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Methodology to Customize Maximal Isometric Forces for Hill-Type Muscle Models.

Fabien Dal Maso1,2, Mickaël Begon1,2, Maxime Raison1

  • 11 CHU Ste Justine.

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|October 14, 2016
PubMed
Summary

Optimizing maximal voluntary isometric contraction (MVIC) trials enhances muscle force estimation accuracy in musculoskeletal models. This method significantly reduced root mean square error (RMSE) by selecting specific MVIC trial combinations.

Keywords:
antagonist musclesbest combinationelectromyographymaximum muscle stressmusculoskeletal modelnumerical optimization

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Area of Science:

  • Biomechanics
  • Human Movement Science
  • Computational Modeling

Background:

  • Accurate muscle force estimation is crucial for understanding human movement and developing effective rehabilitation strategies.
  • Electromyographic-driven musculoskeletal models offer a powerful tool for estimating muscle forces, but their confidence can be limited by model-to-participant variability.
  • Minimizing the root mean square error (RMSE) between model-estimated and inverse dynamics-computed joint torques is a key approach to enhance model confidence.

Purpose of the Study:

  • To develop and validate a novel method for reducing RMSE in electromyographic-driven musculoskeletal models.
  • To improve the accuracy and confidence of muscle force estimations by optimizing the selection of maximal voluntary isometric contraction (MVIC) trials.
  • To investigate the variability of maximal isometric forces across different upper limb muscles.

Main Methods:

  • Developed an upper-limb electromyographic-driven musculoskeletal model for twelve participants.
  • Optimized maximum muscle stress and estimated maximal isometric forces for biceps brachii, brachialis, brachioradialis, and triceps brachii.
  • Computed maximal isometric forces from all possible combinations of flexion-extension MVIC trials to identify subsets minimizing RMSE.

Main Results:

  • The proposed method significantly reduced normalized RMSE to 7.4%, compared to 9.0% when using all MVIC trials.
  • This 7.4% RMSE is a notable improvement over the previously reported smallest RMSE of 10.3%.
  • Maximal isometric forces exhibited substantial inter-individual variability, highlighting the need for personalized model customization.

Conclusions:

  • Selecting specific combinations of MVIC trials effectively minimizes RMSE, thereby increasing confidence in muscle force estimations from electromyographic-driven musculoskeletal models.
  • The proposed method offers a significant advancement in reducing discrepancies between model predictions and actual physiological measurements.
  • Personalized estimation of maximal isometric forces is essential for accurate and reliable musculoskeletal modeling.