###### Abstract
This oversight leads to increased model parameters and complexity due to excessive feature extraction, necessitating higher levels of computational resources. Addressing this challenge requires methodologies capable of both extracting valuable inter-channel feature information and efficiently filtering and compressing redundant features. This paper introduces a novel deep learning-based approach for gesture recognition. The methodology presented in this paper was inspired by the work of [PERSON] et al. [28], particularly the multimodal squeeze-and-excite feature fusion module in the SEN-DAL model. Given the unequal contributions of electroencephalogram (EEG) and electro-coculogram (EOG) data in different sleep stages, it is necessary to assign varying levels of importance to them. Hence, by modeling the interdependencies among channels through squeeze-and-excite (SE), SEN-DAL recalibrates the response contributions of channel features. Building upon the aforementioned process, this paper considers how to design modules to extract inter-channel feature information, thereby establishing the correlation between channel features and actions. Importance is assigned to different channel features based on their contributions during different action processes, thereby filtering and compressing the model's features. The model incorporates a channel feature selection unit to comprehensively address the interplay between distinct feature channels. It excels at amplifying the importance of valuable features, suppressing redundant attributes, and employs partial convolution to efficiently capture essential feature channel information while economizing on parameter count. This not only enhances algorithm robustness but also fosters generalization. Our innovative approach not only offers a fresh vantage point in sEMG signal processing and analysis but also ushers in new horizons for the application of deep learning in the realm of biomedical signal processing. Experimental results unequivocally underscore the effectiveness of our method and provide profound insights for the advancement of sEMG gesture recognition. The structure of this paper is outlined as follows. First, we briefly introduce the importance of gesture recognition and its application areas to outline the research theme. Next, we provide an overview of existing gesture recognition methods and point out the existing issues. Then, we present the details of the method proposed in this paper. Subsequently, we describe the experimental results, including accuracy, recall, and other metrics, followed by a comparative analysis of the experimental results. Finally, we summarize the research achievements of this paper and provide insights into future research directions. The main contributions of this paper are summarized as follows:  
* We introduce a channel selection mechanism empowered by gating unit mechanisms to selectively filter channel feature vectors. This innovation compresses and selects key features within high-dimensional feature vectors of the model, reducing its complexity and laying a foundation for further enhancements. * We implement a combination of partial convolution and channel selection units. Through the collaborative operation of these modules, our approach not only extracts inter-channel features but also efficiently filters intrinsic redundant features in feature maps while maintaining recognition accuracy. Consequently, it significantly reduces the model's parameter count, marking progress in model efficiency and resource optimization. * Our model is evaluated across various publicly available databases to demonstrate its effectiveness and robustness. Through comprehensive analysis and comparison with state-of-the-art deep learning methods, we show that our model achieves higher accuracy with fewer trainable parameters across different databases while maintaining exceptional recognition stability. These results provide strong evidence of the efficacy of our proposed model.