CFP last date
22 April 2024
Reseach Article

Real Time Implementation for Monitoring Drowsiness Condition of a Train Driver using Brain Wave Sensor

by Upasana Sinha, Kamal K. Mehta, A.K. Shrivastava
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 9
Year of Publication: 2016
Authors: Upasana Sinha, Kamal K. Mehta, A.K. Shrivastava
10.5120/ijca2016909267

Upasana Sinha, Kamal K. Mehta, A.K. Shrivastava . Real Time Implementation for Monitoring Drowsiness Condition of a Train Driver using Brain Wave Sensor. International Journal of Computer Applications. 139, 9 ( April 2016), 25-30. DOI=10.5120/ijca2016909267

@article{ 10.5120/ijca2016909267,
author = { Upasana Sinha, Kamal K. Mehta, A.K. Shrivastava },
title = { Real Time Implementation for Monitoring Drowsiness Condition of a Train Driver using Brain Wave Sensor },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 9 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number9/24519-2016909267/ },
doi = { 10.5120/ijca2016909267 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:29.821247+05:30
%A Upasana Sinha
%A Kamal K. Mehta
%A A.K. Shrivastava
%T Real Time Implementation for Monitoring Drowsiness Condition of a Train Driver using Brain Wave Sensor
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 9
%P 25-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Driver fatigue and lack of sleep of drivers especially those who drive for a longer period of time as train accidents are a longer standing problem. It has been observed that each year numerous train accidents and fatalities may occur around the world due to driver falling asleep while driving the train. There are various traditional methods that may facilitate to detect drowsiness state of the driver to warn in such a manner, so that such accidents may be prevented to large extent. In this implementation, a system for determining drowsiness state of a driver to avoid an accident is disclosed. In one aspect, the system comprises a brainwave sensor, a microcontroller, and an alarm unit. The sensor which is the Brain Computer Interface (BCI) may be attached to one or more touch points for sensing a brainwave emitted by neurons in a brain of the driver. The microcontroller, coupled with the sensor, configured to analyze the mind in order to determine a category of the brainwaves. The brainwave may be categorized into the category based upon a predefined frequency range associated with the brainwave of types Delta, Theta, Alpha and Beta. The in-built Bluetooth will be paired up with the microcontroller to start messaging regarding the status of the driver through the GSM modem, and soon after it will alarm in the motor or in nearby station if required.

References
  1. Budiharto, W. & Putra,W. (2013). Design and Analysis of Fast Driver’s Fatigue Estimation and Drowsiness Detection System using Android. Journal of Software, vol.8, no.12, pp.3055-3059.
  2. Gulhane, Miss. M., & Mohod, P.S. (2013). INTELLIGENT FATIGUE DETECTION AND AUTOMATIC VEHICLE CONTROL SYSTEM. International Journal of Computer Science & Information Technology (IJCSIT), vol. 6, No.3, pp. 87-92.
  3. HARMA, M. & SALLINEN, M. et al. (2002).The effect of an irregular shift system on sleepiness at work in train drivers and railway traffic controllers. J. Sleep Res., vol.11, pp.141-151.
  4. Lal, S.K., et al. (2003). Development of an algorithm for an EEG- based driver fatigue countermeasures”, Journal of safety Research, vol.34, pp. 321-328.
  5. Sende P. & Warade, et al. (2015). Driver Fatigue Detection System and the Status Transmission. International Journal of Innovative Research in Science, Engineering and Technology, vol.4, no.6, pp. 4923-4927.
  6. Sontakke, K. (2015). Efficient Driver Fatigue Detection and Alerting System. International Journal of Scientific and Research Publications, vol. 5, no. 7, pp.1-4.
  7. Tayade, M. R. et al. (2014). Real Time Eye State Monitoring System for Driver Drowsiness Detection. International Journal of Emerging Technology and Advanced Engineering, vol. 4, Issue 6, pp. 452-456.
  8. Viola, P. & Jones, M. (2004). Robust Real Time Face Detection. International Journal of Computer Vision, vol.57, no. 2, pp.137-154.
  9. Angeline, P.J., Saunders, G.M. & Pollack, J.B. (1994). An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, vol.5, pp.54-65.
  10. Dorrian, J., et al.; (2006),Simulated train driving: Fatigue, self-awareness and cognitive disengagement; Applied Ergonomics, vol. 38, pp.155-166.
  11. Dwivedi, K., Biswaranjan, K. & Sethi, A. (2014). Drowsy Driver Detection using Representation Learning. Advance Computing Conference (IACC), IEEE Conference, Gurgaon, pp.995-999.
  12. Eakandarian, A. & Mortazavi, A. (2007). Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection. Proceeding of the IEEE intelligent vehicle symposium, pp.553-559.
  13. Jap, B.T. et al. (2007). Using Special Analysis To Extract Frequency Components From Electroencephalography: Application for Fatigue Countermeasure in train drivers. Proc.-IEEE 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, pp.13.
  14. Rau, P.S. (2005). Drowsy driver’s detection and warning system for commercial vehicle drivers: Field proportional test design, analysis, and progress. 19th International Technical Conference on the Enhanced safety of Vehicles, Washington, D.C.
  15. Salakhutdinov, R. & SMnih, A. et al. (2007). Restricted Boltzmann machines for collaborative filtering. Proceeding- 24th International conference on machine learning, pp.791-798.
  16. Smith, P., Shah, M. & Lobo, N.V. (2000). Monitoring head/eye motion for driver alertness with one camera”, Proceedings- 15th International Conference on Pattern Recognition on Pattern Recognition, vol.4.
  17. U. Shenyang Technology; Multimodal driver fatigue detection method and special equipment thereof, Publication no. CN102073857, Published on May 25, 2011.
  18. A. Nagakoshi Toyota (JP), et al.; Drowsy state determination device and method, Publication no. US 7830266, Published on January 29, 2009.
  19. P. Pirim Paris (FR), et al.; Method and apparatus for detection of drowsiness, Publication no. US6717518, Published on April 6, 2004.
  20. Sinha, U., Mehta, K., A system and method for determining Drowsiness condition of a Train Driver, Application no. 2778/MUM/2015, and Filed on July 22, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Brain wave sensor (BCI) Railway derailment accident EEG signal processing.s