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


Karan Ahuja (HCII, Carnegie Mellon University)
Abhishek Bose (Indian Institute of Information Technology, Guwahati)
Mohit Jain (IBM Research)
Kuntal Dey (IBM Research)
Anil Joshi (IBM Research)
Krishnaveni Achary (Tamana NGO)
Blessin Varkey (Tamana NGO)
Chris Harrison (HCII, Carnegie Mellon University)
Mayank Goel (HCII, Carnegie Mellon University)

Session: 1.2C: Improving health outcomes

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.