000168041 001__ 168041
000168041 005__ 20260123152959.0
000168041 0247_ $$2doi$$a10.3390/bioengineering12121311
000168041 0248_ $$2sideral$$a147630
000168041 037__ $$aART-2025-147630
000168041 041__ $$aeng
000168041 100__ $$aRico-González, Markel
000168041 245__ $$aMachine learning methods in posture-related applications in children up to 12 years old: a systematic review
000168041 260__ $$c2025
000168041 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168041 5203_ $$aOne of the most important factors in how infants and young children learn to move is postural control. This systematic review aims to evaluate the machine learning methods in posture-related applications for children aged 0–12. Following PRISMA guidelines, we systematically searched the PubMed, Web of Sciences, SCOPUS, and ProQuest Central databases. Twenty-two studies were included in the qualitative synthesis following screening of 199 articles, with methodological quality assessed as moderate to good using the MINORS scale (scores ranging from 8/16 to 19/24). The reviewed research involved diverse samples of infants and children up to 12 years old, employing sensor-based technologies such as inertial measurement units, force plates, pressure mats, and video cameras to extract kinematic and postural features for machine learning applications. Reported accuracies, typically exceeding 85%, reflected considerable methodological heterogeneity related to sensor modality, data quality, and model architecture. Algorithms such as Random Forest, SVM, and CNN were most frequently and effectively applied for posture classification, early detection of developmental delays, and diagnosis of conditions such as cerebral palsy and autism spectrum disorder, demonstrating promising potential for at-home monitoring and clinical interventions.
000168041 536__ $$9info:eu-repo/grantAgreement/ES/DGA/S53-23R
000168041 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168041 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168041 700__ $$0(orcid)0000-0002-4084-8124$$aGómez-Carmona, Carlos D.$$uUniversidad de Zaragoza
000168041 700__ $$aOuergui, Ibrahim
000168041 700__ $$aArdigò, Luca Paolo
000168041 7102_ $$13001$$2187$$aUniversidad de Zaragoza$$bDpto. Expres.Music.Plást.Corp.$$cÁrea Didáctica Expres.Corporal
000168041 773__ $$g12, 12 (2025), 1311 [18 pp.]$$pBioengineering$$tBioengineering$$x2306-5354
000168041 8564_ $$s635480$$uhttps://zaguan.unizar.es/record/168041/files/texto_completo.pdf$$yVersión publicada
000168041 8564_ $$s2608906$$uhttps://zaguan.unizar.es/record/168041/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168041 909CO $$ooai:zaguan.unizar.es:168041$$particulos$$pdriver
000168041 951__ $$a2026-01-23-14:33:56
000168041 980__ $$aARTICLE