• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1119-1123
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• 2006

Wireless sensor network plays a prominent role in tracking the location of the target outdoor or indoor. This paper describes the implementation of the passive indoor location tracking system using ultrasonic and RF technologies that provides accurate location in the form of user space and position in three dimensions. Our system used a combination of RF and ultrasonic technologies to provide a location-support service to users and applicants. Ceiling-mounted beacons were spread through the building, publishing location information on an RF signal. The person carried a listener and the listener determined the location by calculating the distance from three beacons using triangulation algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1141-1142
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• 2011

유비쿼터스 기술의 결합을 통해 u-헬스케어라는 보건 의료의 새로운 패러다임을 탄생시켜가고 있으며, 원격에서 건강 상태를 검진하여 질병을 방지하거나 만성질환자의 건강상태를 장기적으로 관찰할 수 있는 IT 기술에 대한 연구가 진행되고 있으며 IT와 BT, NT 등의 관련 기술의 융합발전 등으로 U-헬스케어는 정보 네트워크로의 접근을 용이하게 하고 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.144-147
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• 2021

헬스케어 분야에서 제공되는 서비스는 의료 및 건강과 같은 의료정보를 다루기 때문에 개인의 생명과 밀접한 관계를 가지고 있다. 개인 의료 정보는 매우 민감하고 개인적인 정보이기 때문에 만약 이 정보가 불법적으로 유출되거나 악용된다면 개인의 프라이버시 침해뿐만 아니라 생명까지도 위협받을 것이다. 그렇기 때문에 개인의 건강 및 의료 정보가 체계적으로 관리되고 의료 서비스 기관에 의한 개인 의료 정보 유출 및 남용을 방지하기 위해 정보 보안 시스템이 강화되어야 한다. 따라서 본 논문에서는 헬스 케어 분야에서 발생할 수 있는 정보 보안 취약점에 대한 이슈를 분석하고 블록체인 기술을 바탕으로 보안 취약점에 대한 해결책을 제시하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.753-758
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• 2009

This paper is proposed the hardware structure and signal processing method of the RF-Tag based on heart sound to develop the mobile biomedical information device for the u-healthcare system. The RF-Tag in this study is consisted of a skin temperature sensor, a dynamic microphone for heart sound detection, Bluetooth communication to transmute healthcare data, and pulse period detection algorithm with adaptive gain controller. We experimented to evaluate performance of the RF-Tag in noisy environments. In addition, the RF-Tag has shown the good performance in the results of experiment. If the proposed methods in this study apply to design the u-healthcare device, we will be able to get the exact health data on real time in mobile environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1791-1798
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• 2016

Recently, research about ESS(Emergency Support System) has been actively carried out to provide a variety of medical services using smart devices and wearable devices. Smart healthcare provides a personalized health care service using various types of bio-signal measuring sensors and smart devices. For the smart healthcare using a smart device, it is need to research about personal health monitoring using a smart wearable devices, and also need to research on service methods for first aid measures after an emergency. In this paper, we proposed about group management based emergency support system, that is monitoring about personal bio signal using smart devices and wearable devices to protect patient's life. The system notices to the medical volunteers based on the position information when an emergency situation. In addition, we have designed and implemented an emergency support system providing the information of the patient on the display when transmitting a picture of a patient using a smart device to the server.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.312-315
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• 2008

Recently, owing to the development of ubiquitous, RFID and local area wireless communication technology, many studies on the system which can measure biomedical signals are being carried out. In this paper, we have designed and implemented an u-Healthcare system based on sensor network using biomedical signal measurement sensors such as ECG, blood pressure, and heartbeats. The biomedical signals from sensor nodes pass through the gateway and are finally transmitted to a healthcare renter. The acquired biomedical signals are processed in the healthcare center and the analyzed results are transmitted to the patients to improve patients' health using either kinesitherapy or dietary treatment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2159-2164
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• 2008

Ubiquitous healthcare system is system that monitors and manages user's health information, and most important in the healthcare system is accuracy of the measured health data. But, the accuracy changes remarkably according to user's motion artifacts in real life. To elevate accuracy of health data, we proposed new algorithm to detect maximum point of pulse wave for heart rate extraction. and the proposed algorithm is to detect maximum points detect of pulse wave in photo-plethysmography signal included motion artifacts by fuzzy inference and multi sub-band filters. In results of experiment to evaluate the performance of the proposed algorithm, we could verify the proposed algorithm extracted maximum point of pulse wave in complex motion artifacts.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.45-50
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• 2019

When PPG signal is used in mobile healthcare devices, the accuracy of the measured heartbeat decreases from the influence by the movement of the user. The reason is that the frequency band of the noise overlaps the frequency band of the PPG signal. In order to remove these same noises, the methods using frequency analysis method or application of acceleration sensor have been investigated and showed excellent performance. However, in applying these methods to low-cost healthcare devices, it is difficult to apply these methods because of much processing time and sensor's cost. In order to solve these problems, this study proposed the filter design method using an adaptive notch filter and the fuzzy inference system to extract more accurate heart rate in real time and evaluated its performance. As results, it showed better results than the other methods. Based on the results, when applying the proposed method to design the mobile healthcare device, it is possible to measure the heartbeat more accurately in real time.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1759-1767
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• 2018

As our society is rapidly entering an aging society, interest in mobile health care services for disease prevention and health care for the elderly is increasing. To increase the usability of mobile healthcare services, UI design should be considered from an older person's perspective and a specific evaluation system is required to diagnose these UI usability levels. This study aimed to develop a UI usability evaluation area of mobile-healthcare application for the elderly. For this, we conducted a literature analysis to explore the comprehensive UI usability evaluation factors, and FGI for expert groups and user groups to derive UI usability evaluation areas and detailed evaluation factors. As a result, the usability evaluation areas for the elderly were divided into four areas 'design', 'contents', 'process', and 'system'. A total of 13 sub-factors and 32 detailed constructions have been derived.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.129-142
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• 2010

The ubiquitous healthcare environment is one of the systems that benefit from wireless sensor network. But one of the challenges with wireless sensor network is its high loss rates when transmitting data. Data from the biosensors may not reach the base stations which can result in missing values. This paper proposes the Health Monitor Agent (HMA) to gather data from the base stations, predict missing values, classify symptom patterns into medical conditions, and take appropriate action in case of emergency. This agent is applied in the Ubiquitous Healthcare Environment and uses data from the biosensors and from the patient’s medical history as symptom patterns to recognize medical conditions. In the event of missing data, the HMA uses a predictive algorithm to fill missing values in the symptom patterns before classification. Simulation results show that the predictive algorithm using the HMA makes classification of the symptom patterns more accurate than other methods.