Eye Tracker

Abstract

The high prevalence of autism spectrum disorder (ASD) has provided a spectrum of diagnostic methodologies ranging from screening scales to technological techniques. The technology-based techniques, especially eye trackers, are shifting the traditional subjective approaches to objective, leading to early ASD screening and intervention. The eye gaze deficits marked by eye trackers are the valid biomarkers of ASD, but the trackers are not clinically available. Another reason for non-availability is the limited number of methodologies which can meaningfully analyze gaze data. The assistance of new technologies into eye tracker system explored here can (1) detect gaze patterns and cognitive abilities of individuals at the single platform and (2) analyze eye movements and events automatically using deep learning system rather than manual interpretation of raw data. These types of systems, if implemented, have the potential to assist clinicians for better ASD diagnosis and intervention approaches.