IALH Research Fellow Giles, Joshua has co-authored a new research article entitled OpenSim Moco Tracking Simulations Efficiently Replicate Predictive Simulation Results Across Morphologically Diverse Shoulder Models. Collaborating authors include Hamad Jaylan and Kuchinka Kaitlyn. The article was published in Computer Methods in Biomechanics and Biomedical Engineering.
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.
To read the full article, see https://doi.org/10.1080/10255842.2024.2384481.