• Journal of the Korean Magnetics Society
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• v.23 no.4
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• pp.135-143
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• 2013

The clip-type pulsimeter equipped with a Hall sensor has a permanent magnet attached in the "Chwan" position to the center of a radial artery. The clip-type pulsimeter is composed of a hardware system measuring voltage signals. These electrical bio-signals display pulse rate, non-invasive blood pressure, respiratory rate, pulse wave velocity (PWV), and spatial pulse wave velocity (SPWV) simultaneously measured by using the radial artery pulsimeter, the electrocardiograph (ECG), and the photoplethysmograph (PPG). The findings of this research may be useful for developing a oriental-western biomedical signal storage device, that is, the new and fusion patient monitor, for a U-health-care system.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.287-297
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• 2019

Monitoring for the physiological state of a solider is essential to the realization of individual combat system. Despite all efforts over the last decades, there is no report to point out the optimal location of the wearable biosensors considering both monitoring accuracy and operational robustness. In response, we quantitatively measure body temperature and heartrate from 34 body parts using 2 kinds of biosensor arrays, each of which consists of a thermocouple(TC) sensor and either a photoplethysmography(PPG) sensor or an electrocardiography(ECG) sensor. The optimal location is determined by scoring each body part in terms of signal intensity, convenience in use, placement durability, and activity impedance. The measurement leads to finding the optimal location of wearable biosensor arrays. Thumb and chest are identified as best body parts for TC/PPG sensors and TC/ECG sensors, respectively. The findings will contribute to the successful development of individual combat system.

• Journal of Sensor Science and Technology
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• v.17 no.2
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• pp.87-94
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• 2008

Blood pressure (BP), one of the most important vital signs, is used to identify an emergency state and reflects the blood flow characteristics of the cardiovascular system. The conventional noninvasive method of measuring BP is inconvenient because patients must wear a cuff on their arm and the measurement process takes time. This paper proposes an algorithm for estimating the BP using the pulse transit time (PTT) of the photoplethysmography (PPG) and pressure pulse from finger at the same time as a more convenient way to measure the BP. After recording the electrocardiogram (ECG), measuring the pressure pulse, and performing PPG, we calculated the PTT from the acquired signals. Then, we used a multiple regression analysis to measure the systolic and diastolic BP indirectly. Comparing the BP measured indirectly using the proposed algorithm and the real BP measured with a sphygmomanometer, the systolic pressure had a mean error of ${\pm}3.240$ mmHg and a standard deviation of 2.530 mmHg, while the diastolic pressure had a satisfactory result, i.e., a mean error of ${\pm}1.807$ mmHg and a standard deviation of 1.396 mmHg. These results are more superior than existing method estimating blood pressure using the one PTT and satisfy the ANSI/AAMI regulations for certifying a sphygmomanometer i.e., the measurement error should be within a mean error of ${\pm}5$ mmHg and a standard deviation of 8 mmHg. These results suggest the possibility of applying our method to a portable, long-term BP monitoring system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.2
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• pp.187-193
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• 2015

In this study, regressive models were employed to predict the blood velocity by using independent variables which measured by 3 sensors, such as electrocardiogram (ECG), photo-plethysmogram (PPG), pulse diagnosis sensor, without ultrasound device at high cost. In experiment, the high predictable model was induced to estimate the blood velocity correctly by comparing correlation values and significance probabilities between independent variables and blood velocities. Results showed that the model induced by two or three independent variables had a higher predictability than those by a single independent variable.

• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.834-837
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• 2005

In this paper, we describe the method of non-invasive blood pressure measurement using pulse wave transit time(PWTT). PWTT is a new parameter involved with a vascular that can indicate the change of BP. PWTT is measured by continuous monitoring of ECG and pulse wave. No additional sensors or modules are required. In many cases, the change of PWTT correlates with the change of BP. We measure pulse wave using the photo plethysmograph(PPG) sensor in an earlobe and we measure ECG using the ECG monitoring device our made in the chest. The measurement device for detecting pulse wave consists of infrared LED for transmitted light illumination, pin photodiode as light detector, amplifier and filter. We composed 0.5Hz high pass, 60Hz notch and 10Hz low pass filter. ECG measurement device consists of multiplexer, amplifier, filter, micro-controller and RF module. After amplification and filtering, ECG signal and pulse wave is fed through micro-controller. We performed the initial work towards the development of ambulatory BP monitoring system using PWTT. An earlobe is suitable place to measure PPG signal without the restraint in daily work. From the results, we can know that the dependence of PWTT on BP is almost linear and it is possible to monitoring an individual BP continuously after the individual calibration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.660-662
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• 2022

Due to the prolonged aftermath of COVID-19, the number of modern people suffering from stress and mental illness is increasing. Recently, various methods of color therapy are being studied using LED lighting to improve concentration, relieve stress, manage skin, and improve sleep quality. In this paper, pulse waves are measured and heart rate variability is extracted using a PPG (Photoplethysmogram) sensor to analyze a person's mental and physical state. Using RGBLED and Arduino, we propose a mood lighting system that automatically changes colors according to changes in mental and physical conditions by producing color lighting of various wavelengths, which are mainly used to stabilize the mental state.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.109-114
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• 2010

In this paper, the implementation of a human signal sensing module that has capabilities to check and restore the weak signals from the human body is presented. A module presented in this paper consists of processing and sensing elements related to human pulse and body temperature and a controller implemented with SoC design method. PPG data is detected by a noise filtering process toward the amplified signal which is from the operating frequency between 0.1Hz - 10Hz. A digital temperature sensor is used to check the body temperature. A sensor outputs the corresponding value of the electric voltage according to the body temperature. Moreover, this paper discusses the implementation of an enhanced microprocessor which is synthesized with VHDL as a part of the SoC development and used to control the entire module. The SoC processor is implemented on a Xilinx Spartan 3 XC3S1000 device and has the achieved operating frequency of 10MHz. The implemented SoC processor core is successfully tested with macro memories in FPGA and the experimental results are hereby shown.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.178-186
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• 2007

This study is attempted to correct an error of electronic blood pressure meter with an optical sensor. In general, for a hospitalized patient, ECG, blood pressure, oxygen saturation, and respiration are basically measured to monitor the patient's condition. Opening of a blood vessel after it is occluded by pressurizing the cuff influences the blood flow of peripheral blood vessels as well as oscillation changes in the cuff. Blood vessels are occluded and peripheral blood flow disappears at cuff pressure above the examinee's blood pressure, while blood vessels are opened and peripheral blood flow appears again at cuff pressure under the examinee's blood pressure. Then Disappear-Appear Point Length(DAPL) of peripheral blood flow can be judged with the signal of peripheral blood flow, thus is available as a factor of error correction for electronic blood pressure meter. Also, systolic or diastolic blood pressure can be corrected with Appear-Point-Pressure(APP) of cuff pressure at a point where blood flow occurs and Appear-Maximum Pressure(AMP) of cuff pressure at the maximum amplitude point of peripheral blood flow after peripheral blood flow appears again. For verification, 27 examinees were selected, and their blood value was obtained through experimental procedure of 4 stages including induction of blood pressure change. The examinees were divided into two groups of experimental group and control group, regression analysis was conducted for experimental group, and correction of a blood pressure error was verified with optical signal by applying the regression equation calculated in experimental group to control group. As an experimental result, mean of the whole measurement errors was 5mmHg or more, which did not meet the standard fur blood pressure meter. As a result of correcting blood pressure measurements with data of DAPL, APP, and AMP as drawn out of PPG signal, systolic blood pressure, mean blood pressure, and diastolic blood pressure were $-0.6{\pm}4.4mmHg,\;-1.0{\pm}3.9mmHg$ and $-1.3{\pm}5.4mmHg$, respectively, indicating that mean of the whole measurement errors was greatly improved, and standard deviation was decreased.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.91-98
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• 2019

Accidents of worker that occur in isolated places are difficult to rescue, unlike general construction accidents. There is a problem of communication limitation when an accident occurs in an isolated place. Also, it is difficult to search the accident place due to the absence of CCTV. In order to solve these problems, this paper proposes a device that combines IoT technology with a safety helmet, which must be worn in the workplace. The proposed device additionally designs and implements a real-time PPG(Photoplethysmography) sensor, body temperature sensor, accelerometer sensor and a camera sensor on the helmet. The proposed helmet system allows the user and the control center to monitor the state of the worker. In addition, when an abnormal biological signal or fall occurs to the worker, the image is transmitted to the control center. By using the proposed system, it is possible to check the status of the worker in real time, so that it has an advantage that it can cope with the accident quickly.