Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders

We present results of a fully automated computer vision pipeline for the analysis of interactions between caregivers and young children in a free play setting. For both caregiver and child, we extract binary time-series signal of ‘reaching’ (1) and ’not reaching’ (0) to the toy and perform dyadic analysis of these data using Markovian models. Our results show that caregiver-child dyads can be clustered into two groups that differ by probabilities of transitions between the model states, serving as a proxy for leading-following behavior characteristics. We found that these two cluster groups differ in terms of the child’s level of social skills as measured by clinical Vineland Adaptive Behavior Scales - Socialization and Communication Subscale. Our results suggest the potential of digital assessment of caregiver-child interactions via computer vision analysis and using it as a tool for screening and providing behavioral biomarkers for neurodevelopmental disorders.