• Science of Emotion and Sensibility
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• v.1 no.1
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• pp.41-58
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• 1998

얼굴표정과 내적상태의 관계 모형을 수립하기 위한 기초 자료로서 얼굴표정과 내적상태의 대응 관계를 조사하였다. 심리적으로 최소유의미거리에 있는 두 내적상태는 서로 구별되는 얼굴표정으로 대응된다는 것을 확인함으로써 얼굴표정과 내적상태의 일대일 대응 관계가 성립한다는 것을 발견하였다. 얼굴표정 차원값과 내적상태 차원값의 관계 구조를 파악하기 위하여 중다희귀분석 및 정준상관분석을 실시한 결과, 쾌-불쾌는 입의 너비에 의해서 각성-수면은 눈과 입이 열린 정도에 의해서 얼굴표정에 민감하게 반영되는 것으로 나타났다. 얼굴표정 차원 열 두 개가 내적상태 차원상의 변화를 설명하는 정도는 50%내외였다. 선형모형이 이처럼 높은 예측력을 갖는다는 것은 이 두 변수 사이에 비교적 단순한 수리적 대응 구조가 존재한다는 것을 암시한다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.118-123
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• 1998

얼굴표정과 내적상태의 관계 모형을 수립하기 위한 기초 자료로서 얼굴표정과 내적상태의 대응관계를 조사하였다. 심리적으로 최소유의미거리에 있는 두 내적상태는 서로 구별되는 얼굴표정과 내적상태의 일대일 대응 관계가 성립한다는 것을 발결하였다. 얼굴표정 차원값과 내적상태 차원값의 관계 구조를 파악하기 위하여 중다회귀분석을 실시한 결과, 쾌-불쾌상태는 입의 너비에 의해서, 각성-수면상태는 눈과 입이 열린 정도에 의해서 얼굴표정에 민감하게 반영되는 것으로 나타났다. 얼굴표정 차원 열 두개가 내적상태 차원 상의 변화를 설명하는 정도는 40%내외였다. 선형모형이 이처럼 높은 예측력을 갖는다는 것은 이 두 변수 사이에 비교적 단순한 수리적 대응 구조가 존재한다는 것을 암시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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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 drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM 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.

• Asia Marketing Journal
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• v.9 no.1
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• pp.23-41
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• 2007

The current study analyzed the relationship between expression type of humor ads and their advertising effects and the differences in advertising effects by expression type according to temperament as categorized by the Myers-Briggs Type Indicator (MBTI). Expression type of humor was classified into arousal-, incongruity-, and superiority-type humor ads. Advertising effects were measured by consumers' cognitive, affective, and conative responses. Three ads were created based on expression type of humor. A personality type, as measured by the MBTI, was categorized into four types of temperament, namely SP, SJ, NF, NT and used as moderating variables. As a result, the advertising effects varied according to the expression type of humor advertising. Interaction effects between ad expression type and temperament on ad feeling and ad preference were also found.

• 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.

• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.461-480
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• 2008

There are innumerable studies on technology adoption and usage continuance; most examine cognitive factors while affective factors or the feelings of users are left relatively unexplored. Although attitude and user satisfaction are factors commonly considered in Information Systems(IS) research, they represent only some aspects of feelings. In contrast, researchers in diverse fields have begun to note the importance of feelings in understanding and predicting human behavior. Feelings are anticipated to be essential particularly in the context of modern applications, such as mobile internet(M-internet) services, where users are not simply technology users but also service consumers. Drawing on the support of consumer research, social psychology and computer science, this study proposes a balanced cognition-affect model of IS continuance. Prior works in relation to IS research have already considered the emotional factors. The common factors are enjoyment, anxiety, affect and satisfaction. The main difference in our study is that the factors that we used are the primary dimensions of affect according to Circumplex Model of Affect. The horizontal axis of the model represents the pleasure dimension and the vertical represents the arousal dimension. Other emotional factors such as enjoyment and anxiety can be viewed as a combination of these two dimensions, and they can be placed in the vector space formed by these two primary dimensions. Affect has been defined as the enjoyment a person derives from using computers. Satisfaction has different conceptualizations. It has been conceptualized as judgment based on the expectation disconfirmation theory. Thus, while prior works considered the direct and indirect effects of "feeling-related constructs"(enjoyment and anxiety) on usage behavior, our study proposes effects of "feeling-based constructs"(pleasure and arousal). The balanced cognition-affect model is tested in a survey of, M-internet service users. The results establish the validity of the model.

• 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.

• 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 Convergence Society
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• v.11 no.10
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• pp.349-353
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• 2020

Background: The dosage of the anesthetic drugs is generally determined by the total body weight of the patients. However, the drugs can be overdosed when the patient is morbidly obese. We have determined anesthetic induction dose based on lean body mass estimated from bioelectrical impedance analysis (BIA). Case: We report a case of morbidly obese patient (161 cm, 138 kg and body mass index 53.1) who had an elective laparoscopic cholecystectomy. The dose of induction agent was determined by lean body mass estimated by BIA, and the sedation was assessed by the observer's assessment alertness/sedation scale. Conclusions: Dose determination through lean body mass measured by BIA is useful in highly obese patients.