Machine Learning Applications for In-School Physical Activity Data Using IMUs in Children and Adolescents: A Systematic Review for Health Promotion

BACKGROUND: Caring health from childhood is a most important challenge. To date, machine learning (ML) algorithms have been introduced to several fields of knowledge, while in education it is a novel perspective. This review aims to evaluate the effectiveness of ML applications on data registered by inertial measurement units collected from preschool to secondary education children's physical activity during school-hours. Furthermore, the review aims to explore how ML is used to process and interpret this data for outcomes like motor competence, physical activity intensity, sedentary behavior and academic/developmental indicators. METHODS: Following PRISMA guidelines, we systematically searched PubMed, Web of Sciences, SCOPUS, SPORTDiscus and ProQuest Central databases. RESULTS: 13 studies met the inclusion criteria, covering preschool to secondary education settings across multiple countries. The methodological quality ranged from moderate to high (11-17/18 MINORS points). ML algorithms, mainly Random Forest, Support Vector Machines, Gradient Boosting and Convolutional Neural Networks, were successfully applied to classify or predict various outcomes such as motor competence, physical activity intensity, sedentary behavior and developmental or academic indicators. CONCLUSION: Reported accuracies ranged from approximately 70% to 99%, demonstrating the strong potential of wearable sensor data combined with ML to objectively monitor and assess school-related physical activity.