Hoill Jung

Knowledge-based dietary nutrition recommendation for obese management

As the basic paradigm of health management has changed from diagnosis and treatment to preventative management, health improvement and management has received growing attention in societies around the world. Recently the number of obese youth has risen globally and obesity has caused serious problems regarding almost all of the diseases of these days. This study presents dietary nutrition recommendations based on knowledge for obese youth. The knowledge-based dietary nutrition recommendations herein include not only static dietary nutritional data but also individualized diet menus for them by utilizing knowledge-based context data through a collaborative filtering method. The suggested method utilizes the basic information on obese youth, forms a similarity clustering with a high correlation, applies the similarity weight on {user-menu} matrix within the similarity clustering and utilizes the knowledge based collaborative filtering to recommend the dietary nutritional menu. Also by using the knowledge-based context-aware modeling, the study constitutes a {user-menu} merge matrix and solves the sparse problem of previous recommendation system. The suggested method herein, unlike the conventional uniformed dietary nutrition recommendations for obesity management, is capable of providing the personalized recommendations. Also through mobile devices, users can receive personalized recipes and menus anytime and anywhere. By using the proposed method, the researcher develops a mobile application of dietary nutrition recommendation service for obese management. A mobile interface will be built herein and applied in an experiment to test its logical validity and effectiveness.

Sequential pattern profiling based bio-detection for smart health service

Due to the development of IT convergence technologies, increased attention has focused on smart health service platforms to detect emergency situations related to chronic disease, telemedicine, silvercare, and wellness. Moreover, there is a high demand for technologies that can properly judge a situation and provide suitable countermeasures or health information if an emergency situation occurs. In this paper, we propose the sequential pattern analysis based bio-detection for smart health services. A smart health service platform is able to save bio-images and their locations detected in a smart health surveillance area where CCD cameras are installed. When a person’s figure is saved, the route tracing detects any movement and then traces its location. In addition, the platform analyzes the perceived bio-images and sequential patterns in order to determine whether or not the emergency situation is normal. Using AprioirAll algorithm-based sequential pattern profile analysis, bio-detection can detect a user who is undergoing an emergency based on abnormal patterns. It performs this task by managing information obtained from data and trace analyses, and it starts bio-detection only when there are patterns not conforming to sequential patterns. In other words, bio-detection detects the maximum sequence that can satisfy the minimum support in a given transaction. Sequential pattern profile analysis based on life-logs can analyze normal and abnormal profiles to provide health guidelines.

M2M-based smart health service for human UI/UX using motion recognition

Home networks currently dominated by human–object or human–human information production, exchange, processing, and paradigms are transitioning to machine to machine (M2M) due to the sudden introduction of embedded devices. Recently, due to the spread of IT equipment, more M2M-related devices are being used, and M2M-based projects are underway in various fields such as M2M-based u-city, u-port, u-work, u-traffic, etc. M2M has been applied in various fields, and u-healthcare is attracting attention in the M2M medical field. U-healthcare refers to technology in which ordinary patients can receive prescription services from experts by continuously monitoring changes in their health status during daily life at home based on wired and wireless communications infrastructures. In this paper, we propose an M2M-based smart health service for human UI/UX using motion recognition. Non-IP protocol, not TCP/IP protocol, has been used in sensor networks applied to M2M-based u-healthcare. However, sensors should be connected to the Internet in order to expand the use of services and facilitate management of the M2M-based sensor network. Therefore, we designed an M2M-based smart health service considering network mobility since data measured by the sensors should be transferred over the Internet. Unlike existing healthcare platforms, M2M-based smart health services have been developed for motion recognition as well as bio-information. Smart health services for motion recognition can sense four kinds of emotions, anger, sadness, neutrality, and joy, as well as stress using sensors. Further, they can measure the state of the individual by recognizing a user’s respiratory and heart rates using an ECG sensor. In the existing medical environment, most medical information systems managing patient data use a centralized server structure. Using a fixed network, it is easy to collect and process limited data, but there are limits to processing a large amount of data collected from M2M devices in real-time. Generally, M2M communication used in u-healthcare consists of many networked devices and gateways. An M2M network may use standardized wireless technology based on the requirements of a particular device. Network mobility occurs when the connecting point changes according to the movement of any network, and the terminal can be connected without changing its address. If the terminal within the network communicates with any corresponding node, communication between the terminal and corresponding node should be continuously serviced without discontinuation. The method proposed in this paper can easily respond to dynamic changes in the wireless environment and conduct systematic management based on user’s motion recognition using technology to support mobility among sensor nodes in M2M.

Discovery of automotive design paradigm using relevance feedback

According to the development of convergence technology and diversification of lifestyle, the importance of automotive design about sensibility products is rapidly increasing. According to the technical development, product performance and reliability of more than certain standards are recognized as basal conditions of market entry. And design and usability of products and subjective satisfaction are being magnified as the success factors of products. As functional performance including maximum speed, horsepower, and mileage reaches the satisfactory level in the automobile industry, consumers think the emotional aspects of the exterior and interior decor of automobiles as important. An analysis of customers’ sensibility and preferences is an important business strategy in the automobile industry that is increasingly becoming more customer oriented. In this paper, we proposed the discovery of automotive design paradigm using the relevance feedback. The proposed method applies a method that supports automotive design using the image-based collaborative filtering utilizing sensibility as a starting point in the development process. Although a collaborative filtering process can well predict the users’ interests and preferences, it cannot efficiently analyze design information while considering the contents of specific contents. The process extracts features from the image data that the users are interested to mitigate a problem, which is regarded as an undulated analysis, and then recommends an automotive design by combining the image filtering and collaborative filtering that use the static relationship between the users’ preference and the image color information. The proposed automotive design paradigm prediction system provides a recommendation by the image-based collaborative filtering. Paradigm recommendation according to sensibility and tendency is possible by applying the relevance feedback to react the paradigm which changes according to various lifestyles. Ultimately, this paper suggests empirical applications to verify the adequacy and validity of this system.

PHR Based Life Health Index Mobile Service Using Decision Support Model

In modern society, interest in health is increasing and the development of medical devices and wireless communication enabled people to get healthcare services easily anytime and anywhere, i.e. ubiquitous healthcare. The development of IT convergence and network technology enabled users to obtain user-centered useful information easily through portable mobile devices as well as computers. Currently, healthcare related user-centered healthcare contents are being actively served and demand related to disease prevention or health promotion is steadily increasing. This paper proposes PHR-based life health index mobile services using a decision support model. A decision support model is developed by using health index related data of existing health weather index service and national health and nutrition survey provided by the Korea Meteorological Administration (KMA) and applications of the mobile environment are developed so that users can receive healthcare services easily anytime and anywhere. The developed mobile service application implemented its interface for the user’s convenient healthcare and was developed to enable interlocking with the user’s PHR information through web server. Unlike comprehensive and standardized index services of existing KMA health weather index and life health index service, the developed mobile service is serving the user’s health status in three stages of danger, alert, safety by using personalized PHR information. The development of the PHR-based life health index mobile service using the decision making model allowed users to check current health status index and obesity measurement, body mass index (BMI), abdominal obesity, potential obesity risk index etc. easily anytime and anywhere only with simple input in the interface of mobile application. Also, accurate and subdivided services can be offered to users and more personalized services enable users to use efficient healthcare service.

Slope Based Intelligent 3D Disaster Simulation Using Physics Engine

In the modern society, the forms of disasters are getting larger and more diverse by abnormal weather phenomena due to rapid social changes. As human and economic damage is increasing by these disasters, interest in disaster observation and prevention is increasing and studies have been carried out in various fields. In order to minimize disaster damage, National Disaster Information Center provides disaster-related information such as disaster situations, risk index, local situation and so on. At present, however, various descriptive studies on action tips in the event of a disaster, disaster alert, disaster communications network have been in progress but there are few studies on prediction or simulation for preventing disasters etc. Therefore, this paper proposes a slope-based intelligent 3D disaster simulation using a physics engine. The proposed method is to infer a variety of disaster situations that may occur in reality through ontology and apply it to 3D simulation. Disaster situation ontology constitutes context information of outside, inside, disasters etc. and generates rule bases using Jena 2.0 inference engine. Inference results are derived in the form of XML and applicable to a variety of devices. The expression of 3D simulation virtual space is divided into interior and exterior. Interior constitutes the index information based on actually measured terrain and then expresses it into 3D compound type terrain data. Exterior expresses six planes into a hexahedron by using Sky Box technique. Simulation operates object collision, movement, surroundings etc. in real time by using a physics engine and represents a disaster situation in 3D virtual space by using particles function. Based on ontology based context awareness, environment context information on slope, including landslides, snow, rain, fogs, fire, explosion, and smoke, is developed into the slope based intelligent 3D disaster simulation. As a result, depending on geomorphic characteristics, disaster management service can realistically and visually be offered to users in 3D virtual reality. In addition, it is possible to experimentally apply disaster situations virtually through 3D virtual reality, and thereby judge their potential risk.

Decision supporting method for chronic disease patients based on mining frequent pattern tree

As the development of IT convergence technology reaches its zenith, data in almost all areas have been developed and operated as a system after digitalization. To acquire more diverse and in-depth information, humans are actively engaged in information filtering. In the medical and health industries, most medical information is organized in a system and utilized for efficient health management as well as in various areas such as U-healthcare. Due to aging and chronic disease, interest in health management has intensified. As a result, health prevention and management through U-healthcare has been developed. However, there has been no study on pain in patients suffering from chronic disease. Regarding pain-related decisions by patients, sustainable and effective management is required, unlike acute disease patients. In this paper, we proposes the decision supporting method for chronic disease patients based on mining frequent pattern tree. The proposed method is measures for pain-related decision making by chronic disease-suffering patients using a frequent pattern tree for data preprocessing, extraction, and data mining of conventional medical data. By utilizing the basic information of patients, which are the foundation for pain-related decision making, normalization can be applied to the frequent pattern tree of data mining. The pain forecast supports pain-related decision making by extracting similar patients’ information in a frequent pattern tree based on Electronic Medical Records (EMR).

Associative context mining for ontology-driven hidden knowledge discovery

The modern society has been developing new paradigms in diverse fields through IT convergence based on information technique development. In the field of construction/transportation, such IT convergence has been attracting attention as a new generation technology for disaster prevention and management. Researches on disaster prevention and management are continuously being performed. However, the development of safety technology and simulation for prediction and prevention is comparatively slow. For the new generation IT convergence to efficiently secure safety and manage disaster prevention, it is more important than anything else to construct systematic disaster prevention system and information technology. In this study, we suggested the associative context mining for ontology-driven hidden knowledge discovery. Such method reasons potential new knowledge information through the association rule mining in the ontology-driven context modeling, a preexisting research, and uses the semantic reasoning engine to create and apply rules to the context simulation. The ontology knowledge base consists of internal, external, and service context information such as user profile, weather index, industry index, location information, environment information, and comprehensive disaster situation. Apriori mining algorithm of the association rule is applied to reason the potential relationship among internal, external, and service context information and discovers and applies hidden knowledge to the semantic reasoning engine. The accuracy and validity are verified through evaluating the performance of the developed ontology-driven associative context simulation. Such developed simulation is expected contribute to enhancing public safety and quality of life through determining potential risk involved in disaster prevention and quick response.

Life style improvement mobile service for high risk chronic disease based on PHR platform

As IT convergence technique develops, medical technology and apparatus are being modernized opening the era that we can obtain variable information easily anywhere, anytime thanks to wireless communication developed, further. These social changes enabled us to obtain information related to health more efficiently. Modern society is rapidly aging and more people experience chronic diseases because of their wrong eating habit, obesity and insufficient exercise. Thus a demand for health improvement and management at a certain term is increasing rather than complete therapy. Previously, major medical institutions managed personal medical history regarding patients mainly in health management but it is not changing its method to self-utilization and management by individual patient as of now along with medical institutions as fusion technology develops, and individual health record information can easily be checked anywhere, anytime through personal health record (PHR) platform. Unlike developing speed of related technology, however, there is a limitation in expansion, development of individual health record service, personal information security currently. In this paper, we propose mobile service regarding life style improvement targeting high risk chronic diseases based on PHR platform. PHR platform determines high blood pressure, diabetes, hyperlipidemia diseases which are three main chronic diseases using users’ data and can monitor chronic diseases in portable mobile device. Also, the service provides by organically, mutually connected form through feedback towards input from health states of users in mobile device. By proposing contents about service based on efficient individual health record through mobile device that maximized transportability based on PHR platform, proposed method will contribute to industry development and activation of application service development of individual health record. Increase in consistency and reliability through standardization of afterwards health management service is expected to contribute to reduction in social cost and improvement of national health being the basis to realize communication activation of health record between medical institutions, efficient management and education of patients, reduction in dual examinations.

Picocell based telemedicine health service for human UX/UI

Telemedicine health created from the combination of IT and BT technologies has received increased attention for improved quality of life in medically vulnerable regions. As the health care paradigm shifts to preventive management in diagnosis and treatment, the importance of prevention of chronic diseases such as obesity is growing. In this paper, we proposed a picocell-based telemedicine health service for the human UX/UI based on a BT-IT fusion technology considering user convenience. The proposed medical service is a BT-IT fusion technology based on the telemedicine health service that can overcome the spatial limitations of hospital-oriented medical services in order to improve user convenience while naturally combining life and medical service spaces. Human UX/UI technology, which is based on sensor network and biomedical technology, requires next generation wireless communication between devices that connects the inside of the human body with the outside. A heterogeneous network is composed within a single domain, as the frequency bandwidth used by the medical device in the ISM bandwidth is different. If a wireless device and low output ISM device spatially access a heterogeneous network, then an interference problem will occur between the small cells. Additionally, there can be interference as the traffic is off-loaded from the base station at the grouped region of a hotspot. A fatal problem may occur due to an information error of the patient due to interference. To solve the interference problem generated by the telemedicine health platform, the performance of the picocell-based telemedicine health service can be improved by applying scheduling using ABS(Almost Blank Subframe) in the time domain. Therefore, the human UX/UI and the provided guidelines can quickly provide patient information, thereby increasing safety of patients.