Advertisement

Driver Sleepiness Detection Using LSTM Neural Network

  • Yini Deng
  • Yingying Jiao
  • Bao-Liang LuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

Driver sleepiness has become one of the main reasons for traffic accidents. Previous studies have shown that two alpha-related phenomena - alpha blocking phenomenon and alpha wave attenuation-disappearance phenomenon - respectively represent two different sleepiness levels: the relaxed wakefulness and the sleep onset. Thus, we proposed a novel model to detect those two alpha-related phenomena based on EEG and EOG signals so as to determine sleepiness level. EOG and EEG signals inherently have temporal dependencies, and the sleepiness level transition is also a temporal process. Correspondingly, continuous wavelet transform represents physiological signals well, and LSTM is capable of handling long-term dependencies. Thus, our proposed dectection model utilized continuous wavelet transform and LSTM neural network for detecting driver sleepiness. The performance of our detection model are twofold: the recall and precision for detecting start and end points of alpha waves are generally high, and the LSTM classifier reaches a mean accuracy of 98.14%.

Keywords

Driver sleepiness detection EEG EOG Continuous wavelet transform LSTM 

Notes

Acknowledgments

This work was supported in part by the grants from the National Key Research and Development Program of China (Grant No. 2017YFB1002501), the National Natural Science Foundation of China (Grant No. 61673266), and the Fundamental Research Funds for the Central Universities.

References

  1. 1.
    Anund, A., Åkerstedt, T.: Perception of sleepiness before falling asleep. Sleep Med. 11(8), 743–744 (2010)CrossRefGoogle Scholar
  2. 2.
    Balandong, R.P., Ahmad, R.F., Saad, M.N.M., Malik, A.S.: A review on EEG-based automatic sleepiness detection systems for driver. IEEE Access 6, 22908–22919 (2018)CrossRefGoogle Scholar
  3. 3.
    Bengio, Y., Simard, P.Y., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5(2), 157–166 (1994)CrossRefGoogle Scholar
  4. 4.
    Guyton, A.C.: Structure and Function of the Nervous System. Saunders Limited. (1976)Google Scholar
  5. 5.
    Harland, C.J., Clark, T.D., Prance, R.J.: Remote detection of human electroencephalograms using ultrahigh input impedance electric potential sensors. Appl. Phys. Lett. 81(17), 3284–3286 (2002)CrossRefGoogle Scholar
  6. 6.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  7. 7.
    Horne, J.A., Baulk, S.D.: Awareness of sleepiness when driving. Psychophysiology 41(1), 161–165 (2004)CrossRefGoogle Scholar
  8. 8.
    Jiao, Y., Lu, B.L.: An alpha wave pattern from attenuation to disappearance for predicting the entry into sleep during simulated driving. In: 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 21–24 (2017)Google Scholar
  9. 9.
    Jiao, Y., Lu, B.L.: Detecting driver sleepiness from EEG alpha wave during daytime driving. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 728–731 (2017)Google Scholar
  10. 10.
    Sagberg, F.: Road accidents caused by drivers falling asleep. Accid. Anal. Prev. 31(6), 639–649 (1999)CrossRefGoogle Scholar
  11. 11.
    Shabani, H., Mikaili, M., Noori, S.M.R.: Assessment of recurrence quantification analysis (RQA) of EEG for development of a novel drowsiness detection system. Biomed. Eng. Lett. 6(3), 196–204 (2016)CrossRefGoogle Scholar
  12. 12.
    Shi, L.C., Lu, B.L.: EEG-based vigilance estimation using extreme learning machines. Neurocomputing 102, 135–143 (2013)CrossRefGoogle Scholar
  13. 13.
    Tang, Y.: Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 (2013)
  14. 14.
    Zheng, W.L., Lu, B.L.: A multimodal approach to estimating vigilance using EEG and forehead EOG. J. Neural Eng. 14(2), 26017 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognition EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Brain Science and Technology Research CenterShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations