Calibrated muscle models improve tracking simulations without enhancing gait predictions.

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.