## 4 Experiments
### Ablation Study  
To verify the effectiveness of each module, we conducted ablation studies on the proposed EHC-GCN model using the NTU RGB+D 60 dataset with the X-Sub benchmark. First, we validated the effectiveness of the hierarchical co-occurrence (HC) framework and the attention module. As shown in Table 1, when only the hierarchical co-occurrence framework is removed (Non-HC), the model's recognition accuracy decreases by 1.6% compared to the proposed EHC-GCN. Similarly, when only the attention module is removed (Non-Attention), the model's recognition accuracy decreases by 0.9% compared to the EHC-GCN. Additionally, as seen in Table 1, the HC structure and attention module can effectively improve the performance of the model, while the model parameters and FLOPs only increase slightly. Next, to verify the necessity of each input branch, we designed a tabular comparison. As shown in Table 2, the model performance with the two-stream structure significantly improved. This demonstrates the effectiveness of data preprocessing. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Acc (\%)** & **GFLOP** & **Params (M)** \\ \hline Non-HC & 86.9 & 1.89 & 0.15 \\ Non-Attention & 87.6 & 2.07 & 0.14 \\ EHC-GCN & 88.5 & 2.09 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The effectiveness of HC and the attention module. Finally, we investigated the impact of the basic convolution layer versus the depth-wise separable convolution layer structure on the performance. In previous studies, separable convolution layers have proven their effectiveness and robustness [40]. As shown in Table 3, by adopting the advanced depth-wise separable structure, we can achieve performance comparable to that of the basic convolution layer with significantly reduced computational cost. Additionally, The efficient temporal convolution module uses a convolution kernel of a certain size to extract temporal features. Since the size of the convolution kernel determines its receptive field, convolution kernels of different sizes have varying abilities to extract features. To explore the optimal temporal convolution kernel size for the model, a comparative experiment was conducted using convolution kernels of various sizes on the NTU RGB+D 60 dataset under the X-Sub benchmark. As shown in Table 4, when the temporal convolution layer uses a \(5\times 1\) convolution kernel to extract temporal features, the model's performance reaches its optimal; hence, the temporal convolution kernel size is set to \(5\times 1\). The model in this paper consists of L EHC-GCN modules, and the number of network layers is directly related to the recognition accuracy. Few layers may fail to extract discriminative features sufficiently, while too many layers can lead to overfitting, and the increased computational cost can also affect the practical application of the model. As shown in Table 5, we conducted experiments with different numbers of EHC-GCN modules. When L = 3, the model achieved a recognition accuracy of 88.5% on the X-Sub benchmark, with Gflops of 2.09 and a parameter amount of 0.16 M. However, when we continued to increase the number of GCN modules, we found that the network's performance did not improve. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Kernel Size** & **Acc (\%)** & **GFLOP** & **Params (M)** \\ \hline
3 \(\times\) 1 & 88.4 & 1.84 & 0.14 \\
5 \(\times\) 1 & 88.5 & 2.09 & 0.16 \\
7 \(\times\) 1 & 88.5 & 2.34 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The impact of different temporal convolution kernel sizes (%). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Inputs** & **Acc (\%)** & **GFLOP** & **Params (M)** \\ \hline Velocity & 84.9 & 1.37 & 0.11 \\ Bone & 85.6 & 1.37 & 0.11 \\ Velocity + Bone & 88.5 & 2.09 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The effectiveness of data preprocessing.