• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.197-204
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• 2015

Recent changes in health care environment including aging population and prevalence of chronic disease encourage the adoption of new innovative technological solutions including wearable vital sensors, wireless networks, and smart phone. In this paper, we present an effective at-home lifestyle monitoring system that can be used for self-management and health intervention of patient himself in the Management-by-Exception perspectives. We implemented the filtering and queuing algorithms as a preprocessor of monitoring system to enhance efficiency of proposed system, and the effective UX design for self-management of patients themselves. The 94,467 actual clinic data was used to test the efficiency of the proposed system. As as a result, 64.8% of the incoming vital data was identified to be filtered out.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.11
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• pp.1330-1338
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• 2011

Social activities of the elderly have been increasing as our society progresses toward an aging society. As their activities increase, so does the occurrence of falls that could lead to fractures. Falls are serious health hazards to the elderly. Therefore, development of a device that can detect fall accidents and prevent fracture is essential. In this study, we developed a portable fall detection system for the fracture prevention system of the elderly. The device is intended to detect a fall and activate a second device such as an air bag deployment system that can prevent fracture. The fall detection device contains a 3-axis acceleration sensor and two 2-axis tilt sensors. We measured acceleration and tilt angle of body during fall and activities of daily(ADL) living using the fall detection device that is attached on the subjects'. Moving mattress which is actuated by a pneumatic system was used in fall experiments and it could provide forced falls. Sensor data during fall and ADL were sent to computer and filtered with low-pass filter. The developed fall detection device was successful in detecting a fall about 0.1 second before a severe impact to occur and detecting the direction of the fall to provide enough time and information for the fracture preventive device to be activated. The fall detection device was also able to differentiate fall from ADL such as walking, sitting down, standing up, lying down, and running.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.303-308
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• 2019

The personal health record platform can store and manage medical records, health-monitoring data such as blood pressure and blood sugar, and life logs generated from various wearable devices. It provides services such as international standard-based medical document management, data pattern analysis and an intelligent inference engine, and disease prediction and domain contents. This study aims to construct a foundation for the transmission of international standard-based medical documents by mapping the diagnosis items of a general health examination, special health examination, life logs, health data, and life habits with the international standard terminology systems. The results of mapping with international standard terminology systems show a high mapping rate of 95.6%, with 78.8% for LOINC, 10.3% for SNOMED, and 6.5% when mapped with both LOINC and SNOMED.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.453-456
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• 2018

본 논문에서는 장시간 동안 모바일 기기를 통하여 건강관리를 할 수 있는 웨어러블 심전도 측정 시스템의 구현에 대하여 기술한다. 웨어러블 심전도 측정 시스템은 1-/6-채널의 심전도를 측정하여 블루 투스 통신으로 모바일 기기로 전송하기 위한 심전도 측정기와 모바일 기기에서 측정된 신호를 실시간으로 보여주는 앱으로 구성된다. 구현한 웨어러블 시스템을 이용하여 일상생활 및 수면동안의 심전도와 맥박 및 스트레스의 변화를 관측할 수 있고, 특히 심장의 이상으로 인한 부정맥 신호를 실시간으로 관찰하는 데 구현한 시스템이 매우 유용한 것으로 파악되었다.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.457-463
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• 2019

The blockchain and crypto currency has become one of the most essential components of a communication network in the recent years. Through communication networking, we browse the internet, make VoIP phone calls, have video conferences and check e-mails via computers. A lot of researches are being conducting to address the blockchain and crypto currency challenges in communication networking and provide corresponding solutions. In this paper, a diverse kind of novel research works in terms of mechanisms, techniques, architectures, and frameworks have been proposed to provide possible solutions against the existing challenges in the communication networking. Such novel research works involve thermal load capacity techniques, intelligent sensing mechanism, secure cloud computing system communication algorithm for wearable healthcare systems, sentiment analysis, optimized resources.

• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.503-509
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• 2009

The development of technology has led to ubiquitous health care service, which enables many patients to receive medical services anytime and anywhere. For the ubiquitous health care environment, real-time measurement of biomedical signals is very important, and the medical instruments must be small and portable or wearable. So, such devices have been developed to measure biomedical signals. In this study, we develop the biomedical monitoring device which is sensing the PPG signal, one of the useful signal in the field of ubiquitous healthcare. We design a watch-like biomedical signal monitoring system without a finger probe to prevent the user's inconvenience. This system obtains the PPG from the radial artery using a sensor in the wrist band. But, new device developed in this paper is easy to get the motion artifacts. So, we proposed new algorithm removing the motion artifacts from the PPG signal. The method detects motion artifacts by changing the degree of brightness of the light source. If the brightness of the light source is reduced, the PPG pulses will disappear. When the PPG pulses have disappeared completely, the remaining signal is not the signal that results from the changing blood flow. We believe that this signal is the motion artifact and call it the noise reference signal. The motion artifacts are removed by subtracting the noise reference signal from the input signal. We apply this algorithm to the system, so we can stabilize the biomedical monitoring system we designed.

• Journal of Korea Entertainment Industry Association
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• v.14 no.2
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• pp.165-176
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• 2020

Customized medical care and services timely providing effective prevention and treatment by collecting and using individuals' biomedical data are recently possible and utilized for users' health care. They are developed as the real-time health care services and information is provided to individuals by using smart phones, PC, tablet, etc. Interactive communication is supported by informing managers of analysis data and results, through collected data. It is therefore the time for constructing health care. This study attempts to prepare for patent applications of technical development at this time, by analyzing the tendency of smart wearable health care technologies, including biological signal-based health care devices and real-time health care system. Patents regarding smart wearable health care technologies were reported to have the relatively higher concentration of research development. Korea focuses on patent activities for real-time health care systems across the intervals of analysis, while U.S and European countries actively make efforts for patent activities regarding health care devices Japan conduct patent activities across health care devices and systems, based on bio-technologies. Korea has recently dominated the market of patents for bio-technologies-based health care devices and real-time health care devices and also appears to secure patents for the technologies and the market, so entry barriers to the market of smart wearable health care technologies are determined to be higher in Korea. It is important to establish the portfolios of patents, by securing patent rights for the figures of products, manufacturing methods and other related technical systems, if technologies are planned to be commercialized.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.1-6
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• 2019

As mobile technologies such as Internet of Things (IoT)-based smart homes and financial technologies (FinTech) are developed, authentication by smart devices is used everywhere. As a result, presence-based biometric authentication using smart devices has become a new mainstream in knowledge-based authentication methods like the existing passwords. The electrocardiogram (ECG) is less prone to forgery, and high-level personal identification is its unique feature from among various biometric authentication methods, such as the pulse, fingerprints, the face, and the iris. Biometric authentication using an ECG is receiving a great deal of attention due to its uses in healthcare and FinTech. In this study, we implemented an ECG authentication system that allows users to easily measure and authenticate their ECG waveforms using a miniaturized wearable device, rather than a large and expensive measurement device. The implemented ECG authentication system identifies ECG features through P-Q-R-S-T feature point identification, and was user-certified under the proposed authentication protocols. Finally, assessment of measurements in a majority of adult males showed a relatively low false acceptance rate of 1.73%, and a low false rejection rate of 4.14%, in a stable normal state. In a high-activity state, the false acceptance rate was 13.72%, and the false rejection rate was 21.68%. In a high-heart rate state, the false acceptance rate was 10.48%, and the false rejection rate was 11.21%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.293-297
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• 2009

This paper describes a real-time reliable measurement and analysis system for ubiquitous healthcare based on IEEE802.15.4 standard. In order to obtain and monitor physiological body signals continuously, wearable pulse oximeter is designed in wrist that could used to measure oxygen saturation of a patient unobtrusively. The measured data was transferred to a central PC or server by using wireless sensor nodes via a wireless sensor network for storage and analysis purposes. LabVIEW server program was designed to monitor and process the measured photoplethysmogram(PPG) to accelerated plethysmogram(APG) by appling second order derivatives in server PC. These experimental results demonstrate that APG can precisely describe the features of an individual's PPG and be used as estimation of vascular elasticity for blood circulation.