Modeling Driver Fatigue Using ECG Signals and Machine Learning Techniques

To improve road traffic safety, this study investigates the potential and feasibility of using electrocardiogram (ECG) signals for driver fatigue detection. A simulated driving experiment was designed to collect raw ECG data from participants, from which typical time-domain, frequency-domain, and non-linear features were extracted. A fatigue recognition model was then constructed using a support vector machine (SVM). Grid search combined with cross-validation was employed to optimize the model’s hyperparameters. The results demonstrated that the optimal classification performance was achieved when the penalty parameter C = 1 and the kernel parameter γ = 0.1. Under this configuration, further evaluation yielded classification accuracy, precision, recall, specificity, and F1-score of 84.9%, 80.0%, 86.5%, 83.7%, and 83.1%, respectively. These findings indicate that the proposed ECG-based SVM model can effectively identify driver fatigue states and exhibits robust classification performance. This study provides a feasible technical approach for intelligent fatigue detection and offers theoretical and practical support for the development of driver monitoring and safety systems.