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 end-to-end latency. Physical validation using standardized benchmarks shows 82. 71 success on YCB object manipulation and reliable performance across 27. 00 Activities of Daily Living (ADL) tasks. With a total cost under \250. 00 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.
Ho et al. (Mon,) studied this question.
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