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

Machine learning approaches in automated infant General Movements Assessment: A scoping review.

Developmental medicine and child neurology·2026
Same author

Augmented Reality and Artificial Intelligence for the Assessment and Rehabilitation of Spatial Neglect: A Systematic Review.

Neurorehabilitation and neural repair·2026
Same author

Have we come to the end of the patient-reported outcome measure (PROM)? : wearable sensors highlight improved rate of recovery and range of motion following robotic-assisted total knee arthroplasty that are overlooked by conventional PROMs.

The bone & joint journal·2026
Same author

Surgical fixation versus non-surgical care for children with a displaced medial epicondyle fracture of the elbow (the SCIENCE study): a multicentre, randomised controlled, superiority trial and economic evaluation.

Lancet (London, England)·2026
Same author

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Model Benchmarking.

Journal of biomechanical engineering·2026
Same author

Prediction accuracy of femoral and tibial stress and strain using statistical shape and density model-based finite element models in paediatrics.

Biomechanics and modeling in mechanobiology·2025

Related Experiment Video

Updated: Dec 21, 2025

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 Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy.

Julie Choisne1, Nicolas Fourrier2, Geoffrey Handsfield1

  • 1Auckland Bioengineering Institute, University of Auckland, 70 Symonds street, Auckland 1010, New Zealand.

Journal of Clinical Medicine
|May 16, 2020
PubMed
Summary

Three-dimensional gait analysis (3DGA) data reveal distinct gait patterns in children with cerebral palsy (CP). Data-driven models show inconsistent orthotics effectiveness and prescription variability, suggesting a need for quantitative approaches in CP gait management.

Keywords:
3D gait analysisankle foot orthosiscerebral palsydata-driven modelgait variable score

More Related Videos

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

14.6K
Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

8.9K

Related Experiment Videos

Last Updated: Dec 21, 2025

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
Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

14.6K
Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

8.9K

Area of Science:

  • Biomechanical analysis
  • Pediatric orthopedics
  • Data-driven modeling

Background:

  • Ankle and foot orthoses are frequently prescribed for children with cerebral palsy (CP).
  • Current clinical practice lacks clarity on the reliability of 3D gait analysis (3DGA) for consistent orthotic prescription.
  • Data-driven modeling offers potential to uncover complex relationships between 3DGA parameters and orthotic outcomes.

Purpose of the Study:

  • To develop a data-driven model for classifying CP gait biomechanics.
  • To identify associations between prescribed orthotic types and observed gait patterns.
  • To evaluate the utility of 3DGA in guiding orthotic interventions for children with CP.

Main Methods:

  • Utilized 3D gait analysis (3DGA) data from typically developing children and children with CP using orthoses.
  • Employed unsupervised self-organizing maps and k-means clustering to categorize gait patterns based on gait variable scores (GVSs).
  • Analyzed GVSs derived from the gait profile score, measuring deviations from typically developing (TD) gait.

Main Results:

  • Identified five distinct pathological gait patterns in children with CP, showing significant differences in GVSs.
  • Observed that only 43% of children demonstrated improved gait patterns with orthotic use.
  • Found considerable variability in orthotics prescriptions even among children with similar gait patterns.

Conclusions:

  • Data-driven modeling can classify CP gait patterns, offering objective insights beyond traditional analysis.
  • Current orthotic prescription practices show variability and may not consistently optimize gait outcomes in children with CP.
  • Quantitative, data-driven approaches are recommended to enhance the clarity and specificity of orthotic prescription in pediatric CP management.