Smart Data Collection and Management in Heterogeneous Ubiquitous Healthcare
Luca Catarinucci, Alessandra Esposito, Luciano Tarricone, Marco Salvatore Zappatore, Riccardo Colell · InTech eBooks · 2011
The increasing availability of network connection and the progress in information technology and in hardware miniaturization techniques, are determining new computing scenarios, where software applications are able to "configure themselves" based on information coming from heterogeneous sources (sensors, RFID, GPS, databases, user input, etc.) which form the so called "context".In other terms, such applications are based on the representation and codification of different kinds of data, such as biomedical parameters, environmental data, device location, user preferences, resource availability, every time, from every location and through different modalities (pervasiveness), and on the provision of services and contents adapted to current context (context-awareness).Such computing scenarios find application in a large number of real-life domains, as environment monitoring, supply-chain management and so on.Health-care is perhaps one of the most relevant and promising.Indeed, the perspectives opened by such technologies are wide and variegate: they range from the home-care of mobility-impaired people to the harmonization and presentation to hospital workers of information gathered from distributed heterogeneous sources.The implementation of context-aware systems is based on two distinct but strongly interlaced tasks: 1) monitoring and collection of sensorial data, with the related issues concerning data transmission, costs, enabling technologies as well as the heterogeneity and the number of data to be collected 2) processing and integration of data with available context information in order to activate decision processes which are in many cases not trivial.Key points are the selection of the enabling technologies for the collection, transmission and smart management of data gathered from heterogeneous sources, and the design of a system architecture having the following characteristics: a. simple to use; b. low-cost and low-power consumption in order to make possible the implementation of systems with a high number of nodes; c. interoperable with any type of sensors; d. customizable to different kinds of application domains with a limited effort; e. scalable with the number of nodes; www.intechopen.comBiomedical Engineering Trends in Electronics, Communications and Software 686 f. suitable response times so to be adopted also in emergency situations.Based on the above considerations, we developed a cost-effective RFID-based device for the monitoring and collection of sensorial data, and a pervasive and context-oriented system which operates on sensor data and is based on an innovative, flexible and versatile framework.The description of the RFID device and of the software framework is provided in this chapter, which starts with an introduction to potential applications for healthcare and to emerging techniques for both data collection and smart data management, and concludes with the validation of both hardware and software solutions in the real-life use-case of patient remote assistance. Potential applicationsThis paragraph proposes an overview of the possible applications of emerging technologies for data sensing, gathering and smart data elaboration with a special focus to the healthcare domain.We partition applications into four groups, even though some overlapping between such groups exist. Knowledge sharing, availability and integrationManagement and delivery of healthcare is critically dependent on access to data.Such data are normally provided by several heterogeneous sources, such as physiological sensors, imaging technology or even handwriting and often spans different organisations.Moreover they are generally stored by using different formats and terminologies, such as electronic health records, clinical databases, or free-text reports.If such a rich collection of health and community data could be linked together, we would improve enormously our capability of finding answers to health and social questions and of tackling complex diseases (Walker, 2005).Unfortunately, sharing such a complex aggregate of information and deriving useful knowledge from it is currently very difficult.A very promising answer to this need is provided by the enabling technologies for smart data collection and management which are named " ontologies" [Section 3.4].Ontologies define a formal semantically rich machine-readable specification of concepts and relationships which is unique and sharable among dispersed consumers.They provide coherent representations of biomedical reality thus enabling wide-area multi-organizational sharing of data by supporting publishing, retrieving and analysis of data in large-scale, diverse and distributed information systems.Moreover, when integrated with other enabling technologies (such as those listed in Section 3.3 and 3.5) ontologies allow the identification of the correct information to be forwarded to health care professional depending on available information about context, such as time, place, patient current state, physiological data, and so on. Independent livingThe combination of sensors, RFID, wireless protocols and software solutions, is going to enhance aging population and impaired people capability of conducting a possibly autonomous independent living.Indeed, pervasive and context-aware applications are more and more recognized as promising solutions for providing continuous care services, while improving quality of life of people suffering from chronic conditions.