• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.313-316
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• 2009

A novel approach for electrocardiogram (ECG) analysis within a functional sensor node has been developed and evaluated. The main aim is to reduce data collision, traffic over loads and power consumption in healthcare applications of wireless sensor networks (WSN). The sensor node attached on the patient's bodysurface around the heart can perform ECG analysis based on a QRS detection algorithm to detect abnormal condition of the patient. Data transfer is activated only after detected abnormality in the ECG. This system can reduce packet loss during transmission by reducing traffic overload. In addition, it saves power supply energy leading to more reliable, cheap and user-friendly operation in the WSN based ubiquitous health monitoring.

• Korean Medical Education Review
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• v.22 no.2
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• pp.99-114
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• 2020

The rapid development of artificial intelligence (AI), including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, drug development, and healthcare management and administration. However, in order for AI technology to improve the quality of medical care, technical problems and the efficacy of algorithms should be evaluated in real clinical environments rather than the environment in which algorithms are developed. Further consideration should be given to whether these models can improve the quality of medical care and clinical outcomes of patients. In addition, the development of regulatory systems to secure the safety of AI medical technology, the ethical and legal issues related to the proliferation of AI technology, and the impacts on the relationship with patients also need to be addressed. Systematic training of healthcare personnel is needed to enable adaption to the rapid changes in the healthcare environment. An overall review and revision of undergraduate medical curriculum is required to enable extraction of significant information from rapidly expanding medical information, data science literacy, empathy/compassion for patients, and communication among various healthcare providers. Specialized postgraduate AI education programs for each medical specialty are needed to develop proper utilization of AI models in clinical practice.

• The Research Journal of the Costume Culture
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• v.27 no.6
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• pp.615-631
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• 2019

Smart healthcare clothing combines IoT, new technology, and clothing construction to perform specific care functions, and its utility has been expanding rapidly within aging and diversified societies. However, the related market remains at an early stage of development due to limited regulation, lack of consumer awareness, and the need for not only technical development but promotion plans for potential users. This paper aims to analyze factors influencing purchase intention for smart healthcare clothing with biosignal monitoring, including variables in the Technology Acceptance Model (TAM), clothing attributes, health-related lifestyle factors, and fashion innovativeness. A survey was conducted on a sample of 300 males and 300 females ranging in age from 20 to 50 years, and data were analyzed using SPSS 21.0. The results show that perceived usefulness, perceived aesthetic attributes, health responsibility, and fashion innovativeness were overall significant predictors of using smart healthcare clothing. Additionally, perceived ease of use and physical activity in the male subsample, and perceived compatibility within the female group, also had significant effects. Furthermore, age was a determining factor; for subjects in the 30s age group, perceived usefulness, compatibility, and health responsibility had significant positive associations. The results of this study can provide basic guidelines for designing merchandising plans to expand user acceptance of smart healthcare clothing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1793-1798
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• 2007

Recently there are some trends to construct smart home system infrastructure depending upon the development of Information and Communication Technology. Also requirements of the Ubiquitous Healthcare Systems at home which can monitor the status of health continuously are increased rapidly comparing with hospitals. Healthcare service can be divided into two categories. The first one is Alarm Service that can be used for the emergency status and the other one is Remote Support Service which can monitor the patient including home environments and give those diagnosis information to medical office or to his family. Generally wired networks and fixed healthcare measuring system have some limits to transmit reliable realtime based information for both categories described above comparing with portable monitoring system. Getting over the inefficiency we will design and implement portable healthcare system under the wireless Zigbee network environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.6
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• pp.55-61
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• 2008

In this paper, we proposed a multi-agent based healthcare system (MAHS) which is the combination of medical sensor module and wireless communication technology. This MAHS provides wide services to mobile telemedicine, patient monitoring, emergency management, doctor's diagnosis and prescription, patients and doctors, information exchange between hospital workers in a long distance. Also, MAHS is connected to Body Area Network (BAN) and a doctor and hospital workers. In addition, we designed and implemented extended JADE based MAHS that reduces hospital server's burden. Agents gather, integrate, and deliver the collected patient's information from sensor, and provide presentation in healthcare environment. Proposed MAHS has advantage that can handle urgent situation in the far away area from hospital like Islands through PDA and mobile device. In addition, by monitoring condition of patient (old man) in a real time base, it shortens time and expense and supports medical service efficiently.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.514-517
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• 2008

Ubiquitous healthcare (U-healthcare) system is one of potential applications of embedded system. Conventional U-healthcare systems are used in health monitoring or chronic disease care based on measuring and transmission of various vital signs. However, future U-healthcare system can be of benefit to more people such as stroke patients which have limited activity by providing them proper medical care as well as continuous monitoring. Recently, an electric brain stimulation treatments have been found to be a better way compared to conventional ones and many are interested in using the method toward the treatment of stroke. In this study, we proposed a remote medical treatment system using ZigBee-based wireless electric brain stimulator that can help them to get a treatment without visiting their doctors. The developed remote medical treatment system connects the doctors to the brain stimulator implanted in the patients via the internet and ZigBee communication built in the brain stimulator. Also, the system receive personal information of the connected patients and cumulate the total records of electric stimulation therapy in a database. Doctors can easily access the information for better treatment planning with the help of graphical visualization tools and management software. The developed remote medical treatment system can extend their coverage to outdoors being networked with hand-held devices through ZigBee.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1551-1560
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• 2016

With the advancement of wearable devices and wireless body are networks, smart healthcare systems based on such technologies have been emerging to effectively monitor patient health and disease progression. In order to implement viable smart healthcare systems, the security and privacy of patient's personal health information must be considered. Yang et al. proposed a privacy-preserving authentication scheme using key-insulation technique for remote health monitoring system, however, key-insulation technique is not properly adapted to their scheme which in turn causes a security pitfall contrary to their assertions. Besides, Yang et al.'s scheme does not guarantee user anonymity against healthcare service provider. Therefore, in this paper, we discuss the security concerns for Yang et al.'s scheme and present an improved anonymous authentication scheme.

• Journal of Sensor Science and Technology
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• v.21 no.4
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• pp.241-248
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• 2012

Recently healthcare industry such as pharmaceutical, medical device and healthcare service technology is growing significantly. Mobile healthcare has attracted big attention due to IT convergence technology. Paradigm of healthcare has been changed from the 1st generation(communicable disease prevention) and the 2nd generation(treatment of disease due to extended life expectancy) to the 3rd generation(extended life expectancy due to prevention and control). In our study, we suggest the 3rd generation mobile healthcare system using Bluetooth based wearable ECG monitoring system and smart phone technology. The mobile healthcare system consists of wearable shirts with Bluetooth communication module, ECG sensor, battery, and mobile phone. The ECG data is obtained by a miniaturized sensor and the data is transferred to a mobile phone using Bluetooth communication. Then, user can monitor his/her own ECG signal on an application using Android in mobile phone. The Bluetooth communication device is used due to highly reliable data transmission property and the Bluetooth chip is embedded in every mobile phone. The wearable shirts with chest belt of Bluetooth ECG module is designed with a focus on convenience in the daily life of a wearer. The ECG signal evaluation software in Android based mobile phone is developed for the health check and the ECG signal variation is tested according to the activities of the wearer such as walking, climbing stairs, stand up and sit down, and so on.

• Journal of Digital Policy
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• v.1 no.1
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• pp.15-20
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• 2022

Recently, the healthcare field is trying to develop a model that can improve service quality by reflecting the requirements of various industrial fields. In this paper, we propose an Internet of Behavior (IoB) environment model that can process users' healthcare information in real time in a 5G environment to improve healthcare services. The purpose of the proposed model is to analyze the user's healthcare information through deep learning and then check the health status in real time. In this case, the biometric information of the user is transmitted through communication equipment attached to the portable medical equipment, and user authentication is performed through information previously input to the attached IoB device. The difference from the existing IoT healthcare service is that it analyzes the user's habits and behavior patterns and converts them into digital data, and it can induce user-specific behaviors to improve the user's healthcare service based on the collected data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.417-423
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• 2017

This paper intend to implement monitoring systems for individual customized diagnostics to maintain ongoing disease management to promote human health. Analyze the threshold of a measured biological signal using a number of measuring sensors. Performance assessment revealed that the SVM algorithm for bio-signal analysis showed an average error rate of 2 %. The accuracy of the classification is 97.2%, and reduced the maximum of 19.2% of the storage space when you split the window into 5,000 pieces. Out of the total 5,000 bio-signals, 84 results showed that results from the system were differently the results of the expert's diagnosis and showed about 98 % accuracy. However, the results of the monitoring system did not occur when the results of the monitoring system were lower than that of experts. And About 98% accuracy was shown.