• Journal of Electrochemical Science and Technology
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• v.12 no.1
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• pp.33-37
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• 2021

In electrochemical glucose sensing, the enhancement of the sensitivity and the response time is essential in developing stable and reliable sensors, especially for continuous glucose monitoring. We developed a method to increase the sensitivity and to shorten the response time for the sensing upon the appropriate addition of single wall carbon nanotube onto the osmium polymer-based hydrogel electrode. Also, the background stabilization is dramatically enhanced.

• Molecules and Cells
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• v.27 no.3
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• pp.337-343
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• 2009

We developed a novel on-chip activity assay using protein arrays for quantitative and rapid analysis of transglutaminase activity in mammalian cells. Transglutaminases are a family of $Ca^{2+}$-dependent enzymes involved in cell regulation as well as human diseases such as neurodegenerative disorders, inflammatory diseases and tumor progression. We fabricated the protein arrays by immobilizing N,N'-dimethylcasein (a substrate) on the amine surface of the arrays. We initiated transamidating reaction on the protein arrays and determined the transglutaminase activity by analyzing the fluorescence intensity of biotinylated casein. The on-chip transglutaminase activity assay was proved to be much more sensitive than the $[^3H]putrescine$-incorporation assay. We successfully applied the on-chip assay to a rapid and quantitative analysis of the transglutaminase activity in all-trans retinoic acid-treated NIH 3T3 and SH-SY5Y cells. In addition, the on-chip transglutaminase activity assay was sufficiently sensitive to determine the transglutaminase activity in eleven mammalian cell lines. Thus, this novel on-chip transglutaminase activity assay was confirmed to be a sensitive and high-throughput approach to investigating the roles of transglutaminase in cellular signaling, and, moreover, it is likely to have a strong potential for monitoring human diseases.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.3
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• pp.168-174
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• 2004

On-line monitoring system(OMS) has been developed for the pumped storage generator/motor The OMS is applied to diagnosis of the PD(partial discharge) activity of stator insulation, the shorted-turn of rotor winding and the variation of the air-gap between stator and rotor. The OMS consists of DAS(data acquisition system), main server system, gateway and display PC. The DAS measures the PD, the shorted-turn and air-gap from three sensors installed on the generator/motor. The gateway controls the data which sent by DAS. The main server system saves the data, analyzes the data and conducts the diagnostic algorithm. The display PC shows the diagnostic results of partial discharge, shorted-turn and air-gap. Field tests were conducted using PDA(partial discharge analyzer). The results of the OMS and PDA measurements can be directly correlated with normalized quantity number(NQN), PD magnitude(Qm) and PD pattern.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.229-232
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• 2008

A Neural network classification of human activity data is presented. The data acquisition system involves a tri-axial accelerometer in wireless sensor network environment. The wireless ad-hoc system has the advantage of small size, convenience for wearability and cost effectiveness. The system can further improve the range of user mobility with the inclusion of ad-hoc environment. The classification is based on the frequencies of the involved activities. The most significant Fast Fourier coefficients, of the acceleration of the body movement, are used for classification of the daily activities like, Rest walk and Run. A supervised learning approach is used. The work presents classification accuracy with the available fast batch training algorithms i.e. Levenberg-Marquardt and Resilient back propagation scheme is used for training and calculation of accuracy.

• 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 Society of Computer and Information
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• v.27 no.11
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• pp.157-163
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• 2022

In this paper, we propose to develop a Bluetooth Health Device Profile (HDP)-based smartphone system to utilize it for early detection of urinary tracts diseases that occur a lot in cats. Therefore, based on Bluetooth HDP, we developed a device and mobile application system (Mycatner®) that can monitor cat activity, toilet usage, urinary disease, and health status, and evaluated its availability to monitor cat health status. The specific feature of this system is that it can measure the number of cat urination frequencies to identify abnormal conditions suspected of urinary tract diseases early, and second, it can be tested with urine test paper and shared with animal hospitals, reducing time and cost. As a result, it is evaluated that the developed device capable of wireless monitoring the urinary system health status of cats is the first in our knowledge.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.29-38
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• 2023

Although the biosignal is the best way to represent the human condition, it is difficult to acquire the biosignal of a driver driving for detecting driver's condition. As one of the methods to overcome this limitation, this paper proposes a driving stress monitoring system based on information provided by OBD-II(on-board diagnostics version II). The driving information and EDA(Electrodermal activity) data are obtained through the OBD-II scanner and E4 wristband, respectively. EDA data is used as ground truth to distinguish whether driver is stressed or not. MLP(multi-layer perceptron) neural network is used as a model to detect driving stress and is trained using driving data for about a month. To evaluate the proposed system, we used about 1 hour of driving data and the accuracy is 92%.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.61-69
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• 2016

Software testing is an activity to find defects included in software through creating test cases from the software system specification. In order to perform software testing effectively, it is required to prepare the full test plan, to create well-defined test cases, and to execute test monitoring activities systematically. Most existing researches for the test approaches focus on automating the activities from the test cases generation to the test execution. Contrary to those approaches, we study automatic approaches for test monitoring activities. For this, we identify the research issues that should be solved to automate test monitoring activities. Next, with those solutions, we suggest the construction method for an automatic framework for test monitoring.

• Journal of Sensor Science and Technology
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• v.19 no.1
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• pp.8-16
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• 2010

This research proposes measuring of BCG(ballistocardiogram) to monitor heart activities in a non-constrained environment, at home or work. Unlike with ECG, measuring BCG does not require the attachment of leads on the subject's body and allows signal measuring in a non-constrained state. It enables effective long-term monitoring of cardiac conditions. In this study a chair type BCG measurement system to continuous monitor the activity of the heart is implemented. The instrument consists of upper petal and ready for press of chair load cell sensor is attached to measure the change of the object's weight. In order to extract the output ballistic signal from the weight and force sensor signals. Beside the signal processing circuit for the digital conversion, the ballistic signal is detected using DAQ equipment. Signal processing algorithm including wavelet transforms for noise cancellation, template matching for normalization and peak detection in BCG is developed. ECG and BCG were concurrently measured to evaluate the performance of the system, and comparing the characteristics of the two signals verified the possibility of the system in non-constrained and nonconscious health monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.141-143
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• 2021

The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.