A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and Its Application in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with deficits in the processing of social information and difficulties in social interaction, and individuals with ASD exhibit atypical attention and gaze. Traditionally, gaze studies have relied upon precise and constrained means of monitoring attention using expensive equipment in laboratories. In this work we develop a low-cost off-the-shelf alternative for measuring attention that can be used in natural settings. The head and iris positions of 104 16-31 months children, an age range appropriate for ASD screening and diagnosis, 22 of them diagnosed with ASD, were recorded using the front facing camera in an iPad while they watched on the device screen a movie displaying dynamic stimuli, social stimuli on the left and non-social stimuli on the right. The head and iris position were then automatically analyzed via computer vision algorithms to detect the direction of attention. We validate the proposed framework and computational tool showing that children in the ASD group paid less attention to the movie, showed less attention to the social as compared to the non-social stimuli, and often fixated their attention to one side of the screen. These results are expected from the ASD literature, here obtained with significantly simpler and less expensive attention tracking methods. The proposed method provides a low-cost means of monitoring attention to properly designed stimuli, demonstrating that the integration of stimuli design and automatic response analysis results in the opportunity to use off-the-shelf cameras to assess behavioral biomarkers.