• Journal of KIISE
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• v.44 no.11
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• pp.1149-1155
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• 2017

Recently, with the development of communication technology, it has become possible to collect various sensor data that indicate the environmental stimuli within a space. In this paper, we propose a group emotion prediction system using a modular Bayesian network that was designed considering the psychological impact of environmental stimuli. A Bayesian network can compensate for the uncertain and incomplete characteristics of the sensor data by the probabilistic consideration of the evidence for reasoning. Also, modularizing the Bayesian network has enabled flexible response and efficient reasoning of environmental stimulus fluctuations within the space. To verify the performance of the system, we predict public emotion based on the brightness, volume, temperature, humidity, color temperature, sound, smell, and group emotion data collected in a kindergarten. Experimental results show that the accuracy of the proposed method is 85% greater than that of other classification methods. Using quantitative and qualitative analyses, we explore the possibilities and limitations of probabilistic methodology for predicting group emotion.

• Science of Emotion and Sensibility
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• v.20 no.1
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• pp.67-74
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• 2017

The goal of this study is to present quantification method for empathy. The micro-movement technology (non-contact sensing method) was used to identify empathy level. Participants were first divided into two groups: Empathized and not empathized. Then, the upper body data of participants were collected utilizing web-cam when participants carried expression tasks. The data were analyzed and categorized into 0.5 Hz, 1 Hz, 3 Hz, 5 Hz, 15 Hz. The average movement, variation, and synchronization of the movement were then compared. The results showed a low average movement and variation in a group who empathized. Also, the participants, who empathized, synchronized their movement during the task. This indicates that the people concentrates with each other when empathy has been established and show different levels of movement. These findings suggest the possibility of empathy quantification using non-contact sensing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.494-500
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• 2008

In the field of development of human interface technology, the interactions between human and machine are important. The research on emotion recognition helps these interactions. This paper presents an algorithm for emotion recognition based on personalized speech signals. The proposed approach is trying to extract the characteristic of speech signal for emotion recognition using PLP (perceptual linear prediction) analysis. The PLP analysis technique was originally designed to suppress speaker dependent components in features used for automatic speech recognition, but later experiments demonstrated the efficiency of their use for speaker recognition tasks. So this paper proposed an algorithm that can easily evaluate the personal emotion from speech signals in real time using personalized emotion patterns that are made by PLP analysis. The experimental results show that the maximum recognition rate for the speaker dependant system is above 90%, whereas the average recognition rate is 75%. The proposed system has a simple structure and but efficient to be used in real time.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.115-124
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• 2021

This research empirically tested the mediating role of customer emotion on the relationship between customer tumbler experience and social legitimacy. For this purpose, data were collected by surveying consumers who use tumblers. Structural equation model analysis was used for hypothesis testing, and the method proosed by Hoyle and Smith was used to additionally test the mediating effect of customer experience and social legitimacy. The empirical analysis results are as follows. First, customer experience has a positive effect on customer emotion and social legitimacy. Second, customer emotion has a positive effect on social legitimacy. This study proposes a exploratory relationship among customer tumbler experience, customer emotion, and social legitimacy and empirically shows that customer experience and customer emotion are identified as antecedents to social legitimacy.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.47-47
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• 2004

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.773-777
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• 2009

In recent years, the field of synthesizing voice has been developed rapidly, and the technologies such as reading aloud an email or sound guidance of a car navigation system are used in various scenes of our life. The sound quality is monotonous like reading news. It is preferable for a text such as a novel to be read by the voice that expresses emotions wealthily. Therefore, we have been trying to develop a system reading aloud novels automatically that are expressed clear emotions comparatively such as juvenile literature. At first it is necessary to identify emotions expressed in a sentence in texts in order to make a computer read texts with an emotionally expressive voice. A method on the basis of the meaning interpretation that utilized artificial intelligence technology for a method to specify emotions of texts is thought, but it is very difficult with the current technology. Therefore, we propose a method to determine only emotion every sentence in a novel by a simpler way. This method determines the emotion of a sentence according to an emotion that words such as a verb in a Japanese verb sentence, and an adjective and an adverb in a adjective sentence, have. The emotional characteristics that these words have are prepared beforehand as a emotional words dictionary by us. The emotions used here are seven types: "joy," "sorrow," "anger," "surprise," "terror," "aversion" or "neutral."

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1121-1125
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• 2004

The RT(Robot Technology) has been developed as the next generation of a future technology. According to the 2002 technical report from Mitsubishi R&D center, IT(Information Technology) and RT(Robotic Technology) fusion system will grow five times larger than the current IT market at the year 2015. Moreover, a recent IEEE report predicts that most people will have a robot in the next ten years. RCP(Robotic Cellular Phone), CP(Cellular Phone) having personal robot services, will be an intermediate hi-tech personal machine between one CP a person and one robot a person generations. RCP infra consists of $RCP^{Mobility}$, $RCP^{Interaction}$, $RCP^{Integration}$ technologies. For $RCP^{Mobility}$, human-friendly motion automation and personal service with walking and arming ability are developed. $RCP^{Interaction}$ ability is achieved by modeling an emotion-generating engine and $RCP^{Integration}$ that recognizes environmental and self conditions is developed. By joining intelligent algorithms and CP communication network with the three base modules, a RCP system is constructed. Especially, the RCP interaction system is really focused in this paper. The $RCP^{interaction}$(Robotic Cellular Phone for Interaction) is to be developed as an emotional model CP as shown in figure 1. $RCP^{interaction}$ refers to the sensitivity expression and the link technology of communication of the CP. It is interface technology between human and CP through various emotional models. The interactive emotion functions are designed through differing patterns of vibrator beat frequencies and a feeling system created by a smell injection switching control. As the music influences a person, one can feel a variety of emotion from the vibrator's beats, by converting musical chord frequencies into vibrator beat frequencies. So, this paper presents the definition, the basic theory and experiment results of the RCP interaction system. We confirm a good performance of the RCP interaction system through the experiment results.

• The Korean Journal of Emergency Medical Services
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• v.6 no.1
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• pp.199-207
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• 2002

This is a study to evaluate the effects of the safety of ambulance driving and the occurrence of ambulance traffic accidents and to provide basic information for the description of various factors to reduce the ambulance traffic accidents. The major instruments of this study were Korean Self-Analysis Driver Opinionnaire. Questionnaire contains 8 items which measure driver's opinions or attitudees: driving courtesy, emotion, traffic law, speed, vehicle condition, the use of drugs, high-risk behavior, human factor. To take the analysis of data, the total of 187 drivers were investigated ambulance drivers in Gwangju Metropolitan City from 2002. 1. September to 2002. 20. September. The data were analyzed by the path analysis SPSS program. The result are as follows : 1. There was desirable attitude group(58.4%) and undesirable attitude group(41.7%) on safety ambulance driving. 2. It have suggested that rist factors of ambulance traffic accident much affected with emotion and speed control on safety ambulance driving(Y(Accident) = -2.00 + 0.6 X1(Emotion Control) + 0.4 $X_2$(Speed control) + E). 3. Almost 92.1% of respondents have agreed to necessity of emergency medical technics for ambulance drivers.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.