RELEVANT PAPERS

This section provides a list of some of the papers that have utilized the data acquired from the experiment described in this chapter. The following papers should be cited when using the experiment design or the data provided with this chapter.

  • 1. Awais M, Badruddin N, Drieberg M. A non-invasive approach to detect drowsiness in a monotonous driving environment. In: TENCON 2014-2014 IEEE Region 10 Conference; 2014, pp. 1-4.
  • 2. Awais M, Badruddin N, Drieberg M. Driver drowsiness detection using EEG power spectrum analysis. In: Region 10 Symposium, 2014 IEEE, 2014, pp. 244-247.

ACKNOWLEDGMENTS

This chapter provides the details of the experiment design available in the papers mentioned in Section 13.5.5 and in the MS thesis of Mr. M. Awais, which is available at Universiti Teknologi PETRONAS.

REFERENCES

  • 1. Sahayadhas A, Sundaraj K, Murugappan M. Detecting driver drowsiness based on sensors: a review. Sensors. 2012;12(12):16937—16953.
  • 2. Slater JD. A definition of drowsiness: one purpose for sleep? Med Hypotheses. 2008;71(5):641-644.
  • 3. Traffic Safety Facts. 2010;854(March).
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  • 9. Forsman PM, Vila BJ, Short RA, Mott CG, Van Dongen HPA. Efficient driver drowsiness detection at moderate levels of drowsiness. Accid Anal Prev. 2013;50:341-350.
  • 10. Grace R, Steward S. Drowsy driver monitor and warning system. Paper presented at: International driving symposium on human factors in driver assessment, training and vehicle design; 2001.
  • 11. Grace R, Byrne VE, Bierman DM, et al. A drowsy driver detection system for heavy vehicles. Paper presented at: Digital Avionics Systems Conference, 1998. Proceedings., 17th DASC. The AIAA/IEEE/SAE; 1998.
  • 12. Moller HJ, Kayumov L, Bulmash EL, Nhan J, Shapiro CM. Simulator performance, microsleep episodes, and subjective sleepiness: normative data using convergent methodologies to assess driver drowsiness.J Psychosom Res. 2006;61(3):335-342.
  • 13. Amin HU, Malik AS, Kamel N, Hussain M. A novel approach based on data redundancy for feature extraction of EEG signals. Brain Topogr. 2016;29(2):207-217.
  • 14. Amin HU, Malik AS, Ahmad RF, et al. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australas Phys Eng Sci Med. 2015;38(1):139-149.
  • 15. Amin HU, Malik AS, Subhani AR, Badruddin N, Chooi W-T. Dynamics of scalp potential and autonomic nerve activity during intelligence test. In: Lee M, Hirose A, Hou Z-G, Kil R, editors. Neural Information Processing,Vol 8226. Heidelberg: Springer Berlin; 2013:9-16.
  • 16. Thiffault P, Bergeron J. Monotony of road environment and driver fatigue: a simulator study. Accid Anal Prev. 2003;35(3):381-391.
  • 17. Sahayadhas A, Sundaraj K, Murugappan M. Drowsiness detection during different times of day using multiple features. Australas Phys Eng Sci Med. 2013;36(2):243-250.
 
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