Background: Avoidance to look others in the eye is a characteristic symptom of\nAutism Spectrum Disorders (ASD), and it has been hypothesised that quantitative monitoring\nof gaze patterns could be useful to objectively evaluate treatments. However,\ntools to measure gaze behaviour on a regular basis at a manageable cost are missing.\nIn this paper, we investigated whether a smartphone-based tool could address this\nproblem. Specifically, we assessed the accuracy with which the phone-based, state-ofthe-\nart eye-tracking algorithm iTracker can distinguish between gaze towards the eyes\nand the mouth of a face displayed on the smartphone screen. This might allow mobile,\nlongitudinal monitoring of gaze aversion behaviour in ASD patients in the future.\nResults: We simulated a smartphone application in which subjects were shown an\nimage on the screen and their gaze was analysed using iTracker. We evaluated the\naccuracy of our set-up across three tasks in a cohort of 17 healthy volunteers. In the\nfirst two tasks, subjects were shown different-sized images of a face and asked to alternate\ntheir gaze focus between the eyes and the mouth. In the last task, participants\nwere asked to trace out a circle on the screen with their eyes. We confirm that iTracker\ncan recapitulate the true gaze patterns, and capture relative position of gaze correctly,\neven on a different phone system to what it was trained on. Subject-specific bias can\nbe corrected using an error model informed from the calibration data. We compare\ntwo calibration methods and observe that a linear model performs better than a previously\nproposed support vector regression-based method.\nConclusions: Under controlled conditions it is possible to reliably distinguish between\ngaze towards the eyes and the mouth with a smartphone-based set-up. However,\nfuture research will be required to improve the robustness of the system to roll angle\nof the phone and distance between the user and the screen to allow deployment in a\nhome setting. We conclude that a smartphone-based gaze-monitoring tool provides\npromising opportunities for more quantitative monitoring of ASD.
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