Video-based Analysis Reveals Atypical Social Gaze in People with Autism Spectrum Disorder

TL;DR

This study uses third-person video analysis and computational models to quantitatively examine social gaze patterns in individuals with autism spectrum disorder, aiming to improve naturalistic ASD diagnosis.

Contribution

It introduces a novel third-person perspective approach and computational framework for analyzing social gaze in ASD from routine clinical videos.

Findings

Gaze features effectively differentiate ASD from neurotypical individuals.

The classifier accurately identifies gaze abnormalities associated with ASD.

Third-person video analysis enhances naturalistic social gaze assessment.

Abstract

In this study, we present a quantitative and comprehensive analysis of social gaze in people with autism spectrum disorder (ASD). Diverging from traditional first-person camera perspectives based on eye-tracking technologies, this study utilizes a third-person perspective database from the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) interview videos, encompassing ASD participants and neurotypical individuals as a reference group. Employing computational models, we extracted and processed gaze-related features from the videos of both participants and examiners. The experimental samples were divided into three groups based on the presence of social gaze abnormalities and ASD diagnosis. This study quantitatively analyzed four gaze features: gaze engagement, gaze variance, gaze density map, and gaze diversion frequency. Furthermore, we developed a classifier trained on these features to identify gaze abnormalities in ASD participants. Together, we demonstrated the effectiveness of analyzing social gaze in people with ASD in naturalistic settings, showcasing the potential of third-person video perspectives in enhancing ASD diagnosis through gaze analysis.