Situation-Aware Ambient Assisted Living and Ambient Intelligence Data Integration for Efficient Eldercare - E-Healthcare Systems and Wireless Communications - page 325

and retrieval of electronic health information
(Health Level Seven, 2010). Nowadays, HL7 is
the standard for communication between medical
subsystems like Hospital Information Systems
(HIS). HL7 v.2.5 is used as a message protocol to
exchange data between devices and a repository
or between hospital information systems, whereas
HL7 v.3 Clinical Document Architecture (CDA)
release 2 is used for document exchange. CDA
is an XML-based markup standard intended to
specify the encoding and structure of clinical
documents for exchange. It provides an exchange
model for clinical documents. By leveraging
XML, a Reference Information Model (RIM) and
coded vocabularies, the CDA makes documents
machine- and human-readable (Health Level
Seven, 2010). Examples for CDA documents of
various countries include the (iii) German ePre-
scription, (iii) the French Discharge Summary,
(iii) the Croatian Consultation Note, and (iv) the
US Clinical Document (Spronk, 2008). A CDA
document consists of a structured and encoded
header that provides context critical information
and of a body. The body of a standard conform
document consists of structured content with
coded sections. Each section component contains
at least a section label to identify the section as
well as a title and a separate text. Patient master
data as well as additional information like author
and recipient are mainly defined in the header.
Care-relevant data and medical data are defined
in certain sections in the structured body of the
document. Care-relevant data contain information
about mobility, skin care, eating habits, sleeping
habits, and information concerning functional
assignments conducted by nurses, which can be
used to analyze A ctivities of Daily Living (ADL).
Medical data contain the reason for referral, aller-
gies, diagnosis, medications, vital signs, pain, etc.
CDA is based on the RIM, an object model
representing the HL7 v.3 methodology. This model
is the cornerstone of the HL7 v.3 development
process. It represents the afore mentioned clinical
data (domains), identifies the life cycle of events

that a message or groups of related messages
can carry and can be used as a base structure for
building ontologies.
Since AmI systems usually process data from
various sources, it becomes apparent that a proper
ontology is needed to define a common knowledge
base between the affected domains. An ontology
has to provide a shared vocabulary, which can be
used to model a domain, its objects, properties and
relations. A sound ontology eases the transforma-
tion and exchange of data and allows describing
situations properly.
The processing of data faces many challenges
depending on the amount of data, the safety and
law restrictions, which differ enormous in different
countries. Data processing must at least guarantee
some kind of basic security (encryption) along
communication channels and data storage, so
that misuse is avoided in advance. Furthermore, a
sufficient scalability, especially when a lot of data
sources are used and the system has to monitor
multiple patients, must be ensured. In some of these
cases a near real-time data processing capability
is mandatory, e.g. for fall detection. Nevertheless,
the general difficulty of data processing depends
on the supported use case or provided service.
AAL systems typically provide services for:
( i) fall or collapse detection, ( ii) localization,
( iii) physiological parameter monitoring, and
( iv) recognition of daily living activities, which
are discussed in the following section in detail.
Fall or collapse detection . Falls are the lead-
ing cause for injury-related hospitalization among
people at the age of 65 years and older (Bourke &
Lyons, 2008). Recent projects offering fall detec-
tion mechanisms are BelAmi from the Fraunhofer
Institute for Experimental Software Engineering
IESE (Becker, 2008),(Nick & Becker, 2007),
eShoe from the Central European Institute of
Technology (Jagos & Oberzaucher, 2008), and
Information Technology for Assisted Living at
Home (ITALH) from the University of California,
Berkeley (Hansen, Eklund, Sprinkle, Bajcsy, &
Sastry, 2005). Since the number of related research