Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Amplification Chambers and Belief Persistence in Commercial Health Communication.

Journal of health communication·2026
Same author

Acute Creatine Ingestion Before Resistance Training Enhances Strength Performance More than Ingestion During or After Training: A Randomized Crossover Pilot Trial.

Nutrients·2026
Same author

<i>ACTN3</i> rs1815739 and <i>BDNF</i> rs6265 Polymorphisms May Not Be Associated with Handgrip Strength in Elite Wrestlers.

Genes·2026
Same author

Correction: Mahdi et al. Melatonin Supplementation Enhances Next-Day High-Intensity Exercise Performance and Recovery in Trained Males: A Placebo-Controlled Crossover Study. <i>Sports</i> 2025, <i>13</i>, 190.

Sports (Basel, Switzerland)·2026
Same author

Melatonin, Caffeine, or Their Combination: Effects on Sleep, Performance, Perceived Exertion in a Placebo-Controlled Crossover Study.

Nutrients·2026
Same author

Dry Needling and Exercise in the Treatment of Musculoskeletal Pain: A Systematic Review.

Sportverletzung Sportschaden : Organ der Gesellschaft fur Orthopadisch-Traumatologische Sportmedizin·2026

Related Experiment Video

Updated: Jan 7, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.0K

Machine Learning Methods in Posture-Related Applications in Children up to 12 Years Old: A Systematic Review.

Markel Rico-González1,2, Carlos D Gómez-Carmona2,3,4, Ibrahim Ouergui5,6

  • 1Department of Didactics of Music, Plastic and Body Expression, University of Basque Country (UPV-EHU), 48940 Leioa, Spain.

Bioengineering (Basel, Switzerland)
|December 30, 2025
PubMed
Summary

Machine learning accurately assesses postural control in children (0-12 years) using sensors. This technology shows promise for early developmental delay detection and diagnosing conditions like cerebral palsy.

Keywords:
computer sciencehealthmachine learningpredictiontechnology

More Related Videos

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

7.2K
Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

10.0K

Related Experiment Videos

Last Updated: Jan 7, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.0K
An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

7.2K
Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

10.0K

Area of Science:

  • Pediatrics
  • Biomedical Engineering
  • Machine Learning

Background:

  • Postural control is crucial for motor development in infants and young children.
  • Machine learning (ML) offers potential for analyzing complex movement data.

Purpose of the Study:

  • To systematically review ML methods applied to posture-related applications in children aged 0-12.
  • To evaluate the effectiveness of ML in posture assessment and related diagnostics.

Main Methods:

  • Systematic literature search following PRISMA guidelines across major databases (PubMed, Web of Science, Scopus, ProQuest).
  • Inclusion of 22 studies with moderate to good methodological quality (MINORS scale).
  • Analysis of sensor-based technologies (IMUs, force plates, pressure mats, video) for extracting kinematic and postural features.

Main Results:

  • ML algorithms, particularly Random Forest, SVM, and CNN, achieved accuracies often exceeding 85%.
  • Heterogeneity in sensor modalities, data quality, and model architectures was noted.
  • Effective application in posture classification, early detection of developmental delays, and diagnosing conditions like cerebral palsy and autism spectrum disorder.

Conclusions:

  • ML demonstrates significant potential for posture-related applications in pediatric populations.
  • These methods show promise for both at-home monitoring and clinical interventions.
  • Further standardization may enhance the reliability and generalizability of ML approaches in pediatric postural control research.