• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.699-706
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• 2009

The convergence tendency accelerates the realization of the ubiquitous healthcare (u-Healthcare) between the technology including the power generaation and IT-BT-NT of the ubiquitous computing technology. By rapidly analyzing a large amount of collected from the sensor network with processing and delivering to the medical team an u-Healthcare can provide a patient for an inappropriate regardless of the time and place. As to the existing u-Healthcare, since the sensor node all transmitted collected data by using with the Zigbee protocol the processing burden of the base node was big and there was many communication frequency of the sensor node. In this paper, the u-Healthcare system in which it can efficiently apply to mobile apparatuses it provided the transfer rate in which it is superior to the bio-signal delivery where there are the life and direct relation which by using the Bluetooth instead of the Zigbee protocol and in which it is variously used in the ubiquitous environment was designed. Moreover, by applying the EEF(Embedded Event Filtering) technique in which data in which it includes in the event defined in advance selected and it transmits with the base node, the communication frequency and were reduced. We confirmed to be the system in which it is efficient through the simulation result than the existing Electrocardiogram Measurement system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.1-6
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• 2019

As mobile technologies such as Internet of Things (IoT)-based smart homes and financial technologies (FinTech) are developed, authentication by smart devices is used everywhere. As a result, presence-based biometric authentication using smart devices has become a new mainstream in knowledge-based authentication methods like the existing passwords. The electrocardiogram (ECG) is less prone to forgery, and high-level personal identification is its unique feature from among various biometric authentication methods, such as the pulse, fingerprints, the face, and the iris. Biometric authentication using an ECG is receiving a great deal of attention due to its uses in healthcare and FinTech. In this study, we implemented an ECG authentication system that allows users to easily measure and authenticate their ECG waveforms using a miniaturized wearable device, rather than a large and expensive measurement device. The implemented ECG authentication system identifies ECG features through P-Q-R-S-T feature point identification, and was user-certified under the proposed authentication protocols. Finally, assessment of measurements in a majority of adult males showed a relatively low false acceptance rate of 1.73%, and a low false rejection rate of 4.14%, in a stable normal state. In a high-activity state, the false acceptance rate was 13.72%, and the false rejection rate was 21.68%. In a high-heart rate state, the false acceptance rate was 10.48%, and the false rejection rate was 11.21%.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.73-86
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• 2019

After the 4th industrial revolution, the healthcare industry is striving to find new business models through new technologies. Among them, blockchain technology is one of the technologies that have great interest in the healthcare industry. Most providers of personal health record systems have difficulty in securing marketability due to various problems. Therefore, they try to integrate blockchain technology to develop new systems and gain marketability. However, blockchain has limitations in solving the problems of the personal health record system. In this study, we have designed a personalized health data management framework that enables information subjects to acquire full ownership rights of individual's health data, based on distributed ledger technology. For the framework design, we refer to the structure of R3 Corda. It was designed with a different network structure than the existing blockchain systems so that the node can be operated on the personal user's mobile device. This allows information subjects to directly store and manage their own data and share data with authorized network members. Through the proposed system, the information utilization of the healthcare industry can be improved and the public health promotion and medical technology development can be realized.

• Information Systems Review
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• v.25 no.1
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• pp.165-187
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• 2023

The continuous increase in life expectancy and high interest in health has brought about significant changes in the use of health information by the public according to the development of information technology represented by the Internet and smartphones. As the medical market expands to the mobile health environment, many health-related apps have been created and distributed, but the acceptance rate is slow as it has become challenging to provide services due to various regulations. In this study, perceived value, perceived risk factors (psychological risk, risk of time-loss, legal risk), and perceived benefits (usefulness, interaction, autonomy) were derived and verified as factors that affect the acceptance resistance of personal health record apps based on the privacy calculation model. In addition, by analyzing the moderating effect of trust in the manufacturer, how the perceived risk and perceived benefit affect the perceived value was verified. A survey was conducted on Korean college students who recognized the personal health record apps but did not use them, and 127 samples were analyzed using structural equations. As a result of hypothesis verification, perceived value has a negative effect on acceptance resistance, perceived risk (risk of time-loss) has a negative effect on perceived value, and perceived benefits (usefulness, interaction, autonomy) were found to have a positive effect on perceived value. Trust in manufacturers has weakened the impact of perceived risks (legal risk) on perceived values. This study is expected to play an important role in maintaining a competitive advantage in the personal health record app market environment by identifying and proposing detailed criteria for reducing the acceptance resistance of personal health record apps.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1619-1637
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• 2016

With wide distribution of smart phones and development of mobile network, social network service (SNS) is displaying remarkable growth rates. Users build new social relations by sharing their interests, which brings surging growth to the SNS based on the combination between the strength of expanding the place for communication and distribution of smart phones featured with easy portability. This study is designed to understand impact factors of SNS on users in Korea and to conduct empirical research on casual relationship between the factors above and the factors affecting personal information behavior through the privacy protection and self-efficacy. In order to accomplish the objective above, the study presented a research model applied with key variables of the Health Belief Model (HBM) predicting behaviors capable of recognizing and preventing individual diseases in the field of health communication. To perform empirical verification on the research model of this study, a survey was conducted upon college students at N university located in Chungcheongnam-do and K university in rural area, who have experiences using the SNS. Through this survey, a total of 186 samples were collected, and path analysis was performed in order to analyze the relationship between the factors. Based on the findings from the survey, first, variables Perceived probability, Perceived severity, Perceived impairment of the HBM, key factors of personal information protection behavior on the SNS, were found to exhibit negative relationship with self-efficacy, and Perceived probability, Perceived benefit, Perceived impairment were found to exhibit negative relationship with privacy protection. But the above, Perceived severity showed positive relationship with privacy protection, and Perceived benefit and self-efficacy also displayed positive relationship. Second, although self-efficacy, a parameter, showed positive relationship with privacy protection, it demonstrated negative relationship with personal information protection behavior. Lastly, privacy protection exhibited positive relationship with personal information protection behavior. By presenting theoretical model reflected with characteristics of prevention based on these findings above unlike previous studies on personal information protection using technologies threatening personal information, this study is to provide theoretical and operational foundation capable of offering explanations how to predict personal information protection behavior on the SNS in the future.