Computer Vision to Automatically Assess Infant Neuromotor Risk.

TitleComputer Vision to Automatically Assess Infant Neuromotor Risk.
Publication TypeJournal Article
Year of Publication2020
AuthorsChambers C, Seethapathi N, Saluja R, Loeb H, Pierce SR, Bogen DK, Prosser L, Johnson MJ, Kording KP
JournalIEEE Trans Neural Syst Rehabil Eng
Volume28
Issue11
Pagination2431-2442
Date Published2020 11
ISSN1558-0210
KeywordsBayes Theorem, Computers, Humans, Infant, Movement, Video Recording, Vision, Ocular
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 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.

DOI10.1109/TNSRE.2020.3029121
Alternate JournalIEEE Trans Neural Syst Rehabil Eng
PubMed ID33021933
PubMed Central IDPMC8011647
Grant ListR01 HD097686 / HD / NICHD NIH HHS / United States
R01 NS063399 / NS / NINDS NIH HHS / United States
R21 HD084327 / HD / NICHD NIH HHS / United States