EMG signal classification for human computer interaction: a review

With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologie...

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Main Authors:	Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran
Format:	Article
Language:	English
Published:	EuroJournals Publishing, Inc. 2009
Subjects:	QA76 Computer software
Online Access:	http://irep.iium.edu.my/1473/ http://irep.iium.edu.my/1473/ http://irep.iium.edu.my/1473/1/EMG_Signal_Classification_for_Human_Computer_Interaction-A_Review.pdf

id	iium-1473
recordtype	eprints
spelling	iium-14732011-11-24T05:59:03Z http://irep.iium.edu.my/1473/ EMG signal classification for human computer interaction: a review Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran QA76 Computer software With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologies. In recent years, there has been a tremendous interest in introducing intuitive interfaces that can recognize the user's body movements and translate them into machine commands. For the neural linkage with computers, various biomedical signals (biosignals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various direction including the development of continuous EMG signal classification for graphical controller, that enables the physically disabled to use word processing programs and other personal computer software, internet. It also enable manipulation of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipments etc. Most of the developmental area is based on pattern recognition using neural networks. The EMG controller can be programmed to perform gesture recognition based on signal analysis of groups of muscles action potential. This review paper is to discuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into computer command. EuroJournals Publishing, Inc. 2009-07 Article PeerReviewed application/pdf en http://irep.iium.edu.my/1473/1/EMG_Signal_Classification_for_Human_Computer_Interaction-A_Review.pdf Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2009) EMG signal classification for human computer interaction: a review. European Journal of Scientific Research, 33 (3). pp. 480-501. ISSN 1450-216X, 1450-202X http://www.eurojournals.com/ejsr_33_3_10.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran EMG signal classification for human computer interaction: a review
description	With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologies. In recent years, there has been a tremendous interest in introducing intuitive interfaces that can recognize the user's body movements and translate them into machine commands. For the neural linkage with computers, various biomedical signals (biosignals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various direction including the development of continuous EMG signal classification for graphical controller, that enables the physically disabled to use word processing programs and other personal computer software, internet. It also enable manipulation of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipments etc. Most of the developmental area is based on pattern recognition using neural networks. The EMG controller can be programmed to perform gesture recognition based on signal analysis of groups of muscles action potential. This review paper is to discuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into computer command.
format	Article
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	EMG signal classification for human computer interaction: a review
title_short	EMG signal classification for human computer interaction: a review
title_full	EMG signal classification for human computer interaction: a review
title_fullStr	EMG signal classification for human computer interaction: a review
title_full_unstemmed	EMG signal classification for human computer interaction: a review
title_sort	emg signal classification for human computer interaction: a review
publisher	EuroJournals Publishing, Inc.
publishDate	2009
url	http://irep.iium.edu.my/1473/ http://irep.iium.edu.my/1473/ http://irep.iium.edu.my/1473/1/EMG_Signal_Classification_for_Human_Computer_Interaction-A_Review.pdf
first_indexed	2023-09-18T20:08:50Z
last_indexed	2023-09-18T20:08:50Z
_version_	1777407340513853440

EMG signal classification for human computer interaction: a review

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