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Insights into muscle metabolic energetics: modelling muscle-tendon mechanics and metabolic rates during walking across speeds
KTH, School of Engineering Sciences (SCI), Engineering Mechanics.ORCID iD: 0000-0002-1561-4000
Faculty of Behavioural and Movement Sciences, VU Amsterdam, Amsterdam, The Netherlands.
Department of Movement Sciences, KU Leuven, Leuven, Belgium.
KTH, School of Engineering Sciences (SCI), Engineering Mechanics. Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.ORCID iD: 0000-0001-5417-5939
2024 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 20, no 9, article id e1012411Article in journal (Refereed) Published
Abstract [en]

The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model individualization and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, inclusion of calibrated muscle passive forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated with a subset of metabolic energy models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and were similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed. Our prediction might inform the design of assistive devices and rehabilitation treatment. The code and experimental data are available online.

Place, publisher, year, edition, pages
Public Library of Science (PLoS) , 2024. Vol. 20, no 9, article id e1012411
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Physiotherapy
Identifiers
URN: urn:nbn:se:kth:diva-353917DOI: 10.1371/journal.pcbi.1012411ISI: 001312585800002PubMedID: 39269982Scopus ID: 2-s2.0-85203879764OAI: oai:DiVA.org:kth-353917DiVA, id: diva2:1900992
Note

QC 20240927

Available from: 2024-09-25 Created: 2024-09-25 Last updated: 2025-02-11Bibliographically approved

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Luis, IsraelGutierrez-Farewik, Elena M.

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