• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.699-706
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• 2009

The convergence tendency accelerates the realization of the ubiquitous healthcare (u-Healthcare) between the technology including the power generaation and IT-BT-NT of the ubiquitous computing technology. By rapidly analyzing a large amount of collected from the sensor network with processing and delivering to the medical team an u-Healthcare can provide a patient for an inappropriate regardless of the time and place. As to the existing u-Healthcare, since the sensor node all transmitted collected data by using with the Zigbee protocol the processing burden of the base node was big and there was many communication frequency of the sensor node. In this paper, the u-Healthcare system in which it can efficiently apply to mobile apparatuses it provided the transfer rate in which it is superior to the bio-signal delivery where there are the life and direct relation which by using the Bluetooth instead of the Zigbee protocol and in which it is variously used in the ubiquitous environment was designed. Moreover, by applying the EEF(Embedded Event Filtering) technique in which data in which it includes in the event defined in advance selected and it transmits with the base node, the communication frequency and were reduced. We confirmed to be the system in which it is efficient through the simulation result than the existing Electrocardiogram Measurement system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1095-1105
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• 2013

This paper proposes the RDMS(Rough Set Theory based Disease Monitoring System) which efficiently manages diseases in Healthcare System. The RDMS is made up of DCM(Data Collection Module), RDRGM(RST based Disease Rules Generation Module), and HMM(Healthcare Monitoring Module). The DCM collects bio-metric informations from bio sensor of patient and stores it in RDMS DB according to the processing procedure of data. The RDRGM generates disease rules using the core of RST and the support of attributes. The HMM predicts a patient's disease by analyzing not only the risk quotient but also that of complications on the patient's disease by using the collected patient's information by DCM and transfers a visualized patient's information to a patient, a family doctor, etc according to a patient's risk quotient. Also the HMM predicts the patient's disease by comparing and analyzing a patient's medical information, a current patient's health condition, and a patient's family history according to the rules generated by RDRGM and can provide the Patient-Customized Medical Service and the medical information with the prediction result rapidly and reliably.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.395-402
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• 2016

In these days, the wearable devices have been developed for measuring biological data effectively. However, wearable devices have tissue allege and noise problem. Also, it is impossible for a remote center to identify the person whose data are measured by wearable devices, which could trigger a communication problem over treatment. To solve these problems, biometric measurement based on a non-contact method, such as face image sequencing is necessary. This makes it possible to measure biometric data without any operation and side effects. This system can monitor the biological signals of people in real time without allege and noise and simultaneously identify them. In this paper, we propose an authentication process while measuring biometric data, through a non-contact method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.417-418
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• 2014

본 논문에서는 무선 바이오센서 기반에서 동물 생체측정 정보를 이용한 질병 예방 감시 시스템을 설계하였다. 제안 시스템은 바이오센서를 이용하여 동물의 심박, 호흡수, 운동량을 측정할 수 있는 모듈을 개발하고 생체 신호를 계측하여 신호 처리된 정보를 Zigbee 무선 통신모듈을 이용하여 원격 데이터베이스에 전송하도록 설계하였다. 모니터링 시스템에서는 수집된 생체 정보의 연속적인 변화를 통하여 가축의 이상여부를 판단하고 질병 발생여부를 조기에 발견할 수 있게 가축의 상태정보를 제공한다. 본 연구결과는 향후 축산 농가의 질병 감시를 통하여 질병의 확산을 차단할 수 있는 긴급대처시스템을 구축하는데 활용 될 것이다.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.979-989
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• 2022

We examined changes in the shell valve movements (SVMs) of Mytilus edulis and Crassostrea gigas using a Hall element sensor to investigate the early detection of the toxic dinoflagellate Alexandrium pacificum. No increase or decrease was observed in SVMs caused by the non-toxic algae Isochrysis galbana in both M. edulis and C. gigas. However, when M. edulis and C. gigas were exposed to A. pacificum, which causes paralytic shellfish poisoning, the average SVMs for 12 hours before and after exposure increased from 1.25 times/hr to 2.13 times/hr and 2.23 times/hr to 8.91 times/hr, respectively. After exposure to A. pacificum, the SVMs of M. edulis increased rapidly within 1 hour and then decreased gradually. However, C. gigas showed high SVMs until 4 hours after exposure. SVMs of C. gigas appeared to be more sensitive to toxic dinoflagellate than those of M. edulis. Therefore, these results are expected to be used as basic data for the establishment of a biological monitoring system for early detection of the toxic dinoflagellate A. pacificum.

• Korean Journal of Optics and Photonics
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• v.19 no.1
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• pp.68-72
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• 2008

Fiber-optic strain sensors based on Bragg reflection gratings produce the change of reflection spectrum when an external stress is applied on the sensor. To measure the Bragg reflection wavelength in high speed, an arrayed waveguide grating device is incorporated in this work. By monitoring the output power from each channel of the AWG, the peak wavelength corresponding to the applied strain could be obtained. To enhance the accuracy of the AWG wavelength interrogation system, a chirped fiber Bragg grating with a 3-dB bandwith of 5.4 nm is utilized. The high-speed response of the proposed system is demonstrated by measuring a fast varying strain produced by the damped oscillation of a cantilever. An oscillation frequency of 17.8 Hz and a damping time constant of 0.96 second are obtained in this measurement.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.28-36
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• 2006

In this paper, an algorithm to classify the 4 motions of arm and a control system to position control the prosthesis are studied. To classify the 4 motions, we use flex sensors which is electrical resistance type sensor that can measure warp of muscle. The flex sensors are attached to the biceps brchii muscle and coracobrachialis muscle and the sensor signals are passed the sensing system. 4 motion of the forearm - flexion and extension, the pronation and supination are classified from this. Also position of forearm is measured from the classified signals. Finally, A two D.O.F prosthesis arm with RC servo-motor is designed to verify the validity of the algorithm. At this time, fuzzy controller is used to reduce the position error by rotary inertia and noise. From the experiment, the position error had occurred within about 5 degree.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.660-662
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• 2022

Due to the prolonged aftermath of COVID-19, the number of modern people suffering from stress and mental illness is increasing. Recently, various methods of color therapy are being studied using LED lighting to improve concentration, relieve stress, manage skin, and improve sleep quality. In this paper, pulse waves are measured and heart rate variability is extracted using a PPG (Photoplethysmogram) sensor to analyze a person's mental and physical state. Using RGBLED and Arduino, we propose a mood lighting system that automatically changes colors according to changes in mental and physical conditions by producing color lighting of various wavelengths, which are mainly used to stabilize the mental state.