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Related Experiment Videos

Spanning set defines variability in locomotive patterns.

M J Kurz1, N Stergiou, D Blanke

  • 1HPER Biomechanics Laboratory, University of Nebraska at Omaha, Omaha, Nebraska, USA. mkurz@mail.unomaha.edu

Medical & Biological Engineering & Computing
|April 15, 2003
PubMed
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The spanning set method offers a sensitive approach to quantify variability in locomotive patterns, outperforming traditional measures like coefficient of variation (CV) and mean deviation (MD) in detecting differences between running conditions.

Area of Science:

  • Biomechanics
  • Locomotion Analysis
  • Kinematics

Background:

  • Locomotive patterns exhibit variability influenced by factors like footwear.
  • Quantifying this variability is crucial for understanding biomechanical changes.
  • Traditional methods may lack sensitivity in detecting subtle differences.

Purpose of the Study:

  • To apply the spanning set methodology for quantifying locomotive pattern variability.
  • To compare the sensitivity of the spanning set method against traditional variability measures (CV, MD).
  • To evaluate changes in gait variability between barefoot and shod conditions.

Main Methods:

  • Subjects ran on a treadmill, with sagittal plane kinematic data captured using high-speed videography.
  • Mean ensemble curves of knee angle during stance were generated for barefoot and shod conditions.

Related Experiment Videos

  • Variability was assessed using traditional measures (CV, MD) and the spanning set method, comparing polynomial fits to standard deviation curves.

    • Main Results:

    • The spanning set method detected a statistically significant difference (98% normalized difference) in variability between barefoot and shod conditions.
    • Traditional measures (MD: 6.6%, CV: 6.9%) failed to identify statistically significant differences.
    • The spanning set demonstrated higher sensitivity in distinguishing between the two running conditions.

    Conclusions:

    • The spanning set methodology provides a statistically robust and sensitive alternative for assessing variability in locomotive patterns.
    • It is more effective than traditional CV and MD measures for detecting subtle changes in gait.
    • This method enhances the evaluation of biomechanical differences in locomotion.