• Science of Emotion and Sensibility
• /
• v.17 no.3
• /
• pp.57-64
• /
• 2014

In this paper, we verify the relation between elements (active and inactive) of Russell's emotional dimension ("A Circumplex Model") to propose a new representing method. Russell's emotional dimension expresses emotional words (happy, joy, sad, nervous, etc.) as a point on the two dimensions (Arousal and Valence). It is most commonly used in many filed such as Science of Emotion & Sensibility, Human-Computer Interaction (HCI), and Psychology etc. But other researchers have insisted that Russell's emotional dimension have to be modified because of its inherent problems. Such problems included the possibility of mixed feelings, the difference of emotion and sensibility, and the difference of Arousal axis and Valence axis. Therefore, we verify relationship of A Circumplex Model's elements (active and inactive) and find how to people express their Arousal feelings using survey. We finally propose new method to express emotion in Russell's emotional dimension. Using this method, we can solve Russell's problems and compensate other researches.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.36 no.4
• /
• pp.41-49
• /
• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• Korean Journal of Child Studies
• /
• v.28 no.4
• /
• pp.229-244
• /
• 2007

A comprehensive program for children's leadership improvement by enhancing their self-esteem, empathy, communication competence and emotion regulation utilized core concepts and techniques or the Solution-Focused Model of Chung et al. (2005), Satir's Experiential Model(Chung, 2003), Cognitive-Behavioral Model (Kim, 2002) and problem-solving games (Chung & Kim, 2005). Twenty-six $3^{rd}$, $4^{th}$, and $5^{th}$ graders from public schools in Seoul participated in six 2.5 hour sessions and their mothers participated in two 2.5 hour sessions. All subjects received pre-, post- and follow-up tests. The results showed that children's self-esteem, empathy, communication competence, and emotion regulation increased as a result of this program and the effects lasted for at least three months after the termination of the program.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.1958-1962
• /
• 2005

A driver's emotion is a very important factor of safe driving. This paper classified a driver's emotion into 3 major emotions, can be occur when driving a car: Surprise, Joy, Tired. And It evaluated the classifier using Hidden Markov Models, which have observation sequence as bio-signals. It used the 2-D emotional plane to classfiy a human's general emotion state. The 2-D emotional plane has 2 axes of pleasure-displeasure and arsual-relaxztion. The used bio-signals are Galvanic Skin Response(GSR) and Heart Rate Variability(HRV), which are easy to acquire and reliable. We classified several moving pictures into 3 major emotions to evaluate our HMM system. As a result of driving simulations for each emotional situations, we can get recognition rates of 67% for surprise, 58% for joy and 52% for tired.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.29 no.9_10 s.146
• /
• pp.1306-1315
• /
• 2005

Impulse buying has been considered a pervasive and distinctive phenomenon in the modern lifestyle and has been receiving increasing attention from consumer researchers and theorists. In the modern marketplace, spontaneous urges to buy and consume often compete with the practical necessity to delay the immediate gratification that purchasing provides. The purpose of this study is to conceptualize and test the framework of impulse buying behavior for fashion products using structural equation model. Data were obtained from 413 students attending universities during schedules classes in Busan. Analysis of the data, utilizing AMOS 4.1, supported most of the predictions. The results showed that situational variable(time available) and individual variable(fashion involvement) have direct effects on consumers' shopping emotions, including positive and negative emotion. Positive emotions had effects on all types of impulse buying(planned impulse buying, reminded impulse buying, and fashion-oriented impulse buying), while negative emotion affected two types of impulse buying(reminded impulse buying and fashion-oriented impulse buying). These emotional experiences influence impulse buying behaviors for fashion products serving as critical mediators. The findings suggest that time available and fashion involvement are good predictors mediated by shopping emotion to impulse buying behavior for fashion products. The implications of this research for future work on the shopping emotion and impulse buying behavior are discussed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.10
• /
• pp.957-962
• /
• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.92-96
• /
• 2004

The researches of the emotions are currently great interest in speech processing as well as in human-machine interaction domain. In the recent years, more and more of researches relating to emotion synthesis or emotion recognition are developed for the different purposes. Each approach uses its methods and its various parameters measured on the speech signal. In this paper, we proposed using a short-time parameter: MFCC coefficients (Mel­Frequency Cepstrum Coefficients) and a simple but efficient classifying method: Vector Quantification (VQ) for speaker-dependent emotion recognition. Many other features: energy, pitch, zero crossing, phonetic rate, LPC... and their derivatives are also tested and combined with MFCC coefficients in order to find the best combination. The other models: GMM and HMM (Discrete and Continuous Hidden Markov Model) are studied as well in the hope that the usage of continuous distribution and the temporal behaviour of this set of features will improve the quality of emotion recognition. The maximum accuracy recognizing five different emotions exceeds $88\%$ by using only MFCC coefficients with VQ model. This is a simple but efficient approach, the result is even much better than those obtained with the same database in human evaluation by listening and judging without returning permission nor comparison between sentences [8]; And this result is positively comparable with the other approaches.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.754-771
• /
• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.11
• /
• pp.1496-1505
• /
• 2018

The technology of emotion recognition is necessary for human computer interaction communication. There are many cases where one cannot communicate without considering one's emotion. As such, emotional recognition technology is an essential element in the field of communication. n this regard, it is highly utilized in various fields. Various bio-sensor sensors are used for human emotional recognition and can be used to measure emotions. This paper proposes a system for recognizing human emotions using two physiological sensors. For emotional classification, two-dimensional Russell's emotional model was used, and a method of classification based on personality was proposed by extracting sensor-specific characteristics. In addition, the emotional model was divided into four emotions using the Support Vector Machine classification algorithm. Finally, the proposed emotional recognition system was evaluated through a practical experiment.

• Journal of the Korean Society of Food Culture
• /
• v.26 no.6
• /
• pp.641-648
• /
• 2011

The two purposes of this study were to understand service encounters in Korean restaurants by foreigners living in Korea and to examine the effect of service encounters on the customer's emotion feelings, customer satisfaction, and behavioral intention. Based on the reactions of a total of 614 foreigners obtained by empirical research, this study reviews the reliability and fitness of the research model, and verifies a total of 4 hypotheses using the Amos program. The hypothesized relationships in the model were tested simultaneously using a structural equation model (SEM). The proposed model provided an adequate fit to the data: ${\chi}^2$ 683.466 (df=216), CMIN/df 3.164, RMR 0.095, GFI 0.911, AGFI 0.886, NFI 0.933, CFI 0.953, and RMSEA 0.059. As a result of empirical analysis, the physical environment, interactions with employees, and interactions with other customers were quantified as service encounter factors in Korean restaurants. These factors were indicated to have an influence on customer's emotion feelings. Also, customer's emotion feelings had a positive influence on customer satisfaction and behavioral intent. Limitations and future research are also discussed.