Detection of driver's drowsiness by means of HRV analysis

Abstract

It is estimated that 10–30% of road fatalities are related to drowsy driving or driver fatigue. Driver's drowsiness detection based on biological and vehicle signals is being studied in preventive car safety. Autonomous Nervous System (ANS) activity, which can be measured non-invasively from the Heart Rate Variability (HRV) signal obtained from surface ECG, presents alterations during stress, ex-trem fatigue and drowsiness episodes. Our hypothesis is that these alterations manifest on HRV. In this work we de-velope an on-line detector of driver's drowsiness based on HRV analysis. Two databases have been analyzed: one of driving simulation in which subjects were sleep deprived, and the other of real situation with no sleep deprivation. An external observer annotated each minute of the recordings as drowsy or awake, and constitutes our reference. The proposed detector classified drowsy minutes with a sensitivity of 0.85 and a predictive positive value of 0.93, using 25 features.