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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Bibliographic Details
Published in:PLoS computational biology 2020-12, Vol.16 (12), p.e1008350-e1008350
Main Authors: Sobinov, Anton, Boots, Matthew T, Gritsenko, Valeriya, Fisher, Lee E, Gaunt, Robert A, Yakovenko, Sergiy
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Approximation
Arm
Biological complexity
Biology and Life Sciences
Biomechanical Phenomena
Biomechanics
Biomimetics
Cognitive ability
Computational Biology
Constraint modelling
Datasets
Forearm - physiology
Humans
Kinematics
Machine learning
Mechanical properties
Medicine and Health Sciences
Methods
Motor task performance
Muscle, Skeletal - physiology
Muscles
Musculoskeletal Physiological Phenomena
Musculoskeletal system
Pattern recognition
Physical Sciences
Physiology
Polynomials
Posture
Prostheses
Research and Analysis Methods
Software
Technology application
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Summary: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
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1008350