Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas

  • Rongrong Fu Yanshan University, 438 Hebei Street, Qinhuangdao 066004
  • Shutao Wang Yanshan University
  • Shiwei Wang Yanshan University
Keywords: driver fatigue, EEG, real-time alarm, wireless communication,

Abstract

The purpose of this paper was to develop a real-time alarm monitoring system that can detect the fatigue driving state through wireless communication. The drivers’ electroencephalogram (EEG) signals were recorded from occipital electrodes. Seven EEG rhythms with different frequency bands as gamma, hbeta, beta, sigma, alpha, theta and delta waves were extracted. They were simultaneously assessed using relative operating characteristic (ROC) curves and grey relational analysis to select one as the fatigue feature. The research results showed that the performance of theta wave was the best one. Therefore, theta wave was used as fatigue feature in the following alarm device. The real-time alarm monitoring system based on the result has been developed, once the threshold was settled by using the data of the first ten minutes driving period. The developed system can detect driver fatigue and give alarm to indicate the onset of fatigue automatically.

Author Biographiesaaa replica rolex repwatches replica rolex watches for men replica iwc watch

Rongrong Fu, Yanshan University, 438 Hebei Street, Qinhuangdao 066004
Electrical Engineering
Shutao Wang, Yanshan University
Electrical Engineering
Shiwei Wang, Yanshan University
Electrical Engineering

References

Budi TJ, Sara L, Peter F, Evangelos B. Using EEG spectral components to assess algorithms for detecting fatigue. Expert Systems with Applications. 2009;36:2352-2359.

Kaplan S, Guvensan MA, Yavuz AG, Karalurt Y. Driver Behavior Analysis for Safe Driving: A Survey. IEEE Transactions on Intelligent Transportation Systems. 2015;16(6):3017-3032.

Pylkkonen M, Sihvola M, Hyvarinen HK, Puttonen S, Hublin C, Sallinen M. Sleepiness, sleep, and use of sleepiness countermeasures in shift-working longhaul truck drivers. Accident Analysis and Prevention. 2015;80:201-210.

Li DH, Liu Q, Yuan W, Liu HX. Relationship between fatigue driving and traffic accident. Journal of Traffic and Transportation Engineering. 2010;10(2):104-109.

You F, Zhang R, Guo L, et al. Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Systems with Applications. 2015;42(14):5932-5946.

Hou Y, Edara P, Sun C. Situation assessment and decision making for lane change assistance using ensemble learning methods. Expert Systems with Applications. 2015;42(8):3875-3882.

Ebrahemzadih M, Giahi O, Foroginasab F. Analysis of traffic accidents leading to death using tripod beta method in Yazd, Iran. Promet – Traffic & Transportation. 2015;27(5):291-297.

Eugene A, Carolyn C, Kayla J, John R. Real-time driver drowsiness feedback improves driver alertness and self-reported driving performance. Accident Analysis and Prevention. 2015;81:8-13.

Pribyl O, Koukol M, Kuklova J. Computational intelligence in highway management: A review. Promet – Traffic & Transportation. 2015;28(3):439-450.

Lela RW, David RD, Kris T, Judith RD, Alistair WM. Young drivers’ perceptions of culpability of sleep-deprived versus drinking drivers. Journal of Safety Research. 2012;43:115-122.

Julie H, Ralston F. The role of risk-propensity in the risky driving of younger drivers. Accident Analysis and Prevention. 2009;41:25-35.

Smith S, Carrington M, Trinder J. Subjective and predicted sleepiness while driving in young adults. Accident Analysis and Prevention. 2005;37:1066-1073.

Hatfield J, Murphy S, Kasparian N. Risk perceptions, attitudes and behaviors regarding driver fatigue in NSW Youth: the development of an evidence-based driver fatigue educational intervention strategy. Motor

Accidents Authority of NSW; 2005.

Conati C. Probabilistic assessment of user’s emotions in educational games. Applied artificial intelligence. 2012:16(7):555-575.

Ji Q, Zhu Z, Lan P. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technology. 2004:53(4):1052-1068.

Lin CT, Huang KC, Chao CF, Chen JA, Chiu TW, Ko LW, Jung TP. Tonic and phasic EEG and behavioral changes induced by arousing feedback. NeuroImage. 2010:52(2):633-642.

Horne JA, Reyner LA. Driver sleepiness. Journal of Sleep Research. 1995;4(S2):23-29.

Eike AS, Michael S, Michael S, Axel B, Wilhelm EK. The short-term effect of verbally assessing drivers’ state on vigilance indices during monotonous daytime driving. Transportation Research Part F. 2011;14:251-260.

Kayvan N, Robert S. Biomedical Signal and Image Processing. Taylor & Francis Group Boca Raton, London New York; 2006.

Cajochen C, Brunner DP, Krauchi K, Graw P, Wirz-Justice A. Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. Sleep. 1995;18(10):890-894.

Stampi C, Stone P, Michimori A. A new quantitative method for assessing sleepiness: the alpha attenuation test. Work & Stress. 1995;9(2):368-376.

Alloway CE, Ogilvie RD, Shapiro CM. The alpha attenuation test: assessing excessive daytime sleepiness in narcolepsy-cataplexy. Sleep. 1997;20(4):258-266.

Cantero JL, Atienza M. Spectral and topographic microstructure of brain alpha activity during drowsiness at sleep onset and REM sleep. Psychophysiol. 2000;14(3):151-158.

Li W, He QC, Fan XM, Fei ZM. Evaluation of driver fatigue on two channels of EEG data. Neuroscience Letters. 2012;506:235-239.

Nevzat A, Alireza AS, Hadi A. Multi-objective optimization of some process parameters of a lab-scale thickener using grey relational analysis. Separation and Purification Technology. 2012;90:189-195.

Fung CP. Manufacturing process optimization for wear property of fiber reinforced polybutylene terephthalate composites with grey relational analysis. Wear. 2003;254:298-236.

Nevzat A. Multi-objective optimization of some process parameters of lead flotation using grey relational analysis. Separations Science Technology. 2012;47(4):599-605.

Ulas C, Ahmet H. Use of the grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics. Optics & Laser Technology. 2008;40:987-994.

Yung KY, Ming TC, Show SL. Optimization of dry machining parameters for high purity graphite in end-milling process. Journal of Materials Processing Technology. 2009;209:4395-4400.

Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford: Oxford University Press; 2003.

Tang LS, Du P, Wu CQ. Compare diagnostic tests using transformation-invariant smoothed ROC curves. Journal of Statistical Planning and Inference. 2010;140:3540-3551.

Liu XX, Zhao YC. Semi-empirical likelihood inference for the ROC curve with missing data. Journal of Statistical Planning and Inference. 2012;142:3123-3133.

Roselina S, Siti MH. Grey Relational with BP_PSO for Time Series Forecasting. Proceedings of 2009 IEEE International Conference on Systems, Man, and Cybernetics; 2009 Oct 11-14; San Antonio, TX, USA; 2009. p. 4895-4900.

Lu Y, Wei HY. Research on the Motivation of the Customer Participation Based on Grey Relational Analysis. Business Management and Electronic Information. 2011;438-441.

Sibsambhu MB, Aurobinda R. EEG signal analysis for the assessment and quantification of driver’s fatigue. Transportation Research Part F. 2010;13:297-306.

Published
2017-04-20
How to Cite
1.
Fu R, Wang S, Wang S. Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas. Promet [Internet]. 2017Apr.20 [cited 2024Dec.30];29(2):165-74. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/2058
Section
Articles