## 5 Conclusions  
In this study, by incorporating an attention mechanism and multi-scale module into the ResNet50 model, we propose the InRes-ACNet model for gesture recognition based on surface electromyography (sEMG) signals. Initially, the study constructs the inception-attention module based on the multi-scale inception module, which is then integrated into the ResNet50 model to enhance its multi-scale feature extraction capabilities. Subsequently, the self-attention mechanism, the ACmix module, is incorporated into ResNet50, enabling the model to maintain a lower parameter count while improving its feature extraction performance. Ultimately, employing the InRes-ACNet model on the NinaPro DB1 and NinaPro DB5 datasets for gesture recognition yielded accuracy rates of 87.94% and 87.04%, respectively. Additionally, the InRes-ACNet model was applied to the prediction of grasping modes in an electromyography manipulator, achieving an average recognition accuracy of 88.37%. These results confirm the effectiveness of the InRes-ACNet model for gesture recognition tasks based on sEMG signals.  
Our research enhances gesture recognition performance by incorporating multi-scale modules and attention mechanisms, alongside utilizing the grayscale images of the original sEMG signals. Nevertheless, the InRes-ACNet model, integrating the inception-attention module, ACmix module, and ResNet50, entails a considerable number of parameters. This complexity results in extensive computations and slower training speeds. Variations in the dataset sizes, attributable to the differing collection times and frequencies for each gesture, also affect the duration of each training step. Given the non-stationary and random characteristics of sEMG signals, coupled with significant variations among individuals, the model's generalization capacity in real-time recognition is constrained. Direct application of model training tailored to specific individuals to others may lead to a suboptimal recognition performance.  
Conceptualization, X.L. and W.H.; methodology, X.L.; software, X.L.; validation, X.L.; formal analysis, X.L.; investigation, X.L.; resources, X.L. and W.H.; data curation, X.L. and W.H.; writing--original draft preparation, X.L. and W.H.; writing--review and editing, X.L., W.H. and Y.L.; visualization, X.L.; supervision, W.H. and Z.W.; project administration, X.L., W.H., and X.D.; funding acquisition, W.H. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hunan Natural Science Foundation (No. 2022J[31015]), General Project of the National Social Science Fund (No. 22 BGL173), Major Project of the Social Science Evaluation Committee of Hunan Province (No. XSP22 ZDA006), Hunan Teaching Reform Research Project (No. HNJG-20230470), and the Graduate Science and Technology Innovation Fund of Central South University of Forestry and Technology (No. 2023 CX02070).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Average Recognition Rate** & **Epoch** & **Number of Movements** \\ \hline ResNet50 & 85.63\% & 50 & 8 \\ InRes-ACNet & 88.37\% & 50 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of recognition performance for the models ResNet50 and InRes-ACNet.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.