• Journal of Sensor Science and Technology
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• v.23 no.6
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• pp.402-408
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• 2014

Heart rate is one of the most important signal to monitor the health condition of the patient or exerciser. Various wearable devices have been developed for the continuous monitoring of ECG signal from human body during exercise. Among these, ECG chest belt has been widely used. However wearing chest belt with ECG sensor is uncomfortable in normal life due to the electrode contact between metal electrodes of ECG sensor and skin of the human body. So we develop the royal healthcare backpack that can measure ECG signal without skin contact by using capacitor-type ECG sensor. The position of the measurement point is critical to collect a clear ECG signal in the capacitive ECG measurement from backpack. Various tests were conducted to find the optimal ECG measurement position which has less noise and could get strong and clear ECG signal during exercise, walking, hiking, mountain climbing and cycling.

• Journal of Sensor Science and Technology
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• v.16 no.5
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• pp.361-368
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• 2007

Small size real-time ECG signal analysis function by QRS-complex detection was put into sensor nodes. Wireless sensor nodes attached on the patient’s body transmit ECG data continuously in normal u-healthcare system. So there are heavy communication traffics between sensor nodes and gateways. New developed platform for real-time analysis of ECG signals on sensor node can be used as an advanced diagnosis and alarming system for healthcare. Sensor node does not need to transmit ECG data all the time in wireless sensor network and to server PC via gateway. When sensor node detects suspicion or abnormality in ECG, then the ECG data in the network was transmitted to the server PC for further powerful analysis. This system can reduce data packet overload and save some power in wireless sensor network. It can also increase the server performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.518-521
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• 2007

The functional wireless sensor node for u-healthcare application was developed. The developed sensor node can check the abnormality of ECG in some simple software in ROM of microprocess in the sensor node. The ECG signal is one of very important health signal form human body, and wavelike signal which is sampled as a sampling frequency between 100 and 400 Hz for digitalization, so the wireless data dor ECG signal is some heavy in Zigbee communication. Thus the sensor send the ECG signal to other sensor nodes or base station when it find abnormality in ECG signal is key technology to reduce the traffic between sensor nodes in wireless sensor network for u-healthcare, The sensor node does not need to transmit ECG data all time in wireless sensor network and to server. Using these sensor nodes, the healthcare system can dramatically reduce wireless data packet overload, the power consumption of battery in the sensor nodes and thus increase the reliability of the wireless system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.1022-1028
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• 2006

A portable ECG sensor module and monitoring system which has the powerline noise reduction and the baseline wander removal is proposed. A small-szie ECG sensor H/W module with the 8-bits microprocessor is implemented. The ECG waveform can be inspected anytime with PDA in real time, and transmitted to the PC through wireless LAN. Portable ECG system can offer the environment that give the lasting medical service to the elderly and the long-time hospitalized patients at the wanted place, and the system can be attached to the chair, wheel chair, treadmill, elderly walker and used to monitor the health condition of man

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.137-144
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• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.227-232
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• 2011

The QRS complex in ECG analysis is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. This paper presents the preprocessor method to detect R-peak, RR interval, and HRV in wireless sensor node. The derivative of the electrocardiogram is efficiency of preprocessing method for resource hungry wireless sensor node with low computation. We have implemented R-peak and RR interval detection application based on dECG for wireless sensor node. The sensor node only transfers meaning parameter of ECG. Thus, implementation of sensor node can save power, reduce traffic, and eliminate congestion in a WSN.

• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.4
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• pp.300-308
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• 2010

This paper describes the development of a low-power electrocardiogram (ECG) touch sensor intended for the use with two dry metal electrodes. An equivalent ECG extraction circuit model encountered in a ground-free two-electrode configuration is investigated for an optimal sensor read-out circuit design criteria. From the equivalent circuit model, (1) maximum sensor resolution is derived based on the electrode's background thermal noise, which originates from high electrode-skin contact impedance, together with the input referred noise of instrumentation amplifier (IA), (2) 60 Hz electrostatic coupling from mains and motion artifact are also considered to determine minimum requirement of common mode rejection ratio (CMRR) and input impedance of IA. A dedicated ECG read-out front end incorporating chopping scheme is introduced to provide an input referred circuit noise of 1.3 ${\mu}V_{rms}$ over 0.5 Hz ~ 200 Hz, CMRR of IA > 100 dB, sensor resolution of 7 bits, and dissipating only 36 ${\mu}W$. Together with 8 bits synchronous successive approximation register (SAR) ADC, the sensor IC chip is implemented in 0.18 ${\mu}m$ CMOS technology and integrated on a 5 cm $\times$ 8 cm PCB with two copper patterned electrodes. With the help of proposed touch sensor, ECG signal containing QRS complex and P, T waves are successfully extracted by simply touching the electrodes with two thumbs.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.362-370
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• 2006

The wireless sensor network (WSN) based ECG and body temperature measuring system for ubiquitous health-care were designed and developed. The system was composed of a wireless sensor network node, base station and server computer for the continuous monitoring of ECG signals and body temperatures of patients at home or hospital. ECG signal and body temperature data, important vital signals which are commonly used in clinical and trauma care, were displayed on a graphical user interface (GUI). The data transfer from sensor nodes on patients' body to server computer was accomplished through a base-station connected to a server computer using Zigbee compatible IEEE802.15.4 standard wireless communication. Real-time as well as historical, ECG data of elderly persons or patients, can also be retrieved and played back to assist the diagnosis. The ubiquitous health care system presented in this study can effectively reduce social medical expenses, which will be increased greatly in the coming aging society.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.403-409
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• 2012

This paper proposes an energy efficient ECG monitoring system by putting some intelligence on the sensor node to reduce the number of transmissions. The sensor node is mostly put into the processing mode and just connects the base station when necessary. Therefore, the transmission energy is greatly reduced while the energy for processing is increased a little bit. Our proposed ECG analysis method classifies ECG cycles by computing the Euclidean distance between the sensed ECG cycle and the reference ECG cycle. This work is a detailed and full explanation of our former work. Extended experimental results show that the proposed trade is very effective in saving energy and the Euclidean distance based classification method is accurate. Furthermore, the PowerTOSSIM energy simulation method is also demonstrated as very accurate in evaluating the energy consumption of the sensor node in our application scenario.