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Updated: Jun 8, 2026

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

Methods to temporally align gait cycle data.

Nathaniel E Helwig1, Sungjin Hong, Elizabeth T Hsiao-Wecksler

  • 1Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820-6232, USA. nhelwig2@illinois.edu

Journal of Biomechanics
|October 5, 2010
PubMed
Summary
This summary is machine-generated.

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Accurate temporal alignment of gait cycle data is crucial. Piecewise alignment methods offer superior results compared to traditional techniques for biomechanical and clinical gait analysis.

Area of Science:

  • Biomechanics
  • Gait Analysis
  • Movement Science

Background:

  • Temporal alignment of gait cycle data is essential for accurate analysis.
  • Current methods lack consensus, leading to variability in research findings.
  • Understanding gait requires precise temporal synchronization of kinematic and kinetic data.

Purpose of the Study:

  • To discuss and evaluate common temporal gait cycle alignment methods.
  • To compare the efficacy of normalization, dynamic time warping, and piecewise alignment techniques.
  • To identify the optimal method for accurate temporal alignment in gait analysis.

Main Methods:

  • Empirical evaluation of normalization to percent gait cycle, dynamic time warping (DTW), derivative dynamic time warping (DDTW), and piecewise alignment.

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  • Mapping test gait cycle trajectories to target trajectories to assess alignment success.
  • Analysis of intensity and temporal differences using piecewise alignment techniques.

    • Main Results:

    • Piecewise temporal alignment techniques demonstrated superior performance over normalization, DTW, and DDTW.
    • The proposed methods achieved successful temporal alignment in typical biomechanical and clinical tasks.
    • Piecewise alignment enables independent examination of intensity and temporal gait variations.

    Conclusions:

    • Piecewise alignment methods are recommended for temporal gait cycle data analysis.
    • These techniques enhance the ability to discern subtle differences in movement patterns.
    • Improved gait analysis through superior temporal alignment offers greater insight into human locomotion.