• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.515-517
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• 2016

In this paper, we develope a stretchable wearable device containing services for processing physiological signals to extract emotion information. The emotion extracting algorithm conducts to recognize emotion from EDR, SKT, and HRV signals measured with the fabric sensors. In addition, the suggested wearable device can also solve the problems faced with today's many other wearable devices: 1) limited battery life 2) the lack of compatibility and expandability due to run on internal components designed for smart phone 3) the design has always been a crucial factor in determining the success of main stream consumer wearable devices.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Design Convergence Study
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• v.15 no.1
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• pp.233-246
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• 2016

Mobile health industry has been combined with IT technology and is attracting attention. The health application has been developed to provide users a healthy life style. First of all, 5 mobile health applications were selected and reviewed in terms of their service trend. It turned out that none of those applications had any emotional data but physical one. Secondly, to extract users' emotion, technological researches were sorted into different categories. And the result implied that text-based emotion recognition technology is the most suitable for the mobile health service. To implement the service, the application was designed and developed the process of emotion recognition system based on the contents of the research. One-dimension emotion model, which is the standard of classifying emotional data and social network service, was set up as a source. In last, to suggest the usage of health application has been combined with persuasive technology. As a result, this paper prospered a overall service process, concrete service scheme and a guidelines containing 15 services in accordance with the five emotions and time. It is expected to become a direction for indicators considering a psychological individual context.

• Science of Emotion and Sensibility
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• v.13 no.2
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• pp.327-336
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• 2010

The purposes of this study are to evaluate mechanical properties and sensibility of tencel jacquard fabrics treated with ginkgo biloba extract and silicon softener, and to contribute to the research and development of the bedclothes made of the tencel jacquard fabrics. Mechanical properties and objective fabric hand evaluation were measured by using KES-FB system. Subjective sensibilities such as sensory, touch, and purchasing preference were estimated by using blind field test. The tensile properties such as EM, WT, and RT of tencel jacquard fabrics treated with ginkgo biloba extract and silicon softener showed increase. Bending properties and shear properties were decreased, but compression properties were increased compared to untreated fabric. With ginkgo biloba extract and silicon softener treatment, thickness and weight were increased. Therefore, tencel jacquard fabrics became more stretchable, softer, and bulkier than untreated fabrics. Consequently, THV of tencel jacquard fabrics treated with ginkgo biloba extract and silicon softener were increased. When fabrics were treated sequentially with ginkgo biloba extract and silicon softener, fabrics were estimated softer, more flexible, and bulkier than untreated fabrics. Also, tencel jacquard fabrics treated with ginkgo biloba extract and silicon softener were estimated to have good touch and preference.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.122-129
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• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.479-487
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• 2016

People can listen to almost every type of music by music streaming services without possessing music. Ironically it is difficult to choose what to listen to. A music recommendation system helps people in making a choice. However, existing recommendation systems have high computation complexity and do not consider context information. Emotion is one of the most important context information of music. Lyrics can be easily computed with various language processing techniques and can even be used to extract emotion of music from itself. We suggest a music-level similarity evaluation method using emotion and structure. Our result shows that it is important to consider semantic information when we evaluate similarity of music.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3925-3932
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• 2013

Emotion is closely related to the life of human, so has effect on many parts such as concentration, learning ability, etc. and makes to have different behavior patterns. The purpose of this paper is to extract important features based on physiological signals to recognize negative emotion. In this paper, after acquisition of electrocardiography(ECG), electroencephalography(EEG), skin temperature(SKT) and galvanic skin response(GSR) measurements based on physiological signals, we designed an accurate and fast algorithm using combination of linear discriminant analysis(LDA) and genetic algorithm(GA), then we selected important features. As a result, the accuracy of the algorithm is up to 96.4% and selected features are Mean, root mean square successive difference(RMSSD), NN intervals differing more than 50ms(NN50) of heart rate variability(HRV), ${\sigma}$ and ${\alpha}$ frequency power of EEG from frontal region, ${\alpha}$, ${\beta}$, and ${\gamma}$ frequency power of EEG from central region, and mean and standard deviation of SKT. Therefore, the features play an important role to recognize negative emotion.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.802-816
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• 2010

To create gesture from a new text input is an important problem in computer games and virtual reality. Recently, there is increasing interest in gesture stylization to imitate the gestures of celebrities, such as announcer. However, no attempt has been made so far to stylize a gestures using emotion such as happiness and sadness. Previous researches have not focused on real-time algorithm. In this paper, we present a system to automatically make gesture animation from SMS text and stylize the gesture from emotion. A key feature of this system is a real-time algorithm to combine gestures with emotion. Because the system's platform is a mobile phone, we distribute much works on the server and client. Therefore, the system guarantees real-time performance of 15 or more frames per second. At first, we extract words to express feelings and its corresponding gesture from Disney video and model the gesture statistically. And then, we introduce the theory of Laban Movement Analysis to combine gesture and emotion. In order to evaluate our system, we analyze user survey responses.