Computer Vision to Automatically Assess Infant Neuromotor Risk.

ABSTRACT: An infant's risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as videos recorded on a mobile device. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N = 19). For each infant, we calculate how much they deviate from a group of healthy infants (N = 85 online videos) using a Naïve Gaussian Bayesian Surprise metric. After pre-registering our Bayesian Surprise calculations, we find that infants who are at high risk for impairments deviate co

SUBMITTER: Chambers C 

PROVIDER: S-EPMC8011647 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature