## 5 Experimental Results and Analysis
### Visualization  
In order to better illustrate the need for modeling non-physical connections, this section provides visualizations for some samples on the NTU RGB+D 60 dataset. From Figure 8, it can be seen that the action of 'drinking water' is mainly an upper limb movement, and the upper limb should be given more attention in the hypergraph. Meanwhile, it can also be observed that when drinking water, the arm and the head have a strong correlation, but they are not directly physically connected. In conclusion, it is shown that constructing a non-physically connected hypergraph can capture more complex and higher-order dependence among the joints and further improve the recognition accuracy of the model. In addition, the proposed method performs well in both CS (cross-subject) protocols and CV (cross-view) protocols, indicating that the proposed method has excellent robustness.  
The NTU RGB+D 60 dataset contains common human actions such as drinking water, eating a meal, etc., and we analyze the accuracy of the action recognition for each category on this dataset. In order to visualize the improvement in the accur  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**NTU RGB+D 60**} \\ \cline{2-3}  & **CS(\%)** & **CV(\%)** \\ \hline Coequal fusion method & 88.4 & 93.2 \\ \hline Multi-feature fusion module based on cross-channel & 91.2 & 96.7 \\ attention mechanism & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of action recognition accuracy of different ways of fusing multi-channel features.  
Figure 8: Visual examples on the NTU RGB + D dataset: (**a**) drink water in CS (cross-subject) protocols; and (**b**) drink water in CV (cross-view) protocols.  
and to analyze the causes of misclassified actions, we plot bar charts based on the accuracy of 2S-AGCN and our proposed method on the NTU RGB+D 60 dataset for 60 action categories. As shown in Figure 9, the blue bars indicate the performance of 2S-AGCN, and the orange bars and the numbers on them indicate the performance improvement of our proposed method compared to using 2S-AGCN. Observation reveals that our proposed method has some performance improvement over 2S-AGCN for the vast majority of action categories. Figure 9a shows the CS (cross-subject) protocols of the NTU RGB+D 60 dataset. We can observe that the recognition accuracy of 'write' and'read' is lower compared to other actions when using 2S-AGCN. For these two actions, the recognition accuracy is improved by about 13.9% and 4.4%, respectively, when using our proposed method. Figure 9b shows the CV (cross-view) protocols of the NTU RGB+D 60 dataset. We found that the recognition accuracy of some action, such as 'writing','reading', 'playing with the phone', and 'typing on keyboard' is lower when using 2S-AGCN. The accuracy of action recognition is improved in all cases after using our proposed method. From the above experimental results, it can be seen that, compared to 2S-AGC, there is a large performance improvement for actions such as 'writing','reading', etc., which only use part of the human body region. This is due to the fact that the proposed method can focus on regions with large variation and focus on the effective information of the action.  
In addition, in order to comprehensively evaluate the model, it is necessary to consider not only the accuracy but also the convergence speed. To better demonstrate the comparative performance with other methods, we conducted accuracy tests on the NTU RGB+D 60 dataset using the CS (cross-subject) and CV (cross-view) protocols, and generated corresponding charts. As shown in Figure 10a,b, it can be observed that, as the number of epochs increases, all curves tend to reach their maximum values and stabilize. Our proposed method exhibits the fastest convergence speed, while Hyper-GNN, 2S-AGCN, AS-GCN, and ST-GCN show slower convergence rates. This indicates that our proposed network can achieve optimal results at the fastest speed compared to other methods.  
Figure 9: _Cont._