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is huge, we just name a few approaches for fall
detection, like the use of motion analysis (Nyana,
Taya, & Maha, 2008), floor-vibration (Alwan, et
al., 2006), acoustic fall detectors (Popescu, Li,
Skubic, & Rantz, 2008) or camera based systems.
Localization
. Localizing individuals and
objects is a basic service of each AmI system. It
is necessary for finding elderly with dementia,
behavior and/or activity analysis. Without proper
localization mechanisms is not possible to build
situation-aware systems, which allow a realistic
view of the current situation. In addition real-
ible location information is an important part in
situation description, e.g. the use of a shower or
tooth brush is often bound to a specific location
like the bathroom. The BelAmi project from the
Fraunhofer Institute for Experimental Software
Engineering IESE (Becker, 2008),(Nick & Becker,
2007), as well as the Harvard project CodeBlue
(Shnayder, Chen, Lorincz, Fulford-Jones, &
Welsh, 2005) provide such localization services.
Physiological parameters monitoring.
Param-
eters, like blood pressure, weight, electrocardio-
gram (ECG), pulse, respiration, skin conductance,
can be used in many different ways. eHealth
applications process them with the purpose to
maintain and/or improve a person's health. The
gathered parameters could be forwarded into a
patient record information system of a healthcare
provider or stored in an IHE compliant system.
Unfortunately actual AmI applications only per-
ceive and visualize such data, but do not share or
match them with other information systems. Other
applications process physiological parameters lo-
cally and provide user feedback (Seto, et al., 2009).
In addition to previously mentioned projects, the
following projects provide processing facilities for
physiological parameters:
(iii)
AlarmNet (Wood,
et al., 2006) is a medical information system
designed for smart healthcare with the focus of
continuous, long-term and remote monitoring of
an assisted individual's health parameters and life
habits,
(iii)
TigerPlace (Rantz, et al., 2005)(Rantz,
Skubic, Miller, & Krampe, 2008) from the Center
for Eldercare and Rehabilitation Technology at
the University of Missouri is designed to pro-
mote aging-in-place, and (
iii)
Mercury (Patel, et
al., 2009) from the Harvard University is geared
towards long-term motion analysis studies with
special focus on Parkinson's disease and Epilepsy.
Activities of daily living.
With respect to the
amount of gathered data it is possible to use the data
for analyzing
Activities of Daily Living
(ADL). An
ADL can be a quite simple activity, like entering
and leaving a restroom, but it can also be much
more complex and longer lasting, like cooking a
meal. The challenge in activity detection increases,
when one activity is interrupted by another or when
activities can be performed in parallel with other
activities. A project that addresses these issues is
BehaviorScope (Bamis, Lymberopoulos, Teixeira,
& Savvides, 2009) from the Yale University. They
use high-level grammar to describe activities in
advance and are able to successfully generate
summaries of everyday activities and trigger
notifications at run-time. As showed in a recent
study (Rantz, et al., 2005), the analysis of ADLs
can also provide medical relevant information.
SOLUTIONS AND
RECOMMENDATIONS
Due to the large amount of projects and prototypes
in the AAL and healthcare domain providing a
detailed overview of all of them would be too
extensive. Concerning smart homes the surveys
provided by (Chan, Esteve, Escriba, & Campo,
2008), (Buchmayr & Kurschl, 2010) and (Baldauf,
Dustdar, & Rosenberg, 2007) give an extensive
introduction.
The work of (Chan, Esteve, Escriba, & Campo,
2008) focuses on various functions that can be
implemented in a smart home by using adequate
equipment. Based on these functions and evalu-
ated projects and prototypes a general architecture
of a smart system, as shown in Figure 3, can be
defined. The available sensor equipment allows
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