## 5 Conclusions  
This study presents a novel, lightweight, and real-time method for dangerous driving action recognition based on 3D skeletal data, specifically designed for deployment in resource-constrained embedded devices within a DMS. The proposed approach leverages GSFR to dynamically select relevant 3D skeletal keypoints, enhancing feature representation while reducing computational complexity and improving robustness to noisy or incomplete data. Additionally, the BLS, integrated with sparse feature selection and PCA, optimizes computational efficiency and supports real-time processing. A dual smoothing strategy, combining sliding window smoothing and EMA, stabilizes the classification outputs, making the system more reliable in dynamic driving conditions.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Modules** & **Time Cost** \\ \hline Image Capture & 0.0022 s \\ Detection & 0.0225 s \\ Inference & 0.0018 s \\ Smoothing & \(8.2133\times 10^{-5}\) s \\ Total Workflow & 0.0375 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average time cost of workflow modules.  
Figure 8: Noise reduction performance of smoothing.  
Figure 7: The time-cost statistics of modules in the workflow.  
Experimental validation on several public datasets, including the State Farm and Driver Skeleton datasets, demonstrates that the GSFR-BLS model significantly outperforms existing methods in terms of accuracy, efficiency, and robustness, highlighting its suitability for practical DMS applications. However, the model's performance is still influenced by the accuracy of human pose estimation and potential data imbalance issues. Future work will focus on incorporating raw image data alongside skeletal information to improve classification accuracy and address challenges in real-world applications, further enhancing the system's robustness in dynamic environments. Future work will aim to extend the system's applicability by collecting data from more diverse driving scenarios, including real-world environments with a broader range of driver demographics and driving conditions (e.g., urban vs. rural settings, varying age groups, and varying experience levels). This will enable testing in complex and dynamic settings to ensure robustness and reliability in safety-critical applications, ultimately improving the model's performance across a wider range of real-world situations. Future advancements in DMS applications should be anticipated to be deeply integrated with Intelligent Transportation Systems. Therefore, it is imperative to explore the development of edge-cloud collaborative DMS technologies and applications involving Vehicle-to-Everything [44] communication and Unmanned Aerial Vehicles [45].  
Conceptualization, P.L. and H.S.; methodology, P.L.; validation, P.L., Z.L., H.S., and C.C.; writing--original draft preparation, P.L.; writing--review and editing, Z.L., H.S., and C.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (62072360, 62172438), the Zhejiang Provincial Natural Science Foundation of China under Grant LR23F010006, the Key Research and Development Plan of Shaanxi province(2021 ZDLGY02-09, 2023-GHZD-44, 2023-ZDLGY-54), the National Key Laboratory Foundation(2023-JCJCJQ-LB-007), the Natural Science Foundation of Guangdong Province of China(2022A1515010988), Key Project on Artificial Intelligence of Xi'an Science and Technology Plan(23 ZDCY)SGG0021-2022, 23 ZDCYYYCJ0008, 23 ZDCYJSGG0002-2023), Xidian-UTAR China Malaysia Science and Technology Institute--the Fundamental Research Funds for the Central Universities/XURF-2024-QTZZX4089, and the Proof-of-concept fund from Hangzhou Research Institute of Xidian University (GNYZ2023 QC0201, GNYZ2024 QC004, GNYZ2024 QC015).  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy restrictions.  
The authors declare no conflicts of interest.