• Journal of information and communication convergence engineering
• /
• v.7 no.3
• /
• pp.268-274
• /
• 2009

Human beings can represent state of mind such as psychological state, personality or emotional trouble by the pictures painted on one's own initiative. But in general, it is hard to understand a consulter's unconscious state through one's objective and intentional descriptions only. So one's psychological state and emotional trouble can be understood and cured by color and location information of objects drawn in one's picture. By this reason, a consultant can help and settle a consulter's growth stages of life and emotional trouble through treatment by pictures. In this paper, we proposed a method to find out state of sensitivity by analysis of color and location information represented in a picture and fuzzy inference rules. We applied the proposed method to the states of sensitivity from color information proposed by Alschuler and Hattwick and the psychological states from location information proposed by Grunwald. In the experimental results by the two applications, we verified the proposed sensitivity analysis method is efficient.

• Journal of Oriental Neuropsychiatry
• /
• v.18 no.2
• /
• pp.25-34
• /
• 2007

Objective : This study was done to investigate the differences of HRV(Heart Rate Variability) change as the response to the emotional stimulus in each Sasang constitutions. Method : We investigated 44 healthy volunteers consisted of 10 Soyangin, 14 Soeumin and 20 Taeumin. After diagnosing the Sasang constitution by specialist of Sasang medicine, we measured the baseline emotional state(100 sec) before the stimulus, the changing emotional state(100 sec) while fearful film was being played. And we rechecked the emotional state(100 sec) while taking a rest. At last, volunteers checked the questionnaire for emotional response. We analysed ECG data with HRV-time domain and frequency domain analysis. Results : (1) There were no significant difference in Mean-RR , SDNN between each groups. (2) LF of Taeumin group significantly increased by the emotional stimulation compared with other groups. (3) There was no significant difference in TP, HF, normalized LF, normalized HF, but the variation of each period in Taeumin group was bigger than those of other groups. (4) Soeumin group reported that they felt significant disgust-emotion in the questionnaire compared with other groups. Conclusion : Taeumin group showed significant emotional changes on HRV by fear stimulus film.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.1
• /
• pp.27-32
• /
• 2006

Verbal communication is the most commonly used mean of communication. A spoken word carries a lot of informations about speakers and their emotional states. In this paper we designed a model to recognize emotional states in a speech, a first phase of two phases in developing a toy machine that recognizes emotional states in a speech. We conducted an experiment to extract and analyse the emotional state of a speaker in relation with speech. To analyse the signal output we referred to three characteristics of sound as vector inputs and they are the followings: frequency, intensity, and period of tones. Also we made use of eight basic emotional parameters: surprise, anger, sadness, expectancy, acceptance, joy, hate, and fear which were portrayed by five selected students. In order to facilitate the differentiation of each spectrum features, we used the wavelet transform analysis. We applied ANFIS (Adaptive Neuro Fuzzy Inference System) in designing an emotion recognition model from a speech. In our findings, inference error was about 10%. The result of our experiment reveals that about 85% of the model applied is effective and reliable.

• Science of Emotion and Sensibility
• /
• v.25 no.4
• /
• pp.63-76
• /
• 2022

Emotional labor, characterized by a dysfunctional type of emotional regulation called surface acting, has detrimental psychological consequences on employees, including depression and social anxiety. Because such disorders exhibit psychological characteristics manifested through brain activation, previous studies have succeeded in distinguishing individuals with depression and social anxiety from healthy controls using their functional connectivity characteristics. However, it has not been established whether the functional connectivity characteristics associated with emotional labor are distinguishable. Thus, we obtained resting-state fMRI data from participants in the emotion labor (EL) group and control (CTRL) group, and we subjected their whole-brain functional connectivity matrices to a linear support vector machine classifier. Our analysis revealed that the EL and CTRL groups could be successfully distinguished on the basis of individuals' connectivity patterns, and confidence in the classification was correlated with the scores on the depression and social anxiety scales. These results are expected to provide insight on the neurobiological characteristics of emotional labor and enable the sorting of employees undergoing adverse emotional labor utilizing neurobiological observations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.6
• /
• pp.2056-2069
• /
• 2014

In order to implement interactive and personalized Web services properly, it is necessary to understand the tangible and intangible responses of the users and to recognize their emotional states. Recently, some studies have attempted to build emotional state estimation models based on facial expressions. Most of these studies have applied multiple regression analysis (MRA), artificial neural network (ANN), and support vector regression (SVR) as the prediction algorithm, but the prediction accuracies have been relatively low. In order to improve the prediction performance of the emotion prediction model, we propose a novel SVR model that is optimized using a genetic algorithm (GA). Our proposed algorithm-GASVR-is designed to optimize the kernel parameters and the feature subsets of SVRs in order to predict the levels of two aspects-valence and arousal-of the emotions of the users. In order to validate the usefulness of GASVR, we collected a real-world data set of facial responses and emotional states via a survey. We applied GASVR and other algorithms including MRA, ANN, and conventional SVR to the data set. Finally, we found that GASVR outperformed all of the comparative algorithms in the prediction of the valence and arousal levels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.958-961
• /
• 2015

Emotional expression is universal and emotional state impacts important areas in our life. Until now, analyzing the acquired EEG signals under circumstances caused by invoked feelings and efforts to define their emotional state have been made mainly by psychologists based on the results. But, recently emotion-related information was released by research results that it is possible to identify mental activity through measuring and analyzing the brain EEG signals. So, this study has compared and analyzed emotional expressions of human by using brain waves. To get EEG difference for a particular emotion, we showed specific subject images to the people for changing emotions that peace, joy, sadness and stress, etc. After measured EEG signals were converged into frequence domain by FFT signal process, we have showed EEG changes in emotion as a result of the performance analyzing each respective power spectrum of delta, theta, alpha, beta and gamma waves.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.3
• /
• pp.395-400
• /
• 2011

In this paper, we propose an study of the facial emotion recognition considering the dynamical variation of emotional state in facial image sequences. The proposed system consists of two main step: facial image based emotional feature extraction and emotional state classification/recognition. At first, we propose a method for extracting and analyzing the emotional feature region using a combination of Active Shape Model (ASM) and Facial Action Units (FAUs). And then, it is proposed that emotional state classification and recognition method based on Hidden Markov Model (HMM) type of dynamic Bayesian network. Also, we adopt a Harmony Search (HS) algorithm based heuristic optimization procedure in a parameter learning of HMM in order to classify the emotional state more accurately. By using all these methods, we construct the emotion recognition system based on variations of the dynamic facial image sequence and make an attempt at improvement of the recognition performance.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.27 no.3
• /
• pp.210-220
• /
• 2017

Objectives: This study was conducted to prepare fundamental data and prevention measures on health promotion and occupational disease, and to assess the effects of the working environment on subjective health status and absenteeism among workers using data from the third working environment survey in Korea. Methods: This study's subjects were composed of 29,711 wage workers from the 3rd working environment survey data. The dependent variables were several diseases, subjective health status and absences, and the independent variable was the working environment. The collected data were analyzed by One-Way ANOVA, Pearson's correlation and stepwise multiple analysis using the IBM SPSS(ver. 20.0) statistical package program. Results: The effecting factors for cardiovascular disease were age, working shift and emotional state. The effecting factors for anxiety and depression were years of education, working condition, duties, and emotional state. The effecting factors of insomnia were duty and emotional state. The positive effecting factors for absent days were work standing, working shift, number of night shifts, autonomy, and duties. The positive effecting factors of subjective health status were age, work standing, working years, working shift, appropriateness of working hours, leadership of superiors, duties and emotional state. Conclusions: Based on the above results, the author considers that it is necessary to improve the working environment to reduce absent days, such as by reducing of number of night shifts and giving autonomy regarding duties, and to improve the working environment for subjective health status such as by controlling the appropriateness of working hours and stability of the emotional state. In addition, this study provides fundamental data on health promotion and occupational disease among workers.

• International Journal of Contents
• /
• v.18 no.3
• /
• pp.11-20
• /
• 2022

Human Emotion Recognition is an exciting topic that has been attracting many researchers for a lengthy time. In recent years, there has been an increasing interest in exploiting contextual information on emotion recognition. Some previous explorations in psychology show that emotional perception is impacted by facial expressions, as well as contextual information from the scene, such as human activities, interactions, and body poses. Those explorations initialize a trend in computer vision in exploring the critical role of contexts, by considering them as modalities to infer predicted emotion along with facial expressions. However, the contextual information has not been fully exploited. The scene emotion created by the surrounding environment, can shape how people perceive emotion. Besides, additive fusion in multimodal training fashion is not practical, because the contributions of each modality are not equal to the final prediction. The purpose of this paper was to contribute to this growing area of research, by exploring the effectiveness of the emotional scene gist in the input image, to infer the emotional state of the primary target. The emotional scene gist includes emotion, emotional feelings, and actions or events that directly trigger emotional reactions in the input image. We also present an attention-based fusion network, to combine multimodal features based on their impacts on the target emotional state. We demonstrate the effectiveness of the method, through a significant improvement on the EMOTIC dataset.

• Korean Journal of Adult Nursing
• /
• v.22 no.2
• /
• pp.211-220
• /
• 2010

Purpose: This study was done to identify the relationship of trait anger, health state, physical symptoms. and general characteristics to physical symptoms and to identify factors affecting physical symptoms of elderly in urban areas. Methods: The research design for this study was a descriptive survey design using a convenience sampling. Elders (n=276), who agreed to participate in this study completed a self-reporting questionnaire. The collected data were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and multiple regression. Results: Study participants reported low trait anger (M=18.61), physical symptoms (M=7.15), and moderate health state (M=3.30). The 45.4%of variance in physical symptoms was significantly explained by emotional function health state (${\beta}$=-.284, p=.013), which is one of the sub-domain of the elderly health state, and trait anger (${\beta}$=3.841, p<.001). Conclusion: Findings of this study provide that the most important factors in explaining physical symptoms for the elders in Korea were emotional function health state and trait anger. Based on the findings of this study, further nursing practice and nursing research for the elders with physical symptoms should be focused on emotional support.