• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1252-1256
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• 2016

Internet of Things(IoT)-devices are now expanding inter-connecting networking technologies to invent healthcare monitoring system especially for assessing physiological conditions of the chronically-ill patients those with cardiovascular diseases. Hence, IoT system is expected to be utilized for home healthcare by dedicating the original usage of IoT devices to collect the biomedical data such as electrocardiogram(ECG) and photoplethysmography(PPG) signal. The aim of this work is to implement health monitoring system by integrating IoT devices with Raspberry-pi components to measure and analyze ECG and the multi-channel PPG signals. The acquired data and fiducial features from our system can be transmitted to mobile devices via wireless networking technology to support the concept of tele-monitoring services based on IoT devices.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.1-10
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• 2021

Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.

• ETRI Journal
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• v.44 no.3
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.11a
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• pp.169-172
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• 2001

There are many people who suffer from simulation sickness when immersing in virtual reality. In this study, we analyzed two photoplethysmogram(PPG) parameters - a second derivative parameter and power spectral density ratios - in order to relate PPG parameters with simulation sickness. 36 young, healthy subjects were participated in the experiment, and each subject was equipped with a PPG electrode during his or her immersion. Simulation sickness section was defined as a 7 - second section which starts from the point where a subject reported simulation sickness, and normal section as a same-length section where no physical stimuli was presented to him or her. We compared the PPG parameters of the simulation sickness sections with the normal sections, - d/a ratio is believed to have lower value during vasodilation and higher value during vasoconstriction, however, we could not find much difference in the parameter between normal and simulation sickness sections. We also compared 1 to 10Hz power spectral density ratios in normal sections with in simulation sickness section, and found that 6 density ratios among them have different value. Therefore, the density ratios might be utilized as parameters to detect simulation sickness of subjects.

• Annals of Clinical Neurophysiology
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• v.25 no.1
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• pp.32-37
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• 2023

Background: Type 2 diabetic mellitus (T2DM) is an emerging global pandemic which is associated with lots of co-morbidities and reported vascular dysfunctions. T2DM associated vascular dysfunctions leads to vasculopathy in the form of altered peripheral vascular dynamics. Cold stress test (CST) is a reliable sympathetic reactivity test used for assessing vascular dysfunctions. In this study we are trying to quantify vascular dysfunctions in T2DM patients non invasively by various parameters of photoplethysmography (PPG) of cold stress test. Methods: Case control study had done in referral health center AIIMS, Raipur. Parameters are recorded by finger-PPG before, during and after CST (1 min) in 2 groups, control (n = 20 healthy volunteers) and case (n = 20 diagnosed T2DM patients). Results: Due to cold stress, PPG parameter peak amplitude was significantly decreased in both healthy and T2DM groups (p <0.001 and p <0.001, respectively). However, recovery trend of amplitude was significantly slow in T2DM compared to healthy subjects. Another PPG parameter peak to peak interval was significantly higher in healthy group compared to T2DM patients. Conclusions: This study showed that T2DM patients has significant deranged pulse volume parameters like amplitude and peak to peak interval can be used to objectively quantify the vasculopathy in T2DM patients by using sympathetic reactivity to cold stress.

• The Journal of Korean Medicine
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• v.23 no.3
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• pp.1-10
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• 2002

Objective: There exist gender differences in pulsatile contour waveform. Women have a greater age-related increase in left ventricular mass than do men and more likely to experience symptomatic heart failure after infarction. SDPTG (the second derivative of photoplethysmogram waveform) is a noninvasive method for evaluating the pulse wave and is correlated with age and other risk factors for atherosclerosis. We studied the effect of gender on SDPTG and made clear why the gender differences appear. Methods: To study the effects of effect factors, including height and blood pressure, on SDPTG in the fourth decade, data on height, weight, PTD (pulse transit distance), blood pressure, serum lipid levels, and SDPTG were collected in 115 laboratory healthy men and women. SDPTG is derived from double-differential processing of fingertip photoplethysmography and consists of a, b, c, and d waves in systole and an e wave in diastole; SDPTG aging index (AI) was calculated as (b-c-d-e)/a. Results: There were significant gender-related differences of SDPTG AI, height, and blood pressure. Age, height, and mean blood pressure were respectively and significantly correlated with SDPTG AI. SDPTG is dependent upon age, height, and blood pressure. Restricting analysis to SDPTG AI, age, height, and mean blood pressure, yielded that there were gender-related differences in SDPTG AI (P<0.05) which were derived from those of height (F<0.001, df=l, P=0.994). Conclusions: These new data may help to explain previous findings about age-related differences in pulsatile contour waveforms and why gender differences of SDPTG appear. The results of this study suggest that SDPTG AI, used for evaluation of biological vascular aging, should be calibrated by height as well as age and blood pressure.

• Science of Emotion and Sensibility
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• v.11 no.2
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• pp.235-244
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• 2008

This study is to develop automatic extraction system of continuous blood pressure using ECG (Electrocardiogram) and PPG(Photoplethysmography) for u-health care technology. PTT (Pulse Transit Time) was determined from peak difference between ECG and PPG and its inverse made to get blood pressure. Since the peaks were vulnerable to be contaminated from noise and variation of amplitude, this study developed the adaptive algorithm for peak calculation in any noise condition. The developed method of the adaptive peak calculation was proven to make the standard deviations of PPT decrease to 28% and the detection of noise increase to 18%. Also, the correlation model such as blood pressure = -0.044 $\cdot$ PTT + 133.592 has successfully been determined for predicting the continuous pressure measured without using cuff but with using PPG and ECG, only.

• 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 the Korea Contents Association
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• v.13 no.12
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• pp.27-37
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• 2013

Today, livestock industry grows faster and bigger. The number of livestock numbers per farm also grows rapidly. The bigger farms need more sophisticated control of livestock to prevent from all possible diseases, especially contagious diseases. In Korea, diseases cause serious economic loss of 2 trillion won every year, which is about 20% of the total production output. Researches on the wireless bio-signal monitoring technology for livestock are of great importance in the world. In this paper, as a way to predict the possible diseases, we propose a measurement method of the pulse of dairy cows for the continuous health monitoring. It is possible to measure a pulse from central artery and the left chest-wall of the cow. The pulse from central artery is measured by the sensor attached at the tail winding. The pulse at the left chest-wall can also be measured with our newly designed harness.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.261-268
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• 2014

This paper presents a electrocardiogram(ECG), electromyogram(EMG), and Photoplethysmography(PPG) signal wireless monitoring system based on Bluetooth Low Energy (BLE). ECG and EMG sensor interface analog front-end circuits are designed by using off-the-shelf parts. Texas Instruments(TI)'s CC2540DK is used for BLE-based communication. Two CC2540DK modules are used as Peripheral and Central nodes. In peripheral device, vital signals are acquired by the analog front-ends and fed to ADC for analog-to-digital conversion. The peripheral transmitts the data through the air to the central device. The central device receive the data and sends them to PC using UART. GUI is designed using Labview for displaying the acquired vital signals. The developed system can be used for future ubiquitous wireless healthcare system based on bluetooth 4.0.