Electromyography Classification Techniques Analysis for Upper Limb Prostheses Control

Abstract

The classification of surface electromyographic signals is an important task for the control of active upper-limb prostheses. This article aims to analyze and evaluate techniques to classify surface electromyographic signals for the control of upper limb prostheses. The electromyographic signals were obtained from a public database. Machine learning algorithms and seven features of the EMG signal were used to classify the signals. Random samples were created for the training and testing tasks in subsets with 80% and 20% of the data, respectively. Machine learning algorithms for classifying electromyographic signals were trained with different configurations, allowing evaluation between combinations of techniques and parameters. It was observed that signal feature extraction is an important process for obtaining accurate results. The best result produced an average accuracy of 95% with a Random Forest classifier and three features extracted from surface electromyography signals of two channels.