## 5 Conclusions  
In this paper, we introduce a PCS-EMGNet based on a channel selection mechanism powered by gating units mechanisms to tackle the enduring challenges within the domain of sEMG gesture recognition. By recognizing the imperative for more precise and computationally efficient methodologies, our proposed model significantly enhances feature extraction capabilities, yielding a model that is both more generalizable and robust. The empirical validation of our model on diverse datasets, including NinaPro DB4, NinaPro DB5, BioPatRec DB1, BioPatRec DB2, and BioPatRec DB3, demonstrated an impressive average classification accuracy of 88.34%, with a notable increase in accuracy and a substantial reduction in computational requirements compared to existing methods. Our findings indicate that our model not only achieves superior performance in terms of accuracy but also in computational efficiency, marking a significant advancement in the practical application of sEMG gesture recognition technologies. Furthermore, by incorporating a novel partial convolution module alongside a channel feature selection unit, our approach effectively capitalizes on the redundancy within feature maps. This methodical application of convolution to select portions of input channels drastically reduces the model's parameter count, thereby streamlining the computational \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **Methodology** & **Accuracy (\%)** &
\begin{tabular}{c} **Number of** \\ **Parameters (M)** \\ \end{tabular} \\ \hline \multirow{4}{*}{NinaPro DB4} & Multi-View CNN [26] & 54.3 & 3.44 \\  & HVPN [36] & 67.0 & 4.76 \\  & Attention sEMG [37] & 73.0 & 1.44 \\  & EMGHandNet [38] & **89.5** & 5.76 \\  & Ours & 83.0 & **0.22** \\ \hline \multirow{4}{*}{NinaPro DB5} & Multi-View CNN [26] & **90.0** & 4.03 \\  & HVPN [36] & 87.1 & 5.54 \\ \cline{1-1}  & Attention sEMG [37] & 87.0 & 1.44 \\ \cline{1-1}  & TDCT [39] & 72.8 & 1.20 \\ \cline{1-1}  & Ours & 88.3 & **0.21** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the accuracy and computational complexity on NinaPro databases with different models. Bold indicates the best results under different models for the same dataset. process. This innovation is poised to set a new benchmark for resource efficiency in the field of sEMG gesture recognition. Gesture recognition through sEMG signals is rapidly gaining traction for its non-invasive nature, ease of acquisition, and high signal stability, making it an exemplary choice for dynamic gesture recognition applications. However, the field continues to grapple with challenges stemming from the diversity and complexity of gestures, as well as vulnerability to signal interference. Current gesture recognition techniques are often limited by their low accuracy and high computational demands. In response to these challenges, future research will focus on optimizing model architectures, advancing feature extraction methods, and broadening the scope of this technology's applications to tackle a wider array of real-world issues, thereby enhancing the accessibility and efficacy of sEMG-based gesture recognition systems. Conceptualization, Y.N. and W.C.; formal analysis, B.X. ; methodology, Y.N. and W.C.; validation, H.Z. and Z.G. ; writing--original draft, Y.N. and W.C.; writing--review and editing, B.X. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under Grant 62073271, the Natural Science Foundation of Fujian Province (2020)[01890, 2020][01891], and the Scientific Fund Projects in Fujian University of Technology (GY-Z17144) and in part by the Scientific Research Projects of the Science and Technology Department in Fujian of China (2020)[01890, 2023][0024], the Provincial Project of Education Department in Fujian of China (JT180344, FBJY20230078, FJJKBK23-045), and the Scientific Research Projects in Fujian University of Technology (KF-X19002, KF-19-22001, GY-H-21191, GY-Z21049, GY-Z220210).