## 4 Experiments
### GSFR Ablation Experiment  
To verify the effectiveness of GSFR, the difference between the presence and absence of GSFR processes is tested on the original BLS, deep neural network (DNN), and GCN. The values are set as \(N_{1}=15\), \(N_{2}=10\), and \(N_{3}=500\). These values correspond to the number of feature nodes in each window (15 nodes per window with a total of 10 windows) and the number of enhancement nodes (500 nodes). The model settings of the BLS are as  
Figure 3: Skeleton representation in datasets.  
follows: the number of feature nodes in each window is 15, with 10 windows. The number of enhancement nodes is 500. The hidden layers of the DNN and GCN consist of three layers, with 64, 128, and 64 neurons, respectively. The dropout probability is 0.2. The Adam optimizer with \(beta_{1}\) = 0.9 and \(beta_{2}\) = 0.999 is used, with a learning rate of 0.001 and a learning rate decay of 0.001. The model is trained for 100 epochs. Table 2 demonstrates the improvement of GSFR on classifier performance. Whether utilizing a BLS or DNN, employing GSFR significantly improves accuracy.  
Figure 4 shows the confusion matrices of the BLS without and with GSFR. From the confusion matrices, it can be seen that the number of correct classifications in most categories has increased after using GSFR. However, the model is prone to confusion between c0 to c9, meaning the data for safe driving and chatting with passengers are relatively similar. In the skeleton coordinate data, only the facial keypoints are slightly offset, while other keypoints remain almost the same. Distinguishing these two actions is challenging. The GSFR-BLS model demonstrates consistent improvements in precision and recall across most categories, especially for challenging ones like c8 (reaching behind) and c0 (safe driving). These enhancements highlight GSFR's ability to dynamically focus on relevant skeletal features, improving discrimination of overlapping or subtle behaviors. The overall accuracy improvement from 93.30% to 93.64% underscores the added value of GSFR-BLS, making it a more robust choice for driver behavior monitoring. The model exhibits significant differences in performance across various driving behavior categories. High-performance categories include c1 (texting--right), c3 (texting--left), c5 (operating the radio), and c7 (hair and makeup), where both precision and recall exceed 95%, demonstrating the model's superior ability to recognize behaviors with prominent skeletal dynamics. These actions often involve distinct hand or head movements, which are easily captured by the skeletal extraction module. However, for low-performance categories like c8 (reaching behind) and c0 (safe driving), the model faces considerable challenges. For instance, c8 achieves precision and recall of 79.1% and 76.2%, respectively, due to its subtle behavioral features, which are often confused with c7 (hair and makeup). Similarly, c0 achieves a recall of 94.6%, but its precision is only 89.1%, mainly due to overlapping dynamic features with c9 (talking to passenger). These results indicate that while the model excels in capturing distinct behaviors, it remains limited in distinguishing between behaviors with similar or less pronounced dynamics.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Classifier** & **GSFR** & **Acc(\%)** \\ \hline \multirow{6}{*}{The State Farm dataset} & BLS & ✓ & 93.639 (+1.103) \\  & & ✕ & 92.536 \\  & & ✓ & 93.287 (+1.253) \\  & DNN & ✕ & 92.034 \\  & GCN & ✓ & 73.892 (+3.219) \\  & & ✕ & 70.673 \\ \hline \multirow{6}{*}{The Driver