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Journal of Advanced Transportation
Volume 2017, Article ID 9509213, 14 pages
https://doi.org/10.1155/2017/9509213
Research Article

Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Correspondence should be addressed to Shuan-Feng Zhao; nc.ude.tsux@fsz and Chuan-wei Zhang; moc.361@gnahzydxs

Received 19 June 2016; Revised 25 December 2016; Accepted 7 March 2017; Published 21 May 2017

Academic Editor: Serge Hoogendoorn

Copyright © 2017 Shuan-Feng Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. K. L. Lal, A. Craig, P. Noord et al., “Development of an algorithm for an eeg-based driver fatigue countermeasure,” Journal of Safety Research, vol. 34, pp. 321–328, 2002. View at Google Scholar
  2. R. Fu, H. Wang, Y. Zhang et al., “Electrocardiogram analysis of driving fatigue based on wearable sensor,” Automotive Engineering, vol. 35, no. 12, pp. 1143–1148, 2013. View at Google Scholar
  3. I. Hostensa and H. Ramonb, “Assessment of muscle fatigue in low level monotonous task performance during car driving,” International Journal of Automotive Technology, vol. 10, no. 3, pp. 391–404, 2005. View at Google Scholar
  4. S. Ahn, T. Nguyen, H. Jang, J. G. Kim, and S. C. Jun, “Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data,” Frontiers in Human Neuroscience, vol. 10, no. 2016, article 219, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Jagannath and V. Balasubramanian, “Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator,” Applied Ergonomics, vol. 45, no. 4, pp. 1140–1147, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. W. W. Wierwile, L. A. Ellsworth, S. S. Wreggit et al., “Research on vehicle-based driver status/performance monitoring, development, validation, and refinement of algorithms for detection of driver drowsiness,” National Highway Traffic Safety Administration Report, 1994, No. DOT HS 808247. View at Google Scholar
  7. T. Azim, M. A. Jaffar, and A. M. Mirza, “Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems,” Applied Soft Computing Journal, vol. 18, pp. 25–38, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. M. L. Jackson, G. A. Kennedy, C. Clarke et al., “The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness,” Accident Analysis & Prevention, vol. 87, pp. 127–133, 2016. View at Publisher · View at Google Scholar
  9. J. Izquierdo-Reyes, R. A. Ramirez-Mendoza, M. R. Bustamante-Bello, S. Navarro-Tuch, and R. Avila-Vazquez, “Advanced driver monitoring for assistance system (ADMAS),” International Journal on Interactive Design and Manufacturing, (IJIDeM), pp. 1–11, 2016. View at Google Scholar
  10. X. Fan, B.-C. Yin, and Y.-F. Sun, “Yawning detection for monitoring driver fatigue,” in Proceedings of the 6th International Conference on Machine Learning and Cybernetics (ICMLC '07), vol. 2, pp. 664–668, Hong Kong, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Mortazavi, A. Eskandarian, and R. A. Sayed, “Effect of drowsiness on driving performance variables of commercial vehicle drivers,” International Journal of Automotive Technology, vol. 10, no. 3, pp. 391–404, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Liang and J. D. Lee, “Combining cognitive and visual distraction: less than the sum of its parts,” Accident Analysis and Prevention, vol. 42, no. 3, pp. 881–890, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. R. K. Satzoda and M. M. Trivedi, “Drive analysis using vehicle dynamics and vision-based lane semantics,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 9–18, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. R. K. Satzoda and M. M. Trivedi, “Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies,” in Proceedings of the 17th IEEE International Conference on Intelligent Transportation Systems (ITSC '14), pp. 697–702, Qingdao, China, October 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Ohn-Bar, S. Martin, and M. M. Trivedi, “Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies,” Journal of Electronic Imaging, vol. 22, no. 4, Article ID 041119, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. A. Ghoneim, “Vehicle dynamics approach to driver warning,” International Journal of Vehicular Technology, vol. 2013, Article ID 109650, 18 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. A. M. Jaber, M. T. Ismail, and A. M. Altaher, “Empirical mode decomposition combined with local linear quantile regression for automatic boundary correction,” Abstract and Applied Analysis, vol. 2014, Article ID 731827, 8 pages, 2014. View at Publisher · View at Google Scholar
  18. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” The Royal Society of London. Proceedings. Series A. Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at MathSciNet
  19. P. Flandrin, G. Rilling, and P. Gonçalvés, “Empirical mode decomposition as a filter bank,” IEEE Signal Processing Letters, vol. 11, no. 2, pp. 112–114, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. Z. H. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046, pp. 1597–1611, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Zhao, J. Li, and W. Cheng, “Feature extraction of faulty rolling element bearing under variable rotational speed and gear interferences conditions,” Shock and Vibration, vol. 2015, Article ID 425989, 9 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. G. G. Pegram, M. C. Peel, and T. A. McMahon, “Empirical mode decomposition using rational splines: an application to rainfall time series,” Proceedings of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences, vol. 464, no. 2094, pp. 1483–1501, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. B. S. Love, A. J. Matthews, and G. J. Janacek, “Real-time extraction of the Madden-Julian oscillation using empirical mode decomposition and statistical forecasting with a VARMA model,” Journal of Climate, vol. 21, no. 20, pp. 5318–5335, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. C.-Y. Lee and Y.-C. Lee, “BPNN based processing for end effects of HHT,” World Academy of Science, Engineering and Technology, vol. 72, pp. 95–97, 2010. View at Google Scholar · View at Scopus
  25. Y. Yuan, Z. K. Yang, and Q. F. Li, “End effect processing for empirical mode decomposition using fuzzy inductive reasoning,” Applied Mechanics and Materials, vol. 55-57, pp. 407–412, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. Y. Bao, T. Xiong, and Z. Hu, “Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 431512, 12 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. S. M. Pincus, “Approximate entropy as a measure of system complexity,” Proceedings of the National Academy of Sciences of the United States of America, vol. 88, no. 6, pp. 2297–2301, 1991. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. W. Liao, G. Hu, and F. Yang, “Nonlinear dynamic analysis of heart rate variability and its application,” Chinese Journal of Biomedical Engineering, vol. 15, no. 3, pp. 193–201, 1996. View at Google Scholar
  29. X. Yong, J. Xu, H. Yang et al., “Phase space reconstruction and nonlinear feature extraction of cortical EEG time series,” Journal of Physics, vol. 51, no. 2, pp. 205–214, 2002. View at Google Scholar
  30. R. Yan and R. X. Gao, “Approximate Entropy as a diagnoistic tool for machine health monitoring,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 824–839, 2007. View at Publisher · View at Google Scholar
  31. X. U. Yong, “Approximate entropy and its applications in mechanical fault diagnosis,” Information & Control, vol. 31, no. 6, pp. 547–551, 2002. View at Google Scholar
  32. C. Biao, X. Lv, C. Min et al., “Approximate entropy analysis of arc welding current signal of short circuit,” Journal of Physics, vol. 55, no. 4, pp. 1696–1705, 2006. View at Google Scholar
  33. R. Yan and R. X. Gao, “Approximate Entropy as a diagnostic tool for machine health monitoring,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 824–839, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Ledesma, D. Liu, and D. Hernández, “Two approximation methods to synthesize the power spectrum of fractional Gaussian noise,” Computational Statistics and Data Analysis, vol. 52, no. 2, pp. 1047–1062, 2007. View at Publisher · View at Google Scholar · View at Scopus