A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes

TL;DR

This paper introduces a robust gait phase estimation method using piecewise monotonic models and a Kalman filter, enabling improved control of transfemoral prostheses across various walking modes.

Contribution

It proposes a novel gait phase estimation approach based solely on thigh angle, enhancing robustness and accuracy for prosthetic control across different locomotion modes.

Findings

The method reduces measurement errors and noise impact.

The real-time controller improves prosthesis stability.

Offline analysis confirms high estimation accuracy.

Abstract

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this paper, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis.