• Korean Journal of Biological Psychiatry
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• v.6 no.2
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• pp.246-252
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• 1999

Objectives : Daytime drowsiness or sedation and changes in night sleep are commonly seen in patients treated with clozapine. There is, however, very limited information on their degree and nature during the course of treatment. The purpose of this study was to understand the sleep patterns in chronic schizophrenic patients with clozapine treatment over a period of 24 weeks. Method : The sleep pattern was evaluated using a set of 5-point scale questionnaire, to record subjective impressions of the night sleep induction, maintenance and quality, and daytime drowsiness and fatigue. In addition, unusual experiences associated with night sleep were recorded. The sleep questionnaire was repeatedly administered at baseline and at 1, 2, 4, 8, 12 and 24 weeks of drug treatment. At present, data on 12 patients has been collected. Results : All the components of night sleep were significantly improved in the 1st through the 12th week after treatment with clozapine. Daytime drowsiness was significantly higher in the 1st to the 2nd week after the treatment and fatigue was also significantly higher in the 1st to the 4th week after the treatment. Eight patients experienced noticeable increases in salivation during night sleep, and of these, one also reported frequent nocturnal urination and even enuresis. However, all these adverse factors did not affect the major sleep patterns. Conclusions : These findings suggest that the beneficial effects of clozapine on night sleep might last much longer than the undesirable effect of daytime drowsiness and fatigue. In other words, tolerance of the hypnotic action of clozapine might develop late and tolerance of the daytime drowsiness and fatigue might be evident earlier.

• 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%.

• 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 Environmental Health Sciences
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• v.32 no.5 s.92
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• pp.412-417
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• 2006

A self-administrated non-malignant respiratory symptoms questionnaire was sent to 1,099 citizens who take subway running in Seoul city. Symptom prevalence rate was high: 70.6% of subjects reported 'chest tightness', 43.4%, 'dysphnea'; 76.2%, 'dry cough'; 49.5%, 'runny nose'; 94.4%, 'drowsiness' when they take subway. The groups responding significant higher respiratory and drowsiness symptoms were 'young passengers' (vs elderly passengers), 'the female' (vs male), 'using subway everyday' (vs often), 'using subway for rush-hour time' (vs other than rush-hour), 'using transfer subway' (no transfer), 'using underground track' (vs ground track). Logistic. regression model was employed to find personal and subway characteristics affecting non-malignant respiratory symptoms. This study concluded that respiratory diseases history such as asthma, rhinitis, sinusitis, hypersensitivity pneumonitis significantly affect 'dry cough' and 'runny nose'. Thus, passengers with respiratory diseases history shows 2.8 times greater 'dry cough' than and 3.4 times greater 'runny nose' than those passengers without respiratory diseases history felt. This results indicated that several measures have to take to protect sensitive groups such as passengers with respiratory diseases, children and elderly people. Also passenger who use to transfer shows 1.7 times higher runny nose symptoms than that passenger who do not transfer felt.

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.142-147
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• 2014

Real time driver's respiration monitoring method for detecting driver's drowsiness is investigated. The sensor to obtain driver's respiration signal was a piezoelectric pressure sensor attached at the abdominal region of the seat belt. The resistance of the pressure sensor was changed according to the pressure applied to the seat belt due to the driver's respiration. Monitoring driver's respiration was carried out by driving on the virtual road in a driving simulator from Cheonan to Seoul and monitoring results were compared to the PELCLOS. Experiment results show that the driver's respiration signal can be used for detecting driver's drowsiness.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.153-154
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• 2019

There are many jobs, not actually sleeping. Sleepy driving is one of the biggest problems in modern society. In this paper, we propose a system to control underwater guns by using deep learning (YOLO) to check eyes and to check drowsiness. So let your mind be clear.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2265-2275
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• 2014

The purpose of this study was to investigate the factors related to the stress of orthodontic patients and their quality of life. The subjects in this study were 181 patients who respectively received orthodontic treatment at three different dental clinics located in the metropolitan city of S and K province. The collected data were analyzed by using a structural inventory designed to investigate the general characteristics, stress, the weekly amount of drowsiness and the quality of sleep, and the statistical package SPSS WIN 18.0 was employed. As a result, they got a mean of $40.51{\pm}20.43$ in the level of self-perceived stress, $11.06{\pm}5.42$ in the weekly amount of drowsiness and $13.24{\pm}9.72$ in the quality of life. And it's ascertained that there was a statistically significant correlation among all the stress of the orthodontic patients, their weekly amount of drowsiness and the quality of life.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.382-387
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• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.90-94
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• 1996

We studied the problem of driver's low vigilance state which is related to the one reason of traffic accidents. In this paper, we developed the drowsiness warning system for a drowsy driver. To extract the eyes and mouth from the driver's facial image in real time, a computer vision method was used. The eye blink duration and yawning were used as measurement parameters of drowsiness detection. When the drowsy state of a driver was detected, the driver was refreshed by the scent generator and the alarm. Also, the driver's bio-signal was acquired and analyzed to measure the vigilance state.