Title

Computer Vision to Automatically Assess Infant Neuromotor Risk.

Year of Publication

2020

Number of Pages

2431-2442

Date Published

2020 11

ISSN Number

1558-0210

Abstract

<p>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.</p>

DOI

10.1109/TNSRE.2020.3029121

Alternate Title

IEEE Trans Neural Syst Rehabil Eng

PMID

33021933

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