REST-HANDS：使用智能眼镜的自我中心视觉在中风幸存者手部治疗中的康复应用
REST-HANDS: Rehabilitation with Egocentric Vision Using Smartglasses for Treatment of Hands after Surviving Stroke
AI总结：
本文提出REST-HANDS数据集，利用RayBan Stories智能眼镜的自我中心视频实现中风后手部远程康复，在运动识别、形式评估和重复计数上取得高精度基准，验证了该方法的可行性。
摘要：
中风是全球第三大死亡和致残原因，被公认为一个重大的全球健康问题。中风幸存者面临的一个主要挑战是持续的手部功能障碍，这严重影响了他们进行日常活动的能力和整体生活质量。为了恢复手部功能，中风幸存者需要接受康复治疗。然而，传统康复需要持续的医疗支持，导致对负担过重的医疗系统产生依赖。在本文中，我们探索使用市售智能眼镜（特别是RayBan Stories）的自我中心记录来进行远程手部康复。我们的方法包括离线实验，以评估智能眼镜在自动运动识别、运动形式评估和重复计数方面的潜力。我们提出了REST-HANDS，这是第一个自我中心手部运动视频数据集。使用最先进的方法，我们在运动识别（98.55%）、形式评估（86.98%）和重复计数（平均绝对误差为1.33）方面建立了高准确率的基准。我们的研究证明了使用智能眼镜的自我中心视频进行远程康复的可行性，为进一步研究铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
