• 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 Sensor Science and Technology
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• v.21 no.5
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• pp.352-358
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• 2012

A sensor network system can be an efficient tool for healthcare telemetry for multiple users due to its power efficiency. One drawback is its limited data size. This paper proposed a real-time application of data compression/decompression method in u-Health monitoring system in order to improve the network efficiency. Our high priority was given to maintain a high quality of signal reconstruction since it is important to receive undistorted waveform. Our method consisted of down sampling coding and differential Huffman coding. Down sampling was applied based on the Nyquist-Shannon sampling theorem and signal amplitude was taken into account to increase compression rate in the differential Huffman coding. Our method was successfully tested in a ZigBee and WLAN dual network. Electrocardiogram (ECG) had an average compression ratio of 3.99 : 1 with 0.24% percentage root mean square difference (PRD). Photoplethysmogram (PPG) showed an average CR of 37.99 : 1 with 0.16% PRD. Our method produced an outstanding PRD compared to other previous reports.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.4
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• pp.42-50
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• 2008

The real-time bio-signal monitoring system based on the ZigBee and SIP/RTP has proposed and implemented for telemedicine but that has some problems at the stabilities to transmit bio-signal from the sensors to the other sides. In this paper, we designed and implemented a real-time bio-signal monitoring system that is focused on the reliability and efficiency for transmitting bio-signal at real-time. We designed the system to have enhanced architecture and performance in the ubiquitous sensor network, SIP/RTP real-time transmission and management of the database. The Bluetooth network is combined with ZigBee network to distribute traffic of the ECG and the other bio-signal. The modified and multiplied RTP session is used to ensure real-time transmission of ECG, other bio-signals and speech information on the internet. The modified ECG compression method based on DWLT and MSVQ is used to reduce data rate for storing ECG to the database. Finally we implemented a system that has improved performance for transmitting bio-signal from the sensors to the monitoring console and database. This implemented system makes possible to make various applications to serve U-health care services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.3
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• pp.781-793
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• 2010

Recently, owing to the development of ubiquitous sensor network and mobile communication technologies, many studies on healthcare system are being carried out. In this paper, we have designed and implemented a mobile u-Healthcare system based on sensor network. The u-Healthcare system is composed of three components: wireless sensor network at home, healthcare center located at remote site, and gateway which relays sensing physiological signals to healthcare center. In order to measure patient's physiological signal three sensors are used: three channel ECG sensor, pulse oximeter, and blood pressure sensor. Each sensor is mounted on a mote which can send gathered signal to the base node using Zigbee communication protocol. Once the base node receives physiological signal from each sensor, the client in the base node transfers the signal to the healthcare center. The received physiological signal at the healthcare center is analyzed and processed using various algorithms. The processed results are compared to the standard healthcare database and appropriate treatment including dietetics and exercise cure would be sent to the patient as feedback using SMS message or healthcare center web site. Each patient can check and manage one's health state every day using the healthcare system and gain a recovery under the treatments from minor health problems.

• Journal of Sensor Science and Technology
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• v.20 no.5
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• pp.343-350
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• 2011

The compliance and stiffness of artery are closely related with disease of arteries. Pulse wave velocity(PWV) in the blood vessel is a basic and common parameter in the hemodynamics of blood pressure and blood flow wave traveling in arteries because the PWV is affected directly by the conditions of blood vessels. However, there is no standardized method to measure the PWV and it is difficult to measure. The conventional PWV measurement has being done by manual calculation of the pulse wave transmission time between coronary arterial proximal and distal points on a strip chart on which the pulse wave and ECG signal are recorded. In this study, a pressure sensor consisting of strain gauges is used to measure the blood pressure of arteries in invasive method and regular ECG electrodes are used to record the ECG signal. The R-peak point of ECG is extracted by using a reference level and time windowing technique and the ascending starting point of blood pressure is determined by using differentiation of the blood pressure signal and time windowing technique. The algorithm proposed in this study, which can measure PWV automatically, shows robust and good results in the extraction of feature points and calculation of PWV.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.129-130
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• 1998

In this paper, We designed the ECG-NIBP-$SpO_2$ patient monitor. This production can measure Electrocardiograph, Heart Rate, Noninvasive Blood Pressure, and Oxygen Saturation for Noninvasive Mehod and can display each information. These informations were implemented by the electrodes of ECG part, the cuff of NIBP module and the finger probe with light sensor of $SpO_2$ without injection of needle or catheter. In addition, We developed a new analysis algorithm and measurement technique for NIBP and $SpO_2$ to observe patient's conditions correctly.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.364-371
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• 2015

In this study, we developed a feasible structure of a textile-based inductive sensor using a machine embroidery method, and applied it to a non-contact type vital sign sensing device based on the principle of magnetic-induced conductivity. The mechanical heart activity signals acquired through the inductive sensor embroidered with conductive textile on fabric were compared with the Lead II ECG signals and with respiration signals, which were simultaneously measured in every case with five subjects. The analysis result showed that the locations of the R-peak in the ECG signal were highly associated with sharp peaks in the signals obtained through the textile-based inductive sensor (r=0.9681). Based on the results, we determined the feasibility of the developed textile-based inductive sensor as a measurement device for the heart rate and respiration characteristics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.357-360
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• 2008

Vital sign monitoring system using IEEE 502.IS.4 based wireless sensor network(WSN) is designed and developed on mobile environment and sensor node platform. WSN and CDMA are integrated to create a wide coverage to support various environments like inside and outside. We developed query processor to use selective any devices(ECG, Blood pressure and sugar module) and control of the self-organizing network of sensor nodes in a wireless sensor network. Vital sign from wireless medical any devices are analysed in cell phone first for real time signal analyses and the abnormal vital signs are sent and save to hospital server for detail signal processing. wireless signal traffic in wireless sensor network environment or data communication inside the cell phone is reduced.

• Journal of IKEEE
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• v.10 no.1 s.18
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• pp.55-61
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• 2006

In modern society, development of medical technology has extended the human life span. However, it has also caused some side-effects. Mostly old people who live alone are not available the medical service quickly when they are in emergency situations. Moreover heart related diseases as well are rapidly increasing with aging. This study proposes the emergency medical alarm system. This system measures the physiological signals such as ECG(electrocardiogram), temperature, and motion data, analyzes those data automatically, and sends the urgent message to the Emergency Medical Center and to their family. There are two main parts in the system. In the first part, physiological data acquisition part, the troublesome addition and deletion of body signals on existing proposed systems have been supplemented, which led to the modulized production by means of ECG sensor module, temperature sensor module, acceleration sensor module. The other part is mobile unit, which includes signal processing and transmission functions. And bluetooth allows two parts to communicate with each other. Data that are processed in the mobile unit are stored in the PC database through the WLAN using TCP/IP protocol.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.136-142
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• 2014

Distinct features in heart rate signals during the driver's wake and sleep states could provide an initiative for the development of a safe driving systems such as drowsiness detecting sensor in a smart wheel. We measured ECG from health subjects ($23.5{\pm}2.5$ in age) during the wake and drowsiness states. The proposed method is able to detect R waves and R-R interval calculation in the ECG even when the signal includes in abnormal signals. Heart rate variability(HRV) was investigated for the time domain and frequency domains. The STD HR(0.029), NN50(0.044) and VLF power(0.0018) of the RR interval series of the subjects were significantly different from those of the control group (p < 0.05). In conclusion, there are changes in heart rate from wake to drowsiness that are potentially to be detected. The results in our study could be useful for the development of drowsiness detection sensors for effective real-time monitoring.