EMG signal classification techniques for the development of human computer interaction system

With the rapid development of information technology, the quantity of information sharing by human is increasing accordingly. Since early eighty, numbers of researchers are engaged to develop alternative interfaces for elder and disabled people. More recently, the advancement of technology attractin...

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Main Authors:	Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran
Format:	Book Chapter
Language:	English
Published:	IIUM Press 2011
Subjects:	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Online Access:	http://irep.iium.edu.my/21658/ http://irep.iium.edu.my/21658/ http://irep.iium.edu.my/21658/1/Chapter_25.pdf

id	iium-21658
recordtype	eprints
spelling	iium-216582012-09-05T08:14:01Z http://irep.iium.edu.my/21658/ EMG signal classification techniques for the development of human computer interaction system Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices With the rapid development of information technology, the quantity of information sharing by human is increasing accordingly. Since early eighty, numbers of researchers are engaged to develop alternative interfaces for elder and disabled people. More recently, the advancement of technology attracting the researcher attention with respect to extracting user's intention data from neural signals. These types of signals can provide information related to body or limb motion faster than other means. On the basis of central nervous system and peripheral nervous system, various types of techniques have been developed to execute user's intention. The brain signals from central nervous system have the potential for revealing human thoughts. The EEG is a noninvasive monitoring method of recording and analyzing brain activities on the scalp [1]. However, the acquired signals not only represent the massed activities of many cortical neurons but also provide a low spatial resolution and a low signal-to-noise ratio (SNR). Afterwards, there are many technical difficulties need to be solved, and extensive training is usually required for interface methods based on brain activities [2]. At the level of the peripheral nervous system, the signals due to body motion can be detected and acquired by an ENG [3] and an EMG [4]. However, ENG signal based interfaces have limitations with respect to the SNR, dimensions, and drifts. Due to the damage in neural tissue and differential motion of the electrode within the fascicle causes a reduction in the SNR and a gradual drift in the recorded nerve fiber population. On the other side, EMG signal can be measured more conveniently and safely than other bio-signals. EMG signal can be easily generated by voluntary muscle movement and it has better properties of SNR and high amplitude. Hence, an EMG-based HCl is most practical with current technologies. IIUM Press 2011 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/21658/1/Chapter_25.pdf Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2011) EMG signal classification techniques for the development of human computer interaction system. In: Human Behaviour Recognition, Identification and Computer Interaction. IIUM Press, Kuala Lumpur, pp. 224-243. ISBN 978-967-418-156-7 http://rms.research.iium.edu.my/bookstore/default.aspx
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
spellingShingle	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran EMG signal classification techniques for the development of human computer interaction system
description	With the rapid development of information technology, the quantity of information sharing by human is increasing accordingly. Since early eighty, numbers of researchers are engaged to develop alternative interfaces for elder and disabled people. More recently, the advancement of technology attracting the researcher attention with respect to extracting user's intention data from neural signals. These types of signals can provide information related to body or limb motion faster than other means. On the basis of central nervous system and peripheral nervous system, various types of techniques have been developed to execute user's intention. The brain signals from central nervous system have the potential for revealing human thoughts. The EEG is a noninvasive monitoring method of recording and analyzing brain activities on the scalp [1]. However, the acquired signals not only represent the massed activities of many cortical neurons but also provide a low spatial resolution and a low signal-to-noise ratio (SNR). Afterwards, there are many technical difficulties need to be solved, and extensive training is usually required for interface methods based on brain activities [2]. At the level of the peripheral nervous system, the signals due to body motion can be detected and acquired by an ENG [3] and an EMG [4]. However, ENG signal based interfaces have limitations with respect to the SNR, dimensions, and drifts. Due to the damage in neural tissue and differential motion of the electrode within the fascicle causes a reduction in the SNR and a gradual drift in the recorded nerve fiber population. On the other side, EMG signal can be measured more conveniently and safely than other bio-signals. EMG signal can be easily generated by voluntary muscle movement and it has better properties of SNR and high amplitude. Hence, an EMG-based HCl is most practical with current technologies.
format	Book Chapter
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 techniques for the development of human computer interaction system
title_short	EMG signal classification techniques for the development of human computer interaction system
title_full	EMG signal classification techniques for the development of human computer interaction system
title_fullStr	EMG signal classification techniques for the development of human computer interaction system
title_full_unstemmed	EMG signal classification techniques for the development of human computer interaction system
title_sort	emg signal classification techniques for the development of human computer interaction system
publisher	IIUM Press
publishDate	2011
url	http://irep.iium.edu.my/21658/ http://irep.iium.edu.my/21658/ http://irep.iium.edu.my/21658/1/Chapter_25.pdf
first_indexed	2023-09-18T20:32:59Z
last_indexed	2023-09-18T20:32:59Z
_version_	1777408860322004992

EMG signal classification techniques for the development of human computer interaction system

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