Gaze-based Autism Detection for Adolescents and Young Adults using Prosaic Videos
This enables passive screening for autism during web media consumption, potentially aiding diagnosis and interface adaptation, though it is incremental as it builds on known atypical gaze patterns.
The paper tackled the problem of undiagnosed autism in adolescents and adults by monitoring gaze patterns while watching commonplace videos, achieving 92.5% accuracy in identifying autism within 15 seconds.
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.