• Journal of the Korea Institute of Information and Communication Engineering
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• 제26권6호
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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.

• Journal of Biomedical Engineering Research
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• 제28권2호
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• pp.280-286
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• 2007

The wearable sensor glove was developed using EDA sensors and conducting fabric. EDA(Electro-dermal Activity) signal is an electric response of human skin. There are SIL(Skin Impedance Level) and SIR(Skin Impedance Response) in EDA. SIL consists mostly of a DC component while SIR consists of an AC component. The relationship between drowsiness and the EDA signal is utilized. EDA sensors were made using a conducting fabric instead of AgCl electrodes, for a more suitable, more wearable device. The EDA signal acquisition module was made by connecting the EDA sensor gloves through conductive fabric lines. Also, the EDA signal acquisition module can be connected to a PC that shows the results of the EDA signal processing analysis and gives proper feedback to the user. This system can be used in various applications to detect drowsiness and prevent accidents from drowsiness for automobile drivers.

• Journal of Sensor Science and Technology
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• 제20권6호
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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..

• Journal of Convergence Society for SMB
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• 제3권1호
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• pp.31-41
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• 2013

There have been various recent methods proposed in detecting driver drowsiness (DD) to avert fatal accidents. This work proposes a hardware/software (HW/SW) co-design approach in implementation of a DD detection system adapted from an AdaBoost-based object detection algorithm with Haar-like features [1] to monitor driver's eye closure rate. In this work, critical functions of the DD detection algorithm is accelerated through custom hardware components in order to speed up processing, while the software component implements the overall control and logical operations to achieve the complete functionality required of the DD detection algorithm. The HW/SW architecture was implemented on an Altera DE2 board with a video daughter board. Performance of the proposed implementation was evaluated and benchmarked against some recent works.

• Proceedings of the KOSOMBE Conference
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• 대한의용생체공학회 1996년도 추계학술대회
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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.

• Journal of Korea Multimedia Society
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• 제21권2호
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• pp.166-172
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• 2018

The distraction of the driver's attention causes as much traffic accidents as drowsiness driving. Yet though there have been many studies on drowsiness driving, research on distraction driving is insufficient. In this paper, we divide distraction of attention into visual distraction and cognitive distraction and analyze the EEG of subjects while viewing images of distracting situations. The results show that more information is received and processed when distractions occur. It is confirmed that the probability of accident increases when the driver receives overwhelming amount of information that he or she cannot concentrate on driving.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제67권7호
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• pp.934-939
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• 2018

Recently, there have been a lot of sleepy driving accidents. In this study, we conducted a preliminary study for detecting drowsiness using posture and image processing technology. We used pressure sensors to study posture. We also investigated the possibility of drowsy recognition using histogram. As a result of the experiment, it was possible to distinguish positions through pressure sensors. Also, it was confirmed that the drowsiness phenomenon can be distinguished by using the histogram.

• Proceedings of the KOSOMBE Conference
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.322-325
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• 1997

In this study, we propose a non-invasive method to detect the drowsiness of a driver. The computer vision technology was used to extract an eye, track eyelids and measure the parameters related to the blink. We examined the blink patterns of a driver in drowsy state. For the evaluation of our image processing algorithm, the blink patterns were compared with the measured EOG signals. The result showed that our algorithm might be available in detection of drowsiness.

• Journal of Biomedical Engineering Research
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• 제21권2호
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• pp.175-180
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• 2000

본 논문은 주행 중의 졸음방지를 목적으로 하는 각성시스템의 평가에 관한 연구이다. 졸음운전을 방지하는 데에는 각성도의 저하상태를 높은 정확도로 검출하는 기술과 그것을 해소하는 기술이 필요하다. 본 논문에서는 졸음운저자를 위해서 졸음각성시스템을 향상시켰다. 개발된 각성시스템의 평가를 위해서 졸음을 유도하는 단조로운 행위를 수행하면서 뇌파, 심전도, 안전도와 같은 생체신호를 측정하였다. 피험자가 졸음상태에 있을 때에 산소, 향, 여러 가지 색 자극을 제시함으로써 각성효과를 평가하였다. 졸음의 해소에 효율적인 일정한 양의 산소와 멘톨 성분이 함유된 향을 동시에 각성자극으로 제시하였을 때와, 노란색의 색 자극을 주었을 때 가장 각성에 효과적임을 확인할 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2019년도 제59차 동계학술대회논문집 27권1호
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• pp.449-450
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• 2019

본 논문에서는 차량 내 이산화탄소 농도 측정을 통해 운전자의 졸음운전을 예방하는 모델을 제안한다. 제안된 모델은 이산화탄소 농도 측정 센서를 연결한 아두이노 보드를 차량 내부에 부착하여 측정된 수치를 실시간으로 분석한다. 분석된 수치를 운전자, 탑승자에게 전송하여 자발적으로 졸음 방지를 유도한다. 또한 설정된 수치 이상인 경우 차량 내 사용자와 차량 외 보호자에게도 경고 메시지를 전송하고 차량 내 공기 상태를 알린다. 추후 차량 내 환경과 운전 시간, 탑승자 정보 등을 활용하여 전송된 수치를 분석하면 운전 환경 개선을 위한 방안을 모색할 수 있다.