• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.113-121
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• 2020

With the rapid development of information and communication technology (ICT), various sensors are being embedded in wearable devices. Consequently, these devices can continuously collect data including health data from individuals. The collected health data can be used not only for healthcare services but also for analyzing an individual's lifestyle by combining with other external data. This helps in making an individual's life more convenient and healthier. However, collecting health data may lead to privacy issues since the data is personal, and can reveal sensitive insights about the individual. Thus, in this paper, we present a method to collect an individual's health data from a smart band in a privacy-preserving manner. We leverage the local differential privacy to achieve our goal. Additionally, we propose a way to find feature points from health data. This allows for an effective trade-off between the degree of privacy and accuracy. We carry out experiments to demonstrate the effectiveness of our proposed approach and the results show that, with the proposed method, the error rate can be reduced upto 77%.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.67-73
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• 2019

The blood pressure measurement is calculated as a value corresponding to the pressure of the blood vessel using the pressure from the outside for a long time. Due to the recent miniaturization of measurement equipment and the ICT combination of personal healthcare systems, a system that enables continuous and real-time measurement of blood pressure with a sensor is required. In this study, blood pressure was measured using pulse transit time using Photoplethysmography. In this study, blood pressure was estimated by using systolic blood pressure. And it is possible to make measurement only with PPG itself, which can contribute to making a micro blood pressure measuring device. As a result, systolic blood pressure and PPG's S1-P and P-S2 were used to analyze the possibility of blood pressure estimation.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Communications of Mathematical Education
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• v.34 no.1
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• pp.1-15
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• 2020

Today's healthcare, intelligent robots, smart home systems, and car sharing are already innovating with cutting-edge information and communication technologies such as Artificial Intelligence (AI), the Internet of Things, the Internet of Intelligent Things, and Big data. It is deeply affecting our lives. In the factory, robots have been working for humans more than several decades (FA, OA), AI doctors are also working in hospitals (Dr. Watson), AI speakers (Giga Genie) and AI assistants (Siri, Bixby, Google Assistant) are working to improve Natural Language Process. Now, in order to understand AI, knowledge of mathematics becomes essential, not a choice. Thus, mathematicians have been given a role in explaining such mathematics that make these things possible behind AI. Therefore, the authors wrote a textbook 'Basic Mathematics for Artificial Intelligence' by arranging the mathematics concepts and tools needed to understand AI and machine learning in one or two semesters, and organized lectures for undergraduate and graduate students of various majors to explore careers in artificial intelligence. In this paper, we share our experience of conducting this class with the full contents in http://matrix.skku.ac.kr/math4ai/.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.163-168
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• 2022

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.238-255
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• 2020

As COVID-19, which occurred at the end of 2019, has become a global pandemic, it has emerged as an unprecedented event that quickly destroys a nation's medical and healthcare system in both developed and developing countries. In the 21st century, most of the civil society that aimed for hyperconnected society is facing a new crisis that has not been experienced so far. Indeed, lack of personal protective equipment, isolation of clustered communities, disruption of medical systems necessary for diagnosis and treatment, and disruption of educational and economic activities due to social isolation are emerging. Since the COVID-19 has occurred, many of the difficulties that have occurred in the past six months indicate the basic infrastructure a society should have particularly in a pandemic. These include personal protective equipment (PPE), decontamination and quarantine tools essential for effective response, rapid and precise large-scale diagnosis, medical devices required for patient care, and identification and fast and wide on-line networks that can be used in social isolation. In this first part, we would like to introduce some representative examples of 1) personal protective equipment, 2) prevention of personal and community health, 3) social response through big data and networks within the framework of appropriate technology.

• Design Convergence Study
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• v.16 no.5
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• pp.1-20
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• 2017

Cardio-cerebrovascular disease is one of the chronic diseases that often attack people in Korea, and in fact, it ranks second in terms of death rate. This disease can be prevented by improving lifestyle, usual health care is important. But, in Korea most of the prevention or management programs adopt passive methods like using guide books or giving lectures, so it is not very effective in preventing the disease. Presently, the smart health care market is being developed in Korea and overseas. As an example, quantified self is being spread through wearable devices which are intended to measure each individual's health conditions and quantify body data into numbers for bettering habits. Accordingly, this author will explore and discuss wearable health care devices so as to prevent and manage cardio-cerebrovascular disease in a more active way. First, this study has classified wearable health care devices presently commercialized or related with cardio-cerebrovascular disease into wrist, clothes, or attaching types by the way of their attachment and analyzed them. After that, summing that up, this author performed cross-tabulations with other ways of preventing cardio-cerebrovascular disease. This will contribute to improving one's health care behavior about disease more actively and also work as an active interdisciplinary mechanism in research dealing with how to prevent disease afterwards.