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:
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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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
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