## 4 Experiments
### Experimental Results Analysis  
#### 4.3.1 Comparison Experiments  
As shown in Tables 4 and 5, the PCK values of the four network models of AGCN [34], CTR-GCN [35], ST-GCN, and the improved ST-GCN (proposed model) on the FSD and MPII test set data, respectively, are shown, where the values in the columns (Ankle*, Knee*, etc.) indicate the average values of the corresponding left and right nodes for that node. It can be seen that the average PCK of the AGCN network is 91.8% and 91.5% on both FSD and MPII datasets, respectively, the average PCK of the CTR-GCN network is 91.7% and 91.1% on both FSD and MPII datasets, respectively, and the average PCK of the ST-GCN network is 91.3% and 91.0% on both FSD and MPII datasets, respectively. In addition to the targeted preprocessing of the original dataset to improve as much as possible the model performance due to the irregularity of the dataset, the method mentioned in this paper redesigned the network model structure, network model configuration, model training, etc., so that the performance of the improved network model was improved to a certain extent on the FSD and MPII datasets. The average PCKs on the FSD and MPII datasets are 93.2% and 92.7%, respectively, as shown in Figure 13, and it can be seen that the method has a high recognition accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{3}{c}{**True Results**} \\ \cline{2-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Prediction Results} & Positive & TP & FP \\ \cline{2-4}  & Negative & FN & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion Matrix. As shown in Figures 14 and 15, the confusion matrices of the method on the FSD and MPII datasets show that the method has the highest probability of misclassifying the 3 Loop action as FlyCamelSpin4 action among the 10 classifications of the FSD dataset (16%) and the highest probability of misclassifying the R-wrist node as L-knee action among the 16 classifications of the MPII dataset (14%). Among the 16 classifications of the MPII dataset, this method has the highest probability of misclassifying R-wrist nodes as L-knee actions, at 14%. In addition, the confusion matrix produced by this method on these two datasets has the largest element values on the diagonal, so it can be considered that this method has very good classification results. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Ankle\({}^{*}\)** & **Knee\({}^{*}\)** & **Hip\({}^{*}\)** & **Shoulder\({}^{*}\)** & **Wrist\({}^{*}\)** & **Elbow\({}^{*}\)** & **Thorax** & **Head Top** & **Mean** \\ \hline AGCN & 90.2\% & 95.3\% & 90.2\% & **98.1\%** & 88.6\% & 87.5\% & 89.1\% & 95.6\% & 91.8\% \\ \hline CTR-GCN & 98.1\% & 96.1\% & 89.5\% & 90.7\% & 87.4\% & 89.8\% & 90.0\% & 91.8\% & 91.7\% \\ \hline ST-GCN & 94.3\% & 93.0\% & 87.9\% & 87.6\% & 86.2\% & 90.9\% & **96.7\%** & 94.0\% & 91.3\% \\ \hline Proposed & **99.7\%** & **99.1\%** & **99.8\%** & 87.6\% & **89.0\%** & **91.2\%** & 83.2\% & **95.7\%** & **93.2\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PCK of different methods on the FSD dataset. Figure 13: Experiment results on MPII of our proposed model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Ankle\({}^{*}\)** & **Knee\({}^{*}\)** & **Hip\({}^{*}\)** & **Shoulder\({}^{*}\)** & **Wrist\({}^{*}\)** & **Elbow\({}^{*}\)** & **Thorax** & **Head Top** & **Mean** \\ \hline AGCN & 98.7\% & 95.4\% & 87.6\% & 89.7\% & **97.3\%** & 89.2\% & 83.0\% & 91.2\% & 91.5\% \\ \hline CTR-GCN & 93.4\% & 94.1\% & 89.4\% & 90.3\% & 90.9\% & 91.3\% & 85.2\% & 93.9\% &  
The performance of the ST-GCN, AGCN, and CTR-GCN networks and the proposed method in this paper, which contains two indexes, model computation, and MACs, is shown in Table 6. It can be seen that the model computation of the proposed method is about 1.7 G, which is similar to the model computation of the proposed method in the CTG-GCN network.