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A Database-Centric Architecture for Home-Based Health Monitoring
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Embedded Systems (CERES).ORCID iD: 0000-0001-6708-0816
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-8804-5884
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4143-2948
2013 (English)In: Ambient Assisted Living and Active Aging: 5th International Work-Conference, IWAAL 2013, Carrillo, Costa Rica, December 2-6, 2013, Proceedings / [ed] Christopher Nugent, Antonio Coronato, José Bravo, Heidelberg, Germany: Springer, 2013, Vol. 8277, 26-34 p.Chapter in book (Refereed)
Abstract [en]

Traditionally, database management systems (DBMSs) have been employed exclusively for data management in infrastructures supporting Ambient Assisted Living (AAL) systems. However, DBMSs provide other mechanisms, such as for security, dependability, and extensibility that can facilitate the development, use, and maintenance of AAL applications. This work utilizes such mechanisms, particularly extensibility, and proposes a database-centric architecture to support home-based healthcare applications. An active database is used to monitor and respond to events taking place in the home, such as bed-exits. In-database data mining methods are applied to model early night behaviors of people living alone. Encapsulating the processing into the DBMS avoids transferring and processing sensitive data outside of database, enables changes in the logic to be managed on-the-fly, and reduces code duplication. As a result, such an approach leads to better performance and increased security and privacy, and can facilitate the adaptability and scalability of AAL systems. An evaluation of the architecture with datasets collected in real homes demonstrated the feasibility and flexibility of the approach.

Place, publisher, year, edition, pages
Heidelberg, Germany: Springer, 2013. Vol. 8277, 26-34 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Vol. 8277
Keyword [en]
Healthcare technology, ambient assisted living, active data-bases, in-database processing, machine learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-25012DOI: 10.1007/978-3-319-03092-0_4Scopus ID: 2-s2.0-84893927504ISBN: 978-3-319-03091-3 ISBN: 978-3-319-03092-0 OAI: oai:DiVA.org:hh-25012DiVA: diva2:711609
Available from: 2014-04-10 Created: 2014-04-10 Last updated: 2016-03-09Bibliographically approved

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Citation style
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