AST: An OpenSim-based tool for the automatic scaling of generic musculoskeletal models

• Published in: Computers in biology and medicine Vol. 175; p. 108524
• Main Authors: Di Pietro, Andrea, Bersani, Alex, Curreli, Cristina, Di Puccio, Francesca
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2024; Elsevier Limited
• Subjects: Accuracy; Automation; Biomechanical Phenomena - physiology; Biomechanics; Computer Simulation; Error correction; Gait - physiology; Humans; Internal Medicine; Inverse kinematics; Kinematics; Marker placement; Matlab; Models, Biological; Musculoskeletal models; OpenSim; Optimization; Other; Root-mean-square errors; Scaling; Scaling factors; Scaling tool; Segments; Software; OpenSim; Automation; MSK; GM; Musculoskeletal models; SSF; RMSE; Scaling tool; ASF; DoFs; Marker placement; SFR; Matlab; Scaling Tool; root mean square error; degrees of freedom; segment scaling factor; scaling factor range; generic model; average scaling factor; musculoskeletal
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108524

The paper introduces a tool called Automatic Scaling Tool (AST) designed for improving and expediting musculoskeletal (MSK) simulations based on generic models in OpenSim. Scaling is a crucial initial step in MSK analyses, involving the correction of virtual marker locations on a model to align with actual experimental markers. The AST automates this process by iteratively adjusting virtual markers using scaling and inverse kinematics on a static trial. It evaluates the root mean square error (RMSE) and maximum marker error, implementing corrective actions until achieving the desired accuracy level. The tool determines whether to scale a segment with a marker-based or constant scaling factor based on checks on RMSE and segment scaling factors. Testing on three generic MSK models demonstrated that the AST significantly outperformed manual scaling by an expert operator. The RMSE for static trials was one order of magnitude lower, and for gait tasks, it was five times lower (8.5 ± 0.76 mm vs. 44.5 ± 7.5 mm). The AST consistently achieved the desired level of accuracy in less than 100 iterations, providing reliable scaled MSK models within a relatively brief timeframe, ranging from minutes to hours depending on model complexity. The paper concludes that AST can greatly benefit the biomechanical community by quickly and accurately scaling generic models, a critical first step in MSK analyses. Further validation through additional experimental datasets and generic models is proposed for future tests. •Musculoskeletal analyses require scaling a generic model to the subject dimensions.•The scaling procedure is a critical and time-consuming step in MSK simulations.•An automatic tool for scaling OpenSim generic models is described and tested.•Three models, each with 10 different marker placements, were scaled on 3 subjects.•Results prove the reliability of the tool with reduced RMSE and CPU time (ca 1:10).