Unobtrusive Multimodal Driver Stress Detection from ECG and PPG Using TCNs

Given its profound economic and societal impact, the automotive industry calls for enhancing driver and driving safety. Current approaches mainly focus on detecting unsafe vehicle behavior, often overlooking crucial factors related to the driver's physiological state, such as stress or drowsiness. This study introduces a multimodal, unobtrusive driver monitoring system for stress detection. It integrates photoplethysmography (PPG) and electrocardiography (ECG) sensors into a smart steering wheel. Data were collected from five participants using a driving simulator under controlled stress-inducing scenarios. A Temporal Convolutional Network (TCN) was used to classify stress levels based solely on physiological signals, achieving an average cross-validated accuracy of 95.14%, an F1-score of 82.29%. This system outperforms many state-of-the-art wearable approaches while performing unobtrusive multimodal driver vital signs monitoring. An ablation study was also conducted to assess the individual and combined contribution of each signal modality. Results confirmed that the sensor fusion of PPG and ECG provides a performance improvement compared to using either signal alone.