315
Chapter 15
Situation-Aware Ambient
Assisted Living and Ambient
Intelligence Data Integration
for Efficient Eldercare
Werner Kurschl
Upper Austria University of Applied Sciences, Austria
Mario Buchmayr
Upper Austria University of Applied Sciences, Austria
Barbara Franz
Upper Austria University of Applied Sciences, Austria
Margit Mayr
Upper Austria University of Applied Sciences, Austria
ABSTRACT
Pervasive healthcare systems are designed to support elderly and care-dependent people to live an independent life. Recent developments are driven by technological advances of wireless sensor networks
and mobile devices, which ease their application in the health- and homecare domain. The integration
into pervasive healthcare systems helps to improve the impact and the efficiency of eldercare, while
keeping financial efforts at a moderate level. The importance of these issues leads to the development
of systems covering situation-aware, ambient assisted living and health data exchange between care
institutions and ambient assistant solutions. Various projects within the Ambient Assisted Living (AAL)
domain have proven that remarkable results can be achieved by using wireless sensor technology and
mobile devices for data collection, but there are still several problems concerning the exchange and
integration of healthcare data. This chapter gives an overview about AAL, healthcare related standards,
and state of the art approaches for data integration. In addition, best practice projects, which deal with
patient-oriented care information, ambient assisted living, as well as ambient intelligence, are covered.
DOI: 10.4018/978-1-61350-123-8.ch015
Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Situation-Aware Ambient Assisted Living and Ambient Intelligence Data Integration for Efficient Eldercare
INTRODUCTION
Due to population aging and longer life expectancy
our society faces serious challenges. This concerns
especially the growing group of elderly people
(Madsen, Serup-Hansen, & Kristiansen, 2002).
One important challenge for elderly people is to
extend the time they can remain (independently)
in their familiar environment (Rennemark & al.,
2009). Pervasive home healthcare services and
systems help to maintain the health and functional capabilities of individuals, support people
with chronic illnesses or disabilities and are one
prerequisite for an independent ageing at home.
Nowadays, pervasive home healthcare systems
are often composed of numerous independent living assistants with the purpose to support elderly
persons and persons with special needs in their
activities of daily living. Proposed living assistants often include features, like item tracking and
searching, warning of household dangers (slippery
floor, unattended stove, running water taps), recognition of critical situations (collapse, fall), as well
as remote health monitoring and data exchange
with nursing facilities and care institutions. The
coherent information gained from various living
assistants increases the scope of detectable situations, improves the fault tolerance of the systems,
and leads to a richer user experience.
Various projects within the Ambient Assisted
Living (AAL) domain have achieved remarkable
results by using wireless sensor technology for
data collection, but still face problems concerning
the exchange and integration of healthcare data
(Wozak, Ammenwerth, Hörbst, Sögner, Mair, &
Schabetsberger, 2008), (Burgsteiner & al., 2009).
Thus, feasible healthcare systems have to cope
with several problems: (i) patient data exchange
across institutional borders, such as nursing homes,
hospitals or AAL equipped retirement homes, (ii)
data integration from various sources, and (iii)
automated processing of unstructured or semi
structured data.
316
The integration of data from various sources
is a typical data fusion problem. One possible
solution is to apply the JDL Data Fusion Model
(Llinas, Bowman, Rogova, & Steinberg, 2004).
The process described by this model covers the
perception of sensor data, the detection of specific
situations and the evaluation of a possible situation
evolution. Besides, the JDL Data Fusion Model
there are other models and/or processes that cope
with the problem of multi sensor data fusion,
like Luo and Kay’s architecture for information
systems (Paradis & Treurniet, 1998), Pau’s Sensor Data Fusion Process (Esteban, Starr, Willetts,
Hannah, & Bryanston-Cross, 2005), the Omnibus
Data Fusion Model (Bedworth & O’Brien, 2000)
or the Waterfall Data Fusion Model (Bailey, Dodd,
& Harris, 1998). Most are based on the findings
of Endsley, who developed a model of situation
awareness in dynamic decision making based on
human beings (Endsley M. R., 2000), (Endsley
M., 1998).
A situation aware system, based on data fusion,
can support eldercare in the field of homecare,
but lacks the ability to integrate data from different external data providers (hospitals, care
institutions) in a standardized way. To solve
such integration issues researchers, like (Kokar,
Matheus, & Baclawski, 2009) and (Baumgartner
& Retschitzegger, 2006) propose models for data
fusion and situation awareness based on ontologies
to support knowledge sharing among different domains. Transforming theses concepts into the AAL
domain enables electronic exchange of patient
data and establishes compliance with international
standard recommendations, like Integrating the
Healthcare Enterprise (IHE).
Efficient exchange of electronic patient record
data between different health service environments
is a challenge, since: (i) semantically correct exchange of patient data requires appropriate care
data models and (ii) incomplete or contradictory
care information must be identified and completed
or corrected (intelligent mapping process). Stan-
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