Associative Feature Information Extraction Using Text Mining from Health Big Data

Wireless Personal Communications - With the development of big data computing technology, most documents in various areas, including politics, economics, society, culture, life, and public health,...     

Context aware based user customized light therapy service using security framework

Current IT market has increased significantly boosted by the development of network technology and IT technology. As we move into an aging society, global people have paid attentions to the matters related to health while the many technologies related to health have been developed, too. Such a change of social paradigm has improved the quality of life but various mental diseases show its growing trend. The past mental disease matters were limited to the area of chronic mental disease patients, but recently light mental diseases including the stress caused by excessive work and internet addiction are included in this area. Especially there are many cases where early treatable depressions have been developed into the serious diseases as they had no chance to recognition and healing or missed the appropriate time for healing. Even if various health related mobile services to solve this show it gradual growing trend now, providing services which correspond to the needs of users, are difficult in the actual situation, while many researches are performed to provide the customized service by utilizing PHR but they are very vulnerable in the aspect of security. Therefore, in this paper, we proposed the context aware based user customized light therapy service using security framework. The proposed service applied the context aware based security framework to enhance the security of health service for PHR interlocking, which can protect the medical information by complying with standard based encryption, security guideline. For this purpose, we arranged that object group support component of DOGF should be existed in server system and components such as user information and distributed sources re-composed by smart health for service providing. Based on this, the user’s states was analyzed in real time through Personal Health Record and Galvanic Skin Response of users, the brightness and chromaticity of bulb was adjusted depending on the states of user for soothing effect, the real-time states was figured out through questionnaires for mental states analysis of smartphone application to provide the real-time treatment for users stress alleviating and concentration enhancement. Also focused on Psychological illness, we arranged to provide the real-time light therapy according to the states of users based on the data of patients collected from smart device in order to mental disease prevention and treatment caused from the stress of workers who work with computer for a long time such as office workers, program developer and home workers.     

Mining health-risk factors using PHR similarity in a hybrid P2P network

In an era of many diseases and increased longevity, more attention has been paid to chronic diseases that require constant health care. Under this circumstance, the development of research and development (R&D) for smart-device-based constant health care has drawn great attention. With the emergence of wearable devices, personal health devices (PHDs), and smartphones, various contents for constant health care have been developed. By using these devices, the users are able to collect personal health records (PHRs) that include data such as activity amount, heart rate, stress, and blood sugar. The range of the collected PHRs can be limited depending on the equipment or the surrounding environment. To overcome this problem, it is necessary to make a comparison with similar users in a cluster. Also, it is necessary to provide a service that can analyze and visually display the collected personal-health information. In this paper, we propose the mining of health-risk factors using the PHR similarity in a hybrid P2P network. This is a method of predicting a user’s health status using similarity-based data mining, where the PHRs are employed in a hybrid P2P environment consisting of a peer, a server, and a gateway. In a hybrid P2P environment, a user receives feedback on the result of a structured-data analysis. A peer searches for a different peer and gateway through a server and exchanges information. Depending on the data type, the PHR is divided into medical health examination, self-diagnosis, and personal-health data. The medical health examination contains the personal-health data that are generated regularly by a medical institution. Self-diagnosis represents the data of mental health, pains, and fatigue that can be changed often but cannot be collected by devices. Personal-health data mean the data that can be collected by individuals in everyday life. For the PHR-data analysis, an index is given to each attribute, and preprocessing is performed after a binary-code conversion. To predict a user’s health status, the PHR data are clustered on the basis of similarity in a hybrid P2P environment. The similarity between a user’s PHR and a PHR that is searched for in the network is measured. After the measurement, an index is given to the PHR that meets the minimum similarity and the PHR is incorporated into a Similarity PHR Group. The Similarity PHR Group flexibly changes depending on a user’s PHR status and the statuses of the users who have accessed the hybrid P2P network. A representative value of the Similarity PHR Group is extracted and is then compared with the user’s PHR to judge the user’s health status. The proposed method is suitable for a smart health service for chronic diseases requiring constant care, elderly health, and aftercare. This is a user-oriented health-care and promotion service wherein a user’s health status can be predicted through the mining of the health-risk factors of PHRs.     

Mining Based Time-Series Sleeping Pattern Analysis for Life Big-Data

Wireless Personal Communications - Humans require rest in order to recover from physical and psychological fatigue caused by daily routines, and either medication or sleep is needed to achieve...     

Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P

Recently, with changing paradigms in health, the focus of healthcare is shifting from treatment after contracting disease to prevention and early diagnosis of disease. Accordingly, the healthcare paradigm is changing from diagnosis and treatment to preventive management, emphasizing prevention of chronic diseases, such as obesity. In particular, obesity in children and adolescents has become a global issue. Lifestyle and health management using BT–IT convergence is needed to improve and manage the health of children and adolescents, and convenience and accessibility must be improved. For that, use of a machine-to-machine (M2M) u-health cluster that allows wireless network connection is increasing, along with wireless networks for measuring biometrics. Expanded to communications between people and objects as well as between objects, M2M refers to the next-generation convergence infra-architecture that offers intelligent services through various media. Because various wireless devices form a cluster when building a service platform using M2M, when the number of users with various M2M devices increases, data traffic increases and causes network overload, deteriorating system performance. To solve this problem, services are increasingly being built by combining a conventional network and Wi-Fi technology. However, in an M2M network, there is a limitation due to low transfer speed, because the network processes biometrics and data through different sensor nodes, and wireless communications based on the system is composed of different wireless sensor nodes. Thus, in this paper, we proposed a knowledge-based health service considering user convenience using a hybrid wireless fidelity (Wi-Fi) peer-to-peer (P2P) architecture. For knowledge-based health services in conventional M2M-based smart health services, hybrid Wi-Fi P2P and wireless devices must be linked. Because there are different ways to link hybrid Wi-Fi P2P devices, depending on the network environment, in this study, a dynamic configuration mechanism is applied to Wi-Fi P2P linkage of wireless devices in an M2M environment. The proposed service provides a high-quality health service (whenever patients use the knowledge-based health service) by building a network using a dispersed cross-layer optimization algorithm that optimizes variables of the transmission control protocol/internet protocol stack in order to improve the energy efficiency of the u-health sensor network and system reliability.