• Korean Institute of Interior Design Journal
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• v.22 no.4
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• pp.41-51
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• 2013

Lately in many cases, Spaces are designed by stimulating user's sensibility, emotion, psychological software over based on the user behavior and purpose of space. Because space is not only practical using and use purpose but also the place for expressing ourselves by experiences. But space is a physical thing. so we need to arrange a structure of abstract and formless source of design by sophisticated grammaticalization. Especially storytelling of that abstract thing have characteristics of relation, process among users and space. because storytelling is focused on the discourse of stories and relationship record of that discourses. So this is used by expression device for user's emotion things and subjects in numerous cases. And storytelling is the basic, important element for user's experience in a space. By the way, Overlapping is also basic element for being possible to make experiences by concrete thing. that is because, Overlapping is layering phenomenon of some objects. Overlapping is made distance, distance make depth, and this relations take us an experience. Finally storytelling, Overlapping and space of experience are relation of cause and effect. So in this thesis, researcher looks for the relation characteristics between storytelling and overlapping by experience. and makes the abstract source into concrete and sophisticated formular.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.39-43
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• 2002

Various electronic bulletin board systems (BBS) exist on the Internet. A user has to use two or more BBS. Because his/her interests may not be restricted to only one subject and BBS satisfying his/her interests may not be restricted to only one also. Furthermore, in each BBS, two or more subjects are parallel discussed while the newest contribution is always carried out. Therefore, when a user tries to keep on perusing about a specific subject, it is necessary for the user to remember in which BBS the subject exists and in the past what article of the subject was read. In this paper, we are aiming to construct an electronic bulletin board integrated management system. In this system, a user is able to use two or more BBS just like one BBS. In this paper, we present a framework of the electronic bulletin board integrated management system and implementation of a prototype system.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.235-241
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• 2006

This paper predict user acceptance of smart clothing. The present research develops and validates new products for smart clothing. Studies suggest that further analysis of the process be undertaken to better establish properties for smart clothing, underlying structures and stability over innovative technologies. The findings reported in this paper should be useful methods which identify user needs. such findings in now provide a way to explain technology acceptance. Both of qualitative and quantitative methods, were applied to this study in order to find out user needs for smart clothing. We are writing scenarios and conducting both focused group interviews and a survey to assess the user's interest. The purpose of the survey is to evaluate the importance of the functions and to evaluate the degree of the participant's feeling and attitude. Furthermore, we explore the nature and specific influences of factors that may affect the user perception and usage.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.132-141
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• 2015

Smart television(TV) is replacing the traditional television model and the importance of user experience(UX) is rising. User experience evaluates the emotion state of users such as immersion, pleasure, and interest. User experience together with usability is a principle to be considered as for designing a smart television. It contributes to improve user satisfaction and lead to the long-term purchase. User experience is more difficult to measure than usability, because UX evaluation requires to biological and psychological techniques. However, the disadvantages of these physiological and psychological techniques require high experimental costs and the restriction of experimental environment. The objective of this paper is first to review conventional methods regarding UX evaluation and suggests a new method for measuring the UX of smart TV which detects keywords related emotional representation. The text is acquired from purchase postscripts of smart TV in the Internet shopping malls. This method costs less than the questionnaire survey to detect emotion.

• Journal of Fashion Business
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• v.26 no.1
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• pp.1-15
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• 2022

Digital transformation has induced changes in human life patterns; consumption patterns are also changing to digitalization. Entering the era of industry 4.0 with the 4th industrial revolution, it is important to pay attention to a new paradigm in the fashion industry, the shift from developer-centered to user-centered in the era of the 3rd industrial revolution. The meaning of storing users' changing life and consumption patterns and analyzing stored big data are linked to consumer sentiment. It is more valuable to read emotions, then develop and distribute products based on them, rather than developer-centered processes that previously started in the fashion market. An AI(Artificial Intelligence) deep learning algorithm that analyzes user emotion big data from user experience(UX) to emotion and uses the analyzed data as a source has become possible. By combining AI technology, the fashion industry can develop various new products and technologies that meet the functional and emotional aspects required by consumers and expect a sustainable user experience structure. This study analyzes clear and useful user experience in the fashion industry to derive the characteristics of AI algorithms that combine emotions and technologies reflecting users' needs and proposes methods that can be used in the fashion industry. The purpose of the study is to utilize information analysis using big data and AI algorithms so that structures that can interact with users and developers can lead to a sustainable ecosystem. Ultimately, it is meaningful to identify the direction of the optimized fashion industry through user experienced emotional fashion technology algorithms.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.653-662
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• 2006

In ubiquitous computing that is to build computing environments to provide proper services according to user's context, human being's emotion recognition based on facial expression is used as essential means of HCI in order to make man-machine interaction more efficient and to do user's context-awareness. This paper addresses a problem of rigidly basic emotion recognition in context-sensitive facial expressions through a new Bayesian classifier. The task for emotion recognition of facial expressions consists of two steps, where the extraction step of facial feature is based on a color-histogram method and the classification step employs a new Bayesian teaming algorithm in performing efficient training and test. New context-sensitive Bayesian learning algorithm of EADF(Extended Assumed-Density Filtering) is proposed to recognize more exact emotions as it utilizes different classifier complexities for different contexts. Experimental results show an expression classification accuracy of over 91% on the test database and achieve the error rate of 10.6% by modeling facial expression as hidden context.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.74-77
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• 2008

Online shoppers are not able to try clothing on. Therefore, the pictures of clothing on the website play a significant role when shoppers make decision on their purchase. There are generally three different ways to show clothing at online shops. The first one is showing only clothing images, and the second one is showing the pictures that have actual fitting models wearing clothing on (In this case, Model's face is mostly not shown in the picture.), and the third is showing the pictures of professional fitting models who wear goods. The shopping malls adopt each of the different ways but little is known about affect on purchasing from these three ways. The aim of this study is to figure out how the online shopper's emotional status is affected by these three ways of presenting pictures of clothing. At first, we developed a set of adjective words of human emotion to set up the evaluation criteria for user's emotional status. Those adjectives are originally from the precedent research on human emotion. To cut 99 adjectives down to a proper number for the criteria, we conducted a preliminary survey, and finally, 5 adjectives are selected as appropriate criteria for evaluating users' emotional status while they are shopping. Those five adjectives are 'possess','sensual', 'unique', 'tasteful', and 'stylish'. Then, we conducted the main survey showing 10 kinds of cloth (each cloth was consist of 3 ways). And in the page of model images, we measured the model's preference for understanding the relation with customer's emotion criteria of the product. As a result of the test there was statistically significant difference between product only images and anonymous images, but there was no significant difference between anonymous images and model images. And the preference of the model and value of the emotion criteria have large correlation except 'unique' criteria. It is expected that the result in this study will help to build new marketing strategy which satisfy customers' emotion.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.528-531
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• 2021

Nowadays recommendation systems are so ubiquitous, where our many decisions are being done by the means of them. We can see recommendation systems in all areas of our daily life. Therefore the research of this sphere is still so active. So far many research papers were published for clothing recommendations as well. In this paper, we propose the clothing-recommendation system according to user emotion and weather information. We used social media to analyze users' 6 basic emotions according to Paul Eckman theory and match the colour of clothing. Moreover, getting weather information using visualcrossing.com API to predict the kind of clothing. For sentiment analysis, we used Emotion Lexicon that was created by using Mechanical Turk. And matching the emotion and colour was done by applying Hayashi's Quantification Method III.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.381-387
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• 2013

Recently, emotional contents that induce emotions and respond to emotions are given attention in the field of cultural industries, and extracting emotion caused by multimedia contents is being noted. Furthermore, since multimedia contents have been quickly produced and distributed these days, researches automatically to extract the feeling of multimedia contents are being accelerated. In this paper, we will study the method of emotional value extraction in the multimedia contents using the volume value of the multimedia contents in a certain frequency among sound informations. This study allows to extract the emotion of multimedia contents automatically, and the extracted information will be used to provide user's current emotion, weather, etc. for the users.