Electromyographic Pattern Analysis and Classification for a Robotic Prosthetic Arm

M José H Erazo Macias,S Alejandro Vega

doi:10.1155/2006/176825

M José H Erazo Macias, S Alejandro Vega

Open Access

https://doi.org/10.1155/2006/176825

Copy DOI

Abstract

This paper deals with the statistical analysis and pattern classification of electromyographic signals from the biceps of a person with amputation below the humerus. Such signals collected from an amputation simulator are synergistically generated to produce discrete elbow movements. The purpose of this study is to utilise these signals to control an electrically driven prosthetic or orthotic elbow with minimum extra mental effort on the part of the subject. The results show very good separability of classes of movements when a learning pattern classification scheme is used, and a superposition of any composite motion to the three basic primitive motions—humeral rotation in and out, flexion and extension, and pronation and supination. Since no synergy was detected for the wrist movement, different inputs have to be provided for a grip. In addition, the method described is not limited by the location of the electrodes. For amputees with shorter stumps, synergistic signals could be obtained from the shoulder muscles. However, the presentation in this paper is limited to biceps signal classification only.

Highlights

Since World War II, many attempts have been made to use external power to operate artificial prosthetic or orthotic arms (Alderson 1954)
The introduction of microcomputers has made it possible to experiment with more sophisticated signal detection and motion control of human prostheses
This paper presents an approach to detect, analyse, and classify synergistic EMG signals generated by the biceps of an above-the-elbow amputee in an attempt to move the artificial limb without extra mental effort

Summary

Introduction

Since World War II, many attempts have been made to use external power to operate artificial prosthetic or orthotic arms (Alderson 1954). Electromyographic (EMG) signals from the body’s intact musculature have been suggested and utilised by many researchers as an effective noninvasive method to provide commands to control an electrically powered artificial limb (Graupe et al 1978; Lyman et al 1974; Saidis and Stephenou 1977). This paper presents an approach to detect, analyse, and classify synergistic EMG signals generated by the biceps of an above-the-elbow amputee in an attempt to move the artificial limb without extra mental effort. An elbow of three degrees of freedom is considered It was built using a newly designed product (made in the Department of Electric and Electronic Engineering, Technological Institute of Reynosa, Tamaulipas, Mexico), while the mechanism and all its parts were made in the Technological Institute of Reynosa (Figure 1). The elbow could be driven to perform all possible simultaneous combinations in the following six primitive motions:

Objectives

Methods

Conclusion