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Updated: Jul 16, 2025

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
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On Gait Consistency Quantification Through ARX Residual Modeling and Kernel Two-Sample Testing.

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    Quantifying gait consistency using a novel AutoRegressive model and hypothesis testing reveals differences between healthy individuals and those with multiple sclerosis (MS). This method highlights how varying conditions impact gait analysis.

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    Area of Science:

    • Biomechanics
    • Neurology
    • Medical Technology

    Background:

    • Gait analysis is crucial for understanding neuromuscular diseases.
    • Quantifying gait consistency helps differentiate natural variability from disease progression or treatment effects.
    • Objective methods are needed to assess gait consistency accurately.

    Purpose of the Study:

    • To present a novel objective method for assessing gait consistency.
    • To quantify gait consistency in healthy individuals and those with multiple sclerosis (MS).
    • To evaluate the impact of varying assessment conditions on gait consistency.

    Main Methods:

    • Utilized AutoRegressive with eXogenous input (ARX) models with inertial sensor accelerometer data from shanks and lower back.
    • Employed model residuals as key features for gait consistency monitoring.
    • Applied the maximum mean discrepancy (MMD) hypothesis test to compare residual distributions.

    Main Results:

    • Multiple sclerosis (MS) patients showed reduced gait consistency, even under controlled conditions.
    • Gait inconsistency was observed in both healthy individuals and MS patients when retested after one week.
    • The study identified detrimental effects of varying assessment conditions on gait pattern consistency.

    Conclusions:

    • Successfully quantified gait consistency in both healthy and MS individuals.
    • The novel approach effectively highlights gait alterations in MS.
    • Varying assessment conditions can mask gait pattern consistency, impacting follow-up assessments.