@inproceedings{2d5db535d6e54b7fa756808a4e65097b,
title = "MyoBit: A Public Dataset Based on An Armband with 16 sEMG Channels for Gesture Recognition under Non-ideal Conditions",
abstract = "The robustness of surface electromyography (sEMG)-based gesture recognition in practical applications has received much attention due to the influence of external non-ideal factors. Unlike most existing sEMG-based gesture recognition datasets that use sparse or high-density resolution instruments for data acquisition under ideal conditions, this paper proposes a sEMG armband (Biofrontier) with semi-dense resolution that records 7 gestures from 24 subjects (12 male, 12 female) under 9 non-ideal conditions as a public dataset, MyoBit. The results demonstrate that Biofrontier has a high signal-to-noise ratio and repeatability, and the MyoBit is able to achieve a high accuracy of gesture recognition by classical classifiers. Furthermore, two methods for dataset augmentation, increasing resolution and expanding rotation data, have been proposed for researchers. The dataset link: www.biofrontier.cn/dataset.html",
keywords = "Dataset, gesture recognition, non-ideal conditions, robustness, surface electromyography",
author = "Wei Chen and Lihui Feng and Jihua Lu and Bian Wu and Dewei Liu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 27th International Conference on Methods and Models in Automation and Robotics, MMAR 2023 ; Conference date: 22-08-2023 Through 25-08-2023",
year = "2023",
doi = "10.1109/MMAR58394.2023.10242547",
language = "English",
series = "2023 27th International Conference on Methods and Models in Automation and Robotics, MMAR 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "69--74",
booktitle = "2023 27th International Conference on Methods and Models in Automation and Robotics, MMAR 2023 - Proceedings",
address = "United States",
}