• Proceedings of the KSPS conference
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• 2005.11a
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• pp.163-166
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• 2005

We propose a speech emotion recognition method for natural human-robot interface. In the proposed method, emotion is classified into 6 classes: Angry, bored, happy, neutral, sad and surprised. Features for an input utterance are extracted from statistics of phonetic and prosodic information. Phonetic information includes log energy, shimmer, formant frequencies, and Teager energy; Prosodic information includes pitch, jitter, duration, and rate of speech. Finally a patten classifier based on Gaussian support vector machines decides the emotion class of the utterance. We record speech commands and dialogs uttered at 2m away from microphones in 5different directions. Experimental results show that the proposed method yields 59% classification accuracy while human classifiers give about 50%accuracy, which confirms that the proposed method achieves performance comparable to a human.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.389-401
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• 2019

The purpose of this study is to clarify the relationship between servicescape experience of visitors to professional baseball stadium, control perception on the spot, consumption emotion, and revisit intention. A total of 273 questionnaires were analyzed using SPSS 20.0 and AMOS 20.0. The validity of the data was verified through frequency analysis, reliability analysis, confirmatory factor analysis, and correlation analysis. The hypothesis was verified by structural equation model analysis. First, servicescape has a statistically significant effect on control perception. Second, the control perception in the servicescape has a significant effect on the consumption emotion. Third, servicescape effected consumption emotion. Fourth, consumption emotion had a significant influence on the revisit intention. The results of this study suggest that visitors to baseball stadiums can induce revisit intention through positive experience of servicescape. The specific discussions and implications are described in the text.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.513-519
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• 2013

The current vision-based approaches for emotion recognition, such as facial expression analysis, have many technical limitations in real circumstances, and are not suitable for applications that use them solely in practical environments. In this paper, we propose an approach for emotion recognition by combining extrinsic representations and intrinsic activities among the natural responses of humans which are given specific imuli for inducing emotional states. The intrinsic activities can be used to compensate the uncertainty of extrinsic representations of emotional states. This combination is done by using PRMs (Probabilistic Relational Models) which are extent version of bayesian networks and are learned by greedy-search algorithms and expectation-maximization algorithms. Previous research of facial expression-related extrinsic emotion features and physiological signal-based intrinsic emotion features are combined into the attributes of the PRMs in the emotion recognition domain. The maximum likelihood estimation with the given dependency structure and estimated parameter set is used to classify the label of the target emotional states.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2373-2378
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• 2005

An emotion detection algorithm using frontal facial image is presented in this paper. The algorithm is composed of three main stages: image processing stage and facial feature extraction stage, and emotion detection stage. In image processing stage, the face region and facial component is extracted by using fuzzy color filter, virtual face model, and histogram analysis method. The features for emotion detection are extracted from facial component in facial feature extraction stage. In emotion detection stage, the fuzzy classifier is adopted to recognize emotion from extracted features. It is shown by experiment results that the proposed algorithm can detect emotion well.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.328-329
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• 2000

A viscosity resistance control method of the intelligent prosthetic legs is studied using an optimal control theory. The simulated results suggests that it is important to control the viscosity of the prosthetic knee joint in one period of walking to improve the usability. In this paper we describe modeling of the thigh prosthetic legs, optimal control and simulated results.

• Horticultural Science & Technology
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• v.28 no.3
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• pp.484-491
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• 2010

This study investigated the effects of the intergenerational horticultural activity program on the improvement of emotion and self-esteem for the elderly and young children. When the pre- and post-treatments of the elderly was compared, both the control and treatment didn't show a significant difference in emotion. In self-esteem of the elderly the control didn't show a significant difference; on the contrary, treatment showed a highly significant difference ($p$<0.01). When a comparison was made on the pre- and post-treatment of young children, the control didn't show a significant difference in emotional intelligence; on the contrary, treatment showed a highly significant difference ($p$<0.001). In self-esteem of young children both the control and the treatment didn't show a significant difference. The results suggest that intergenerational horticultural activity program can improve young children's emotional intelligence and the elderly's self-esteem.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.55-56
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• 2011

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1776-1780
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• 2004

In this paper, we propose the new emotion model and the robot behavior decision model based on proposed emotion model. As like in human, emotions are hierarchicalized in four levels (momentary emotions, mood, attitude, and personality) and are determined from the robot behavior and human responses. They are combined with motivation (which is determined from the external stimuli) to determine the robot behavior.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.138.2-138
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• 2001

This paper proposes the technique for recognizing the human´s emotion by using the CCD color image. To do this, we first acquire the color image from the CCD camera. And then propose the method for recognizing the expressing to be represented the structural correlation of man´s feature points(eyebrows, eye, nose, mouse), In the proposed method. Human´s emotion is divided into four emotion(surprise, anger, happiness, sadness). Finally, we have proven the effectiveness of the proposed method through the experimentation.