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Causal interactions and dynamic stability between limbs while walking with imposed leg constraints.

Genevieve K R Williams1, Domenico Vicinanza2, Michael Attias3

  • 1Department of Public Health and Sports Sciences, University of Exeter, Exeter, United Kingdom.

Frontiers in Human Neuroscience
|September 20, 2024
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Summary

This study reveals that leg motion redundancy supports causal interactions, enhancing walking stability. Gait constraints increase complexity and reduce inter-leg coordination, impacting motor control dynamics.

Keywords:
clinical gait analysisexoskeletonnonlinear dynamicspathological gaitsymmetry

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Area of Science:

  • Biomechanics
  • Motor Control
  • Nonlinear Dynamics

Background:

  • Understanding gait dynamics is crucial for motor control research.
  • Investigating leg motion complexity, stability, and causal relationships provides insights into motor control.
  • Passive exoskeletons can simulate conditions like gastrocnemius contractures to study gait.

Purpose of the Study:

  • To examine motor control system dynamics during walking.
  • To analyze leg motion complexity, stability, and causal interactions under bilateral and unilateral constraints.
  • To investigate the effects of simulated gastrocnemius contractures on gait.

Main Methods:

  • Collected kinematic data from 10 healthy participants during self-selected speed walking.
  • Defined a Complexity-Instability Index (CII) using Correlation Dimension and Largest Lyapunov Exponent.
  • Employed Convergent Cross Mapping to explore causal interactions between leg motions.

Main Results:

  • Normal walking exhibits high inter-leg drive and low CII (high stability, low complexity).
  • Bilateral constraints reduced inter-leg drive and increased CII.
  • Unilateral constraints led to the constrained leg driving the unconstrained leg, with higher CII in the constrained leg.

Conclusions:

  • Limb motion redundancy facilitates causal interactions, reducing complexity and enhancing walking stability.
  • Redundancy enables adaptability and optimal system interaction.
  • Nonlinear and causal variables, alongside biomechanical factors, capture functional movement patterns.