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 from the
mutual gaze detection framework with the human-coded ratio values. We analyzed
the mutual gaze frequency and duration across different therapy settings,
activities, and sessions. We created mutual gaze-related measures for social
visual behavior score prediction using multiple machine learning-based
regression models. The results show that our method provides mutual gaze
measures that reliably represent (or even replace) the human coders' hand-coded
social gaze measures and effectively evaluates and predicts ASD children's
social visual performance during the intervention. Our findings have
implications for social interaction analysis in small-group behavior
assessments in numerous co-located settings in (special) education and in the
workplace.Comment: Accepted to IEEE/ACM international conference on Connected Health:
Applications, Systems and Engineering Technologies (CHASE) 202