• 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.

• Science of Emotion and Sensibility
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• v.6 no.1
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• pp.33-37
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• 2003

The objective of the present study is to develop a thermal comfort measurement system for ergonomic sensibility analysis. The system can measure basic components for thermal comfort, such as skin temperature and clothing temperature/humidity level. A study on the linearization of temperature and humidity sensors has been conducted for more accurate and stable sensor development. The software has been developed for thermal comfort analysis for both clothing thermal environments and indoor environments.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.245-249
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• 2002

인간 감성변화의 기본인 피부온도 및 피복내 온습도 측정을 위한 시스템과 감성공학적 해석을 위한 보다 정밀하고 안정성이 있는 센서의 응답성 및 회로의 선형화에 대한 연구를 수행하고, 의복내 환경평가 및 실내 온습도 측정등의 다양한 감성공학적 해석을 위한 소프트웨어의 개발이 본 연구의 목적이다. 본 연구에는 손쉬운 온습도 변환장치와 풍부한 저장능력등 다양한 분야에서 활용이 가능한 온습도 측정기와 이에 필요한 센서를 개발하고 측정기의 선형화특성을 평가하였다.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.12
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• pp.55-64
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• 2019

The purpose of this study was to analyze the affection of plan configuration on human emotion using Virtual Reality. A total of four different plan configuration was selected according to the prior study and built using Virtual Reality. The EEG was measured and then calculated using FFT to measure human emotion in different plan configurations. The measurements were shown to lead there was a significant statistical difference in four types of brainwaves between the plan types(p<0.05). This indicates that there is a possibility of plan configuration may exacerbate psychological disorder among single-person household and suggests that it is possible to counteract those stress among single-person household by changing the plan configuration in the earlier designing stage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1076-1094
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• 2022

Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.2
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• pp.9-13
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• 2001

• Journal of IKEEE
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• v.22 no.3
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• pp.816-821
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• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.171-175
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• 2012

Recently, the research on ICT (Information and Communication Technology), which cognizes and communicates human's emotion through information technology, is increasing. For instance, there are researches on phones and services that perceive users' emotions through detecting people's voices, facial emotions, and biometric data. In short, emotions which were used to be predicted only by humans are, now, predicted by digital equipment instead. Among many ICT researches, research on emotion recognition from face is fully expected as the most effective and natural human interface. This paper studies about sensitivity ICT and examines mechanism of facial expression recognition system as an example of sensitivity ICT.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.76-81
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• 2009

We propose an effective emotion recognition algorithm based on the minimum classification error (MCE) incorporating multi-modal system The emotion recognition is performed based on a Gaussian mixture model (GMM) based on MCE method employing on log-likelihood. In particular, the reposed technique is based on the fusion of feature vectors based on voice signal and galvanic skin response (GSR) from the body sensor. The experimental results indicate that performance of the proposal approach based on MCE incorporating the multi-modal system outperforms the conventional approach.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.539-546
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• 1998

The purpose of this study is to estimate human emotion quantitatively under colors and music stimuli and to examine the for correlation between color and music sensibility. Physiological signals(electroencephalogram, electrocardiogram, Galvanic skin conductivity and respiration rate) were measured to compare color with music sensibilities. The personality of the subject were investigated using factor analysis and semantic differential method of 20 items(7 interval scaled). The results showed that red, yellow and violets color provoked active and exciting senses mainly as dance, rock and blues music. While blue, cyan and pink colors ware involved in tranquil and resting emotions deeply as classic and ballade music.