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      Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

      IEEE transactions on bio-medical engineering
      Adult, Aged, Algorithms, Electrodiagnosis, methods, Fuzzy Logic, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Sleep Stages, physiology

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          Abstract

          Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

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          Author and article information

          Journal
          20858575
          10.1109/TBME.2010.2077291

          Chemistry
          Adult,Aged,Algorithms,Electrodiagnosis,methods,Fuzzy Logic,Humans,Male,Middle Aged,Signal Processing, Computer-Assisted,Sleep Stages,physiology

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