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Applied Bionics and Biomechanics
Volume 3, Issue 2, Pages 113-119

Electromyographic Pattern Analysis and Classification for a Robotic Prosthetic Arm

M. José H. Erazo Macias1 and S. Alejandro Vega2

1Department of Electric and Electronic Engineering, Technological Institute of Reynosa, Reynosa Tamaulipas, Mexico
2Technological Institute of Superior Studies of Monterrey, Campus Queretaro, Queretaro, Mexico

Copyright © 2006 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.