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Errors in Predicting Muscle Fiber Lengths from Joint Kinematics Point to the Need to Include Tendon Tension in Computational Neuromuscular Models

View ORCID ProfileDaniel A Hagen, Francisco J Valero-Cuevas
doi: https://doi.org/10.1101/2020.07.08.194381
Daniel A Hagen
aDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
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Francisco J Valero-Cuevas
aDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
bDivision of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA
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  • For correspondence: valero@usc.edu
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Abstract

Accurate predictions of tendon forces must consider musculotendon mechanics; specifically muscle fiber lengths and velocities. These are either predicted explicitly by simulating musculoskeletal dynamics or approximated from measured limb kinematics. The latter is complicated by the fact that tendon lengths and pennation angles vary with both limb kinematics and tendon tension. We now derive the error in kinematically-approximated muscle fiber lengths as a general equation of muscle geometry and tendon tension. This enables researchers to objectively evaluate this error’s significance—which can reach ~ 80% of the optimal muscle fiber length—with respect to the scientific or clinical question being asked. Although this equation provides a detailed functional relationship between muscle fiber lengths, joint kinematics and tendon tension, the parameters used to characterize musculotendon architecture are subject- and muscle-specific. This parametric uncertainty limits the accuracy of any generic musculoskeletal model that hopes to explain subject-specific phenomena. Nevertheless, the existence of such a functional relationship has profound implications to biological proprioception. These results strongly suggest that tendon tension information (from Golgi tendon organs) is likely integrated with muscle fiber length information (from muscle spindles) at the spinal cord to produce useful estimates of limb configuration to enable effective control of movement.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • All authors were fully involved in the study and preparation of this preprint manuscript.

  • https://daniel8hagen.com/images/tendon_length_change_parallel_coords

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 10, 2020.
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Errors in Predicting Muscle Fiber Lengths from Joint Kinematics Point to the Need to Include Tendon Tension in Computational Neuromuscular Models
Daniel A Hagen, Francisco J Valero-Cuevas
bioRxiv 2020.07.08.194381; doi: https://doi.org/10.1101/2020.07.08.194381
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Errors in Predicting Muscle Fiber Lengths from Joint Kinematics Point to the Need to Include Tendon Tension in Computational Neuromuscular Models
Daniel A Hagen, Francisco J Valero-Cuevas
bioRxiv 2020.07.08.194381; doi: https://doi.org/10.1101/2020.07.08.194381

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