## 5 Experiments and Results
### Action Recognition Results Analysis  
The final results across the five categories are as follows: an average accuracy of 85.9%, an average precision of 86.9%, an average recall of 87.6%, and an average F1 score of 86.3%. These results indicate strong performance, though there remains significant room for optimization.  
In Figure 8, the validation metrics' variations with epoch during the training process are given. We conducted a series of experiments evaluating key performance metrics for the final classification task. As shown in Table 3, within the human body detection module, the transition from YOLOv3 to YOLOX resulted in concurrent improvements in accuracy, precision, recall, and F1 score, while maintaining relative stability in the number of model parameters. This performance boost can be attributed to the careful design of the YOLOX architecture, which improves detection performance while remaining fast and efficient. Table 4 demonstrates the performance comparison of YOLOX for different categories, which show relatively similar results. Table 5 illustrates that in the pose estimation module, although ResNet50 outperforms HRNet in accuracy, it comes with a higher parameter count. In contrast, HRNet, with our modifications, achieves a slight increase in accuracy while reducing the parameter size and enhancing inference speed, leading to better real-time performance. Finally, as shown in Table 6, within the action recognition module, our enhancements over existing graph convolutional neural networks (GCNs) yield improvements of 3.1% in accuracy, 1.7% in precision, 2.4% in recall, and 1.1% in F1 score, all while maintaining a relatively low parameter count, indicating a well-rounded performance gain. The PR curve of the model is shown in Figure 9. The PR curve  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Number of Parameters (M)** \\ \hline aagcn [29] & 79.3 & 78.1 & 79.3 & 78.5 & 3.75 \\ ctrgcn [30] & 80.4 & 79.9 & 80.4 & 80.1 & 1.42 \\ msg3d [31] & 82.8 & 85.2 & 85.2 & 85.2 & 2.95 \\ stgcn [32] & 80.9 & 83.5 & 83.0 & 83.2 & 3.07 \\ Ours & 85.9 & 86.9 & 87.6 & 86.3 & 1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of various GCN models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline Coats & 87.6 & 83.3 & 93.7 & 88.1 \\ Hats & 91.7 & 81.6 & 91.7 & 86.4 \\ Masks & 81.6 & 96.8 & 73.5 & 83.6 \\ Gloves & 93.7 & 79.0 & 93.8 & 85.7 \\ Normal & 83.3 & 93.8 & 83.3 & 88.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of YOLOX for different PPE classes.  
Figure 8: The validation metrics’ variations by epoch during model training process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **Speed (FPS)** & **Number of Parameters (M)** \\ \hline ResNet50 & 84.1 & 86.1 & 82.1 & 3.2 & 40.58 \\ HRNet & 85.3 & 87.9 & 83.9 & 3.1 & 31.16 \\ Ours & 85.9 & 86.9 & 87.6 & 5.9 & 28.54 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of baseline and improved HRNet.  
The smoking action is a generalization of PPE action, so only the optimized model was used to analyze and process the smoking data. Table 7 illustrates the smoking detection in the two-category classification: the results show an average accuracy of 87.0%, an average precision of 85.0%, an average recall of 90.6%, and an average F1 score of 86.0%. The notably high recall suggests that some smokers were not correctly identified, leading to missed detection, and further optimizations could focus on reducing these misses.