SVM-based Real-Time Classification of Prosthetic Fingers using Myo Armband-acquired Electromyography Data

Akmal, Muhammad and Qureshi, Muhammad Farrukh and Amin, Faisal and Ur Rehman, Muhammad Zia and Niazi, Imran Khan (2021) SVM-based Real-Time Classification of Prosthetic Fingers using Myo Armband-acquired Electromyography Data. In: 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE). (In Press)

Text SVM_based_real_time_classification_of_prosthetic_fingers_using_myo_armband_acquired_electromyography_data.pdf Download (3MB)

Abstract

In this work we applied real-time classification of
prosthetic fingers movements using surface electromyography
(sEMG) data. We employed support vector machine (SVM) for
classification of fingers movements. SVM has some benefits over
other classification techniques e.g. 1) it avoids overfitting, 2)
handles nonlinear data efficiently and 3) it is stable. SVM is
employed on Raspberry pi which is a low-cost, credit-card sized
computer with high processing power. Moreover, it supports
Python which makes it easy to build projects and it has
multiple interfaces available. In this paper, our aim is to perform
classification of prosthetic hand relative to human fingers. To
assess the performance of our framework we tested it on ten
healthy subjects. Our framework was able to achieve mean
classification accuracy of 78%

Actions (login required)

View Item