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.

翻译：本文提出ReGlove系统，该系统将低成本商用气动康复手套转化为视觉引导的辅助性矫形器。慢性上肢功能障碍影响着全球数百万人，然而现有的辅助技术要么价格极其昂贵，要么依赖于不可靠的生物信号。我们的平台将腕戴式摄像头与边缘计算推理引擎（Raspberry Pi 5）相结合，无需依赖可靠的肌肉信号即可实现情境感知抓握。通过采用基于YOLO的实时计算机视觉模型，该系统实现了96.73%的抓握分类准确率，且端到端延迟低于40.00毫秒。使用标准化基准进行的物理验证显示，该系统在YCB物体操作任务中取得了82.71%的成功率，并在27项日常生活活动任务中表现出稳定性能。凭借低于250美元的总成本及完全商业化的组件，ReGlove为可普及的、基于视觉的上肢辅助技术提供了基础，有望惠及被传统肌电控制设备排除在外的群体。