# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 4 Experimental Evaluation
### Ablation Study and Discussion  
In order to assess the efficacy of the suggested approach, we established three networks with distinct configurations to conduct ablation experiments on the NTU-RGB+D 60 (X-Sub) dataset. The objective was to test the usefulness of each module in the ACE-GCN. This study used the ST-GCN [5] as the baseline. The ST-GCN is a non-adaptive topology-sharing graph convolutional network, and its topology is fixed and cannot be learned. In this study, we enhance the ST-GCN model by using residual connections as the fundamental convolutional module. Additionally, we substitute the time convolution module of the ST-GCN with the time convolution module outlined in Section 3.3. The experimental results of human action recognition by the ACE-GCN without integrated Edgeconv are shown in Table 1. The symbol in Edgeconv indicates whether the network uses Edgeconv for feature aggregation. Joint (J) and bone (B) represent the human action recognition ability of the model in different data streams. The ACE-GCN enhances the Top-1 accuracy of the joint stream by 0.1% compared to the baseline. Similarly, it improves the Top-1 accuracy of the bone stream by 0.8%. Furthermore, the fusion of the J and B stream with ACE-GCN results in a 0.7% increase in Top-1 accuracy. The joint stream primarily consists of the 3D coordinates of human joints, while the bone stream is formed by the differences in coordinates between interconnected joints in space. The proposed ACE-GCN can obtain the attention weight matrix of joint points through the attention mechanism and adaptively optimize the graph topology in the channel through the attention weight matrix so as to better capture the correlation between paired joint points. Therefore, the improvement of the Top-1 accuracy of the ACE-GCN in the bone flow is more significant. The Top-1 accuracy of the joint flow (J) is improved by 0.2%, the Top-1 accuracy of the bone flow (B) is improved by 1%, and the Top-1 accuracy of the joint flow and bone flow fusion (J+B) is enhanced by 0.8% when the ACE-GCN includes Edgeconv for human action recognition. Edgeconv is one of the parallel aggregation branches. Without destroying the topology of the graph, Edgeconv can effectively aggregate the spatio-temporal features of the local topology of the human skeleton through clustering. The other aggregation branch focuses on global information aggregation, and through global-local information aggregation, it achieves global feature aggregation that retains local key features. Therefore, adding the Edgeconv aggregation branch to the ACE-GCN improves the Top-1 accuracy. In the confusion matrices shown in Figures 7 and 8, the data on the diagonal represents the Top-1 classification accuracy of the model in each action class. The pictures in Figure 8 show the intercepted results of some of the confusion matrices in Figures 7 and 8. The left-hand-side plots (a), (c), and (e) show the confusion matrices of the BASELINE model on different action classes, and the right-hand-side plots (b), (d), and (f) show the confusion matrices of the ACE-GCN model on the corresponding classes. The horizontal coordinates of plots (a)-(f) are the predicted action labels, the vertical coordinates are the correct action labels, and the data in the plots represents the recognition accuracies. The values in Figure 9 show that compared to the baseline model, the Top-1 classification accuracy of the ACE-GCN is improved in each action classes, and the overall accuracy of human action classification is better than the baseline model. Figure 10 further compares the Top-1 classification accuracies of the ACE-GCN and baseline in different action classes, and the results show that the recognition performance of the ACE-GCN is better than that of the baseline in general. In summary, the ACE-GCN proposed in this paper has excellent action recognition performance. However, through the analysis of the experimental data and its visualization, we also found some limitations of the ACE-GCN model.