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Reseach Article

Analysis of EEG Signals and Facial Expressions to Detect Drowsiness and Fatigue using Gabor Filters and SVM Linear Classifier

by N Mohammed Abu Basim, P Sathyabalan, P Suresh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 11
Year of Publication: 2015
Authors: N Mohammed Abu Basim, P Sathyabalan, P Suresh
10.5120/20194-2433

N Mohammed Abu Basim, P Sathyabalan, P Suresh . Analysis of EEG Signals and Facial Expressions to Detect Drowsiness and Fatigue using Gabor Filters and SVM Linear Classifier. International Journal of Computer Applications. 115, 11 ( April 2015), 9-14. DOI=10.5120/20194-2433

@article{ 10.5120/20194-2433,
author = { N Mohammed Abu Basim, P Sathyabalan, P Suresh },
title = { Analysis of EEG Signals and Facial Expressions to Detect Drowsiness and Fatigue using Gabor Filters and SVM Linear Classifier },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 11 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number11/20194-2433/ },
doi = { 10.5120/20194-2433 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:54:32.477127+05:30
%A N Mohammed Abu Basim
%A P Sathyabalan
%A P Suresh
%T Analysis of EEG Signals and Facial Expressions to Detect Drowsiness and Fatigue using Gabor Filters and SVM Linear Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 11
%P 9-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

More sophistication in vehicle's state of art technologies in driver assistance systems and stringent laws implemented by the governments did not stop any of the road accidents in the developing countries like India. The report shows that India contributes nearly 9. 5% of the total 1. 2 million road accidents globally. Among that, nearly 60-70% of road accidents are due to manmade faults like attention-less driving, usage of mobile phones while driving, intoxication of alcohol or any other drugs. The proposed system is designed based on the ground breaking concept known as "humanizing technology" which monitors the physiological changes especially in human brain and facial expressions of the driver and get processed using Gabor filters and SVM linear kernel classifier. The system can crisscross autonomously whether the ignition should get initiated or not. This type of system not only helps the drivers from the accidents, but also a great paradise for pedestrians.

References
  1. Klaus-Robert M¨uller , Michael Tangermann , Guido Dornhege ,Matthias Krauledat , Gabriel Curio ,Benjamin Blankertz (2008) " Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring" Journal of Neuroscience Methods 167 (2008) 82–90
  2. Teplan M (2002) "Fundamental Of EEG Measurement" Measurement Science Review, Volume 2, Section 2
  3. David A. Kaiser (2006)"What Is Quantitative EEG? "Journal of Neuro therapy, Vol. 10(4) 2006
  4. Jessy Parokaran Varghese (2009) "Analysis of EEG Signals For EEG-based Brain-Computer Interface School" School of Innovation, Design and Technology Mälardalen University Vasteras, Sweden July 2009
  5. Nurettin Acir, Cluneyt Guzelils (2004) "Automatic spike detection in EEG by a two-stage procedure based on support vector machines" Computers in Biology and Medicine 34 (2004) 561–575
  6. Ali Bashashati, Mehrdad Fatourechi, Rabab K Ward and Gary E Birch (2007) "A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals Journal of Neural Engineering 4 (2007) R32–R57
  7. M. Patel, S. K. L. Lal, D. Kavanagh, P. Rossiter (2011) "Applying neural network analysis on heart rate variability data to assess driver fatigue" Expert Systems with Applications 38 (2011) 7235–7242
  8. Nandita Sharma, Tom Gedeon (2014) "Modeling the stress signal" Applied Soft Computing 14 (2014) 53–61
  9. Aleksandra Vuckovic , Vlada Radivojevic , Andrew C. N. Chen , Dejan Popovic (2002) "Automatic recognition of alertness and drowsiness from EEG By An Artificial Neural Network" Medical Engineering & Physics 24 (2002) 349-360
  10. Jian-DaWu, Tuo-Rung Chen(2008) "Development of a drowsiness warning system based on the Fuzzy Logic images analysis" Expert Systems with Applications 34 (2008) 1556–1561
  11. Chin-Teng Lin, Che-Jui Chang, Bor-Shyh Lin, Shao-Hang Hung, Chih-Feng Chao and IiJan Wang(2010) "A real time wireless BCI system for drowsiness detection" IEEE Transactions on Biomedical Circuits and Systems, Vol 4. No 4, August 2010 214-222
  12. HuShuyan, ZhengGangtie (2009)" Driver drowsiness detection with Eyelid related parameters by Support Vector Machine" Expert Systems with Applications 36 (2009) 7651–765
  13. Chunlin Zhao, Min Zhao, Jianpin Liu, Chongxun Zheng (2012), "Electroencephalogram and Electrocardiograph assessment of mental fatigue in a driving simulator" Accident Analysis and Prevention 45 (2012) 83– 90
  14. Gianluca Borghini, Laura Astolfi, Giovanni Vecchiato, Donatella Mattia, Fabio Babiloni (2012) "Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness" Neuroscience and Bio-behavioral Reviews (2012) NBR-1640; No. of Pages 18
  15. Jaeik Jo , Sung Joo Lee , Kang Ryoung Park , Ig-Jae Kim , Jaihie Kim (2014) "Detecting driver drowsiness using feature-level fusion and user-specific classification" Expert Systems with Applications 41 (2014) 1139–1152
  16. Michael Lewis, Jeannette M. Haviland-Jones, Lisa Feldman Barrett. Handbook of emotions — 3rd ed. 2008 The Guilford Press
  17. file:///D:/22012015/FACS%20(Facial%20Action%20Coding%20System). htm
  18. Paul Ekman (2003) "Emotions Revealed- Recognizing faces and feelings to improve communication and emotional life"
  19. Gabor D, Dr. Ing (1945) "Theory of communication"
  20. E. Osuna, R. Freund, and F. Girosit. Training support vector machines: an application to face detection. Proc. of CVPR, pages 130–136, 1997.
Index Terms

Computer Science
Information Sciences

Keywords

Driver assistance systems road accidents manmade faults humanizing technology physiological changes facial expressions Gabor filter SVM linear kernel