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The validity of stability measures: a modelling approach.

Sjoerd M Bruijn1, Daan J J Bregman, Onno G Meijer

  • 1Motor Control Laboratory, Research Center for Movement Control and Neuroplasticity, Department of Biomedical Kinesiology, KU Leuven, Belgium. s.m.bruijn@gmail.com

Journal of Biomechanics
|July 19, 2011
PubMed
Summary

Predicting falls in human walking is crucial. This study found that step time variability, especially its log transform, better predicts fall probability in passive dynamic walkers than maximum Floquet multipliers or kinematic variability.

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Area of Science:

  • Biomechanics
  • Robotics
  • Human locomotion

Background:

  • Stability measures like maximum Floquet multipliers and variability are used to study walking.
  • The correlation between these measures and the actual probability of falling remains unclear.

Purpose of the Study:

  • To investigate if maximum Floquet multipliers, kinematic state variability, or step time variability can predict the probability of falling in a passive dynamic walker model.
  • To determine which variability measure is the most consistent predictor of fall probability.

Main Methods:

  • Utilized an extended passive dynamic walker model with arced feet and a hip spring.
  • Manipulated fall probability by adjusting foot radius, hip spring stiffness, and slope.
  • Analyzed correlations between fall probability and maximum Floquet multipliers, kinematic variability, and step time variability (including its log transform).

Main Results:

  • Maximum Floquet multipliers and kinematic state variability showed inconsistent correlations with the probability of falling.
  • Step time variability demonstrated a good correlation with fall probability.
  • The variability of the log transform of step time exhibited the most consistent correlation with the probability of falling.

Conclusions:

  • Maximum Floquet multipliers are not reliable predictors of fall probability in this model.
  • Variability of critical variables, particularly the log transform of step time, may serve as a robust predictor of fall probability.
  • Findings suggest focusing on step time variability for fall prediction in dynamic walking models.