Database Reference
In-Depth Information
that shows visual snapshots arranged as a series of panels to enable re-
view of activities for users. A similar system can be used to support
safe and complete medication adherence. This technology has also been
used for diabetes management using a mobile phone to which a glucose
meter can be connected via Bluetooth.
Besides these laboratory settings, there are also several smart homes
that have been implemented in actual community settings, apartment
complexes, and retirement housing units. These include a smart home
in Vinson Hall Retirement Community in Missouri that is dedicated
to serving former U.S. military ocers and their families. Eskaton, Ltd.
has created the National Demonstration Home in California with a range
of technologies. The University of Missouri-Columbia has integrated
sensor networks into privately owned apartments called TigerPlace II. A
community wide comprehensive smart home deployment is under devel-
opment in McKeesport, Pennsylvania. The University at Buffalo, State
University of New York, has utilized X10 devices to retrofit 50 homes
for older adults with chronic conditions living alone in their own home.
More details on these and other such smart home projects can be ob-
tained from [96].
Researchers have recently investigated the use of domestic robots as
a promising technology for persuasive telehealth [101]. Domestic robots
have several unique features as compared against other devices in smart
environments. One reason some technologies are dicult to use in per-
suasive telehealth systems is because they require the user to spend effort
learning and becoming familiar with the technologies. Domestic robots
are easier to use through their natural human-like communication, which
can provide a pleasant experience for the user. Their friendliness can
create an emotional bond that helps users, such as the elderly, feel more
comfortable using them. Domestic robots are in fact effective inform-
ers, educators, reminders, and even readers of the users feelings and
thoughts, which are hard to detect using other devices. While this effort
is preliminary, and requires several technological advances, it is likely of
significant interest for effective pervasive healthcare.
Multiple sensor mining technologies have been combined with such
smart environment data gathering infrastructures to build healthcare
applications targeting different requirements. The work in [81] uses
frequent pattern mining to identify repeating structures in the routine
patterns of human activity from environmental sensor data and detect
changes in these patterns. This is important as the onset or complication
of a life threatening episode may be marked by changes in behavior and
activity patterns. This has been shown to be true for several conditions
including prostatism, degenerative joint disease, bursitis, and gastro-
