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Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders
Dmitry Isaev · J. Matias Di Martino · Kimberley Carpenter · Guillermo Sapiro · geraldine Dawson

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.

Author Information

Dmitry Isaev (Duke University)
J. Matias Di Martino (Duke)
Kimberley Carpenter (Duke University)
Guillermo Sapiro (Duke University)
geraldine Dawson (Duke University)

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