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Related Experiment Video

Updated: Nov 4, 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

14.4K

Gait Analysis by Causal Decomposition.

Xiaohang Peng, Yukun Feng, Shengjie Ji

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 24, 2021
    PubMed
    Summary
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    This study introduces a new causal-decomposition method to analyze bilateral gait by examining frequency-specific interactions in vertical ground reaction forces. High-frequency gait patterns effectively distinguish Parkinson

    Area of Science:

    • Biomechanics
    • Neuroscience
    • Signal Processing

    Background:

    • Bilateral gait analysis using kinetic/kinematic signals is common.
    • Previous methods overlooked simultaneous causation and frequency domain interactions.
    • Accurate causality inference in gait signals requires advanced analysis.

    Purpose of the Study:

    • To develop and apply a causal-decomposition approach for bilateral gait analysis.
    • To investigate frequency-specific causal interactions in gait signals.
    • To assess the utility of this method in distinguishing Parkinson's disease (PD) patients from healthy controls (HC).

    Main Methods:

    • Utilized ensemble empirical mode decomposition to break down vertical ground reaction force (VGRF) signals into intrinsic mode functions (IMFs).

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    Related Experiment Videos

    Last Updated: Nov 4, 2025

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

    Published on: March 4, 2018

    14.4K
    3D Kinematic Gait Analysis for Preclinical Studies in Rodents
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    3D Kinematic Gait Analysis for Preclinical Studies in Rodents

    Published on: August 3, 2019

    11.0K
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  • Analyzed left and right VGRF signals from PD patients and HC individuals.
  • Assessed causal interaction strength (CIS) between IMFs using instantaneous phase dependency.

    • Main Results:

    • Causal interaction strengths (CISes) between high-frequency IMFs of VGRF signals significantly distinguished PD patients from HC individuals.
    • High-frequency CISes showed a significant correlation with Parkinson's disease progression.
    • Other frequency bands did not yield significant distinctions or correlations.

    Conclusions:

    • Frequency-specific causality in bilateral gait, particularly in high-frequency VGRF signals, reflects an individual's health status and disease progression.
    • This novel approach offers insights into the mechanisms of walking and related diseases.
    • Potential broad applications in medicine and engineering for gait analysis and disease monitoring.