• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.5
• /
• pp.486-494
• /
• 2014

Intelligent homes consist of ubiquitous sensors, home networks, and a context-aware computing system. These homes are expected to offer many services such as intelligent air-conditioning, lighting control, health monitoring, and home security. In order to realize these services, many researchers have worked on various research topics including smart sensors with low power consumption, home network protocols, resident and location detection, context-awareness, and scenario and service control. This paper presents the real-time metabolic rate estimation method that is based on measured heart rate for human adaptive appliance (air-conditioner, lighting etc.). This estimation results can provide valuable information to control smart appliances so that they can adjust themselves according to the status of residents. The heart rate based method has been experimentally compared with the location-based method on a test bed.

• Annals of Occupational and Environmental Medicine
• /
• v.35
• /
• pp.24.1-24.15
• /
• 2023

Background: The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance. Methods: We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state. Results: Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point. Conclusion: The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

• 유체기계공업학회:학술대회논문집
• /
• 2006.08a
• /
• pp.501-504
• /
• 2006

The estimation of the work of heart can be treated as one of the most important parameters for determining the amount of circulating blood needed for harmonious metabolism in the human body. By monitoring the work of heart, one can detect increased work load of heart and start the treatment at the early stage of CHF. Thus it is necessary to estimate the work of heart. The contractility of the left ventricle, the second important parameter for representing the motion of heart, can be estimated through information on the work of heart. In this study, the modified Windkessel model, which has been used for a measure of vascular hemodynamic impedance parameters, was adapted to estimate the work of heart.

• Clinical and Experimental Pediatrics
• /
• v.64 no.2
• /
• pp.49-59
• /
• 2021

Since the initial International Society of Heart Lung Transplantation registry was published in 1982, the number of pediatric heart transplantations has increased markedly, reaching a steady state of 500-550 transplantation annually and occupying up to 10% of total heart transplantations. Heart transplantation is considered an established therapeutic option for patients with end-stage heart disease. The long-term outcomes of pediatric heart transplantations were comparable to those of adults. Issues affecting long-term outcomes include acute cellular rejection, antibody-mediated rejection, cardiac allograft vasculopathy, infection, prolonged renal dysfunction, and malignancies such as posttransplant lymphoproliferative disorder. This article focuses on medical issues before pediatric heart transplantation, according to the Korean Network of Organ Sharing registry and as well as major problems such as graft rejection and cardiac allograft vasculopathy. To reduce graft failure rate and improve long-term outcomes, meticulous monitoring for rejection and medication compliance are also important, especially in adolescents.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.7
• /
• pp.892-897
• /
• 2018

Due to population aging, an increase in the number of patients with chronic illnesses, and an increase in the number of single-person households, monitoring of health status in everyday life without the need for a hospital has become very important. For this reason, researches on various health care devices have been attempted, among which wearable devices are attracting much attention. In this paper, we propose a new ring-type wearable device for next generation healthcare. On the inner side of the ring, a metal electrodes for GSR measurement and an optical sensor for measurement of pulse wave signals of two wavelengths of red and near-infrared light were mounted. In addition, it was equipped with an acceleration sensor, and information about the degree of motion could be obtained. In this paper, it is shown that a health monitoring device can be implemented in the form of a ring, and the measured signals can be used to calculate heart rate, oxygen saturation, sleep time and sleep efficiency. Through the advanced algorithm, it is expected that we can extract various health information, especially sleep related health information by using the ring device, and it is also expected that it can contribute to the implementation of wearable healthcare effectively.

• Biomedical Science Letters
• /
• v.19 no.3
• /
• pp.254-260
• /
• 2013

The purpose of this study was to survey the effects of Karvonen exercise prescription in coronary artery disease patients reaching age-predicted maximal heart rates with the exercise stress test on hemodynamic responses and cardiorespiratory fitness. The subject group was comprised of acute coronary syndrome (ACS) patients, who were divided into the maximal heart rate (MHR) group that included those who completed the test with their heart rates reaching the number of 220-age and the maximal dyspnea (MD) group that included those who could not continue the test due to respiratory difficulty and were asked to stop the test. Both groups had the exercise stress test before and after the experiment. In the exercise stress test before the experiment, the exercise prescription intensity of Karvonen was set at the target heart rates of 50~85% with a six-week exercise monitoring arrangement. As a result, there were no interactive effects in rest heart rate (RHR) according to time and group, but interactive effects were observed in maximal heart rate (MHR) (P=0.000). Both rest systolic blood pressure (RSBP) and rest diastolic blood pressure (RDBP) had no interactive effects according to time and group. Maximal systolic blood pressure (MSBP) showed significant interactive effects according to time and group (P=0.017). Maximal diastolic blood pressure (MDBP) showed no interactive effects according to time and group, while maximal rate pressure product (MRPP) showed significant interactive effects according to time and group (P=0.003). Maximal time (MT) had no interactive effects according to time and group. $VO_{2max}$ and maximal metabolic equivalent (MMET) showed significant interactive effects according to time and group (P=0.000, P=0.002, respectively), whereas maximal respiratory exchange ratio (MRER) and maximal rating of perceived exertion (MRPE) showed no interactive effects according to time and group. The exercise test that was discontinued as the subjects reached the predicted maximal heart rates considering age did not reach the maximal exercise intensity and accordingly showed low exercise effects when applied to Karvonen exercise prescription intensity. That is, the test should keep going by monitoring cardiac events, MRER and MRPE until the heart rates exceed the predicted MHR by up to 10~12 even after the subject reaches the predicted MHR considering age in the exercise stress test.

• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.1
• /
• pp.14-19
• /
• 2018

Recently, the smart health care industry has been rising rapidly and interest and efforts for public health have been greatly increased. As a result, the public does not visit medical specialists and medical facilities, but the desire to check their health condition in everyday life is increased. Therefore, many domestic and foreign companies continuously research and develop wearable devices that can measure body activity information anytime and anywhere And the market. Especially, it is used for heart activity measurement device using pulse wave sensor and electrocardiogram sensor. However, in this study, a monitoring system that can detect cardiac activity using cardiac sounds, heart sound measurement rather than pulse wave measurement and electrocardiogram measurement, was performed and its performance was evaluated. Experimental results confirmed the predictability of cardiac heart rate and heart valve disease during daily living.

• Science of Emotion and Sensibility
• /
• v.12 no.2
• /
• pp.161-168
• /
• 2009

In this study, we have developed the patch type HAMS (Heart Activity Monitoring System) which is non-restricted, non-awarable and non-invasive. The module using wireless telecommunication to receive the ECG (electrocardiogram) signal at the computer has mobility which it easily monitors the heart activity of subjects in no time for long term at any time and places. We developed the small patch type electrode which can be attached on the chest. Also the reliability and moving artifact of ECG signal measured by this electrode have been verified. Using HAMS, we measured the HRV (Heart Rate Variability) parameters, the questionnaire evaluation for anxiety and stress and the amount of stress hormone (cotisol) to evaluate the stress effect in HRV on the same subject. As a result of comparing the values under non stressed and stressed condition, there was significant difference on many parameters. And the parameter highly related with stress on Pearson's Correlation Coefficient has been examined. These show that using HAMS is able to evaluate the function of autonomic nervous system. Therefore, we can predict heart problem in daily life by using HAMS. Also we expect that this module can be applied for more application as health monitoring system.

• Journal of Digital Convergence
• /
• v.18 no.8
• /
• pp.231-242
• /
• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.10
• /
• pp.1444-1450
• /
• 2013

Esophageal stethoscope is used for monitoring the heart sounds and breath sounds of patients during surgery under a general anesthesia. Recently, an electronic esophageal stethoscope (EES)[1] has been developed for the purpose of real-time monitoring these information visually. This system uses only a microphone as the sound sensor. A drawback of the EES system is that it may be difficult to distinguish the first sound ($S_1$) and the second sound ($S_2$) of heart, because their periods are irregular depending on patients. In this paper, we propose an improved EES system in which the infrasound is measured by adding a pressure sensor as well as a sound sensor. We investigate some correlations between the infrasound and characteristics of the heart sound. The proposed system has been tested on 15 patients. The results show that the new system is capable of detecting the first sound more reliably and easily determining the heart rate and breathing period.