A cost effective eye movement tracker based wheel chair control algorithm for people with paraplegia

TL;DR

This paper presents a cost-effective eye movement-based control system for wheelchairs, enabling mobility for quadriplegic patients by converting eye signals into control commands using simple image processing and pattern recognition.

Contribution

It introduces a novel, affordable eye-tracking algorithm integrated with an Android app for refined wheelchair control in quadriplegic patients.

Findings

System effectively translates eye movements into control signals

Cost-effective approach suitable for real-world application

Android app enhances control precision

Abstract

Spinal cord injuries can often lead to quadriplegia in patients limiting their mobility. Wheelchairs could be a good proposition for patients, but most of them operate either manually or with the help of electric motors operated with a joystick. This, however, requires the use of hands, making it unsuitable for quadriplegic patients. Controlling eye movement, on the other hand, is retained even by people who undergo brain injury. Monitoring the movements in the eye can be a helpful tool in generating control signals for the wheelchair. This paper is an approach to converting obtained signals from the eye into meaningful signals by trying to control a bot that imitates a wheelchair. The overall system is cost-effective and uses simple image processing and pattern recognition to control the bot. An android application is developed, which could be used by the patients' aid for more refined control of the wheelchair in the actual scenario.