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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| Format: | Thesis |
| Language: | English English English |
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2015
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| Online Access: | http://eprints.uthm.edu.my/1502/ 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_ | 1848887477575614464 |
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| author | Meor Zainol, Wan Saidatulakma |
| author_facet | Meor Zainol, Wan Saidatulakma |
| author_sort | Meor Zainol, Wan Saidatulakma |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:55:01Z |
| format | Thesis |
| id | uthm-1502 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T19:55:01Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-15022021-10-03T07:45:46Z http://eprints.uthm.edu.my/1502/ Analysis of electromyograph (EMG) for controlling wheelchair motion 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 NonPeerReviewed text en http://eprints.uthm.edu.my/1502/2/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1502/1/24p%20WAN%20SAIDATULAKMA%20MEOR%20ZAINOL.pdf text en http://eprints.uthm.edu.my/1502/3/WAN%20SAIDATULAKMA%20MEOR%20ZAINOL%20WATERMARK.pdf Meor Zainol, Wan Saidatulakma (2015) Analysis of electromyograph (EMG) for controlling wheelchair motion. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Meor Zainol, Wan Saidatulakma Analysis of electromyograph (EMG) for controlling wheelchair motion |
| title | 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_short | Analysis of electromyograph (EMG) for controlling wheelchair motion |
| title_sort | analysis of electromyograph (emg) for controlling wheelchair motion |
| topic | TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television |
| url | http://eprints.uthm.edu.my/1502/ 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 |