• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.994-996
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• 1999

In this paper, we proposed complex demodulation method(CDM) to visualize the instantaneous frequency change of LF component and HF component of HRV signals, which represent the dynamics of sympathetic and parasympathetic (vagal) tone, respectively. As we know the range of the center frequencies of each autonomic tones, we could apply complex demodulation method. To simulate the heart rate variability signal, the IFPM model was adopted for generation of simulated cardiac event series. Then, we can visualize and access the dynamic changes of LF and HF component of autonomic tones in the time-frequency domain.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.136-142
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• 2014

Distinct features in heart rate signals during the driver's wake and sleep states could provide an initiative for the development of a safe driving systems such as drowsiness detecting sensor in a smart wheel. We measured ECG from health subjects ($23.5{\pm}2.5$ in age) during the wake and drowsiness states. The proposed method is able to detect R waves and R-R interval calculation in the ECG even when the signal includes in abnormal signals. Heart rate variability(HRV) was investigated for the time domain and frequency domains. The STD HR(0.029), NN50(0.044) and VLF power(0.0018) of the RR interval series of the subjects were significantly different from those of the control group (p < 0.05). In conclusion, there are changes in heart rate from wake to drowsiness that are potentially to be detected. The results in our study could be useful for the development of drowsiness detection sensors for effective real-time monitoring.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2080-2085
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• 2018

There have been numerous studies that extract the R-peak from electrocardiogram (ECG) signals. All of these studies can extract R-peak from ECG. However, these methods are complicated and difficult to implement in a real-time portable ECG device. After filtration choosing a threshold value for R-peak detection is a big challenge. Fixed threshold scheme is sometimes unable to detect low R-peak value and adaptive threshold sometime detect wrong R-peak for more adaptation. In this paper, a simple and robustness algorithm is proposed to detect R-peak with less complexity. This method also solves the problem of threshold value selection. Using the adaptive filter, the baseline drift can be removed from ECG signal. After filtration, an appropriate threshold value is automatically chosen by using the minimum and maximum value of an ECG signals. Then the neighborhood searching scheme is applied under threshold value to detect R-peak from ECG signals. Proposed method improves the detection and accuracy rate of R-peak detection. After R-peak detection, we calculate heart rate to know the heart condition.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.978-980
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• 2014

본 연구에서는 스트레스 상태 측정을 위한 심전도 신호 QRS 검출 알고리즘을 제안한다. 심전도 신호의 QRS 검출 과정은 4단계로 wavelet, moving average, squaring, threshold method로 구성된다. wavelet은 기저선 변동과 노이즈를 제거하고 moving average는 전체 신호를 부드럽게 하고 잔여 노이즈를 제거하며 squaring은 신호를 강조하는 역할을 한다. 마지막으로 threshold 기법을 이용해 검출간격을 설정하여 QRS를 검출하였다. 그 결과 Sensitivity는 99.54%, Positive Predictivity는 99.69%, Detection Error는 0.76%를 보였다. 또한, 피험자를 대상으로 게임을 이용해 스트레스 상태 변화에 대한 실험을 하였고, HRV 시간-주파수 파라미터를 분석함으로써 스트레스 상태 변화를 관찰할 수 있었다.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1245-1246
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• 2022

Walkability is an indicator of how much pedestrians are willing to walk and how well a walking environment is created. As walking can promote pedestrians' mental and physical health, there has been increasing focus on improving walkability in different ways. Thus, plenty of research has been undertaken to measure walkability. When measuring walkability, there are many objective and subjective variables. Subjective variables include a feeling of safety, pleasure, or comfort, which can significantly affect perceived walkability. However, these subjective factors are difficult to measure by making the walkability index more reliant on objective and physical factors. Because many subjective variables are associated with human emotional states, understanding pedestrians' emotional states provides an opportunity to measure the subjective walkability variables more quantitatively. Pedestrians' emotions can be examined through surveys, but there are social and economic difficulties involved when conducting surveys. Recently, an increasing number of studies have employed physiological data to measure pedestrians' stress responses when navigating unpleasant environmental barriers on their walking paths. However, studies investigating the emotional states of pedestrians in the walking environment, including assessing their positive emotions felt, such as pleasure, have rarely been conducted. Using wearable devices, this study examined the various emotional states of pedestrians affected by the walking environment. Specifically, this study aimed to demonstrate the feasibility of monitoring biometric data, such as electrodermal activity (EDA) and heart rate variability (HRV), using wearable devices as an indicator of pedestrians' emotional states-both pleasant-unpleasant and aroused-relaxed states. To this end, various walking environments with different characteristics were set up to collect and analyze the pedestrians' biometric data. Subsequently, the subjects wearing the wearable devices were allowed to walk on the experimental paths as usual. After the experiment, the valence (i.e., pleasant or unpleasant) and arousal (i.e., activated or relaxed) scale of the pedestrians was identified through a bipolar dimension survey. The survey results were compared with many potentially relevant EDA and HRV signal features. The research results revealed the potential for physiological responses to indicate the pedestrians' emotional states, but further investigation is warranted. The research results were expected to provide a method to measure the subjective factors of walkability by measuring emotions and monitoring pedestrians' positive or negative feelings when walking to improve the walking environment. However, due to the lack of samples and other internal and external factors influencing emotions (which need to be studied further), it cannot be comprehensively concluded that the pedestrians' emotional states were affected by the walking environment.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.6
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• pp.271-280
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• 2017

In this paper, we propose a new safety system composed of wearable devices, driver's seat belt, and integrating controllers. The wearable device and driver's seat belt capture driver's biological information, while the integrating controller analyzes captured signal to alarm the driver or directly control the car appropriately according to the status of the driver. Previous studies regarding driver's safety from driver's seat, steering wheel, or facial camera to capture driver's physiological signal and facial information had difficulties in gathering accurate and continuous signals because the sensors required the upright posture of the driver. Utilizing wearable sensors, however, our proposed system can obtain continuous and highly accurate signals compared to the previous researches. Our advanced wearable apparatus features a sensor that measures the heart rate, skin conductivity, and skin temperature and applies filters to eliminate the noise generated by the automobile. Moreover, the acceleration sensor and the gyro sensor in our wearable device enable the reduction of the measurement errors. Based on the collected bio-signals, the criteria for identifying the driver's condition were presented. The accredited certification body has verified that the devices has the accuracy of the level of medical care. The laboratory test and the real automobile test demonstrate that our proposed system is good for the measurement of the driver's condition.

• 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 Fisheries and Marine Sciences Education
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• v.18 no.1
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• pp.11-18
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• 2006

Man has always striven to create a thermally comfortable environment. This is reflected in building traditions around the world - from ancient history to present day. Today, creating a thermally comfortable environment is still one of the most important parameters to be considered when designing buildings. It is defined in the ISO 7730 standard as being "That condition of mind which expresses satisfaction with the thermal environment". A definition most people can agree on, but also a definition is not easily converted into physical parameters. Thermal comfort is a matter of many physical parameters, and not just one, as for example the air temperature that is set by air-conditioner. The most important matter Today's common offices and homes are only depending on air-conditioning as a cooling system during the summer. This kind of system tends to be focused on the person who controls it and those who are around the air-conditioner while thermal-comfort is neglected. Futhermore, the people's body conditions are not considered during each time that beginning, middle, last of the air-conditioning which causing displeasure of the residents more and more. This kind of operating system is set for a long time may causes unbalanced air condition and man's psychologic displeasure goes to increase.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4021-4030
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• 2015

Pulse wave is the physiological responses through the autonomic nervous system such as ECG. It is relatively convenient because it can measure the signal just by applying a sensor on a finger. So, it can be usefully employed in the field of U-Healthcare. The objects of this study are acquiring the PPG (Photoplethysmography) one of the way of measuring the pulse waves in non-invasive way using the CMOS image sensor on a smartphone camera, developing the portable system judging stressful or not, and confirming the applicability in the field of u-Healthcare. PPG was acquired by using image data from smartphone camera without separate sensors and analyzed. Also, with that image signal data, HRV (Heart Rate Variability) and stress index were offered users by just using smartphone without separate host equipment. In addition, the reliability and accuracy of acquired data were improved by developing additional hardware device. From these experiments, we can confirm that measuring heart rate through the PPG, and the stress index for analysis the stress degree using the image of a smartphone camera are possible. In this study, we used a smartphone camera, not commercialized product or standardized sensor, so it has low resolution than those of using commercialized external sensor. However, despite this disadvantage, it can be usefully employed as the u-Healthcare device because it can obtain the promising data by developing additional external device for improvement reliability of result and optimization algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.126-134
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• 2017

There have been numerous studies on extracting the R-peak from electrocardiogram (ECG) signals. However, most of the detection methods are complicated to implement in a real-time portable electrocardiograph device and have the disadvantage of requiring a large amount of calculations. R-peak detection requires pre-processing and post-processing related to baseline drift and the removal of noise from the commercial power supply for ECG data. An adaptive filter technique is widely used for R-peak detection, but the R-peak value cannot be detected when the input is lower than a threshold value. Moreover, there is a problem in detecting the P-peak and T-peak values due to the derivation of an erroneous threshold value as a result of noise. We propose a robust R-peak detection algorithm with low complexity and simple computation to solve these problems. The proposed scheme removes the baseline drift in ECG signals using an adaptive filter to solve the problems involved in threshold extraction. We also propose a technique to extract the appropriate threshold value automatically using the minimum and maximum values of the filtered ECG signal. To detect the R-peak from the ECG signal, we propose a threshold neighborhood search technique. Through experiments, we confirmed the improvement of the R-peak detection accuracy of the proposed method and achieved a detection speed that is suitable for a mobile system by reducing the amount of calculation. The experimental results show that the heart rate detection accuracy and sensitivity were very high (about 100%).