Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions

This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand moti...

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Main Authors:	Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran
Format:	Article
Language:	English
Published:	Versita Open, Versita Ltd. London, Great Britain 2013
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/30535/ http://irep.iium.edu.my/30535/ http://irep.iium.edu.my/30535/1/Ibrahimy.pdf

id	iium-30535
recordtype	eprints
spelling	iium-305352013-10-04T10:46:18Z http://irep.iium.edu.my/30535/ Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions Ibrahimy, Muhammad Ibn Ahsan, Md. Rezwanul Khalifa, Othman Omran T Technology (General) This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to discriminate EMG signals. Versita Open, Versita Ltd. London, Great Britain 2013-06-10 Article PeerReviewed application/pdf en http://irep.iium.edu.my/30535/1/Ibrahimy.pdf Ibrahimy, Muhammad Ibn and Ahsan, Md. Rezwanul and Khalifa, Othman Omran (2013) Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions. Measurement Science Review, 13 (3). pp. 142-151. ISSN 1335 - 8871 http://www.measurement.sk/
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 optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
description	This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to discriminate EMG signals.
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 optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
title_short	Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
title_full	Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
title_fullStr	Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
title_full_unstemmed	Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
title_sort	design and optimization of levenberg-marquardt based neural network classifier for emg signals to identify hand motions
publisher	Versita Open, Versita Ltd. London, Great Britain
publishDate	2013
url	http://irep.iium.edu.my/30535/ http://irep.iium.edu.my/30535/ http://irep.iium.edu.my/30535/1/Ibrahimy.pdf
first_indexed	2023-09-18T20:44:42Z
last_indexed	2023-09-18T20:44:42Z
_version_	1777409597606199296

Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions

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