Classification of EMG signals based on CNN features with continuous wavelet transformation and LS-SVM

Abstract

The various hand EMG signal grasps are classified in this study. Because EMG signals offer critical information about muscle activity, they are commonly used as input to electro muscular control systems. Each muscle performs a specific function in each movement. Electromyography is a medical, healthcare, and human-machine interaction diagnostic technique for acquiring an EMG signal (MMI). e most important component of the locomotion system is the muscular system. Accordingly, sensors were developed to detect the movement system and diagnose the electromyogram. Nowadays, While maintaining a modest size, it has improved and become more accurate. In this paper, The EMG signals are converted into images using CWT, then the EMG images features are extracted based on convolutional neural network (CNN) , and finally, the EMG features are categorized by an LS-SVM classifier in Matlab. The main objective of this study is to classify grasps into six basic hand movements: (1) cylindrical, (2) palm, (3) lat (4) sphere(5) Tip, and (6) Hook. Finally, electrophysiological patterns of each movement were extracted by extracting features from the images using CNN where EMG images are divided into (70 percent ) training and (30 percent ) validation, and then these features are fed into classification using the least square support vector machine. It produced an accuracy of 95.33%.