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SÜRÜCÜLER İÇİN DERİN ÖĞRENME TABANLI YORGUNLUK VE UYUŞUKLUK TESPİTİ

Year 2021, Volume: 29 Issue: 3, 311 - 315, 31.12.2021
https://doi.org/10.31796/ogummf.891255

Abstract

Sürüş sırasında uyumak, trafik kazalarının önemli bir parçasıdır. Trafik kazaları bir halk sağlığı sorunu olarak değerlendirilmekle beraber uyuşturucu, dinlenmeden araç kullanma, uyku bozuklukları, alkol tüketimi gibi çeşitli faktörler uykusuzluğu etkilemektedir. Ayrıca sürücüler, otoyol hipnozu gibi uykuya dalma durumunun da farkına varmayabilirler. Tüm bu faktörler, sürüş sırasında kazalara neden olur ve genellikle ölümcüldür. Sürücülerin kazadan hemen önce etkili sürücü uyarı sistemleri ve diğer karşı önlemleri uygulamaları için etkili yöntem sağlanmalıdır. Bu çalışmada, trafik kazalarını önlemek için Uzun-Kısa Süreli Hafıza (LSTM) derin öğrenme tabanlı sürücü uyarı sistemi önerilmiştir. Sürücülerin Elektrokardiyogram (EKG) sinyalleri, uykuya geçip geçmediklerini kontrol etmek için anlık olarak işlenmektedir. Uyku halinde ve uyanık halde olmak üzere iki farklı insan veri seti üzerinde deneysel çalışmalar yapılmıştır. Simülasyon sonuçları, önerilen yöntemin etkinliğini kanıtlamakta ve diğer klasik teknoloji yöntemlere göre üstünlüğünü göstermektedir.

References

  • Babaeian, M., Bhardwaj, N., Esquivel, B., et al. (2016). Real time driver drowsiness detection using a logistic-regression-based machine learning algorithm, 2016 IEEE Green Energy and Systems Conference (IGSEC), IEEE.
  • Chui, K. T., Tsang, K. F., Chi, H. R., et al. (2015). Electrocardiogram based classifier for driver drowsiness detection, 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), IEEE.
  • Edison, T., Ulagapriya, K. and Saritha, A., (2020). Prediction of Drowsy Driver Detection by Using Soft Computing Technique. Journal of Critical Reviews, 7 (6), 678-682.
  • Ford (2020). Retrieved 11/03/2020, from https://bolha.com.br/work/ford-safe-cap/.
  • Harken. Retrieved 11/03/2020, from http://harken.ibv.org/.
  • Jeong, J.-H., Yu, B.-W., Lee, D.-H., et al., (2019). Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional LSTM network using electroencephalography signals. Brain sciences, 9 (12), 348.
  • KERAS. Retrieved 11/03/2020, from https://keras.io/.
  • Panasonic. Retrieved 11/03/2020, from https://www.gzt.com/teknoloji/panasonicten-muthis-teknoloji-direksiyon-basinda-uyumaya-son-2769871.
  • Radha, M., Fonseca, P., Moreau, A., et al., (2019). Sleep stage classification from heart-rate variability using long short-term memory neural networks. Scientific Reports, 9 (1), 1-11.
  • Shahrudin, N. N. and Sidek, K. (2020). Driver drowsiness detection using different classification algorithms, Journal of Physics: Conference Series, IOP Publishing.

A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS

Year 2021, Volume: 29 Issue: 3, 311 - 315, 31.12.2021
https://doi.org/10.31796/ogummf.891255

Abstract

Falling asleep while driving is a major part of road accidents. Traffic accidents can be considered as a public health problem and several factors like drugs, driving without rest, sleep disorders, alcohol consumption affect sleep deprivation. Furthermore, drivers are also unaware of falling asleep situations, such as highway hypnosis. All these factors cause accidents while driving and are often fatal. A good background should be provided for drivers to implement effective driver warning systems and other countermeasures just before the accident. In this study, Long Short-Term Memory (LSTM) deep learning based driver warning system has been proposed to prevent road accidents. The Electrocardiogram (ECG) signals of the drivers are processed instantaneously to check whether they go into sleep or not. Experimental studies have been carried out on two different human data sets as sleep mode and awake mode. The simulation results confirm the effectiveness of the proposed method and show its superiority over other state-of-the art methods.

References

  • Babaeian, M., Bhardwaj, N., Esquivel, B., et al. (2016). Real time driver drowsiness detection using a logistic-regression-based machine learning algorithm, 2016 IEEE Green Energy and Systems Conference (IGSEC), IEEE.
  • Chui, K. T., Tsang, K. F., Chi, H. R., et al. (2015). Electrocardiogram based classifier for driver drowsiness detection, 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), IEEE.
  • Edison, T., Ulagapriya, K. and Saritha, A., (2020). Prediction of Drowsy Driver Detection by Using Soft Computing Technique. Journal of Critical Reviews, 7 (6), 678-682.
  • Ford (2020). Retrieved 11/03/2020, from https://bolha.com.br/work/ford-safe-cap/.
  • Harken. Retrieved 11/03/2020, from http://harken.ibv.org/.
  • Jeong, J.-H., Yu, B.-W., Lee, D.-H., et al., (2019). Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional LSTM network using electroencephalography signals. Brain sciences, 9 (12), 348.
  • KERAS. Retrieved 11/03/2020, from https://keras.io/.
  • Panasonic. Retrieved 11/03/2020, from https://www.gzt.com/teknoloji/panasonicten-muthis-teknoloji-direksiyon-basinda-uyumaya-son-2769871.
  • Radha, M., Fonseca, P., Moreau, A., et al., (2019). Sleep stage classification from heart-rate variability using long short-term memory neural networks. Scientific Reports, 9 (1), 1-11.
  • Shahrudin, N. N. and Sidek, K. (2020). Driver drowsiness detection using different classification algorithms, Journal of Physics: Conference Series, IOP Publishing.
There are 10 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Şahin Işık 0000-0003-1768-7104

Yıldıray Anagün 0000-0002-7743-0709

Publication Date December 31, 2021
Acceptance Date August 19, 2021
Published in Issue Year 2021 Volume: 29 Issue: 3

Cite

APA Işık, Ş., & Anagün, Y. (2021). A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 29(3), 311-315. https://doi.org/10.31796/ogummf.891255
AMA Işık Ş, Anagün Y. A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. ESOGÜ Müh Mim Fak Derg. December 2021;29(3):311-315. doi:10.31796/ogummf.891255
Chicago Işık, Şahin, and Yıldıray Anagün. “A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 29, no. 3 (December 2021): 311-15. https://doi.org/10.31796/ogummf.891255.
EndNote Işık Ş, Anagün Y (December 1, 2021) A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 29 3 311–315.
IEEE Ş. Işık and Y. Anagün, “A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS”, ESOGÜ Müh Mim Fak Derg, vol. 29, no. 3, pp. 311–315, 2021, doi: 10.31796/ogummf.891255.
ISNAD Işık, Şahin - Anagün, Yıldıray. “A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 29/3 (December 2021), 311-315. https://doi.org/10.31796/ogummf.891255.
JAMA Işık Ş, Anagün Y. A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. ESOGÜ Müh Mim Fak Derg. 2021;29:311–315.
MLA Işık, Şahin and Yıldıray Anagün. “A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 29, no. 3, 2021, pp. 311-5, doi:10.31796/ogummf.891255.
Vancouver Işık Ş, Anagün Y. A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. ESOGÜ Müh Mim Fak Derg. 2021;29(3):311-5.

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