AI-Powered Camera and Sensors for the Rehabilitation Hand Exoskeleton

TL;DR

This paper introduces a vision-enabled rehabilitation hand exoskeleton that assists disabled individuals with hand movements using a camera and embedded processor, offering an accessible, training-free interface for grasping and releasing objects.

Contribution

It presents a novel passive vision-based control system integrated into a commercially available glove for hand rehabilitation, eliminating the need for individualized training.

Findings

Prototype successfully assists hand movements using vision and air pressure.

The system enables grasping and releasing without user training.

Future work aims to reduce cost, weight, and power consumption.

Abstract

Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.