• Journal of the Korea Safety Management & Science
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• v.21 no.4
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• pp.1-6
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• 2019

This study relates to acquiring biological signal without attaching directly to the user using UWB(Ultra Wide Band) radar. The collected information is the respiratory rate, heart rate, and the degree of movement during sleep, and this information is used to measure the sleep state. A breathing measurement algorithm and a sleep state detection algorithm were developed to graph the measured data. Information about the sleep state will be used as a personalized diagnosis by connecting with the medical institution and contribute to the prevention of sleep related diseases. In addition, biological signal will be linked to various sensors in the era of the 4th industrial revolution, leading to smart healthcare, which will make human life more enriching.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.1-5
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• 2021

Sleep apnea refers to a condition in which a person does not breathe during sleep, and is a dangerous symptom that blocks oxygen supply in the body, causing various complications, and the elderly and infants can die if severe. In this paper, we present an algorithm that classifies sleep breathing by analyzing the intensity of breathing with images alone in preparation for the risk of sleep apnea. Only the chest of the person being measured is set to the Region of Interest (ROI) to determine the breathing strength by the differential image within the corresponding ROI area. The adult was selected as the target of the measurement and the breathing strength was measured accurately, and the difference in breathing intensity was also distinguished using depth information. Two videos of sleeping babies also show that even microscopic breathing motions smaller than adults can be detected, which is also expected to help prevent infant death syndrome (SIDS).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.794-798
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• 2018

There is a growing need for a care system that can continuously monitor, manage and effectively relieve stress for modern people. In recent years, mobile healthcare devices capable of measuring heart rate have become popular, and many stress monitoring techniques using heart rate variability analysis have been actively proposed and commercialized. In addition, respiratory biofeedback methods are used to provide stress relieving services in environments using mobile healthcare devices. In this case, breathing information should be measured well to assess whether the user is doing well in biofeedback training. In this study, we extracted the heart beat interval signal from the PPG and used the oscillator based notch filter based on the IIR band pass filter to track the strongest frequency in the heart beat interval signal. The respiration signal was then estimated by filtering the heart beat interval signal with this frequency as the center frequency. Experimental results showed that the number of breathing could be measured accurately when the subject was guided to take a deep breath. Also, in the timeing measurement of inspiration and expiration, a time delay of about 1 second occurred. It is expected that this will provide a respiratory biofeedback service that can assess whether or not breathing exercise are performed well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.214-217
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• 2017

In this paper, we propose a sleep efficiency measurement algorithm based on IR-UWB radar sensor in distance. Among the vital signs which can be measured by the IR-UWB radar sensor such as breathing rate, heartbeat rate, and movement, we analyzed correlation between the movement and the sleep efficiency, and based on the result, we propose a sleep efficiency measurement algorithm. In order to verify the performance of the proposed algorithm, we applied the algorithm to three polysomnography patients in hospitals and obtained the performance of an average absolute error within 3.9%.

• Journal of the Semiconductor & Display Technology
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• v.17 no.3
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• pp.108-112
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• 2018

In this paper, a respiration measurement method using FMCW signal for off-the-shelf smartphone is presented and investigated. The proposed algorithm transmits FMCW signal periodically instead of transmitting continuously so that one can reduce the power consumption from speaker in smartphone and the algorithm complexity. In order to eliminate the clicking noise generated when transmitting FMCW signal, Tukey window with ${\alpha}=0.01$ is applied to prevent the noise from being heard. An application program for Android OS which can transmit FMCW signal through speaker and record the reflected signals through MIC has been developed. Since the total duration of the signal transmission is set to 20msec per 1 second for the experiments, the power consumption can be decreased by 80% compared to the continuous transmission. It was confirmed that the clicking noise is inaudible as long as a smartphone is located at more than 10cm from ears. In the experiments on a sleeping child, the breathing signal of about 0.27Hz was measured.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.11 s.254
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• pp.1051-1056
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• 2006

The mean and RMS velocity field of the respiratory gas flow in the human airway was studied experimentally by particle image velocimetry (PIV). Some researchers investigated the airflow for the mouth breathing case both experimentally and numerically. But it is very rare to investigate the airflow of nose breathing in a whole airway due to its geometric complexity. We established the procedure to create a transparent rectangular box containing a model of the human airway for PIV measurement by combination of the RP and the curing of clear silicone. We extend this to make a whole airway including nasal cavities, larynx, trachea, and 2 generations of bronchi. The CBC algorithm with window offset (64 $\times$ 64 to 32 $\times$ 32) is used for vector searching in PIV analysis. The phase averaged mean and RMS velocity distributions in Sagittal and coronal planes are obtained for 7 phases in a respiratory period. Some physiologic conjectures are obtained. The main stream went through the backside of larynx and trachea in inspiration and the frontal side in expiration. There exist vortical motions in inspiration, but no prominent one in expiration.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.17-23
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• 2017

In this paper, we implemented a respiration measurement system consisting of piezoelectric sensor, respiration signal processing device, and a viewer on a notebook. We tried an experiment for measuring respiration and detecting sleep apnea syndrome when a subject lay on a bed. We applied the respiration measurement algorithm to sensor data obtained from four subjects. In order to get a good graph shape, data manipulation methods such as moving averages and maximum values were applied. The window size for moving average was chosen as N=70, and the threshold value for each subject was customized. In this case, the proposed system showed 96.0% accuracy. When the maximum value among 90 data was applied instead of moving average, our system achieved 95.1% accuracy. In an experiment for detecting sleep apnea syndrome, the system showed that sleep apnea occurred correctly and calculated the average interval of sleep apnea. While infants or the elderly as well as patients with sleep apnea syndrome are lying down on a bed, our results are also expected to be able to cope with some accidental emergency situation by observing their respiration and detecting sleep apnea.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.322-327
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• 2020

Recently, a research on obtaining a vital signal using a Doppler radar has been developed and is used as a technology applied to patients in bed. However, in the case of the measured pulse, the respiration signal is generated as noise, resulting in a problem of lowering accuracy. In this paper, we propose a bio-signal measurement algorithm based on the sampling point to improve the accuracy of the signal for measuring the pulse rate when measuring bio-signals using a Doppler radar. The proposed algorithm improves the accuracy of the measured bio-signal by removing noise generated when measuring biosignals based on two sampling points. Compared with actual medical equipment and existing bio-signal algorithms, it is more than 90% similar to medical equipment. In addition, it was confirmed that severe amplitude change was minimized compared to the existing algorithm.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.957-966
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• 2018

Recently, the demand for remote patient monitoring based on IoT has been increased due to aging population and an increase in single-person household. A non-contact biological signal measurement system using multiple IR-UWB radars for remote patient monitoring is proposed in this paper. To reduce error signals, a multilayer Subtraction algorithm is applied because when the background subtraction algorithm was applied to the biological signal processing, errors occurred such as voltage noise and staircase phenomenon. Therefore, a multilayer background subtraction algorithm is applied to reduce error occurrence. The multilayer background subtraction algorithm extracts the signal by calculating the amount of change between the previous clutter and the current clutter. In this study, the SVD algorithm is used. We applied the improved multilayer background subtraction algorithm to biological signal measurement and computed the respiration rate through Fast Fourier Transform (FFT). To verify the proposed system using IR-UWB radars and multilayer background subtraction algorithm, the respiration rate was measured. The validity of this study was verified by obtaining a precision of 97.36% as a result of a control experiment with Neulog's attachment type breathing apparatus. The implemented algorithm improves the inconvenience of the existing contact wearable method.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.240-246
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• 2022

In order to measure the respiratory rate, one of the major vital signs, many devices have been developed and related studies have been conducted. In particular, as the number of wearers of respirators increases in the COVID-19 pandemic situation, studies have been conducted to measure the respiratory rate of the wearer by attaching an electronic sensor to the respirator, but most of them are cases in which an air flow sensor or a microphone sensor is used. In this study, we design and develop a system that measures the respiratory rate of the wearer using an air pressure sensor in a respirator. Air pressure sensors are inexpensive and consume less power than the other sensors. In addition, since the amount of data required for calculation is small and the algorithm is simple, it is suitable for small-scale and low-power processing devices such as Arduino. We developed an algorithm to measure the respiratory rate of a respirator wearer by analysing air pressure change patterns. In addition, variables that can affect air pressure changes were selected, and experimental scenarios were designed according to the variables. According to the designed scenario, we collected air pressure data while the respirator wearer was breathing. The performance of the developed system was evaluated using the collected data.