• Science of Emotion and Sensibility
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• v.10 no.2
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• pp.147-154
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• 2007

Electrocardiogram(ECG) being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many researches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm in the con due to inaccuracy of domestic diagnosis results for a heart disease. This paper proposes ST-segment extraction technique diagnosing heart disease parameter from raw ECG data. As the ST-segment is used for prediction of Coronary Artery Disease, we can predict heart disease using classification approach in data mining technique. We can also predict patient's clinical characterization from patient clinical data.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.55-64
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• 2019

Stroke is a health problem experienced by many elderly people around the world. Stroke has a devastating effect on quality of life, causing death or disability. Hemiplegia is clearly an early sign of a stroke and can be detected through patterns of body balance and gait. The goal of this study was to determine various feature vectors of foot pressure and gait parameters of patients with stroke through the use of a wearable sensor and to compare the gait parameters with those of healthy elderly people. To monitor the participants at all times, we used a simple measuring device rather than a medical device. We measured gait data of 220 healthy people older than 65 years of age and of 63 elderly patients who had experienced stroke less than 6 months earlier. The center of pressure and the acceleration during standing and gait-related tasks were recorded by a wearable insole sensor worn by the participants. Both the average acceleration and the maximum acceleration were significantly higher in the healthy participants (p < .01) than in the patients with stroke. Thus gait parameters are helpful for determining whether they are patients with stroke or normal elderly people.

• Science of Emotion and Sensibility
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• v.18 no.2
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• pp.31-36
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• 2015

This study employed the first-person shooter task(FPST: Correll, Park, Judd, & Wittenbrink, 2002) paradigm to examine racial bias toward Blacks in a population unrelated to the Black-White racial context. We tested whether having Korean participants play the role of a White police officer portrayed as nonracist (vs. racist) would attenuate the bias to shoot Black suspects. Participants were told that they would perform a police simulation task as a White police officer, who was described as racist or nonracist, or was presented without a description. They then performed the FPST. Although nonracist description lowered shooter bias, racist description weakened it even more, contrary to our prediction. The latter result is interpreted as due to activation of an egalitarian goal after reading about racism-related description, especially as the description was about someone who was to be incorporated to the self. Supporting this interpretation, a mediation analysis involving Racist and Control conditions revealed that the racist description was associated with stronger perception of the officer's racial bias, which in turn was correlated with weaker shooter bias.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.224-228
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• 2002

제품의 품질 및 가격 뿐만 아니라 물질적 풍요로움과 더불어 다변화 되어가는 생활 환경 속에서 소비자의 감성과 선호도를 파악하는 것은 제품 판매 전략의 중요한 성공요소가 되고 있다. 이를 위하여 제품의 기능적 측면 뿐만 아니라 개개인의 정서적 감정과 선호도가 반영된 제품의 설계나 디자인 또한 요구되고 있다. 본 연구에서는 소재 개발의 프로세스가 고객 중심으로 변화하는 것에 대응하여 사용자의 감성과 선호도를 중심으로 소재를 개발하는 방법의 하나로 협력적 필터링 개인화 기법을 응용하여 섬유 패션 디자인 추천 시스템을 제안한다. Textile 기반의 협력적 필터링 시스템에서 예측에 사용될 이웃의 수를 결정하기 위해서 Representative Attribute-Neighborhood를 사용한다. 이웃들간의 사용자 유사도 가중치는 피어슨 상관 계수(Pearson Correlation Coefficient)를 사용한다. 소재에 대한 사용자의 감성이나 선호도에 대한 Textile의 대표 감성 형용사를 추출함으로써 소재 개발을 위한 감성 형용사 데이터 베이스를 구축한다. 구축된 감성 형용사 데이터 베이스를 기반으로 성향이 비슷한 사용자에게 Textile을 추천한다. 사용자 선호도 예측과 Textile 기반의 협력적 필터링 기술을 이용한 섬유 패션 디자인 추천 에이전트를 구축하여 시스템의 논리적 타당성과 유효성을 검증하기 위해 실험적인 적용을 시도하고자 한다.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.103-112
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• 2018

In this paper, we propose a method of recognizing emotions using PPG sensor which measures blood flow according to emotion. From the existing PPG signal, we use a method of determining positive emotions and negative emotions in the frequency domain through PSD (Power Spectrum Density). Based on James R. Russell's two-dimensional prototype model, we classify emotions as joy, sadness, irritability, and calmness and examine their association with the magnitude of energy in the frequency domain. It is significant that this study used the same PPG sensor used in wearable devices to measure the top four kinds of emotions in the frequency domain through image experiments. Through the questionnaire, the accuracy, the immersion level according to the individual, the emotional change, and the biofeedback for the image were collected. The proposed method is expected to be various development such as commercial application service using PPG and mobile application prediction service by merging with context information of existing smart phone.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.1-6
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• 2011

A perceptual reaction to human being's gaits has "regularity" that possibly obtains sympathy among people. This thesis is in the vein of the study that performs the research on the quantificational extraction of the regularity, reconstitute the result, and apply it to controlling behavior. The purpose of this thesis lies in assuring the validity of the future research by demonstrating the following hypothesis: when the physical numerical values of the gait "A" whose perceptual reaction is "a" and those of the gait "B" whose perceptual reaction is "b" are arbitrarily blended, the perceptual reaction to this blended gait also corresponds to the blend of "a" and "b", "a/b". I blended the samples of two types of gaits in the form of Bipeds using the EAM made by 3D Studio Max Script. Blending outcomes were obtained successfully for four times out of the six tries in total. It implies that without utilizing other methods such as Motion Capturing, the basic Bipeds data itself has an enough capability to generate various gaits of Bipeds. Although the present research targets only the Bipeds samples equipped with the 1Cycle moving condition of arms and legs, I acknowledge that a tool that makes blending possible under various moving conditions is necessary for a completed system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.9-14
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• 2004

Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.

• Science of Emotion and Sensibility
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• v.10 no.1
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• pp.99-111
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• 2007

In order to identify tactile sensibility factors of traditional silk fabrics and to provide prediction models for the sensibility factors by mechanical properties, seventeen different traditional silk fabrics were evaluated in terms of both tactile sensation and sensibility by using a modified magnitude estimation line scale Gongdan and Newttong with lower values for surface roughness(SMD), bending rigidity(B), and compression resilience(RC) were rated as softer, smoother, fluffier, and more pliable in tactile sensation than any other traditional silk fabrics whereas Nobangju haying higher B, SMD, and tensile resilience(RT) was touched as crisper, more rustling, and springier. Three different tactile sensibility factors including 'Feminine', 'Natural', and 'Casual' were obtained significantly by grouping fifteen different tactile sensibility descriptors. In the prediction models sensibility 'Feminine' was explained positively by SMD, which was supported by the fact that both Gongdan and Newtton were perceived as more feminine. Sensibility 'Natural' that was felt stronger as for Myoungju and Sa was predicted negatively by both fabric thickness(T) and RT. Finally, RC, elongation at maximum load (EM), and T predicted sensibility 'Casual' negatively, which results in its higher factor scores for Myoungju and Shantung, respectively.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.1033-1042
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• 2003

In this study, the economic population prediction by computer simulation has been studied by using statistical model. The forecast of future population based on that of the past is a very difficult problem as uncertain conditions are modeled in it. Even if a thought forecast is possible, world-wide cultures and the local culture emotion the cultures of the world and out country can not be predicted due to rapid change and the estimation of population is ‘nowadays more and more’ difficult to be made good guess. In the estimation of economic population, by using the census population from 1960 to 1990, and using ARIMA model developed by Box and Jenkins, the estimation has been done on the economic population until 2021 according to age as appeared table and appendix. This kind of forecast would have both good point and weak point of ARIMA model theory saying that prediction can be done only by the economic population.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.