• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1287-1290
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• 2003

Generally, it is known that human beings have both emotion and rationality. Especially, emotion is so subjective that human beings might act in different way for the same environment according to their own emotion. Emotion also plays very important role in communication with someone else For an agent, even though it is designed to act delicately, when it is designed without internal emotion, it can not interact dynamically just like human beings. In this paper, we suggest an agent which action is effected by not only rationality but also emotion to make it interact with human beings dynamically. It is composed of supervised learning, SOM (Self-Organizing Map) and fuzzy decision.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.276-279
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• 2003

Generally, it is known that human beings have both emotion and rationality. Especially, emotion is so subjective that human beings might act in different way for the same environment according to their own emotion. Emotion also plays very important role in communication with someone else. For an agent, even though it is designed to act delicately, when it is designed without internal emotion, it can not interact dynamically just like human beings. In this paper, we suggest an agent which action is effected by not only rationality but also emotion to make it interact with human beings dynamically. It is composed of supervised learning, SOM (Self-Organizing Map) and fuzzy decision.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.832-837
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Proceedings of the ESK Conference
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• 1993.04a
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• pp.64-68
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• 1993

The idia of modern product design is translated from the concept of functional importance as the basic function to that of emotional importance as the supplement function. In other words, the interests of the emotion in human performance side based on psychological factors of human are increased as well as the function in technical performance side based on physical factors of product. The standard emotional works for understanding the human's emotion are arranged in this paper. The standard emotional words are composed of words expressing the humaa's emotion. The adjectives adaptable to human's emotional works are collected from Korean dictionaries and arranged in the semantic differential(SD) scale. Next, the words with great marks evaluated by SD method are analyzed by factor analysis(FA) method and characterized as emotional words for understanding the human's emotion. The standard emotional words arranged in this paper are important because they are basic information for the development of product or technology as well as for the matter of emotional measurement technical development.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1363-1368
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• 2015

Recently, computer form were smaller than before because of computing technique's development and many wearable device are formed. So, computer's cognition of human emotion has importantly considered, thus researches on analyzing the state of emotion are increasing. Human voice includes many information of human emotion. This paper proposes a discriminative feature vector selection for emotion classification based on speech. For this, we extract some feature vectors like Pitch, MFCC, LPC, LPCC from voice signals are divided into four emotion parts on happy, normal, sad, angry and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification.

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.119-128
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• 2007

Emotion interaction between human and robot is an important element for natural interaction especially for service robot. We propose a hybrid emotion generation architecture and detailed design of reactive process in the architecture based on insight about human emotion system. Reactive emotion generation is to increase task performance and believability of the service robot. Experiment result shows that it seems possible for the reactive process to function for those purposes, and reciprocal interaction between different layers is important for proper functioning of robot's emotion generation system.

• The Journal of Korea Robotics Society
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• v.2 no.4
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• pp.341-345
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• 2007

This paper proposes an emotional interaction model between human and robot using an android. An android is a sort of humanoid robot that the outward shape of robot is almost the same as that of human. The android is a robot platform to implement and test emotional expressions and human interaction. In order to behave for the android like human, a structure of internal emotion system is very important. In our research, we propose a novel emotional model of android based on biological hormone and emotion space. Proposed emotion model has an advantage that it can represent emotion change as time by hormone dynamics.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.228-236
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• 2011

Emotion recognition plays an important role in the research area of human-computer interaction, and it allows a more natural and more human-like communication between humans and computer. Most of previous work on emotion recognition focused on extracting emotions from face, speech or EEG information separately. Therefore, a novel approach is presented in this paper, including face, speech and EEG, to recognize the human emotion. The individual matching scores obtained from face, speech, and EEG are combined using a weighted-summation operation, and the fused-score is utilized to classify the human emotion. In the experiment results, the proposed approach gives an improvement of more than 18.64% when compared to the most successful unimodal approach, and also provides better performance compared to approaches integrating two modalities each other. From these results, we confirmed that the proposed approach achieved a significant performance improvement and the proposed method was very effective.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.132-134
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• 2004

Emotion recognition between human and human is done compositely using various features that are face, voice, gesture and etc. Among them, it is a face that emotion expression is revealed the most definitely. Human expresses and recognizes a emotion using complex and various features of the face. This paper proposes hybrid feature extraction for emotions recognition from facial expression. Hybrid feature extraction imitates emotion recognition system of human by combination of geometrical feature based extraction and color distributed histogram. That is, it can robustly perform emotion recognition by extracting many features of facial expression.