Social Visual Behavior Analytics for Autism Therapy of Children Based on Automated Mutual Gaze Detection
Zhang Guo, Vuthea Chheang, Jicheng Li, Kenneth E. Barner, Anjana Bhat,, Roghayeh Barmaki

TL;DR
This paper presents an automated framework for detecting mutual gaze in children with autism during therapy, providing reliable social visual behavior measures that can replace manual coding and assist in intervention evaluation.
Contribution
It introduces a novel automated mutual gaze detection method and validates its effectiveness in analyzing social visual behavior in autism therapy sessions.
Findings
Automated mutual gaze measures closely match human-coded data.
The framework effectively predicts social visual performance in children with autism.
Results support use in small-group social interaction assessments.
Abstract
Social visual behavior, as a type of non-verbal communication, plays a central role in studying social cognitive processes in interactive and complex settings of autism therapy interventions. However, for social visual behavior analytics in children with autism, it is challenging to collect gaze data manually and evaluate them because it costs a lot of time and effort for human coders. In this paper, we introduce a social visual behavior analytics approach by quantifying the mutual gaze performance of children receiving play-based autism interventions using an automated mutual gaze detection framework. Our analysis is based on a video dataset that captures and records social interactions between children with autism and their therapy trainers (N=28 observations, 84 video clips, 21 Hrs duration). The effectiveness of our framework was evaluated by comparing the mutual gaze ratio derived…
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Taxonomy
TopicsAutism Spectrum Disorder Research · Behavioral and Psychological Studies · Child Development and Digital Technology
