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Motor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles.

dc.contributor.authorOliveira, Anderson S.
dc.contributor.authorGizzi, Leonardo
dc.contributor.authorFarina, Dario
dc.contributor.authorKersting, Uwe G.
dc.date.accessioned2015-05-13T07:32:53Z
dc.date.available2015-05-13T07:32:53Z
dc.date.issued2014
dc.identifier.citationOliveira, Anderson S; Gizzi, Leonardo; Farina, Dario; Kersting, Uwe G (2014): Motor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles. - Frontiers in human neuroscience, Vol. 8, p. 335
dc.relation.ISSN1662-5161
dc.identifier.urihttp://resolver.sub.uni-goettingen.de/purl?gs-1/11791
dc.description.abstractLocomotion can be investigated by factorization of electromyographic (EMG) signals, e.g., with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analyzed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMGSNG), from averaging 2, 3, 5, 10, 20, and 40 consecutive cycles (EMGAVR), and from the concatenation of the same sets of consecutive cycles (EMGCNC). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality >90%), regardless of the type of data structure used. However, EMGCNC was associated with a slightly reduced reconstruction quality with respect to EMGAVR. Most motor modules were similar when extracted from different data structures (similarity >0.85). However, the quality of the reconstructed 40-step EMGCNC datasets when using the muscle weightings from EMGAVR was low (reconstruction quality ~40%). On the other hand, the use of weightings from EMGCNC for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality ~80%). Although EMGSNG and EMGAVR showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion.
dc.languageeng
dc.language.isoeng
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/600698/EU//H2R
dc.rightsopenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.subjectlocomotion;variability;EMG;musclesynergies;motormodules;neuralcontrol
dc.titleMotor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles.
dc.typejournalArticle
dc.identifier.doi10.3389/fnhum.2014.00335
dc.type.versionpublishedVersion
dc.identifier.fs612831
dc.bibliographicCitation.volume8
dc.bibliographicCitation.firstPage1
dc.bibliographicCitation.lastPage9
dc.type.subtypejournalArticle
dc.identifier.pmid24904375
dc.bibliographicCitation.articlenumber335
dc.relation.euprojectH2R
dc.description.statuspeerReviewed
dc.bibliographicCitation.journalFrontiers in human neuroscience


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