## 1 Introduction  
sEMG signals constitute bioelectric signals that record sequential muscle contraction processes within skeletal muscles, encompassing crucial information regarding muscle contraction modes and intensities; they can serve as a straightforward, reliable information source for the purpose of gesture recognition. In recent years, sEMG signals have found widespread applications in diverse fields, notably in human-computer interaction [1, 2] and prosthetic limb control [3, 4, 5]. Gesture recognition based on sEMG signals presents itself as a multi-classification problem within the domain of pattern recognition; currently, two primary methodologies are employed to address this challenge: traditional machine-learning methods and deep-learning methods. Traditional machine-learning approaches typically involve the extraction of features from sEMG signals in the time domain, frequency domain, or time-frequency domain. Following dimensionality reduction in these features [6, 7], conventional classification methods, such as support vector machines (SVMs) [8, 9, 10, 11], random forest (RF) [12, 13], linear discriminant analysis (LDA) [14, 15, 16], and others, are subsequently applied for effective gesture recognition. Deep-learning methods have a strong ability to learn features from data and images; they have been confirmed to perform well in pattern recognition, so in recent years, various deep-learning structures and methods have been gradually applied for gesture recognition based on sEMG signals. For instance, in Ref. [17], a convolutional neural network (CNN)was employed for electromyography gesture recognition, yielding superior accuracy compared to the traditional SVM. [PERSON] et al. [18] utilized the LeNet model for recognition, achieving a recognition accuracy equivalent to traditional classification methods in 53 gesture recognition tasks. [PERSON] et al. [19] introduced an eight-layer CNN for gesture recognition. [PERSON] et al. [20] proposed a topic transfer learning method for gesture recognition on the NinaPro DB2 dataset, elevating the recognition accuracy from 81.43% to 82.87%. [PERSON] et al. [21] utilized a CNN for gesture recognition on the NinaPro DB2 dataset, achieving a correct recognition rate of 78.7%. [PERSON] et al. [22] proposed a deep CNN model for gesture recognition, with the highest recognition accuracy reaching 82.54% on the NinaPro DB1 dataset. [PERSON] et al. [23] employed a multi-stream CNN fusion network for gesture recognition, achieving a recognition accuracy of 85%. Attention mechanisms in deep learning have proven instrumental in enhancing system focus on key information within signals, thereby improving classification accuracy and decoding. They have been used in gesture recognition problems based on sEMG signals. For instance, [PERSON] et al. [24] integrated attention mechanism modules into a neural network's input layer for electromyography gesture recognition based on the CappMyo and CSLHDEMG datasets, resulting in accuracy improvements of 4.44% and 2.71%, respectively. [PERSON] et al. [25] enhanced the LSTM-CNN network by introducing the attention mechanism CBAM, leading to a notable 5.3% increase in recognition accuracy. [PERSON] et al. [26] proposed the CSAC-Net network model, leveraging attention mechanisms to focus on crucial information in the channel space, achieving a gesture recognition accuracy of 82.50%. [PERSON] et al. [27] employed the attention mechanism and temporal convolution in the TC-HGR architecture, achieving a gesture recognition accuracy of 81.65%. [PERSON] et al. [28] proposed a hybrid CNN-RNN network structure based on the attention mechanism, achieving an average gesture recognition accuracy of 84.80% based on the NinaPro DB1 dataset. Additionally, multi-scale modules in deep learning use convolution kernels and pooling operations of various sizes concurrently to facilitate feature extraction across different scales.