Gesture recognition framework for upper-limb prosthetics using entropy features from electromyographic signals and a Gaussian kernel SVM classifier

Abstract

This paper puts forward a novel entropy features based multi-class SVM classifier framework to predict the limb movement of the transradial amputees from the surface electromyography (sEMG) signals. The major challenges with the sEMG signal are nonlinear and non-stationary characteristics and susceptibility to noise. Consequently, a robust and an effective feature extraction framework which is invariant to force level variations is central in sEMG based prosthesis control. To address the aforementioned challenges, this study leverages the potential of variational mode decomposition (VMD) technique to identify the prominent frequency modes of the sEMG signals, and performs the spectral evaluation of the decomposed sEMG modes to identify the dominant ones to extract the entropy features. Subsequently, we evaluate the efficacy of four nonlinear optimal feature selection techniques and identify the prominent entropy features to train the multi-class SVM model that can predict the gestures. Specifically, to handle the nonlinearly separable input data, this study implements a kernelization named a radial basis function (RBF), which has good generalization and noise tolerance features. The efficacy of the proposed framework is tested using the publicly available datasets that contain gestures from transradial and congenital amputees for functional gestures. Experimental results obtained for various gestures with dynamic force levels underscore that the proposed framework is highly robust against the force level variations and can achieve a classification accuracy of 99.07%.