• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2006.05a
• /
• pp.426-429
• /
• 2006

A distributed healthcare monitoring system prototype for clinical and trauma patients, was developed, using wireless sensor network node. The proposed system aimed to measure various vital physiological health parameters like ECG and body temperature of patients and elderly persons and transfer his/ her health status wirelessly in Ad-hoc network, to remote base station which was connected to doctor's PDA/PC or to a hospital's main Server using wireless sensor node. The system also aims to save the cost of healthcare facility for patients and the operating power of the system because sensor network is deployed widely and the distance from sensor to base station was shorter than in general centralized system. The wireless data communication will follow IEEE 802.15.4 frequency communication with ad-hoc routing thus enabling every motes attached to patients, to form a wireless data network to send data to base-station, providing mobility and convenience to the users in home environment.

• Journal of information and communication convergence engineering
• /
• v.8 no.2
• /
• pp.219-222
• /
• 2010

An ubiquitous sensor network (USN) system to monitor the bio information and the emergency of the elderly in the silver town is presented. The USN system consists of the sensor node platforms based on MCU of Atmage128L and RF Chip of CC2420 satisfying IEEE 802.15.4, which includes the bios sensor module such as the electrocardiogram (ECG) sensor and the temperature sensor. Additionally, when an emergency of the elderly is occurred in the silver town, the routing algorithm suitable to find and inform the location of the elderly is proposed, and the proposed routing algorithm is applied to the USN. To collect and manage the ECG data at the PC connected to the sink node, LabView software is used. The bio information and the emergency of the elderly can also be monitored at the client PC by TCP/IP networks in the USN system.

• Journal of Sensor Science and Technology
• /
• v.29 no.1
• /
• pp.19-26
• /
• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• Transactions on Electrical and Electronic Materials
• /
• v.16 no.1
• /
• pp.16-19
• /
• 2015

A wearable sensing ECG T-shirt for ubiquitous vital signs sensing is proposed. The sensor system consists of a signal processing board and capacitive sensing electrodes which together enable measurement of an electrocardiogram (ECG) on the human chest with minimal discomfort. The capacitive sensing method was employed to prevent direct ECG measurement on the skin and also to provide maximum convenience to the user. Also, low power integrated circuits (ICs) and passive electrodes were employed in this research to reduce the power consumption of the entire system. Small flexible electrodes were placed into cotton pockets and affixed to the interior of a worn tight NIKE Pro combat T-shirt. Appropriate signal conditioning and processing were implemented to remove motion artifacts. The entire system was portable and consumed low power compared to conventional ECG devices. The ECG signal obtained from a 24 yr. old male was comparable to that of an ECG simulator.

• Science of Emotion and Sensibility
• /
• v.26 no.3
• /
• pp.149-160
• /
• 2023

This study develops a time-varying system-based noncontact fabric sensor that can measure cerebral blood-flow signals to explore the possibility of brain blood-signal detection and emotional evaluation. The textile sensor was implemented as a coil-type sensor by combining 30 silver threads of 40 deniers and then embroidering it with the computer machine. For the cerebral blood-flow measurement experiment, subjects were asked to attach a coil-type sensor to the carotid artery area, wear an electrocardiogram (ECG) electrode and a respiration (RSP) measurement belt. In addition, Doppler ultrasonography was performed using an ultrasonic diagnostic device to measure the speed of blood flow. The subject was asked to wear Meta Quest 2, measure the blood-flow change signal when viewing the manipulated image visual stimulus, and fill out an emotional-evaluation questionnaire. The measurement results show that the textile-sensor-measured signal also changes with a change in the blood-flow rate signal measured using the Doppler ultrasonography. These findings verify that the cerebral blood-flow signal can be measured using a coil-type textile sensor. In addition, the HRV extracted from ECG and PLL signals (textile sensor signals) are calculated and compared for emotional evaluation. The comparison results show that for the change in the ratio because of the activation of the sympathetic and parasympathetic nervous systems due to visual stimulation, the values calculated using the textile sensor and ECG signals tend to be similar. In conclusion, a the proposed time-varying system-based coil-type textile sensor can be used to study changes in the cerebral blood flow and monitor emotions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.249-252
• /
• 2008

A wearable ubiquitous health care monitoring system using integrated ECG and accelerometersensors based on WSN is designed and developed. Wireless sensor network technology is applied for non intrusive healthcare in some wide area coverage with small battery support for RF transmission. We developed wearable devices which are wearable USN node, sensor board and base-station. Low power operating ECG and accelerometer sensor board was integrated to wearable USN node for user's health monitoring. The wearable ubiquitous healthcare monitoring system allows physiological data to be transmitted in wireless sensor network from on body wearable sensor devices to a base-station connected to server PC using IEEE 802.15.4. Physiological data displays and stores on server PC continuously.

• The Journal of the Korea Contents Association
• /
• v.6 no.12
• /
• pp.96-104
• /
• 2006

In this study, data transmission ratio and power consumption issues of Zigbee based sensor module and personal digital assistant(PDA) were addressed to develop ECG telemetry device. PDA processes the data transmitted through serial port using non-blocking method. The transmission rate was dependent on the packet structure. It was 300 ECG samples/sec, when each packet was composed of 2 ECG data and 3-axial acceleration vector. Using two AAA batteries in series, operating time of the wireless sensor module was above 28 hours in average. Power consumption of PDA was dependent on screen ON/OFF condition and serial port usage. In this application, operating time of PDA was 5 hours in average. In conclusion, there was no problem in the power consumption of wireless sensor module and transmission rate, when the developed device was used as 24 hour Holter device. But, PDA has the problem of power consumption, which should be solved.

• Journal of Practical Engineering Education
• /
• v.16 no.4
• /
• pp.491-497
• /
• 2024

Over the past five years, the proportion of patients with arrhythmia heart disease among teenagers and those in their 20s has been increasing. Heart disease has consistently remained the second leading cause of death in Korea and as the number has increased, electrocardiogram testing for arrhythmia has become important. However, specialized electrocardiogram medical devices are economically burdensome and are difficult to store individually in hospitals due to their large size and difficulty in operation. Testing is conducted through visits. Therefore, it is essential to enable individuals to perform ECG self-examinations using an Arduino-based ECG sensor that is affordable and easy to use in daily life, so that arrhythmia can be identified through individual ECG measurement. In this study, data is measured using an electrocardiogram sensor (AD8232), and changes in bio signals are visually provided through real-time monitoring, allowing users to make intuitive decisions and at the same time understand test results. To safeguard sensitive personal information, we have developed a web service that provides individual heart disease and customized health guides that can protect personal information through web vulnerability security using session and user authentication and SSL.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.6
• /
• pp.1257-1264
• /
• 2019

The traditional cuff-based method for BP(Blood Pressure) measurement is not suitable for continuous real-time BP measurement techniques. For this reason, the previous studies estimated various blood pressures by fusion with the electrocardiography (ECG) and photoplethysmogram (PPG) sensor signals. However, conventional techniques based on PPG bio-sensing measurement face many challenging issues such as noisy supply fluctuation, small pulsation, and drifting non-pulsatile. This paper proposed a novel BP estimation methods using PPG and ECG sensors, which can be derived from the relationship between PPG and ECG using PTT(Pulse Transit Time) and PWV(Pulse Wave Velocity). Unlike conventional height ratio features, which are extracted on the basis of the peaks in the PPG and ECG waveform. The proposed method can be reliably obtained even if there are missing peaks among the sensed PPG signal. The increased reliability comes from periodical estimation of the peak-to-peak interval time using ECG and PPG. After 250,000 times trials of the blood pressure measurement, the proposed estimation technique was verified with the accuracy of ±28.5% error, compared to a commercialized BP device.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.10
• /
• pp.1469-1476
• /
• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.