• MALSORI
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• no.56
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• pp.173-183
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• 2005

We propose a speech emotion recognition method for affective 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 pattern 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 5 different directions. Experimental results show that the proposed method yields $48\%$ classification accuracy while human classifiers give $71\%$ accuracy.

• 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 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.256-261
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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 developed, 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 the reinforcement learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.737-743
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• 2017

The intelligent life-log system proposed in this paper is intended to identify and record a myriad of everyday life information as to the occurrence of various events based on when, where, with whom, what and how, that is, a wide variety of contextual information involving person, scene, ages, emotion, relation, state, location, moving route, etc. with a unique tag on each piece of such information and to allow users to get a quick and easy access to such information. Context awareness generates and classifies information on a tag unit basis using the auto-tagging technology and biometrics recognition technology and builds a situation information database. In this paper, we developed an active modeling method and an application that recognizes expressionless and smile expressions using lip lines to automatically record emotion information.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.58-61
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• 2000

The purpose of this study is to investigate the characteristics of electroencephalogram (EEG) during emotional occurrence of likes and dislikes in humans subjected to visual stimuli. Fifteen healthy male subjects participated in the study. Portrayals of females and cars on a visual screen, one photographic display at a time. were used as the stimulus. The subjects, with their EEGs concomitantly monitored, were asked to record their likes and dislikes for each portrayal of a total of 50 sequential displays. The results indicated that beta ($\beta$)=wave was more prevalent with dislikes than likes, and the arousal level was higher when dislikes predominated over likes, implying that more cerebral information processing activity was involved in answering dislikes than likes. In the case of cars, the difference between likes and dislikes was focused within a frequency band of 15-20 Hz in the right cerebral hemisphere. Our findings suggest that the activity in the right brain predominates with increases in negative emotion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.10 no.15
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• pp.1-22
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• 1987

From birth to death, every man is under the control of the three biorhythm curves that initiate from inner body. body. Those are called 'Physical rhythm, emotion I rhythm and intellectual rhythm'. These biorhythms have influence each other in human behaviour like physical endurance, creativity, record of examination. The result of investigation indicates that the students' records in low level period are lower than those of in high. Therefore, it is verified statistically whether the biorhythm has effects on human ability in scholarly record or not. And also, this research calculates the average nixed-biorhythm which is representable for a group by using mode mixed-biorhythm.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.322-327
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• 2007

Researchers in the field of psychology used Electroencephalographic (EEG) to record activities of human brain lot 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 physiological signals and gestures in the existing research. In this paper, we use together physiological 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 physiological signals and gestures gets high recognition rates better than using physiological signals or gestures. Both physiological signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• The Korean Journal of Archival Studies
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• no.44
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• pp.199-215
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• 2015

This study suggest the social role of record information management for Sewol ferry disaster on sea near Jindo-gun at April 16, 2014. Social each part and the discrete member be going to carry out the social role for the disaster so that record information management part may be gather, operate, provide related record informations. Record informations is ways to reflect, to supplement about something and means to effect it. They naturally generated though automatic managed, it need to purposeful activity. From finding to lack, to remedy a problem, Korean society and record information management part shall be reinforcing directions and competency of the solution with various angles. Practical union and assistance of record information management part for Sewol ferry disaster, at first it be a help to recognize officially evidence for the accident. Secondary it producing and using better than current state of the area's record information part. Finally it may be actively comprise and implement our competency and emotion.

• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.709-714
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• 2024

In this paper, we introduce the application 'Mindlog' system that helps patients seeking psychiatric treatment and counseling to record their thoughts and medical records. This system was developed to prevent patients from ending the treatment with an uncomfortable feeling due to not being able to clearly convey what they want to say during a short consultation time, or from not receiving detailed treatment instructions due to this. It allows patients to objectively observe and organize their feelings through simple tasks and systematic guides. In addition, it helps patients more conveniently check what changes have occurred compared to previous treatment and what they want to discuss in actual treatment by allowing them to systematically manage the treatment schedule and content.