An Automated Early Motor Development Risk Screener from Observational Video Recordings of Infants and Toddlers

Motor development plays a pivotal role in an infant’s overall development and has a cascading effect on social, cognitive, memory, and language ability. These functions can be compromised when motor development is disrupted; motor disruption is associated with generalized developmental delays. Early screening and identification of delays can allow early interventions with the goal of not just improving motor function but overall development in other domains. Experience with independent or motorized crawling is shown to improve spatial memory in infants. Active motor training, even as early as 3 months, is shown to achieve gains in object exploration and social engagement that persist long after the training sessions, indicating the benefits of early intervention. Children who get developmental screening are more likely to be identified with delays and receive early intervention services compared to those that only receive age-appropriate milestone checks as part of pediatric well-child visits. However, comprehensive motor screening, using standardized instruments, for developmental surveillance is labor intensive and requires specialized training in administration and evaluation. Shortage of resources to perform frequent, gold-standard, comprehensive motor assessments can lead to missed opportunities for early intervention and contribute negatively to lifelong outcomes for infants at risk for behavioral and developmental disorders. In the US, 15-20% of children have a developmental or behavioral disability, but less than a third of them get diagnosed before entering school, preventing opportunities for early intervention during a critical period of brain development. In Phase I, we built a prototype Human Action Recognition Engine (HARE) to automate extraction of infant motor movement from video; then utilized this engine to demonstrate the feasibility of an automated developmental risk screener by leveraging recent advances in machine learning to achieve state of the art accuracies in assessment of infant motor development. In Phase II, we aim to build out the risk screener, SCOREIT, as a clinically deployable, cost-effective, developmental risk assessment tool to identify children who need further clinical follow-up. SCOREIT has the potential to transform early developmental screening by bringing the power of comprehensive motor screening, usually administered only to at-risk children utilizing specialized resources, to underserved and economically-fragile communities. Less than a third of US children receive developmental screening. SCOREIT can bring risk screenings to the over 70% of children that currently do not receive them, increasing opportunities for early intervention for behavioral and developmental disorders. Additionally, the HARE system’s ability to automatically extract and quantify motor movements from video can be an enabler for explorations of early markers for behavioral and developmental disorders, significantly easing manual video coding burdens and accelerating the pace of discoveries.This proposal introduces an automated, portable, low-cost developmental risk assessment tool, SCOREIT, that leverages advances in machine learning to extract motor movements from infant videos and generate a motor developmental score based on a standardized instrument (Alberta Infant Motor Scale; AIMS). SCOREIT has the potential to transform early developmental risk screening by bringing the power of standardized, comprehensive motor screening to underserved, economically-fragile communities, and increase opportunities for early intervention of developmental disorders.