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With the development of information technology, ambient intelligence has been combined with various application areas so as to create new convergence service industries. Through IT convergence, human-oriented technologies for improving people’s quality of life has continued to be developed. Healthcare service that has been provided along with the development of various smart IT devices makes it possible to realize more efficient healthcare of people. Therefore, along with such a medical service, the advanced lifecare service for physical and mental health has been demanded. In order to meet the healthcare demands, an advanced healthcare platform has been developed. Lifecare service has been expanded to healthcare, the disease with the highest mortality induced by complications so that the service for disease survivals have been offered. Accordingly, a big-data integration and advanced healthcare platform based on patients’ life logs are developed in order for health service. In this platform, it is possible to establish an optimized model with the knowledge base and predict diseases and complications and judge a degree of risk with the use of information filtering. The conventional filtering based on a data model using scatter life logs makes use of user attribute information only for clustering so that it has low accuracy. Also, in calculating the similarity of actual users, such a method does not apply social relationships. Therefore, this study proposes a social mining based cluster process for big-data integration. The proposed method uses conventional static model information and the information extracted from the social network in order to create reliable user modeling and applies a different level of weight depending on users’ relations. In the clustering process for disease survivals’ health conditions, it is possible to predict their health risk. Based on the risk and expectation of healthcare event occurrence, their health conditions can be improved. Lifecare forecasting model that uses social relation performs social sequence mining using PrefixSpan to complement the weak point that spends a long time to scan it repeatedly in the candidate pattern. For performance evaluation, the social mining based cluster process was compared with a conventional cluster method. More specifically, the estimation accuracy of the conventional model-based cluster method was compared with the accuracy of the social mining based cluster process. As a result, the proposed method in the mining-based healthcare platform had better performance than the conventional model-based cluster method.

There is a rapidly growing interest in health care due to the recent development of IT convergence technologies according to the 4th industrial revolution. More services for personal health management of users are available and studies on the establishment of knowledge base for an efficient health log data management in the health care field are being carried out with the emergence of block chain technology which is the next generation information security technology. In this paper, a knowledge-based block chain network for health log data management mobile service is suggested. The user’s log data and context information are applied to block chain technology that is difficult to forge and falsify in the knowledge-based health platform, enabling a large amount of users’ log data and context information accumulated continuously to be stored in a block in the knowledge base using the side chain structure that stores information through the configuration of knowledge-based data transaction. This enables high expandability and security to be secured in mobile environment as well. The result of comparative evaluation with the existing ontology knowledge model for verifying the validity shows that the suggested method presented approximately 16.5% higher performance in accuracy and reproducibility.

Due to recent growing interest, the importance of preventive and efficient healthcare using big data scattered throughout various IoT devices is being emphasized in healthcare, as well in the IT field. The analysis of information in healthcare is mainly prediction using a user’s basic information and static data from a knowledge base. In this study, a knowledge-based dynamic cluster model using a convolutional neural network (CNN) is suggested for healthcare recommendations. The suggested method carries out a process to extend static data and a previous knowledge base from an ontology-based ambient-context knowledge base beyond knowledge-based healthcare management, which was the focus of previous study. It is possible to acquire and expand a large amount of high-quality information by reproducing inferred knowledge using a CNN, which is a deep-learning algorithm. A dynamic cluster model is developed, and the accuracy of the predictions is improved in order to enable recommendations on healthcare according to a user environment that changes over time and based on environmental factors as dynamic elements, rather than static elements. Also, the accuracy of the predictions is verified through a performance evaluation between the suggested method and the previous method to validate effectiveness, and an approximate 13% performance improvement was confirmed. Currently, the acquisition of knowledge from unstructured data is in its early stages. It is expected that symbolic knowledge-acquisition technology from unstructured information that is produced and that changes in real time, and the dynamic cluster model method suggested in this study, will become the core technologies that promote the development of healthcare management technology.

Cognitive manufacturing has brought about an innovative change to the 4th industrial revolution based technology in combination with blockchain distributed ledger, which guarantees reliability, safety, and security, and mining-based intelligence information technology. In addition, artificial intelligence, machine learning, and deep learning technologies are combined in processes for logistics, equipment, distribution, manufacturing, and quality management, so that an optimized intelligent manufacturing system is developed. This study proposes a topic mining process in blockchain-network-based cognitive manufacturing. The proposed method exploits the highly universal Fourier transform algorithm in order to analyze the context information of equipment and human body motion based on a variety of sensor input information in the cognitive manufacturing process. An accelerometer is used to analyze the movement of a worker in the manufacturing process and to measure the state energy of work, movement, rest, and others. Time is split in a certain unit and then a frequency domain is analyzed in real time. For the vulnerable security of smart devices, a side-chain-based distributed consensus blockchain network is utilized. If an event occurs, it is processed according to rules and the blocking of a transaction is saved in a distributed database. In the blockchain network, latent Dirichlet allocation (LDA) based topic encapsulation is used for the mining process. The improved blockchain distributed ledger is applied to the manufacturing process to distribute the ledger of information in a peer-to-peer blockchain network in order to jointly record and manage the information. Further, topic encapsulation, a formatted statistical inference method to analyze a semantic environment, is designed. Through data mining, the time-series-based sequential pattern continuously appearing in the manufacturing process and the correlations between items in the process are found. In the cognitive manufacturing, an equalization-based LDA method is used for associate-clustering the items with high frequency. In the blockchain network, a meaningful item in the manufacturing step is extracted as a representative topic. In a cognitive manufacturing process, through data mining, potential information is extracted and hidden rules are found. Accordingly, in the cognitive manufacturing process, the mining process makes decision-making possible, especially advanced decision-making, such as potential risk, quality prediction, trend prediction, production monitoring, fault diagnosis, and data distortion.

With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined.

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.

As augmented reality---related technologies become commercialized due to requests for 3D content, they are developing a pattern whereby users utilize and consume the realism and reality of 3D content. Rather than using absolute position information, the pattern characteristics of gestures are extracted by considering body-proportion characteristics around the shoulders. Even if performing the same gesture, position coordinate values of the skeleton measured by a motion recognition sensor can vary, depending on the length and direction of the arm. In this paper, we propose a 3D human-gesture interface for fighting games using a motion recognition sensor. Recognizing gestures in the motion recognition sensor environment, we applied the gestures to a fighting action game. The motion characteristics of gestures are extracted by using joint information obtained from the motion recognition sensor, and 3D human motion is modeled mathematically. Motion is effectively modeled and analyzed with a method of expressing it in space via principal component analysis and then matching it with the 3D human-gesture interface for new input. Also, we propose an advanced pattern matching algorithm as a way to reduce motion constraints in a motion recognition system. Finally, based on the results of motion recognition, an example used as the interface of a 3D fight action game is presented. By obtaining high-quality 3D motion, the developed technology provides more realistic 3D content through real-time processing technology.

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.

In regards to information technology of modern society, IT convergence technology is applied in various fields. Particularly, active studies are conducted for creative design products with the use of IT convergence technology in design industry which transforms sensibility of people into various expressions. People started to put high significance to design elements and sensibility accordingly with diverse and distinctive lifestyle and active studies are also conducted on sensibility engineering interaction method which connects sensibility of people with design to satisfy such demand. Also, since distributed processing became available, advancement from server centered information processing and network, such strength is applied to design industry as well. The purpose of this study lies in recommending and proposing P2P context awareness based sensibility design using color and bio-signal analysis. In order to express design that coincides with distinctive and differentiated sensibility of people, the proposed method analyzes relation between visual sensibility and color design with the use of statistic analysis tool R 3.1.0 and SPSS 21.0 and the clustering of users with similar sensibility is conducted with the use of P2P network based context awareness. It recommends color design that coincides with the sensibility of new user by using the P2P network based collaborative filtering and applying it to color design based on clustered users. Proposed method reduces the time and cost spent to estimate design that satisfies the sensibility and requirement of user and supports companies to have concrete and clarified grasp on ambiguous personal requirement of user.

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.

Efficient healthcare management has increasingly drawn much attention in healthcare sector along with recent advances in IT convergence technology. Population aging and a shift from an acute to a chronic disease with a long duration of illness have urgently necessitated healthcare service for efficient, systematic health management. Clinical decision support system (CDSS) is an integrated healthcare system that effectively guides health management and promotion, recommendation for regular health check-up, tailor-made diet therapy, health behavior change for self-care, alert service for drug interaction in patients with chronic diseases with a high prevalence. Although CDSS rule-based algorithm aids guidelines and decision making according to a single chronic disease, it is unable to inform unique characteristics of each chronic disease and suggest preventive strategies and guidelines of complex diseases. Therefore, this study proposes evolutionary rule decision making using similarity based associative chronic disease patients to normalize clinical conditions by utilizing information of each patient and recommend guidelines corresponding detailed conditions in CDSS rule-based inference. Decision making guidelines of chronic disease patients could be systematically established according to various environmental conditions using database of patients with different chronic diseases.

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.

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.

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.

In modern society, the amount of information has significantly increased due to the development of BT-IT convergence technology. This leads to developing information obtaining and searching technologies from much data. Although system integration for medicare has been largely established to accumulate large amounts of information, there is a lack of provision and support of information for nursing activities, using such an established database. In particular, the judgment for pain intervention depends on the experience of individual nurses, leading to usually making subjective decisions. Thus, there is some danger in applying unwanted anesthesia and drug abuse. In this paper, we proposed the interactive pain nursing intervention system for smart health service. The proposed method uses collaborative filtering that extracts some pain strengths, which represent a high relative level, based on similar pain strengths. Pain strength estimation method using collaborative filtering calculates patient similarities through Pearson correlation coefficients in which a neighbor selection method is used based on the pain strength. In general, medical data in patients shows various distributions due to its own characteristics, as sample data demonstrates. Therefore, this is determined as an applicable theory to the sparsity problem. In addition, it is compensated using a default voting method. The medical data evaluated by applying standard data and its accuracy in pain prediction is verified. The test of the proposed method yielded excellent extraction results; it is possible to provide the fundamental data and guideline to nurses for recognizing the pain of patients based on the results of this study. This represents increased patient welfare for smart health services.

With the emergence of the 4th industrial revolution, IT convergence engineering based artificial intelligence and intelligent system has constantly been researched in today's society. In particular, healthcare service based on IT-BT convergence helps to improve quality of people's life and provide user-oriented healthcare contents actively. Currently, the healthcare industry has gradually changed its healthcare paradigm from conventional healthcare to mental diseases care and tries to solve the social problem with depression, one of mental disorders. This study proposes the context mining based mental health model for the lifecare platform. This study makes use of users’ profiles about depression and health weather index provided by Korea Meteorological Administration to classify and define semantic ontology based context information, and to develop the context mining model for depression index service. The proposed context mining based mental health model uses personalized context information so that it is possible to provide personalized depression index service, rather than unified healthcare service. Also, the proposed one uses user-based information for modeling so that it can provide guidelines for developing data model of depression. In addition, it is possible to provide accurate and specified service for users and efficient depression index service through customized service. The result of the proposed method shows that the context mining model not only promotes the theory and practical ability but also consolidates their understanding of web engineering models and concepts.

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).