Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/1167
Title: Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures
Authors: Ozdemir, Mehmet Akif
Kisa, Deniz Hande
Guren, Onan
Akan, Aydin
Keywords: Biomedical signals
Biosignals
Classification
Data
Electromyography (EMG)
Gesture
Movement
Muscle
Deep Learning
Machine Learning
Publisher: Elsevier
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/)
URI: https://doi.org/10.1016/j.dib.2022.107921
https://hdl.handle.net/20.500.14365/1167
ISSN: 2352-3409
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

Files in This Item:
File SizeFormat 
185.pdf1.34 MBAdobe PDFView/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

30
checked on Nov 6, 2024

WEB OF SCIENCETM
Citations

22
checked on Nov 6, 2024

Page view(s)

208
checked on Nov 11, 2024

Download(s)

54
checked on Nov 11, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.