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Parameter estimation and experimental design for Hill-type muscles: Impulses from optimization-based modeling.

R Rockenfeller1, J L Herold2, T Götz1

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Summary
This summary is machine-generated.

Optimization-based modeling enhances parameter estimation for Hill-type muscle models. Specific isometric and quick-release experiments are proposed to reliably identify all model parameters, reducing experimental effort.

Keywords:
BiomechanicsGauss–Newton method

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Area of Science:

  • Biomechanics
  • Computational Modeling
  • Muscle Physiology

Background:

  • Hill-type muscle models are crucial for understanding muscle mechanics.
  • Parameter estimation is essential for accurate muscle model predictions.
  • Previous studies have faced challenges in comprehensive parameter identification.

Purpose of the Study:

  • To demonstrate the benefits of optimization-based modeling for parameter estimation in Hill-type muscle models.
  • To identify optimal experimental conditions for reliable parameter estimation.
  • To propose a refined experimental protocol for macroscopic muscle model parameterization.

Main Methods:

  • Utilized optimization-based modeling techniques.
  • Analyzed existing piglet calf muscle contraction data (isometric, concentric, quick-release).
  • Performed local sensitivity analysis to guide experimental design.

Main Results:

  • Isometric contractions are suitable for derivative-based parameter estimation.
  • Concentric and quick-release contractions offered limited additional value in the original analysis.
  • Proposed a set of four new experiments (two isometric, two quick-release) for comprehensive parameter identification.

Conclusions:

  • Optimization-based modeling significantly improves muscle model parameter estimation.
  • A combination of specific isometric and quick-release experiments can reliably identify all model parameters.
  • The proposed experimental guidelines can reduce effort for practitioners in muscle modeling.