I found an intriguing article by Martha E. Pollack
that emphasizes the socioeconomic concerns with regard to an aging population,
in contrast to the last blog post from the IT Perspective. The article began by
establishing the need for further innovation in intelligent assistive technology
and follows through to survey some of the technology being deployed. (I should
also mention that this research is a few years old, so some of the information
is a little dated; however, the concepts remain very relevant .)
Currently,
over 10 percent of the world’s population is over the age of 60. Despite the
overall problem of worldwide population growth, there will continue to be an disproportionate increase of the old and the oldest old age groups. This is
especially a concern considering the strong correlation between age and
cognitive impairment involving Alzheimer’s, in particular. There will also be
fewer young people to assist in their endeavors. The author states that “although the shift is
most dramatic in the more industrialized regions of the world, a significant
growth in the percentage of older adults is expected in virtually every
country.”
With advances in technology that will help assist,
compensate, and assess the impaired, society as a whole may become
sounder. After all, institutionalization
comes as a great financial burden. The
article claims that over 132 billion dollars are spent on U.S. nursing home
bills a year, of those 60 percent are covered by the government. Many of the elderly prefer to age at home,
but fail in performing everyday activities or feel socially isolated. The
process of aging is quite difficult for the caregivers as well. Many people do
not have the available time, and even then struggle to be consistently
attentive. It is often more feasible to rely on today’s technology even with
non-computerized inventions such as lift chairs or ergonomic handles. Nonetheless, there is much AI –based research
being performed with the goal of curbing some of the difficulties involved with
cognitive and physical impairments.
As I implied, research can be placed into three main
categories: those that assist,
compensate, or assess. With advances it
will be easier to assure safety in performing daily activities by assisting or
assessing deviations. Systems may even warn the user or caregiver of any
errors. If for instance, a person falls or forgets to take their medicine, a
caregiver’s phone could be called. Activity monitoring is essential in any of
the methods.
One method is using radio frequency identification
tags. RFID chips are definitely a major concern when it comes to privacy, but
the convenience in data collecting is tremendous. By tagging objects or clothes,
detailed data can be collected about the user’s interactions, locations, and
physical well-being. This way a system can infer specific daily activities.
Most technology used for recognition use dynamic Bayesian networks that filter
and derive probabilities. For example, the PROACT uses a DBN and the user wear
s a glove that includes an RFID reader. Such as making tea can be broken down
into a three-step process. In step two the system may derive that there is a
high probability of using a tea kettle. Time can be monitored along with it.
Based on trends, the system can target unusual activity.
Because wandering is a significant concern, systems
such as Opportunity Knocks employ GPS tracking. The system may learn typical
behavior and preferences. Deviations can be spotted based on normal trends. The
IMP or intelligent mobility platform uses similar techniques. It uses a
“semi-Markov” model having three layers:
metric position, topology, and current activity. This system makes a map
as it goes through a partially observable environment and monitors trends. A
user can then select rooms/locations for sequentially displayed directions—and
of course, it uses a large arrow to show the current direction needed.
Planning systems are in high demand as
well. Many of the elderly simply struggle to remember what they need to do
next. Interesting enough, many modern
systems don’t even require exact times to be inputted. If a person is typically awake and eating at
8 a.m. every morning, schedules can be shifted accordingly or a caregiver may
be notified with lack of activity. If a person needs to take a pill an hour after
eating, then it can be scheduled based on when sensors find the user has eaten.
Some even will avoid specific times that are normally set aside. If a user
likes a particular television show, the ‘eat’ notification may occur before or
after the typical time. The current focus is for them to be even more dynamic.
Critics argue that the systems are too fixed with regard to the
decision logic; problems may occur if a user becomes ill or the user ages more.
Personally, I had barely considered that
some of the elderly struggle to simply following sequences. Simple tasks, like
washing hands, have become not all that simple. Many try to dry their hands
before even washing them. I also think advances in assistive technology will revolve
around AI. Nonetheless, I remain generally unconcerned. As the need for the
technology increases, I am confident that even better solutions will be
employed. Hopefully the technology can then be applied to an even wider
audience, further improving everyday conveniences.
