• Sim-seong Yeon-gu
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• v.25 no.2
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• pp.123-162
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• 2010

The author tried to analyse a Korean fairy tale "the traveller and the fox". The essence of the story is as follows; A traveller who was wandering in mountains found a house with a light. There was a beautiful woman who was very kind to give food and shelter. But she was a fox that tried to kill him with a knife. He asked her to bring a basket of water and he broke the wall with it to run away. The fox chased and he fell down a cliff to ride on the back of a tiger. The tiger ran into a cave and give him to her babies as a prey. He killed them by throwing stones and climbed a tree out of the cave. There came foxes and the tiger and they killed each other. He came back to the village with the fur of the foxes and the tiger. The author tried to understand the contents of the story symbolically and interpret them from the perspective of analytical psychology. On conclusion, the traveller was on the individuation process and experienced the negative anima (the fox) and the negative mother archetype (the tiger) and its negative subsidiaries (the tiger's babies). He tried to be consciously alert and paid continuous attention so that he could get out of the status and get new insight. During this process, it was meaningful that he could actively get an appropriate aid of positive mother archetype and Self symbolized by the water and the tree respectively.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.1-14
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• 2014

Both researchers and practitioners are showing an increased interested in interactive exhibition services. Interactive exhibition services are designed to directly respond to visitor responses in real time, so as to fully engage visitors' interest and enhance their satisfaction. In order to install an effective interactive exhibition service, it is essential to adopt intelligent technologies that enable accurate estimation of a visitor's emotional state from responses to exhibited stimulus. Studies undertaken so far have attempted to estimate the human emotional state, most of them doing so by gauging either facial expressions or audio responses. However, the most recent research suggests that, a multimodal approach that uses people's multiple responses simultaneously may lead to better estimation. Given this context, we propose a new multimodal emotional state estimation model that uses various responses including facial expressions, gestures, and movements measured by the Microsoft Kinect Sensor. In order to effectively handle a large amount of sensory data, we propose to use stratified sampling-based MRA (multiple regression analysis) as our estimation method. To validate the usefulness of the proposed model, we collected 602,599 responses and emotional state data with 274 variables from 15 people. When we applied our model to the data set, we found that our model estimated the levels of valence and arousal in the 10~15% error range. Since our proposed model is simple and stable, we expect that it will be applied not only in intelligent exhibition services, but also in other areas such as e-learning and personalized advertising.

• Tuberculosis and Respiratory Diseases
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• v.60 no.1
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• pp.76-82
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• 2006

Background : The objective of this study was to understand sleep-related problems, and to determine whether the sleep questionnaires is a clinically useful method in patients who need polysomnography. Methods : Subjects were patients who performed polysomnography and who asked to answer a sleep questionnaires at the Sleep Disorders Clinic of St. Paul's Hospital, Catholic University of Korea. Baseline characteristics, past medical illness, behaviors during sleep-wake cycle, snoring, sleep-disordered breathing and symptoms of daytime sleepiness were analyzed to compare with data of polysomnography. Results : The study population included 1081 patients(849 men, 232 female), and their mean age was $44.2{\pm}12.8years$. Among these patients, 38.9% had an apnea-hypopnea index(AHI)<5, 27.9% had $5{\leq}AHI<20$, 13.2% had $20{\leq}AHI<40$, and 20.0% had $40{\leq}AHI$. The main problems for visiting our clinic were snoring(91.7%), sleep apnea(74.5%), excessive daytime sleepiness(8.0%), insomnia(4.3%), bruxism(1.1%) and attention deficit(0.5%). The mean value of frequency of interruptions of sleep was 1.6 and the most common reason was urination(46.3%). Epworth Sleepiness Scale(ESS) had a weak correlation with AHI(r=0.209, p<0.01). When we performed analysis of sleep questionnaires, there were significant differences in the mean values of AHI according to the severity of symptoms including snoring, daytime sleepiness, taking a nap and arousal state after wake(p<0.05). Conclusion : On the basis of statistical analysis of sleep questionnaires, the severity of subjective symptoms such as ESS, snoring, daytime sleepiness and arousal state after wake correlated with the AHI significantly. Therefore the sleep questionnaires can be useful instruments for prediction of the severity of sleep disorder, especially sleep-disordered breathing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.721-729
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• 2012

In this paper, human sensibility is measured on multi-modal environment and a sensitivity lighting system is implemented according to driven emotional indexes. We use LED lighting because it supports ecological circumstance, high efficiency, and long lifetime. In particular, the LED lighting provides various color schemes even in single lighting bulb. To cognize the human sensibility, we use the image information and the arousal state information, which are composed of multi-modal basis and calculates emotional indexes. In experiments, as the LED lighting color vision varies according to users' emotional index, we show that it provides human friendly lighting system compared to the existing systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1572-1580
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• 2010

This thesis presents an emotion-based contents player platform that can change its visual and aural components as user's emotions. It analyzes the emotion as pleasant, unpleasant, aroused, and relaxed based on the physiological signals and the user's active response. Accordingly. the system reorganizes graphical and aural stimuli, such as, light, color, sound, in real-time. It can be used to develop and show the emotional contents and also be applied for the systematic analysis to find out how the components would affect the emotion. This paper describes overall the system architecture and the implementations of the sub-systems, as well as the actual contents built on top of the platform.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1471-1476
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• 2015

Due to the anesthesia process is inappropriate on the operation, awakening state was appeared. To prevent the state, it is necessary to monitor the patients by measuring the depth of anesthesia. In this study, we investigate the possibility of the development of actual surgery available quantitative indicators. The DFA which is included the correlation property of the EEG is used to analysis the depth of anesthesia and bispctrum index. In the results, at the pre-operation, the peak of bispectrum was widely distributed, DFA value was decreased. At the during operation, bispectrum was concentrically appeared in the low frequency area. At the post operation, bispectrum and DFA was both returned to the pre-operation state. We confirmed to be close correlation between the peaks of the bispectrum and DFA value.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.397-400
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• 2015

Due to the anesthesia process is inappropriate on the operation, awakening state was appeared. Because of that patients suffered from severe mental and physical pain. To prevent the state, it is necessary to monitor the patients by measuring the depth of anesthesia. In this study, we investigate the possibility of the development of actual surgery available quantitative indicators. The DFA(detrended fluctuation analysis) which is included the correlation property of the EEG is used to analysis the depth of anesthesia and bispctrum index. In the results, at the pre-operation, the peak of bispectrum was widely distributed, DFA value was decreased. At the during operation, bispectrum was concentrically appeared in the low frequency area. At the post operation, bispectrum and DFA was both returned to the pre-operation state. As a result, we confirmed to be close correlation between the peaks of the bispectrum and DFA value.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.2
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• pp.45-51
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• 2013

In this paper, a driver's drowsy detection sensor system based on the respiration is investigated. The sensor system consists of a piezoelectric pressure sensor attached at the abdominal region of the seat belt and a personal computer. The piezoelectric pressure sensor was utilized for the measurement of pressure variations induced by the movement of the driver abdomen during breathing. The signal processing software for detecting driver's drowsiness was produced using the Labview. The experiments were performed with 30 years male driver. The amplitude of the respiration at awake state was larger than one at the drowsy state. On the contrary, the respiration rate at awake state was lower than one at the drowsy state. The drowsy detection sensor system developed based on the experimental could successfully detect the driver's drowsy on real-time.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.