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Tremor Eye Movements as an Objective Marker of Driver’s Fatigue

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Abstract

Fatigue is a functional state characterized by a persistent decrease in performance. A system for recording the degree of driver fatigue based on the tremor modulation signal model is proposed. The amplitude and frequency of natural tremor oscillations of the eye are taken as an indicator of the degree of fatigue. It has been experimentally proven that tremor is an adequate marker of the condition of fatigue. The objectivity and effectiveness of the proposed method were evaluated in comparison with classical methods for assessing the functional state of the central nervous system—the simple visual-motor reaction and visual-motor coordination times. It was found that the probability of correct prediction of the condition of fatigue by measuring the parameters of natural tremor oscillations of the eye (amplitude and frequency) with the data of simple visual-motor response and visual-motor coordination task is 87.4 and 88.6%, respectively.

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REFERENCES

  1. Muto, V., Shaffii-le Bourdiec, A., Matarazzo, L., et al., Influence of acute sleep loss on the neural correlates of alerting, orientating and executive attention components, J. Sleep Res., 2012, vol. 21, no. 6, p. 648.

    Article  Google Scholar 

  2. Awais, M., Badruddin, N., and Drieberg, M., A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability, Sensors (Basel), 2017, vol. 17, no. 9, p. 1991.

    Article  Google Scholar 

  3. Sahayadhas, A., Sundaraj, K., Murugappan, M., and Palaniappan, R., Physiological signal based detection of driver hypovigilance using higher order spectra, Expert Syst. Appl., 2015, vol. 42, no. 22, p. 8669.

    Article  Google Scholar 

  4. Jing, D., Zhang, S., and Guo, Z., Fatigue driving detection method for low-voltage and hypoxia plateau area: a physiological characteristic analysis approach, Int. J. Transp. Sci. Technol., 2020, vol. 9, no. 2, p. 148.

    Article  Google Scholar 

  5. Junior, A.F., Chierotti, P., Gabardo, J.M., et al., Residual effects of mental fatigue on subjective fatigue, reaction time and cardiac responses, Rev. Psicol. Deporte, 2020, vol. 29, no. 2, p. 27.

    Google Scholar 

  6. Senders, J.W. and Cruzen, M., Tracking Performance on Combined Compensatory and Pursuit Tasks: WADC Technical Report Vol. 52, No. 39, Dayton, OH: Wright Air Dev. Center, US Air Force, 1952.

  7. Boiko, E.I., Vremya reaktsii cheloveka (Human Reaction Time), Moscow: Meditsina, 1964.

  8. Il’in, E.P., Psikhomotornaya organizatsiya cheloveka (Psychomotor Organization of a Man), St. Petersburg: Piter, 2003.

  9. Fan, J., McCandliss, B.D., Fossella, J., et al., The activation of attentional networks, NeuroImage, 2005, vol. 26, no. 2, p. 471.

    Article  Google Scholar 

  10. Nekhoroshkova, A.N., Gribanov, A.V., and Deputat, I.S., Sensomotor reactions in psychophysiological research (a review), Zh. Med.-Biol. Issled., 2015, no. 1, p. 38.

  11. Shutova, S.V. and Murav’eva, I.V., Sensomotor reactions as a characteristic of the functional state of the central nervous system, Vestn. Tomsk. Gos. Univ., 2013, vol. 18, no. 5, p. 2831.

    Google Scholar 

  12. Eichele, H., Juvodden, H.T., Ullsperger, M., and Eichele, T., Mal-adaptation of event-related EEG responses preceding performance errors, Front. Hum. Neurosci., 2010, vol. 4, p. 65.

    PubMed  PubMed Central  Google Scholar 

  13. Lin, C.-T., Huang, K.-C., Chao, C.-F., et al., Arousing feedback rectify lapses in driving? Prediction from EEG power spectra, J. Neural Eng., 2013, vol. 10, p. 056024.

    Article  Google Scholar 

  14. Golz, M., Sommer, D., Trutschel, U., et al., Evaluation of fatigue monitoring technologies, Somnol. Schlafforsch. Schlafmedizin, 2010, vol. 14, no. 3, p. 187.

    Article  Google Scholar 

  15. Golz, M., Sommer, D., Trutschel, U., et al., Driver drowsiness immediately before crashes—A comparative investigation of EEG pattern recognition, Proc. Seventh Int. Driving Symp. on Human Factors in Driver Assessment, Training and Vehicle Design, June 17–20, 2013. Bolton Landing, Iowa City, IA: Univ. of Iowa, 2013, p. 516.

  16. Wang, Y.-T., Huang, K.-C., Wei, C.-S., et al., Developing an EEG-based on-line closed-loop lapse detection and mitigation system, Front. Neurosci., 2014, vol. 8, no. 321, p. 321.

    PubMed  PubMed Central  Google Scholar 

  17. Simon, M., Schmidt, E.A., Kincses, W.E., et al., EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions, Clin. Neurophysiol., 2011, vol. 122, no. 6, p. 1168.

    Article  Google Scholar 

  18. Golz, M., Sommer, D., and Krajewski, J., Prediction of immediately occurring microsleep events from brain electric signals, Curr. Dir. Biomed. Eng., 2016, vol. 2, no. 1, p. 149.

    Article  Google Scholar 

  19. Bergasa, L.M., Nuevo, J., Sotelo, M.A., et al., Real-time system for monitoring driver vigilance, IEEE Trans. Intell. Transp. Syst., 2006, vol. 7, no. 1, p. 63.

    Article  Google Scholar 

  20. D’Orazio, T., Leo, M., Guaragnella, C., and Distan-te, A., A visual approach for driver inattention detection, Pattern Recognit., 2007, vol. 40, no. 8, p. 2341.

    Article  Google Scholar 

  21. Al-Anizy, G.J., Nordin, M.J., and Razooq, M.M., Automatic driver drowsiness detection using HAAR algorithm and support vector machine techniques, Asian J. Appl. Sci., 2015, vol. 8, no. 2, p. 149.

    Article  Google Scholar 

  22. Hossain, M.Y. and George, F.P., IOT based real-time drowsy driving detection system for the prevention of road accidents, Proc. Int. Conf. on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Bangkok, 2018, p. 190.

  23. Snodderly, D.M., A physiological perspective on fixational eye movements, Vision Res., 2016, vol. 118, p. 31.

    Article  Google Scholar 

  24. Ko, H.K., Snodderly, D.M., and Poletti, M., Eye movements between saccades: measuring ocular drift and tremor, Vision Res., 2016, vol. 122, p. 93.

    Article  Google Scholar 

  25. Rucci, M. and Victor, J.D., The unsteady eye: an information-processing stage, not a bug, Trends Neurosci., 2015, vol. 38, no. 4, p. 195.

    Article  CAS  Google Scholar 

  26. Kubarko, A.I., Likhachev, S.A., and Kubarko, N.P., Zrenie (Vision), Minsk: Bel. Gos. Med. Univ., 2009, vol. 2.

    Google Scholar 

  27. Schwartz, S.H., Visual Perception: A Clinical Orientation, New York: McGraw-Hill, 2010.

    Google Scholar 

  28. Khazova, I.V., Shoshmin, A.V., and Devyatova, O.F., Polifunktsional’noe psikhofiziologicheskoe testirovanie v otsenke funktsionirovaniya, ogranichenii zhiznedeyatel’nosti i zdorov’ya. Metodicheskie ukazaniya (Multifunctional Psychophysiological Testing in the Assessment of Functioning, Disabilities, and Health: Methodical Guide), St. Petersburg: Nauchn. Tsentr Rebil. Invalidov im. G.A. Al’brekhta, 2011.

  29. Lyapunov, S.I., Response of the visual system to sine waves under external conditions, J. Opt. Technol., 2018, vol. 85, no. 2, p. 100.

    Article  Google Scholar 

  30. Lyapunov, S.I., Visual acuity and contrast sensitivity of the human visual system, J. Opt. Technol., 2017, vol. 84, no. 9, p. 613.

    Article  Google Scholar 

  31. Lyapunov, S.I., Visual-perception depth of field as a function of external conditions, J. Opt. Technol., 2017, vol. 84, no. 1, p. 16.

    Article  Google Scholar 

  32. Lyapunov, S.I., Threshold contrast of the visual system as a function of the external conditions for various test stimuli, J. Opt. Technol., 2014, vol. 81, no. 6, p. 349.

    Article  Google Scholar 

  33. Yaroshenko, E.I., Use of eye-tracking technology to identify socio-psychological characteristics of emotional burnout, Org. Psikhol., 2019, vol. 9, no. 1, p. 96.

    Google Scholar 

  34. Baiguzhin, P.A., Factors of the effectiveness of psychophysiological study of the functional state of the central nervous system of students, Vestn. Yuzn.-Ural. Gos. Univ., 2011, no. 26, p. 131.

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ACKNOWLEDGMENTS

The authors are grateful to Olesya Mikhailovna Lisevskaya for valuable assistance in organizing a study to assess driver fatigue in the interests of road safety, collecting and providing testing data using a set of methods described in the article as well as to Denis Viktorovich Yavna, Associate Professor of the Southern Federal University, for his help with writing a program for recording visual-motor coordination.

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Authors and Affiliations

  1. Prokhorov General Physics Institute, Russian Academy of Sciences, 119991, Moscow, Russia

    S. I. Lyapunov & I. S. Lyapunov

  2. Pavlov Institute of Physiology, Russian Academy of Sciences, 188680, St. Petersburg, Russia

    I. I. Shoshina

  3. St. Petersburg State University, 199034, St. Petersburg, Russia

    I. I. Shoshina

Authors
  1. S. I. Lyapunov
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  2. I. I. Shoshina
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  3. I. S. Lyapunov
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Correspondence to S. I. Lyapunov or I. I. Shoshina.

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COMPLIANCE WITH ETHICAL STANDARDS

All procedures performed in studies involving human participants were in accordance with the biomedical ethics principles formulated in the 1964 Helsinki Declaration and its later amendments and approved by the local bioethical committee of the Bechterev National Research Medical Center for Psychiatry and Neurology (St. Petersburg).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

INFORMED CONSENT

Each study participant provided a voluntary written informed consent signed by him after explaining to him the potential risks and benefits, as well as the nature of the upcoming study.

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Translated by E. Babchenko

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Lyapunov, S.I., Shoshina, I.I. & Lyapunov, I.S. Tremor Eye Movements as an Objective Marker of Driver’s Fatigue. Hum Physiol 48, 71–77 (2022). https://doi.org/10.1134/S0362119722010091

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  • DOI: https://doi.org/10.1134/S0362119722010091

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