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Related Experiment Video

Updated: Dec 24, 2025

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

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Gait pattern analysis and clinical subgroup identification: a retrospective observational study.

Sunghyon Kyeong1, Seung Min Kim2, Suk Jung3

  • 1Institute of Behavioral Science in Medicine, Yonsei University College of Medicine.

Medicine
|April 14, 2020
PubMed
Summary

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This study identified common abnormal gait patterns across neurological and musculoskeletal conditions. A simple prediction model using gait speed and hip extension accurately classifies these gait patterns for clinical use.

Area of Science:

  • Biomechanics
  • Neurology
  • Orthopedics

Background:

  • Gait analysis is crucial for diagnosing and managing neurological and musculoskeletal disorders.
  • Identifying common gait features across diverse conditions can improve diagnostic accuracy and treatment strategies.

Purpose of the Study:

  • To identify fundamental gait characteristics and abnormal patterns shared across conditions like stroke, Parkinsonian disorders, radiculopathy, and musculoskeletal pain.
  • To develop a predictive model for classifying gait abnormalities within these conditions.

Main Methods:

  • Retrospective analysis of temporal-spatial, kinematic, and kinetic gait parameters in 1328 subjects (1012 patients, 316 controls).
  • Utilized a community detection algorithm to identify gait subgroups within each condition.

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Related Experiment Videos

Last Updated: Dec 24, 2025

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
3D Kinematic Gait Analysis for Preclinical Studies in Rodents
10:19

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

Published on: August 3, 2019

11.2K
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

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  • Developed a prediction model based on gait speed and maximal hip extension during the stance phase.

    • Main Results:

    • Observed distinct gait alterations: asymmetric knee/ankle flexion in hemiplegia and reduced hip/knee range of motion in Parkinsonian disorders.
    • Identified three prevalent abnormal gait patterns: fast gait with adequate hip extension, fast gait with inadequate hip extension, and slow gait.
    • The prediction model demonstrated high accuracy in classifying gait subgroups within conditions.

    Conclusions:

    • Specific gait patterns exist both within and across various neurological and musculoskeletal conditions.
    • The developed subgrouping algorithm offers a clinically applicable tool for classifying abnormal gait and guiding therapeutic interventions.