@inproceedings{77d85c1a89b848fc863067eeeb76defc,
title = "A New Dataset and Recognition for Egocentric Microgesture Designed by Ergonomists",
abstract = "Virtual and Augmented reality (VR/AR) are widely deployed in industrial, medical, educational, and entertaining fields. The design of interactive interfaces has an impact on usability, comfort, and efficiency. Hand controllers and gestures are popularly used in VR/AR devices. However, users may suffer from overloading on the upper extremities while raising the hand or controller. Therefore, we released a microgesture library with 19 microgestures designed by ergonomists. Users can perform microgestures for an extended duration by resting the forearm on the tables to reduce the load on the upper extremity. Additionally, we collected a microgesture dataset of 2900 samples and utilized the C3D model to recognize the microgesture dataset. Finally, we achieved a recognition accuracy of 93.4% on the microgestures dataset.",
keywords = "Dataset, Ergonomist, Gesture recognition, Microgestures, VR/AR",
author = "Guangchuan Li and Yue Liu and Weitao Song and Cong Wang and Yongtian Wang",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 11th International Conference on Image and Graphics, ICIG 2021 ; Conference date: 06-08-2021 Through 08-08-2021",
year = "2021",
doi = "10.1007/978-3-030-87358-5_7",
language = "English",
isbn = "9783030873578",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "80--89",
editor = "Yuxin Peng and Shi-Min Hu and Moncef Gabbouj and Kun Zhou and Michael Elad and Kun Xu",
booktitle = "Image and Graphics - 11th International Conference, ICIG 2021, Proceedings",
address = "Germany",
}