• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1826-1829
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• 2005

Recent advancement in wireless communications and electronics has enabled the development of low power sensor network. Wireless sensor network are often used in remote monitoring control applications, health care, security and environmental monitoring. Wireless sensor networks are an emerging technology consisting of small, low-power, and low-cost devices that integrate limited computation, sensing, and radio communication capabilities. Sensor network platform for health care has been designed, fabricated and tested. This system consists of an embedded micro-controller, Radio Frequency (RF) transceiver, power management, I/O expansion, and serial communication (RS-232). The hardware platform uses Atmel ATmega128L 8-bit ultra low power RISC processor with 128KB flash memory as the program memory and 4KB SRAM as the data memory. The radio transceiver (Chipcon CC1000) operates in the ISM band at 433MHz or 916MHz with a maximum data rate of 76.8kbps. Also, the indoor radio range is approximately 20-30m. When many sensors have to communicate with the controller, standard communication interfaces such as Serial Peripheral Interface (SPI) or Integrated Circuit ($I^{2}C$) allow sharing a single communication bus. With its low power, the smallest and low cost design, the wireless sensor network system and wireless sensing electronics to collect health-related information of human vitality and main physiological parameters (ECG, Temperature, Perspiration, Blood Pressure and some more vitality parameters, etc.)

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.865-867
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• 2016

본 논문에서는 섬유근접센서를 이용하여 측정한 맥박을 평가하기 위해 Biopac MP150에서 획득한 Electrocardiography(ECG)와의 관계를 보았다. 섬유근접센서는 요골동맥에서의 맥박을 측정하기 위해 $5{\times}5$ 크기로 설계하였고, 전처리 과정과 필터링을 거쳐 획득한 데이터 값은 ECG 데이터와 Peak Point의 개수를 비교하여 올바른 맥박이 측정되었는지를 판단하였다. 그 결과 섬유근접센서와 MP150에서 측정한 두 데이터의 Peak Point가 모두 동일한 결과를 보였다.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.80-84
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• 2014

As an integrated technology with IT and biomedical sciences, U-health offers various healthcare services without time and space limit. In order to make a proper diagnosis, doctors need two key technologies: biosignal measurement and high reliability communication technologies. In this paper, we introduce an implementation process of a bio signal system with using an electrocardiography(ECG) sensor, video, global positioning system(GPS), communication module and micro controller unit(MCU).

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.6
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• pp.841-846
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• 2013

Recently, personal health monitoring system using intelligent biosensors and equipments has been realized. This system can be adopted for soldier's biosignal monitoring system. In this paper, we propose a soldier biosignal monitoring system using personal biosensors, such as the ECG sensors and accelerometer.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.371-376
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• 2014

Consistently collecting a variety of vital signs is crucial in u-Healthcare. In order to do so, IoT is being considered as a top solution nowadays as an efficient network environment between the sensor and the server. This paper proposes a transmission method and compression algorithm which are appropriate for IoT environment. Results were compared to widely used compression methods, and were compared to other prior researches. The results showed that the compression ratio of our proposed algorithm was 11.7.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.220-226
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• 2011

Device of the ECG and pulse signal was made to measure PTT signal using non-invasive method and possible to wearable. PTT alterations were observed according to position change using implemented system.It was needed to ECG and pulse to detect the PTT, used the photoplethymorgraphy appeared to change the blood volume. And also wireless sensor node which was able to Zigbee compatibility was used to transfer the detected ECG and pulse signal to PC. Noise was removed from transit data and algorithm was applied to calculate the PTT. After the evaluation of both the conventional measuring systems and the proposed photoplethymography measuring system, a highly effective and efficient formation and distribution sequences were found within the proposed photoplethymography measuring system.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.261-268
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• 2014

This paper presents a electrocardiogram(ECG), electromyogram(EMG), and Photoplethysmography(PPG) signal wireless monitoring system based on Bluetooth Low Energy (BLE). ECG and EMG sensor interface analog front-end circuits are designed by using off-the-shelf parts. Texas Instruments(TI)'s CC2540DK is used for BLE-based communication. Two CC2540DK modules are used as Peripheral and Central nodes. In peripheral device, vital signals are acquired by the analog front-ends and fed to ADC for analog-to-digital conversion. The peripheral transmitts the data through the air to the central device. The central device receive the data and sends them to PC using UART. GUI is designed using Labview for displaying the acquired vital signals. The developed system can be used for future ubiquitous wireless healthcare system based on bluetooth 4.0.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.105-112
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• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.797-804
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• 2009

Recently, To provide various service for user, there are some trends to construct health care system using bio-sensors and wireless network technologies. Accordingly, An effective middleware is needed to manage and control the various bio-sensors. Also, it is important to extend network and interoperate the various application system in healthcare service. In this paper, we propose to be connected with network based on UPnP by design and implementation of UPnP-ZigBee bridge. The proposed UPnP-ZigBee bridge manage and control ZigBee sensor module effectively. we implemented and tested ECG, PPG, and environment sensor module based on ZigBee wireless network to apply in health care service.