Title : ( A Multi-Class SVM for Decoding the Human Activity Mode from sEMG Signals )
Authors: Hadi Kalani , Seyed Mohammad Tahamipour Zarandi , Iman Kardan , Alireza Akbarzadeh Tootoonchi , Amirali Ebrahimi , reza sede ,
Full TextAbstract
Nowadays, the relationship between muscles\\\\\\\\\\\\\\\' electrical activity and body movements has been investigated in many medical applications. This Paper proposes the classification of activity mode of healthy human subjects based on surface Electromyography (sEMG) signals. Support vector machine (SVM) methodology is used to predict human activity mode, using the sEMG signals recorded from four main muscles in flexion and extension of the left leg. The presented method shows promising results with classification accuracies of up to 93%. This method provides a reliable solution for the classification of human activity modes, required in many applications like control of exoskeleton robots.
Keywords
Surface Electromyography (sEMG) Support Vector Machine (SVM) Flexion and Extension Muscles Exoskeleton Robot
@inproceedings{paperid:1082327,
author = {هادی کلانی and Tahamipour Zarandi, Seyed Mohammad and Kardan, Iman and Akbarzadeh Tootoonchi, Alireza and Ebrahimi, Amirali and Sede, Reza},
title = {A Multi-Class SVM for Decoding the Human Activity Mode from sEMG Signals},
booktitle = {7th International Conference on Robotics and Mechatronics (ICRoM)},
year = {2019},
location = {تهران, IRAN},
keywords = {Surface Electromyography (sEMG)
Support Vector Machine (SVM)
Flexion and Extension Muscles
Exoskeleton Robot},
}
%0 Conference Proceedings
%T A Multi-Class SVM for Decoding the Human Activity Mode from sEMG Signals
%A هادی کلانی
%A Tahamipour Zarandi, Seyed Mohammad
%A Kardan, Iman
%A Akbarzadeh Tootoonchi, Alireza
%A Ebrahimi, Amirali
%A Sede, Reza
%J 7th International Conference on Robotics and Mechatronics (ICRoM)
%D 2019
