Non-intrusive drowsiness detection by employing Support Vector Machine

Abas, Ashardi; Mellor, John; Chen, Xun

doi:10.1109/iconac.2014.6935484

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…Then, the classifier can be obtained by solving Lagrangian dual optimization problem for maximum-margin using Support Vector Machine (SVM) [19]. (4) where the a i is the Lagrange multiplier, y={-1,1} is the class label of the input data, and K(x i ,x j ) is the kernel function for mapping the data into higher dimensions' feature space.…”

Section: Features Extraction and Classifier Constructionmentioning

confidence: 99%

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Electrocardiogram based classifier for driver drowsiness detection

Chui

Tsang

Ran

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

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Section: Features Extraction and Classifier Constructionmentioning

confidence: 99%

“…Nonintrusive method includes vehicle-based detection [4,5] and image-based detection [6,7]. Vehicle-based method utilized camera and data logger to measure the steering angle, and distance to outside lane, to be the input of driver drowsiness detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram based classifier for driver drowsiness detection

Chui

Tsang

Ran

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

View full text Add to dashboard Cite

show abstract

“…This is a non-intrusive measurement because the sensor is not attached to the conductor. In behavior-based methods [1][2][3][4][5][6][7], the visual behavior of the driver is, blink, close your eyes, yawn, lower your head, etc. Analysis to detect drowsiness.…”

Section: Introductionmentioning

confidence: 99%

Driver Drowsiness Detection Using Face Recognition

Goud¹,

Aravind²

2023

IJRASET

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Every year, hundreds of people die in traffic accidents all over the world. According to national statistics, human factors play a determining role in 90-95% of car accidents in Iran. Globally, 25% of accidents are caused by driver fatigue and around 60% of road accidents result in death or serious injury. In a National Transportation Research Institute (NTSRB) study of 107 randomly selected car crashes, fatigue accounted for 58% of all crashes. Drowsy driving is a major factor in serious road accidents that claim thousands of lives every year. The application of intelligent systems in automobiles has developed considerably in recent years. These systems use wireless sensor networks to monitor and transmit car and driver status. Smart cars that use software technology to control engine speed, steering, transmission, braking, etc. Management and control, the quality of management has been greatly improved. Ad hoc networks are the first systems to develop automatic navigation in cars. A notable weakness of these systems is that their response to changes in the environment is not in real time. This is especially important in driving, where time is a key factor in a driver's decision. On the other hand, another way to check driver fatigue is to monitor the physical condition and facial expression of the driver, which cannot be processed and transmitted accurately enough by wireless sensor networks. Driver fatigue is a major factor in a large number of road accidents. Recent statistics estimate that 1,200 deaths and 76,000 injuries are attributable to fatigue-related accidents each year.

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An Automated System for Driver Drowsiness Monitoring Using Machine Learning

Nandyal

Sushma

2019

Intelligent Data Communication Technologies and Internet of Things

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Non-intrusive drowsiness detection by employing Support Vector Machine

Cited by 10 publications

References 6 publications

Electrocardiogram based classifier for driver drowsiness detection

Electrocardiogram based classifier for driver drowsiness detection

Driver Drowsiness Detection Using Face Recognition

An Automated System for Driver Drowsiness Monitoring Using Machine Learning

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