An Electrocardiogram-Based Driver Stress Detection Scheme Using Ensembled Multiscale Classifier

Improving driving safety is the key to reducing traffic accidents caused by human factors such as cognitive errors, judgment errors, slow emergency response, etc. Studies have shown these human factors are highly correlated with drivers stress levels. Hence, various driver stress detection (DSD) schemes have been developed to improve driving safety. However, existing approaches face great difficulties in achieving high detection accuracy and practicability. To address this challenge, an Ensembled Multiscale Classifier (EMC) is proposed to realize the DSD and further reduce traffic accidents. In EMC, the stress level is classified into three categories, namely Low-Stress Level (LSL), Mid-Stress Level (MSL), and High-Stress Level (HSL). Fiducial features and non-fiducial features are considered to reach a balance between detection accuracy and implementation practicability. Specifically, fiducial features are extracted directly from ECG signals and analyzed by the neural network with backpropagation (NNBP). For non-fiducial features, they are extracted from the transformed ECG signals and analyzed by the 1-D convolutional neural network (1-D CNN). The outputs of the NNBP and 1-D CNN are coordinated by an ensembled decision-making layer, thereby deriving a probabilistic prediction of drivers stress levels. Experimental results reveal the developed method has high model fitness and 95.9% detection accuracy. © 2024 IEEE.