• Journal of the Ergonomics Society of Korea
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• v.29 no.1
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• pp.47-53
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• 2010

An analysis of biosignal and performance data collected during driving has increasingly employed in research to explore a human-vehicle interface design for better safety and comfort. The present study developed a protocol and a system to effectively analyze biosignal and driving performance measurements in various driving conditions. Electrocardiogram (ECG), respiration rate (RR), and skin conductance level (SCL) were selected for biosignal analysis in the study. A data processing and analysis protocol was established based on a comprehensive review of related literature. Then, the established analysis protocol was implemented to a computerized system so that immense data of biosignal and driving performance can be analyzed with ease, efficiency, and effectiveness for an individual and/or a group of individuals of interest. The developed analysis system would be of use to examine the effects of driving conditions to cognitive workload and driving performance.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.19-26
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• 2010

In this paper, we studied about the Embedded System of the biosignal measurement and analysis to sensibility evaluation in daily life for non-intrusive. This system is two kinds of measuring biosiganls(Electrocardiogram:ECG, Photoplethysmography:PPG) and analyzed by real-time wireless transmission to notebook PC using bluetooth for consistent and reliability of physiological way to assess continuously changing sensibility. Comparative studied of an autonomic nerve system activity ratio on characteristics frequency band of two kinds of biosignal analyzed frequency way using the Fast Fourier Transform(FFT) and Power Spectrum Density(PSD). Also the key idea of this system is to minimize computing of analysis algorithm for faster and more accurate to assess the sensibility, and the result of the visualization using graph. In this paper, we evaluated the analysis system to assess sensibility that measuring various situation in daily life using a non-intrusive biosignal measurement system, and the accuracy and reliability in comparison with difference of result by development analysis system.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.12-18
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• 2004

본 글에서는 인체로부터 생체전기신호를 측정하고 처리 및 해석하는 기술을 소개한다. 일반적인 계측 시스템을 구성하는 필수적인 네 가지 요소는 측정대상, 센서부, 신호처리부, 그리고 출력부이다. 생체전기신호의 측정에서 측정대상은 인체를 포함하는 생명체이다. 경우에 따라서는 생명체로부터 떼어 낸 특정 부위가 측정대상이 될 수도 있으나 본 글에서는 살아 있는 인체를 측정대상으로 설정하기로 한다. 또한 인체로부터 방사되는 에너지를 측정하는 비접촉 방식은 다루지 않고, 측정 부위를 인체의 내부 또는 표면으로 제한한다. 즉, 센서를 측정 부위에 직접 부착하는 접촉형 인체-센서 인터페이스 방법을 사용하는 경우만을 다루기로 한다.(중략)

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.21-22
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• 2011

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.139-145
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• 2017

PURPOSES : This study analyzed the difference in a driver's workload between using a driving simulator and field driving in tunnel, highway. METHODS : Based on the literature review, it was found that a driver's workload could be quantified using biosignals. This study analyzed the biosignal data of 30 participants using data collected while they were using a driving simulator and during a field test involving tunnel driving. Relative energy parameter was used for biosignal analysis. RESULTS : The driver's workload was different between the driving simulator and field driving in tunnels, highway. Compared with the driving simulator test, the driver's workload exhibited high value in field driving. This result was significant at the 0.05 level. The same result was observed before the tunnel entrance section and 200 m after the entrance section. CONCLUSIONS : This study demonstrates the driving simulator effect that drivers feel safer and more comfortable using a driving simulator than during a field test. Future studies should be designed considering the result of this study, age, type of simulator, study site and so on.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.85-89
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• 1997

We present several biosignal measurement results and analysis algorithms for the visual stimulus from International Affective Picture Sytem. Sine human body is nonlinear dynamic system, we investigated both linear and nonlinear methods. We found that the alpha wave of EEG, the chaos of peripheral blood pressure, the LF/HF of HRV and thd retutn map of RR interval were good parameters for the measuremet of human sensibility. These can be used as the parameters for the measurement of human sensibility.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.23-26
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• 1997

We present several biosignal measurement results and analysis algorithms or the visual stimulus from International Affective Picture System. Since human body is nonlinear dynamic system, we investigated both linear and nonlinear methods. We found that the chaos was diminished when unpleasant picture is presented relative to pleasant picture, and the alpha wave of EEG was slightly augmented in pleasant picture, but was not convincing result. These can be used as the parameter or the measurement of human sensibility.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.35-38
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• 2010

본 논문에서는 일상생활 속에서 무자각적으로 생체신호를 측정하고 분석하여 감성을 평가할 수 있는 임베디드 시스템에 관하여 연구하였다. 지속적으로 변화하는 감성을 일관적이며 신뢰성이 높은 생리적인 방법으로 평가하기 위해 심전도(ECG:Electrocardiogram), 맥파(PPG:Photoplethysmogram)의 두 가지 생체신호를 측정하고, 무선전송(Bluetooth) 장치를 이용하여 측정한 생체신호를 실시간으로 노트북PC로 전송하여 분석하였다. 생체신호의 분석방법은 FFT(Fast Fourier Transform)과 전력스펙트럼밀도(Power Spectrum Density)를 이용한 주파수 분석방법으로 두 생체신호의 특정 주파수 대역이 가지는 자율신경계의 활성도의 비율을 분석하여 비교 연구하였다. 또한 보다 빠르고 정확한 감성을 평가하기 위하여 분석알고리즘의 연산을 최소화 하였으며 그래프를 이용한 분석결과의 시각화를 하였다. 본 논문에서는 무자각적인 생체신호 측정 시스템을 이용하여 다양한 상황에서 생체신호를 측정하고, 개발한 분석 알고리즘으로 분석한 결과의 차이를 연구하여 정확도 및 신뢰도를 기준으로 감성을 평가하기 위한 분석 시스템을 평가하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1049-1050
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• 2015

e-health보드를 이용하여 측정한 생체신호를 실시간으로 블루투스통신을 통한 무선통신을 함으로서 PC와 연결한다. PC에서 송신된 데이터를 텍스트로 저장한 뒤 c#으로 체온, 심전도, 근전도, 피층 전기 반응, 호흡 5가지의 결과 값을 그래프로 보여준다.

• Archives of Craniofacial Surgery
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• v.22 no.5
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• pp.223-231
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• 2021

The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.