用智能眼镜拍摄的手部康复视频，可远程自动评估动作与次数。
REST-HANDS: Rehabilitation with Egocentric Vision Using Smartglasses for Treatment of Hands after Surviving Stroke
- 通过智能眼镜采集第一视角视频，自动识别手部动作
- 动作识别准确率98.55%，重复计数误差仅1.33次
- 首个第一视角手部康复数据集，适合远程康复研究
中风是全球第三大死亡和致残原因，严重手功能障碍显著影响日常生活质量。传统康复依赖持续医疗支持，加重医疗系统负担。本文探索使用商用智能眼镜（如RayBan Stories）的穿戴式第一视角视频，实现远程手部康复。提出REST-HANDS，首个第一视角手部康复视频数据集。通过离线实验验证了智能眼镜在动作识别、动作形态评估和重复次数统计方面的可行性。基于先进方法建立基准：动作识别准确率达98.55%，形态评估准确率为86.98%，重复计数平均绝对误差为1.33。结果证明第一视角视频可用于远程康复，为后续研究铺平道路。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。