Prototype demonstrates high grasp classification accuracy and low latency in assistive technologies for upper-limb impairment.
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.
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Ho et al. (2025) studied this question.
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