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Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking

Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularl...

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Autores principales: Markowitz, Jared, Herr, Hugh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4866735/
https://www.ncbi.nlm.nih.gov/pubmed/27175486
http://dx.doi.org/10.1371/journal.pcbi.1004912
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author Markowitz, Jared
Herr, Hugh
author_facet Markowitz, Jared
Herr, Hugh
author_sort Markowitz, Jared
collection PubMed
description Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularly their force-length relations. Current walking models that use stock parameters when simulating muscle-tendon dynamics tend to significantly overestimate metabolic consumption, perhaps because they do not adequately consider the role of elasticity. As an alternative, we posit that the muscle-tendon morphology of the human leg has evolved to maximize the metabolic efficiency of walking at self-selected speed. We use a data-driven approach to evaluate this hypothesis, utilizing kinematic, kinetic, electromyographic (EMG), and metabolic data taken from five participants walking at self-selected speed. The kinematic and kinetic data are used to estimate muscle-tendon lengths, muscle moment arms, and joint moments while the EMG data are used to estimate muscle activations. For each subject we perform an optimization using prescribed skeletal kinematics, varying the parameters that govern the force-length curve of each tendon as well as the strength and optimal fiber length of each muscle while seeking to simultaneously minimize metabolic cost and maximize agreement with the estimated joint moments. We find that the metabolic cost of transport (MCOT) values of our participants may be correctly matched (on average 0.36±0.02 predicted, 0.35±0.02 measured) with acceptable joint torque fidelity through application of a single constraint to the muscle metabolic budget. The associated optimal muscle-tendon parameter sets allow us to estimate the forces and states of individual muscles, resolving redundancies in joint actuation and lending insight into the potential roles and control objectives of the muscles of the leg throughout the gait cycle.
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spelling pubmed-48667352016-05-18 Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking Markowitz, Jared Herr, Hugh PLoS Comput Biol Research Article Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularly their force-length relations. Current walking models that use stock parameters when simulating muscle-tendon dynamics tend to significantly overestimate metabolic consumption, perhaps because they do not adequately consider the role of elasticity. As an alternative, we posit that the muscle-tendon morphology of the human leg has evolved to maximize the metabolic efficiency of walking at self-selected speed. We use a data-driven approach to evaluate this hypothesis, utilizing kinematic, kinetic, electromyographic (EMG), and metabolic data taken from five participants walking at self-selected speed. The kinematic and kinetic data are used to estimate muscle-tendon lengths, muscle moment arms, and joint moments while the EMG data are used to estimate muscle activations. For each subject we perform an optimization using prescribed skeletal kinematics, varying the parameters that govern the force-length curve of each tendon as well as the strength and optimal fiber length of each muscle while seeking to simultaneously minimize metabolic cost and maximize agreement with the estimated joint moments. We find that the metabolic cost of transport (MCOT) values of our participants may be correctly matched (on average 0.36±0.02 predicted, 0.35±0.02 measured) with acceptable joint torque fidelity through application of a single constraint to the muscle metabolic budget. The associated optimal muscle-tendon parameter sets allow us to estimate the forces and states of individual muscles, resolving redundancies in joint actuation and lending insight into the potential roles and control objectives of the muscles of the leg throughout the gait cycle. Public Library of Science 2016-05-13 /pmc/articles/PMC4866735/ /pubmed/27175486 http://dx.doi.org/10.1371/journal.pcbi.1004912 Text en © 2016 Markowitz, Herr 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
Markowitz, Jared
Herr, Hugh
Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking
title Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking
title_full Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking
title_fullStr Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking
title_full_unstemmed Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking
title_short Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking
title_sort human leg model predicts muscle forces, states, and energetics during walking
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4866735/
https://www.ncbi.nlm.nih.gov/pubmed/27175486
http://dx.doi.org/10.1371/journal.pcbi.1004912
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