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Review and Implementation of Driving Fatigue Evaluation Methods Based on RR Interval

  • Weiwei Guo
  • Chunling Xu
  • Jiyuan Tan
  • Yinghong Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

The evaluation methods of driving fatigue have been the hot topics in the study of traffic. This paper studied the main existing driving fatigue evaluation methods based on RR intervals and verified the effectiveness of these methods by experiments. There were 15 drivers selected in the simulation driving experiment, and the pulse signal of them was collected and recorded during the test period. Average heart rate, time and frequency domain features of HRV, nonlinear features of HRV, and statistic of MSPC were chosen as the indexes to verify. The experimental results show that “T2” of HRV time domain indexes, including MSPC, SDNN, RMSSD and total power, HRV frequency domain indexes like LF/HF, and the longer axis of Poincare scatter plot can distinguish the fatigue state and the waking state of drivers with a favorable effect.

Keywords

Driver fatigue RR intervals Heart rate Heart rate variability MSPC 

Notes

Acknowledgments

This research is partially supported by the National Natural Science Foundation of China (Grant No. 61503007, 61603005), Technology Project General Project of Beijing Municipal Education Commission. No. SQKM201810009007, and Beijing Youth Talent Support Program. The authors gratefully thank anonymous referees for their useful comments and editors for their work.

References

  1. 1.
    Phillips RO (2015) A review of definitions of fatigue—and a step towards a whole definition. Transp Res Part F Traffic Psychol Behav 29:48–56CrossRefGoogle Scholar
  2. 2.
    Ting PH, Hwang JR, Doong JL et al (2008) Driver fatigue and highway driving: a simulator study. Physiol Behav 94(3):448–453CrossRefGoogle Scholar
  3. 3.
    Dewar RE, Olson PL (2002) Human factors in traffic safety. Lawyers & Judges, Tucson, AZGoogle Scholar
  4. 4.
    Yang G, Lin Y, Bhattacharya P (2010) A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inf Sci 180(10):1942–1954CrossRefGoogle Scholar
  5. 5.
    Fadda P, Meloni M, Fancello G et al (2015) Multidisciplinary Study of biological parameters and fatigue evolution in quay crane operators. Procedia Manuf 3:3301–3308CrossRefGoogle Scholar
  6. 6.
    Lal SKL, Craig A (2002) Driver fatigue: electroencephalography and psychological assessment. Psychophysiology 39(3):313–321CrossRefGoogle Scholar
  7. 7.
    Malik M, Bigger JT, Camm AJ et al (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17(3):354–381CrossRefGoogle Scholar
  8. 8.
    Toscani L, Gangemi PF, Parigi A, Silipo R, Ragghianti P, Sirabella E, Morelli M, Bagnoli L, Vergassola R, Zaccara G (1996) Human heart rate variability and sleep stages. Ital J Neurol Sci 17:437–439CrossRefGoogle Scholar
  9. 9.
    Elsenbruch S, Harnish M, Orr WC (1999) Heart rate variability during waking and sleep in healthy males and females. Sleep 22:1067–1071CrossRefGoogle Scholar
  10. 10.
    Patel M, Lal SKL, Rossiter P et al (2011) Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst Appl 38(6):7235–7242CrossRefGoogle Scholar
  11. 11.
    Shinar Z, Akselrod S, Dagan Y, Baharav A (2006) Autonomic changes during wake-sleep transition: a heart rate variability based approach. Auton Neurosci 130:17–27CrossRefGoogle Scholar
  12. 12.
    Takahashi I, Yokoyama K (2011) Development of a feedback stimulation for drowsy driver using heartbeat rhythms. 2011(4):4153–4158Google Scholar
  13. 13.
    Vicente J, Laguna P, Bartra A et al (2016) Drowsiness detection using heart rate variability. Med Biol Eng Compu 54(6):927–937CrossRefGoogle Scholar
  14. 14.
    Baharav A, Kotagal S, Gibbons V, Rubin BK, Pratt G, Karin J, Akselrod S (1995) Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variability. Neurology 45(6):1183–1187CrossRefGoogle Scholar
  15. 15.
    Furman G, Baharav A, Cahan C, Akselrod S (2009) Early detection of falling asleep at the wheel: a heart rate variability approach. Comput Cardiol 35:1109–1112Google Scholar
  16. 16.
    Rodriguez-Ibañez N et al (2012) Changes in heart rate variability indexes due to drowsiness in professional drivers measured in a real environment. Comput Cardiol IEEE 39:913–916Google Scholar
  17. 17.
    Woo MA, Stevenson WG, Moser DK et al (1992) Patterns of beat-to-beat heart rate variability in advanced heart failure. Am Heart J 123(3):704–710CrossRefGoogle Scholar
  18. 18.
    Kamen PW, Krum H, Tonkin AM (1996) Poincare plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clin Sci 91(2):201–8CrossRefGoogle Scholar
  19. 19.
    Kano M, Nagao K, Hasebe S, Hashimoto I, Ohno H, Strauss R, Bakshi BR (2002) Comparison of multivariate statistical process monitoring methods with applications to the eastman challenge problem. Comput Chem Eng 26(2):161–174CrossRefGoogle Scholar
  20. 20.
    Nomikos P, MacGregor JF (1994) Control procedures for residuals associated with principal component analysis. AIChE J 40:1361–1375CrossRefGoogle Scholar
  21. 21.
    Abe E, Fujiwara K, Hiraoka T et al (2014) Development of drowsy driving accident prediction by heart rate variability analysis. Asia-Pacific Signal and Information Processing Association, 2014 Summit and Conference. Siem Reap, CambodiaGoogle Scholar
  22. 22.
    Fujiwara K, Miyajima M, Yamakawa T et al (2016) Epileptic seizure prediction based on multivariate statistical process control of heart rate variability features. IEEE Trans Biomed Eng 63(6):1321–1332CrossRefGoogle Scholar
  23. 23.
    Li PF, Wang DH, Liu DB et al (2011) Analysis on driving fatigue before and after lunch based on indices of physiology and psychology. J Changan Univ 31(4):81–86Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Weiwei Guo
    • 1
  • Chunling Xu
    • 1
  • Jiyuan Tan
    • 1
  • Yinghong Li
    • 1
  1. 1.Beijing Key Lab of Urban Intelligent Traffic Control TechnologyNorth China University of TechnologyShijingshanChina

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