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Lookup NU author(s): Claire Marcroft, Dr Aftab Khan, Professor Nicholas EmbletonORCiD, Professor Mike TrenellORCiD, Dr Thomas Ploetz
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Preterm birth is associated with increased risks of neurological and motor impairments such as cerebral palsy. The risks are highest in those born at the lowest gestations. Early identification of those most at risk is challenging meaning that a critical window of opportunity to improve outcomes through therapy-based interventions may be missed. Clinically, the assessment of spontaneous general movements is an important tool, which can be used for the prediction of movement impairments in high risk infants. Movement recognition aims to capture and analyze relevant limb movements through computerized approaches focusing on continuous, objective, and quantitative assessment. Different methods of recording and analyzing infant movements have recently been explored in high risk infants. These range from camera-based solutions to body-worn miniaturized movement sensors used to record continuous time-series data that represent the dynamics of limb movements. Various machine learning methods have been developed and applied to the analysis of the recorded movement data. This analysis has focused on the detection and classification of atypical spontaneous general movements. This article aims to identify recent translational studies using movement recognition technology as a method of assessing movement in high risk infants. The application of this technology within pediatric practice represents a growing area of inter-disciplinary collaboration, which may lead to a greater understanding of the development of the nervous system in infants at high risk of motor impairment.
Author(s): Marcroft C, Khan A, Embleton N, Trenell M, Ploetz T
Publication type: Article
Publication status: Published
Journal: Frontiers in Neurology
Year: 2015
Volume: 5
Online publication date: 09/01/2015
Acceptance date: 15/12/2014
Date deposited: 02/06/2015
ISSN (electronic): 1664-2295
Publisher: Frontiers Research Foundation
URL: http://dx.doi.org/10.3389/fneur.2014.00284
DOI: 10.3389/fneur.2014.00284
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