Stroke represents the third cause of death and disability worldwide, and is recognised as a significant global health problem. A major challenge for stroke survivors is persistent hand dysfunction, which severely affects the ability to perform daily activities and the overall quality of life. In order to regain their functional hand ability, stroke survivors need rehabilitation therapy. However, traditional rehabilitation requires continuous medical support, creating dependency on an overburdened healthcare system. In this paper, we explore the use of egocentric recordings from commercially available smart glasses, specifically RayBan Stories, for remote hand rehabilitation. Our approach includes offline experiments to evaluate the potential of smart glasses for automatic exercise recognition, exercise form evaluation and repetition counting. We present REST-HANDS, the first dataset of egocentric hand exercise videos. Using state-of-the-art methods, we establish benchmarks with high accuracy rates for exercise recognition (98.55%), form evaluation (86.98%), and repetition counting (mean absolute error of 1.33). Our study demonstrates the feasibility of using egocentric video from smart glasses for remote rehabilitation, paving the way for further research.

本研究解决了中风幸存者手部功能持久障碍的问题，提出利用智能眼镜进行远程康复的新方法。通过离线实验评估智能眼镜在运动识别、姿势评估及重复计数中的潜力，研究表明此方法具有高准确率，首次建立了自我中心手部运动视频的数据集，为未来康复研究奠定了基础。

REST-HANDS：使用智能眼镜进行中风康复的自我中心视觉治疗手部功能