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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Gait phase normalization resolves the problem of different phases being compared in gait cycle normalization.

Elham Alijanpour1, Daniel M Russell1

  • 1School of Exercise Science, Ellmer College of Health Sciences, Old Dominion University, United States.

Journal of Biomechanics
|August 2, 2024
PubMed
Summary
This summary is machine-generated.

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Gait phase normalization offers a robust method for analyzing gait data, accurately capturing timing and magnitude differences across various walking speeds. This approach overcomes limitations of traditional gait cycle normalization, improving analysis reliability.

Area of Science:

  • Biomechanics
  • Human Movement Analysis
  • Gait Analysis

Background:

  • Traditional gait cycle normalization methods struggle with variations in cycle durations and ignore differences in gait phase durations.
  • This can lead to inaccurate averaging and comparison of gait data across different phases.

Purpose of the Study:

  • To introduce and validate a comprehensive method for analyzing time-continuous gait data, focusing on independent analysis of magnitude and timing differences.
  • To propose gait phase normalization as a superior alternative to gait cycle normalization.

Main Methods:

  • Gait phases were identified, and differences in absolute/relative timing of phase durations were analyzed using standard statistics.
  • Time-continuous gait data was normalized to gait phases, and statistical parametric mapping (SPM) was used to assess magnitude differences.
Keywords:
Gait speedLandmark registrationSPM analysisTime normalization methodsTime-continuous gait analysis

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  • The method was demonstrated on sagittal knee angle data from ten healthy adults walking at five different speeds.

    • Main Results:

    • Significant changes in gait phase durations were observed across different walking speeds, a factor overlooked by gait cycle normalization.
    • Statistical parametric mapping results for knee angle varied depending on whether data was normalized to the gait cycle or gait phase.
    • Gait phase normalization demonstrated robustness to the definition of the gait cycle start event, unlike gait cycle normalization.

    Conclusions:

    • The proposed method, incorporating gait phase duration analysis and gait phase normalization, overcomes limitations of previous approaches.
    • This comprehensive approach enables a more accurate analysis of both magnitude and timing differences in time-continuous gait data.
    • The methodology is adaptable to other time-continuous movement analysis tasks.