Automatic Classification of General Movements in Newborns
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Date
2025
Publication Type
Conference Paper
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yes
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Abstract
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.
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published
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ETH Zürich
Event
4th Machine Learning for Health Symposium (ML4H 2024)
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Subject
Automated General Movements Assessment (GMA); Neurodevelopmental Disorder Screening Tool; Time Series; Paediatrics
Organisational unit
09670 - Vogt, Julia / Vogt, Julia
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Is variant form of: 10.48550/arXiv.2411.09821