Authors:
Shili Eddine
1
;
Youssef Serrestou
2
;
Slim Yacoub
3
;
Ali Al-Timemy
4
and
Kosai Raoof
4
Affiliations:
1
ENSIM- LAUM ENISO, Le Mans University, University of Sousse (ENISO), France
;
2
Le Mans Univeristy, Le Mans, France
;
3
INSAT-Carthage University, Tunis, Tunisa
;
4
Biomedical Eng. Department, Al-Khwarizmi College of Engineeing, University of Baghdad, Iraq
Keyword(s):
Acousto Myography (AMG), Electromyography (EMG), Mechanomyography (MMG), Support Vector Machine (SVM).
Abstract:
This article presents a hand movement classification system that combines acoustic myography (AMG) signals, electromyography (EMG) signals and mechanomyogram signal (MMG) data. The system aims to accurately predict hand movements, with the potential to improve the control of hand prostheses. A dataset was collected from 9 individuals who repeated 10 times each of 4 hand movements (hand close, hand open, fine pinch and index flexion). The system, with a Support Vector Machine (SVM) classifier, achieved an accuracy score of 97%, demonstrating its potential for real-time hand prosthesis control. The combination of AMG, EMG, and MMG signals proved to be effective in accurately classifying hand movements.