Classification of Hand Movements via EMG using Machine Learning Methods for Prosthesis

Abstract

The recognition of hand movements using surface electromyography (sEMG) and a machine learning technique is becoming increasingly significant to control a prosthetic hand in a rehabilitation facility for people who have had their hands amputated in order to regain lost capability. However, in real life, controlling a prosthetic hand utilizing non-invasive methods is still a challenge. Existing research results are limited and not meeting the needs of amputee. The objective of this work is to fulfill the gap by proposing empirical mode decomposition (EMD) based machine learning (ML)classifier to recognize hand movements of the Ninapro dataset, this benchmark standard is used to evaluate four classifiers by comparing the performance accuracy results. The outcome of this work is better movement recognition achieved using one of the four distinct classifiers.