A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition

Computer Modeling in Engineering & Sciences(2023)

引用 2|浏览9
暂无评分
摘要
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.
更多
查看译文
关键词
Hand gesture recognition,sEMG,CNN,temporal squeeze-and-excite,attention
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要