Design a 1D-CNN'S to classify surface EMG (SEMG) signals

Abstract

Amputate forearm, finger, or hand is a biggest problem for the disabled subject. Therefore, Prosthetic does a significant role for the amputees to modify the capability and mobility of their systematic activities. Using the EMG signals of hand and finger motion discrimination are continuously growth for numerous hand and finger gestures. The main problem in designing a prosthetic hand is the classification of EMG signals. Machine learning (ML) algorithms present a solution to this problem by providing a way to classify EMG signals with simply and less costly scheme. This study presents more than one experiment on two datasets in order to classify individual fingers (IF) with wrist and victory based on a normative dataset of EMG signals and Deep Learning DL. These experiments show that the overall performance (average accuracy) of the proposed method is 98.83% and the overall error classification rate (error rate) is 1.17%.