Paper
ReGlove: A Soft Pneumatic Glove for Activities of Daily Living Assistance via Wrist-Mounted Vision
Authors
Rosh Ho, Jian Zhang
Abstract
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 \SI{96.73}{\percent} grasp classification accuracy with sub-\SI{40.00}{\milli\second} end-to-end latency. Physical validation using standardized benchmarks shows \SI{82.71}{\percent} success on YCB object manipulation and reliable performance across \SI{27.00}{} Activities of Daily Living (ADL) tasks. With a total cost under \$\SI{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.