Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Real-time drowsiness detection using wearable, lightweight brain sensing headbands

Real-time drowsiness detection using wearable, lightweight brain sensing headbands

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The feasibility of real-time drowsiness detection using commercially available, off-the-shelf, lightweight, wearable electroencephalogram (EEG) sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. However, the use of lightweight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines (SVMs) is shown to classify drowsy states with high accuracy. The system is validated using data collected on 23 subjects in fresh and drowsy states. An accuracy of 81% is obtained at a per-subject level and 74% in cross-subject validation using SVM with radial basis kernel. Using a temporal aggregation strategy, the cross-subject validation accuracy is shown to improve to 87%. The EEG signals are also used to characterise the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis.

References

    1. 1)
      • 5. Sahayadhas, A., Sundaraj, K., Murugappan, M.: ‘Detecting driver drowsiness based on sensors: a review’, Sensors, 2012, 12, (12), pp. 1693716953.
    2. 2)
      • 27. Melia, U., Guaita, M., Vallverdú, M., et al: ‘Characterization of daytime sleepiness by time–frequency measures of EEG signals’, J. Med. Biol. Eng., 2015, 35, (3), pp. 406417.
    3. 3)
      • 1. Kulathumani, V., Kecojevic, V., Nimbarte, A., et al: ‘Integrated surface mine safety system: Alpha mining foundation technical report’. Available at http://www.alpha-foundation.org/wp-content/uploads/2016/01/AFC113-15_WVU_-FinalRpt_Approved.pdf, 2015.
    4. 4)
      • 33. Aizerman, A., Braverman, E.M., Rozoner, L.I.: ‘Theoretical foundations of the potential function method in pattern recognition learning’, Autom. Remote Control, 1964, 25, pp. 821837.
    5. 5)
      • 14. Zhe, M., Xin-Ping, Y., Chao-Zhong, W.: ‘Driving fatigue identification method based on physiological signals’. Seventh Int. Conf. of Chinese Transportation Professionals, 2008, pp. 341352.
    6. 6)
      • 31. Chin, T., Che, J., Bor, S., et al: ‘A real-time wireless brain–computer interface system for drowsiness detection’, IEEE Trans. Biomed. Circuits Syst., 2010, 4, (1), pp. 214222.
    7. 7)
      • 15. Liu, C., Hosking, S., Lenn, M.: ‘Predicting driver drowsiness using vehicle measures: recent insights and future challenges’, J. Saf. Res., 2009, 40, (1), pp. 239245.
    8. 8)
      • 10. Lew, M., Sebe, N., Huang, T., et al: ‘Drowsy driver detection through facial movement analysis’, Hum.-Comput. Interact., 2007, 4796, (1), pp. 618.
    9. 9)
      • 8. Zhu, Z., Ji, Q., Lan, P.: ‘Real-time nonintrusive monitoring and prediction of driver fatigue’, IEEE Trans. Veh. Technol., 2004, 53, (4), pp. 10521068.
    10. 10)
      • 11. Li, G., Chung, W.-Y.: ‘Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier’, Sensors, 2013, 13, (12), pp. 1649416511.
    11. 11)
      • 26. Melia, U., Guaita, M., Vallverdú, M., et al: ‘Correntropy measures to detect daytime sleepiness from EEG signals’, Physiol. Meas., 2014, 35, (10), pp. 20672083.
    12. 12)
      • 24. Shabani, H., Mikaili, M., Noori, S.M.R.: ‘Assessment of recurrence quantification analysis (RQA) of EEG for development of a novel drowsiness detection system’, Biomed. Eng. Lett., 2016, 6, (3), pp. 196204.
    13. 13)
      • 12. Maven machines: ‘Maven co-pilot’. Available at http://www.mavenmachines.com/co-pilot/.
    14. 14)
      • 28. Chen, L., Zhao, Y., Zhang, J., et al: ‘Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning’, Expert Syst. Appl., 2015, 42, (21), pp. 73447355.
    15. 15)
      • 34. Garces, C., Orosco, L., Laciar, E.: ‘Automatic detection of drowsiness in EEG records based on multimodal analysis’, Med. Eng. Phys., 2014, 2, (36), pp. 244249.
    16. 16)
      • 4. Goodbody, A.: ‘Feeling tired?’, Min. Mag., 2013, 7, pp. 3848.
    17. 17)
      • 29. Johnson, M.J., Chahal, T., Stinchcombe, A., et al: ‘Physiological responses to simulated and on-road driving’, Int. J. Psychophysiol., 2011, 81, (1), pp. 203208.
    18. 18)
      • 20. Yu, S., Li, P., Lin, H., et al: ‘Support vector machine based detection of drowsiness using minimum EEG features’. Int. Conf. on Social Computing (SocialCom), 2013, pp. 827835.
    19. 19)
      • 16. Bergasa, L., Nuevo, J., Sotelo, M., et al: ‘Real-time system for monitoring driver vigilance’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (1), pp. 6377.
    20. 20)
      • 6. CTC and Associates: ‘Monitoring vehicle driver fatigue’. Technical Report, TRS-1501, Minnesota Department of Transportation, MN, January 2015.
    21. 21)
      • 7. Garca, I., Bronte, S., Bergasa, L.M., et al: ‘Vision-based drowsiness detector for real driving conditions’. Intelligent Vehicles Symp. (IV), 2012, pp. 618623.
    22. 22)
      • 32. Park, J., Xu, L., Sridhar, V., et al: ‘Wireless dry EEG for drowsiness detection’. IEEE Int. Conf. on Engineering in Medicine and Biology Society (EMBC), 2011, pp. 32983301.
    23. 23)
      • 18. Seeing machines: ‘Seeing machines – fleet’. Available at https://www.seeingmachines.com/solutions/fleet/.
    24. 24)
      • 23. Ko, L.-W., Lai, W.-K., Liang, W.-G., et al: ‘Single channel wireless EEG device for real-time fatigue level detection’. Int. Joint Conf. on Neural Networks (IJCNN), 2015, pp. 15.
    25. 25)
      • 30. Fu, C.L., Li, W., Chun, H., et al: ‘Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system’, IEEE Trans. Circuits Syst., 2012, 59, (1), pp. 20442055.
    26. 26)
      • 17. DOrazio, T., Leo, M., Guaragnella, C., et al: ‘A visual approach for driver inattention detection’, Pattern Recognit.., 2007, 40, (8), pp. 23412355.
    27. 27)
      • 22. Li, G., Chung, W.: ‘A context-aware EEG headset system for early detection of driver drowsiness’, Sensors, 2015, 15, (8), pp. 2079320873.
    28. 28)
      • 2. Mine Safety and Health Administration: ‘Coal mine fatalgrams and investigation reports’. Available at http://www.msha.gov/fatals/fabc.htm, 2013.
    29. 29)
      • 19. De Gennaro, L., Ferrara, M., Bertini, M.: ‘The boundary between wakefulness and sleep: quantitative electroencephalographic changes during the sleep onset period’, Neuroscience, 2001, 107, (1), pp. 111.
    30. 30)
      • 3. Zhang, M., Kecojevic, V.: ‘Intervention strategies to eliminate truck-related fatalities in surface coal mining in West Virginia’, Int. J. Inj. Contr. Saf. Promot., 2015, 23, (2), pp. 115129.
    31. 31)
      • 25. Melia, U., Guaita, M., Vallverdú, M., et al: ‘Mutual information measures applied to {EEG} signals for sleepiness characterization’, Med. Eng. Phys., 2015, 37, (3), pp. 297308.
    32. 32)
      • 21. Interaxon Inc.: ‘MUSE the brain sensing headband’. Available at http://xwww.choosemuse.com.
    33. 33)
      • 13. Hu, S., Zheng, G.: ‘Driver drowsiness detection with eyelid related parameters by support vector machine’, Expert Syst. Appl., 2009, 36, (4), pp. 76517658.
    34. 34)
      • 9. Abe, T., Nonomura, T., Komada, Y., et al: ‘Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests’, Int. J. Psychophysiol., 2011, 82, (3), pp. 269274.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0183
Loading

Related content

content/journals/10.1049/iet-its.2016.0183
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address