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Direct comparison of muscle force predictions using linear and nonlinear programming.

D R Pedersen1, R A Brand, C Cheng

  • 1Orthopaedic Biomechanics Laboratory, University of Iowa, Iowa City 52242.

Journal of Biomechanical Engineering
|August 1, 1987
PubMed
Summary

This study introduces a flexible optimization program for estimating muscle and joint forces during locomotion. Nonlinear formulations improve EMG prediction accuracy and reveal synergistic and antagonistic muscle activity, unlike linear models.

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Area of Science:

  • Biomechanics
  • Musculoskeletal modeling
  • Computational dynamics

Background:

  • Estimating muscle and joint forces during locomotion is complex due to the indeterminate nature of the problem.
  • Previous comparisons of optimization methods are hindered by variations in models, algorithms, and data.
  • A unified approach is needed to directly evaluate optimization criteria and constraints.

Purpose of the Study:

  • To present a new, flexible optimization program for analyzing human locomotion.
  • To compare linear and nonlinear optimization techniques for predicting muscle and joint forces.
  • To evaluate the impact of different optimization criteria and constraints on model predictions.

Main Methods:

  • Developed a novel optimization program with linear and nonlinear techniques.

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  • Incorporated various cost functions and flexible problem formulation.
  • Applied the program to estimate muscle and joint forces during locomotion.
  • Compared predictions with electromyography (EMG) data.

    • Main Results:

    • Nonlinear formulations enable synergistic and antagonistic muscle activity, unlike linear formulations.
    • Nonlinear predictions show better concurrence with EMG data compared to linear predictions.
    • Allowing muscles to not resolve the entire intersegmental moment in nonlinear models reduces joint contact forces while maintaining synergism.

    Conclusions:

    • Unified solution methods facilitate direct evaluation of optimization criteria and constraints.
    • Nonlinear optimization offers a more realistic representation of muscle activity during locomotion.
    • This approach provides a flexible platform for exploring new optimization formulations and comparing them to existing methods.