## 3 Technical Design of GSFR-BLS
### Graph-Based Skeleton Feature Representation  
Figure 2 shows the 33 3D keypoints skeleton model defined by BlazePose [28]. The skeleton model can be represented by a graph structure, where each keypoint can be defined as a vertex, and the connections between keypoints are defined as edges. Therefore, the skeleton keypoints of a frame can be defined as an undirected graph \(G=(V,E)\). In this study, the GSFR algorithm is improved by incorporating spatio-temporal dynamic selection. Unlike the traditional GSFR that processes static adjacency matrices, a dynamic mechanism that selects the most relevant keypoints by analyzing the movement of each point over multiple frames is introduced. This allows the system to focus on the keypoints that exhibit significant movement, which is more indicative of dangerous driving behaviors. For each frame, the skeleton data consist of the 3D positions of keypoints, denoted as \(P_{i}^{t}=(x_{i}^{t},y_{i}^{t},z_{i}^{t})\), where \(i\) represents the keypoint index and \(t\) represents the frame number. To capture the temporal dynamics, the displacement of each keypoint between consecutive frames is computed. The displacement of keypoint \(P_{i}\) between frame \(t\) and \(t-1\) is defined as  
\[\Delta P_{i}^{t}=P_{i}^{t}-P_{i}^{t-1}=(x_{i}^{t}-x_{i}^{t-1},y_{i}^{t}-y_{i}^ {t-1},z_{i}^{t}-z_{i}^{t-1}). \tag{1}\]  
The displacements across multiple frames are accumulated to evaluate the significance of each keypoint's movement over time. To dynamically select keypoints that are most relevant for action recognition, a thresholding mechanism based on the magnitude of movement is introduced. Instead of using a fixed threshold, the threshold is defined dynamically based on the average movement  
Figure 1: The architecture of the proposed method. Figure 2: The 33 3D keypoints skeleton model defined by BlazePose. magnitude of all keypoints in a frame. The accumulated movement of each keypoint over a window of \(T\) frames is calculated as  
\[\Delta P_{i}^{\text{total}}=\sum_{k=1}^{T}\Delta P_{i}^{t-k}. \tag{2}\]  
The global average movement of keypoints in frame \(t\) is defined as  
\[\bar{\Delta}P^{\text{total}}=\frac{1}{N}\sum_{i=1}^{N}\Delta P_{i}^{\text{ total}} \tag{3}\]  
where \(N\) is the total number of keypoints. The threshold is dynamically set as a multiple of the global average movement:  
\[\theta=\beta\cdot\bar{\Delta}P^{\text{total}} \tag{4}\]  
where \(\beta\) is a hyperparameter controlling the sensitivity of the threshold. A keypoint is considered significant if its accumulated movement \(\Delta P_{i}^{\text{total}}\) exceeds the threshold \(\theta\):  
\[\text{important\_points}=\{P_{i}|\Delta P_{i}^{\text{total}}>\beta\cdot\bar{ \Delta}P^{\text{total}}\}. \tag{5}\]  
This ensures that only keypoints with significant movement are used to form the dynamic neighborhood, reducing computational overhead by excluding less important points. After selecting the most relevant keypoints based on their movement over time, the representation matrix \(R\), by combining the selected keypoints' information and the adjacency relationships between them, is constructed. The adjacency matrix \(A\) is split into two parts: the out-degree matrix \(A_{1}\) and the in-degree matrix \(A_{2}\). Both matrices are filtered using a keypoint importance matrix \(K\), which reduces the contribution of less significant keypoints (such as those in the lower body). This decomposition allows us to separately account for the spatial influence of incoming and outgoing connections between keypoints. After filtering and normalization, the representation matrix \(R\) is constructed by concatenating the following matrices:  
\[R=[I^{\prime},A_{1}^{\prime},A_{2}^{\prime}] \tag{6}\]  
where \(I^{\prime}\) represents the identity matrix filtered by keypoint importance, capturing the self-information of each keypoint. \(A_{1}^{\prime}\) and \(A_{2}^{\prime}\) represent the normalized out-degree and in-degree matrices, capturing the relationships between keypoints based on their spatial and temporal dynamics.