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Area of Science:

  • Biomechanics
  • Musculoskeletal modeling
  • Human motion analysis

Background:

  • Musculoskeletal models (MSMs) are crucial for understanding human movement.
  • Functionally-calibrated models (FCMs) offer potential improvements over non-linearly scaled models (NSMs).

Purpose of the Study:

  • To compare the performance of FCMs against NSMs in tracking and predictive simulations of human motion.
  • To evaluate the impact of different functional calibration variations on simulation accuracy.

Main Methods:

  • Motion capture data from six functional activities were collected from three subjects.
  • Musculotendon parameters were estimated using optimal control to create four FCMs per subject.
  • FCMs and NSMs were compared in tracking simulations (motions excluded from calibration) and predictive gait simulations.

Main Results:

  • FCMs demonstrated higher accuracy in joint torque estimations during tracking simulations.
  • Including gait in calibration improved knee torque accuracy (NRMSE 0.31 vs. 0.70).
  • NSMs outperformed FCMs in predictive gait simulations for subtalar torques and knee angles, while muscle excitation prediction accuracy was similar.

Conclusions:

  • FCMs enhance tracking simulation accuracy for joint torques.
  • FCMs did not surpass NSMs in fully predictive gait simulations.
  • Calibration strategies significantly influence simulation outcomes.