Quantifying spontaneous infant movements using state-space models.

Autor: Passmore E; Developmental Imaging, MCRI, Melbourne, Australia.; Biomedical Engineering, University of Melbourne, Melbourne, Australia.; Department of Paediatrics, University of Melbourne, Melbourne, Australia.; Gait Analysis Laboratory, Royal Children's Hospital, Melbourne, Australia., Kwong AKL; Department of Paediatrics, University of Melbourne, Melbourne, Australia.; Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia.; Newborn Research Centre, Royal Women's Hospital, Melbourne, Australia., Olsen JE; Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia.; Newborn Research Centre, Royal Women's Hospital, Melbourne, Australia., Eeles AL; Department of Paediatrics, University of Melbourne, Melbourne, Australia.; Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia.; Newborn Research Centre, Royal Women's Hospital, Melbourne, Australia., Cheong JLY; Department of Paediatrics, University of Melbourne, Melbourne, Australia.; Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia.; Newborn Research Centre, Royal Women's Hospital, Melbourne, Australia., Spittle AJ; Department of Paediatrics, University of Melbourne, Melbourne, Australia.; Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia., Ball G; Developmental Imaging, MCRI, Melbourne, Australia. gareth.ball@mcri.edu.au.; Department of Paediatrics, University of Melbourne, Melbourne, Australia. gareth.ball@mcri.edu.au.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 19; Vol. 14 (1), pp. 28598. Date of Electronic Publication: 2024 Nov 19.
DOI: 10.1038/s41598-024-80202-x
Abstrakt: Over the first few months after birth, the typical emergence of spontaneous, fidgety general movements is associated with later developmental outcomes. In contrast, the absence of fidgety movements is a core feature of several neurodevelopmental and cognitive disorders. Currently, manual assessment of early infant movement patterns is time consuming and labour intensive, limiting its wider use. Recent advances in computer vision and deep learning have led to the emergence of pose estimation techniques, computational methods designed to locate and track body points from video without specialised equipment or markers, for movement tracking. In this study, we use automated markerless tracking of infant body parts to build statistical models of early movements. Using a dataset of infant movement videos (n = 486) from 330 infants we demonstrate that infant movement can be modelled as a sequence of eight motor states using autoregressive, state-space models. Each, motor state Is characterised by specific body part movements, the expression of which varies with age and differs in infants at high-risk of poor neurodevelopmental outcome.
Competing Interests: Declarations Competing interests A.S. is a tutor with the General Movements Trust. All other authors have no conflicts of interest to declare.
(© 2024. The Author(s).)
Databáze: MEDLINE
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