• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.220-223
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• 2011

Respiration signal of the vital signs is an important parameter in clinical parts. To extract the respiration signal from PPG signal for mobile healthcare system is difficult because the bands of the motion artifacts and respiration in the frequency domain are overlapped. This study to improve this problem suggested a respiration extraction method using the independent component analysis and evaluated its performances. In results of evaluation, the ICA method showed better performance than LPF suggested recently.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1862-1870
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• 2016

A respiration is one of the most useful techniques for vital checking and an abnormal respiration is often the earliest sign of critical illness. Detection of respiration is based on the photo-plethysmography (PPG) with photodiode technique. Because PPG sensor using photodiode can be easily miniaturized, it is suitable for wearable devices. A system to measure respiration rate based on PPG signal is implemented and for a reliable measurement an improved algorithm in accuracy using PPG signal pattern is proposed in this paper. As results regarding to three types of respirations (regular interval, free interval, and weak respiration) the proposed algorithm showed error rate of 0.047, 0.067, and 0.122 respectively.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.334-343
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• 2014

In this paper, we suggest a method to improve the fusion of an accelerometer and gyro sensor by using a Kalman filter to produce a more high-quality respiration signal to supplement the weakness of using a single accelerometer. To evaluate our proposed algorithm's performance, we developed a chest belt-type module. We performed experiments consisting of aerobic exercise and muscular exercises with 10 subjects. We compared the derived respiration signal from the accelerometer with that from our algorithm using the standard respiration signal from the piezoelectric sensor in the time and frequency domains during the aerobic and muscular exercises. We also analyzed the time delay to verify the synchronization between the output and standard signals. We confirmed that our algorithm improved the respiratory rate's detection accuracy by 4.6% and 9.54% for the treadmill and leg press, respectively, which are dynamic. We also confirmed a small time delay of about 0.638 s on average. We determined that real-time monitoring of the respiration signal is possible. In conclusion, our suggested algorithm can acquire a more high-quality respiration signal in a dynamic exercise environment away from a limited static environment to provide safer and more effective exercises and improve exercise sustainability.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.357-359
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• 2009

In this paper, the 2.4GHz doppler radar system consisting of the doppler radar module and a baseband module were designed to detect heartbeat and respiration signal without direct skin contact. A bio-radar system emits continuous RF signal of 2.4GHz toward human chest, and then detects the reflected signal so as to investigate cardiopulmonary activities. The heartbeat and respiration signals acquired from quadrature signal of the doppler radar system are applied to the pre-processing circuit, amplification circuit, and the offset circuit of the baseband module. ECG(electrocardiogram) and reference respiration signals are measured simultaneously to evaluate the doppler radar system. As a result, the respiration signal of doppler radar signal is detected to 1m without complex digital signal processing. The sensitivity and calculated from I/Q respiration signal were $98.29{\pm}1.79%$, $97.11{\pm}2.75%$, respectively, and positive predictivity were $98.11{\pm}1.45%$, $92.21{\pm}10.92%$, respectively. The sensitivity and positive predictivity calculated from phase and magnitude of the doppler radar were $95.17{\pm}5.33%$, $94.99{\pm}5.43%$, respectively. In this paper, we confirmed that noncontact real-time heartbeat and respiration detection using the doppler radar system has the possibility and limitation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.163-170
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• 2022

Currently, as the number of families living with companion animals increases, the demand for information about the health status of companion animals has increased. As the demand for this increases, there is a need for a method to measure respiration and pulse in companion animals. Considering the characteristics of hairy companion animals, we want to measure respiration and pulse signals using BCG, which is different from adsorption ECG. Since this BCG method is made by mixing respiration and pulse signals into one signal, it is necessary to separate the respiration signal waveform and the pulse signal waveform from one signal waveform. In this paper, a wearable device for BCG measurement was implemented to measure the signal, and a method of separating the signal input from the BCG wearable device into a respiration signal and a pulse signal was proposed.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.733-740
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• 2004

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) is proposed. The whole system consists of two-lead electrocardiogram acquisition (lead Ⅰ and Ⅱ), baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problem of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we proposed a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 subjects, and we could obtain satisfactory respiration signals that shows high correlation (r>0.9) with the signal acquired from the chest-belt respiration sensor.

• Journal of Satellite, Information and Communications
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• v.12 no.1
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• pp.28-33
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• 2017

In this paper, new signal model for bio-signal detection, i.e heart beat and respiration, using CW radar. Most research on this similar topic are based on the conventional signal model which is not correct in envisaging reflected signal from the human body. The system developed based on this conventional model can not predict exact performance of the system. So in this paper modified signal model for bio-radar is proposed and then simulation for detecting heartbeat and respiration signal in AWGN, multipath environment. The detection performance difference between two signal models are discussed.the modified

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.142-147
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• 2014

Real time driver's respiration monitoring method for detecting driver's drowsiness is investigated. The sensor to obtain driver's respiration signal was a piezoelectric pressure sensor attached at the abdominal region of the seat belt. The resistance of the pressure sensor was changed according to the pressure applied to the seat belt due to the driver's respiration. Monitoring driver's respiration was carried out by driving on the virtual road in a driving simulator from Cheonan to Seoul and monitoring results were compared to the PELCLOS. Experiment results show that the driver's respiration signal can be used for detecting driver's drowsiness.

• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.326-332
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• 2008

A respiration measurement system for vital signs was developed. Respiration signals were measured, processed, and analyzed. Four electrodes, attached on the surface of the skin, were used to monitor respiration signals by impedance pneumography. The measured signals were amplified, detrended, filtered, and transferred toan embedded module. The Kalman filter was used to remove motion artifact from the respiration signals. Experiments were conducted at stable condition and walking condition to evaluate the performance of the system. Respiration rates of five males and five females were measured and analyzed at each condition. The referenced respiration signal was determined by temperature of nose surroundings. The results showed that the respiration rates at the walking condition had more motion artifacts than the stable condition. The accuracies of the respiration measurement system with Kalman filter were found as 96% at the stable condition and 95% at the walking condition. The results showed that the Kalman filter was an effective tool to remove the motion artifact from the respiration signal.

• Journal of Biomedical Engineering Research
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• v.34 no.3
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• pp.105-110
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• 2013

Respiratory signal is one of the important physiological information indicating the status and function of the body. Recent studies have provided the possibility of being able to estimate the respiratory signal by using a change of PWV(pulse width variability), PRV(pulse rate variability) and PAV(pulse amplitude variability) in the PPG (photoplethysmography) signal during daily life. But, it is not clear whether the respiratory monitoring is possible even during sleep. Therefore, in this paper, we estimated the respiration from PWV, PRV and PAV of PPG signals during sleep. In addition, respiration rates of the estimated respiration signal were calculated through a time-frequency analysis, and errors between respiration rates calculated from each parameter and from reference signal were evaluated in terms of 1 sec, 10 sec and 1 min. As a result, it showed the errors in PWV(1s: $36.38{\pm}37.69$ mHz, 10s: $36.53{\pm}38.16$ mHz, 60s: $30.35{\pm}38.72$ mHz), in PRV(1s: $1.45{\pm}1.38$ mHz, 10s: $1.44{\pm}1.37$ mHz, 60s: $0.45{\pm}0.56$ mHz), and in PAV(1s: $1.05{\pm}0.81$ mHz, 10s: $1.05{\pm}0.79$ mHz, 60s: $0.56{\pm}0.93$ mHz). The errors in PRV and PAV are lower than that of PWV. Finally, we concluded that PRV and PAV are more effective than PWV in monitoring the respiration in daily life as well as during sleep.