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2018-10-29Zeitschriftenartikel DOI: 10.18452/19644
Modular Control of Human Movement During Running: An Open Access Data Set
dc.contributor.authorSantuz, Alessandro
dc.contributor.authorEkizos, Antonis
dc.contributor.authorJanshen, Lars
dc.contributor.authorMersmann, Falk
dc.contributor.authorBohm, Sebastian
dc.contributor.authorBaltzopoulos, Vasilios
dc.contributor.authorArampatzis, Adamantios
dc.date.accessioned2018-12-28T10:09:54Z
dc.date.available2018-12-28T10:09:54Z
dc.date.issued2018-10-29
dc.identifier.issn1664-042X
dc.identifier.other10.3389/fphys.2018.01509
dc.identifier.urihttp://edoc.hu-berlin.de/18452/20420
dc.description.abstractThe human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the 20th century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of 5 to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males and 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization (NMF) and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using NMF. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing, and late swing. We foresee many possible applications of our data that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modeling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods. All the data is available at Zenodo (doi: 10.5281/zenodo.1254380).eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin
dc.relation.haspart10.5281/zenodo.1435592
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectmuscle synergieseng
dc.subjectlocomotioneng
dc.subjectrunningeng
dc.subjectmotor controleng
dc.subjectEMGeng
dc.subjectdata seteng
dc.subject.ddc610 Medizin und Gesundheit
dc.titleModular Control of Human Movement During Running: An Open Access Data Set
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/20420-4
dc.identifier.doihttp://dx.doi.org/10.18452/19644
dc.type.versionpublishedVersion
local.edoc.container-titleFrontiers in physiology
local.edoc.pages11
local.edoc.anmerkungThis article was supported by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionLebenswissenschaftliche Fakultät
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameFrontiers Research Foundation
local.edoc.container-publisher-placeLausanne
local.edoc.container-volume9
local.edoc.container-alephidBV045281312
dc.description.versionPeer Reviewed
local.edoc.container-articlenumber1509

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