Classification of Amputee EMG Signals Using Machine Learning Techniques

In the field of prosthetics and assistive technology, the accurate classification of EMG signals from amputees is of paramount importance. These signals provide insights into the intended movements of the user and are essential for designing intuitive and responsive prosthetic devices. This research is primarily centered on the meticulous classification of EMGsignals using advanced machine-learningtechniques. This research contributes by achieving high accuracy (95.77%, 97.36%, and 95.77%) using SVM, ANN, and CNN, respectively, on EMG signals from 11 amputees in the Ninapro database, offering an innovative approach to improve amputee assistance.Weemployed SVM, ANN, and CNN algorithms to classify EMG signals from 11 amputees in the Ninapro database, utilizing a robust methodology.This research yielded impressive accuracy rates of 95.77%, 97.36%, and 95.77% for SVM, ANN, and CNN, respectively, demonstrating the effectiveness of machine-learningtechniques in amputee EMG signal classification. The discussion highlights the potential implications for improving prosthetic control and rehabilitation.This research presentspromising results and highlightsthe potential of machine learning for advancing amputee assistance, opening new avenues for research and application.