RT Journal Article
SR Electronic
T1 Computer vision to automatically assess infant neuromotor risk
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 756262
DO 10.1101/756262
A1 Claire Chambers
A1 Nidhi Seethapathi
A1 Rachit Saluja
A1 Helen Loeb
A1 Samuel Pierce
A1 Daniel Bogen
A1 Laura Prosser
A1 Michelle J. Johnson
A1 Konrad P. Kording
YR 2019
UL http://biorxiv.org/content/early/2019/09/10/756262.abstract
AB 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 video cameras. 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 Naïve Gaussian Bayesian Surprise. After pre-registering our Bayesian Surprise calculations, we find that infants that are at higher risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.