A distance robust EOG-based feature for gaze trajectory inference

Abstract

The vast majority of eye controlled applications rely on a mapping function which relates the estimated gaze angle of the user to a pixel coordinate on the screen. In turn, such systems heavily depend on the user maintaining an unnatural, fixed and known distance from the computer screen in order to ensure proper operation of the designed application. This work has investigated the use of trajectory bearing angles as a distance-robust electrooculography (EOG)-based feature which can be used for gaze trajectory inference. The trajectory bearing angles are extracted directly from EOG data and are shown to be robust to the distance of the user from the screen. Three different EOG-based bearing angle estimation methods are investigated and results of a proof-of-concept Hidden Markov Model (HMM) swipe typing application carried out by ten subjects, show that an average top-five rate of over 80% can be achieved across three different user distances from the screen.