• Journal of Institute of Control, Robotics and Systems
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• v.22 no.5
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• pp.361-366
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• 2016

In this paper, we propose a technique for drowsiness detection using the histogram and edge information of eyes. The drowsiness of vehicle drivers is the main cause of many vehicle accidents. Therefore, the checking of eye images in order to detect the drowsiness status of a driver is very important for preventing accidents. In our suggested method, we analyze the changes of the histograms and edges of eye region images, which are acquired using a CCD camera. The experimental results show that our proposed method enhances the accuracy of detecting drowsiness to nearly 99%, and can be used for preventing vehicle accidents caused by the driver's drowsiness.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.56-59
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• 1996

The purpose of this study is to identify the cause of driver's drowsiness and to get information about driver's drowsiness from facial image using computer vision. We measured the driver's movements of a head and shoulders in the highway arid street. We also measured the eye blink duration and yawning duration of normal and drowsy drivers. from the results, we confirmed that the measurement of eye blink and yawning might be a way of drowsy detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.134-141
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• 2017

The purpose of this study was to analyze physiological changes via a cold air reaction experiment to generate basic data that are useful for the development of an automobile active air conditioning system to prevent drowsiness. The $CO_2$ concentration causing drowsiness in vehicle operation was kept below a certain level. Air was blown to the driver's face by using an indoor air cooling apparatus. Sleepiness and the arousal state of the driver in cold wind were measured by physiological signals. It was evident in the EEG that alpha waves decreased and beta waves increased, caused by cold air stimulation. The ${\alpha}/{\beta}$ ratio was reduced by about 52.9% and an alert state confirmed. In the electrocardiogram analysis, the efficiency of cold air stimulation was confirmed by the mean heart rate interval change. The R-R interval had a delay time of about one minute compared to the EEG response. The findings confirmed an arousal effect from sleepiness due to cold air stimulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1303-1310
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• 2010

Recently, researches related with automative mechanism have been widely studied to increase the driver's safety by continuously monitoring the driver's health condition to prevent driver's drowsiness. This paper describes the design of wearable chest belt for ECG and reflectance pulse oximetry for SpO2 sensors based on wireless sensor network to monitor the driver's healthcare status. ECG, SpO2 and heart rate signals can be transmitted via wireless sensor node to base station connected to the server. Intelligent monitoring system is designed at the server to analyze the SpO2 and ECG signals. HRV (Heart Rate Variability) signals can be obtained by processing the ECG and PPG signals. HRV signals are further analyzed based on time and frequency domain to determine the driver's drowsiness status.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.481-484
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• 2011

Recent Automotive technology is driven from mechanical device to the electronic components which improve the vehicle's safety and convenience. The future competitiveness of the car will come from safety issues and energy efficiency, convenience and the application of the technologies. In this study, various techniques for driver drowsiness detection are introduced and compared with each others. The advantages and disadvantages of commercially available technologies and developed technologies are compared. To enhance the detection resolution, multiple sensing technologies are introduced in this paper. The feasibility of two drowsiness detection methods, that is, existing camera image recognition method and bio signal analysis method, are tested. The direct drowsiness detection by the camera image of eyes and driver's vital signs detected indirectly are combined and analyzed by the developed noble algorithm for stress, fatigue, drowsiness detection with a more accurate high-drowsiness detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.136-142
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• 2014

Distinct features in heart rate signals during the driver's wake and sleep states could provide an initiative for the development of a safe driving systems such as drowsiness detecting sensor in a smart wheel. We measured ECG from health subjects ($23.5{\pm}2.5$ in age) during the wake and drowsiness states. The proposed method is able to detect R waves and R-R interval calculation in the ECG even when the signal includes in abnormal signals. Heart rate variability(HRV) was investigated for the time domain and frequency domains. The STD HR(0.029), NN50(0.044) and VLF power(0.0018) of the RR interval series of the subjects were significantly different from those of the control group (p < 0.05). In conclusion, there are changes in heart rate from wake to drowsiness that are potentially to be detected. The results in our study could be useful for the development of drowsiness detection sensors for effective real-time monitoring.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.322-325
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• 1997

In this study, we propose a non-invasive method to detect the drowsiness of a driver. The computer vision technology was used to extract an eye, track eyelids and measure the parameters related to the blink. We examined the blink patterns of a driver in drowsy state. For the evaluation of our image processing algorithm, the blink patterns were compared with the measured EOG signals. The result showed that our algorithm might be available in detection of drowsiness.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1667-1670
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• 2014

This paper describes a method for detecting the pupil region and identification of the eye status for driver drowsiness detection system. This program detects a driver's face and eyes using viola-jones face detection algorithm and extracts the pupil area by utilizing mean values of each row and column on the eye area. The proposed method uses binary images and the number of black pixels to identify the eye status. Experimental results showed that the accuracy of classification eye status(open/close) was above 90%.