• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.15-15
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• 2004

• Archives of design research
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• v.19 no.2 s.64
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• pp.343-354
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• 2006

Human emotion is the issue which is subjective, difficult to define and even more difficult to measure because it's personal. In general, the methods for measuring human emotion is divided in two, one is the psychological way based on user's subjective evaluation and another is the physiological way based on physiological signs. However, these methods have some limitations. Therefore, in this study we suggested the methods for measuring users emotion in the natural and accessible environment for the design field. In order to observe user's emotional changes while they interact with a product, we have extracted some emotional words and representative emotions, and made a set of subjective evaluation scales. With these scales, emotion logging program 'VideoTAME' was developed as an effective measurement tool for complementing the current psychological methods. In the testing module of 'VdeoTAME,' participants evaluate their emotional changes through playing and watching the video dips of their performing tasks in the experiment room. In the analyzing module, the researchers replay the results created by participants during the experiment and analyze the results using Microsoft Exel. It is hoped that this study will be of great help for designers for effective measurement of user's emotions naturally expressed while using a product.

• Science of Emotion and Sensibility
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• v.8 no.1
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• pp.17-28
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• 2005

With the advent of computer technology, the fundamental nature of products has shaped from physical forms towards product interactivity, The focus is now on usability of the product with ease and efficiency rather than conversing with just the looks of the product. However, most definitions of usability and contemporary usability-related researches, have focused on the performance-oriented functional aspects of usability (i.e., how well users perform tasks using a product). Today, user expectations are higher; products that bring not only functional benefits but also emotional satisfaction. So far, there have been many studies on human emotions and the emotional side of products in the field of emotional engineering. Contemporary emotion-related researches have focused mainly on the relationship between product aesthetics and the emotional responses elicited by the products, but little is known about emotions elicited from using the products. The main objective of our research is analyzing user's emotional changes while using a product, to reveal the influence of usability on human emotions. In this research, we suggested conceptual framework for the study on the relationship between usability of products, and human emotions with emphasis on mobile phones. We also extracted emotional words for measuring user's emotions expressed not from looking at the product's appearance, but from using the product. First, we assembled a set of emotions that is sufficiently extensive to represent a general overview of the full repertoire of Korean emotions from the literature study. Secondly, we found emotional words in the after note by the users on the websites. Finally, verbal protocols in which the user says out loud what he/she ks feeling while he/she ks carrying out a task were collected. And then, the appropriateness of extracted emotional words was verified by the members of the consumer panel of a company through web survey. It is expected that emotional words extracted in this research will be used to measure user's emotional changes while using a product. Based on the conceptual framework suggested in this research, basic guidelines on interface design methods that reflect user's emotions will be illustrated.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.83-91
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• 2020

Environmental conditions are known to impact human health and behavior, emotions such as pleasure, anxiety, and depression, and reduce stress. Interior design that elevates emotional comfort and satisfaction can help improve mental health and well-being. This study is a systematic review that analyzed previous empirical studies that explored the effect of interior design elements on the user's emotional response which is quantitatively evaluated by bio-signal and qualitatively evaluated through self-reported questionnaire surveys. This paper aims to derive the attributes of interior design and biometric indicators that affect the user's positive emotion through the synthesis of previous studies and to confirm the feasibility of measuring bio-signals as an objective evaluation tool for architectural design and as a quantitative research method. As a result of the review, the biometric data from EEG, fMRI, ECG, EMG, GSR, and eye-tracking were used to measure the participants' emotional responses, which were manifested as positive or negative depending on certain attributes of interior design such as the form, color, lighting, material and furniture. The attributes of interior design related to the positive emotional response were the curved shape, high ceiling, openness of space, and subdued tone colors. Standard lighting conditions and wooden spaces were related to stress reduction in terms of comfort and relaxation. The free arrangement of furniture was related to the user's positive emotions. On the other hand, consistent experimental protocols could not be found, and although the sample sizes of the studies were small, the studies have demonstrated the feasibility of the emotional response measurement by using the biometric data. Therefore this method can be a useful objective tool in the measurement of human-centric data in architectural design, and to develop the evidence-based design to induce positive emotions and minimize stress.

• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.761-768
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• 2010

Research from many marketing fields shows that the importance of factors which ultimately reflect customers' satisfaction towards a product or service vary with the progression of time. Likewise, the emotions that users experience from using the product can vary with prolonged interaction with the product itself. Thus, research was conducted under the notion that users' emotions at the initial stage of product usage is likely to vary with that at a later stage, when users would have had more experience with the product. In this research, a comparison was made between the emotions felt by customers with first-hand experience with the iPhone and those felt by long-time iPhone users who already had much more opportunities to familiarize themselves with the product. The results of the research conducted above show that, with first usage of the iPhone, users experience a relatively low level of negative emotions(Uncomfortableness) due to the predominance of the users' positive emotions. Over prolonged usage, however, the level of negative emotions associated with the usage of the iPhone has been shown to increase. Still, it is impossible to state that the Uncomfortableness associated with iPhone usage is high in general. In fact, it has been shown that positive emotions were rather experienced by iPhone users both before and after usage, suggesting that the emotions involved during iPhone usage was generally positive. Although more research would need to be conducted to safeguard this claim, it can be generally deduced from the results of this research that:when using a product, positive emotions in terms of the reflective level accounts for much of the negative emotions(i.e. discomfort) associated with behavioral level with regard to the product itself.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.167-177
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• 2006

In this study, we developed a tool for measuring user's emotions expressed while using a product in the natural and accessible environment for the design field. Also, using emotional word logging software VideoTAME, we measured a user's emotions expressed while using a product. In the testing module of VideoTAME, participants evaluate their emotional changes through playing and watching the video clips of their performing tasks in the experiment room. In the analyzing module, the researchers replay the results created by participants during the experiment and analyze the results using Microsoft Excel. In this research, we have asked users to examine their emotional changes while watching the recorded video clip of them in the experiment room performing a series of tasks using a cellular phone. In this experiment, there were no big differences in the representative emotions expressed for each characteristics of task. The reason for this can be assumed it is because of the emotional changes occurred while facing specific situations when performing a task rather than the task itself. If more data is collected and concrete statistical analysis is done, it is expected that we can clarify what effect a product's usability has on user's emotions.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.10a
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• pp.232-233
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• 2004

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.37-43
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• 2018

In this paper, we propose a framework that automatically suggests emotion using emotion analysis method based on facial expression change. We use Microsoft's Emotion API to calculate and analyze emotion values in facial expressions to recognize emotions that change over time. In this step, we use standard deviations based on peak analysis to measure and classify emotional changes. The difference between the classified emotion and the normal emotion is calculated, and the difference is used to recognize the emotion abnormality. We match user's emotions to relatively relaxed emotions using histograms and emotional meshes. As a result, we provide relaxed emotions to users through images. The proposed framework helps users to recognize emotional changes easily and to train their emotions through emotional relaxation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.239-246
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• 2021

In the field of computer graphics and HCI, there are many studies on systems that create characters and interact naturally. Such studies have focused on the user's response to the user's behavior, and the study of the character's behavior to elicit positive emotions from the user remains a difficult problem. In this paper, we develop a prototype of an interaction system to elicit positive emotions from users according to the movement of virtual characters using artificial intelligence technology. The proposed system is divided into face recognition and motion generation of a virtual character. A depth camera is used for face recognition, and the recognized data is transferred to motion generation. We use imitation learning as a learning model. In motion generation, random actions are performed according to the first user's facial expression data, and actions that the user can elicit positive emotions are learned through continuous imitation learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1759-1768
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• 2022

This paper proposes a model to detect depression-related emotions in a user's speech using wellness dialogue scripts provided by AI Hub, topic-specific daily conversation datasets, and chatbot datasets published on Github. There are 18 emotions, including depression and lethargy, in depression-related emotions, and emotion classification tasks are performed using KoBERT and KOELECTRA models that show high performance in language models. For model-specific performance comparisons, we build diverse datasets and compare classification results while adjusting batch sizes and learning rates for models that perform well. Furthermore, a person performs a multi-classification task by selecting all labels whose output values are higher than a specific threshold as the correct answer, in order to reflect feeling multiple emotions at the same time. The model with the best performance derived through this process is called the Depression model, and the model is then used to classify depression-related emotions for user utterances.