A Hybrid Classification Model for EMG Signals Using Grey Wolf Optimizer and SVMs

Abstract

Electromyography (EMG) signal is an electrical indicator for neuromuscular activation. It provides direct access to physiological processes enabling the muscle to generate force and produce movement in order to accomplish countless functions. As a successful classification of the EMG signal is basically dependent on the selection of the best parameters carefully, this paper proposes a hybrid optimized classification model for EMG signals classification. The proposed system implements grey wolf optimizer (GWO) combined with support vector machines (SVMs) classification algorithm in order to improve the classification accuracy via selecting the optimal settings of SVMs parameters. The proposed approach consists of three phases; namely pre-processing, feature extraction, and GWO-SVMs classification phases. The obtained experimental results obviously indicate that significant enhancements in terms of classification accuracy have been achieved by the proposed GWO-SVMs classification system. It has outperformed the typical SVMs classification algorithm via achieving an accuracy of over 90 % using the radial basis function (RBF) kernel function.