A cost effective eye movement tracker based wheel chair control algorithm for people with paraplegia
This addresses mobility challenges for people with paraplegia by providing an alternative to hand-operated controls, though it appears incremental as it builds on existing eye-tracking methods.
The paper tackles the problem of enabling quadriplegic patients to control wheelchairs by developing a cost-effective system that converts eye movement signals into control commands for a bot, using simple image processing and pattern recognition.
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.