Driver stress level detection using HRV analysis

This paper intends to investigate stress level detection of a driver during real world driving experiment. This detection is based on heart rate variability (HRV) analysis which is derived from ECG signal and reflects autonomic nervous system state of the human body. The alteration of autonomic nervous system predicts the stress level of drivers during driving operation and permits a safe driving by the possibility of an early warning. This stress, taking place during driving, is caused by diverse factors such as changing mood, bio rhythm, fatigue, boredom or disease which can prevent the driver from reaching inappropriate state for driving. In our study, the ECG signal of the driver is extracted and preprocessed in order to perform the HRV analysis. This analysis is accomplished using one of the domain analysis approach such as time, frequency, time-frequency or non-linear methods including Wavelet and STFT. After HRV analysis, several parameters are extracted to build a vector of features for the classification phase. Our experimentation is performed with data issued from 16 different subjects from the Stress Recognition in Automobile Driver database (DRIVEDB). Several classification techniques were investigated including support vector machine with radial basis function (SVM-RBF) kernel, K nearest neighbor (KNN), and radial basis function (RBF) classifiers. Our results indicate that stress detection could be predicted with an accuracy of 83% using SVM-RBF classifier. This also points out the robustness of ECG biometric as an accurate physiological indicator of a driver state.