Configuring the baseline of activity for older adults

Less movement and non-movement matter.  The Washington Post ran an article recently about GreatCall's partnership with an AI company so that patterns and changes in behavior could be reported to family members from its wearable PERS device.  Of course, tracking and reporting about changes from baselines -- that's nothing new for sensor-based home monitoring systems.   But it is a surprisingly big deal in the PERS industry -- where even those who once supported pattern-detecting big ideas dropped them like a hot rock -- in favor of the transactional PERS world -- press the button and someone will come.   The most radical changes in that industry over five years, fall detection and GPS tracking, have still been transactional -- Mrs. Smith, we are responding, are you okay? -- versus, I'm Mrs. Smith's device, and based on her behavior changes, she is not okay.

Business intelligence from wearables is a tough upsell -- but by now, it should be a given. Even the health care industry is gun-shy about the concept of configuring a baseline -- let alone have your volumes of information dumped on doctors or their unsuspecting nurses and assistants.  PERS is a pretty big market -- $1 billion or more.  Think about the numbers and price points in the PERS industry, assuming you can find the data -- they are mostly in the $29-39/month range. Buy the device, open the box, call to start the service, and done. But as the Frost and Sullivan report observes, PERS is a gateway to in-home and connected home offerings -- even complementing other health and telehealth deployments -- and fitting nicely with sensor-based monitoring.

Solve pattern detection -- and families will be reassured.  We are a long-distance family world.  Adult children worry.  Older adults want to stay in their homes -- families cannot get them to move. There are plenty of tips and suggestions to address long-distance caregiving (did you know about the Carrier Alert program of the USPS?) but nothing that really gets to the inactivity, pattern change and non-movement aspect. So if 45% of women aged 75+ live alone -- does that mean we're counting on the mail carrier to notice if something changes?  And what if a woman at that age lives in a shared mailbox community -- will someone notice if the mail is not picked up?  Or does the post office worker just move that mail into a larger storage box?  

AI is a fancy term for detecting, recording and making sense of patterns. We're not talking about IBM and Watson -- just the collection of elements of usage and comparing that usage to a baseline pattern.  Step one, of course, record the baseline after purchase. Step two, determine a price uplift for tracking changes to baseline -- at an agreed-upon interval that cannot be every minute or even every hour. Decline in activity is a pattern -- researchers get that.  Absence of activity is a pattern -- the fitness wearable folks get that.  Not wearing a device -- that's a non-transmitting, but very trackable incidence, but after multiple days, it is a pattern. Store the data somewhere -- monitor over time, analyze and compare.  Combine it with room-based sensor information.  If it is useful enough, actionable enough, and cost-avoiding enough, healthcare providers and insurers will wish to detect it. And for far-away family members of the frail and alone, it represents an improvement.