A Portable Fuzzy Driver Drowsiness Estimation System
The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/15/4093 |
| _version_ | 1827712516148428800 |
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| author | Alimed Celecia Karla Figueiredo Marley Vellasco René González |
| author_facet | Alimed Celecia Karla Figueiredo Marley Vellasco René González |
| author_sort | Alimed Celecia |
| collection | DOAJ |
| description | The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach. |
| first_indexed | 2024-03-10T18:16:50Z |
| format | Article |
| id | doaj.art-4ed748c2fb7d45628164f26add488dd9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:16:50Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4ed748c2fb7d45628164f26add488dd92023-11-20T07:37:54ZengMDPI AGSensors1424-82202020-07-012015409310.3390/s20154093A Portable Fuzzy Driver Drowsiness Estimation SystemAlimed Celecia0Karla Figueiredo1Marley Vellasco2René González3Electrical Engineering Department, PUC-Rio, Rio de Janeiro 22451900, BrazilDepartment of Informatics and Computer Science, Institute of Mathematics and Statistics, State University of Rio de Janeiro (UERJ), Rio de Janeiro 20550-900, BrazilElectrical Engineering Department, PUC-Rio, Rio de Janeiro 22451900, BrazilResearch & Development Department, Solinftec, Araçatuba 16013337, BrazilThe adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.https://www.mdpi.com/1424-8220/20/15/4093drowsiness detectiondrowsiness measuresfuzzy inference systemRaspberry Piembedded hardwareeyes closing detection |
| spellingShingle | Alimed Celecia Karla Figueiredo Marley Vellasco René González A Portable Fuzzy Driver Drowsiness Estimation System Sensors drowsiness detection drowsiness measures fuzzy inference system Raspberry Pi embedded hardware eyes closing detection |
| title | A Portable Fuzzy Driver Drowsiness Estimation System |
| title_full | A Portable Fuzzy Driver Drowsiness Estimation System |
| title_fullStr | A Portable Fuzzy Driver Drowsiness Estimation System |
| title_full_unstemmed | A Portable Fuzzy Driver Drowsiness Estimation System |
| title_short | A Portable Fuzzy Driver Drowsiness Estimation System |
| title_sort | portable fuzzy driver drowsiness estimation system |
| topic | drowsiness detection drowsiness measures fuzzy inference system Raspberry Pi embedded hardware eyes closing detection |
| url | https://www.mdpi.com/1424-8220/20/15/4093 |
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