Gaze-based Autism Detection for Adolescents and Young Adults using Prosaic Videos

TL;DR

This study demonstrates that monitoring gaze patterns during everyday video viewing can accurately identify autism spectrum disorder in adolescents and young adults within 15 seconds, enabling passive screening.

Contribution

We introduce a simple, non-invasive gaze-based method using off-the-shelf eye trackers to detect autism in real-world video viewing scenarios.

Findings

92.5% accuracy within 15 seconds

Effective identification with off-the-shelf eye trackers

Potential for passive autism screening during media consumption

Abstract

Autism often remains undiagnosed in adolescents and adults. Prior research has indicated that an autistic individual often shows atypical fixation and gaze patterns. In this short paper, we demonstrate that by monitoring a user's gaze as they watch commonplace (i.e., not specialized, structured or coded) video, we can identify individuals with autism spectrum disorder. We recruited 35 autistic and 25 non-autistic individuals, and captured their gaze using an off-the-shelf eye tracker connected to a laptop. Within 15 seconds, our approach was 92.5% accurate at identifying individuals with an autism diagnosis. We envision such automatic detection being applied during e.g., the consumption of web media, which could allow for passive screening and adaptation of user interfaces.