Classification of Amputee EMG Signals Using Machine Learning Techniques

Shabana Hajano; Bushra Naz; Shanwaz Talpur; Sofia Hajano

Authors

Shabana Hajano Information Technology, MUET, Jamshoro, Jamshoro, 76090, Sindh, Pakistan.
Bushra Naz Computer System Engineering, MUET, Jamshoro, Jamshoro, 76090, Sindh, Pakistan.
Shanwaz Talpur Computer System Engineering, MUET, Jamshoro, Jamshoro, 76090, Sindh, Pakistan.
Sofia Hajano Computer System Engineering, MUET, Jamshoro, Jamshoro, 76090, Sindh, Pakistan.

Keywords:

EMG, SVM, CNN, ANN, Feature Extraction

Abstract

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 EMG signals using advanced machine-learning techniques. 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. We employed 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-learning techniques in amputee EMG signal classification. The discussion highlights the potential implications for improving prosthetic control and rehabilitation. This research presents promising results and highlights the potential of machine learning for advancing amputee assistance, opening new avenues for research and application.

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