## 5 Ablation Study and Discussion
### Comparison of Specific Actions  
Figures 8 and 9 shows the confusion matrix of our proposed model on the PAMAP2, USC-HAD, Skoda, and Opportunity datasets. The confusion matrix is used to measure the effectiveness of a classifier in recognizing different categories. The row and column labels of a confusion matrix represent the true and predicted categories, respectively. The diagonal elements of the confusion matrix indicate the correct recognition rate for each action, while the off-diagonal elements represent the proportion of actions that are incorrectly recognized as other categories. In Figure 8a, there is some confusion between \"standing\" and \"sitting\", which is reasonable because the two actions are relatively similar. Other categories such as \"walking\", \"running\", and \"descending stairs\" are well recognized. In Figure 8b, there is some confusion about the type of action due to the division of the sensor's position and action category at the time of data acquisition. However, for some categories such as \"Walking Forward\", \"Walking Right\", \"Walking Upstairs\", etc., our model is still able to distinguish the confusing actions well. In Figure 9a, there is some confusion between the actions \"open left front door\" and \"close left front door\" due to the similarity of the two actions, resulting in similar data collected by the accelerometer. However, other action categories, such as \"open hood\", \"close hood\", and \"close both left doors\" were well recognized. This is because these actions are process-oriented and can be distinguished without serious confusion, and the model is more sensitive to the data collected by the sensors. In Figure 9b, human action recognition on the Opportunity dataset is challenging due to the highly unbalanced sample distribution. Nevertheless, our model can still distinguish some easily confused actions, such as \"Open Door 1\" and \"Open Door 2\", \"Close Door 1\" and \"Close Door 2\", \"Open Drawer 3\" and \"Close Drawer 3\", etc. This shows that our proposed model can effectively and accurately recognize some complex actions with subtle processes and can also distinguish some confusing actions well. In addition, the evaluation metrics scores for each category on the Skoda and Opportunity datasets are presented in Tables 5 and 6, respectively. The main focus here is to analyze the performance of the Macro F1-score. In Table 5, the \"close left front door\" action has the lowest Macro F1-score of 0.85; while the actions such as \"write on notepad\" and \"check steering wheel\" have a higher Macro F1-score of 0.99. In Table 6, the Macro F1-score performance of confusing actions such as \"Open Door 1\", \"Open Door 2\", \"Close Door 1\", and \"Close Door 2\" all reached above 0.79, while the Macro F1-score performance of \"Open Drawer 2\", \"Close Drawer 2\", \"Open Drawer 3\", and \"Close Drawer 3\" also reached above 0.6, which is generally a good performance. These results indicate that our proposed model has good action recognition performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action of Opportunity Dataset** & **Precision** & **Recall** & **Macro F1-Score** \\ \hline Other & 0.949 & 0.964 & 0.956 \\ Open Door 1 & 0.842 & 0.800 & 0.821 \\ Open Door 2 & 0.844 & 0.750 & 0.794 \\ Close Door 1 & 0.944 & 0.739 & 0.829 \\ Close Door 2 & 0.750 & 0.938 & 0.833 \\ Open Fridge & 0.800 & 0.675 & 0.732 \\ Close Fridge & 0.733 & 0.746 & 0.740 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics for each action of the proposed model on the Opportunity dataset.