Design and performance analysis of artificial neural network for hand motion detection from EMG signals

Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This article represents the classification of Electromygraphy (EMG) signal for the detection of different predefin...

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Main Authors:	Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran
Format:	Article
Language:	English
Published:	IDOSI Publication 2013
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/30592/ http://irep.iium.edu.my/30592/ http://irep.iium.edu.my/30592/1/WASJ_2013.pdf

id	iium-30592
recordtype	eprints
spelling	iium-305922013-07-11T03:51:38Z http://irep.iium.edu.my/30592/ Design and performance analysis of artificial neural network for hand motion detection from EMG signals Ibrahimy, Muhammad Ibn Ahsan, Md. Rezwanul Khalifa, Othman Omran T Technology (General) Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This article represents the classification of Electromygraphy (EMG) signal for the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). The neural network is of backpropagation type, trained by Levenberg-Marquardt training algorithm. Before the classification process, the EMG signals have been pre-processed for extracting some features from them. The conventional and most effective time and timefrequency based features are extracted and normalized. The neural network has been trained with the normalized feature set with supervised learning method. The obtained results show that the designed network can successfully classify the hand motions from the EMG signals with the success rate of 88.4%. The performance of the designed network has also been compared to similar research work, whereby it certainly shows the outperformance. IDOSI Publication 2013 Article PeerReviewed application/pdf en http://irep.iium.edu.my/30592/1/WASJ_2013.pdf Ibrahimy, Muhammad Ibn and Ahsan, Md. Rezwanul and Khalifa, Othman Omran (2013) Design and performance analysis of artificial neural network for hand motion detection from EMG signals. World Applied Sciences Journal , 23 (6). pp. 751-758. ISSN 1818-4952 http://www.idosi.org/wasj/wasj.htm
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Ibrahimy, Muhammad Ibn Ahsan, Md. Rezwanul Khalifa, Othman Omran Design and performance analysis of artificial neural network for hand motion detection from EMG signals
description	Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This article represents the classification of Electromygraphy (EMG) signal for the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). The neural network is of backpropagation type, trained by Levenberg-Marquardt training algorithm. Before the classification process, the EMG signals have been pre-processed for extracting some features from them. The conventional and most effective time and timefrequency based features are extracted and normalized. The neural network has been trained with the normalized feature set with supervised learning method. The obtained results show that the designed network can successfully classify the hand motions from the EMG signals with the success rate of 88.4%. The performance of the designed network has also been compared to similar research work, whereby it certainly shows the outperformance.
format	Article
author	Ibrahimy, Muhammad Ibn Ahsan, Md. Rezwanul Khalifa, Othman Omran
author_facet	Ibrahimy, Muhammad Ibn Ahsan, Md. Rezwanul Khalifa, Othman Omran
author_sort	Ibrahimy, Muhammad Ibn
title	Design and performance analysis of artificial neural network for hand motion detection from EMG signals
title_short	Design and performance analysis of artificial neural network for hand motion detection from EMG signals
title_full	Design and performance analysis of artificial neural network for hand motion detection from EMG signals
title_fullStr	Design and performance analysis of artificial neural network for hand motion detection from EMG signals
title_full_unstemmed	Design and performance analysis of artificial neural network for hand motion detection from EMG signals
title_sort	design and performance analysis of artificial neural network for hand motion detection from emg signals
publisher	IDOSI Publication
publishDate	2013
url	http://irep.iium.edu.my/30592/ http://irep.iium.edu.my/30592/ http://irep.iium.edu.my/30592/1/WASJ_2013.pdf
first_indexed	2023-09-18T20:44:47Z
last_indexed	2023-09-18T20:44:47Z
_version_	1777409602173796352

Design and performance analysis of artificial neural network for hand motion detection from EMG signals

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