• 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 Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.167-176
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• 2009

In this paper, a system that detects driver's drowsiness has been implemented based on the automatic extraction and the tracking of pupils. The research also focuses on the compensation of illumination and reduction of background noises that naturally exist in the driving condition. The system, that is based on the principle of Haar-like feature, automatically collects data from areas of driver's face and eyes among the complex background. Then, it makes decision of driver's drowsiness by using recognition of characteristics of pupils area, detection of pupils, and their movements. The implemented system has been evaluated and verified the practical uses for the prevention of driver's drowsiness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4518-4540
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• 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.

• Journal of the Ergonomics Society of Korea
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• v.35 no.5
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• pp.371-381
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• 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.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• 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 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.