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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Finger Movement Discrimination of EMG Signals Towards Improved Prosthetic Control using TFD
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110928
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020.
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.
Keywords: Electromyography; feature extraction; time-frequency distribution; spectrogram; classification; machine learning
E. F. Shair, N.A. Jamaluddin and A.R. Abdullah, “Finger Movement Discrimination of EMG Signals Towards Improved Prosthetic Control using TFD” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110928
@article{Shair2020,
title = {Finger Movement Discrimination of EMG Signals Towards Improved Prosthetic Control using TFD},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110928},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110928},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {E. F. Shair and N.A. Jamaluddin and A.R. Abdullah}
}
