• ETRI Journal
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• v.36 no.5
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• pp.730-740
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• 2014

The rapid increase in the number of patients with chronic diseases is an important public healthcare issue in many countries, which accelerates many studies on a healthcare system that can, whenever and wherever, extract and process patient data. A patient with a chronic disease conducts self-management in an out-of-hospital environment, particularly in an at-home environment, so it is important to provide integrated and personalized healthcare services for effective care. To help provide effective care for chronic disease patients, we propose a service flow and a new cloud-based personalized healthcare system architecture supporting both at-home and at-hospital environments. The system considers the different characteristics of at-hospital and at-home environments, and it provides various chronic disease care services. A prototype implementation and a predicted cost model are provided to show the effectiveness of the system. The proposed personalized healthcare system can support cost-effective disease care in an at-hospital environment and personalized self-management of chronic disease in an at-home environment.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.79-84
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• 2016

A novel diagnosis scheme PCADP(personalized computer aided diagnosis probability) is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method based on ontology and it makes the bio-data analysis just a 'process' in the Smart healthcare service system. In addition, we offer a semantics modeling of the smart healthcare ontology framework in order to describe smart healthcare data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has real-time processing, characteristics of flexible structure, easy monitoring of decision process, and continuous improvement.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.111-119
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• 2010

U-Healthcare provides healthcare and medical services, such as prevention, diagnosis, treatment, and follow-up services whenever and wherever it is needed, and its ultimate goal is to improve quality of life. This study defines the figure of U-Healthcare personalized services for providing U-Healthcare personalized services and proposes a healthcare model. A diet prescription system for personalized services can draw customized calories and rates of nutrition factors and represent a personalized diet through analyzing the personal preference in foods. This system changes the personal preference by monitoring the diet selection behavior of users. Also, this system is designed to be interactively operated with some sensors and devices in various environments using Java-based OSGi middleware.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.102-103
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• 2021

With the development of medical IT technology, personalized medical services are increasing in the silver industry era through the development of smart healthcare business. Therefore, in this paper, using various sensors in the Arduino environment, we implemented a finger modeling that can perform joint rehabilitation exercise that can provide personalized smart healthcare services. By measuring the activity of each individual finger joint using an Arduino-based flex sensor, it is intended to be used for personalized rehabilitation exercise in the smart healthcare field in the future.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.24-32
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• 2009

Our society is rapidly ageing and income level is rising. With the development of IT-based convergence technology and the construction of infrastructure for the u-healthcare services, the importance of the hand acupuncture prescription has known as the folk remedies is being spotlighted. In this paper, we proposed the hand acupuncture prescription using the personalized symptom according to context in the u-healthcare. The proposed method defined the context and environment of the users and predicted the profited hand acupuncture prescription service according to the personalized symptom using the collaborative filtering. The user gets the accurate hand acupuncture prescription as the personalized symptom to input only the name of a disease in the proposed system. We developed GUI for this purpose, and experimented with it to verify the logical validity and effectiveness. Accordingly, the satisfaction and the quality of services will be improved the hand acupuncture prescription by supporting the context information as well as the personalized symptom.

• The Journal of Information Systems
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• v.29 no.2
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• pp.243-263
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• 2020

Purpose The purpose of this study is to analyze the causal relationship between user satisfaction, expected satisfaction, quality of service, perceived value, and expertise that should be emphasized in personalized medical information services for the development of personalized medical information services based on big data analysis and the spread of their demand. Design/methodology/approach This study established research models and hypotheses on the basis of the theory of reuse intent, and applied the PLS methodology for analysis, the factors that make it applicable to personalized medical services in the theory of service quality and satisfaction. Findings According to the empirical analysis result, this study confirmed that it can be seen that the expertise, perceived value, and quality of medical services did not directly affect the user's intention to reuse, but formed a direct causal relationship through variables such as whether they met expectations.

• Journal of the Korean Society of Food Culture
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• v.38 no.4
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• pp.185-190
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• 2023

This paper elucidates the novel direction of food research in the era of the 4^th Industrial Revolution characterized by personalized approaches. Since conventional approaches for identifying novel food materials for health benefits are expensive and time-consuming, there is a need to shift towards AI-based approaches which offer more efficient and cost-effective methods, thus accelerating progress in the field of food science. However, relevant research papers in this field present several challenges such as regional and ethnic differences and lack of standardized data. To tackle this problem, our study proposes to address the issues by acquiring and normalizing food and biological big data. In addition, the paper demonstrates the association between heath status and biological big data such as metabolome, epigenome, and microbiome for personalized healthcare. Through the integration of food-health-bio data with AI technologies, we propose solutions for personalized healthcare that are both effective and validated.

• Journal of Korean Philosophical Society
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• v.145
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• pp.71-90
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• 2018

The purpose of this paper is to examine the concept of autonomy, which is the subject of respect, namely, narrow bioethical autonomy and relational autonomy, before discussing autonomy in the era of personalized healthcare based on self-tracking technology. There is a need to extend the scope of the autonomy concept through a review of the autonomy debates presented by Tom Beauchamp and James Childress. Then, I suggest that relational autonomy based on the capability approach as a broader autonomy than the autonomy of consultation is a suitable concept of autonomy as an object to form ethical guidelines in a new situation.

• Journal of IKEEE
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• v.25 no.1
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.