OpenSim Moco tracking simulations efficiently replicate predictive simulation results across morphologically diverse shoulder models

Abstract

OpenSim Moco enables solving for an optimal motion using Predictive and Tracking simulations. However, Predictive simulations are computationally prohibitive, and the efficacy of Tracking in deviating from its reference is unclear. This study compares Tracking and Predictive approaches applied to the generation of morphology-specific motion in statistically-derived musculoskeletal shoulder models. The signal analysis software, CORA, determined mean correlation ratings between Tracking and Predictive solutions of 0.91 ± 0.06 and 0.91 ± 0.07 for lateral and forward-reaching tasks. Additionally, Tracking provided computational speed-up of 6–8 times. Therefore, Tracking is an efficient approach that yields results equivalent to Predictive, facilitating future large-scale modelling studies.

Keywords:

• Musculoskeletal modeling
• statistical shape model
• predictive simulation
• tracking simulation
• reverse total shoulder arthroplasty

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work was supported by the Canadian Institutes of Health Research under Grant PJT-173505 and the Michael Smith Health Research British Columbia Scholar Award under Grant SCH-2021-1562.