## 4 Experiments
### Classification Experiment  
The visualized results demonstrate that the proposed method effectively extracts skeletal features from drivers of different body types and genders, providing robust support for behavior analysis, as shown in Figure 5. The method accurately captures key skeletal nodes and their dynamic distributions, including head (green nodes and lines); shoulders, arms, and hands (blue nodes and lines); torso (purple nodes and lines); and lower limbs (orange nodes and lines). These features are consistently extracted across diverse scenarios, such as smaller or larger body types, male and female drivers, and varying postures. For instance, in cases like texting with one hand, talking on the phone, or safe driving, the system clearly distinguishes between behaviors based on the spatial and temporal relationships of skeletal keypoints. Additionally, even in complex environments with multiple individuals, the method isolates the driver's skeletal features, ensuring accurate representation. The adaptability and robustness of the skeletal extraction process provide high-quality inputs for driving behavior classification, establishing its suitability for diverse and real-world applications. The accuracy, inference time, and parameters of the proposed driving action recognition method are compared with those of other mainstream classifiers: machine learning classifiers, such as random forests [41], gradient boosting [42], and support vector machine (SVM) [43]; deep learning classifiers, including DNN, 1D-CNN, and GCN, which have identical hidden layer structures consisting of layers with 64, 128, and 64 neurons, respectively; and the original BLS. The hyperparameters of the machine learning classifiers mentioned above are the default parameters in Python's sklearn library. Figure 4: The confusion matrices without GSFR of BLS on the Driver Skeleton dataset. (**a**) CM of BLS without GSFR. (**b**) CM of BLS with GSFR. Table 3 demonstrates the excellent performance of the proposed driving action recognition method, which offers high accuracy, short inference time, and lightweight characteristics across different datasets. Typically, machine learning classifiers require large parameters to achieve high accuracy. Deep learning classifiers often struggle to achieve high accuracy due to a lack of efficient feature representation, and their complex computing processes result in significant inference time. Overall, the original BLS has comprehensive advantages, and the proposed method improves the classification accuracy of the original BLS without incurring significant costs. To validate the effectiveness of the proposed method, comparisons are conducted with several recent state-of-the-art approaches relevant to Driver Monitoring Systems (DMSs). On the State Farm dataset, the proposed method was compared with SC-GCN [39] and DDDS [34]. These models are widely recognized in the DMS domain for driver action recognition but are not optimized for lightweight performance. On the Driver Skeleton dataset, the proposed method was evaluated against 2S-GCN-TAtt [38] and Beh-MSFNet [37]. Although Beh-MSFNet achieves slightly higher accuracy, its parameter size (3614.72 KB) is significantly larger compared to the proposed method (507 KB), highlighting the advantage of the GSFR-BLS in achieving competitive accuracy while maintaining a compact model size. This balance between accuracy and model efficiency makes the proposed method particularly suitable for deployment in resource-constrained environments. Figure 6 evaluates the capabilities of the above classifiers based on accuracy, inference time, and parameters. To accommodate the significant numerical gaps between different indicators, the Min-Max Normalization is applied as in (16) to each indicator across various models, constraining their values to a range of (0, 1). Shorter inference time and smaller parameter costs indicate better performance. Therefore, the values for the normalized inference time and normalized parameters have been inverted.