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https://hdl.handle.net/20.500.14365/1167
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ozdemir, Mehmet Akif | - |
dc.contributor.author | Kisa, Deniz Hande | - |
dc.contributor.author | Guren, Onan | - |
dc.contributor.author | Akan, Aydin | - |
dc.date.accessioned | 2023-06-16T12:59:13Z | - |
dc.date.available | 2023-06-16T12:59:13Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 2352-3409 | - |
dc.identifier.uri | https://doi.org/10.1016/j.dib.2022.107921 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14365/1167 | - |
dc.description.abstract | This 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.sponsorship | Scientific 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.sponsorship | This 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.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Data in Brıef | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Biomedical signals | en_US |
dc.subject | Biosignals | en_US |
dc.subject | Classification | en_US |
dc.subject | Data | en_US |
dc.subject | Electromyography (EMG) | en_US |
dc.subject | Gesture | en_US |
dc.subject | Movement | en_US |
dc.subject | Muscle | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Machine Learning | en_US |
dc.title | Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures | en_US |
dc.type | Data Paper | en_US |
dc.identifier.doi | 10.1016/j.dib.2022.107921 | - |
dc.identifier.pmid | 35198693 | en_US |
dc.identifier.scopus | 2-s2.0-85124262604 | en_US |
dc.department | İzmir Ekonomi Üniversitesi | en_US |
dc.authorid | Ozdemir, Mehmet Akif/0000-0002-8758-113X | - |
dc.authorid | Kisa, Deniz Hande/0000-0002-5882-0605 | - |
dc.authorid | Akan, Aydin/0000-0001-8894-5794 | - |
dc.authorwosid | Ozdemir, Mehmet Akif/G-7952-2018 | - |
dc.authorwosid | Güren, Onan/HKF-6479-2023 | - |
dc.authorscopusid | 57206479576 | - |
dc.authorscopusid | 57221554803 | - |
dc.authorscopusid | 56364720900 | - |
dc.authorscopusid | 35617283100 | - |
dc.identifier.volume | 41 | en_US |
dc.identifier.wos | WOS:000778990600034 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.grantfulltext | open | - |
item.openairetype | Data Paper | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 05.06. 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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