• Journal of the Korean Society of Safety
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• v.30 no.2
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• pp.56-62
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• 2015

The purpose of this study was to identify the relationship between the index of Electrocardiography(LF/HF) and the occurrence of drowsiness driving while driving in a simulated situation. Participants were 31 undergraduate students with an experience in driving and they participated 30 minutes driving under enough sleep condition and 1 hour under the sleep deprivation condition. The Euro Truck Simulator II was used for driving simulation task and ECG and perceived drowsiness of each participants were measured during two driving conditions. Perceived sleepiness recorded by the checklist every 10 minutes and ECG data extracted before and after 15 seconds of every 10 minutes to verify the relationship between two variables. The results showed that the level of perceived sleepiness under sleep deprivation condition was higher than that under the enough sleep condition, and the level of LF/HF under sleep deprivation condition was lower than that under the enough sleep condition. In addition, the result of analysis of repeated measure ANOVA for ECG indicated that authentic sleepiness revealed in 20 minutes after the start of driving under the sleep deprivation condition. However, the result of perceived drowsiness indicated that authentic sleepiness revealed in 30 minutes after the start of driving. These result suggest that the time difference between biological and perceived response on drowsiness may be exist. Finally, the significant negative correlation between the LF/HF level and perceived drowsiness was observed. These findings suggest that ECG(LF/HF) can be an possible index to measure drowsiness driving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4518-4540
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• 2019

With the development of IT convergence technology, autonomous driving has gradually developed; however, the vehicle is still operated by the driver, who should always be in good health - but sometimes, this is not the case. It is especially dangerous to drive when drowsy, and unable to fully concentrate on driving, such as when taking certain medicines, or through fatigue. Drowsy driving is at least eight times more dangerous than normal driving, and as dangerous as drunk driving. Previous research has looked at technology to detect drowsiness, in order to wake up drivers when necessary, or to safely stop the vehicle. Furthermore, many studies have been conducted to find out when drowsiness occurs. However, it is more desirable for the driver to take sufficient rest during a break, in order to be able to continue to focus and drive. In other words, it is important to maintain a normal state before drowsiness. In this study, we introduce a sound source to increase driver concentration and prevent drowsiness, another that can improve the quality of sleep, and a system that produces these sound sources. The proposed system has a noise reduction effect of about 15 dB. We have confirmed that the proposed sound induces an EEG of the desired form.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.330-335
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• 2015

Approximately 23% of traffic accidents appear to be caused by drowsiness while driving. This fact shows that drowsy driving is a big factor in many traffic accidents. Therefore, the development of a drowsiness checking system is necessary to prevent drowsy driving. In this paper, we analyse the changes of the histogram of eye region images which are acquired using a CCD camera. We develop a drowsiness checking system using this histogram change information. The experimental results show that our proposed method enhances the accuracy of checking drowsiness by nearly 98%, and can be used to prevent vehicle accidents due to the drowsiness of a driver.

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.11-15
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• 2018

The purpose of this study is to develop a pulsatile drowsiness detection system that can compensate the limitations of existing camera - based or breathing pressure sensor based Drowsiness driving prevention systems. A heart rate sensor mounted on the driver's finger and an alarm system that sounds when drowsiness is detected. The heart rate sensor was used to measure pulse changes in the wrist, and an alarm system based on the Arduino, which works in conjunction with the laptop, generates an audible alarm in the event of drowsiness. In this paper, we assume that the pulse rate of the drowsy state is 60 ~ 65 times / minute, which is the middle between the awake state and the sleep state. As a result of the experiment, the alarm sounded when the driver's pulse rate was in the drowsy pulse rate range. Based on these experiments, the drowsiness detection system was able to detect the drowsiness of the driver successfully in real time. A more effective drowsiness prevention system can be developed in the future by incorporating the results of the present study on a pulse-based drowsiness prevention system in an existing drowsiness prevention system.

• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.167-176
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• 2009

In this paper, a system that detects driver's drowsiness has been implemented based on the automatic extraction and the tracking of pupils. The research also focuses on the compensation of illumination and reduction of background noises that naturally exist in the driving condition. The system, that is based on the principle of Haar-like feature, automatically collects data from areas of driver's face and eyes among the complex background. Then, it makes decision of driver's drowsiness by using recognition of characteristics of pupils area, detection of pupils, and their movements. The implemented system has been evaluated and verified the practical uses for the prevention of driver's drowsiness.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• Journal of Biomedical Engineering Research
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• v.18 no.3
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• pp.315-323
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• 1997

Driving is a complex vigilance task that includes improper lookout, excessive speed and inattention. The primary objective of this research is to detect driver drowsiness so that the driver can be alerted to an impending traffic accident in performance. We developed the automatic detection and warning system during drowsy driving. A drowsiness detection system must be able to monitor driver status and detect the detrimental changes of a driver performance. Eyeblink has been found to be a reliable factor of drowsiness detection in earlier studies. As an additional parameter, we also considered the yawning which often occurs in a low vigilance state and predicts the drowsy state. We used a computer vision method to extract the eyeblink and yawning in the face image sequences. When the drowsy state was detected, the driver was refreshed by alarming device and menthol scent generator after deciding the warning level by fuzzy logic. For the evaluation of our system, we measured the physiological parameters such as EOG and EEG. The results indicated that it is possible to detect and alert the driver drowsiness temporarily or continuously by using our system.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1024-1033
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• 2016

In this study, a system is proposed for analyzing whether driver's eyes are open or closed on the basis of images to determine driver's drowsiness. The proposed system converts eye areas detected by a camera to a color space area to effectively detect eyes in a dark situation, for example, tunnels, and a bright situation due to a backlight. In addition, the system used a thickness distribution of a detected eye area as a feature value to analyze whether eyes are open or closed through the Support Vector Machine(SVM), representing 90.09% of accuracy. In the experiment for the images of driver wearing glasses, 83.83% of accuracy was obtained. In addition, in a comparative experiment with the existing PCA method by using Eigen-eye and Pupil Measuring System the detection rate is shown improved. After the experiment, driver's drowsiness was identified accurately by using the method of summing up the state of driver's eyes open and closes over time and the method of detecting driver's eyes that continue to be closed to examine drowsy driving.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.223-229
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• 2008

In this paper. we propose the real-time vision system which judges drowsiness driving based on levels of drivers' fatigue. The proposed system is to prevent traffic accidents by warning the drowsiness and carelessness using face-image analysis and fuzzy logic algorithm. We find the face position and eye areas by using fuzzy skin filter and virtual face model in order to develop the real-time face detection algorithm, and we measure the eye blinking frequency and eye closure duration by using their informations. And then we propose the method for estimating the levels of drivel's fatigue based on measured data by using the fuzzy logic and for deciding whether drowsiness driving is or not. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.