用廉价摄像头和边缘计算，让康复手套自动识别抓取动作
ReGlove: A Soft Pneumatic Glove for Activities of Daily Living Assistance via Wrist-Mounted Vision
- 用腕部摄像头+树莓派实现视觉引导的抓握控制
- 抓取识别准确率96.73%，端到端延迟低于40毫秒
- 成本低于250美元，适合日常活动辅助，无需肌电信号
本文提出ReGlove系统，将低成本商用气动康复手套改造为基于视觉的助行装置。全球数百万上肢功能障碍患者面临辅助设备昂贵或依赖不可靠生物信号的问题。本平台通过腕部摄像头与边缘计算推理引擎（Raspberry Pi 5）结合，实现无需可靠肌电信号的上下文感知抓握。通过实时适配YOLO-based计算机视觉模型，系统在端到端延迟低于40.00毫秒条件下达到96.73%的抓取分类准确率。物理验证显示，在YCB物体操作标准测试中成功率达82.71%，并在27项日常生活活动（ADL）任务中表现稳定。系统总成本低于250美元，仅使用商用组件，为可及的、基于视觉的上肢辅助技术提供了技术基础，惠及无法使用传统肌电控制设备的人群。
原文摘要 · Abstract (English)
This paper presents ReGlove, a system that converts low-cost commercial pneumatic rehabilitation gloves into vision-guided assistive orthoses. Chronic upper-limb impairment affects millions worldwide, yet existing assistive technologies remain prohibitively expensive or rely on unreliable biological signals. Our platform integrates a wrist-mounted camera with an edge-computing inference engine (Raspberry Pi 5) to enable context-aware grasping without requiring reliable muscle signals. By adapting real-time YOLO-based computer vision models, the system achieves 96.73% grasp classification accuracy with sub-40.00 millisecond end-to-end latency. Physical validation using standardized benchmarks shows 82.71% success on YCB object manipulation and reliable performance across 27 Activities of Daily Living (ADL) tasks. With a total cost under $250 and exclusively commercial components, ReGlove provides a technical foundation for accessible, vision-based upper-limb assistance that could benefit populations excluded from traditional EMG-controlled devices.
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