AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention

Abstract--Effective stroke recovery requires continuous rehabilitation integrated with daily living. T o 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 Stroke user exercises following the application guidance, the system records skeleton sequences, which are then Assessed by the deep learning model, RAST -G@. The model employs a spatio-temporal graph con-volutional network (ST -GCN) 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. ECENT advancements in Neurology, particularly in motor control and learning, have revealed different mechanisms that induce changes in brain plasticity and behavior over both short-and long-term periods. Physical rehabilitation can be seen as a form of motor learning that occurs under specific conditions [1]-[4], and patients with motor impairments, such as those following a stroke, are capable of limited motor learning, although with variations in learning speed and volume. In particular, usage-based and reward-based learning, which are shaped by habitual, repetitive actions and rewards, play a key role in determining long-term brain and behavioral changes in stroke patients after they are discharged and resume daily activities.