The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques
The operation of a motor vehicle under the influence of alcohol poses a significant risk to the safety of the driver, passengers, and other road users. Electrooculographic (EOG) signal analysis can be used to understand the movements and behavior of the eyes while driving. In our study, we used smar...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000073 |
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| author | Rafał J. Doniec Natalia Piaseczna Konrad Duraj Szymon Sieciński Muhammad Tausif Irshad Ilona Karpiel Mirella Urzeniczok Xinyu Huang Artur Piet Muhammad Adeel Nisar Marcin Grzegorzek |
| author_facet | Rafał J. Doniec Natalia Piaseczna Konrad Duraj Szymon Sieciński Muhammad Tausif Irshad Ilona Karpiel Mirella Urzeniczok Xinyu Huang Artur Piet Muhammad Adeel Nisar Marcin Grzegorzek |
| author_sort | Rafał J. Doniec |
| collection | DOAJ |
| description | The operation of a motor vehicle under the influence of alcohol poses a significant risk to the safety of the driver, passengers, and other road users. Electrooculographic (EOG) signal analysis can be used to understand the movements and behavior of the eyes while driving. In our study, we used smart glasses to collect EOG data from nine participants who used a driving simulator. Their level of alcoholic intoxication was simulated by drunk vision goggles at three different levels of inebriation (0, 1, 2, and 3‰ blood alcohol content). We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. The Bagged Trees achieved the highest accuracy of 79%. The most important features to detect simulated alcohol intoxication were the blink rate and the velocity of the saccade, a rapid simultaneous movement of both eyes in the same direction. Our study shows the potential of using smart glasses and machine learning for the automated detection of alcohol intoxication, even when alcohol consumption is simulated. |
| format | Article |
| id | doaj-art-2eb2142eb5ba42f280cf63a53cb977aa |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-2eb2142eb5ba42f280cf63a53cb977aa2024-12-19T11:02:56ZengElsevierSystems and Soft Computing2772-94192024-12-016200078The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniquesRafał J. Doniec0Natalia Piaseczna1Konrad Duraj2Szymon Sieciński3Muhammad Tausif Irshad4Ilona Karpiel5Mirella Urzeniczok6Xinyu Huang7Artur Piet8Muhammad Adeel Nisar9Marcin Grzegorzek10Department of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, Zabrze, 41-800, PolandDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, Zabrze, 41-800, PolandDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, Zabrze, 41-800, PolandDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, Zabrze, 41-800, Poland; Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany; Corresponding author at: Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany.Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany; Department of Information Technology, University of the Punjab, Katchery Road, Lahore, 54000, PakistanŁukasiewicz Research Network - Krakow Institute of Technology, The Centre for Biomedical Engineering, Zakopiańska 73, Kraków, 30-418, PolandŁukasiewicz Research Network - Krakow Institute of Technology, The Centre for Biomedical Engineering, Zakopiańska 73, Kraków, 30-418, PolandInstitute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, GermanyInstitute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, GermanyDepartment of Information Technology, University of the Punjab, Katchery Road, Lahore, 54000, PakistanInstitute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany; Fraunhofer IMTE, Mönkhofer Weg 239a, Lübeck, 23562, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, Katowice, 40-287, PolandThe operation of a motor vehicle under the influence of alcohol poses a significant risk to the safety of the driver, passengers, and other road users. Electrooculographic (EOG) signal analysis can be used to understand the movements and behavior of the eyes while driving. In our study, we used smart glasses to collect EOG data from nine participants who used a driving simulator. Their level of alcoholic intoxication was simulated by drunk vision goggles at three different levels of inebriation (0, 1, 2, and 3‰ blood alcohol content). We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. The Bagged Trees achieved the highest accuracy of 79%. The most important features to detect simulated alcohol intoxication were the blink rate and the velocity of the saccade, a rapid simultaneous movement of both eyes in the same direction. Our study shows the potential of using smart glasses and machine learning for the automated detection of alcohol intoxication, even when alcohol consumption is simulated.http://www.sciencedirect.com/science/article/pii/S2772941924000073ElectrooculographySmart glassesDrivingAlcoholHuman intoxicationMachine learning |
| spellingShingle | Rafał J. Doniec Natalia Piaseczna Konrad Duraj Szymon Sieciński Muhammad Tausif Irshad Ilona Karpiel Mirella Urzeniczok Xinyu Huang Artur Piet Muhammad Adeel Nisar Marcin Grzegorzek The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques Systems and Soft Computing Electrooculography Smart glasses Driving Alcohol Human intoxication Machine learning |
| title | The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques |
| title_full | The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques |
| title_fullStr | The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques |
| title_full_unstemmed | The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques |
| title_short | The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques |
| title_sort | detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques |
| topic | Electrooculography Smart glasses Driving Alcohol Human intoxication Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000073 |
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