Assessing Driver’s Hypovigilance from Biosignals

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

Abstract

For the assessment of Fatigue Monitoring Technologies (FMT) an independent reference of driver’s hypovigilance is needed. To achieve this goal, we propose to process EEG and EOG biosignals, to apply a feature fusion concept and to utilize Support-Vector Machines (SVM) for classification. Karolinska Sleepiness Scale (KSS) and variation of lane deviation (VLD) were used in order to get independent class labels, whereas KSS are subjective and VLD are objective measures. For simplicity, two classes were determined: slight and strong hypovigilance. 16 young volunteers participated in overnight experiments in our real car driving simulation lab. Results were compared with PERCLOS (percentage of eye closure), an oculomotoric variable utilized in several FMT systems. We conclude that EEG and EOG biosignals contain substantial higher amount of hypovigilance information than the PERCLOS biosignal.

Keywords

Hypovigilance EEG EOG PERCLOS Data Fusion Support-Vector Machines Driving Simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Leproult R, Coleccia E, Berardi A, Stickgold R, Kosslyn S.M., Van Cauter E, (2002) Individual differences in subjective and objective alertness during sleep deprivation are stable and unrelated, Am J Physiol 284, 2002, pp R280–R290.Google Scholar
  2. 2.
    Trutschel U, Sommer D, Aguirre A, Dawson T, Sirois B (2006), Alertness Assessment Using Data Fusion and Discrimination Ability of LVQ-Networks, 10th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems (KES 2006), pp. 1264–1271,Bournemouth, UK, October 2006Google Scholar
  3. 3.
    Golz M, Sommer D, Chen M, Trutschel U, Mandic D, (2007), Feature Fusion for the Detection of Microsleep Events, The Journal of VLSI Signal Processing, vol. 49, pp. 329–342, ISSN 0922-5773, Springer, Netherlands, 2007Google Scholar
  4. 4.
    Pilutti T, Ulsoy G, (1999), Identification of Driver State for Lane-Keeping Tasks, IEEE Transactions on Systems, Man, and Cybernetic, Part A: System and Humans, vol. 29, pp. 486–502, 1999CrossRefGoogle Scholar
  5. 5.
    Golz M, Sommer D, (2005), Detection of Strong Fatigue During Overnight Driving 39th Annual Congress of the German Society for Biomedical Engineering (BMT 2005), pp 479–480, Part 1,Nürnberg, Germany, September 2005Google Scholar
  6. 6.
    Dinges D, Grace R, (1998), PERCLOS: A Valid Psychophysiological Measure of Alertness As Assessed by Psychomotor Vigilance, TechBrief NHTSA, Publication No. FHWA-MCRT-98-006Google Scholar
  7. 7.
    Johns M, (2003). The Amplitude-Velocity Ratio of Blinks: A new Method for Monitoring Drowsiness. Sleep, vol. 26, pp.A51–52.Google Scholar
  8. 8.
    Schleicher R, Galley N, Briest S, Galley L (2007) Looking Tired? Blinks and Saccades as Indicators of Fatigue. Ergonomics 51: 982–1010CrossRefGoogle Scholar
  9. 9.
    AWAKE-System for Effective Assessment of Driver Vigilance and Warning According to Traffic Risk Estimation, (2004), Road Safety Workshop, Balocco (Italy),, http://www.awake-eu.org/index.htmlGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of Applied Sciences — SchmalkaldenSchmalkaldenGermany
  2. 2.Circadian Technologies Inc.StonehamUSA
  3. 3.Institute for System Analysis and Applied NumericsTabarzGermany
  4. 4.Caterpillar Inc.PeoriaUSA

Personalised recommendations