Step Placement Swing Control for Powered Knee-Ankle Prostheses

Abstract

Humans engage in alternating locomotion patterns in daily life by continuously adjusting step placement. Step placement control in powered prostheses could benefit prosthesis users by supporting speed-adaptation and improving gait stability. This paper uses a data-driven predictive step placement model and a task-space swing controller to achieve human-like step placement patterns on a powered prosthesis platform in simulation. We designed the predictive model to estimate future desired step placement from current user-prosthesis states by analyzing biological gait patterns from a motion-capture dataset. We also present a novel 3D human-prosthesis simulation for evaluating prosthesis controllers with inputs from human walking experiments. In this simulation, we demonstrate our step placement controller with 22 subject models, each with 28 steady-state and 35 non-steady-state walking conditions. Simulation results show that this speed-adaptive control framework achieves human-like step placement and Margin of Stability patterns with respect to walking speed.