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arXiv · 2510.00049

AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention

Suhyeon Lim·Ye-eun Kim·Andrew J. Choi
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Abstract

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.

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Suhyeon Lim, Ye-eun Kim, Andrew J. Choi. 2025-09-27. AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention. https://arxiv.org/abs/2510.00049

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