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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/30535/1/Ibrahimy.pdf
http://irep.iium.edu.my/30535/
http://www.measurement.sk/
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spelling my.iium.irep.305352020-10-26T00:37:34Z 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/
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
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/30535/1/Ibrahimy.pdf
http://irep.iium.edu.my/30535/
http://www.measurement.sk/
_version_ 1683230273945206784
score 13.211869

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