Abstract
BackgroundAvoidance to look others in the eye is a characteristic symptom of Autism Spectrum Disorders (ASD), and it has been hypothesised that quantitative monitoring of gaze patterns could be useful to objectively evaluate treatments. However, tools to measure gaze behaviour on a regular basis at a manageable cost are missing. In this paper, we investigated whether a smartphone-based tool could address this problem. Specifically, we assessed the accuracy with which the phone-based, state-of-the-art eye-tracking algorithm iTracker can distinguish between gaze towards the eyes and the mouth of a face displayed on the smartphone screen. This might allow mobile, longitudinal monitoring of gaze aversion behaviour in ASD patients in the future.ResultsWe simulated a smartphone application in which subjects were shown an image on the screen and their gaze was analysed using iTracker. We evaluated the accuracy of our set-up across three tasks in a cohort of 17 healthy volunteers. In the first two tasks, subjects were shown different-sized images of a face and asked to alternate their gaze focus between the eyes and the mouth. In the last task, participants were asked to trace out a circle on the screen with their eyes. We confirm that iTracker can recapitulate the true gaze patterns, and capture relative position of gaze correctly, even on a different phone system to what it was trained on. Subject-specific bias can be corrected using an error model informed from the calibration data. We compare two calibration methods and observe that a linear model performs better than a previously proposed support vector regression-based method.ConclusionsUnder controlled conditions it is possible to reliably distinguish between gaze towards the eyes and the mouth with a smartphone-based set-up. However, future research will be required to improve the robustness of the system to roll angle of the phone and distance between the user and the screen to allow deployment in a home setting. We conclude that a smartphone-based gaze-monitoring tool provides promising opportunities for more quantitative monitoring of ASD.
Highlights
Avoidance to look others in the eye is a characteristic symptom of Autism Spectrum Disorders (ASD), and it has been hypothesised that quantitative monitoring of gaze patterns could be useful to objectively evaluate treatments
A series of eye-tracking studies since have established that, when shown the image of a face, ASD patients spend less time fixating on the eyes and more time exploring the mouth or objects in the surroundings
While abnormal gaze behaviour is not the cause for the difficulties which subjects with ASD experience, it has been proposed that it might provide a quantifiable feature for monitoring the condition over time, and to evaluate the efficacy of treatments [4, 5]
Summary
Avoidance to look others in the eye is a characteristic symptom of Autism Spectrum Disorders (ASD), and it has been hypothesised that quantitative monitoring of gaze patterns could be useful to objectively evaluate treatments. We assessed the accuracy with which the phone-based, state-ofthe-art eye-tracking algorithm iTracker can distinguish between gaze towards the eyes and the mouth of a face displayed on the smartphone screen. This might allow mobile, longitudinal monitoring of gaze aversion behaviour in ASD patients in the future. Thanks to recent advances new solutions are emerging that allow eye-tracking using only the camera of a laptop or smartphone These approaches use machine-learning techniques, such as support vector methods [8], Gaussian processes [9], or neural networks, [10, 11], and hold great potential for longitudinal gaze behaviour monitoring in ASD patients. We envision the development of a mobile-monitoring tool in which patients regularly perform a series of tests on their smartphone that measure their gaze behaviour and so provide insights into the development of their condition over time
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