## 4 Experiment and Results
### Comparison With the Current Gesture Recognition Methods  
In this section, we conduct a performance comparison with existing research methods on five publicly available datasets, namely Multi-view CNN [26], HVPN [36], Attention sEMG [37], EMGHandNet [38], TDCT [39], and SE-CNN [40]. The SE-CNN [40] method utilizes a strategy that integrates squeeze-and-excite (SE) modules within a CNN framework to suppress irrelevant features while enhancing important ones. This approach integrates SE, CNN, and attention mechanisms to recognize features. EMGHandNet [38] is built upon a hybrid architecture of CNN and Bi-LSTM for the learning of inter-channel and temporal features. Spatial and short-term temporal relationships are encoded by convolutional layers, while long-term temporal relationships are learned by the Bi-LSTM layer. The proposed framework is capable of extracting cross-channel and temporal features, where one-dimensional convolution encodes cross-channel and short-term temporal information, while Bi-LSTM encodes long-term temporal information in both forward and backward directions. In contrast, Attention sEMG [37] and TDCT [39] are variants based on attention mechanisms to extract global feature information, facilitating the learning of global long-term features for gesture classification. Attention sEMG utilizes a feed-forward simple attention mechanism to extract representation features in the time domain from multiple channels, while TDCT enhances the extraction of local temporal and channel correlation features in sEMG by replacing the linear transformation in the multi-head self-attention mechanism with temporal depth convolution. This modification boosts feature-learning  
Figure 6: The SHAP visualization of our model for the agree gesture (class 9) in BioPatRec DB1 with a window size of 250 ms and 4 channels (red highlight areas indicate higher correlations). Figure 7: The SHAP visualization of our model for the fine grip gesture (class 8) in BioPatRec DB1 with a window size of 250 ms and 4 channels (red highlight areas indicate higher correlations). capabilities and decreases parameter size. Both Multi-view CNN [26] and HVPN [36] employ similar approaches by integrating multiple sets of feature information. They extract feature information from various perspectives and integrate it using different network architectures. In multi-view learning, each perspective has specific viewpoint features, and all perspectives can access common viewpoint features. These models learn feature information from multi-channel sEMG signals from both local and global perspectives. The key aspect of these models lies in their rich feature information, which may, however, introduce feature redundancy. Due to variations in the window sizes used by different models, for the sake of fairness, we unify the comparison based on each model's best classification results, as shown in Table 5.  
From Table 5, it is evident that our proposed model delivers strong performance across multiple datasets, achieving an average classification accuracy of 88.98% across the five datasets. For the NinaPro DB4 dataset, our model achieved an accuracy of 83.3%--slightly lower than EMGHandNet's 89.5%. However, it is important to note that our model maintains a significantly lower parameter count, with only 0.20 million parameters compared to EMGHandNet's 6.4 million. This underscores the effectiveness of our approach. For the Ninapro DB5 dataset, our model achieved a classification accuracy of 87.6%, coming close to the best results. In contrast, Multi-View CNN exhibits notable instability and significant performance variations across different datasets, with an average classification accuracy of 73.4% across the five datasets, trailing our model by 15.4%. Our model leverages compact convolution to learn temporal dependencies and dynamically model correlations between different channels, enabling strong generalization across diverse datasets.