• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.191-198
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• 2020

In this study, we proposed a real-time ECG analytical method for predicting stress and dangerous heart condition using the ECG signal in playing AR/VR device. A real-time diagnosis is used as R-R interval based HRV(:Heart rate variability), BPM(:Beats Per Minitue) and autonomic nervous research with through mapping method of two-dimensional planes. The ECG data were analyzed every 5 minutes and derived from autonomic nervous system diagnosis.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.496-503
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• 2009

In the life environment changed with not only the material abundance but also the quality, it is the most crucial factor for the strategy of sensibility engineering to investigate vital signal according to the sensibility. In this perspective, it is necessary to design and merchandise the products in cope with each sensibility and needs as well as its functional aspects. In this paper, we proposed the correlation between the visual sensibility and the vital signal using the wearable based electrocardiogram sensing clothes. We measured the electrocardiogram (ECG) signal by wearing the electrocardiogram sensing clothes. The heart rate variability (HRV) is calculated form the acquired ECG signal by wearing the electrocardiogram sensing clothes. And the power spectrum analysis using the Fast Fourier Transform (FFT) is evaluated the correlation between the visual sensibility and the vital signal. we plan to conduct empirical applications to verify the adequacy and the validity of the proposed method.

• Progress in Medical Physics
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• v.3 no.1
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• pp.71-78
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• 1992

This study describes the design of the biophysiological signal processing analyzer which can collect and analyze the biosignal raw data. System hardware is consisted of the IBM PC AT. pre-amplifier. AID converter, Counter/Timer. and RS-232C processor. Biophysiological signal data were processed by the software digital filter. FFT and graphic processing routine. The tachogram and FFT of the the peak to peak interval time was accomplished by the Graphic user interface software using the biophysiological signal processed data. Using this system. the powerspectrum of the heart rate variability during the long term could be observed. Experimental results of this system approach our purpose. which is improved the cost performance. easy to use. reducing raw-data noise and optimizing model for digital filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1431-1437
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• 2016

In mobile healthcare device, when to measure the heart rate by using the PPG signal, its performance is reduced according to the motion artifacts that is the movement of user. This is because the frequency range of motion (0.01-10 Hz) and that of PPG signals overlap. Also, the motion artifacts cannot be rectified by general filters. To solve the problem, this paper proposes a method using filter banks and independent component analysis (ICA). To evaluate the performance of the proposed method, we were artificially applied various movements and compared heart rate errors of the moving average filter and ICA. In the experimental results, heart rate error of the proposed method showed very low than moving average filter and ICA. In this way, it is possible to measure stable heart rate if the proposed method is applied to the healthcare terminal design.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.93-102
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• 2019

As interest in wellness grows, There is a lot of research about monitoring individual health using wearable devices. Accordingly, a variety of methods have been studied to distinguish exercise from daily activities using wearable devices. Most of these existing studies are machine learning methods. However, there are problems with over-fitting on individual person's learning, data discontinuously recognition by independent segmenting and fake activity. This paper suggests a detection method for exercise activity based on the physiological response principle of heart rate up and down during exercise. This proposed method calculates activity intensity and heart rate from triaxial and photoplethysmography sensor to determine a heart rate recovery, then detects exercise by estimating activity intensity or detecting a heart rate rising state. Experimental results show that our proposed algorithm has 98.64% of averaged accuracy, 98.05% of averaged precision and 98.62% of averaged recall.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.14-19
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• 2018

Recently, the smart health care industry has been rising rapidly and interest and efforts for public health have been greatly increased. As a result, the public does not visit medical specialists and medical facilities, but the desire to check their health condition in everyday life is increased. Therefore, many domestic and foreign companies continuously research and develop wearable devices that can measure body activity information anytime and anywhere And the market. Especially, it is used for heart activity measurement device using pulse wave sensor and electrocardiogram sensor. However, in this study, a monitoring system that can detect cardiac activity using cardiac sounds, heart sound measurement rather than pulse wave measurement and electrocardiogram measurement, was performed and its performance was evaluated. Experimental results confirmed the predictability of cardiac heart rate and heart valve disease during daily living.

• Journal of Sensor Science and Technology
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• v.23 no.6
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• pp.402-408
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• 2014

Heart rate is one of the most important signal to monitor the health condition of the patient or exerciser. Various wearable devices have been developed for the continuous monitoring of ECG signal from human body during exercise. Among these, ECG chest belt has been widely used. However wearing chest belt with ECG sensor is uncomfortable in normal life due to the electrode contact between metal electrodes of ECG sensor and skin of the human body. So we develop the royal healthcare backpack that can measure ECG signal without skin contact by using capacitor-type ECG sensor. The position of the measurement point is critical to collect a clear ECG signal in the capacitive ECG measurement from backpack. Various tests were conducted to find the optimal ECG measurement position which has less noise and could get strong and clear ECG signal during exercise, walking, hiking, mountain climbing and cycling.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1087-1094
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• 2008

This study describes a system for human sensibility evaluation using PPG(photoplethysmogram) signal and biofeedback algorithm to respond the bad(negative) mood. For this objective, PPG signals for two emotional states(positive/negative) are collected. To evoke the test emotions, happy(or joyful) and sad(or irritating) movie files are collected and played in subjects' monitor. From the acquired PPG signal, the heart rate variability(HRV) is calculated. Using the HRV and its FFT spectra, the human sensibility is evaluated. Since the heart is a representative organ which is controlled by the autonomic nervous system(ANS), the ANS may reflect the changes in emotion. The biofeedback algorithm is designed with motion image player interacting with the results of the sensibility evaluation. It was shown that HRV was changed according to the subject's emotions. Accordingly, the sensibility evaluation test showed feasibility of the our method.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2010-2012
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• 2008

This study describes a system for human sensibility evaluation using PPG(photoplethysmogram) signal and biofeedback algorithm to respond the bad(negative) mood. For this objective, PPG signals for two emotional states(positive/negative) are collected. To evoke the test emotions, happy(or joyful) and sad(or irritating) movie files are collected and played in subjects' monitor. From the acquired PPG signal, the heart rate variability(HRV) is calculated. Using the HRV and its FFT spectra, the human sensibility is evaluated. Since the heart is a representative organ which is controlled by the autonomic nervous system(ANS), the ANS may reflect the changes in emotion. The biofeedback algorithm is designed with motion image player interacting with the results of the sensibility evaluation. It was shown that HRV was changed according to the subject's emotions. Accordingly, the sensibility evaluation test showed feasibility of the our method.