Analysis of electrooculography (EOG) for controlling wheelchair motion

Rehabilitation devices are increasingly being used to improve the quality of the life of differentially abled people. Human Machine Interface (HMI) has been studied extensively to control electromechanical rehabilitation aids using bio signals such as EEG, EOG and EMG. Among the various bio signals,...

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Main Author:	Baharom, Nor Azurah
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TJ163.13-163.25 Power resources
Online Access:	http://eprints.uthm.edu.my/1395/2/NOR%20AZURAH%20BAHAROM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1395/1/24p%20NOR%20AZURAH%20BAHAROM.pdf http://eprints.uthm.edu.my/1395/3/NOR%20AZURAH%20BAHAROM%20WATERMARK.pdf
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spelling	my-uthm-ep.13952021-10-03T06:39:26Z Analysis of electrooculography (EOG) for controlling wheelchair motion 2015-06 Baharom, Nor Azurah TJ163.13-163.25 Power resources Rehabilitation devices are increasingly being used to improve the quality of the life of differentially abled people. Human Machine Interface (HMI) has been studied extensively to control electromechanical rehabilitation aids using bio signals such as EEG, EOG and EMG. Among the various bio signals, EOG signals have been studied in depth due to the occurrence of a definite signal pattern. Persons suffering from extremely limited peripheral mobility like Spinal Cord Injury (SCI) usually have the ability to coordinate eye movements. This project focuses on the analysis of EOG signals for controlling wheelchair motion. The EOG signal is obtained from the eye muscle by using disposable electrodes. For the acquisition of EOG raw signal, NI MyDAQ is used. The features are extracted from the conditioned EOG signal such as root mean square value and average rectifier value. The signals are usually non-repeatable and contradictory in nature. Therefore, to classify such kind of signal, a classifier able to withstand uncertainties in data is required. Fuzzy theory is well known for its capability to deal with imprecise environment. 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/1395/ http://eprints.uthm.edu.my/1395/2/NOR%20AZURAH%20BAHAROM%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1395/1/24p%20NOR%20AZURAH%20BAHAROM.pdf text en public http://eprints.uthm.edu.my/1395/3/NOR%20AZURAH%20BAHAROM%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	TJ163.13-163.25 Power resources
spellingShingle	TJ163.13-163.25 Power resources Baharom, Nor Azurah Analysis of electrooculography (EOG) for controlling wheelchair motion
description	Rehabilitation devices are increasingly being used to improve the quality of the life of differentially abled people. Human Machine Interface (HMI) has been studied extensively to control electromechanical rehabilitation aids using bio signals such as EEG, EOG and EMG. Among the various bio signals, EOG signals have been studied in depth due to the occurrence of a definite signal pattern. Persons suffering from extremely limited peripheral mobility like Spinal Cord Injury (SCI) usually have the ability to coordinate eye movements. This project focuses on the analysis of EOG signals for controlling wheelchair motion. The EOG signal is obtained from the eye muscle by using disposable electrodes. For the acquisition of EOG raw signal, NI MyDAQ is used. The features are extracted from the conditioned EOG signal such as root mean square value and average rectifier value. The signals are usually non-repeatable and contradictory in nature. Therefore, to classify such kind of signal, a classifier able to withstand uncertainties in data is required. Fuzzy theory is well known for its capability to deal with imprecise environment. 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	Baharom, Nor Azurah
author_facet	Baharom, Nor Azurah
author_sort	Baharom, Nor Azurah
title	Analysis of electrooculography (EOG) for controlling wheelchair motion
title_short	Analysis of electrooculography (EOG) for controlling wheelchair motion
title_full	Analysis of electrooculography (EOG) for controlling wheelchair motion
title_fullStr	Analysis of electrooculography (EOG) for controlling wheelchair motion
title_full_unstemmed	Analysis of electrooculography (EOG) for controlling wheelchair motion
title_sort	analysis of electrooculography (eog) 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/1395/2/NOR%20AZURAH%20BAHAROM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1395/1/24p%20NOR%20AZURAH%20BAHAROM.pdf http://eprints.uthm.edu.my/1395/3/NOR%20AZURAH%20BAHAROM%20WATERMARK.pdf
_version_	1747830780519776256

Analysis of electrooculography (EOG) for controlling wheelchair motion

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