• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.102-107
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• 2015

In this paper, we propose a technique of drowsiness detection using a histogram for vehicle safety. The drowsiness of vehicle drivers is often 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 analyse the changes of a histogram of eye region images which are acquired using a CCD camera. We develop a drowsiness detection system using this histogram change information. The experimental results show that the proposed method enhances the accuracy of detecting drowsiness to nearly 97%, and can be used to prevent accidents due to driver drowsiness.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.266-270
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• 2016

In this paper, we propose a technique of drowsiness detection using machine vision. The drowsiness of vehicle driver is often the primary cause of motor vehicle accidents. Therefore, the checking of eye images for detecting drowsiness status of driver is critical for preventing these accidents. In our suggested method, we analyze the changes of histogram and edge of eye region images which are acquired using CCD camera. We developed a drowsiness detection system using the histogram and edge change information. The experimental results show that our proposed method enhances the accuracy of detecting drowsiness nearly to 98%, and can be used for preventing vehicle accidents due to the drowsiness of drivers.

• Korean Journal of Clinical Pharmacy
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• v.22 no.1
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• pp.30-40
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• 2012

In Korea, elderly population aged 65 and older are about 5.0% and 10.7% in 1990 and 2009, respectively. Since elderly people may experience physiologic changes with aging and their pharmacodynamic and pharmcokinetic parameters also have been undergone changes, several adverse drug reactions can occur more frequently than young people. Especially, neuropsychiatric adverse drug reactions such as delirium and drowsiness endanger elderly patients more. The purpose of this study is to evaluate the outpatient prescriptions using drug causing delirium and drowsiness in elderly patients aged 65 and older. We retrospectively reviewed prescriptions for elderly patients collected from four community pharmacies from January 2nd to February 1st, 2010. One pharmacy was located closed to a general hospital, and others were located closed to a internal medicine or an ENT clinic. The each number of the collected prescriptions was followings; Group A (n=496) from internal medicine department of a general hospital; Group B (n=44) from ENT department of general hospital; Group C (n=144) from internal medicine clinic; Group D (n=110) from ENT clinic. In result, in Group A, the average number of prescribed drugs causing delirium or drowsiness per Rx was 2.38 In Group B, the average number of prescribed drugs causing delirium or drowsiness per Rx was 2.09 In Group C, the average number of prescribed drugs causing delirium or drowsiness per Rx was 2.51. In Group D, the average number of prescribed drugs causing delirium or drowsiness per Rx was 2.72. Especially, in Group D, the percentage of prescription that drugs causing delirium or drowsiness per Rx prescribed more than 3 is 52.73% In all the 4 groups, over the 60% of drugs causing delirium and/or drowsiness per prescription of elderly patients were prescribed. It means elderly patients take 2 drugs causing delirium and/or drowsiness among 3 drugs, which is very serious. Frequently prescribed drugs causing delirium and/or drowsiness were followings; GI agents, antitussives & expectorants, histamine H1 antagonist, analgesics, antibiotics. Among these drugs, GI agents was high raking in all the 4 groups, and pharmacists should caution elderly patients when counseling. In the internal medicine groups (Group A,C), drugs concerning chronic diseases were prescribed frequently. In conclusion, pharmacist's role is important. Pharmacists are well informed of the drugs causing delirium or drowsiness and it is important to explain about ADRs slowly and easily to the elderly patients that receive drugs causing delirium or drowsiness. And institutional device is needed. For example, when doctors prescribe drugs for the elderly patients, message is needed that supply some informations about drugs causing delirium or drowsiness.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.699-704
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• 2016

In this paper, we propose a development method for a drowsiness detection system using retinex theory and edge information for vehicle safety. Detection of a drowsy state of a driver is very important because the drowsiness of driver is often the main cause of many car accidents. After acquiring an image of the entire face, we executed the pre-process step using the retinex theory. We then applied a technique for the detection of the white pixels using edge information. Experimental results showed that the proposed method improved the accuracy of detecting drowsiness to nearly 98%, and can be used to prevent a car accident caused by the driver's drowsiness.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.189-194
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• 2018

In this paper, we propose the drowsiness detection method for car driver. This paper determines whether or not the driver's eyes are closed using the depth and infrared videos. The proposed method has the advantage to detect drowsiness without being affected by illumination. The proposed method detects a face in the depth picture by using the fact that the nose is closest to the camera. The driver's eyes are detected by using the extraction of harr-like feature within the detected face region. This method considers to be drowsiness if eyes are closed for a certain period of time. Simulation results show the drowsiness detection performance for the proposed method.

• 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 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 Multimedia Society
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• v.19 no.11
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• pp.1852-1861
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• 2016

Drowsy driving is a significant factor in traffic accidents, so driver drowsiness detection system based on computer vision for convenience and safety has been actively studied. However, it is difficult to accurately detect the driver drowsiness in complex background and environmental change. In this paper, it proposed the driver drowsiness detection algorithm to determine whether the driver is drowsy through the measurement standard of a yawn, eyes drowsy status, and nod based on facial features. The proposed algorithm detect the driver drowsiness in the complex background, and it is robust to changes in the environment. The algorithm can be applied in real time because of the processing speed faster. Throughout the experiment, we confirmed that the algorithm reliably detected driver drowsiness. The processing speed of the proposed algorithm is about 0.084ms. Also, the proposed algorithm can achieve an average detection rate of 98.48% and 97.37% for a yawn, drowsy eyes, and nod in the daytime and nighttime.