This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors’ main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.
References
-
-
1)
-
2. Klauer, S.G., Dingus, T.A., Neale, T.V., et al: ‘The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data’ (U.S. Department of Transportation Washington D.C, 2006).
-
2)
-
23. Correa, A.G., Orosco, L., Laciar, E.: ‘Automatic detection of drowsiness in EEG records based on multimodal analysis’, Med. Eng. Phys., 2014, 36, (2), pp. 244–249 (doi: 10.1016/j.medengphy.2013.07.011).
-
3)
-
4. NHTSA.: ‘Traffic safety facts 2011 data-pedestrians’, Retour Au Numéro, 2013, 62, (6), pp. 612–613.
-
4)
-
22. Kaur, R., Singh, K.: ‘Drowsiness detection based on EEG signal analysis using EMD and trained neural network’, Int. J. Sci. Res., 2013, 10, pp. 157–161.
-
5)
-
12. Zhao, C., Zheng, C., et al: ‘Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic’, Expert. Syst. Appl., 2011, 38, (3), pp. 1859–1865 (doi: 10.1016/j.eswa.2010.07.115).
-
6)
-
18. Reyes-Muñoz, A., Domingo, M.C., López-Trinidad, M.A., et al: ‘Integration of body sensor networks and vehicular ad-hoc networks for traffic safety’, Sensors, 2016, 16, pp. 107–135 (doi: 10.3390/s16010107).
-
7)
-
1. Phillips, R.O.: ‘A review of definitions of fatigue – and a step towards a whole definition’, Transp. Res. F, Traffic Psychol. Behav., 2015, 29, pp. 48–56 (doi: 10.1016/j.trf.2015.01.003).
-
8)
-
8. Lal, S.K.L., Craig, A.: ‘A critical review of the psychophysiology of driver fatigue’, Biol. Psychol., 2001, 55, (3), pp. 173–194 (doi: 10.1016/S0301-0511(00)00085-5).
-
9)
-
21. Xiong, Y., Gao, J., Yang, Y., et al: ‘Classifying driving fatigue based on combined entropy measure using EEG signals’, Int. J. Control Autom., 2016, 9, (3), pp. 329–338 (doi: 10.14257/ijca.2016.9.3.30).
-
10)
-
16. Lee, B.G., Chung, W.Y.: ‘A smartphone-based driver safety monitoring system using data fusion’, Sensors, 2012, 12, pp. 17536–17552 (doi: 10.3390/s121217536).
-
11)
-
17. Lee, B.G., Lee, B.L., Chung, W.Y.: ‘Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals’, Sensors, 2014, 14, pp. 17915–17936 (doi: 10.3390/s141017915).
-
12)
-
12. Li, W., He, Q.-C., Fan, X.-M., et al: ‘Evaluation of driver fatigue on two channels of EEG data’, Neurosci. Lett., 2012, 506, (2), pp. 235–239 (doi: 10.1016/j.neulet.2011.11.014).
-
13)
-
19. Xiang, J., Li, C.G., Li, H.F., et al: ‘The detection of epileptic seizure signals based on fuzzy entropy’, J. Neurosci. Method, 2015, 243, pp. 18–25 (doi: 10.1016/j.jneumeth.2015.01.015).
-
14)
-
9. Gharagozlou, F., Nasl Saraji, G., et al: ‘Detecting driver mental fatigue based on EEG alpha power changes during simulated driving’, Iran J. Public Health, 2015, 44, (12), pp. 1693–1700.
-
15)
-
7. Hopstaken, J.F., van der Linden, D., et al: ‘Shifts in attention during mental fatigue: evidence from subjective, behavioral, physiological, and eye-tracking data’, J. Exp. Psychol. Hum. Percept. Perform., 2016, 42, (6), pp. 878–889 (doi: 10.1037/xhp0000189).
-
16)
-
10. Khushaba, R.N., Kodagoda, S., et al: ‘Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm’, IEEE Trans. Biomed. Eng., 2011, 58, (1), pp. 121–131 (doi: 10.1109/TBME.2010.2077291).
-
17)
-
13. Liu, J., Zhang, C., Zheng, C.: ‘EEG-based estimation of mental fatigue by using KPCA-HMM and complexity parameters’, Biomed. Signal Process. Control, 2010, 5, (2), pp. 124–130 (doi: 10.1016/j.bspc.2010.01.001).
-
18)
-
3. Lee, M.L., Howard, M.E., Horrey, W.J., et al: ‘High risk of near-crash driving events following night-shift work’, Proc. Nat. Acad. Sci., 2016, 113, (1), pp. 176–181 (doi: 10.1073/pnas.1510383112).
-
19)
-
6. Owen, N., King, H., Lamb, M.: ‘Literature review of race driver fatigue measurement in endurance motor sport’, Proc. Eng., 2015, 112, pp. 344–348 (doi: 10.1016/j.proeng.2015.07.260).
-
20)
-
5. Dawson, D., Searle, A.K., Paterson, J.L.: ‘Look before you sleep: evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry’, Sleep Med. Rev., 2014, 18, (2), pp. 141–152 (doi: 10.1016/j.smrv.2013.03.003).
-
21)
-
24. Hu, J.F., Mu, Z.D., Wang, P.: ‘Multi-feature authentication system based on event evoked electroencephalogram’, J. Med. Imaging Health Inf., 2015, 5, pp. 862–870 (doi: 10.1166/jmihi.2015.1471).
-
22)
-
25. Hu, J.F., Mu, Z.D., Yin, J.H.: ‘EEG-based identification system for mobile devices’, Comput. Model. New Technol., 2014, 18, pp. 672–677.
-
23)
-
20. Sharma, R., Pachori, R.B., Acharya, R.: ‘Application of an entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals’, Entropy, 2015, 17, pp. 669–691 (doi: 10.3390/e17020669).
-
24)
-
11. Gurudath, N., Riley, H.B.: ‘Drowsy driving detection by EEG analysis using wavelet transform and K-means clustering’, Proc. Comput. Sci., 2014, 34, pp. 400–409 (doi: 10.1016/j.procs.2014.07.045).
-
25)
-
14. Mu, Z.D., Yin, J.H., Hu, J.F.: ‘Mobile healthcare system for driver based on drowsy detection using EEG signal analysis’, Metall. Min. Ind., 2015, 7, pp. 266–273.
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2016.0053
Related content
content/journals/10.1049/htl.2016.0053
pub_keyword,iet_inspecKeyword,pub_concept
6
6