Gesture recognition network based on the interaction of global and local myoelectric features
XIAO Cheng-gang
MIN Hua-song
Abstract：In order to capture the long-term dynamic dependencies and local detail information in the electromyography(EMG)signal more effectively and reduce the impact of the loss of intrinsic EMG feature information on the gesture classification accuracy,we propose a gesture recognition network the global and local-electromyography-network(GL-EMG-Net)based on the interaction of global and local features.Firstly,the dilated convolution and multi-head self-attention mechanism are integrated to design the global feature extraction block the global-dilation transformer(Global-DT)to extract the global information in the EMG signal.Then,with the help of the depth separable convolution and attention mechanism,the local feature extraction block the local-selective kernel(Local-SK)is designed to capture the local detail information of different scales in the EMG signal,and feedback the extracted detail information to the Global-DT module through the feedback mechanism to complete the interaction between local features and global features.Finally,the global features and local features are fused for classification.The experimental results show that the gesture recognition network shows high gesture classification accuracy and strong robustness in the 52 gestures of Ninapro DB5 dataset and 12 actual common gestures.
Keywords：surface electromyographyhand gesture recognitiondilated convolutional networksattention mechanismfeature fusion
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 609-617 )