###### Abstract
[PERSON] and [PERSON] [15] employed a KNN classifier using 25 features of sEMG and accelerometer signals to classify Persian sign language symbols, achieving an average accuracy of 96.13%. [PERSON] et al. [16] classified 15 hand movements for individuals with intact limbs and 12 hand movements for amputees using linear discriminant analysis (LDA) and SVM with AutoRegressive (AR) features. Given that EMG signals represent sequential data, hidden Markov models are suitable for modeling the latent information in EMG signals. [PERSON] et al. [17] used an HMM classifier to create an sEMG-based sign language recognition system. Despite the significant potential of sEMG technology, traditional gesture recognition methods still face several challenges. sEMG signals are usually of high dimensionality and complexity, involving multi-channel and high-sampling-rate data, which poses challenges for feature extraction and dimensionality management. Moreover, sEMG signals are susceptible to interference from muscle fatigue, electrode displacement, and environmental noise, which affects the stability and accuracy of the model. Finally, existing gesture recognition methods usually rely on complex feature engineering, which may limit the performance of the model. In recent years, deep learning has gained substantial popularity and made ground-breaking advances in various domains, such as image processing [18] and speech recognition [19]. More recently, the use of deep learning for sEMG-based gesture recognition has started to capture researchers' attention. They have begun exploring the application of deep neural networks (DNNs) for gesture recognition. DNNs offer powerful feature learning and classification capabilities, eliminating the need for manual feature engineering and enabling the automatic learning of gesture feature representations, consequently significantly enhancing gesture recognition accuracy. Among various deep learning techniques used for sEMG-based gesture recognition, the Convolutional Neural Network (CNN) architecture stands out as one of the most widely employed. Researchers have categorized these into two primary types based on different evaluation methods. For example, [PERSON] et al. [5] conducted sEMG classification tasks on four publicly available datasets using a deep convolutional neural network (CNN) architecture comprising two convolutional layers. Their work demonstrated a performance improvement of 2-5% compared to existing machine learning classifiers such as KNN, SVM, random forests, and LDA [20]. [PERSON] et al. [21] proposed a deep learning model that combines Convolutional Autoencoders (CAEs) and CNNs for classification of sEMG datasets consisting of ten hand gestures. The results indicated high levels of performance and robustness. [PERSON] et al. [22] fed the spectral representations of sEMG into a CNN for gesture recognition, but they achieved only 78.7% accuracy on the second sub-database of the NinaPro dataset. Furthermore, due to the temporal characteristics of sEMG, Recurrent Neural Networks (RNNs) and LongShort-Term Memory (LSTM) architectures have been applied to hand-related problems based on surface electromyography. In [23], [PERSON] et al. employed a ConvLSTM cascaded with an LSTM architecture for gesture sequence classification. In [24], a two-level network composed of a fully connected network and stacked RNNs was implemented to classify high-density (HD) and sparse sEMG signals. [PERSON] et al. [25] collected sEMG signals using the Myo armband for sign language recognition and applied an RNN architecture to extract features from sequential data for analysis of sign language gestures. Despite the promising outcomes in sEMG signal analysis, the broader application of existing methods is impeded by their complexity, computational demands, and extended training times. Current models predominantly focus on augmenting feature information from temporal and frequency domains [26], neglecting inter-channel correlations [27].