• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.160-163
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• 2009

본 연구는 생리신호에 의한 집중된 시선과 집중하지 않는 시선을 분류하고자 한다. 이를 검증하기 위해 시각적으로 높은 집중과 낮은 집중을 요구하는 두가지 과제를 피실험자에게 제시하고 PPG(Photoplethysmogram), GSR(Galvanic Skin Response) 그리고 SKT(Skin Temperature)센서를 사용한 자율신경계 반응과 시선 움직임을 측정하였다. 과제는 $3{\times}3$으로 화면 구역을 나누고 각 구역에 문자를 제시하고 역방향 문자를 찾도록 하였다. 실험에는 20 명의 대학생이 참여하였으며, 1 번의 실험에 12 종류의 다른 문자배열을 제시 받았으며 1 번의 연습을 포함하여 총 5 회 실시후 데이터를 분석하였다. 높은 집중일 경우와 낮은 집중일 경우를 T-test 분석 결과, 자율신경계에서는 높은 집중일 경우 PPG 주파수가 증가하고 GSR과 SKT는 감소한 결과를 보였다. 따라서 시선의 집중도에 따라 다른 자율신경계 반응과 시선반응을 보이는 것을 확인하였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1294-1299
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• 2014

The emotion is deeply affected by human behavior and cognitive process, so it is important to do research about the emotion. However, the emotion is ambiguous to clarify because of different ways of life pattern depending on each individual characteristics. To solve this problem, we use not only physiological signal for objective analysis but also hybrid unsupervised-supervised learning classifier for automatic emotion detection. The hybrid emotion classifier is composed of K-means, genetic algorithm and support vector machine. We acquire four different kinds of physiological signal including electroencephalography(EEG), electrocardiography(ECG), galvanic skin response(GSR) and skin temperature(SKT) as well as we use 15 features extracted to be used for hybrid emotion classifier. As a result, hybrid emotion classifier(80.6%) shows better performance than SVM(31.3%).

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.4
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• pp.499-508
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• 2014

In this paper, we implemented a LED sensitivity illumination system, being driven in response to changes in the biological signals of GSR and PPG signal. After measuring biological signals of a human body from GSR and PPG sensor modules, MCU decided the state of relaxation or arousal of the subject, being based on the wake relaxation identifying map proposed in this paper. A developed LED sensitivity illumination system makes the subject to reach a normal state by giving a change of the LED illumination color, corresponding to a state of the subject.

• Child Health Nursing Research
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• v.20 no.3
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• pp.142-148
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• 2014

Purpose: This study was done to evaluate the effectiveness of EMLA cream on pain related to venipuncture among children. Methods: In this study, 48 children were evaluated using a sequential measurement for level of pain by Skin Conductance Level (SCL) based on Galvanic Skin Response (GSR), heart rate, and the Visual Analogue Scale (VAS) at four times. Results: The maximum and mean of the SCL were each significantly different between the experimental and control groups and furthermore, the two were also significantly different among observed times. In addition there was a significant interaction between group and time. The children's perceived pain using VAS was not significantly different between the experimental and control groups. There was no significant difference in the heart rate between the experimental and control groups; however, the interaction between group and time was significant. Conclusion: In conclusion, applying topical anesthetic cream to the venipuncture site to reduce pain was effective among the children and therefore it is highly recommended that topical anesthetic cream be applied at the venipuncture site as a nursing intervention to reduce pain when a child has to undergo a venipuncture.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.559-564
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• 2006

최근에 들어서 생체신호분석을 통하여 여러 가지 사용자 상태를 파악하려는 연구가 많이 진행되고 있다. 대표적인 것이 GSR(전기피부반응, galvanic skin response), BVP(blood volume pressure), 호흡 등의 생체신호가 사람의 흥분 정도, 정신적 부담, 감정변화에 따라 달라지는 특성을 활용하는 것이다. 본 연구에서는 디지털 TV, 혹은 IPTV 의 컨텐츠를 감상하는 환경 하에서 시청자의 생체신호의 변화 패턴을 분석하여, 그 분석 결과로부터 TV 프로그램이나 디지털 컨텐츠에 대해 시청자가 느끼는 만족도, 집중도, 흥미 여부 등을 추론하고자 하였다. 즉, 주어진 컨텐츠를 감상하는 동안 시청자로부터 얻어낸 생체신호를 분석한 시청 정보 데이터가 프로그램에 대한 선호도와 관련을 가질 수 있는지 검증한 기초 연구 결과를 제시한다. 또한 이 결과를 통해 프로그램에 대한 시청자의 반응을 객관적으로 측정하고 실시간으로 반영할 수 있도록 하는 TV 프로그램 추천 시스템의 구현 가능성을 검증한다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.120-122
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• 2003

In this study, an experiment was conducted in order to investigate the feasibility and effectiveness of and on-line subjective assessment (OLSA) system. The present study compared Galvanic Skin Response (GSR) with the OLSA by presenting 28 subjects in their 20s with pictures arousing either positive or negative sensibility. According to the correlation coefficients, changes in subjective sensibility caused by the positive visual stimulus were related more closely to GSR, from the positive visual stimulus, and changes in subjective sensibility caused by the negative visual stimulus were related more closely to GSR from the negative visual stimulus. In conclusion, the most remarkable characteristic of the present system is that it not only assesses the average sensibility when stimuli are presented, but also shows the changing strength of sensibility over time.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.878-879
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• 2016

본 논문에서는 기존의 고비용-고정형 거짓말탐지기의 단점을 극복하기 위하여 거짓말 시 발생되는 인체의 생리적 변화를 측정하기 위한 GSR (Galvanic Skin Response) 센서와 스트레스수치 알고리즘을 활용하여 스마트폰 기반 어플리케이션을 개발 및 그 활용방안을 제안한다. 사용자에게 부착하여 사용자의 생체 피부를 통해 외적자극 또는 심적 흥분/동요 상태에 의한 활동전위 발생 정도를 측정하고 이를 스트레스 지수로 변환할 수 있는 방법을 제시한다. 또한, 사용성 및 저비용성을 보장하기 위하여, 제안하는 거짓말 탐기기능을 스마트폰 어플리케이션화 하였다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.87-90
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• 2009

본 연구는 HMD(Head Mounted Display), 적외선 카메라, 웹 카메라, 데이터 글러브, 그리고 생리신호 측정 센서를 이용한 증강현실 게임 플랫폼 설계를 목적으로 하고 있다. HMD 는 사용자의 머리의 움직임을 파악하고, 사용자에게 가상 물체를 디스플레이화면에 제공한다. 적외선 카메라는 HMD 하단에 부착하여 사용자의 시선을 추적한다. 웹 카메라는 HMD 상단에 부착하여 전방 영상을 취득 후, 현실영상을 HMD 디스플레이를 통하여 사용자에게 제공한다. 데이터 글러브는 사용자의 손동작을 파악한다. 자율신경계반응은 GSR(Galvanic Skin Response), PPG(PhotoPlethysmoGraphy), 그리고 SKT(SKin Temperature) 센서로 측정한다. 측정된 피부전기반응, 맥파, 그리고 피부온도는 실시간 데이터분석을 통하여 집중 정도를 파악하게 된다. 사용자의 머리 움직임, 시선, 그리고 손동작은 직관적 인터랙션에 사용되고, 집중 정도는 직관적 인터랙션과 결합하여 사용자의 의도파악에 사용된다. 따라서, 본 연구는 멀티모달 인터랙션을 이용하여 직관적 인터랙션 구현과 집중력 분석을 통하여 사용자의 의도를 파악할 수 있는 새로운 증강현실 게임 플랫폼을 설계하였다.

• Journal of the Ergonomics Society of Korea
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• v.18 no.3
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• pp.27-39
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• 1999

The purpose of this study was to approve the capability of human sensibility evaluation based on physiological responses and real-time subjective assessments. Three well-trained healthy human subjects were participated in the experiments. We measured physiological responses such as Heart Rate Variability(HRV), Galvanic Skin Response(GSR) and skin temperature under rest and visual stimulation conditions, respectively. Off-time subjective assessments were recorded before and after visual stimulations. Real-time subjective assessments were recorded during visual stimulations. The results of physiological responses and off-time and real-time subjective assessments were quantified and compared. The results showed that the correlation between physiological responses and real-time subjective assessments was high (83%) for both the positive and negative visual stimulation. The correlation between the physiological responses and off-time subjective assessments was high (83%) for the negative visual stimulation but was low (15%) for the positive visual stimulation. Although the current observation is preliminary and requires more careful experimental study, it appears that the correlation between real-time subjective assessment and physiological responses is higher than that of the off-time subjective assessment and physiological responses.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.146-154
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• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.