• 제어로봇시스템학회논문지
• /
• 제22권4호
• /
• pp.266-270
• /
• 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.

• 대한인간공학회지
• /
• 제35권5호
• /
• pp.371-381
• /
• 2016

Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver's drowsiness and waking status in order to develop the classification model for a driver's drowsiness and waking status using those features. Background: Driver's drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver's drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver's drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver's drowsiness prevention systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권9호
• /
• pp.4518-4540
• /
• 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.

• 제어로봇시스템학회논문지
• /
• 제22권5호
• /
• pp.361-366
• /
• 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.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1996년도 추계학술대회
• /
• pp.90-94
• /
• 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.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1997년도 추계학술대회
• /
• pp.48-51
• /
• 1997

The purpose of this paper is to propose a method to protect the drowsiness of a driver. We measured the physiological signals, response time, and ace expression of the subjects in normal and drowsy state. Those data are used to establish the drowsiness index and fuzzy system. We employed the computer vision technology to extract and eye, track eyelids and measure the parameters related to drowsiness. These parameters were ed into the fuzzy system to decide the drowsiness level, When the drowsiness was detected, the fuzzy system generated warning signals which cons ist of sound and fragrance. Our system was available in decision of the drowsiness level and improvement of subjects' state.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1996년도 춘계학술대회
• /
• pp.56-59
• /
• 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.

• 한국정보통신학회논문지
• /
• 제26권6호
• /
• pp.859-864
• /
• 2022

매년 교통사고의 가장 큰 원인으로 손꼽히는 졸음운전은 운전자의 수면 부족, 산소 부족, 긴장감의 저하, 신체의 피로 등과 같은 다양한 요인을 동반한다. 졸음 유무를 확인하는 일반적인 방법으로 운전자의 표정과 주행패턴을 파악하는 방법, 심전도, 산소포화도, 뇌파와 같은 생체신호를 분석하는 방법들이 연구되고 있다. 본 논문은 영상을 검출하는 딥러닝 모델과 생체 신호 측정 기술을 이용한 운전자 피로 감지 시스템을 제안한다. 제안 방법은 일차적으로 딥러닝을 이용하여 운전자의 눈 모양과 하품 유무, 졸음으로 예상되는 신체 동작을 파악하여 졸음 상태를 감지한다. 이차적으로 맥파 신호와 체온을 이용하여 운전자의 피로 상태를 파악하여 시스템의 정확도를 높이도록 설계하였다. 실험 결과, 실시간 영상에서 운전자의 졸음 유무 판별이 안정적으로 가능하였으며 각성상태와 졸음 상태에서의 분당 심박수와 체온을 비교하여 본 연구의 타당성을 확인할 수 있었다.

• 설비공학논문집
• /
• 제29권3호
• /
• pp.134-141
• /
• 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.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2011년도 춘계학술대회
• /
• pp.481-484
• /
• 2011

최근의 자동차 기술이 기계적 장치 위주에서 전장부품 특히, 차량의 안전 및 편의 기술로서 발전되고 있어서, 추후 자동차의 경쟁력은 에너지 효율성문제와 안전편의 기술의 적용에 의해 그 경쟁력이 결정될 것으로 판단된다. 본 연구에서는 자동차 운전자 졸림의 검지하기 위한 각종 기술을 소개하고 상용화된 기술의 장단점을 비교하여서, 이의 문제점을 해결하기 위한 복합 센싱기술을 소개한다. 기존의 카메라에 의한 눈동자인식을 기반으로한 직접적인 졸림검지와 운전자의 생체신호를 검출하여 간접적으로 스트레스, 피로도, 졸림을 검출하는 방법을 결합하여, 보다 정확도가 높은 졸림검지가 가능한 알고리즘을 개발하였다.