MLP Based Continuous Gait Recognition of a Powered Ankle Prosthesis with Serial Elastic Actuator

TL;DR

This paper introduces a novel powered ankle prosthesis with serial elastic actuator and an MLP-based gait recognition system that accurately predicts multiple gait parameters in real time, enhancing prosthesis control and user mobility.

Contribution

It presents a new SEA design with improved stiffness and endurance, and a continuous gait recognition method using a single IMU for better prosthesis performance.

Findings

Prosthesis supports walking speeds up to 4 m/s.

Gait recognition accurately predicts speed, phase, ankle angle, and velocity.

The system demonstrates high continuity and adaptability across speeds.

Abstract

Powered ankle prostheses effectively assist people with lower limb amputation to perform daily activities. High performance prostheses with adjustable compliance and capability to predict and implement amputee's intent are crucial for them to be comparable to or better than a real limb. However, current designs fail to provide simple yet effective compliance of the joint with full potential of modification, and lack accurate gait prediction method in real time. This paper proposes an innovative design of powered ankle prosthesis with serial elastic actuator (SEA), and puts forward a MLP based gait recognition method that can accurately and continuously predict more gait parameters for motion sensing and control. The prosthesis mimics biological joint with similar weight, torque, and power which can assist walking of up to 4 m/s. A new design of planar torsional spring is proposed for the SEA, which has better stiffness, endurance, and potential of modification than current designs. The gait recognition system simultaneously generates locomotive speed, gait phase, ankle angle and angular velocity only utilizing signals of single IMU, holding advantage in continuity, adaptability for speed range, accuracy, and capability of multi-functions.