## 4 Experiment and Results
### Ablation Studies of the Proposed Network  
In this section, to demonstrate the effectiveness of our proposed feature extraction method with partial convolutional fusion, we conduct ablation experiments. Additionally, we perform ablation experiments on the channel selection unit to demonstrate its capability in reducing computational complexity and compressing the channel features. We compare the recognition accuracy of partial convolution with feature importance learning. These comparisons were performed continuously with three different window sizes (50 ms, 150 ms, and 250 ms) on the Ninapro DB4 and DB5 and BioPatRec DB1, DB2, and DB3 datasets while maintaining constant experimental settings. The results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Ablation**} & \multicolumn{2}{c}{**Params**} & \multirow{2}{*}{**50 ms**} & \multirow{2}{*}{**150 ms**} & \multirow{2}{*}{**250 ms**} \\  & & **(M)** & & & \\ \hline \multirow{3}{*}{NinaPro DB4} & Full & 0.22 & 77.0 & 81.2 & 83.0 \\  & Without partial block & 0.21 & 76.0 & 80.6 & 82.1 \\  & Without channel selection & 0.48 & 77.7 & 81.8 & 83.3 \\ \hline \multirow{3}{*}{NinaPro DB5} & Full & 0.21 & 82.0 & 87.5 & 88.3 \\  & Without partial block & 0.20 & 80.5 & 85.3 & 86.3 \\  & Without channel selection & 0.47 & 82.1 & 87.4 & 87.3 \\ \hline \multirow{3}{*}{BioPatRec DB1} & Full & 0.20 & 84.1 & 89.7 & 91.5 \\  & Without partial block & 0.19 & 81.8 & 86.8 & 87.8 \\  & Without channel selection & 0.45 & 84.4 & 90.3 & 92.3 \\ \hline \multirow{3}{*}{BioPatRec DB2} & Full & 0.21 & 85.0 & 88.4 & 91.6 \\  & Without partial block & 0.20 & 83.9 & 86.2 & 89.7 \\  & Without channel selection & 0.46 & 85.5 & 89.2 & 91.8 \\ \hline \multirow{3}{*}{BioPatRec DB3} & Full & 0.20 & 85.4 & 89.0 & 90.9 \\  & Without partial block & 0.19 & 80.9 & 84.8 & 86.8 \\  & Without channel selection & 0.45 & 85.6 & 89.1 & 90.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy (%) ablation of the proposed method. Figure 4: The t-SNE visualization of BioPatRec DB1 with different window sizes. As shown in Table 4, the use of partial convolution consistently enhances accuracy across all datasets. Across the five datasets and three different window sizes, the average improvement in classification accuracy due to partial convolution is 3%, 2%, 2.23%, 1.03%, and 3.93%, respectively. This highlights the important role of partial convolution, as it is effective in extracting valuable features from the sEMG signals, thus improving the training efficiency. In addition, the introduction of the channel selection unit can significantly reduce the number of parameters with minimal impact on accuracy. To validate the effectiveness of the proposed model in channel selection, a comprehensive analysis was conducted using Shapley Additive exPlanations (SHAP), which is python package shap 0.44.1, as illustrated in Figure 5. Initially, SHAP was employed to delve into the correlation between each sEMG channel and the final gesture action. Specifically, we compared the model integrating both a partial block and a channel selection block with the models lacking either of these blocks in terms of the correlation scores between channel features and gesture action outcomes. Figure 4(a,c) reveal that the introduction of the channel selection block did not alter the model's classification performance, nor did it significantly change the focus on respective channel features. However, the incorporation of this block led to a nearly 50% reduction in the model's parameter count, substantially decreasing its size and thereby highlighting the significant role of the channel selection block in filtering and focusing on relevant channel features. Concurrently, Figure 4(a,b) demonstrate the impact of the presence or absence of the partial block on the model's performance in channel feature extraction, as well as a more dispersed representation of related channel features.