Abstract
According to the portable and real-time problems on the driving fatigue prevention based on electroencephalogram (EEG), a headband integrated with Thinkgear EEG chip, tri-axial accelerometer, gyroscope and Bluetooth is developed to collect the subject’s left prefrontal Attention, Meditation EEG and head movement data. The relation between Attention and Meditation EEG when the subject is in the state of concentration, relaxation, fatigue and sleep is analyzed firstly. As a result, a new method for driving fatigue detection based on the correlation coefficient between subject’s Attention and Meditation EEG is proposed. Meanwhile, the slide windows and k-Nearest Neighbors (k-NN) algorithm are introduced to classify the correlation coefficient between the subject’s Attention and Meditation EEG, so as to detect driving fatigue and alert. Lastly, a software running on an Android smart device is developed based on the above technologies, and the experiment proves that it has noninvasive and real-time advantages, while its sensitivity and specificity are 80.98 % and 90.43 % respectively.
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References
Idogawa, K.: On the brain wave activity of professional drivers during monotonous work. Behav. Metrika 30, 23–34 (1991)
Yaxuan, W., Wenshu, L., Guosheng, S., et al.: Survey on fatigue driving detection method research. Chinese J. Ergon. (2003). (in Chinese)
Khushaba, R.N., Kodagoda, S., Lal, S., Dissanayake, G.: Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans. Biomed. Eng. 58(1), 121–131 (2011)
Lin, C.-T., Ko, L.-W., Chung, I.-F., Huang, T.-Y., Chen, Y.-C., Jung, T.-P., Liang, S.-F.: Adaptive EEG-based alertness estimation system by using ICA-based fuzzy neural networks. IEEE Trans. Circ. Syst. I Regul. Pap. 53(11), 2469–2476 (2006)
Saccade velocity characteristics Intrinsic variability and fatigue, Schmidt, D., Abel, L.A., Dell’Osso, L.F., Daroff, R.B.: Saccade velocity characteristics: Intrinsic variability and fatigue. Aviat. Space Environ. Med. 50, 393–395 (1979)
Lal, S.K.L., Craig, A.: Psychological effects associated with drowsiness: Driver fatigue and electroencephalography. Int. J. Psychophysiol. 15(3), 183–189 (2001)
Ji, Q., Zhu, Z., Lan, P.: Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Trans. Veh. Technol. 53(4), 1052–1068 (2004)
Jap, B.T., Lal, S., Fischer, P., Bekiaris, E.: Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst. Appl. 36(2), 2352–2359 (2009)
Tsuchida, A., Bhuiyan, M.S., Oguri, K.: Estimation of drowsiness level based on eyelid closure and heart rate variability. In: proceedings of the 31st Annual International Conference IEEE Engineering in Medicine and Biology Society (EMBS), pp. 2543–2546 (2009)
Van Orden, K., Jung, T.P., Makeig, S.: Combined eye activity measures accurately estimate changes in sustained visual task performance. Biol. Psychol. 52(3), 221–240 (2000)
Jung, T.P., Makeig, S., Stensmo, M., Sejnowski, T.J.: Estimating alertness from the EEG power spectrum. IEEE Trans. Biomed. Eng. 44(1), 60–69 (1997)
Fu, J.W., Li, M., Lu, B.L.: Detecting drowsiness in driving simulation based on EEG. In: Proceedings of the 8th International Workshop Autonomic System-Self-organization, Manage., Control, pp. 21–28, October 6–7 2008
Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science Fourth Edition. McGraw-Hill, United State of America, pp. 324 (2000)
American Electroencephalographic Society: Guideline thirteen: Guidelines for standard electrode position nomenclature. J. Clin. Neurophysiol. 11, 111–113 (1994)
Erdogan, S.Z., Bilgin, T.T.: A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data. IET Commun. 6(18), 3281–3287 (2012)
He, J., Hu, C., Li, Y.: An autonomous fall detection and alerting system based on mobile and ubiquitous computing. In: 10th International Conference on Ubiquitous Intelligence and Computing, pp. 539–543 (2013)
Kangas, M., Vikman, I., Wiklander, J., Lindgren, P., Nyberg, L., Jämsä, T.: Sensitivity and specificity of fall detection in people aged 40 years and over. Gait Posture 29(4), 571–574 (2009)
Acknowledgment
This work was supported by the Beijing Natural Science Foundation under grant No. 4102005, and partly supported by the National Nature Science Foundation of China (No. 61040039). The authors would also like to thank Dr. Xiaohua Zhao who opens the Beijing Transportation Engineering key Lab to do experiments.
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He, J., Zhou, M., Hu, C., Wang, X. (2015). A Safety Guard for Driving Fatigue Detection Based on Left Prefrontal EEG and Mobile Ubiquitous Computing. In: Hsu, CH., Xia, F., Liu, X., Wang, S. (eds) Internet of Vehicles - Safe and Intelligent Mobility. IOV 2015. Lecture Notes in Computer Science(), vol 9502. Springer, Cham. https://doi.org/10.1007/978-3-319-27293-1_17
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DOI: https://doi.org/10.1007/978-3-319-27293-1_17
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