Predictive Simulation of Human Walking Augmented by a Powered Ankle Exoskeleton

Abstract

The human ankle provides significant positive power during the stance phase of walking, which has resulted in studies focusing on methods to reduce the energetic walking cost by augmenting the ankle with exoskeletons. Recently, a few devices have successfully reduced the metabolic cost of walking by replacing part of the biological ankle plantar flexor torque. Despite these achievements, development of assistive ankle devices remains challenging, partly because the current practice of design and control of powered exoskeletons is highly time and effort consuming, which prevents quickly exploring different design and control parameters. Predictive simulations using musculoskeletal models coupled with robotic devices may facilitate the process of design and control of assistive devices. In this study, we simulate human walking augmented by a powered ankle exoskeleton. The walking problem was formulated as a predictive dynamic optimization in which both the optimal assistive device torque and the gait were solved simultaneously. Cases with exoskeletons assisting one ankle and both ankles were considered. The results showed that the energetic cost of walking could be reduced by 45% with one ankle augmented, and by 52% with both ankles augmented. This study contributes towards the goal of providing optimal assistive torque through external devices and theoretical peak reductions that could be expected from such devices.