000161780 001__ 161780
000161780 005__ 20251017144622.0
000161780 0247_ $$2doi$$a10.3390/app15116296
000161780 0248_ $$2sideral$$a144454
000161780 037__ $$aART-2025-144454
000161780 041__ $$aeng
000161780 100__ $$aRico-González, Markel
000161780 245__ $$aMachine Learning Applications for Physical Activity and Behaviour in Early Childhood: A Systematic Review
000161780 260__ $$c2025
000161780 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161780 5203_ $$aThis systematic review evaluated machine learning applications for analysing physical activity and behaviour in preschool children using accelerometer data. Following the PRISMA guidelines, we systematically searched PubMed, FECYT, and ProQuest Central databases. Fourteen studies implementing machine learning approaches for preschool accelerometry data were identified and assessed using the MINORS scale. Studies focused on two primary domains: physical activity analysis (n = 10) and sleep monitoring (n = 4). The ActiGraph GT3X+ was predominantly used, with placement varying between the hip and wrist. Random Forest algorithms proved most effective, achieving accuracy rates up to 86.4% in activity classification and 96.2% in sleep prediction. Sampling frequencies (0.25–100 Hz) and epoch lengths (1–60 s) varied considerably across studies. Machine learning applications show promising results for preschool physical activity assessment. However, small sample sizes and methodological inconsistencies limit generalizability. Future research should prioritise larger cohorts, explore multiple sensor integrations, and develop standardised protocols to enhance practical applications.
000161780 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000161780 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000161780 700__ $$0(orcid)0000-0002-4084-8124$$aGómez-Carmona, Carlos D.$$uUniversidad de Zaragoza
000161780 7102_ $$13001$$2187$$aUniversidad de Zaragoza$$bDpto. Expres.Music.Plást.Corp.$$cÁrea Didáctica Expres.Corporal
000161780 773__ $$g15, 11 (2025), 6296 [15 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417
000161780 8564_ $$s644444$$uhttps://zaguan.unizar.es/record/161780/files/texto_completo.pdf$$yVersión publicada
000161780 8564_ $$s2619518$$uhttps://zaguan.unizar.es/record/161780/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000161780 909CO $$ooai:zaguan.unizar.es:161780$$particulos$$pdriver
000161780 951__ $$a2025-10-17-14:22:09
000161780 980__ $$aARTICLE