A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS
Yıl: 2021 Cilt: 29 Sayı: 3 Sayfa Aralığı: 311 - 315 Metin Dili: İngilizce DOI: 10.31796/ogummf.891255 İndeks Tarihi: 29-07-2022
A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS
Öz: 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.
Anahtar Kelime: SÜRÜCÜLER İÇİN DERİN ÖĞRENME TABANLI YORGUNLUK VE UYUŞUKLUK TESPİTİ
Öz: 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.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | isik s, Anagun Y (2021). A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. , 311 - 315. 10.31796/ogummf.891255 |
| Chicago | isik sahin,Anagun Yildiray A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. (2021): 311 - 315. 10.31796/ogummf.891255 |
| MLA | isik sahin,Anagun Yildiray A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. , 2021, ss.311 - 315. 10.31796/ogummf.891255 |
| AMA | isik s,Anagun Y A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. . 2021; 311 - 315. 10.31796/ogummf.891255 |
| Vancouver | isik s,Anagun Y A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. . 2021; 311 - 315. 10.31796/ogummf.891255 |
| IEEE | isik s,Anagun Y "A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS." , ss.311 - 315, 2021. 10.31796/ogummf.891255 |
| ISNAD | isik, sahin - Anagun, Yildiray. "A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS". (2021), 311-315. https://doi.org/10.31796/ogummf.891255 |
| APA | isik s, Anagun Y (2021). A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online), 29(3), 311 - 315. 10.31796/ogummf.891255 |
| Chicago | isik sahin,Anagun Yildiray A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online) 29, no.3 (2021): 311 - 315. 10.31796/ogummf.891255 |
| MLA | isik sahin,Anagun Yildiray A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online), vol.29, no.3, 2021, ss.311 - 315. 10.31796/ogummf.891255 |
| AMA | isik s,Anagun Y A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online). 2021; 29(3): 311 - 315. 10.31796/ogummf.891255 |
| Vancouver | isik s,Anagun Y A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online). 2021; 29(3): 311 - 315. 10.31796/ogummf.891255 |
| IEEE | isik s,Anagun Y "A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS." Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online), 29, ss.311 - 315, 2021. 10.31796/ogummf.891255 |
| ISNAD | isik, sahin - Anagun, Yildiray. "A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS". Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online) 29/3 (2021), 311-315. https://doi.org/10.31796/ogummf.891255 |
