Knee Angle Estimation Algorithm for Myoelectric Control of Active Transfemoral Prostheses

Abstract

This paper presents a bioinstrumentation system for the acquisition and pre-processing of surface electromyographic (SEMG) signals, and a knee angle estimation algorithm for control of active transfemoral leg prostheses, using methods for feature extraction and classification of myoelectric signal patterns. The presented microcontrolled bioinstrumentation system is capable of recording up to four SEMG channels, and one electrogoniometer channel. The proposed neural myoelectric controller algorithm is capable of predicting the intended knee joint angle from the measured SEMG signals. The algorithm is designed in three stages: feature extraction, using auto-regressive model and amplitude histogram; feature projection, using self organizing maps; and pattern classification, using a Levenberg-Marquardt neural network. The use of SEMG signals and additional mechanical information such as that provided by the electrogoniometer may improve precision in the control of leg prostheses. Preliminary results are presented.