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Related Experiment Video

Updated: Aug 18, 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

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Singular value decomposition-based gait characterization.

Cem Guzelbulut1, Katsuyuki Suzuki1, Satoshi Shimono2,3

  • 1Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan.

Heliyon
|December 8, 2022
PubMed
Summary

Human gait variations are influenced by factors like walking speed, age, sex, height, and weight. Understanding these gait parameters is crucial for detecting walking problems and designing assistive devices.

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Area of Science:

  • Biomechanics
  • Human Motion Analysis
  • Data Science

Background:

  • Human gait exhibits variability due to personal characteristics and potential walking impairments.
  • Identifying sources of gait variation is critical for diagnosing mobility issues and developing personalized orthotics or prosthetics.

Purpose of the Study:

  • To analyze temporal variations in joint angles and ground reaction forces during human gait.
  • To investigate the influence of demographic factors (age, sex, height, weight) and walking speed on gait parameters.

Main Methods:

  • Singular Value Decomposition (SVD) was employed to analyze temporal variations in joint angles and ground reaction forces.
  • Pearson's correlation coefficient matrix was used to assess relationships between demographic data, walking speed, and SVD-derived coefficients.
Keywords:
Gait analysisGait variationsSingular value decompositionTemporal variations

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Related Experiment Videos

Last Updated: Aug 18, 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

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Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
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Main Results:

  • The primary modes from SVD captured the majority of gait variability (99.9%).
  • Walking speed was identified as the most significant factor affecting joint kinematics and ground reaction forces.
  • Age, gender, height, and weight also influenced gait parameters, though to a lesser extent than walking speed.

Conclusions:

  • Singular Value Decomposition effectively represents complex temporal gait data with a reduced set of coefficients.
  • The study highlights the significant impact of walking speed and demographic factors on gait patterns.
  • This methodology offers potential for monitoring disease progression and informing the design of orthotic and prosthetic devices.