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Data-driven spectral analysis for coordinative structures in periodic human locomotion.

Keisuke Fujii1,2, Naoya Takeishi3, Benio Kibushi4

  • 1Graduate School of Informatics, Nagoya University, Nagoya, Japan. fujii@i.nagoya-u.ac.jp.

Scientific Reports
|November 16, 2019
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Summary
This summary is machine-generated.

This study introduces a novel operator-theoretic spectral analysis to uncover dynamic properties of biological coordinative structures. The method reveals speed-independent and speed-dependent dynamics in human walking, offering new insights into biological periodic systems.

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Area of Science:

  • Nonlinear dynamics
  • Systems biology
  • Biomechanics

Background:

  • Living organisms utilize complex, redundant control mechanisms involving numerous components.
  • Conventional methods for extracting coordinative structures often fail to capture essential dynamical properties.
  • Understanding these structures is crucial for analyzing biological periodic systems.

Purpose of the Study:

  • To develop a data-driven method for analyzing coordinative structures in biological periodic systems.
  • To extract dynamical properties like frequency and phase from these structures.
  • To apply and validate the approach using human locomotion and simulation data.

Main Methods:

  • Modeling biological periodic systems as nonlinear limit-cycle oscillations.
  • Applying operator-theoretic spectral analysis to extract dynamical properties.
  • Utilizing segmental angle series from human walking and simulation data.

Main Results:

  • Identified speed-independent coordinative structures related to gait frequency harmonics.
  • Discovered speed-dependent, time-evolving phase behaviors using estimated eigenfunctions.
  • Validated the approach with double pendulum and walking model simulations.

Conclusions:

  • The proposed operator-theoretic spectral analysis effectively captures dynamical properties of biological coordinative structures.
  • This method provides a new perspective for analyzing biological periodic phenomena through nonlinear dynamical systems.
  • The findings have implications for understanding locomotion and other periodic biological processes.