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Related Experiment Video

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Use of Rotorod as a Method for the Qualitative Analysis of Walking in Rat
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A method to concatenate multiple short time series for evaluating dynamic behaviour during walking.

Stefan Orter1, Deepak K Ravi1, Navrag B Singh1

  • 1Institute for Biomechanics, ETH Zürich, Zurich, Switzerland.

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|June 22, 2019
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Summary

A new algorithm concatenates gait data, enabling nonlinear analysis for mobility impairments. While effective for stability measures, it requires caution for regularity analysis to avoid misinterpreting gait signals.

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Area of Science:

  • Biomechanics
  • Human Movement Science
  • Data Science

Background:

  • Gait variability analysis is crucial for detecting functional deficits in individuals with mobility impairments.
  • Nonlinear measures of gait kinematics necessitate long, continuous time series for accurate temporal evolution representation.
  • Existing methods often struggle with standard overground walking protocols due to data fragmentation.

Purpose of the Study:

  • To introduce and validate a novel algorithm for concatenating multiple gait time series.
  • To enable nonlinear analysis of gait data from unrestricted overground walking.
  • To assess the algorithm's efficacy in preserving gait dynamics for stability and regularity metrics.

Main Methods:

  • Collected full-body gait data from 20 healthy subjects during treadmill walking under varied weight conditions.
  • Segmented time series into shorter segments and applied a novel algorithm to identify similar poses for optimal concatenation.
  • Aligned and concatenated data, ensuring smooth transitions for subsequent nonlinear analysis.
  • Calculated Largest Lyapunov Exponent (LyE) for stability and Sample Entropy (SE) for regularity.

Main Results:

  • The concatenation algorithm demonstrated good agreement with full, uninterrupted time series for LyE (stability).
  • Sample Entropy (SE) analysis revealed higher sensitivity to the concatenation process, potentially leading to misinterpretation of gait regularity.
  • The approach allows for reprocessing of previously collected gait datasets.

Conclusions:

  • The proposed gait data concatenation algorithm facilitates nonlinear analysis, particularly for dynamic stability assessment.
  • Care must be taken when applying the algorithm to regularity measures like Sample Entropy due to potential signal alteration.
  • This method enhances the analysis of gait data from overground protocols and historical datasets.