• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.39-40
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• 2011

• Journal of the Korean Society of Radiology
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• v.9 no.5
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• pp.315-322
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• 2015

The purposes of this study were to compare the sensitivities of two tests, QST(quantitative sensory test)and PPG(photoplethysmography) in normal adults. We evaluated the sensory thresholds of QST and PPG and diabetes mellitus test in 17 normal adults. The finding of this study can be summarized as follow :The diagnostic sensitivities of QST was similar to PPG. There is a direct correlation between the amount of Blood sugar and parasympathetic nerve in ANS. Concluded that the QST might be complement to NCS for early of diabetic polyneuropathy. A new diagnostic approach, QST was introduced and exploited for diagnosis. This study provides support for PPG performed a ANS analysis.

• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.328-334
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• 2010

Over the past few years, a considerable number of methods have been proposed and applied for the classification of photoplethysmography (PPG). Most of the previous studies, however, focused on the qualitative description of the pulse type according to specific disease and thus provided ambiguous criteria to interpreters. In order to screen out this problem, we present a quantitative method for the pulse type classification including the second derivative of photoplethysmography (SDPTG). In the PPG signal, we have classified the signal as 4 types using the position and the presence of the dicrotic wave. In addition, we have categorized the SDPTG signal as 7 types using the position and the presence of "c" and "d" wave and the sign of "c" wave. In order to check the efficacy of the proposed pulse type classification rule, we collected pulse signals from 155 subjects with different ages and sex. From the correlation analysis, Class 1(p<0.01) and Class 2(p<0.01) in the PPG signal are significantly correlated with ages. In a similar manner Class A(p<0.01), Class C(p<0.05), Class D(p<0.01), and Class F(p<0.01) in the SDPTG signal are considerably correlated with the ages. From these observations, and some earlier ones [4], [5], we can conclude that since the newly proposed method has objectivity and clarity in pulse type classification, this method can be used as an alternative of previous classification rules including similar age-related characteristics.

• Advances in nano research
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• v.15 no.2
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• pp.155-163
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• 2023

Exercise is beneficial to the body in some ways. It is vital for people who have heart problems to perform exercise according to their condition. This paper describes how an Android platform can provide early warnings of fatigue during wushu exercise using Photoplethysmography (PPG) signals. Using the data from a micro-electro-mechanical system (MEMS) gyroscope to detect heart rate, this study contributes an algorithm to determine a user's fatigue during wushu exercise. It sends vibration messages to the user's smartphone device when the heart rate exceeds the limit or is too fast during exercise. The heart rate monitoring system in the app records heart rate data in real-time while exercising. A simple pulse sensor and Android app can be used to monitor heart rate. This plug-in sensor measures heart rate based on photoplethysmography (PPG) signals during exercise. Pulse sensors can be easily inserted into the fingertip of the user. An embedded microcontroller detects the heart rate by connecting a pulse sensor transmitted via Bluetooth to the smartphone. In order to measure the impact of physical activity on heart rate, Wushu System tests are conducted using various factors, such as age, exercise speed, and duration. During testing, the Android app was found to detect heart rate with an accuracy of 95.3% and to warn the user when their heart rate rises to an abnormal level.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.363-369
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• 2023

In this study, to check the function of managing the severity of ultraviolet rays with a smart watch, a popular health care IT device, It was tested whether measuring heart rate using a PPG(Photoplethysmography) sensor representatively used in a smart watch could tell skin changes caused by ultraviolet rays. Through this experiment, we examined the possibility that the skin color tanned by ultraviolet rays can be determined only by the heart rate measurement function of the PPG sensor. In addition, the possibility of expanding the heart rate measurement function of the PPG sensor to the use of skin condition management was considered. we used an Arduino-based reflective PPG sensor to measure changes in heart rate by selecting body sites with high and low UV rays exposure. A significant value was derived through tests considering factors such as gender, UV exposure, and age. As a result, the study identified the possibility of adding ultraviolet rays and skincare items to future smart watch healthcare items and the possibility of expanding skin measurement methods. It is also possible to suggest the direction of future research.

• Journal of Magnetics
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• v.19 no.1
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• pp.32-36
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• 2014

The purpose of this study was to investigate the effect of pulsed magnetic field (PMF) and low frequency low level laser (LFLLL) stimuli on acupoint LI4 (Hegu) using photoplethysmography (PPG). Our PMF system was designed to generate maximum intensity of 0.20 T at a transition time of 0.16 ms, with pulse intervals of 1 Hz. The diode laser with wavelength of 650 nm and power of 5 mW was also employed. It was observed the change of the pulsating blood volume through measuring PPG signals from both hands. These results imply that stimulating acupoint LI4 with PMF and LFLLL improves the circulation of peripheral vascular system. In particular, PMF stimulation brings a big improvement of the blood flow even with short term stimulation of 3-4 minutes compared to LFLLL stimulus.

• Journal of the Optical Society of Korea
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• v.10 no.2
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• pp.91-95
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• 2006

Blood pressure was predicted from photoplethysmography (PPG). To obtain PPG, backscattered light from a fingertip was measured and its waveform was analyzed. Systolic upstroke time and diastolic time in the pulse waveform were used as parameters to predict blood pressure. The experiment was carried out with five subjects on five different days. The systolic upstroke time had a correlation coefficient of -0.605 with respect to systolic blood pressure and the diastolic time had a correlation coefficients of -0.764 for diastolic pressure. This PPG method does not require an air-cuff installation on the arm and can predict blood pressure continuously. This simple LED/photo detector setup can be a good candidate for nonconstrained monitoring of blood pressure variations.

• Journal of Magnetics
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• v.20 no.2
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• pp.114-119
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• 2015

This study explored the effect of pulsed magnetic field (PMF) stimulus on the improvement of blood stagnation by means of photoplethysmography (PPG). Our stimulus system was designed to generate PMF with a maximum intensity variation of 0.20 T at a transition time of $160{\mu}s$, with pulse intervals of 1 Hz. In order to quantitatively estimate vascular condition, indices such as blood vessel tension (BVT), stress power (SP), differential pulse wave index (DPI) and remained blood volume (RBV) were calculated from the second derivative of the PPG signal and power density spectrum (PDS). Our results showed that non-invasive PMF stimulus was effective in improving blood stagnation. Therefore, it may be concluded that appropriate PMF stimulus affects the blood circulatory system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.233-234
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• 2012

본 논문에서는 광전용적맥파(photoplethysmography:PPG)를 이용한 맥파 측정에 있어서 움직이는 상태에서의 파장에 따른 노이즈의 영향을 비교 연구하였다. 광전용적맥파는 광을 이용한 맥파 측정방식으로 움직임에 의한 잡음에 매우 취약하다. 이러한 노이즈로 인하여 측정 시 오차가 발생하게 되는데 특히 스마트폰 등을 이용한 상시 측정 시스템의 경우 이러한 노이즈의 발생이 큰 문제로 부각되게 된다. 따라서 본 연구에서는 움직이는 상태에서적외선광과 녹색광원의 노이즈영향을 측정하여 파장에 따른 움직임노이즈를 비교검토 하였다. 결과적으로 녹색광이 적외선광에 비해 움직임의 영향을 덜 받으며 이동 중에서의 측정이 용이하다는 결론을 얻었다.

• ETRI Journal
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• v.43 no.5
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• pp.881-890
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• 2021

Photoplethysmography (PPG) is a noninvasive technique that can be used to conveniently measure heart rate (HR) and thus obtain relevant health-related information. However, developing an automated PPG system is difficult, because its waveforms are susceptible to motion artifacts and between-patient variation, making its interpretation difficult. We use deep neural network (DNN) filters to mimic the cognitive ability of a human expert who can distinguish the features of PPG altered by noise from various sources. Systolic (S), onset (O), and first derivative peaks (W) are recognized by three different DNN filters. In addition, the boundaries of uninformative regions caused by artifacts are identified by two different filters. The algorithm reliably derives the HR and presents recognition scores for the S, O, and W peaks and artifacts with only a 0.7-s delay. In the evaluation using data from 11 patients obtained from PhysioNet, the algorithm yields 8643 (86.12%) reliable HR measurements from a total of 10 036 heartbeats, including some with uninformative data resulting from arrhythmias and artifacts.