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Eye Controlled Wheelchair Using Transfer Learning

The freedom of mobility affects an individual’s sense of prominence and confidence. Because of diseases injuring the nervous system like Amyotrophic Lateral Sclerosis (ALS) and Parkinson disease, people lose their ability to move outmoded wheelchairs. This paper presents a novel technique to control a wheelchair by using eye movements. Eye controlled chair comprised of an electric wheelchair, a webcam in front of the user’s eye capturing eyeball movements with a low-cost Raspberry pi system, serially communicating with Arduino microcontroller to drive wheelchair in the desired direction. The transfer learning approach was adopted instead of traditional image processing techniques, making wheelchair more reliable and accurate. Keras deep learning pre-trained VGG-16 model led us to achieve excellent performance, with very little training dataset. Unlike conventional wheelchairs, presented methodology makes this wheelchair equally suitable for people wearing eye glasses.