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Characterizing intersection variability of butterfly diagram in post-stroke gait using Kernel Density Estimation.

Yun-Ju Lee1, Jing Nong Liang2

  • 1Department of Industrial Engineering and Engineering, Management National Tsing Hua University No. 101, Section 2, Guangfu Road, East District, Hsinchu City, 300 Taiwan.

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|December 22, 2019
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Summary

Kernel density estimation (KDE) offers a novel and sensitive method to analyze gait variability in stroke survivors. This approach provides better insights into locomotor characteristics compared to traditional methods.

Keywords:
Butterfly diagramCenter of pressureGaitKernel Density EstimationLocomotor variability

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Area of Science:

  • Biomechanics
  • Neurology
  • Rehabilitation Science

Background:

  • The butterfly diagram visualizes center of pressure (COP) during walking but is inefficient for displaying locomotor variability.
  • Existing methods struggle to adequately represent gait characteristics in individuals with neurological impairments.

Purpose of the Study:

  • To evaluate post-stroke locomotor variability using Kernel density estimation (KDE) on butterfly diagram intersections.
  • To compare KDE-derived gait metrics with conventional symmetry and variability measures.

Main Methods:

  • Determined bilateral toe-off (TO) and initial contact (IC) points for COP symmetry index calculation.
  • Applied KDE to butterfly diagram intersections to assess gait density and variability.
  • Compared standard deviations of step width and length between groups.

Main Results:

  • Identified 4 distinct KDE surface plot patterns in post-stroke individuals, correlating with functional status (walking speed, Fugl-Meyer scores).
  • Conventional metrics (step width/length variability) did not significantly differ between groups.
  • KDE analysis revealed greater sensitivity in characterizing locomotor COP variability.

Conclusions:

  • KDE analysis presents a novel, more sensitive method for characterizing locomotor COP variability in post-stroke hemiparesis.
  • This approach offers superior visualization of gait characteristics compared to conventional metrics.