A Novel Method for Detecting the Degree of Fatigue Using Mobile Camera

  • Qing Yu
  • Ludi Wang
  • Ying Xing
  • Xiaoguang Zhou
  • Wei Zhou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)

Abstract

This paper presented a novel method for detecting human fatigue using mobile camera and cloud techniques. Photoplethysmography technique and detrended fluctuation analysis (DFA) method are used to fatigue detection. The experimental results confirm the correctness of the proposed method. The proposed method has realistic significance.

Keywords

Fatigue Component Detrended fluctuation analysis (DFA) Photoplethysmography technique 

Notes

Acknowledgments

This work is supported by Engineering Research Center of Information Networks, Ministry of Education.

References

  1. 1.
    Wang, Q., et al.: Driver fatigue detection: a survey. In: Intelligent Control and Automation (WCICA 2006), pp. 8587–8591. IEEE Press, June 2006.  https://doi.org/10.1109/WCICA.2006.1713656
  2. 2.
    Williamson, A., Chamberlain, T.: Review of on-road driver fatigue monitoring devices (2005)Google Scholar
  3. 3.
    Kim, D., Choi, S., Choi, J., Shin, H., Sohn, K.: Visual fatigue monitoring system based on eye-movement and eye-blink detection. In: Proceedings SPIE, vol. 7863, no. 1, pp. 159–172 February 2011.  https://doi.org/10.1117/12.873354
  4. 4.
    Gu, H., Ji, Q.: An automated face reader for fatigue detection. In: IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, South Korea, pp. 111–116 May 2004. IEEE Computer Society.  https://doi.org/10.1109/AFGR.2004.1301517
  5. 5.
    Peng, C.K., Mietus, J.E., Hausdorff, J.M., Havlin, S., Stanley, H.E., Goldberger, A.L.: Long-range anti-correlations and non-gaussian behaviour of the heartbeat. Phys. Rev. Lett. 70(9), 1343–1346 (1993).  https://doi.org/10.1103/PhysRevLett.70.1343CrossRefGoogle Scholar
  6. 6.
    Peng, C.K., Havlin, S., Stanley, H.E., Goldberger, A.L.: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5(1), 82–87 (1995).  https://doi.org/10.1063/1.166141CrossRefGoogle Scholar
  7. 7.
    Peng, C.K., Hausdorff, J.M., Havlin, S., Mietus, J.E., Stanley, H.E., Goldberger, A.L.: Multiple-time scales analysis of physiological time series under neural control. Phys. A Stat. Mech. Appl. 249(1–4), 491–500 (1998).  https://doi.org/10.1016/S0378-4371(97)00508-6CrossRefGoogle Scholar
  8. 8.
    Bunde, A., Havlin, S., Kantelhardt, J.W., Penzel, T., Peter, J.H., Voigt, K.: Correlated and uncorrelated regions in heart-rate fluctuations during sleep. Phys. Rev. Lett. 85(17), 3736–3739 (2000).  https://doi.org/10.1103/PhysRevLett.85.3736CrossRefGoogle Scholar
  9. 9.
    Papasimakis, N., Pallikari, F.: Correlated and uncorrelated heart rate fluctuations during relaxing visualization. EPL 90(4), 1303–1324 (2010).  https://doi.org/10.1209/0295-5075/90/48003CrossRefGoogle Scholar
  10. 10.
    Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1972).  https://doi.org/10.1021/ac60214a047CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Qing Yu
    • 1
  • Ludi Wang
    • 1
  • Ying Xing
    • 1
  • Xiaoguang Zhou
    • 1
  • Wei Zhou
    • 2
  1. 1.Automation SchoolBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Department of NeuroscienceUppsala UniversityUppsalaSweden

Personalised recommendations