study can be used in a toolbox for obtaining subject- and task-specific cost functions and assisting the development of personalized rehabilitation technologies.