Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals
Elbow flexion and extension is a common rehabilitation routine that is widely performed by stroke patients to rehabilitate elbow joints. The biceps and triceps muscles are the responsible muscles for flexing and extending the elbow joint. Hence, analyzing the electrical activity of those muscles pro...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
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IEEE
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/37735/ |
| _version_ | 1848848687507177472 |
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| author | Qassim, Hassan M. Wan Hasan, W. Z. Ramli, H. R. Harith, Hazreen H. Inchi Mat, Liyana Najwa Salim, M. S. F. |
| author_facet | Qassim, Hassan M. Wan Hasan, W. Z. Ramli, H. R. Harith, Hazreen H. Inchi Mat, Liyana Najwa Salim, M. S. F. |
| author_sort | Qassim, Hassan M. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Elbow flexion and extension is a common rehabilitation routine that is widely performed by stroke patients to rehabilitate elbow joints. The biceps and triceps muscles are the responsible muscles for flexing and extending the elbow joint. Hence, analyzing the electrical activity of those muscles provides beneficial information on elbow motion intention and eventually can be used for controlling purposes of potential rehabilitation robots. We investigate the Electromyography (EMG) signals of the biceps and triceps of stroke patients and their roles in elbow flexion and extension. The investigation process involves collecting, processing, filtering, and segmenting the collected surface Electromyography (sEMG) signal to ultimately extract specific features. Then, the optimum feature for elbow motion prediction is identified to be later used for controlling purposes. Six time-domain features, specifically MAV, RMS, SD, SAV, SSC, and ZC, were chosen to evaluate their efficiency in predicting elbow joint motion. MAV, RMS, SD, and SAV are the features that showed similar behavior during elbow flexion and extension. However, SAV showed the highest variation in the magnitude when the muscle's state changed from contraction to relaxation and vice-versa. On the other hand, SSC and ZC features showed an arbitrary behavior, where no reliable results were achieved. Eight stroke patients participated in this study after obtaining the ethics approval and consent agreements. The clinical trials were conducted at the Department of Rehabilitation Medicine, Hospital Pengajar Universiti Putra Malaysia (HPUPM). |
| first_indexed | 2025-11-15T09:38:28Z |
| format | Conference or Workshop Item |
| id | upm-37735 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:38:28Z |
| publishDate | 2023 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-377352023-09-28T05:35:26Z http://psasir.upm.edu.my/id/eprint/37735/ Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals Qassim, Hassan M. Wan Hasan, W. Z. Ramli, H. R. Harith, Hazreen H. Inchi Mat, Liyana Najwa Salim, M. S. F. Elbow flexion and extension is a common rehabilitation routine that is widely performed by stroke patients to rehabilitate elbow joints. The biceps and triceps muscles are the responsible muscles for flexing and extending the elbow joint. Hence, analyzing the electrical activity of those muscles provides beneficial information on elbow motion intention and eventually can be used for controlling purposes of potential rehabilitation robots. We investigate the Electromyography (EMG) signals of the biceps and triceps of stroke patients and their roles in elbow flexion and extension. The investigation process involves collecting, processing, filtering, and segmenting the collected surface Electromyography (sEMG) signal to ultimately extract specific features. Then, the optimum feature for elbow motion prediction is identified to be later used for controlling purposes. Six time-domain features, specifically MAV, RMS, SD, SAV, SSC, and ZC, were chosen to evaluate their efficiency in predicting elbow joint motion. MAV, RMS, SD, and SAV are the features that showed similar behavior during elbow flexion and extension. However, SAV showed the highest variation in the magnitude when the muscle's state changed from contraction to relaxation and vice-versa. On the other hand, SSC and ZC features showed an arbitrary behavior, where no reliable results were achieved. Eight stroke patients participated in this study after obtaining the ethics approval and consent agreements. The clinical trials were conducted at the Department of Rehabilitation Medicine, Hospital Pengajar Universiti Putra Malaysia (HPUPM). IEEE 2023 Conference or Workshop Item PeerReviewed Qassim, Hassan M. and Wan Hasan, W. Z. and Ramli, H. R. and Harith, Hazreen H. and Inchi Mat, Liyana Najwa and Salim, M. S. F. (2023) Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals. In: 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (IEEE ECBIOS 2023), 2-4 June 2023, Tainan, Taiwan. (pp. 54-57). https://ieeexplore.ieee.org/document/10218631 10.1109/ECBIOS57802.2023.10218631 |
| spellingShingle | Qassim, Hassan M. Wan Hasan, W. Z. Ramli, H. R. Harith, Hazreen H. Inchi Mat, Liyana Najwa Salim, M. S. F. Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals |
| title | Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals |
| title_full | Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals |
| title_fullStr | Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals |
| title_full_unstemmed | Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals |
| title_short | Prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals |
| title_sort | prediction of elbow joint motion of stroke patients by analyzing biceps and triceps electromyography signals |
| url | http://psasir.upm.edu.my/id/eprint/37735/ http://psasir.upm.edu.my/id/eprint/37735/ http://psasir.upm.edu.my/id/eprint/37735/ |