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.

翻译：有效的卒中康复需要将持续性康复训练融入日常生活。为满足这一需求，我们提出一种居家康复训练与反馈系统。该系统包含：(1) 配备RGB-D摄像头与可穿戴传感器的硬件装置，用于捕捉卒中患者动作；(2) 提供训练指导的移动应用程序；(3) 进行评估与反馈的AI服务器。当卒中用户依据应用指导进行训练时，系统记录骨骼序列数据，并通过深度学习模型RAST-G@（康复评估时空图注意力网络）进行评估。该模型采用时空图卷积网络提取骨骼特征，并集成基于Transformer的时间注意力机制以评估动作质量。为实现该系统，我们构建了NRC数据集，包含从卒中患者与非残疾参与者采集的10种上肢日常生活活动（ADL）与5种关节活动度（ROM）数据，并由持证物理治疗师提供评分标注。在KIMORE与NRC数据集上的实验结果表明，RAST-G@在平均绝对偏差（MAD）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）指标上均优于基线模型。此外，系统提供融合以患者为中心的评估与监测功能的用户反馈。研究结果证明，所提出的系统为定量化、标准化的居家康复评估提供了可扩展的解决方案。