Consecutive Detection of Extreme Central Fatigue

Part of the IFMBE Proceedings book series (IFMBE, volume 22)

Abstract

In order to establish fatigue monitoring technologies a valid method for automatic detection of extreme central fatigue is needed. At present, acquisition of biosignals and their analysis by computational intelligence methods are most promising. We present experiments during which 10 volunteers drove overnight in our real-car lab following a partial sleep deprivation design. Based on several biosignals (EEG, EOG) recorded during microsleep events a classifier was constructed. We have shown earlier that spectral power densities of EEG and EOG averaged in narrow bands performed best as signal features and that carefully parameterized Support-Vector Machines perform best for classification. Afterwards, classification of approximately 1.5 million consecutively segmented biosignals was performed in order to check utility for real detector application. The independent validation of this step is shown to be crucial. Two different methods based on a subjecttive and an objective measure are presented. A methodological problem remains open in how to proceed with suspicious periods where some behavioral signs point to extreme fatigue, but driving seems still to be possible.

Keywords

EEG EOG Support-Vector Machines Fatigue Microsleep 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gründl M (2005) Fehler und Fehlverhalten als Ursache von Verkehrsunfällen und Konsequenzen für das Unfallvermeidungspotenzial. PhD Thesis (in German), Regensburg, Germany, 2005.Google Scholar
  2. 2.
    Golz M, Sommer D, Chen M, Trutschel U, Mandic D (2007) Feature Fusion for the Detection of Microsleep Events. J VLSI Signal Proc Syst 49: 329–342CrossRefGoogle Scholar
  3. 3.
    Overview at http://www.microsleep.deGoogle Scholar
  4. 4.
    Golz M, Sommer D, Holzbrecher M, Schnupp T (2007) Detection and Prediction of Driver’s Microsleep Events, Proc 14th Int Conf Road Safety on Four Continents; Bangkok, Thailand, 2007Google Scholar
  5. 5.
    Davidson PR, Jones RD, Peiris MT (2007) EEG-Based Behavioral Microsleep Detection with High Temporal Resolution. IEEE Trans Biomed Engin 54: 832–839CrossRefGoogle Scholar
  6. 6.
    Åkerstedt T (1990) Subjective and Objective Sleepiness in the Active Individual. Int J Neurosci 52: 29–37CrossRefGoogle Scholar
  7. 7.
    Ingre M, Åkerstedt T, Peters B, Anund A, Kecklund G (2006) Subjective Sleepiness, Simulated Driving Performance and Blink Duration: Examining Individual Differences. J Sleep Res 15: 47–53CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of Applied Sciences — SchmalkaldenSchmalkaldenGermany
  2. 2.Institute of Work and Organizational PsychologyUniversity of WuppertalWuppertalGermany
  3. 3.Institute for System Analysis and Applied NumericsTabarzGermany

Personalised recommendations