Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD

Shair, Ezreen Farina and Jamaluddin, Nur Asyiqin and Abdullah, Abdul Rahim (2020) Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD. International Journal of Advanced Computer Science and Applications, 11 (9). pp. 244-251. ISSN 2158-107X

Text IJACSA 2020.PDF Download (810kB)

Abstract

Prosthetic is an artificially made as a substitute or replacement for missing part of a body. The function of the missing body part can be replaced by using the prosthesis and it can help disabled people do their activities easily. A myoelectric control system is a fundamental part of modern prostheses. The electromyogram (EMG) signals are used in this system to control the prosthesis movements by taking it from a person's muscle. The problem for the myoelectric control system is when it did not receive the same attention to control fingers due to more dexterous of individual and combined finger control in a signal. Thus, a method to solve the problem of the myoelectric control system by using time-frequency distribution (TFD) is proposed in this paper. The EMG features of the individual and combine finger movements for ten subjects and ten different movements is extracted using TFD, ie. spectrogram. Three machine learning algorithms which are Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Ensemble Classifier are then used to classify the individuals and combine finger movement based on the extracted EMG feature from the spectrogram. The performance of the proposed method is then verified using classification accuracy. Based on the results, the overall accuracy for the classification is 90% (SVM), 100% (KNN) and 100% (Ensemble Classifier), respectively. The finding of the study could serve as an insight to improve the conventional prosthetic control strategies.

Item Type:	Article
Uncontrolled Keywords:	Electromyography, Feature extraction, Timefrequency distribution, Spectrogram, Classification, Machine learning
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	10 Mar 2021 12:45
Last Modified:	10 Mar 2021 12:45
URI:	http://eprints.utem.edu.my/id/eprint/24884
Statistic Details:	View Download Statistic

Actions (login required)

View Item