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Pace and motor control optimization for a runner.

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This summary is machine-generated.

This study introduces a runner model optimizing pace and effort for fixed distances. For long races, a turnpike problem approximates optimal velocity, simplifying parameter inference from race data and estimating slope effects.

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Area of Science:

  • Sports Science
  • Biomechanics
  • Physiological Modeling

Background:

  • Understanding runner's pace and motor control is crucial for performance.
  • Existing models often simplify physiological parameters and external factors like terrain.

Purpose of the Study:

  • To develop a comprehensive model for runner's pace optimization and motor control effort.
  • To approximate optimal velocity for long races using a turnpike problem formulation.
  • To enable inference of physiological parameters and estimation of terrain effects from race data.

Main Methods:

  • Development of a mathematical model integrating pace optimization and motor control effort.
  • Application of turnpike problem principles for long-term behavior approximation.
  • Numerical simulations to validate the model and its approximations.
  • Formulation of a simplified problem for inferring physiological parameters.

Main Results:

  • The turnpike problem provides a good approximation for optimal velocity in long races.
  • Numerical simulations align well with the turnpike approximation.
  • The simplified model allows inference of physiological parameters from velocity data and physiological cost function.
  • The model can estimate the impact of slopes and ramps on running performance.

Conclusions:

  • The proposed model offers a simplified yet effective approach to understanding and predicting running performance.
  • Accurate inference of physiological parameters is possible with velocity data and a known cost function.
  • The model's ability to account for terrain effects enhances its practical applicability in sports science.