AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention
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| Format: | Preprint |
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2025
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| _version_ | 1866908803964862464 |
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| author | Lim, Suhyeon Kim, Ye-eun Choi, Andrew J. |
| author_facet | Lim, Suhyeon Kim, Ye-eun Choi, Andrew J. |
| contents | Effective stroke recovery requires continuous rehabilitation integrated with daily living. To support this need, we propose a home-based rehabilitation exercise and feedback system. The system consists of (1) hardware setup with RGB-D camera and wearable sensors to capture stroke movements, (2) a mobile application for exercise guidance, and (3) an AI server for assessment and feedback. When a stroke user exercises following the application guidance, the system records skeleton sequences, which are then assessed by the deep learning model, RAST-G@ (Rehabilitation Assessment Spatio-Temporal Graph ATtention). The model employs a spatio-temporal graph convolutional network to extract skeletal features and integrates transformer-based temporal attention to figure out action quality. For system implementation, we constructed the NRC dataset, include 10 upper-limb activities of daily living (ADL) and 5 range-of-motion (ROM) collected from stroke and non-disabled participants, with Score annotations provided by licensed physiotherapists. Results on the KIMORE and NRC datasets show that RAST-G@ improves over baseline in terms of MAD, RMSE, and MAPE. Furthermore, the system provides user feedback that combines patient-centered assessment and monitoring. The results demonstrate that the proposed system offers a scalable approach for quantitative and consistent domiciliary rehabilitation assessment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_00049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention Lim, Suhyeon Kim, Ye-eun Choi, Andrew J. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Effective stroke recovery requires continuous rehabilitation integrated with daily living. To support this need, we propose a home-based rehabilitation exercise and feedback system. The system consists of (1) hardware setup with RGB-D camera and wearable sensors to capture stroke movements, (2) a mobile application for exercise guidance, and (3) an AI server for assessment and feedback. When a stroke user exercises following the application guidance, the system records skeleton sequences, which are then assessed by the deep learning model, RAST-G@ (Rehabilitation Assessment Spatio-Temporal Graph ATtention). The model employs a spatio-temporal graph convolutional network to extract skeletal features and integrates transformer-based temporal attention to figure out action quality. For system implementation, we constructed the NRC dataset, include 10 upper-limb activities of daily living (ADL) and 5 range-of-motion (ROM) collected from stroke and non-disabled participants, with Score annotations provided by licensed physiotherapists. Results on the KIMORE and NRC datasets show that RAST-G@ improves over baseline in terms of MAD, RMSE, and MAPE. Furthermore, the system provides user feedback that combines patient-centered assessment and monitoring. The results demonstrate that the proposed system offers a scalable approach for quantitative and consistent domiciliary rehabilitation assessment. |
| title | AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.00049 |