Improvement of a Forward Dynamic Predictive Human Gait Model

Lack of understanding of human gait is detrimental to the development of gait related treatments and devices. This study improves a dynamic, predictive model of human gait which uses model predictive control (MPC) to replicate the control of the central nervous system (CNS). In this work, improved performance criteria, including metabolic cost and dynamic effort, are developed using an existing optimization framework to better mimic control of the CNS. Consistent with existing literature, incorporating dynamic effort and COM energy into the objective function improved gait simulations. This study also demonstrates COM energy and dynamic effort can both be used to predict metabolic energy consumption, which is likely the primary optimization criteria in normal gait generation.