Advertisement

Fuzzy-Logic Decision Fusion for Nonintrusive Early Detection of Driver Fatigue or Drowsiness

  • Mario Malcangi
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)

Abstract

Traffic accidents due to falling asleep at the wheel are a longstanding problem in many countries. This paper presents a novel solution based on fuzzy-logic decision fusion that prevents accidents by detecting driver fatigue or drowsiness early. The proposed method is based on analyzing and inferring about certain biological and behavioral measurements that enable detection of reduced alertness preceding driver-sleep onset. Because wakeful or sleep activity is reflected in several physiological conditions in human beings, such as cardiac, breathing, movement, and skin galvanic conductance, captured bioelectric signal features were extracted and fuzzy decision-fusion logic was tuned to make inferences about oncoming driver fatigue or drowsiness. The proposed method improves the performance by applying the fuzzy logic inference to fuse decisions from independent modules that infer about features measured on the sensed physiologic and/or behavioral information. The method reduces the complexity of the signal processing and of the pattern matching model. Tests have been executed on clinical and in field physiologic and behavioral data. A prototype based on a 32 bit microcontroller and a highly integrated analog front-end has been developed to support the in field tests.

Keywords

fuzzy logic decision fusion sleep onset heart-rate variability breathing rate power-spectrum density ANS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    NHTSA: Drowsy driving. Published by NHTSA’s national center for statistics and analysis 1200 New Jersey Avenue SE., Washington, DC 20590 (2011)Google Scholar
  2. 2.
    Eriksson, M., Papanikolopoulos, N.P.: Eye-tracking for Detection of Driver Fatigue. In: IEEE Proceendings of Intelligent Transport System, Boston, MA, pp. 314–319 (1997)Google Scholar
  3. 3.
    Malcangi, M., Smirne, S.: Fuzzy-logic inference for early detection of sleep onset in car driver. In: Jayne, C., Yue, S., Iliadis, L. (eds.) EANN 2012. CCIS, vol. 311, pp. 41–50. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Dorfman, G.F., Baharav, A., Cahan, C., Akselrod, S.: Early Detection of Falling Asleep at the Wheel: a Heart Rate Variability Approach. Computers in Cardiology 35, 1109–1112 (2008)Google Scholar
  5. 5.
    Zocchi, C., Giusti, A., Adami, A., Scaramellini, F., Rovetta, A.: Biorobotic system for increasing automotive safety. In: 12th IFToMM World Congress, Besançon, France (2007)Google Scholar
  6. 6.
    Estrada, E., Nazeran, H.: EEG and HRV Signal Features for Automatic Sleep Staging and Apnea Detection. In: 20th International Conference on Electronics, Communications and Computer (CONIELECOMP), February 22-24, pp. 142–147 (2010)Google Scholar
  7. 7.
    Manis, G., Nikolopoulos, S., Alexandridi, A.: Prediction techniques and HRV analysis. In: MEDICON 2004, Naples, Italy, July 31-August 5 (2004)Google Scholar
  8. 8.
    Rajendra, A.U., Paul, J.K., Kannathal, N., Lim, C.M., Suri, J.S.: Heart rate variability: a review. Med. Bio. Eng. Comput. 44, 1031–1051 (2006)CrossRefGoogle Scholar
  9. 9.
    Tohara, T., Katayama, M., Takajyo, A., Inoue, K., Shirakawa, S., Kitado, M., Takahashi, T., Nishimur, Y.: Time frequency analysis of biological signal during sleep. In: SICE Annual Conference, September 17-20, pp. 1925–1929. Kagawa University, Japan (2007)Google Scholar
  10. 10.
    Travaglini, A., Lamberti, C., DeBie, J., Ferri, M.: Respiratory signal derived from eight-lead ECG. Computer in Cardiology 25, 65–68 (1998)Google Scholar
  11. 11.
    Felblinger, J., Boesch, C.: Amplitude demodulation of the electrocardiogram signal (ECG) for respiration monitoring and compensation during MR examinations. Magn-Reson-Med. 38(1), 129–136 (1997)CrossRefGoogle Scholar
  12. 12.
    Patel, M., Lal, S.K.L., Kavanagh, D., Rossiter, P.: Applying neural networks analysis on heart rate variability data to asses driver fatigue. Expert systems with Applications (2011)Google Scholar
  13. 13.
    Ranganathan, G., Rangarajan, R., Bindhu, V.: Signal processing of heart rate variability using wavelet transform for mental stress measurement. Journal of Theoretical and Applied Information Technology 11(2), 124–129 (2010)Google Scholar
  14. 14.
    Ranganathan, G., Rangarajan, R., Bindhu, V.: Evaluation of ECG signal for mental stress assessment using fuzzy technique. International Journal of Soft Computing and Engineering (IJSCE) 1(4), 195–201 (2011)Google Scholar
  15. 15.
    Mager, D.E., Merritt, M.M., Kasturi, J., Witkin, L.R., Urdiqui-Macdonald, M., Sollers, J.I., Evans, M.K., Zonderman, A.B., Abernethy, D.R., Thayer, J.F.: Kullback–Leibler Clustering of Continuous Wavelet Transform Measures of Heart Rate Variability. Biomed. Sci. Instrum. 40, 337–342 (2004)Google Scholar
  16. 16.
    Dzitac, S., Popper, L., Secui, C.D., Vesselenyi, T., Moga, I.: Fuzzy Algorithm for Human Drowsiness Detection Devices. SIC 19(4), 419–426 (2010)Google Scholar
  17. 17.
    Sharma, N., Banga, V.K.: Development of a drowsiness warning system based on the fuzzy logic. International Journal of Computer Applications (0975-8887) 8(9) (2010)Google Scholar
  18. 18.
    Picot, A., Charboinner, S., Caplier, A.: Drowsiness detection based on visual signs: blinking analysis based on high frame rate video. In: 2010 IEEE International Instrumentation and Measurement Technology Conference, 2MTC 2010 (2010)Google Scholar
  19. 19.
    Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: A review. Sensors 2012 12, 16937–16953 (2012)Google Scholar
  20. 20.
    Wang, Q., Yang, J., Ren, M., Zheng, Y.: Driver fatigue detection: a survey. In: Proceedings of the 6th World Congress of Intelligent Control and Automation, pp. 8587–8591. IEEE (2006) Google Scholar
  21. 21.
    Bajaj, P., Narole, N., Devi, M.S.: Research on Driver’s Fatigue Detection. eNewsletter System, Man and Cybernetics Society (31) (June 2010)Google Scholar
  22. 22.
    Albu, A.B., Widsten, B., Wang, T., Lan, J., Mah, J.: A Computer Vision-based System for Real-time Detection of Sleep Onset in Fatigued Drivers. In: Proceedings of 2008 IEEE Intelligent Vehicles Symposium, Eindhoven University of Technology Eindhoven, The Netherlands, June 4-6, pp. 25–30 (2008)Google Scholar
  23. 23.
    Bowman, D.S., Schaudt, W.A., Hanowski, R.J.: Advances in Drowsy Driver Assistance Systems through Data Fusion. In: Handbook of Intelligent Vehicles, pp. 895–912. Springer (2012)Google Scholar
  24. 24.
    Malcangi, M., Smirne, S.: Heart Rate Variability Analysis for Prediction of Sleep Onset in Car Drivers. Journal of Sleep Research 21(Suppl. 1), 307–308 (2012)Google Scholar
  25. 25.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On cobining classifier. IEEE Transactions on Pattern Analysis and Mahine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  26. 26.
    Kasabov, N.: Evolving fuzzy neural networks – algorithms, applications and biological motivation. In: Yamakawa, Matsumoto (eds.) Methodologies for the conception, design and application of the soft computing, World Computing, pp. 271–274 (1998)Google Scholar
  27. 27.
    Sandberg, D., Anund, A., Fors, C., Kecklund, G., Karlsson, J.G., Wahde, M., Åkerstedt, T.: The characteristics of sleepiness during real driving at night—A study of driving performance, physiology and subjective experience. Sleep 34(10), 1317–1325 (2011)Google Scholar
  28. 28.
    Lin, C.W., Wang, J.S., Chung, P.C.: Mining Physiological Conditions from Heart Rate Variability Analysis. IEEE Computational Intelligence Magazine, 50–58 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mario Malcangi
    • 1
  1. 1.Department of Computer ScienceUniversità degli Studi di MilanoMilanoItaly

Personalised recommendations