Skip to main content

Research Repository

Advanced Search

The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development

Turner, Alexander; Hayes, Stephen; Sharkey, Don


Alexander Turner

Profile Image

Mr Steve Hayes
Lecturer in Biomechanics & Strength and Conditioning

Don Sharkey


Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient’s home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children’s dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children’s complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring.


Turner, A., Hayes, S., & Sharkey, D. (2023). The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development. Sensors, 23(10), Article 4800.

Journal Article Type Article
Acceptance Date May 12, 2023
Online Publication Date May 16, 2023
Publication Date May 2, 2023
Deposit Date May 24, 2023
Publicly Available Date May 25, 2023
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 23
Issue 10
Article Number 4800
Keywords Neurological development; Infant development; Deep learning; Autonomous monitoring; Movement assessment of infants
Public URL


Published article (1 Mb)

Publisher Licence URL

Copyright Statement
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (

You might also like

Downloadable Citations