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

• Journal of Information Processing Systems
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• v.4 no.3
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• pp.103-112
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• 2008

This paper proposes a policy adjuster-driven Grid workflow management system for collaborative healthcare platform, which supports collaborative heart disease diagnosis applications. To select policies according to service level agreement of users and dynamic resource status, we devised a policy adjuster to handle workflow management polices and resource management policies using policy decision scheme. We implemented this new architecture with workflow management functions based on policy quorum based resource management system for providing poincare geometrycharacterized ECG analysis and virtual heart simulation service. To evaluate our proposed system, we executed a heart disease identification application in our system and compared the performance to that of the general workflow system and PQRM system under different types of SLA.

• Anxiety and mood
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• v.16 no.2
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• pp.83-90
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• 2020

Objective : Heart rate variability (HRV) is known to reflect autonomic nervous system activity. Individuals with posttraumatic stress disorder (PTSD) are reported to have lower HRVs. We attempted to find HRV indices with head up tilt position that reflect the symptoms well in order to evaluate PTSD symptoms. Methods : Sixty-seven patients with PTSD and 72 patients without PTSD were assessed using the PTSD Checklist for DSM-5 (PCL-5), the Beck Depression Inventory, the Beck Anxiety Inventory and the Pittsburgh Sleep Quality Index. HRV was measured in the head-up tilt position. We collected data regarding heart rate (HR), standard deviation of the NN intervals (SDNN), the square root of the mean squared differences of successive NN intervals (RMSSD), log low-frequency (LNLF) and log high-frequency (LNHF). Results : The value of LNHF was different according to presence or absence of PTSD after head-up tilt position. In the findings of the association between PTSD symptoms and HRV indices as based on head-up tilt, LNHF had a significant correlation with the total score of PCL-5. Conclusion : The reduction of the high-frequency component of HRVs in the PTSD group might reflect more PTSD symptoms.

• Journal of People, Plants, and Environment
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• v.22 no.2
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• pp.219-229
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• 2019

This study was carried out to verify the accuracy of fitness tracking devices in monitoring heart rate and energy consumption and to contribute to the development of a forest exercise program that can recommend the intensity and amount of forest exercises based on personal health-related data and provide monitoring and feedback on forest exercises. Among several commercially available wearable devices, Fitbit was selected for the research, as it provides Open API and data collected by Fitbit can be utilized by third parties to develop programs. Fitbit provides users with various information collected during forest exercises including exercise time and distance, heart rate, energy consumption, as well as the altitude and slope of forests collected by GPS. However, in order to verify the usability of the heart rate and energy consumption data collected by Fitbit in forest, the accuracy of heart rate and energy consumption were verified by comparing the data collected by Fitbit and reference. In this study, 13 middle-aged women were participated, and it was found that the heart rate measured by Fitbit showed a very low error rate and high correlation with that measured by the reference. The energy consumption measured by Fitbit was not significantly different from that measured in the reference, but the error rate was slightly higher. However, there was high correlation between the results measured by Fibit and the reference, therefore, it can be concluded that Fitbit can be utilized in developing actual forest exercise programs.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.191-198
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• 2020

In this study, we proposed a real-time ECG analytical method for predicting stress and dangerous heart condition using the ECG signal in playing AR/VR device. A real-time diagnosis is used as R-R interval based HRV(:Heart rate variability), BPM(:Beats Per Minitue) and autonomic nervous research with through mapping method of two-dimensional planes. The ECG data were analyzed every 5 minutes and derived from autonomic nervous system diagnosis.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

• Health Policy and Management
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• v.26 no.4
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• pp.343-351
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• 2016

Background: This study aims to analyze the effects of insurance types on the medical service uses for heart failure inpatients using propensity score matching (PSM). Methods: 2014 National inpatient sample based on health insurance claims data was used in the analysis. PSM was applied to control factors influencing the service uses except insurance types. Negative binomial regression was used after PSM to analyze factors that had influences on the service uses among inpatients. Subjects were divided by health insurance type, national health insurance (NHI) and medical aid (MA). Total charges and length of stay were used to represent the medical service uses. Covariance variables in PSM consist of sociodemographic characteristics (gender, age, Elixhauser comorbidity index) and hospital characteristics (hospital types, number of beds, location, number of doctors per 50 beds). These variables were also used as independent variables in negative binomial regression. Results: After the PSM, length of stay showed statistically significant difference on medical uses between insurance types. Negative binomial regression provided that insurance types, Elixhauser comorbidity index, and number of doctors per 50 beds were significant on the length of stay. Conclusion: This study provided that the service uses, especially length of stay, were differed by insurance types. Health policy makers will be required to prepare interventions to narrow the gap of the service uses between NHI and MA.

• ETRI Journal
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• v.37 no.2
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• 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.

• Journal of Preventive Medicine and Public Health
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• v.50 no.5
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• pp.294-302
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• 2017

Objectives: The objectives of this study were to investigate the agreement between medical history questionnaire data and claims data and to identify the factors that were associated with discrepancies between these data types. Methods: Data from self-reported questionnaires that assessed an individual's history of hypertension, diabetes mellitus, dyslipidemia, stroke, heart disease, and pulmonary tuberculosis were collected from a general health screening database for 2014. Data for these diseases were collected from a healthcare utilization claims database between 2009 and 2014. Overall agreement, sensitivity, specificity, and kappa values were calculated. Multiple logistic regression analysis was performed to identify factors associated with discrepancies and was adjusted for age, gender, insurance type, insurance contribution, residential area, and comorbidities. Results: Agreement was highest between questionnaire data and claims data based on primary codes up to 1 year before the completion of self-reported questionnaires and was lowest for claims data based on primary and secondary codes up to 5 years before the completion of self-reported questionnaires. When comparing data based on primary codes up to 1 year before the completion of selfreported questionnaires, the overall agreement, sensitivity, specificity, and kappa values ranged from 93.2 to 98.8%, 26.2 to 84.3%, 95.7 to 99.6%, and 0.09 to 0.78, respectively. Agreement was excellent for hypertension and diabetes, fair to good for stroke and heart disease, and poor for pulmonary tuberculosis and dyslipidemia. Women, younger individuals, and employed individuals were most likely to under-report disease. Conclusions: Detailed patient characteristics that had an impact on information bias were identified through the differing levels of agreement.