Literature DB >> 19045930

Adaptive surrogate modeling for efficient coupling of musculoskeletal control and tissue deformation models.

Jason P Halloran1, Ahmet Erdemir, Antonie J van den Bogert.   

Abstract

Finite element (FE) modeling and multibody dynamics have traditionally been applied separately to the domains of tissue mechanics and musculoskeletal movements, respectively. Simultaneous simulation of both domains is needed when interactions between tissue and movement are of interest, but this has remained largely impractical due to the high computational cost. Here we present a method for the concurrent simulation of tissue and movement, in which state of the art methods are used in each domain, and communication occurs via a surrogate modeling system based on locally weighted regression. The surrogate model only performs FE simulations when regression from previous results is not within a user-specified tolerance. For proof of concept and to illustrate feasibility, the methods were demonstrated on an optimization of jumping movement using a planar musculoskeletal model coupled to a FE model of the foot. To test the relative accuracy of the surrogate model outputs against those of the FE model, a single forward dynamics simulation was performed with FE calls at every integration step and compared with a corresponding simulation with the surrogate model included. Neural excitations obtained from the jump height optimization were used for this purpose and root mean square (RMS) difference between surrogate and FE model outputs (ankle force and moment, peak contact pressure and peak von Mises stress) were calculated. Optimization of the jump height required 1800 iterations of the movement simulation, each requiring thousands of time steps. The surrogate modeling system only used the FE model in 5% of time steps, i.e., a 95% reduction in computation time. Errors introduced by the surrogate model were less than 1 mm in jump height and RMS errors of less than 2 N in ground reaction force, 0.25 Nm in ankle moment, and 10 kPa in peak tissue stress. Adaptive surrogate modeling based on local regression allows efficient concurrent simulations of tissue mechanics and musculoskeletal movement.

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Year:  2009        PMID: 19045930      PMCID: PMC2891249          DOI: 10.1115/1.3005333

Source DB:  PubMed          Journal:  J Biomech Eng        ISSN: 0148-0731            Impact factor:   2.097


  27 in total

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Journal:  J Biomech       Date:  2004-05       Impact factor: 2.712

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Journal:  J Biomech       Date:  2007-04-12       Impact factor: 2.712

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Authors:  J H Koolstra; T M G J van Eijden
Journal:  J Biomech       Date:  2004-12-30       Impact factor: 2.712

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Authors:  M G Pandy; F E Zajac
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Authors:  S G McLean; A Su; A J van den Bogert
Journal:  J Biomech Eng       Date:  2003-12       Impact factor: 2.097

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  16 in total

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Journal:  J Biomech Eng       Date:  2014-02       Impact factor: 2.097

3.  Efficient Computation of Cartilage Contact Pressures within Dynamic Simulations of Movement.

Authors:  Colin R Smith; Kwang Won Choi; Dan Negrut; Darryl G Thelen
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2016-05-13

4.  A computationally efficient strategy to estimate muscle forces in a finite element musculoskeletal model of the lower limb.

Authors:  Alessandro Navacchia; Donald R Hume; Paul J Rullkoetter; Kevin B Shelburne
Journal:  J Biomech       Date:  2018-12-28       Impact factor: 2.712

5.  Concurrent musculoskeletal dynamics and finite element analysis predicts altered gait patterns to reduce foot tissue loading.

Authors:  Jason P Halloran; Marko Ackermann; Ahmet Erdemir; Antonie J van den Bogert
Journal:  J Biomech       Date:  2010-06-22       Impact factor: 2.712

6.  3D finite element models of shoulder muscles for computing lines of actions and moment arms.

Authors:  Joshua D Webb; Silvia S Blemker; Scott L Delp
Journal:  Comput Methods Biomech Biomed Engin       Date:  2012-09-20       Impact factor: 1.763

7.  Computational Models for Neuromuscular Function.

Authors:  Francisco J Valero-Cuevas; Heiko Hoffmann; Manish U Kurse; Jason J Kutch; Evangelos A Theodorou
Journal:  IEEE Rev Biomed Eng       Date:  2009

8.  An Open-Source Toolbox for Surrogate Modeling of Joint Contact Mechanics.

Authors:  Ilan Eskinazi; Benjamin J Fregly
Journal:  IEEE Trans Biomed Eng       Date:  2015-07-13       Impact factor: 4.538

9.  Surrogate modeling of deformable joint contact using artificial neural networks.

Authors:  Ilan Eskinazi; Benjamin J Fregly
Journal:  Med Eng Phys       Date:  2015-07-26       Impact factor: 2.242

10.  ReadySim: A computational framework for building explicit finite element musculoskeletal simulations directly from motion laboratory data.

Authors:  Donald R Hume; Paul J Rullkoetter; Kevin B Shelburne
Journal:  Int J Numer Method Biomed Eng       Date:  2020-09-01       Impact factor: 2.747

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