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EMG optimization in OpenSim: A model for estimating lower back kinetics in gait.

Jacob J Banks1, Brian R Umberger2, Graham E Caldwell3

  • 1University of Massachusetts Amherst, Department of Kinesiology, 110 Totman Building, 30 Eastman Lane, Amherst, MA 01003, United States; Beth Israel Deaconess Medical Center, Center for Advanced Orthopaedic Studies, 330 Brookline Avenue, RN 115, Boston, MA 02215, United States; Harvard Medical School, Department of Orthopaedic Surgery, Boston, MA 02115, United States.

Medical Engineering & Physics
|May 2, 2022
PubMed
Summary

This study developed an electromyography optimization (EMGopt) approach in OpenSim for personalized musculoskeletal models. This method enhances the accuracy of estimating lower back internal kinetic demands during gait tasks.

Keywords:
ElectromyographyEvaluationIn silicoLoad carriageMusculoskeletalSensitivityStatic optimization

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Area of Science:

  • Biomechanics
  • Musculoskeletal modeling
  • Human movement analysis

Background:

  • Accurate estimation of lower back internal kinetic demands is crucial for assessing injury risk.
  • Participant-specific musculoskeletal models are essential for precise biomechanical analysis.
  • Existing generic models may not fully capture individual variations in movement and muscle recruitment.

Purpose of the Study:

  • To develop and evaluate an electromyography optimization (EMGopt) framework within OpenSim for personalized musculoskeletal modeling.
  • To assess lower back internal kinetic demands during gait and carrying tasks using the developed EMGopt approach.
  • To compare the EMGopt approach with a generic static optimization (SOpt) method.

Main Methods:

  • Recorded kinematic, external kinetic, and electromyography (EMG) data from six participants during walking and carrying tasks.
  • Developed an EMGopt approach integrated within OpenSim to estimate muscle forces and joint loads.
  • Compared predicted lumbar vertebral joint forces and muscle activations against SOpt and recorded EMG data.

Main Results:

  • The EMGopt model produced lumbar vertebral joint forces qualitatively similar to literature values.
  • EMGopt predicted significantly larger joint loads compared to SOpt (p<.01).
  • Muscle activations estimated by EMGopt showed better agreement with individual participant EMG data than SOpt.

Conclusions:

  • The developed EMGopt framework is well-suited for evaluating internal lower back demands during gait and carrying tasks.
  • The approach provides more accurate estimations of lower back kinetic demands compared to generic static optimization.
  • The model demonstrates robustness to day-to-day EMG variability, maintaining reliable between-task comparisons.