Towards a Platform for Widespread Embedded Intelligence - ERCIM

SPECIAL THEME: Embedded Intelligence
Approach
We have identified major
problem areas for the elderly
by talking to care personnel,
geriatricians, medical doctors
and emergency centre staff and
have then determined appropriate
indicators that can be
measured by sensors. Based on
this, we set up our Ambient
Assisted Living Demonstrator
Laboratory (AAL Lab), which
aims to come up with
embedded intelligence solutions
for elderly people living
alone at home.
The AAL Lab will be continuously
extended. In the first version,
to be opened to the public
in October 2006, the following
scenarios are just a few of those
which will be implemented:
Monitored Drinking –
Dementia is a major reason for
moving the elderly into nursing homes.
Dehydration exacerbates dementia and
other health problems. Reminding the
elderly to drink enough is an important
function. Our computerised cup measures
the amount of fluids consumed
each day by the person handling the cup.
Potential dehydration can thus be
detected.
Monitoring Food Quality – Eating
spoiled food is another source of health
problems. An RFID-based system built
into a refrigerator checks for expired
food and issues warnings. Food related
incidents can be prevented or can be
traced back to the root cause, allowing
for a better treatment.
Location Tracking – Knowing the exact
location of a person enables a whole
range of location-aware services in the
AAL environment, such as switching the
lights on or off or activating those communication
devices which are closest to
the person. RFID labels embedded in the
carpet of the AAL Lab are read by a
walking aid, allowing the location of the
person to be determined.
Fall Detection – A sudden fall is a strong
indicator of an emergency situation and
is often detected too late, sometimes
20 ERCIM News No. 67, October 2006
Ambient Intelligent Care and Assistance (amiCA).
hours or days later. Gyro sensors built
into wrist badges, walking aids, or other
body-mounted devices can detect sudden
falls by a person and trigger a staged
emergency reaction of the AAL system.
First, the person is asked to reset the
device to rule out false positives. If the
person fails to react, a telephone or
video-telephone connection will be initiated.
Based on location-tracking information,
communication devices in the
appropriate room will be activated to
establish a communication link between
the person in trouble and the emergency
call centre.
Our approach to reasoning on sensor
data raises the challenge of coping with
inherently unreliable and imprecise data.
This is tackled by combining alternative
or redundant sources. For instance, location
tracking can be fed from different
sensor systems (RFID, ultra-sonic, pressure,
etc.).
Furthermore, the system must evolve
over time, so as to adapt to the specific
conditions and demands of the assisted
person, as they acquire new diseases or
handicaps or recover from impairment.
The central module of amiCA is MonA
(Monitoring and Assistance component).
In MonA, sensor data is collected
and model-based reasoning
is performed. The
model base entails models of
the assisted person, their capabilities
and needs, and of the
person's daily routine,
including recurring medical
treatments, as well as models
of the living environment, for
example devices and their location
and capabilities.
Validation
The impact of our solution will
be evaluated in several steps.
The first step is the prototypical
development and integration
in an apartment-like AAL
Lab (late 2006). The second
step is integration into a
nursing home and with the
emergency call centre in
Kaiserslautern (in 2007). The
third step is validation of emergency
monitoring within the 6
FP project EMERGE on a pan-European
level (2006 – 2008).
This work is part of the joint research
project BelAmI of Fraunhofer IESE, the
University of Kaiserslautern, the
University of Budapest, the University
of Szeged, and the Bay Zoltan Research
Foundation, Budapest. Recent achievements
are described in detail in the
paper: Nehmer, J.; Karshmer, A.;
Becker, M.; Lamm, R.: "Living
Assistance Systems – An Ambient
Intelligence Approach", in Proceedings
of the 28th International Conference on
Software Engineering (ICSE 2006),
Shanghai, China, 2006.
Links:
BelAmi: http://www.belami-project.org
IESE: http://www.iese.fraunhofer.de
University of Kaiserslautern research center
on AmI website:
http://www.eit.uni-kl.de/AmI/frame.html?en
Please contact:
Frank Bomarius, Martin Becker, Thomas
Kleinberger, Fraunhofer IESE, Germany
Tel: +49 631 6800 1200
E-mail: {bomarius, mbecker,
kleinberger}@iese.fraunhofer.de