Research Article
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Drowsiness Detection and Alert System Using Wearable Dry Electroencephalography for Safe Driving

Year 2022, , 300 - 310, 31.01.2022
https://doi.org/10.31202/ecjse.973119

Abstract

Driver drowsiness and fatigue plays a great impact in causing road accidents. Drowsiness can lead to inattentiveness or even microsleep, which involves brief intermittent moments of sleep sometimes without the person even noticing it, and this can sometimes be fatal when driving. In this paper, a drowsiness detection an alert system is proposed to identify the drowsiness level of a driver and trigger an audible alarm, status display on LCD, and a light indicator to alert the driver. The input is captured using MindLink Neuro Sensor which is a wearable dry EEG headset which is wirelessly connected to the microcontroller. The common activities that activate certain brain wave, as well as the activities that deactivate the respective brain wave is examined and presented in the results. It can be seen that a few brain waves can be associated with drowsiness as they are triggered during yawning such as the alpha, beta, and theta waves, but the MindLink EEG headset used in this experiment featured 2 nodes placed at the front of the forehead and is most sensitive to changes in the alpha wave, so alpha wave is used as a drowsiness determinant.

Supporting Institution

Universiti Teknikal Malaysia Melaka (UTeM)

Thanks

The authors would like to thank Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik (FTKEE) and Center for Advanced Computing Technology (C-ACT) of Universiti Teknikal Malaysia Melaka (UTeM) for supporting the work herein. Special thanks to my good friend Mustafa Yücefaydalı for the Turkish translation. The authors would also like to thank the editor and anonymous reviewers for their feedbacks in improving the quality of this work.

References

  • [1] J. L. Connor, “The role of driver sleepiness in car crashes: A review of the epidemiological evidence,” Drugs, Driving and Traffic Safety, pp. 187–205, 2009, doi: 10.1007/978-3-7643-9923-8_12.
  • [2] N. Gurudath and H. Bryan Riley, “Drowsy driving detection by EEG analysis using Wavelet Transform and K-means clustering,” Procedia Computer Science, vol. 34, pp. 400–409, 2014, doi: 10.1016/J.PROCS.2014.07.045.
  • [3] J. N. Mindoro, C. D. Casuat, A. S. Alon, M. A. F. Malbog, and J. A. B. Susa, “Drowsy or not? Early drowsiness detection utilizing arduino based on electroencephalogram (eeg) neuro-signal,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 2, pp. 2221–2226, 2020, doi: 10.30534/IJATCSE/2020/200922020.
  • [4] S. H. Hwang, M. Park, J. Kim, Y. Yun, and J. Son, “Driver drowsiness detection using EEG features,” Communications in Computer and Information Science, vol. 852, pp. 367–374, 2018, doi: 10.1007/978-3-319-92285-0_49.
  • [5] T. Hwang, M. Kim, S. Hong, and K. S. Park, “Driver drowsiness detection using the in-ear EEG,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, pp. 4646–4649, Oct. 2016, doi: 10.1109/EMBC.2016.7591763.
  • [6] G. Li and W. Y. Chung, “A context-aware EEG headset system for early detection of driver drowsiness,” Sensors (Switzerland), vol. 15, no. 8, pp. 20873–20893, Aug. 2015, doi: 10.3390/S150820873.
  • [7] M. Ogino and Y. Mitsukura, “Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram,” Sensors (Switzerland), vol. 18, no. 12, Dec. 2018, doi: 10.3390/S18124477.
  • [8] D. Sandberg, T. Åkerstedt, A. Anund, G. Kecklund, and M. Wahde, “Detecting driver sleepiness using optimized nonlinear combinations of sleepiness indicators,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 1, pp. 97–108, Mar. 2011, doi: 10.1109/TITS.2010.2077281.
  • [9] X. Wang and C. Xu, “Driver drowsiness detection based on non-intrusive metrics considering individual specifics,” Accident Analysis and Prevention, vol. 95, pp. 350–357, Oct. 2016, doi: 10.1016/J.AAP.2015.09.002.
  • [10] J. Chen, S. Wang, E. He, H. Wang, and L. Wang, “Recognizing drowsiness in young men during real driving based on electroencephalography using an end-to-end deep learning approach,” Biomedical Signal Processing and Control, vol. 69, p. 102792, Aug. 2021, doi: 10.1016/J.BSPC.2021.102792.
  • [11] C. Zhao, M. Zhao, J. Liu, and C. Zheng, “Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator,” Accident Analysis and Prevention, vol. 45, pp. 83–90, Mar. 2012, doi: 10.1016/J.AAP.2011.11.019.
  • [12] E. Wascher, S. Getzmann, and M. Karthaus, “Driver state examination—Treading new paths,” Accident Analysis and Prevention, vol. 91, pp. 157–165, Jun. 2016, doi: 10.1016/J.AAP.2016.02.029.
  • [13] J. A. Pineda, “The functional significance of mu rhythms: Translating ‘seeing’ and ‘hearing’ into ‘doing,’” Brain Research Reviews, vol. 50, no. 1, pp. 57–68, Dec. 2005, doi: 10.1016/J.BRAINRESREV.2005.04.005.
  • [14] V. Saini and R. Saini, “Driver Drowsiness Detection System and Techniques: A Review”, Accessed: Jul. 16, 2021. [Online]. Available: www.ijcsit.com
  • [15] Z. Li, S. E. Li, R. Li, B. Cheng, and J. Shi, “Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions,” Sensors (Basel, Switzerland), vol. 17, no. 3, Mar. 2017, doi: 10.3390/S17030495.
  • [16] S. Otmani, T. Pebayle, J. Roge, and A. Muzet, “Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers,” Physiology and Behavior, vol. 84, no. 5, pp. 715–724, Apr. 2005, doi: 10.1016/J.PHYSBEH.2005.02.021.
  • [17] P. Thiffault and J. Bergeron, “Monotony of road environment and driver fatigue: A simulator study,” Accident Analysis and Prevention, vol. 35, no. 3, pp. 381–391, 2003, doi: 10.1016/S0001-4575(02)00014-3.
  • [18] S. H. Fairclough and R. Graham, “Impairment of driving performance caused by sleep deprivation or alcohol: A comparative study,” Human Factors, vol. 41, no. 1, pp. 118–128, Mar. 1999, doi: 10.1518/001872099779577336.
  • [19] S. Majumder, B. Guragain, C. Wang, and N. Wilson, “On-board drowsiness detection using EEG: Current status and future prospects,” IEEE International Conference on Electro Information Technology, vol. 2019-May, pp. 483–490, May 2019, doi: 10.1109/EIT.2019.8833866.
  • [20] M. Awais, N. Badruddin, and M. Drieberg, “Driver drowsiness detection using EEG power spectrum analysis,” IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium, pp. 244–247, Jul. 2014, doi: 10.1109/TENCONSPRING.2014.6863035.
  • [21] M. Oviyaa, P. Renvitha, R. Swathika, I. J. L. Paul, and S. Sasirekha, “Arduino based Real Time Drowsiness and Fatigue Detection for Bikers using Helmet,” 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 - Conference Proceedings, pp. 573–577, Mar. 2020, doi: 10.1109/ICIMIA48430.2020.9074842.
  • [22] A. S. Abdel-Rahman, A. F. Seddik, and D. M. Shawky, “An affordable approach for detecting drivers’ drowsiness using EEG signal analysis,” 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, pp. 1326–1332, Sep. 2015, doi: 10.1109/ICACCI.2015.7275796.
  • [23] P. A. Abhang, B. W. Gawali, and S. C. Mehrotra, “Technological Basics of EEG Recording and Operation of Apparatus,” Introduction to EEG- and Speech-Based Emotion Recognition, pp. 19–50, Jan. 2016, doi: 10.1016/B978-0-12-804490-2.00002-6.
  • [24] “Design and Implementation of Neuro Based Switching System Control for Power Socket”, doi: 10.35940/ijrte.D4218.118419.
  • [25] A. Kübler, B. Kotchoubey, J. Kaiser, J. R. Wolpaw, and N. Birbaumer, “Brain–computer communication: Unlocking the locked in.,” Psychological Bulletin, vol. 127, no. 3, pp. 358–375, 2001, doi: 10.1037/0033-2909.127.3.358.
  • [26] W. Klimesch et al., “Theta synchronization during episodic retrieval: neural correlates of conscious awareness,” Cognitive Brain Research, vol. 12, no. 1, pp. 33–38, Aug. 2001, doi: 10.1016/S0926-6410(01)00024-6.
  • [27] P. L. Nunez, “The brain wave equation: a model for the EEG,” Mathematical Biosciences, vol. 21, no. 3–4, pp. 279–297, Dec. 1974, doi: 10.1016/0025-5564(74)90020-0.

Güvenli Sürüş için Giyilebilir Kuru Elektroensefalografi Kullanan Uyuşukluk Algılama ve Uyarı Sistemi

Year 2022, , 300 - 310, 31.01.2022
https://doi.org/10.31202/ecjse.973119

Abstract

Sürücünün uyuşukluk ve yorgunluğunun yol kazalarına neden olmada büyük etkisi vardır. Uyuşukluk, dikkatsizliğe ve hatta bazen kişinin farkına bile varmadan kısa aralıklı uyku anlarını içeren mikro uykuya yol açabilir ve bu bazen araba kullanırken ölümcül olabilir. Bu yazıda, sürücünün uykululuk seviyesini belirleyen, seviye artınca sesli bir alarmı çalıştıran, LCD'de uykululuk seviyesi durum göstergesi ve sürücüyü uyarmak için bir ışıklı göstergesi olan bir uyuşukluk algılama ve uyarı sistemi önerilmiştir. Veri girişi, mikrodenetleyiciye kablosuz olarak bağlanan giyilebilir bir kuru EEG kulaklığı olan MindLink Neuro Sensor kullanılarak sağlanmıştır. Belirli beyin dalgasını aktive eden ortak faaliyetler ve ilgili beyin dalgasını devre dışı bırakan aktiviteler incelenmiş ve sonuçlarda sunulmuştur. Alfa, beta ve teta dalgaları gibi birkaç beyin dalgasının esneme sırasında tetiklendikleri için uyuşukluk ile ilişkilendirilebileceği görülebilir, ancak bu deneyde kullanılan MindLink EEG kulaklığı alnın önüne yerleştirilmiş 2 düğüm içerdiği ve en çok alfa dalgasındaki değişikliklere duyarlı olduğu için Alfa beyin dalgaları uyku hali belirleyicisi olarak kullanılmıştır.

References

  • [1] J. L. Connor, “The role of driver sleepiness in car crashes: A review of the epidemiological evidence,” Drugs, Driving and Traffic Safety, pp. 187–205, 2009, doi: 10.1007/978-3-7643-9923-8_12.
  • [2] N. Gurudath and H. Bryan Riley, “Drowsy driving detection by EEG analysis using Wavelet Transform and K-means clustering,” Procedia Computer Science, vol. 34, pp. 400–409, 2014, doi: 10.1016/J.PROCS.2014.07.045.
  • [3] J. N. Mindoro, C. D. Casuat, A. S. Alon, M. A. F. Malbog, and J. A. B. Susa, “Drowsy or not? Early drowsiness detection utilizing arduino based on electroencephalogram (eeg) neuro-signal,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 2, pp. 2221–2226, 2020, doi: 10.30534/IJATCSE/2020/200922020.
  • [4] S. H. Hwang, M. Park, J. Kim, Y. Yun, and J. Son, “Driver drowsiness detection using EEG features,” Communications in Computer and Information Science, vol. 852, pp. 367–374, 2018, doi: 10.1007/978-3-319-92285-0_49.
  • [5] T. Hwang, M. Kim, S. Hong, and K. S. Park, “Driver drowsiness detection using the in-ear EEG,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, pp. 4646–4649, Oct. 2016, doi: 10.1109/EMBC.2016.7591763.
  • [6] G. Li and W. Y. Chung, “A context-aware EEG headset system for early detection of driver drowsiness,” Sensors (Switzerland), vol. 15, no. 8, pp. 20873–20893, Aug. 2015, doi: 10.3390/S150820873.
  • [7] M. Ogino and Y. Mitsukura, “Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram,” Sensors (Switzerland), vol. 18, no. 12, Dec. 2018, doi: 10.3390/S18124477.
  • [8] D. Sandberg, T. Åkerstedt, A. Anund, G. Kecklund, and M. Wahde, “Detecting driver sleepiness using optimized nonlinear combinations of sleepiness indicators,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 1, pp. 97–108, Mar. 2011, doi: 10.1109/TITS.2010.2077281.
  • [9] X. Wang and C. Xu, “Driver drowsiness detection based on non-intrusive metrics considering individual specifics,” Accident Analysis and Prevention, vol. 95, pp. 350–357, Oct. 2016, doi: 10.1016/J.AAP.2015.09.002.
  • [10] J. Chen, S. Wang, E. He, H. Wang, and L. Wang, “Recognizing drowsiness in young men during real driving based on electroencephalography using an end-to-end deep learning approach,” Biomedical Signal Processing and Control, vol. 69, p. 102792, Aug. 2021, doi: 10.1016/J.BSPC.2021.102792.
  • [11] C. Zhao, M. Zhao, J. Liu, and C. Zheng, “Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator,” Accident Analysis and Prevention, vol. 45, pp. 83–90, Mar. 2012, doi: 10.1016/J.AAP.2011.11.019.
  • [12] E. Wascher, S. Getzmann, and M. Karthaus, “Driver state examination—Treading new paths,” Accident Analysis and Prevention, vol. 91, pp. 157–165, Jun. 2016, doi: 10.1016/J.AAP.2016.02.029.
  • [13] J. A. Pineda, “The functional significance of mu rhythms: Translating ‘seeing’ and ‘hearing’ into ‘doing,’” Brain Research Reviews, vol. 50, no. 1, pp. 57–68, Dec. 2005, doi: 10.1016/J.BRAINRESREV.2005.04.005.
  • [14] V. Saini and R. Saini, “Driver Drowsiness Detection System and Techniques: A Review”, Accessed: Jul. 16, 2021. [Online]. Available: www.ijcsit.com
  • [15] Z. Li, S. E. Li, R. Li, B. Cheng, and J. Shi, “Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions,” Sensors (Basel, Switzerland), vol. 17, no. 3, Mar. 2017, doi: 10.3390/S17030495.
  • [16] S. Otmani, T. Pebayle, J. Roge, and A. Muzet, “Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers,” Physiology and Behavior, vol. 84, no. 5, pp. 715–724, Apr. 2005, doi: 10.1016/J.PHYSBEH.2005.02.021.
  • [17] P. Thiffault and J. Bergeron, “Monotony of road environment and driver fatigue: A simulator study,” Accident Analysis and Prevention, vol. 35, no. 3, pp. 381–391, 2003, doi: 10.1016/S0001-4575(02)00014-3.
  • [18] S. H. Fairclough and R. Graham, “Impairment of driving performance caused by sleep deprivation or alcohol: A comparative study,” Human Factors, vol. 41, no. 1, pp. 118–128, Mar. 1999, doi: 10.1518/001872099779577336.
  • [19] S. Majumder, B. Guragain, C. Wang, and N. Wilson, “On-board drowsiness detection using EEG: Current status and future prospects,” IEEE International Conference on Electro Information Technology, vol. 2019-May, pp. 483–490, May 2019, doi: 10.1109/EIT.2019.8833866.
  • [20] M. Awais, N. Badruddin, and M. Drieberg, “Driver drowsiness detection using EEG power spectrum analysis,” IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium, pp. 244–247, Jul. 2014, doi: 10.1109/TENCONSPRING.2014.6863035.
  • [21] M. Oviyaa, P. Renvitha, R. Swathika, I. J. L. Paul, and S. Sasirekha, “Arduino based Real Time Drowsiness and Fatigue Detection for Bikers using Helmet,” 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 - Conference Proceedings, pp. 573–577, Mar. 2020, doi: 10.1109/ICIMIA48430.2020.9074842.
  • [22] A. S. Abdel-Rahman, A. F. Seddik, and D. M. Shawky, “An affordable approach for detecting drivers’ drowsiness using EEG signal analysis,” 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, pp. 1326–1332, Sep. 2015, doi: 10.1109/ICACCI.2015.7275796.
  • [23] P. A. Abhang, B. W. Gawali, and S. C. Mehrotra, “Technological Basics of EEG Recording and Operation of Apparatus,” Introduction to EEG- and Speech-Based Emotion Recognition, pp. 19–50, Jan. 2016, doi: 10.1016/B978-0-12-804490-2.00002-6.
  • [24] “Design and Implementation of Neuro Based Switching System Control for Power Socket”, doi: 10.35940/ijrte.D4218.118419.
  • [25] A. Kübler, B. Kotchoubey, J. Kaiser, J. R. Wolpaw, and N. Birbaumer, “Brain–computer communication: Unlocking the locked in.,” Psychological Bulletin, vol. 127, no. 3, pp. 358–375, 2001, doi: 10.1037/0033-2909.127.3.358.
  • [26] W. Klimesch et al., “Theta synchronization during episodic retrieval: neural correlates of conscious awareness,” Cognitive Brain Research, vol. 12, no. 1, pp. 33–38, Aug. 2001, doi: 10.1016/S0926-6410(01)00024-6.
  • [27] P. L. Nunez, “The brain wave equation: a model for the EEG,” Mathematical Biosciences, vol. 21, no. 3–4, pp. 279–297, Dec. 1974, doi: 10.1016/0025-5564(74)90020-0.
There are 27 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Shamsul Fakhar Abd Gani 0000-0002-1925-9309

Publication Date January 31, 2022
Submission Date July 19, 2021
Acceptance Date October 8, 2021
Published in Issue Year 2022

Cite

IEEE S. F. Abd Gani, “Drowsiness Detection and Alert System Using Wearable Dry Electroencephalography for Safe Driving”, ECJSE, vol. 9, no. 1, pp. 300–310, 2022, doi: 10.31202/ecjse.973119.