Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder
Electromyography signal analysis and classification method for Health Screening Program for Social Security Organisation (SOCSO) Malaysia is the first time applied using time-frequency distribution (TFD). This paper presents the classification of EMG signals for health screening task for musculoskel...
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| Format: | Article |
| Language: | en |
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2019
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| Online Access: | http://eprints.utem.edu.my/id/eprint/24152/2/3_2019-CLASSIFICATIONOFEMGSIGNALFORHEALTHSCREENINGTASKFORMUSCULOSKELETALDISORDER.PDF http://eprints.utem.edu.my/id/eprint/24152/ https://www.sciencepubco.com/index.php/ijet/article/view/25980/13335 |
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| author | Zawawi, Tengku Nor Shuhada Tengku Mohd Saad, Norhashimah Too Jing, Wei Shair, Ezreen Farina Abdullah, Abdul Rahim Sudirman, Rubita |
| author_facet | Zawawi, Tengku Nor Shuhada Tengku Mohd Saad, Norhashimah Too Jing, Wei Shair, Ezreen Farina Abdullah, Abdul Rahim Sudirman, Rubita |
| author_sort | Zawawi, Tengku Nor Shuhada Tengku |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Electromyography signal analysis and classification method for Health Screening Program for Social Security Organisation (SOCSO) Malaysia is the first time applied using time-frequency distribution (TFD). This paper presents the classification of EMG signals for health screening task for musculoskeletal disorder. A time-frequency method, i.e spectrogram is employed to obtain the data of time and frequency information of the EMG signal. Four machine learning classifier of k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB) and Support Vector Machine (SVM) are implemented to EMG signal. Three out of six tasks (axial rotational task, kneeling reach and kneeling to standing back reach) which focused on the upper limb was performed using Multi Sensor Management ConsensysPRO and functional range on motion (FROM). From the experiment, SVM classifier is outperformed others using the purposed extracted features from spectrogram which is more than 80% except NB with 73.33%. The finding of the study concludes that SVM is suitable to classify EMG signal and can help rehabilitation center to diagnose their patients |
| format | Article |
| id | my.utem.eprints-24152 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2019 |
| record_format | eprints |
| spelling | my.utem.eprints-241522020-08-04T15:25:50Z http://eprints.utem.edu.my/id/eprint/24152/ Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder Zawawi, Tengku Nor Shuhada Tengku Mohd Saad, Norhashimah Too Jing, Wei Shair, Ezreen Farina Abdullah, Abdul Rahim Sudirman, Rubita Electromyography signal analysis and classification method for Health Screening Program for Social Security Organisation (SOCSO) Malaysia is the first time applied using time-frequency distribution (TFD). This paper presents the classification of EMG signals for health screening task for musculoskeletal disorder. A time-frequency method, i.e spectrogram is employed to obtain the data of time and frequency information of the EMG signal. Four machine learning classifier of k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB) and Support Vector Machine (SVM) are implemented to EMG signal. Three out of six tasks (axial rotational task, kneeling reach and kneeling to standing back reach) which focused on the upper limb was performed using Multi Sensor Management ConsensysPRO and functional range on motion (FROM). From the experiment, SVM classifier is outperformed others using the purposed extracted features from spectrogram which is more than 80% except NB with 73.33%. The finding of the study concludes that SVM is suitable to classify EMG signal and can help rehabilitation center to diagnose their patients 2019 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24152/2/3_2019-CLASSIFICATIONOFEMGSIGNALFORHEALTHSCREENINGTASKFORMUSCULOSKELETALDISORDER.PDF Zawawi, Tengku Nor Shuhada Tengku and Mohd Saad, Norhashimah and Too Jing, Wei and Shair, Ezreen Farina and Abdullah, Abdul Rahim and Sudirman, Rubita (2019) Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder. International Journal Of Engineering & Technology, 8 (1.7). pp. 219-226. ISSN 0975-4024 https://www.sciencepubco.com/index.php/ijet/article/view/25980/13335 10.14419/ijet.v8i1.7.25980 |
| spellingShingle | Zawawi, Tengku Nor Shuhada Tengku Mohd Saad, Norhashimah Too Jing, Wei Shair, Ezreen Farina Abdullah, Abdul Rahim Sudirman, Rubita Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder |
| title | Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder |
| title_full | Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder |
| title_fullStr | Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder |
| title_full_unstemmed | Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder |
| title_short | Classification Of EMG Signal For Health Screening Task For Musculoskeletal Disorder |
| title_sort | classification of emg signal for health screening task for musculoskeletal disorder |
| url | http://eprints.utem.edu.my/id/eprint/24152/2/3_2019-CLASSIFICATIONOFEMGSIGNALFORHEALTHSCREENINGTASKFORMUSCULOSKELETALDISORDER.PDF http://eprints.utem.edu.my/id/eprint/24152/ https://www.sciencepubco.com/index.php/ijet/article/view/25980/13335 |
| url_provider | http://eprints.utem.edu.my/ |