• Proceedings of the KAIS Fall Conference
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• 2003.06a
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• pp.219-221
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• 2003

This system is Emergency patient medical examination and treatment way through biosignal transmission to use PDA. Store measured biosignal that is stored to file to database by Application, This system helps doctor's treatment that transmit patient's biosignal data and patient's symptoms use wireless internet. ThenOffer more than safer medical treatment environment to a doctor and patient

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.1-15
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• 2019

Internet of Things (IoT) is commonly referred to as a future internet technology to provide advanced services by interconnecting physical and virtual things, collecting and using many data from them. The IoT platform is a server platform with a common architecture to collect and share the data independent of the IoT devices and services. Recently, oneM2M, the global standards initiative for Machine-to-Machine (M2M) communications and the IoT announced the availability of oneM2M Release 2 specifications. Accordingly, this paper presents a new oneM2M-compliant IoT platform called Mobius 2.0 and proposes its application to collect the biosignal data from wearable IoT devices for emotion recognition. Experimental results show that we can collect various biosignal data seamlessly and extract meaningful features from the biosignal data to recognize two emotions of joy and sadness.

• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.164-171
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• 2008

Recently, many of hospitals have hurried to computerize the resulting data from medical devices, in order to introduce Electric Medical Record(EMR). In terms of the linkage between medical devices and hospital information systems, however, many difficulties have arisen due to some reasons such as the variety of prescription input, the format difference of the resulting data sheet, and the interface difference between medical devices from different companies. To solve these problems, many researches on standardization of the resulting data of medical devices have been performed. In this study, the linkage between hospital information systems and resulting datum in Electrocardiogram(ECG) generating biosignal waveform was tested by applying Medical waveform Format Encoding Rules(MFER) Version 1.02, which has more advantages than existing global standard. MFER viewer, in addition, was made to display the resulting data on a screen. The MFER viewer was tested and compared to the existing Scalable Vector Graphics (SVG) Viewer. The results showed that this method is more effective in the interface the data storage and application, because of simplicity and easiness in data applications. And the results show that the MFER is convenience and effective for physician. It is considered that the role of MFER as the interface in biosignal waveform including Electrocardiogram medical devices would expand in the near future.

• Journal of the Ergonomics Society of Korea
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• v.29 no.1
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• pp.47-53
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• 2010

An analysis of biosignal and performance data collected during driving has increasingly employed in research to explore a human-vehicle interface design for better safety and comfort. The present study developed a protocol and a system to effectively analyze biosignal and driving performance measurements in various driving conditions. Electrocardiogram (ECG), respiration rate (RR), and skin conductance level (SCL) were selected for biosignal analysis in the study. A data processing and analysis protocol was established based on a comprehensive review of related literature. Then, the established analysis protocol was implemented to a computerized system so that immense data of biosignal and driving performance can be analyzed with ease, efficiency, and effectiveness for an individual and/or a group of individuals of interest. The developed analysis system would be of use to examine the effects of driving conditions to cognitive workload and driving performance.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.501-506
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• 2008

Medical environments incorporate complex and integrated data networks to transfer vast amounts of patient information, such as images, waveforms, and other digital data. To assure interoperability of images, waveforms and patient data, health level seven(HL7) was developed as an international standard to facilitate the communication and storage of medical data. We also adopted medical waveform description format encoding rule(MFER) standard for encoding waveform biosignal such as ECG, EEG and so on. And, the study converted a broad domain of clinical data on patients, including MFER, into a HL7 message, and saved them in a clinical database in hospital. According to results obtained in the test environment, it was possible to acquire the same HL7 message and biosignal data as ones acquired during transmission. Through this study, we might conclude that the proposed system can be a promising model for electronic medical record system in u-healthcare environment.

• Journal of Microbiology and Biotechnology
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• v.28 no.3
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• pp.439-447
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• 2018

The aromatic compound p-hydroxybenzoate (PHBA) is an important material with multiple applications, including as a building block of liquid crystal polymers in chemical industries. The cytochrome P450 (CYP) enzymes are beneficial monooxygenases for the synthesis of chemicals, and CYP53A15 from fungus Cochliobolus lunatus is capable of executing the hydroxylation from benzoate to PHBA. Here, we constructed a system for the bioconversion of benzoate to PHBA in Escherichia coli cells coexpressing CYP53A15 and human NADPH-P450 oxidoreductase (CPR) genes as a redox partner. For suitable coexpression of CYP53A15 and CPR, we originally constructed five plasmids in which we replaced the N-terminal transmembrane region of CYP53A15 with a portion of the N-terminus of various mammalian P450s. PHBA productivity was the greatest when CYP53A15 expression was induced at $20^{\circ}C$ in $2{\times}YT$ medium in host E. coli strain ${\Delta}gcvR$ transformed with an N-terminal transmembrane region of rabbit CYP2C3. By optimizing each reaction condition (reaction temperature, substrate concentration, reaction time, and E. coli cell concentration), we achieved 90% whole-cell conversion of benzoate. Our data demonstrate that the described novel E. coli bioconversion system is a more efficient tool for PHBA production from benzoate than the previously described yeast system.

• 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.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.5-15
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• 2006

In this paper, we measured the biosignal using hot-water system(STYX ford202) on foot. The biosignal transition data is observed from hot-water thermotherapy on foot. pre and post demonstration conditions under 43$^{\circ}C$ and 45$^{\circ}C$ are checked about 9 physiological factors for 10 persons and 10 days. (Checking Time: pre-test, post-test(5, 10, 15, 20 minutes)). The biosignal transition of demonstration's results showed as belows; Forehead Temperature($^{\circ}C$): -0.69 $\pm$ 0.01 dec, Leg Temperature($^{\circ}C$): 1.51 $\pm$ 0.22 inc, Blood Flow($m\ell/min$): 1.18 $\pm$ 0.50 inc, Blood Pressure(mmHg): (max) -1.49$\pm$ 2.81, (min) -0.06 $\pm$ 0.13 dec, Heart Rate(bpm): 6.97 $\pm$ 0.72 inc, Blood Sugar($mg/d\ell$) : -2.41 $\pm$ 1.55 dec, Oxygen Saturation(%): 1.34 $\pm$ 0.28 inc, Body Fat(%) -1.75 $\pm$ 0.15 dec, Weight(kg): -0.10 $\pm$ 0.04 dec. (dec: decrease, inc: increase)

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.139-145
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• 2017

PURPOSES : This study analyzed the difference in a driver's workload between using a driving simulator and field driving in tunnel, highway. METHODS : Based on the literature review, it was found that a driver's workload could be quantified using biosignals. This study analyzed the biosignal data of 30 participants using data collected while they were using a driving simulator and during a field test involving tunnel driving. Relative energy parameter was used for biosignal analysis. RESULTS : The driver's workload was different between the driving simulator and field driving in tunnels, highway. Compared with the driving simulator test, the driver's workload exhibited high value in field driving. This result was significant at the 0.05 level. The same result was observed before the tunnel entrance section and 200 m after the entrance section. CONCLUSIONS : This study demonstrates the driving simulator effect that drivers feel safer and more comfortable using a driving simulator than during a field test. Future studies should be designed considering the result of this study, age, type of simulator, study site and so on.