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

Markel Rico-González1, Eivind Holsbrekken2, Carlos D Gómez-Carmona3,4,5

  • 1University of Basque Country (UPV-EHU), Leioa, Spain.

Journal of Primary Care & Community Health
|April 12, 2026
PubMed
Summary

Machine learning (ML) effectively analyzes children's physical activity data from wearable sensors during school hours. This technology shows strong potential for monitoring motor competence, activity intensity, and sedentary behavior in students.

Keywords:
adolescentchildexercisehealth promotionmachine learningschoolssedentary behavior

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Area of Science:

  • Pediatric Health
  • Educational Technology
  • Sports Science

Background:

  • Childhood health monitoring is crucial.
  • Machine learning (ML) applications are novel in education.
  • Inertial measurement units (IMUs) capture physical activity data.

Purpose of the Study:

  • Evaluate ML effectiveness on children's physical activity data from IMUs.
  • Explore ML's role in interpreting data for motor competence, activity intensity, sedentary behavior, and academic/developmental indicators.

Main Methods:

  • Systematic literature search following PRISMA guidelines.
  • Databases searched: PubMed, Web of Sciences, SCOPUS, SPORTDiscus, ProQuest Central.
  • Included studies from preschool to secondary education.

Main Results:

  • 13 studies met inclusion criteria with moderate to high methodological quality.
  • ML algorithms (Random Forest, SVM, Gradient Boosting, CNNs) successfully classified/predicted outcomes.
  • Accuracies ranged from 70% to 99%.

Conclusions:

  • Wearable sensor data combined with ML offers strong potential for objective monitoring.
  • ML can assess school-related physical activity in children.
  • This approach supports health and developmental indicators.