{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T16:30:52Z","timestamp":1781800252882,"version":"3.54.5"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health Research (NIHR) Children and Young People MedTech Co-operative (CYP MedTech)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2019s dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children\u2019s 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.<\/jats:p>","DOI":"10.3390\/s23104800","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T01:58:06Z","timestamp":1684288686000},"page":"4800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2392-6549","authenticated-orcid":false,"given":"Alexander","family":"Turner","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Hayes","sequence":"additional","affiliation":[{"name":"Department of Engineering, Nottingham Trent University, Nottingham NG4 2EA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4989-8697","authenticated-orcid":false,"given":"Don","family":"Sharkey","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Nottingham, Nottingham NG7 2RD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s12098-008-0226-z","article-title":"Neurological disorders in children and adolescents","volume":"76","author":"Banerjee","year":"2009","journal-title":"Indian J. 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