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

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 prosthe...

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Main Authors: Shair, Ezreen Farina, Jamaluddin, Nur Asyiqin, Abdullah, Abdul Rahim
Format: Article
Language:English
Published: Science and Information Organization 2020
Online Access:http://eprints.utem.edu.my/id/eprint/24884/2/IJACSA%202020.PDF
http://eprints.utem.edu.my/id/eprint/24884/
https://thesai.org/Downloads/Volume11No9/Paper_28-Finger_Movement_Discrimination_of_EMG_Signals.pdf
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spelling my.utem.eprints.248842021-03-10T12:45:52Z http://eprints.utem.edu.my/id/eprint/24884/ Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD Shair, Ezreen Farina Jamaluddin, Nur Asyiqin Abdullah, Abdul Rahim 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. Science and Information Organization 2020-10 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24884/2/IJACSA%202020.PDF 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 https://thesai.org/Downloads/Volume11No9/Paper_28-Finger_Movement_Discrimination_of_EMG_Signals.pdf 10.14569/IJACSA.2020.0110928
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description 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.
format Article
author Shair, Ezreen Farina
Jamaluddin, Nur Asyiqin
Abdullah, Abdul Rahim
spellingShingle Shair, Ezreen Farina
Jamaluddin, Nur Asyiqin
Abdullah, Abdul Rahim
Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD
author_facet Shair, Ezreen Farina
Jamaluddin, Nur Asyiqin
Abdullah, Abdul Rahim
author_sort Shair, Ezreen Farina
title Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD
title_short Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD
title_full Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD
title_fullStr Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD
title_full_unstemmed Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD
title_sort finger movement discrimination of emg signals towards improved prosthetic control using tfd
publisher Science and Information Organization
publishDate 2020
url http://eprints.utem.edu.my/id/eprint/24884/2/IJACSA%202020.PDF
http://eprints.utem.edu.my/id/eprint/24884/
https://thesai.org/Downloads/Volume11No9/Paper_28-Finger_Movement_Discrimination_of_EMG_Signals.pdf
_version_ 1695535229709582336
score 13.211869

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