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 in jump height and RMS errors of less than in ground reaction force, in ankle moment, and 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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January 2009
Research Papers
Adaptive Surrogate Modeling for Efficient Coupling of Musculoskeletal Control and Tissue Deformation Models
Jason P. Halloran,
Jason P. Halloran
Department of Biomedical Engineering (ND-20),
Cleveland Clinic Foundation
, 9500 Euclid Avenue, Cleveland, OH 44195
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Ahmet Erdemir,
Ahmet Erdemir
Department of Biomedical Engineering (ND-20),
Cleveland Clinic Foundation
, 9500 Euclid Avenue, Cleveland, OH 44195
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Antonie J. van den Bogert
Antonie J. van den Bogert
Department of Biomedical Engineering (ND-20),
e-mail: bogerta@ccf.org
Cleveland Clinic Foundation
, 9500 Euclid Avenue, Cleveland, OH 44195
Search for other works by this author on:
Jason P. Halloran
Department of Biomedical Engineering (ND-20),
Cleveland Clinic Foundation
, 9500 Euclid Avenue, Cleveland, OH 44195
Ahmet Erdemir
Department of Biomedical Engineering (ND-20),
Cleveland Clinic Foundation
, 9500 Euclid Avenue, Cleveland, OH 44195
Antonie J. van den Bogert
Department of Biomedical Engineering (ND-20),
Cleveland Clinic Foundation
, 9500 Euclid Avenue, Cleveland, OH 44195e-mail: bogerta@ccf.org
J Biomech Eng. Jan 2009, 131(1): 011014 (7 pages)
Published Online: November 26, 2008
Article history
Received:
February 29, 2008
Revised:
August 21, 2008
Published:
November 26, 2008
Citation
Halloran, J. P., Erdemir, A., and van den Bogert, A. J. (November 26, 2008). "Adaptive Surrogate Modeling for Efficient Coupling of Musculoskeletal Control and Tissue Deformation Models." ASME. J Biomech Eng. January 2009; 131(1): 011014. https://doi.org/10.1115/1.3005333
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