Sleep Deprivation Detection for Real-Time Driver Monitoring Using Deep Learning

  • Miguel García-GarcíaEmail author
  • Alice Caplier
  • Michele Rombaut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)


We propose a non-invasive method to detect sleep deprivation by evaluating a short video sequence of a subject. Computer Vision techniques are used to crop the face from every frame and classify it (within a Deep Learning framework) into two classes: “rested” or “sleep deprived”. The system has been trained on a database of subjects recorded under severe sleep deprivation conditions. A prototype has been implemented in a low-cost Android device proving its viability for real-time driver monitoring applications. Tests on real world data have been carried out and show encouraging performances but also reveal the need of larger datasets for training.


Mobilenet Road safety Driver drowsiness Sleep deprivation 


  1. 1.
    Taheri, S., et al.: Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med. 1(3), e62 (2004). Ed. Philippe Froguel. PMCMathSciNetCrossRefGoogle Scholar
  2. 2.
    Durmer, J.S., Dinges, D.F.: Neurocognitive consequences of sleep deprivation. Semin. Neurol. 25(1), 117–129 (2005). Copyright 2005 by Thieme Medical Publishers Inc, 333 Seventh Avenue, New York, NY 10001, USACrossRefGoogle Scholar
  3. 3.
    Peters, R.D.: Effects of partial and total sleep deprivation on driving performance. US Department of Transportation, February 1999Google Scholar
  4. 4.
    Metzgar, C.: Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication. Prof. Saf. 46(1), 17 (2001)Google Scholar
  5. 5.
    Drowsy Driving NHTSA reports. Accessed 02 June 2017
  6. 6.
    Masten, S.V., Stutts, J.C., Martell, C.A.: Predicting daytime and nighttime drowsy driving crashes based on crash characteristic models. In: 50th Annual Proceedings, Association for the Advancement of Automotive Medicine, Chicago, October 2006Google Scholar
  7. 7.
    Sundelin, T., et al.: Cues of fatigue: effects of sleep deprivation on facial appearance. Sleep 36(9), 1355–1360 (2013)CrossRefGoogle Scholar
  8. 8.
    Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953 (2012)CrossRefGoogle Scholar
  9. 9.
    Ekman, P.: Facial action coding system (FACS). A human face (2002)Google Scholar
  10. 10.
    Vural, E., Cetin, M., Ercil, A., Littlewort, G., Bartlett, M., Movellan, J.: Drowsy driver detection through facial movement analysis. In: Lew, M., Sebe, N., Huang, T.S., Bakker, E.M. (eds.) HCI 2007. LNCS, vol. 4796, pp. 6–18. Springer, Heidelberg (2007). Scholar
  11. 11.
    Weng, C.-H., Lai, Y.-H., Lai, S.-H.: Driver drowsiness detection via a hierarchical temporal deep belief network. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10118, pp. 117–133. Springer, Cham (2017). Scholar
  12. 12.
    Shih, T.-H., Hsu, C.-T.: MSTN: multistage spatial-temporal network for driver drowsiness detection. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10118, pp. 146–153. Springer, Cham (2017). Scholar
  13. 13.
    Huynh, X.-P., Park, S.-M., Kim, Y.-G.: Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10118, pp. 134–145. Springer, Cham (2017). Scholar
  14. 14.
    Lyu, J., Zejian Y., Dapeng C.: Long-term multi-granularity deep framework for driver drowsiness detection. arXiv:1801.02325 (2018)
  15. 15.
    Dwivedi, K., Biswaranjan, K., Sethi, A.: Drowsy driver detection using representation learning. In: 2014 IEEE International Advance Computing Conference (IACC) , 21–22 February 2014Google Scholar
  16. 16.
    Bradski, G., Adrian, K.: OpenCV. Dr. Dobb’s journal of software tools 3 (2000)Google Scholar
  17. 17.
    Schroff, F., Dmitry, K., James, P.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)Google Scholar
  18. 18.
    Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Efficient convolutional neural networks for mobile vision applications. CoRR, Mobilenets (2017)Google Scholar
  19. 19.
    Reed, L.J., Berkson, J.: The application of the logistic function to experimental data. J. Phys. Chem. 33(5), 760–779 (1929)CrossRefGoogle Scholar
  20. 20.
    Massoz, Q., Langohr, T., Francois, C., Verly, J.G.: The ULG Multimodality Drowsiness Database (called DROZY) and Examples of Use, WACV (2016)Google Scholar
  21. 21.
  22. 22.
    García-García, M., Caplier, A., Rombaut, M.: Driver head movements while using a smartphone in a naturalistic context. In: 6th International Symposium on Naturalistic Driving Research, The Hague, Netherlands, vol. 8, Jun 2017Google Scholar
  23. 23.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Innov+, Batiment 503, Centre Universitaire d’OrsayOrsayFrance
  2. 2.Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-labGrenobleFrance

Personalised recommendations