## 4 Experiments
### Comparisons with SOTA Methods  
We compare EHC-GCN model with the SOTA methods on the NTU 60 [35] and NTU 120 [38] datasets, with the results shown in Tables 6 and 7. These tables include methods based on a 2D skeleton extracted from RGB data and a 3D skeleton obtained from Kinect cameras. Our method achieves better or more competitive accuracy on both data forms. For 3D skeleton-based action recognition, compared to the CNN-based HCN algorithm [16], our proposed EHC-GCN model improves the Top-1 accuracy by 2.0% and 2.3% on the X-Sub and X-View benchmarks, respectively. Among the graph-based comparative methods, several models are particularly noteworthy. The first is ST-GCN [9], which is a pioneering graph convolution model. Compared to ST-GCN, our model's recognition accuracy improves by 7.0% on the X-Sub benchmark and 5.1% on the X-View benchmark, respectively, with a reduction in FLOPs by a factor of 7.8 times and a decrease in parameters by a factor of 19.4 times. Compared to 2s-AGCN [12], we achieve similar recognition accuracy, but its FLOPs is about 17.9 times and the number of parameters is about 43.4 times that of our model. The third is MS-G3D [15], which is a high-accuracy model proposed in recent years. Its accuracy is higher than our method, but it also suffers from the problem of being too complex and over-parametrized. In particular, we focus on several lightweight action recognition models [22; 28; 29; 30; 31; 44; 45], which are indicated with an asterisk (*) in Tables 6 and 7. Figure 7 shows the Params (M) and accuracy (%) for different lightweight models. The horizontal axis lists the names of different models; the left side of the vertical axis displays the Params (M), represented by \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Year** & **Data** & **X-Sub** & **X-View** & **GFLOP** & **Params (M)** \\ \hline DSSCA-SSLM [41] & 2017 & RGB + Pose & 74.9 & - & - & - \\
2D-3D-Softargma [42] & 2018 & RGB & 85.5 & - & - & - \\ Glimpse Clouds [43] & 2018 & RGB & 86.6 & 93.2 & - & - \\ IntegralAction [25] & 2021 & RGB & 91.7 & - & - & - \\ B2C-AFM [26] & 2023 & RGB + Pose & 91.7 & - & - & - \\ \hline HCN [16] & 2018 & 3D Skeleton & 86.5 & 91.1 & - & - \\ ST-GCN [9] & 2018 & 3D Skeleton & 81.5 & 88.3 & 16.32 & 3.10 \\ SR-TSL [18] & 2018 & 3D Skeleton & 84.8 & 92.4 & 4.20 & 19.07 \\ RA-GCNv1 [10] & 2019 & 3D Skeleton & 85.9 & 93.5 & 32.80 & 6.21 \\ AS-GCN [12] & 2019 & 3D Skeleton & 86.8 & 94.2 & 26.76 & 9.50 \\
2s-AGCN [12] & 2019 & 3D Skeleton & 88.5 & 95.1 & 37.32 & 6.94 \\ AGC-LSTM [19] & 2019 & 3D Skeleton & 89.2 & 95.0 & 54.40 & 22.89 \\ EleAtt-GRU [44] * & 2020 & 3D Skeleton & 79.8 & 87.1 & - & 0.3 \\ GFNet [45] * & 2020 & 3D Skeleton & 82.0 & 89.9 & 48.7 & 1.6 \\ SGN [28] * & 2020 & 3D Skeleton & 89.0 & 94.5 & - & 0.69 \\ DGNN [17] & 2020 & 3D Skeleton & 89.9 & 96.1 & - & 26.24 \\
4s-ShiftCN [14] & 2020 & 3D Skeleton & 90.7 & 96.5 & 10.0 & 2.76 \\ MS-G3D [15] & 2020 & 3D Skeleton & 91.5 & 96.2 & 48.88 & 6.40 \\ PRGCN [22] * & 2021 & 3D Skeleton & 85.2 & 91.7 & 1.7 & 0.5 \\ RA-GCNv2 [13] & 2021 & 3D Skeleton & 87.3 & 93.6 & 32.80 & 6.21 \\
3s-CrossCLR [46] & 2021 & 3D Skeleton & 86.2 & 92.5 & - & 2.55 \\ Tripool [47] & 2021 & 3D Skeleton & 88.0 & 95.3 & 11.76 & - \\ STAR [32] & 2021 & 3D Skeleton & 83.4 & 89.0 & - & 1.26 \\ ST-TR [23] & 2021 & 3D Skeleton & 89.9 & 96.1 & - & - \\ KA-AGTN [24] & 2022 & 3D Skeleton & 90.4 & 96.1 & - & - \\ CoST-GCN [30] * & 2023 & 3D Skeleton & 85.3 & 93.1 & 0.27 & 3.14 \\ CoAGCN [30] * & 2023 & 3D Skeleton & 86.0 & 93.9 & 0.30 & 3.47 \\ CoS-TR [30] * & 2023 & 3D Skeleton & 86.5 & 93.3 & 0.22 & 3.09 \\ ST-Net [31] * & 2023 & 3D Skeleton & 88.8 & 95.0 & 7.5 & 1.7 \\ STH-DRL [48] & 2023 & 3D Skeleton & 90.8 & **96.7** & - & - \\ EfficientGCN-B0 [29] * & 2023 & 3D Skeleton & 90.2 & 94.9 & 2.73 & 0.29 \\ \hline EHC-GCN & 2024 & 3D Skeleton & 88.5 & 93.4 & 2.09 & 0.16 \\ EHC-GCN & 2024 & Pose & **92.0** & 94.2 & 1.40 & 0.15 \\ \hline \hline \end{tabular}  
* represents the lightweight action recognition model.  
\end{table}