Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/1167
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dc.contributor.authorOzdemir, Mehmet Akif-
dc.contributor.authorKisa, Deniz Hande-
dc.contributor.authorGuren, Onan-
dc.contributor.authorAkan, Aydin-
dc.date.accessioned2023-06-16T12:59:13Z-
dc.date.available2023-06-16T12:59:13Z-
dc.date.issued2022-
dc.identifier.issn2352-3409-
dc.identifier.urihttps://doi.org/10.1016/j.dib.2022.107921-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/1167-
dc.description.abstractThis paper presents an electromyography (EMG) signal dataset for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial deviation of the wrist, grip, abduction of all fingers, adduction of all fingers, supination, and pronation. Data were collected from 4 forearm muscles when simulating 10 unique hand gestures and recorded with the BIOPAC MP36 device using Ag/AgCl surface bipolar electrodes. Each participant's data contains five repetitive cycles of ten hand gestures. A demographic survey was applied to the participants before the signal recording process. This data can be utilized for recognition, classification, and prediction studies in order to develop EMG-based hand movement controller systems. The dataset can also be useful as a reference to create an artificial intelligence model (especially a deep learning model) to detect gesture-related EMG signals. Additionally, it is encouraged to use the proposed dataset for benchmarking current datasets in the literature or for validation of machine learning and deep learning models created with different datasets in accordance with the participant-independent validation strategy. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)en_US
dc.description.sponsorshipScientific and Technical Research Council of Turkey (TUBITAK) [120E512]; Izmir Katip Celebi University Scientific Research Projects Coordination Unit [2021-oDL-MuMF-0004, 2022-GAP-MuMF-0001]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technical Research Council of Turkey (TUBITAK) [grant number 120E512] and the Izmir Katip Celebi University Scientific Research Projects Coordination Unit [grant numbers 2021-oDL-MuMF-0004, 2022-GAP-MuMF-0001] . The authors thank all volunteers who participated in the experiment.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofData in Brıefen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiomedical signalsen_US
dc.subjectBiosignalsen_US
dc.subjectClassificationen_US
dc.subjectDataen_US
dc.subjectElectromyography (EMG)en_US
dc.subjectGestureen_US
dc.subjectMovementen_US
dc.subjectMuscleen_US
dc.subjectDeep Learningen_US
dc.subjectMachine Learningen_US
dc.titleDataset for multi-channel surface electromyography (sEMG) signals of hand gesturesen_US
dc.typeData Paperen_US
dc.identifier.doi10.1016/j.dib.2022.107921-
dc.identifier.pmid35198693en_US
dc.identifier.scopus2-s2.0-85124262604en_US
dc.departmentİzmir Ekonomi Üniversitesien_US
dc.authoridOzdemir, Mehmet Akif/0000-0002-8758-113X-
dc.authoridKisa, Deniz Hande/0000-0002-5882-0605-
dc.authoridAkan, Aydin/0000-0001-8894-5794-
dc.authorwosidOzdemir, Mehmet Akif/G-7952-2018-
dc.authorwosidGüren, Onan/HKF-6479-2023-
dc.authorscopusid57206479576-
dc.authorscopusid57221554803-
dc.authorscopusid56364720900-
dc.authorscopusid35617283100-
dc.identifier.volume41en_US
dc.identifier.wosWOS:000778990600034en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.grantfulltextopen-
item.openairetypeData Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.dept05.06. Electrical and Electronics Engineering-
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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