A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables

dc.contributor.authorYasar, Merve Nuren
dc.contributor.authorSica, Marcoen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.authorMenolotto, Matteoen
dc.contributor.funderScience Foundation Ireland
dc.contributor.funderEuropean Regional Development Fund
dc.date.accessioned2024-06-12T10:42:29Z
dc.date.available2024-06-12T10:42:29Z
dc.date.issued2024-04-27en
dc.description.abstractWearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid433en
dc.identifier.citationYasar, M.N., Sica, M., O’Flynn, B., Tedesco, S. and Menolotto, M. (2024) ‘A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables’, Scientific Data, 11(1), p. 433. Available at: https://doi.org/10.1038/s41597-024-03254-8en
dc.identifier.doihttps://doi.org/10.1038/s41597-024-03254-8en
dc.identifier.endpage14en
dc.identifier.issn2052-4463en
dc.identifier.issued1en
dc.identifier.journaltitleScientific Dataen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16000
dc.identifier.volume11en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofScientific Dataen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 2/12/RC/2289-P2s/IE/INSIGHT Phase 2/
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/
dc.rights© The Authors 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectBiomedical engineeringen
dc.subjectResearch dataen
dc.subjectWearable sensorsen
dc.subjectPhysical fatigue assessmenten
dc.subjectFatigueen
dc.titleA dataset for fatigue estimation during shoulder internal and external rotation movements using wearablesen
dc.typeArticle (peer-reviewed)en
oaire.citation.issue1en
oaire.citation.volume11en
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