Drivers Pressures States Recognition based on Heart Rate Variability

Abstract

: Drivers pressures are major causes of road accidents, and thus drivers’ pressures states recognition become an important topic in Advanced Driver Assistant System (ADAS). Physiological signals provide information about the internal functioning of human body and thereby provide accurate, reliable and robust information on the driver’s state. In this work, the several features, which are 8 heart rate variability features and 10 mathematical features, are trained using three classifiers: Support Vector Machine (SVM), K-nearest-neighbor (KNN) and Ensemble. The algorithms based pNN5 and LF/HF achieved best performance in HRV linear features evaluation, and the accuracy (AC), sensitivity (SE), specificity (SP) for Stress Recognition in Automobile Drivers data are 89.0%, 91.8% and 77.3% respectively. The mathematical features result in 98.6%,99.1% and 91.5% for accuracy (AC), sensitivity (SE), specificity, respectively.