• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.574-576
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• 2021

본 논문에서는 딥러닝을 이용한 구글 ML Kit 를 이용하여 직접적이고 효과적인 졸음운전 예방기술을 구현하였다. 본 연구에서는 눈 상태를 인식하여 졸음을 감지하고 경보음을 발생시켜 교통사고 안전성 향상을 위한 방안을 제안하고 구현하였다. 또한, 정부 공공데이터 활용을 통해 성능테스트를 진행하여 시스템의 성능을 검증하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.7-10
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• 2008

자동차의 수가 점점 증가함에 따라 교통사고도 그 만큼 증가하고 있다. 교통사고의 주요 원인 중 하나가 졸음운전이나 부주의한 운전에 의한 것이다. 따라서 Real-Time으로 운전자의 제스처를 인식하여 졸음운전이나 부주의에 의한 사고를 사전에 예방하여 보다 안전한 운전을 돕는 서비스가 필요시 되고 있다. 본 논문에서는 운전자의 제스처 인식에 전처리 과정으로 운전자의 상반신에 대한 영상데이터에서 Adaboost를 이용하여 복잡한 배경과 다양한 환경에서 강인하게 얼굴 영역을 찾는 알고리즘을 소개한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.860-861
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• 2023

라즈베리파이와 아두이노의 성공적인 실시간 데이터 송수신, 운전자에게 시각적 효과를 줄 수 있는 맞춤형 이산화탄소 농도 데이터 LCD, 자동 환기 시스템 및 쿨링 시트 가동 기능을 통합하여 졸음운전을 방지할 수 있는 시스템을 제안하고자 한다.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.2
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• pp.77-82
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• 2016

The purpose of this paper is to develop the algorithm of human arousal inducing interface using steady-state visual evoked potential(SSVEP) and its verification through experiments. In order to develop the model, computer-based SSVEP program simulation is preliminary performed. From the results of the simulation, stimulus pattern is decided to checkerboard and SSVEP frequency range is set into beta wave (13~30Hz). After the experiment on proving the effect of SSVEP flashing stimulation while driving by installing it at the location of people mostly falling asleep in the highway, the result confirms that both during the night and the day, after SSVEP flashing stimulation, a wave Beta immediately increases and the subjects keep high stimulation for the 5 minute maintaining stage.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.3
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• pp.255-261
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• 2016

The purpose of this study was to investigate the relationship between excessive daytime sleepiness (EDS) and blood pressure (BP) in patients with obstructive sleep apnea-hypopnea (OSAH). Patients were classified into four groups based on their severity of polysomnographic data: the snoring group (n=108)-characterized by Apnea-Hypopnea Index (AHI<5); the mild OSA group (n=186)-AHI $5{\leq}AHI$<15; the moderate OSA group (n=179)- AHI $15{\leq}AHI$<30; and the severe OSA group (n=233)-$AHI{\geq}30$. On the same night of polysomnography (PSG), BP levels were measured before sleeping (bedtime BP) and immediately after waking up on the following morning (morning BP). EDS was recognized as ESS (epworth sleepiness scale)${\geq}9$. The differences and correlations between BP and PSG parameters in the EDS and non-EDS groups of OSAH patients were analyzed. MAP was positively correlated with BMI, AHI, and total arousal (r=0.099, r=0.142, r=0.135, p<0.01, p<0.01, p<0.01), while negatively correlated with mean $SaO_2$ (r=-0.258, p<0.01). The EDS group had overall younger population ($47.2{\pm}11.3$ vs $50.3{\pm}11.4$, p=0.023), higher DBP (both bedtime and morning, $83.1{\pm}9.7$ vs $81.4{\pm}8.8$ and $86.4{\pm}9.2$ vs $83.6{\pm}9.7$)(p=0.031, p=0.047), and higher SBP (both bedtime and morning, $126.7{\pm}11.2$ vs $123.4{\pm}12.4$, $128.9{\pm}12.4$ vs $125.3{\pm}12.9$)(p=0.021, p=0.021) than compared with the non-EDS group. In hypertensive OSAH patients, patients with EDS were also younger and had higher total arousal number, as well as higher morning and bedtime DBP and SBP than compared with the non-EDS group (p<0.005, p=0.008, p<0.001 and p<0.001). EDS in OSAHS patients is a special phenotype characterized by younger age, higher DBP, more severe desaturation, and hypertension.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.89-97
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• 2014

In this paper, we propose real-time program that measure open-close frequency of mouth and eyes to detect drowsiness of a driver. This program detects a face to the CCD camera image using OpenCV library. Then that extracts each area using CDF for eye detection and Active Contour for mouth detection based on detected face. This system measures each frequency of Open-Close using extracted area data of eyes and mouth. We propose foundation technique how to perform sleepiness decision of users based on measurement data.

• 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.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.255-265
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• 2007

In this paper, we propose a new illumination-robust drowsy driver monitoring system with single CCD(Charge Coupled Device) camera for intelligent vehicle in the day and night. For this system that is monitoring driver's eyes during a driving, the eye detection and the measure of eyelid movement are the important preprocesses. Therefore, we propose efficient illumination compensation algorithm to improve the performance of eye detection and also eyelid movement measuring method for efficient drowsy detection in various illumination. For real-time application, Cascaded SVM (Cascaded Support Vector Machine) is applied as an efficient eye verification method in this system. Furthermore, in order to estimate the performance of the proposed algorithm, we collect video data about drivers under various illuminations in the day and night. Finally, we acquired average eye detection rate of over 98% about these own data, and PERCLOS(The percentage of eye-closed time during a period) are represented as drowsy detection results of the proposed system for the collected video data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.729-740
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• 2022

This study analyzed the effects of job stress and sleep disorders on sleepiness during the daytime for a night-shift urban railway crew. As a result of the analysis, measurements on a daytime sleepiness index were higher for the crew than those during other working hours, indicating that the degrees of sleepiness and drowsiness were severe. Afterwards, aromatic essential oils, which are generally known to help relieve sleep disorders and stress, were utilized to verify the effect on stress on night-shift workers. The experimental group treated with aromatherapy was found to show improved stress reduction and overall activity levels in the autonomic nervous system compared to an untreated control group. This study proves that night work by an urban railway crew negatively affects stress levels by increasing stress and negatively affects the autonomous nervous system, also showing that an aromatherapy treatment can be a good alternative to alleviate these conditions. The results of this study are expected to help prevent safety accidents by relieving the stress of nighttime workers.

• Korean Journal of Clinical Laboratory Science
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• v.47 no.4
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• pp.216-224
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• 2015

This study aims to identify the prevalence of sleep related disease in those who experienced car accidents caused by drowsy driving. To this end, a survey of usual sleep habits, polysomnography, and multiple sleep latency tests were conducted in 34 persons who experienced an accident after normal sleep (Group 1), 22 persons who experienced an accident after abnormal sleep (Group 2), and 17 persons who was proven to be normal as a result of polysomnography and had no accident (Group 3). In all, 192 persons responded to the preliminary survey and the results were compared and analyzed. Crossover analysis was conducted to test the homogeneity of statistical characteristics, and the physical characteristics by age were analyzed. In the survey of sleeping habits, there was a significance between groups in how often they woke up while asleep (p<0.01), how difficult it was to go back to sleep again after waking up from sleep (p<0.05), how early they woke up in the morning (p<0.05), how difficult it was to get up in the morning (p<0.05), how sleepy they felt in the daytime (p<0.01), and how tired they felt in the daytime (p<0.01). Furthermore, among 56 subjects who had an accident during drowsy driving, 94.6% (53 persons) were found to have sleep related diseases. This suggests that car accidents during drowsy driving is not simply caused by temporary lack of sleep but by sleep related diseases even when sleep is adequate, leading to car accidents. Therefore, this study is significant identifying the association between car accidents during drowsy driving and sleep related disorders. Furthermore, the data would be considered basic to prepare social measures against drowsy driving related to such sleep related disorders.