Step Placement Swing Control for Powered Knee-Ankle Prostheses
Michael Feldkamp, Rachel Gehlhar Humann
AI summary
Problem
Current prostheses cannot actively modulate step placement, forcing users into compensatory motions that cause injury, while existing controls and simulation tools fail to capture realistic, speed-adaptive human-prosthesis dynamics.
Approach
The authors combine a data-driven predictive model that estimates future step placement from current stance states with a task-space swing controller, evaluated in a novel 3D simulation that integrates real human motion-capture data.
Key results
- Data-driven predictive model for step placement and swing duration
- Task-space swing controller using feedback linearization for trajectory tracking
- Novel 3D human-prosthesis simulation integrating motion-capture inputs
- Validation across 22 subjects demonstrating human-like step placement and Margin of Stability across steady and non-steady speeds
Why it matters
Provides a foundational framework for next-generation powered prostheses to naturally adapt to walking speed, improving stability and reducing user fatigue.
Abstract
Humans engage in alternating locomotion patterns in daily life by continuously adjusting step placement. Step placement control in powered prostheses could benefit pros- thesis 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 walk- ing 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.