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Related Experiment Video

Updated: Feb 3, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Gait variability analysis through phase portrait estimated from the Hilbert transform.

Gustavo Souto de Sá E Souza1, Adriano O Andrade2, Marcus Fraga Vieira1,2

  • 1a Bioengineering and Biomechanics Laboratory , Universidade Federal de Goiás , Goiânia , Brazil.

Computer Methods in Biomechanics and Biomedical Engineering
|October 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Hilbert transform-based method for gait variability analysis. The new approach is more robust to noise than traditional methods, offering a valuable alternative for gait control evaluation.

Keywords:
Gait variabilityHilbert transformgait analysisgait cyclephase portrait

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Area of Science:

  • Biomechanics
  • Human Movement Analysis
  • Signal Processing

Background:

  • Gait variability is a key indicator of gait control ability.
  • Previous research explored gait variability under various conditions (speed, incline, load) and subject groups (age, impairment).

Purpose of the Study:

  • To develop and validate a new method for estimating gait variability within gait cycles.
  • To leverage the Hilbert transform for phase portrait creation from single time series data.

Main Methods:

  • Utilized the Hilbert transform to generate phase portraits from gait time series.
  • Compared the proposed method with a traditional approach using data from inclined surface walking.
  • Assessed the influence of noise on the accuracy of gait variability estimation.

Main Results:

  • The proposed Hilbert transform-based method demonstrated reduced sensitivity to noise compared to traditional methods.
  • The new method does not require signal interpolation, simplifying the analysis process.
  • The method proved effective for gait variability analysis on inclined surfaces.

Conclusions:

  • The novel Hilbert transform method offers a robust and efficient alternative for gait variability analysis.
  • This technique enhances the evaluation of gait control, particularly in noisy or complex conditions.