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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| Format: | Thesis |
| Language: | English English English |
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2015
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| Online Access: | http://eprints.uthm.edu.my/1395/ 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_ | 1848887449232605184 |
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| author | Baharom, Nor Azurah |
| author_facet | Baharom, Nor Azurah |
| author_sort | Baharom, Nor Azurah |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:54:34Z |
| format | Thesis |
| id | uthm-1395 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T19:54:34Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-13952021-10-03T06:39:26Z http://eprints.uthm.edu.my/1395/ Analysis of electrooculography (EOG) for controlling wheelchair motion 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 NonPeerReviewed text en http://eprints.uthm.edu.my/1395/2/NOR%20AZURAH%20BAHAROM%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1395/1/24p%20NOR%20AZURAH%20BAHAROM.pdf text en http://eprints.uthm.edu.my/1395/3/NOR%20AZURAH%20BAHAROM%20WATERMARK.pdf Baharom, Nor Azurah (2015) Analysis of electrooculography (EOG) for controlling wheelchair motion. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | TJ163.13-163.25 Power resources Baharom, Nor Azurah Analysis of electrooculography (EOG) for controlling wheelchair motion |
| title | 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_short | Analysis of electrooculography (EOG) for controlling wheelchair motion |
| title_sort | analysis of electrooculography (eog) for controlling wheelchair motion |
| topic | TJ163.13-163.25 Power resources |
| url | http://eprints.uthm.edu.my/1395/ 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 |