Head-mounted, wireless eyetracker for real-time gaze prediction utilizing machine-learning

Eyetracking opens new possibilities for people with physical disabilities, enabling them to explore new ways of communication and interaction with their environment. However, this field of assistive technology is not available to everyone in need and still faces substantial obstacles: high-cost devices for use in real-world environments, difficulty in enhancing the performance of a calibrated system, difficulty in providing a personalized, adaptive setup. The aim of this work is to provide a lightweight, low cost, wireless device with a software performing real-time 2-D gaze prediction in a fixed coordinate system, and the possibility of enhancing performance by recalibration.