# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 5 Conclusions  
The aim of this study is to propose a new model called the ACE-GCN for recognizing human actions based on skeletal data. Unlike existing methods, the ACE-GCN adaptively encodes the graph topological features in the channel through an attention mechanism, achieving feature refinement between joints and effectively extracting more representative human action features to improve recognition accuracy. Simultaneously, this strategy can also prevent the buildup of unnecessary information and decrease the quantity of computational parameters. In addition, by introducing Edgeconv to build a multi-branch aggregation module, the model enhances its ability to aggregate temporal and spatial features at different scales. The results of the ablation and comparison experiments show that the ACE-GCN model proposed in this study achieves 92% (X-Sub) and 96.3% (X-View) accuracy using the NTU RGB+D 60 dataset and 88.6% (X-Sub) and 90.1% (X-View) using the NTU RGB+D 120 dataset, respectively. It achieved an accuracy of 96.6% for the NW-UCLA dataset while maintaining a relatively low number of computational parameters. These results indicate that the ACE-GCN has significant advantages in spatio-temporal feature extraction and global-local context capture. Since no skeletal features are created for objects in the hand-object action class, the model's accuracy in recognizing fine hand actions is low relative to that for other actions. This may affect its performance in certain tasks with a high proportion of fine action samples. In this paper, we have focused on improving the model's performance in the spatial dimension, and future work will build skeletal features for objects and focus on obtaining a balanced performance of the model on different action types. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Param.** & **NW-UCLA** \\ \hline SGN [32] & 0.69 M & 92.5 \\ AGC-LSTM [29] & - & 93.3 \\ Shift-GCN [25] & 2.76 M & 94.6 \\ NLB-ACSE [37] & 1.21 M & 95.3 \\ FGCN [38] & - & 95.3 \\ LA-SGCN [39] & 0.43 M & 95.7 \\ ACE-GCN (ours) & 1.9 M & 96.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top-1 classification accuracy comparisons for the NW-UCLA dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{3}{c}{**NTU RGB+D 120**} \\
**Model** & **Param.** & \multicolumn{1}{c}{**X-Sub (\%)**} & **X-View (\%)** \\ \hline ST-GCN [5] & 3.1 M & 70.7 & 73.2 \\
2s-AGCN [6] & 9.94 M & 82.5 & 84.2 \\ SGN [32] & 0.69 M & 79.2 & 81.5 \\ Shift-GCN [25] & 2.76 M & 85.9 & 87.6 \\ MS-G3D [31] & 6.4 M & 86.9 & 88.4 \\ MST-GCN [33] & 12 M & 87.5 & 88.8 \\ SMotif-GCN [34] & - & 88.4 & 88.9 \\ DD-GCN [35] & - & 84.9 & 86 \\ EfficientC-BA [26] & 1.1 M & 88.3 & 89.1 \\ SARGCN [36] & 1.09 M & 83.8 & 85.1 \\ ACE-GCN (ours) & 1.9 M & 88.6 & 90.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top-1 classification accuracy comparisons for the NTU RGB+D 120 dataset. **Author Contributions:** Conceptualization, X-Y.C. and X-W.H. ; methodology, X-Y.C. ; software, X-Y.C. ; validation, X.-Y.C. ; formal analysis, X-Y.C. and X-W.H. ; investigation, X-Y.C. ; resources, X.-Y.C. ; data curation, X.-Y.C. and Y.C. ; writing--original draft preparation, X.-Y.C. ; writing--review and editing, X.-Y.C. and Q.-Y.G. ; visualization, X.-Y.C. ; supervision, X.-W.H. and Y.C. ; project administration, X.-W.H. and W.H. ; funding acquisition, X.-W.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was awarded financial assistance under the Heilongjiang Postdoctoral Fund to pursue scientific research in Heilongjiang Province in 2019. The level of financial assistance is II, and the amount of financial assistance is RMB 70,000. Serial number: LBH-Z19072. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Informed consent was obtained from all the subjects involved in this study.