Electromyograph (EMG) signal analysis to predict muscle fatigue during driving
Electromyography (EMG) signal obtained from muscles need advance methods for detection, processing and classification. The purpose of this paper is to analyze muscle fatigue from EMG signals. At beginning, 15 subjects will an-swer a set of questionnaires. The score of the questionnaires will be calc...
| Main Authors: | , , , , , |
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| Format: | Book Chapter |
| Language: | English English English |
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Springer Singapore
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/1/35.%20Electromyograph%20%28EMG%29%20signal%20analysis%20to%20predict%20muscle%20fatigue%20during%20driving.pdf http://umpir.ump.edu.my/id/eprint/22861/13/52.%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf http://umpir.ump.edu.my/id/eprint/22861/14/52.1%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf |
| _version_ | 1848821668169908224 |
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| author | Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari |
| author_facet | Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari |
| author_sort | Muhammad Amzar Syazani, Mohd Azli |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Electromyography (EMG) signal obtained from muscles need advance methods for detection, processing and classification. The purpose of this paper is to analyze muscle fatigue from EMG signals. At beginning, 15 subjects will an-swer a set of questionnaires. The score of the questionnaires will be calculated and the score will determine if the driver is fatigue or mild fatigue or fatigue based on their driving habit. Next, EMG signals will be collected by placing two surface electrodes on the Brachioradialis muscle located at the forearm while driving Need For Speed (NFS) game. A simulation set of steering and pedals will be controlled during the driving game. The drivers drive for two hours and the EMG signal will be collected during they are driving. The output signals will be pre-process to remove any noise in the signal. After that, the data is normalized between value 0 to 1 and the signal is analyzed using frequency analysis and time analysis. Mean and variance will be calculated for time domain analysis and graph of mean vs variance is plotted. In frequency domain analysis, Power Spec-tral Density (PSD) is extracted from the peak frequency of PSD in each signal is obtained. All result will be divided into three classes: non-fatigue, mild-fatigue and fatigue. Based on result obtained in time domain, average normalized mean (non-fatigue: 0.5004), (mild-fatigue: 0.497) and (fatigue: 0.494). While, for fre-quency domain analysis, average peak frequency (non-fatigue: 13.379Hz), (mild-fatigue: 11.969Hz) and (fatigue: 12.782Hz). |
| first_indexed | 2025-11-15T02:29:00Z |
| format | Book Chapter |
| id | ump-22861 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T02:29:00Z |
| publishDate | 2018 |
| publisher | Springer Singapore |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-228612019-05-30T07:04:22Z http://umpir.ump.edu.my/id/eprint/22861/ Electromyograph (EMG) signal analysis to predict muscle fatigue during driving Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari TK Electrical engineering. Electronics Nuclear engineering Electromyography (EMG) signal obtained from muscles need advance methods for detection, processing and classification. The purpose of this paper is to analyze muscle fatigue from EMG signals. At beginning, 15 subjects will an-swer a set of questionnaires. The score of the questionnaires will be calculated and the score will determine if the driver is fatigue or mild fatigue or fatigue based on their driving habit. Next, EMG signals will be collected by placing two surface electrodes on the Brachioradialis muscle located at the forearm while driving Need For Speed (NFS) game. A simulation set of steering and pedals will be controlled during the driving game. The drivers drive for two hours and the EMG signal will be collected during they are driving. The output signals will be pre-process to remove any noise in the signal. After that, the data is normalized between value 0 to 1 and the signal is analyzed using frequency analysis and time analysis. Mean and variance will be calculated for time domain analysis and graph of mean vs variance is plotted. In frequency domain analysis, Power Spec-tral Density (PSD) is extracted from the peak frequency of PSD in each signal is obtained. All result will be divided into three classes: non-fatigue, mild-fatigue and fatigue. Based on result obtained in time domain, average normalized mean (non-fatigue: 0.5004), (mild-fatigue: 0.497) and (fatigue: 0.494). While, for fre-quency domain analysis, average peak frequency (non-fatigue: 13.379Hz), (mild-fatigue: 11.969Hz) and (fatigue: 12.782Hz). Springer Singapore 2018-08 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22861/1/35.%20Electromyograph%20%28EMG%29%20signal%20analysis%20to%20predict%20muscle%20fatigue%20during%20driving.pdf pdf en http://umpir.ump.edu.my/id/eprint/22861/13/52.%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf pdf en http://umpir.ump.edu.my/id/eprint/22861/14/52.1%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf Muhammad Amzar Syazani, Mohd Azli and Mahfuzah, Mustafa and Rafiuddin, Abdubrani and Amran, Abdul Hadi and S. N., Aqida and Zarith Liyana, Zahari (2018) Electromyograph (EMG) signal analysis to predict muscle fatigue during driving. In: Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018. Lecture Notes in Electrical Engineering . Springer Singapore, Singapore, pp. 405-420. ISBN 978-981-13-3708-6 https://link.springer.com/chapter/10.1007/978-981-13-3708-6_35 DOI: https://doi.org/10.1007/978-981-13-3708-6_35 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari Electromyograph (EMG) signal analysis to predict muscle fatigue during driving |
| title | Electromyograph (EMG) signal analysis to predict muscle fatigue during driving |
| title_full | Electromyograph (EMG) signal analysis to predict muscle fatigue during driving |
| title_fullStr | Electromyograph (EMG) signal analysis to predict muscle fatigue during driving |
| title_full_unstemmed | Electromyograph (EMG) signal analysis to predict muscle fatigue during driving |
| title_short | Electromyograph (EMG) signal analysis to predict muscle fatigue during driving |
| title_sort | electromyograph (emg) signal analysis to predict muscle fatigue during driving |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/1/35.%20Electromyograph%20%28EMG%29%20signal%20analysis%20to%20predict%20muscle%20fatigue%20during%20driving.pdf http://umpir.ump.edu.my/id/eprint/22861/13/52.%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf http://umpir.ump.edu.my/id/eprint/22861/14/52.1%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf |