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dc.contributor.authorArones, Marleny M.
Shourijeh, Mohammad S.
Patten, Carolynn
Fregly, Benjamin J.
dc.date.accessioned 2021-02-08T18:37:54Z
dc.date.available 2021-02-08T18:37:54Z
dc.date.issued 2020
dc.identifier.citation Arones, Marleny M., Shourijeh, Mohammad S., Patten, Carolynn, et al.. "Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking." Frontiers in Bioengineering and Biotechnology, 8, (2020) Frontiers: https://doi.org/10.3389/fbioe.2020.588925.
dc.identifier.urihttps://hdl.handle.net/1911/109817
dc.description.abstract Assessment of metabolic cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed walking data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized electromyographic (EMG)-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject’s CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.
dc.language.iso eng
dc.publisher Frontiers
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking
dc.type Journal article
dc.citation.journalTitle Frontiers in Bioengineering and Biotechnology
dc.citation.volumeNumber 8
dc.identifier.digital fbioe-08-588925
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.3389/fbioe.2020.588925
dc.type.publication publisher version
dc.citation.articleNumber 588925


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