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Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, plan surgical interventions, or control realistic high-dimensional articulated prosthetic limbs. If the models are sufficiently accurate, they may embed c...

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Detalles Bibliográficos
Autores principales: Sobinov, Anton, Boots, Matthew T., Gritsenko, Valeriya, Fisher, Lee E., Gaunt, Robert A., Yakovenko, Sergiy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773415/
https://www.ncbi.nlm.nih.gov/pubmed/33326417
http://dx.doi.org/10.1371/journal.pcbi.1008350
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author Sobinov, Anton
Boots, Matthew T.
Gritsenko, Valeriya
Fisher, Lee E.
Gaunt, Robert A.
Yakovenko, Sergiy
author_facet Sobinov, Anton
Boots, Matthew T.
Gritsenko, Valeriya
Fisher, Lee E.
Gaunt, Robert A.
Yakovenko, Sergiy
author_sort Sobinov, Anton
collection PubMed
description Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, plan surgical interventions, or control realistic high-dimensional articulated prosthetic limbs. If the models are sufficiently accurate, they may embed complex relationships within the sensorimotor system. These potential benefits are limited by the challenge of implementing fast and accurate musculoskeletal computations. A typical hand muscle spans over 3 degrees of freedom (DOF), wrapping over complex geometrical constraints that change its moment arms and lead to complex posture-dependent variation in torque generation. Here, we report a method to accurately and efficiently calculate musculotendon length and moment arms across all physiological postures of the forearm muscles that actuate the hand and wrist. Then, we use this model to test the hypothesis that the functional similarities of muscle actions are embedded in muscle structure. The posture dependent muscle geometry, moment arms and lengths of modeled muscles were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in <10 μs, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with increasing muscle complexity; complexity increases linearly instead. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, indicating the high accuracy of approximating models. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models to describe human movement.
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spelling pubmed-77734152021-01-07 Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials Sobinov, Anton Boots, Matthew T. Gritsenko, Valeriya Fisher, Lee E. Gaunt, Robert A. Yakovenko, Sergiy PLoS Comput Biol Research Article Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, plan surgical interventions, or control realistic high-dimensional articulated prosthetic limbs. If the models are sufficiently accurate, they may embed complex relationships within the sensorimotor system. These potential benefits are limited by the challenge of implementing fast and accurate musculoskeletal computations. A typical hand muscle spans over 3 degrees of freedom (DOF), wrapping over complex geometrical constraints that change its moment arms and lead to complex posture-dependent variation in torque generation. Here, we report a method to accurately and efficiently calculate musculotendon length and moment arms across all physiological postures of the forearm muscles that actuate the hand and wrist. Then, we use this model to test the hypothesis that the functional similarities of muscle actions are embedded in muscle structure. The posture dependent muscle geometry, moment arms and lengths of modeled muscles were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in <10 μs, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with increasing muscle complexity; complexity increases linearly instead. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, indicating the high accuracy of approximating models. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models to describe human movement. Public Library of Science 2020-12-16 /pmc/articles/PMC7773415/ /pubmed/33326417 http://dx.doi.org/10.1371/journal.pcbi.1008350 Text en © 2020 Sobinov et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sobinov, Anton
Boots, Matthew T.
Gritsenko, Valeriya
Fisher, Lee E.
Gaunt, Robert A.
Yakovenko, Sergiy
Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials
title Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials
title_full Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials
title_fullStr Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials
title_full_unstemmed Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials
title_short Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials
title_sort approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773415/
https://www.ncbi.nlm.nih.gov/pubmed/33326417
http://dx.doi.org/10.1371/journal.pcbi.1008350
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