Research on Driver Fatigue Detection in Real Driving Environments Based on Semi-Dry Electrodes with Automatic Conductive Fluid Replenishment.

Driving fatigue poses a serious threat to road safety. To detect fatigue accurately and thereby improve vehicle safety, this paper proposes a novel semi-dry electrode with the ability to automatically replenish the conductive fluid for monitoring driving fatigue. This semi-dry electrode not only integrates the advantages of both wet and dry electrodes but also incorporates an automatic conductive fluid replenishment mechanism. This design significantly extends the operational lifespan of the electrode while mitigating the limitations of manual replenishment, particularly the risk of signal interference. Additionally, this study adopts a transfer learning approach to detect driving fatigue by analyzing electroencephalography (EEG) signals. The experimental results indicate that this method effectively addresses the issue of data sparsity in real-time fatigue monitoring, overcomes the limitations of traditional algorithms, shows strong generalization performance and cross-domain adaptability, and achieves faster response times with enhanced accuracy. The semi-dry electrode and transfer learning algorithm proposed in this study can provide rapid and accurate detection of driving fatigue, thereby enabling timely alerts or interventions. This approach effectively mitigates the risk of traffic accidents and enhances both vehicle and road traffic safety.