Goal: Incorporation of elastic joint contact models into simulations of human movement could facilitate studying the interactions between muscles, ligaments, and bones. Unfortunately, elastic joint contact models are often too expensive computationally to be used within iterative simulation frameworks. This limitation can be overcome by using fast and accurate surrogate contact models that fit or interpolate input-output data sampled from existing elastic contact models. However, construction of surrogate contact models remains an arduous task. The aim of this paper is to introduce an open-source program called Surrogate Contact Modeling Toolbox (SCMT) that facilitates surrogate contact model creation, evaluation, and use.
Methods: SCMT interacts with the third-party software FEBio to perform elastic contact analyses of finite-element models and uses MATLAB to train neural networks that fit the input-output contact data. SCMT features sample point generation for multiple domains, automated sampling, sample point filtering, and surrogate model training and testing.
Results: An overview of the software is presented along with two example applications. The first example demonstrates creation of surrogate contact models of artificial tibiofemoral and patellofemoral joints and evaluates their computational speed and accuracy, while the second demonstrates the use of surrogate contact models in a forward dynamic simulation of an open-chain leg extension-flexion motion.
Conclusion: SCMT facilitates the creation of computationally fast and accurate surrogate contact models. Additionally, it serves as a bridge between FEBio and OpenSim musculoskeletal modeling software.
Significance: Researchers may now create and deploy surrogate models of elastic joint contact with minimal effort.
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http://dx.doi.org/10.1109/TBME.2015.2455510 | DOI Listing |
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National Bio- and Agro-Defense Facility, Agricultural Research Services, United States Department of Agriculture, Manhattan, KS 66506, USA.
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Laboratory of Biology of Cellular Interactions, Department of Morphology, Federal University of Minas Gerais, Belo Horizonte 31270-901, MG, Brazil.
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Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain.
In recent years, the application of AI has expanded rapidly across various fields. However, it has faced challenges in establishing a foothold in medicine, particularly in invasive medical procedures. Medical algorithms and devices must meet strict regulatory standards before they can be approved for use on humans.
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Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40127 Bologna, Italy.
Temporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard.
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School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
The geometric error distributed on components' contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution error modeling for precision machining surfaces based on the K-space spectrum is proposed.
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