Authors:
Jessica Snead, Susan Brosnan, Daniel Goins, Patricia Hogan
Abstract:
This disclosure proposes a method to monitor the health and well-being of certain populations of people. It can be difficult to get elderly folks to be willing to participate in program that requires them to wear technology to monitor their safety. With more people of all ages participating in Food and Meal Delivery services, the service and equipment connected to these services can be used to assist in monitoring the health and well-being of vulnerable folks.
Background:
Many wearable technologies exist for monitoring the health of vulnerable folks, but many of those elders are reluctant to participate in them. The technologies can sometimes feel intrusive or requiring wearing bracelets or necklaces that might be uncomfortable or unsightly. It would be best to monitor these vulnerable folks in ways that do not require them to directly interact with technology.
Description:
Since more and more seniors are using food and meal delivery services, Retailers and Food Delivery Providers can assist Elders and/or families in monitoring for health via food behaviors.
Our main idea is to attach a RFID tag to the food containers of the delivered food. The Food Delivery Providers would associate the RFIDs on the tagged food containers to a food delivery order and associate the order with the household whose health they are monitoring.
The Food Delivery Health Tracking application could track the active RFID tags to monitor –
- When the food was delivered to the household
- When was the food taken inside the household
- Is the person ordering less frequently indicating loss of appetite or forgetfulness or are they ordering too frequently indicating confusion?
- Did the food remain on the front porch for many hours or days because the household members were not well enough to pick up the order, or because they forgot about the order?
- When the food was taken inside the house, what path did it take?
- Did it go straight to the kitchen or eating area, or did it get distractedly put in a bathroom or closet or bedroom indicating a confused household member?
- Over time the application could learn where food is normally stored, where it is normally eaten. The application could send alerts if this pattern changes.
- How often were the food containers accessed, and which containers were accessed? When were the containers accessed?
- Did the household members access the containers at a reasonable mealtime? Over time the application could learn the normal eating patterns of the household and send an alert if the pattern changes which could indicate a change in health.
- Were the containers accessed many times during the day? Perhaps indicating people were snacking all day?
- Were the containers accessed over many days? This could indicate a household member taking nibbles of the food each day. Taking nibbles each day may indicate loss of appetite.
- Were only certain containers that contain certain types of food accessed? Is there anything to learn from that? The person only eats deserts and cookies and is not eating a balanced meal? The person only accessed containers with soft food indicating they may not be able to chew or cut some of the other types of food and may need attention to their teeth or to their arthritic or broken hands.
- Was the beverage container accessed enough? Is the person forgetting to drink and getting dehydrated.
This idea is to help monitor vulnerable people in their homes without those people having to wear monitoring devices, or to have cameras watching them all the time, or having Alexa like devices listening to them all the time to detect if they have moved, what room they are in, is their speech pattern normal, is the sound of their walk normal, etc.
The method would work like the following:
- Families or Seniors themselves would register for the monitoring program
- The Enrollee could select what areas to monitor and could optionally provide information about their current eating habits and preferences
- As new orders are made, machine learning determines what is normal for this enrollee
- Then the system monitors the orders for abnormalities in the order (as above)
- The system will have an available modification option
- For instance, a pet passed away, so the system learns that no cat food is normal
TGCS Reference 3088
