SEMG biceps and triceps effort signals classification using 1D-CNN convolution

Abstract

In this paper, we present a system for acquiring and classifying surface physiological muscles signals (sEMG) for the biceps and triceps muscles during movement or work, as normal or aggressive effort in order to control and command the aid prostheses to intervene only during aggressive efforts. Thus, the main objective of our work is to developing and improving the performance of the hand prostheses for daily life tasks for elderly persons or for persons who have hand muscle failure. Our contribution consists of detecting and classifying physiological signals of biceps and triceps muscles hand as normal and aggressive efforts, for that we proposed a technique based on a 1D Convolutional Neural Network (CNN-1D) using the Wavelet Scattering Transform as the sEMG feature extraction technique. Our methodology is carried out in two steps: the first step is crucial to build a database for deep learning network for sEMG signals classification based on fifty-five volunteers spanning various ages and genders. The second step achieves the sEMG signals effort classification as normal or aggressive efforts based on the classification network produced based on sEMG signal sequences treated by the WST. The obtained results for the training and validation sets indicate perfect performance of the proposed technique, with an accuracy, precision, sensitivity, and specificity of 100% for the training process, and 99.3%, 98.6%, 100% and 98.7% for the accuracy, precision, sensibility and specificity respectively. It is important to note that while perfect metrics on the training and the test set might suggest excellent model learning.