A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Eskişehir Osmangazi University
2021-12-01
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| Series: | Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/1618184 |
| _version_ | 1797920594743787520 |
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| author | Şahin IŞIK Yıldıray ANAGÜN |
| author_facet | Şahin IŞIK Yıldıray ANAGÜN |
| author_sort | Şahin IŞIK |
| collection | DOAJ |
| description | 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) based driver warning system has been proposed to prevent road accidents. The Electrocardiogram (ECG) signals 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 %95.52 accuracy rate confirms the effectiveness of the proposed method and show its superiority over some state-of-the art methods. |
| first_indexed | 2024-04-10T14:03:39Z |
| format | Article |
| id | doaj.art-b0a38050d5074be8afc5c6731421895e |
| institution | Directory Open Access Journal |
| issn | 2630-5712 |
| language | English |
| last_indexed | 2024-04-10T14:03:39Z |
| publishDate | 2021-12-01 |
| publisher | Eskişehir Osmangazi University |
| record_format | Article |
| series | Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi |
| spelling | doaj.art-b0a38050d5074be8afc5c6731421895e2023-02-15T16:10:10ZengEskişehir Osmangazi UniversityEskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi2630-57122021-12-01293311315https://doi.org/10.31796/ogummf.891255A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERSŞahin IŞIK0https://orcid.org/0000-0003-1768-7104Yıldıray ANAGÜN1https://orcid.org/0000-0002-7743-0709Eskişehir Osmangazi University, Engineering and Architecture Faculty, Computer Engineering Department, EskişehirEskişehir Osmangazi University, Engineering and Architecture Faculty, Computer Engineering Department, EskişehirFalling 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) based driver warning system has been proposed to prevent road accidents. The Electrocardiogram (ECG) signals 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 %95.52 accuracy rate confirms the effectiveness of the proposed method and show its superiority over some state-of-the art methods.https://dergipark.org.tr/en/download/article-file/1618184deep learningdriver sleepiness detectionelectrocardiogramstaying awakedriving |
| spellingShingle | Şahin IŞIK Yıldıray ANAGÜN A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi deep learning driver sleepiness detection electrocardiogram staying awake driving |
| title | A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS |
| title_full | A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS |
| title_fullStr | A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS |
| title_full_unstemmed | A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS |
| title_short | A DEEP LEARNING BASED SLEEPNESS AND WAKEFULNESS DETECTION FOR DRIVERS |
| title_sort | deep learning based sleepness and wakefulness detection for drivers |
| topic | deep learning driver sleepiness detection electrocardiogram staying awake driving |
| url | https://dergipark.org.tr/en/download/article-file/1618184 |
| work_keys_str_mv | AT sahinisik adeeplearningbasedsleepnessandwakefulnessdetectionfordrivers AT yıldırayanagun adeeplearningbasedsleepnessandwakefulnessdetectionfordrivers AT sahinisik deeplearningbasedsleepnessandwakefulnessdetectionfordrivers AT yıldırayanagun deeplearningbasedsleepnessandwakefulnessdetectionfordrivers |