Enhanced Broad-Learning-Based Dangerous Driving Action Recognition on Skeletal Data for Driver Monitoring Systems

Recognizing dangerous driving actions is critical for improving road safety in modern transportation systems. Traditional Driver Monitoring Systems (DMSs) often face challenges in terms of lightweight design, real-time performance, and robustness, especially when deployed on resource-constrained embedded devices. This paper proposes a novel method based on 3D skeletal data, combining Graph Spatio-Temporal Feature Representation (GSFR) with a Broad Learning System (BLS) to overcome these challenges. The GSFR method dynamically selects the most relevant keypoints from 3D skeletal data, improving robustness and reducing computational complexity by focusing on essential driver movements. The BLS model, optimized with sparse feature selection and Principal Component Analysis (PCA), ensures efficient processing and real-time performance. Additionally, a dual smoothing strategy, consisting of sliding window smoothing and an Exponential Moving Average (EMA), stabilizes predictions and reduces sensitivity to noise. Extensive experiments on multiple public datasets demonstrate that the GSFR-BLS model outperforms existing methods in terms of accuracy, efficiency, and robustness, making it a suitable candidate for practical deployment in embedded DMS applications.