• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.57-63
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• 2012

Effective use of stethoscope is very important for primary clinical diagnosis for the increasing cardiovascular and respiratory disease. This study developed the contact vibration sensor using piezopolymer film which minimizes the ambient noise, and signal processing algorithm was applied for providing better auscultation sounds compare to the existing electronic stethoscopes. Especially, low frequency heart sounds were acquired without distortion, and the quality of lung sounds were improved. Also, auscultating sounds could be transmitted using bluetooth, which made possible to be used for the u-healthcare environment. Results of this study, auscultation of heart and lung sounds, could be applied to the convergence industry of medical and information communication technology through remote diagnosis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.203-210
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• 2011

The current sensor network analyzes the data collected by the sensing of fixed sensor nodes and provides a service. However, this method cannot actively handle the state and the change in the position of people, 'the target for sensing and the change in the environment', including home automation, building automation and real-time road & weather information, and healthcare environment, etc. To support a dynamic situation which is appropriate for an individual in this diverse environment, it is necessary to provide actively differentiated specific information according to the movement of people and the changes in the environment. In this study, a individualized sensor mobile agent middleware which provides the individualized information (the location of fire incidence and the trace for the path of spread), has been realized through the sensor network environment constructed by the installation of wireless sensor nodes mounted with mobile agent middlewares in buildings.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.705-711
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1289-1295
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.324-332
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• 2018

Blockchain technology has the potential to revolutionize trust models and business processes in a variety of industries. However, it is considered to be the initial stage of the system that pursues autonomy rather than efficiency, and it is necessary to monitor and inspect the distributed ledger technology from the price and introduction time as compared with the existing relational DB transaction technology. However, domestic and foreign private sectors have already been activated by applying block-chain technology in the national domain, and the block chain is devoid of doubt that it is an exaggerated technology, characterized by the invariance of the record, transparency, and autonomous execution of business rules. It has begun to be utilized in history, identity, certification and auditing in the financial industry as well as various industries. In this paper, we analyze the problems such as security weakness, insufficient regulatory environment, technical consensus and lack of common standard. In addition, the business sense and possibility of the block chain technology is expected to be the innovation of the industrial ecosystem by entering into the reality system from the concept through monitoring the actual introduction performance in the field of copyright, logistics, health care and environment.

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

• 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 Information and Communication Engineering
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• v.14 no.8
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• pp.1836-1842
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• 2010

Sensor network, which is technology to realize the ubiquitous environment, recently, could apply to the field of Mechanic & electronic Security System, Energy management system, Environment monitoring system, Home automation and health care application. However, feature of wireless networking of sensor network is vulnerable to eavesdropping and falsification about message. Presently, PKC(public key cryptography) technique using ECC(elliptic curve cryptography) is used to build up the secure networking over sensor network. ECC is more suitable to sensor having restricted performance than RSA, because it offers equal strength using small size of key. But, for high computation cost, ECC needs to enhance the performance to implement over sensor. In this paper, we propose the optimizing technique for multiplication, core operation in ECC, to accelerate the speed of ECC.

• Research in Community and Public Health Nursing
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• v.32 no.3
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• pp.404-414
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• 2021

Purpose: Health behaviors for metabolic syndrome (MetS) prevention should be emphasized from early adulthood. There is little information on psychosocial factors associated with health behaviors for MetS prevention. The aim of this study was to determine whether there would be a mediating effect of perceived stress on the association between social support and health behaviors for MetS prevention among university students. Methods: This cross-sectional and correlation study was conducted with 502 university students in South Korea. Social support, perceived stress, and lifestyle evaluation for metabolic syndrome scales were used. Online questionnaire survey was conducted between November and December 2019. The mediating effect of social support on health behaviors for MetS prevention was analyzed using PROCESS macro program with bootstrapping method to test our hypotheses. Results: Social support directly influenced perceived stress (β=-.35, p<.001) and health behaviors for MetS prevention (β=.14, p=.002). Health behaviors for MetS prevention was indirectly influenced by perceived stress (β=-.25, p<.001). The size of indirect effect of social support on health behaviors for MetS prevention was 0.06. Conclusions: The association of social support and health behaviors for MetS prevention was partially mediated by perceived stress among university students. Therefore, a university-based nursing intervention should comprise social support strategies with stress management to promote health behaviors for MetS prevention.