Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)

Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assisti...

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Main Authors:	Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/5887/ http://irep.iium.edu.my/5887/ http://irep.iium.edu.my/5887/ http://irep.iium.edu.my/5887/1/05937135.pdf

id	iium-5887
recordtype	eprints
spelling	iium-58872011-11-22T07:11:51Z http://irep.iium.edu.my/5887/ Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN) Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran T Technology (General) Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based reliable and efficient hand gesture identification can help to develop good human computer interface which in turn will increase the quality of life of the disabled or aged people. The purpose of this paper is to describe the process of detecting different predefined hand gestures (left, right, up and down) using artificial neural network (ANN). ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. The EMG pattern signatures are extracted from the signals for each movement and then ANN utilized to classify the EMG signals based on features. A back-propagation (BP) network with Levenberg-Marquardt training algorithm has been used for the detection of gesture. The conventional and most effective time and time-frequency based features (namely MAV, RMS, VAR, SD, ZC, SSC and WL) have been chosen to train the neural network. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/5887/1/05937135.pdf Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2011) Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN). In: 4th International Conference on Mechatronics (ICOM'11), 17-19 May 2011, Kuala Lumpur. http://www.iium.edu.my/ICOM/2011/ doi:10.1109/ICOM.2011.5937135
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 Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
description	Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based reliable and efficient hand gesture identification can help to develop good human computer interface which in turn will increase the quality of life of the disabled or aged people. The purpose of this paper is to describe the process of detecting different predefined hand gestures (left, right, up and down) using artificial neural network (ANN). ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. The EMG pattern signatures are extracted from the signals for each movement and then ANN utilized to classify the EMG signals based on features. A back-propagation (BP) network with Levenberg-Marquardt training algorithm has been used for the detection of gesture. The conventional and most effective time and time-frequency based features (namely MAV, RMS, VAR, SD, ZC, SSC and WL) have been chosen to train the neural network.
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	Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
title_short	Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
title_full	Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
title_fullStr	Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
title_full_unstemmed	Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
title_sort	electromygraphy (emg) signal based hand gesture recognition using artificial neural network (ann)
publishDate	2011
url	http://irep.iium.edu.my/5887/ http://irep.iium.edu.my/5887/ http://irep.iium.edu.my/5887/ http://irep.iium.edu.my/5887/1/05937135.pdf
first_indexed	2023-09-18T20:14:41Z
last_indexed	2023-09-18T20:14:41Z
_version_	1777407708827222016

Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)

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