Analysis of electromyograph (EMG) for controlling wheelchair motion

Nowadays, due to the aging of the current population, the need of wheelchair has significantly increased not only to the disabled community but also to the old citizen. However, the use of manual wheelchair is restricted to the user with leg impairment only. Yet, for the user with severe impairment...

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Main Author:	Meor Zainol, Wan Saidatulakma
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:	http://eprints.uthm.edu.my/1502/2/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1502/1/24p%20WAN%20SAIDATULAKMA%20MEOR%20ZAINOL.pdf http://eprints.uthm.edu.my/1502/3/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20WATERMARK.pdf
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id	my-uthm-ep.1502
record_format	uketd_dc
spelling	my-uthm-ep.15022021-10-03T07:45:46Z Analysis of electromyograph (EMG) for controlling wheelchair motion 2015-06 Meor Zainol, Wan Saidatulakma TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Nowadays, due to the aging of the current population, the need of wheelchair has significantly increased not only to the disabled community but also to the old citizen. However, the use of manual wheelchair is restricted to the user with leg impairment only. Yet, for the user with severe impairment (arm and leg) the smart wheelchair that controlled by the alternative user interface is necessary. Since the target of this project is for the user who has high level Spinal Cord Injury (SCI), the EMG signal from neck has been chose to trigger the wheelchair. The EMG signals are obtained using disposable electrode from neck muscles which are Sternocleidomastoid and Trapezius muscles, with different direction. For the acquisition of SEMG signal MyDAQ is used. The features are extracted from the conditioned EMG signal such as: root mean square value and mean absolute value. To classify such kind of signal, a classifier able to withstand uncertainties in data is required. So, in this work a fuzzy classifier is designed and implemented using LabVIEW software. The classifier system is tested using 10 subjects. The simulation results have authenticated the capability of implemented system. 2015-06 Thesis http://eprints.uthm.edu.my/1502/ http://eprints.uthm.edu.my/1502/2/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1502/1/24p%20WAN%20SAIDATULAKMA%20MEOR%20ZAINOL.pdf text en public http://eprints.uthm.edu.my/1502/3/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Meor Zainol, Wan Saidatulakma Analysis of electromyograph (EMG) for controlling wheelchair motion
description	Nowadays, due to the aging of the current population, the need of wheelchair has significantly increased not only to the disabled community but also to the old citizen. However, the use of manual wheelchair is restricted to the user with leg impairment only. Yet, for the user with severe impairment (arm and leg) the smart wheelchair that controlled by the alternative user interface is necessary. Since the target of this project is for the user who has high level Spinal Cord Injury (SCI), the EMG signal from neck has been chose to trigger the wheelchair. The EMG signals are obtained using disposable electrode from neck muscles which are Sternocleidomastoid and Trapezius muscles, with different direction. For the acquisition of SEMG signal MyDAQ is used. The features are extracted from the conditioned EMG signal such as: root mean square value and mean absolute value. To classify such kind of signal, a classifier able to withstand uncertainties in data is required. So, in this work a fuzzy classifier is designed and implemented using LabVIEW software. The classifier system is tested using 10 subjects. The simulation results have authenticated the capability of implemented system.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Meor Zainol, Wan Saidatulakma
author_facet	Meor Zainol, Wan Saidatulakma
author_sort	Meor Zainol, Wan Saidatulakma
title	Analysis of electromyograph (EMG) for controlling wheelchair motion
title_short	Analysis of electromyograph (EMG) for controlling wheelchair motion
title_full	Analysis of electromyograph (EMG) for controlling wheelchair motion
title_fullStr	Analysis of electromyograph (EMG) for controlling wheelchair motion
title_full_unstemmed	Analysis of electromyograph (EMG) for controlling wheelchair motion
title_sort	analysis of electromyograph (emg) for controlling wheelchair motion
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Electrical and Electronic Engineering
publishDate	2015
url	http://eprints.uthm.edu.my/1502/2/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1502/1/24p%20WAN%20SAIDATULAKMA%20MEOR%20ZAINOL.pdf http://eprints.uthm.edu.my/1502/3/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20WATERMARK.pdf
_version_	1747830804487077888

Analysis of electromyograph (EMG) for controlling wheelchair motion

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