@Article{cmes.2022.020035,
AUTHOR = {Zhengyuan Xu, Junxiao Yu, Wentao Xiang, Songsheng Zhu, Mubashir Hussain, Bin Liu, Jianqing Li},
TITLE = {A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {1},
PAGES = {157--177},
URL = {http://www.techscience.com/CMES/v134n1/49428},
ISSN = {1526-1506},
ABSTRACT = {In this article, to reduce the complexity and improve the generalization ability of current gesture recognition
systems, we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition. The
proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then
utilizes it to recalibrate the weights of the feature outputs from the convolutional layer. By enhancing important
features while suppressing useless ones, the model realizes gesture recognition efficiently. The last procedure of
the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG
signals to perform multi-channel sEMG-based gesture recognition tasks. To evaluate the effectiveness and accuracy
of the proposed algorithm, we conduct experiments involving multi-gesture datasets Ninapro DB4 and Ninapro
DB5 for both inter-session validation and subject-wise cross-validation. After a series of comparisons with the
previous models, the proposed algorithm effectively increases the robustness with improved gesture recognition
performance and generalization ability.},
DOI = {10.32604/cmes.2022.020035}
}