## 1 Introduction  
In recent years, Driver Monitoring Systems (DMSs) have become a critical component in ensuring road safety and reducing traffic accidents. DMSs typically monitor driver behavior to detect dangerous actions, indicative of states such as fatigue, distraction, or impairment, preventing potential traffic incidents. These systems have evolved from traditional methods based on visual image data to more advanced techniques utilizing skeletal data, which provide unique advantages in terms of computational efficiency and robustness. According to the National Highway Traffic Safety Administration (NHTSA), distracted driving alone causes more than 3000 fatal crashes each year, underscoring the need for effective DMS technologies to enhance road safety [1, 2, 3]. As the reliance on advanced sensor technologies grows, DMSs must operate efficiently under resource constraints, making them a vital aspect of modern transportation infrastructure [4, 5, 6]. Existing DMS algorithms can be broadly categorized into two approaches: direct image-based detection methods and skeletal data-based detection methods. Direct image-based methods typically rely on convolutional neural networks (CNNs) [7, 8] and recurrent neural networks (RNNs) [9, 10] or other deep learning models that process raw pixel data to identify dangerous driving behaviors. However, these models often require significant computational resources, which can hinder real-time performance on resource-constrained embedded devices [11; 12]. In contrast, skeletal data-based methods focus on analyzing the 3D skeletal features of the driver, providing several advantages over traditional image-based approaches. Skeletal data are computationally less intensive, offer better light invariance, and are more robust to occlusions and noise. These characteristics make skeletal data particularly suitable for deployment on embedded systems with limited resources. Despite the critical role DMSs play in preventing dangerous driving, existing skeletal data-based algorithms face several challenges in real-world applications. First, they must operate efficiently on embedded devices with limited computational power and storage capacity, necessitating lightweight algorithms that strike a balance between accuracy and efficiency [13]. Additionally, traffic incidents can occur suddenly, requiring real-time detection and immediate response [14]. Furthermore, DMSs must maintain high accuracy in dynamic environments where sensor data can be noisy, incomplete, or missing due to sensor malfunctions or communication issues [15; 16]. To address these challenges, this paper proposes a novel, lightweight, real-time, and robust DMS for dangerous driving behavior detection that integrates graph-based approaches with a Broad Learning System (BLS). The core of our method lies in its ability to process 3D skeletal data for dynamic driver behavior analysis, capturing key spatio-temporal features that are crucial for detecting dangerous driving actions. Unlike traditional methods that heavily rely on visual data, our approach focuses on skeletal data, which are not only more efficient in terms of computational requirements but also more robust to occlusions, lighting variations, and noise. The key contributions of this paper are as follows:  
1. A Graph Spatio-Temporal Feature Representation (GSFR) method that selects the most relevant keypoints from 3D skeletal data by analyzing spatio-temporal dynamics. This approach improves detection accuracy while reducing computational complexity, ensuring real-time performance on embedded systems. The dynamic selection mechanism also enhances robustness against noisy or missing data. 2. The integration of a Broad Learning System (BLS), a lightweight neural network architecture, to process the selected features efficiently. By using sparse feature selection and Principal Component Analysis (PCA), we reduce the dimensionality of the feature space while retaining essential information, further enhancing real-time detection capabilities. 3.