Feature Matching-Based Gait Phase Prediction for Obstacle Crossing Control of Powered Transfemoral Prosthesis

TL;DR

This paper presents a novel method using inertial sensors and neural networks optimized by genetic algorithms to accurately predict gait phases and joint angles, enhancing obstacle crossing capabilities in powered transfemoral prostheses.

Contribution

It introduces a new approach combining sensor data, neural network optimization, and gait prediction algorithms for improved prosthetic control.

Findings

Achieves 100% gait phase estimation accuracy under 150 Hz noise conditions.

Thigh and knee angle prediction errors are 8.71% and 6.78%, respectively.

Effective noise elimination when Gaussian noise standard deviation is below 1.

Abstract

For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100\% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71\% and knee angle prediction error being 6.78\%. These findings demonstrate the method's ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.