• Journal of the Korean Applied Science and Technology
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• v.40 no.3
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• pp.560-569
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• 2023

This study is a descriptive research study conducted to identify the grit, positive emotions, teamwork, and clinical practice satisfaction of nursing college students, and to identify factors that affect clinical practice satisfaction. The participants of the study were fourth grade nursing students from two universities located in U city and G city. The SPSS 21.0 program was used to analyze the data into descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and multiple regression. As a result of the study, there was a positive correlation between grit, positive emotion, and teamwork in clinical practice satisfaction. Teamwork had a positive correlation with grit and positive emotions, and positive emotions had a positive correlation with grit. Factors affecting clinical practice satisfaction were teamwork and positive emotions, and explanatory power was 43.6%. Therefore, it is necessary to develop educational programs and find ways to improve teamwork and positive emotions as a strategy to improve the satisfaction of nursing college students in clinical practice.

• 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 Korean Society for Quality Management
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• v.51 no.4
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• pp.643-662
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• 2023

Purpose: The purpose of this study was to understand an individuals' COVID-19 vaccine acceptance intention during the peak of the pandemic by utilizing the coping theory and technology threat avoidance theory (TTAT) as a framework. Specifically, we focused on understanding how inward and outward emotion-focused coping (EFC), such as psychological distancing and emotional support seeking, affect problem-focused behavior (PFC), which is vaccine acceptance. Furthermore, we investigate how the individuals' cognitive appraisal to- ward COVID-19, consisted of perceived threat and perceived avoidability act as an antecedent of EFC. Methods: A PLS-SEM analysis was conducted to find the causal relation between the variables. An online survey was conducted targeting vaccination recipients on April, 2021. Participants were asked about their perception toward the virus, their coping strategy, and vaccine acceptance intention. A total of 186 valid samples were collected and used for the analysis. Furthermore, to analyze the out-of-sample predictive power of the research model and ensure the generalizability of the results, a PLSpredict analysis was conducted. Results: The results of the PLS-SEM analysis show that perceived threat toward COVID-19 significantly affect an individuals' EFC strategy. Furthermore, both types of inward EFC (psychological distancing, wishful thinking) negatively affected vaccine acceptance intention. On the other hand, emotional support seeking, which is a type of outward EFC, positively affected vaccine acceptance. The result of the PLSpredict analysis confirms the generalizability of the PLS-SEM result. Conclusion: The results of our study could be utilized to decrease vaccine hesitancy and prevent global pandemics by accelerating and increasing vaccination. Our study provides several meaningful implications to researchers and practitioners regarding vaccine acceptance and threat coping behavior.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.429-440
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• 2010

It is known that as many as 1 in 91 children are diagnosed with an autistic spectrum disorder, and the incidence rate of the autistic spectrum disorder is much higher than that of cancer in Korea. It is necessary to develop a novel technology to sense their emotional status and give proper psychological diagnosis and therapy, since the children with autistic spectrum disorder usually do not express their own emotional status. This article presents the state-of-the-arts on the affective computing technologies that include recognition of emotional status through bio-sensing and virtual affective agent modeling, and then proposes a novel system architecture for diagnosis and therapy of autistic spectrum disorder. The diagnosis and therapy system of autistic spectrum disorder is composed of bio-sensing module, virtual environment module with affective agents, and haptic interface module. The architecture proposed in this paper will enhance the objectivity to diagnose autism spectrum disorders, and enable continuous treatment in daily life.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.63-73
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• 2008

Recent years have seen an explosion of interest in interactive agents motivating human learners to engage in edutainment systems which are designed to be entertaining and educational at the same time. Especially, work on socio-emotional processes has focus on understanding of human's social behavior in training and entertainment a applications. In contrast with work on social emotion, where research groups have developed detailed models of emotional processes, models of personality have emphasized shallow surface behavior. Here, we build on computational appraisal models of emotion to better characterize dispositional differences in how people come to understand social situations. Known as explanatory style, this dispositional factor plays a key role in social interactions and certain socio-emotional disorders, such as depression. Building on appraisal and attribution theories, we model key conceptual variables underlying the explanatory style, and enable agents to exhibit different explanatory tendencies with respect to their personalities. Furthermore, we developed an interactive AR agent based on our framework and applied it into an interactive teaming system that allows participants to explore individual differences in the explanation of social events, with the goal of encouraging the development of perspective laking and emotion-regulatory skills.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.581-586
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• 2023

Recently a drama that features a lawyer with autism spectrum disorder has attracted a lot of attention, raising interest in the difficulties faced by people with autism spectrum disorders. If the Autism spectrum gets detected early and proper education and treatment, the prognosis can be improved, so the development of the treatment is urgently needed. Drugs currently used to treat autism spectrum often have side effects, so Digital Therapeutics that have no side effects and can be supplied in large quantities are drawing attention. In this paper, we introduce 'AEmotion', an application and a Digital Therapeutic that provides emotion and facial expression learning for toddlers with an autism spectrum disorder. This system is developed as an application for smartphones to increase interest in training autistic children and to test easily. Using machine learning, this system consists of three main stages: an 'emotion learning' step to learn emotions with facial expression cards, an 'emotion identification' step to check if the user understood emotions and facial expressions properly, and an 'expression training' step to make appropriate facial expressions. Through this system, it is expected that it will help autistic toddlers who have difficulties with social interactions by having problems recognizing facial expressions and emotions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.519-525
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• 2023

The human brain constantly emits electrical impulses, which is called brain waves, and brain waves can be defined as the electrical activity of the brain generated by the flow of ions generated by the biochemical interaction of brain cells. There is a study that emotion is one of the factors that can cause stress. Brain waves are the most used in the study of emotions. This paper is a study on whether emotions affect stress, and showed two images of fear and joy to four experimenters and divided them into three stages before, during, and after watching. As a measurement tool, brain waves at the positions of Fp1 and Fp2 were measured using the NeuroBrain System, a system that can automate brain wave measurement, analysis, brain wave reinforcement, and suppression training with remote control. After obtaining the brain wave data for each emotion, the average value was calculated and the study was conducted. As for the frequency related to stress, the values of Alpha and SMR, Low Beta, and High Beta were analyzed. Brainwave analysis affects stress depending on the emotional state, and "fear" emotions cause anxiety by raising Beta levels, resulting in higher Mind Stress levels, while "joy" emotions lower Beta levels, resulting in a significant drop in Mind Stress.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.119-130
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• 2017

The purpose of the study is discovering the effects of virtual competitors on user in AR (Augment Reality) home training system. Specifically, the current research examined their effects on immersion, perceived competition, and leaning motivation. The paper tested three unexplored relationship. First, introducing virtual competitors in home training system will enhance user's immersion. Second, presenting virtual competitors in home training system will increase user's perceived competition. Third, virtual competitors in home training system will raise user's learning motivation. For empirical analysis, we developed home training system, which could check and give feedback automatically, based on user's posture. Using this AR home training system, the study empirically shows how and why virtual competitors affect users. The results give implications not only on service design; but also on the idea that virtual other could affect user's behavior.

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.513-524
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• 2011

Digital Technology actively utilized in all spheres by rapid changes in information and knowledge demands transformations not only in the sociocultural and educational spheres but also specifically in the field of arts education. Digital Technology becomes a challenging factor for the field of arts education. In this study purposed a new teaching-learning approach method in arts education, called "Music Technology-Based Learning"(hereafter, MTBL), which is to, first, take interdisciplinary approaches combining various subjects in the field of arts education. In the case study was conducted to examine the educational effects of the MTBL approach to the liberal arts course in university: mind maps derived from 2 sessions (pre-class and post-class), evaluation sheets regarding self-directed learning and in-depth interviews with ten voluntary learners after the class were used as methods for data collection. The result of case study shows positive changes in the terms of the degrees of emotional sensibilities of the learners. Moreover, the research confirmed the potential of MTBL as a new teaching and learning methodology in art education.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.3-16
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• 2018

The emergence of the fourth industrial revolution is rapidly changing the conventional society and the industry, eroding the boundaries among the technology, culture, and finance. In the mobility industry, as the engineering-based industry converges with the information technology, the mobile space is changing from mobility or safety-centric space into space where the passengers can consume infotainment or contents services. The contents evaluation of the future mobility is conducted in terms of usability or technology acceptance aspect, but according to the trend analysis, the mobility industries, such as vehicle OEMs, it is necessary to evaluate the emotional or sensibility factors for the development of their future mobile space design. Herein, this research study evaluates which sensibility factor should be evaluated in priority to develop the contents interaction in the future mobile space. Thus, using Patrick Jordan's Four Pleasure Model, the priority evaluation has been conducted among 116 Korean drivers. As a result of the statistical analysis and AHP (Analytic Hierarchy Process), it has been found that first, it is necessary to evaluate psychological, ideological, social and physical sensibility in the respective order, and second, it is necessary to evaluate based on the contents user type.