EOG-Assist: Real-Time EOG-Based Eye Tracking for Assistive Systems

Paralysed patients find it hard to move various body parts depending on the severity of the injury. In an attempt to recover the motor imagery skills, rehabilitation schemes are recommended. One of the biggest barrier is the continuously dropping motivation for performing physiotherapy, besides the logistics hardships constantly borne by the care-taker. While the latter challenge remains personal, various Human-Computer-Interaction (HCI) emerged applications have shown promise to maintain the zeal to continue the therapy. Eye Tracking has been one of the popular interfaces to the HCI based rehabilitation gaming engines. Besides eye tracking also serves as a measure to evaluate the degree of neurological disorders and recovery from the same. Vision based eye-tracking exists but are cost ineffective, besides being a privacy-disabled solution. Alternately, (Electrooculogram) EOG signals to track eye movements for HCI purpose has emerged and have shown potential. This work establishes real-time EOG signal devised eye and blink tracker system. Two channels of EOG signals are collected and supplied to motion tracker model. Random forest and LGBM classifier models with an accuracy of 96% were found suitable for real-time tracking. For pilot demonstration, the proposed EOG signal tracker system was interfaced with a rotating camera system that follows the eye movement to provide 360 °of vision at 2 ° resolution. The real-time use-case of EOG eye tracker system paves way for integration with much more finer physiotherapy applications for paraplegia patients. The design files and EOG datasets for eye-tracking are made freely available for further usage to the researchers and users.