Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?

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dc.citation.journalTitleFrontiers in Computational Neuroscience
dc.contributor.authorMichaud, Florian
dc.contributor.authorShourijeh, Mohammad S.
dc.contributor.authorFregly, Benjamin J.
dc.contributor.authorCuadrado, Javier
dc.contributor.orgRice Computational Neuromechanics Laboratory
dc.date.accessioned2020-11-04T18:47:51Z
dc.date.available2020-11-04T18:47:51Z
dc.date.issued2020
dc.description.abstractDetermination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.
dc.identifier.citationMichaud, Florian, Shourijeh, Mohammad S., Fregly, Benjamin J., et al.. "Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?." <i>Frontiers in Computational Neuroscience,</i> (2020) Frontiers: https://doi.org/10.3389/fncom.2020.00054.
dc.identifier.doihttps://doi.org/10.3389/fncom.2020.00054
dc.identifier.urihttps://hdl.handle.net/1911/109503
dc.language.isoeng
dc.publisherFrontiers
dc.rightsThis 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.titleDo Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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