Optimization of neural network for efficient EMG signal classification

This paper illustrates the classification of Electromyography (EMG) signals through designing and optimization of artificial neural network. The EMG signals obtained for different kinds of hand movements, which are processed to extract the features. Extracted time and timefrequency based fea...

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Main Authors:	Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran
Format:	Conference or Workshop Item
Language:	English
Published:	2012
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/1/06215165.pdf

id	iium-25207
recordtype	eprints
spelling	iium-252072012-09-18T06:08:58Z http://irep.iium.edu.my/25207/ Optimization of neural network for efficient EMG signal classification Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran T Technology (General) This paper illustrates the classification of Electromyography (EMG) signals through designing and optimization of artificial neural network. The EMG signals obtained for different kinds of hand movements, which are processed to extract the features. Extracted time and timefrequency based feature sets are used to train the neural network. A back-propagation neural network with LevenbergMarquardt training algorithm has been utilized for the classification. The results show that the designed network is optimized for 10 hidden neurons and able to efficiently classify single channel EMG signals with an average rate of 88.4%. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/25207/1/06215165.pdf Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2012) Optimization of neural network for efficient EMG signal classification. In: 2012 8th International Symposium on Mechatronics and its Applications (ISMA), 10 - 12 April 2012, American University of Sharjah. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6215165&contentType=Conference+Publications
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) Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran Optimization of neural network for efficient EMG signal classification
description	This paper illustrates the classification of Electromyography (EMG) signals through designing and optimization of artificial neural network. The EMG signals obtained for different kinds of hand movements, which are processed to extract the features. Extracted time and timefrequency based feature sets are used to train the neural network. A back-propagation neural network with LevenbergMarquardt training algorithm has been utilized for the classification. The results show that the designed network is optimized for 10 hidden neurons and able to efficiently classify single channel EMG signals with an average rate of 88.4%.
format	Conference or Workshop Item
author	Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran
author_facet	Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran
author_sort	Ahsan, Md. Rezwanul
title	Optimization of neural network for efficient EMG signal classification
title_short	Optimization of neural network for efficient EMG signal classification
title_full	Optimization of neural network for efficient EMG signal classification
title_fullStr	Optimization of neural network for efficient EMG signal classification
title_full_unstemmed	Optimization of neural network for efficient EMG signal classification
title_sort	optimization of neural network for efficient emg signal classification
publishDate	2012
url	http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/1/06215165.pdf
first_indexed	2023-09-18T20:37:39Z
last_indexed	2023-09-18T20:37:39Z
_version_	1777409153812135936

Optimization of neural network for efficient EMG signal classification

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