2024/11/14 by Daphné Chopard, Sonia Laguna, Chopard, Daphné +10
Health Professions · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Infant Development and Preterm Care #Infant Health and Development #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2411.09821
openalex publication_date 2024/11/14 · openalex created_date 2024/11/22 · openalex updated_date 2026/07/28
General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification.