The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference)
Stroke is a major cause of disability worldwide that affects many people every year. Stroke rehabilitation is a process that helps stroke patients regain their lost function and improve their quality of life. However, the recovery process varies widely depending on the severity of stroke and other f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22913/ http://journalarticle.ukm.my/22913/1/11%20%282%29.pdf |
| _version_ | 1848815714232696832 |
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| author | Cern, Yong Saan Ze, Yeoh Sheng |
| author_facet | Cern, Yong Saan Ze, Yeoh Sheng |
| author_sort | Cern, Yong Saan |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Stroke is a major cause of disability worldwide that affects many people every year. Stroke rehabilitation is a process that helps stroke patients regain their lost function and improve their quality of life. However, the recovery process varies widely depending on the severity of stroke and other factors such as age, health and type of stroke. Many elderly patients face difficulties in attending rehabilitation centers due to various factors such as cost, distance and congestion. Therefore, this paper proposes methods to help stroke patients do rehabilitation exercises at home using the latest technology. Our project consists of interactive exercises that are customized to the skill level of the patients, hardware sensor inputs that can measure the strength of the hand movement of the patients, embedded processing board with camera that can detect and guide the movement of the patients and machine learning using convolutional neural network (CNN) that can analyze the movement data and provide feedback and motivation to the patients. The effectiveness of the proposed system is evaluated by the improvements in patients’ conditions through pre- and post-exercise tests. Overall, our kinesthetic augmented kinematic inferencing methods appear to be more effective than conventional methods for post-stroke rehabilitation. This project demonstrates a promising solution to enhance stroke rehabilitation, recovery and quality of life. |
| first_indexed | 2025-11-15T00:54:22Z |
| format | Article |
| id | oai:generic.eprints.org:22913 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:54:22Z |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:229132024-01-24T03:25:10Z http://journalarticle.ukm.my/22913/ The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference) Cern, Yong Saan Ze, Yeoh Sheng Stroke is a major cause of disability worldwide that affects many people every year. Stroke rehabilitation is a process that helps stroke patients regain their lost function and improve their quality of life. However, the recovery process varies widely depending on the severity of stroke and other factors such as age, health and type of stroke. Many elderly patients face difficulties in attending rehabilitation centers due to various factors such as cost, distance and congestion. Therefore, this paper proposes methods to help stroke patients do rehabilitation exercises at home using the latest technology. Our project consists of interactive exercises that are customized to the skill level of the patients, hardware sensor inputs that can measure the strength of the hand movement of the patients, embedded processing board with camera that can detect and guide the movement of the patients and machine learning using convolutional neural network (CNN) that can analyze the movement data and provide feedback and motivation to the patients. The effectiveness of the proposed system is evaluated by the improvements in patients’ conditions through pre- and post-exercise tests. Overall, our kinesthetic augmented kinematic inferencing methods appear to be more effective than conventional methods for post-stroke rehabilitation. This project demonstrates a promising solution to enhance stroke rehabilitation, recovery and quality of life. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22913/1/11%20%282%29.pdf Cern, Yong Saan and Ze, Yeoh Sheng (2023) The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference). Jurnal Kejuruteraan, 35 (6). pp. 1383-1391. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3506-2023/ |
| spellingShingle | Cern, Yong Saan Ze, Yeoh Sheng The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference) |
| title | The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference) |
| title_full | The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference) |
| title_fullStr | The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference) |
| title_full_unstemmed | The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference) |
| title_short | The Design of Stroke Rehabilitation Using Artificial Intelligence K.A.K.I (Kinesthetic Augmented Kinematic Inference) |
| title_sort | design of stroke rehabilitation using artificial intelligence k.a.k.i (kinesthetic augmented kinematic inference) |
| url | http://journalarticle.ukm.my/22913/ http://journalarticle.ukm.my/22913/ http://journalarticle.ukm.my/22913/1/11%20%282%29.pdf |