SVM for Decoding the Human Activity Mode from sEMG Signals
- Resource Type
- Conference
- Authors
- Kalani, Hadi; Tahamipour-Z, S. Mohammad; Kardan, Iman; Akbarzadeh, Alireza; Ebrahimi, Amirali; Sede, Reza
- Source
- 2019 7th International Conference on Robotics and Mechatronics (ICRoM) Robotics and Mechatronics (ICRoM), 2019 7th International Conference on. :265-269 Nov, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Robotics and Control Systems
Legged locomotion
Muscles
Support vector machines
Exoskeletons
Feature extraction
Electromyography
Surface Electromyography (sEMG)
Support Vector Machine (SVM)
Flexion and Extension Muscles
Exoskeleton Robot
- Language
- ISSN
- 2572-6889
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.