• The Journal of Korea Robotics Society
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• v.5 no.1
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• pp.7-13
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• 2010

This paper presents the sound-based emotion estimation method and the growing HRI (human-robot interaction) system for a Mon-E robot. The method of emotion estimation uses the musical element based on the law of harmony and counterpoint. The emotion is estimated from sound using the information of musical elements which include chord, tempo, volume, harmonic and compass. In this paper, the estimated emotions display the standard 12 emotions including Eckman's 6 emotions (anger, disgust, fear, happiness, sadness, surprise) and the opposite 6 emotions (calmness, love, confidence, unhappiness, gladness, comfortableness) of those. The growing HRI system analyzes sensing information, estimated emotion and service log in an edutainment robot. So, it commands the behavior of the robot. The growing HRI system consists of the emotion client and the emotion server. The emotion client estimates the emotion from sound. This client not only transmits the estimated emotion and sensing information to the emotion server but also delivers response coming from the emotion server to the main program of the robot. The emotion server not only updates the rule table of HRI using information transmitted from the emotion client and but also transmits the response of the HRI to the emotion client. The proposed system was applied to a Mon-E robot and can supply friendly HRI service to users.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.99-103
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• 2000

There are two approaches in the study of emotion in the physiological psychology. The first is to clarify the brain mechanism of emotion, and the second is to evaluate objectively emotions using physiological responses along with our feeling experience. The method presented here belongs to the second one. Our method is based on the "level-crossing point detection" method. which involves the analysis of frequency fluctuations of EEG and is characterized by estimation of emotionality using coefficients of slopes in the log-power spectra of frequency fluctuation in alpha waves on both the left and right frontal lobe. In this paper we introduce a new theory of estimation on an individual's emotional state by using our non-invasive and easy measurement apparatus.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.1-8
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• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.143-146
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• 2008

The objectives of this study were to evaluate color sensation for yellowish natural dye fabrics using magnitude estimation to determine physical colorimetric factors significantly related to human sensibility by establishing power function in psychophysical analysis. Fourteen different yellowish fabrics dyed with natural colorants were selected as stimuli and subjective color sensations including brightness, heaviness, softness, strength, warmth, activeness, classicalness, femininity, and pleasantness for each stimulus were evaluated. As results, yellowish natural dye fabrics in general seemed to evoke feeling of brightness, femininity, and pleasantness more strongly than that of heaviness and classicalness. Most of color sensation were significantly related with more than one of physical color properties, which leads to establishing reliable power functions between them. In the power functions, these relationships could be utilized to design color-sensible natural dye textiles.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.2
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• pp.100-105
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• 2018

In this paper, we determined the driver's stress and fatigue level through physiological signals of a driver in the connected car environment, accordingly designing and implementing the architecture of the connected cars' platforms needed to provide services to make the driving environments comfortable and reduce the driver's fatigue level. It includes a gateway between AVN and ECU for the vehicle control, a framework for native applications and web applications based on AVN, and a sensing device and an emotion estimation engine for application services. This paper will provide the element technologies for the connected car-based convergence services and their implementation methods, and reference models for the service design.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.16-16
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• 2004

• Speech Sciences
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• v.12 no.4
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• pp.53-70
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• 2005

A Vowel of speech signals are multicomponent signals composed of AM-FM components whose instantaneous frequency and instantaneous amplitude are time-varying. The changes of emotion states cause the variation of the instantaneous frequencies and the instantaneous amplitudes of AM-FM components. Therefore, it is important to estimate exactly the instantaneous frequencies and the instantaneous amplitudes of AM-FM components for the extraction of key information representing emotion states and changes in speech signals. In tills paper, firstly a method decomposing speech signals into AM - FM components is addressed. Secondly, the fundamental frequency of vowel sound is estimated by the simple method based on the spectrogram. The estimate of the fundamental frequency is used for decomposing speech signals into AM-FM components. Thirdly, an estimation method is suggested for separation of the instantaneous frequencies and the instantaneous amplitudes of the decomposed AM - FM components, based on Hilbert transform and the demodulation property of the extended Fourier transform. The estimates of the instantaneous frequencies and the instantaneous amplitudes can be used for modification of the spectral distribution and smooth connection of two words in the speech synthesis systems based on a corpus.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.171-175
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• 1998

This work presents an estimation for stress status using biosignal and fuzzy theory. Stress is estimated by 'coin-build' experiment with two type, relax and stress status. The estimator uses five biosignals, fuzzy logic to combine these signals and physiological knowledge. The system was tested in 10 records of healthy indivisuals and acheived a template of a stress progress. This work presents an estimation for stress status using biosignal and fuzzy theory. Stress is estimated by 'coin-build' experiment with two type, relax and stress status. The estimator uses five biosignals, fuzzy logic to combine these signals and physiological knowledge. The system was tested in 10 records of healthy indivisuals and acheived a template of a stress progress.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.41-46
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• 2003

In recently, Is inhabiting more than 70％ indoors during a day in case of company employee and ordinary people which is looking at usual business. Therefore Thermal comfort of human body about indoor temperature and air flow acting very heftily. When intestine temperature is fallen for external low temperature and air flow in winter in case enter into heated room feel comfort by effect of temperature and feel comfort or discomfort by room heating condition gradually. Therefore it is important that grasp thermal comfort about temperature and air flow in heating to keep continuous comfort in indoor dwelling. Temperature and thermal comfort factor of emotion ＆ sensitivity image exert fair effect since heating middle although thermal comfort change greatly effect on sensation about temperature at actuality heating early. Need much study yet in vantage point of emotion ＆ sensitivity although much study were held about thermal and comfort sensibility and when heat in existing research until now. Therefore this study is targeting that evaluate thermal comfort through introduction of estimation method by emotion ＆ sensibility image real and synthetic sensibility about thermal environment that is becoming winter heating.