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The 360/365 Caregiver Monitoring Model

A modern strategy to help caregivers provide a 360/365 monitoring solution for loved ones

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Who is your idea designed for and how does it enable older adults to live their best possible life by preventing falls?

The model is best designed to help current caregivers monitor status, predict, and react to situations, therefore enabling their patients to live a more active and safe life.

360-365 Caregiver Model

The concept incorporates a three-phase approach to protecting loved ones from accidental injury from a fall while providing peace of mind and preventative care.The solution follows the care recipient 24/7/365 with a 360 visibility and monitoring.

To begin, we build a short story around a specific care recipient to understand a scenario in which this model can be used to benefit the care recipient and caregivers while highlighting the response time.

Meet Debra, an 83-year-old grandmother, whose eldest daughter, Helen age 57 lives 20 minutes away and is Debra’s primary caregiver. Fortunately, the proximity gives Helen the ability to stops by either before or after work each day.

Financially successful, Helen is fortunate enough to utilize technology to assist her with her caregiving responsibilities and would like to safely and securely monitor her mother’s actions without imposing or disrupting Debra’s independence.

Helen understands the risks involved with Debra continuing to live independently, and willing to invest in a safe smart home solution.


Phase 1 

Currently, Helen has several solutions available to her on the market.

Utilizing a web-enabled camera, a GPS-enabled personal fall alarm necklace, a baby sub-mattress monitor, a health monitor wristband, and each device’s associate app, Helen can monitor her mother’s activities daily.

The limitation of this option is that Helen must sign into multiple unintegrated portals and do not provide a connected view of the multiple factors that could indicate an issue. Only a few of these devices have functionality that would alert Helen remotely if Debra had an issue that needed to be addressed urgently, such as a fall.

As comforting as these devices are for Helen, this status quo is solution is not a sufficient model to help in the caregiving process due to its complexity and clunkiness.

 

Phase 2 

The advancement of open sources and api integrated products as well as faster internet speeds has created an environment for real time aggregation of these products data allowing for a single seamless dashboard solution that monitors and alerts to emergencies providing a unified app experience for both Helen and Debra and any other part time caregivers that Debra might encounter.

The smart home features would be expanded in Phase 2 to incorporate additional functionality such as Amazon Echo or Google Home to provide voice recognition to trigger actions (i.e. “Call an Ambulance”, “Call my daughter”). The 360-365 device skills can also trigger actions based on a phrase heard. For example, stating “Alexa, I fell,” will send a distress signal and place a speaker phone call to designated contacts to assess the severity.

Utilizing Smart home devices such as Amazon Echo or Google Home, various skills can assist Debra with her daily activities (ordering an Uber to take her somewhere, ordering delivery of groceries or products).

A scheduling mechanism is incorporated to maintain health and vital schedules. Check points via different device keep reminding the patient to take an action. Cameras and IOT sensors can be triggered by thresholds set with in the app. 

Personalizing the caregiver experiences and maintain vital digital monitoring through three points of communication is ideal.

An IOT device, Cameras, and Mobile Phone always communicating status and situations with the caregiver’s Phone, APP and Home Solution.

 

Phase 3 

With the continuation of rapid advances in technology this platform will easily adapt to consume data from additional products such as stick on health monitoring IOT’s, floor sensing fall detection, and motion trigger home devices.

Once an archive of data has been achieved, the 360-365 algorithm with predict Debra’s behavior to better assess whether something is wrong. Over time, 36-365 will become more accurate at identifying confirmed distress (such as a fall) and potential issues (e.g. an elevated heart rate over time that requires a physician appointment). The application will learn Debra’s behavior, wake time, sleep time, while providing reminders for medication dosage and create alerts and notifications specific to the care recipient.


REFINEMENT

In this phase of the OpenIDEO challenge, the request is to narrow down the concept into a prototype or MVP phase. First, we identified market ready products that have the accessible technology needed to create our MVP. Second, we designed a solution around a specific use case that encompasses the products’ features and functionality. Finally, we will illustrate possible project road map as well as design solutions for the 360-365 product.

The four products we researched all have market ready, proven technology with clear and valuable documentation. We have chosen the iPhone, Amazon Echo (voice and call), Beddit (sleep monitoring system), Nest (video monitoring), and Valdic(wearable IOT Data collection software).

iPhone (/ˈaɪfoʊn/ EYE-fohn) is a line of smartphones designed and marketed by Apple Inc. They run Apple's iOS mobile operating system. The first generation iPhone was released on June 29, 2007, and there have been multiple new hardware iterations since.

The user interface is built around the device's multi-touch screen, including a virtual keyboard. The iPhone has Wi-Fi and can connect to cellular networks. An iPhone can shoot video (though this was not a standard feature until the iPhone 3GS), take photos, play music, send and receive email, browse the web, send and receive text messages, follow GPS navigation, record notes, perform mathematical calculations, and receive visual voicemail. Other functionality, such as video games, reference works, and social networking, can be enabled by downloading mobile apps.

Alexa - It is capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, and other real time information, such as news. Alexa can also control several smart devices using itself as a home automation system.[2] Most devices with Alexa allow users to activate the device using a wake-word (such as Echo); other devices (such as the Amazon app on iOS or Android) require the user to push a button to activate Alexa's listening mode. Currently, interaction and communication with Alexa is only available in English and German. (Wikipedia)

Beddit is a sleep monitoring device and mobile application. It has an ultra-thin film sensor that you place in your bed, under the bed sheet. All you have to do is to sleep on it. Beddit connects wirelessly to your mobile device and provides you with an overall sleep analysis every morning. The Beddit Sleep Monitor measures the user's sleep stages, heart and breathing rates, snoring, and more.(Wikipedia)

Nest is a home automation home monitoring self-learning, sensor-driven, Wi-Fi-enabled thermostats, smoke detectors, and other security systems. It introduced the Nest Learning Thermostat in 2011 as its first product. The Nest Protect smoke and carbon monoxide detector was then introduced in October 2013.  (Wikipedia)

Valdic (this case fitbit) are geo tracking activity trackers, wireless-enabled wearable technology devices that measure data such as the number of steps walked, heart rate, quality of sleep, steps climbed, and other personal metrics involved in fitness. The first product released was the Fitbit Tracker.Some evidence has found that the use of similar devices results in less weight loss rather than more.(Wikipedia)

For us to better understand what we can do with these products and the 360-365 model, let us create a real world scenario for Debra and her mother to illustrate our proposal.

At 6:30 am, Helen is making breakfast at her home and glances at the notifications on her iPhone.  360/365 indicates that motion is detected in her mother Helen’s master bedroom. A quick tap and Helen has navigated to the live shot of her mother awakening. After a quick video of assurance she engages with the dashboard to determine her mother’s daily schedule as well as health status. 

Sleep Status shows all green – Debra must have slept well. No other alerts determined for health status last reported 8:30pm last night. A calendar reminder is present and a event is scheduled for her mother’s prescription refills. Debra schedules a reminder for her mother as well as a Alexa alarm.  She also orders a driver to assist her mother at 11:30.

It is 7:15am Helen has left on her daily commute. Her mother’s house is on the way and she makes a stop by. The 360-365 app notifies her that someone is at the front door of her mother’s house (herself), reassuring her that her mother’s safety is well monitored.

“Good morning, Mother;” a loving exchanges proceeds. Helen routinely does the dishes and reminds her mother to wear her Fitbit. They discuss today’s agenda and the need to refill the prescription as well as Debra’Thursday Bridge Night plans. Helen adds a few other items to the her mother’s notification and determines that the IOT device is sending health monitoring vitals. She kisses her mother good bye and proceeds to work.

It is 11:15 – A reminder is sent to both Helen and her mother that the schedule driver will be arriving in 15 minutes.  Debra looks at a live feed of the last room with movement present on her dashboard. Her mother is dressed and watching TV. Confident and content Debra noticed that mother was quite active today and had an elevator heart rate. I glance at the logs revealed she took her daily walk around the neighborhood. No notifications were generated because this was a recognized pattern of events.

11:20 - Helen calls her mother - after a quick conversation Helen resumes on her daily tasks.

11:33 - Front Door Video Monitoring alert - Helen quickly engages with her phone and watches from the front door camera as her mother climbs into a PRius. 

11:35 - Tracking Alert. Debra can now watch in Map view of her mother's trip to the pharmacy.

11:50 -Helen’s mother arrives at the pharmacy/ driver waits for her  to complete her task.

12:15 -Helen’s tracking Alert senses motion again - Debra is on the move.

12:45 -Front door Motion detection activated - Helen’s mother arrives safely back home. 

1:30 - sleep monitoring system activated daily vitals recorded - system on sleep 

2:45 - Motion detected - Status: Ok

3:30 - Helen’s mom interacts with her daily routine at her house, gardening, reading and conversing with friends. The 360-360 saw no immediate need to alert the caregiver as all vitals and status updates were routine.

4:32 - Helen’s mother begins to  return into the house with an ambitious about of items in her hand. She stumbles and falls into the backdoor kitchen entrance.  Immediately - iphone detects the disruption  flags the app “This could be a FALL”  Trigger event matrix -    ALEXA - prompt - Are You ok - we detected a possible fall. Caregiver is sent an AMBER style notification - Helen opens the apps and see the video where her mother is lying on the ground. She immediately tries to call - and watches as her mom attempts to reach her phone. Helen then begins to engage with the Distress option and a Alexa Call is sent out. Helen is now talking through the Alexa device . Mom .. Are you ok.. Mom.  Helen is relieved that her mom is responded and immediately determines that this is a emergency and triggers the pre planned events . The emergency 911 plan is engaged and all associated members with Helen’s plan are notified via text or email. Helen heads immediately towards the hospital fully engaged with the product using it as the lifeline and recorded of events  for the fallen parent. 

8:30 pm - Debra is still in the hospital explain the events to fellow loved ones - they all are extremely grateful the Helen had installed a system that allowed them to react so fast. 

So what this could be - What could it look like. The technology for this to happen is ready and available. A strong project plan and dedicated team could easily return a MVP within a 3 month - With the use case described as the product's main features and functionality.

Main Features - 

  • Video Monitor
  • Vitals Monitors
  • Events 
  • Alarms
  • Notifications
  • GPS Tracking


Team

Systems Architect

Front End IOS Developer

UX DESIGNER 

Project Manager

TIME-LINE 

3 -Month

3 Weeks / Designs and Discover

8 Weeks  / Build

2 weeks test / refine

 

What early, lightweight experiment might you try out in your own community to find out if the idea will meet your expectations?

Invest in Phase One, dissecting current market ready products to identify data attributes applicable to the modeling. Phase Two, Build an MVP, illustrating how these data points could trigger caregiver actions to eliminate the risk of falls and injury, and thus injury related deaths.

What skills, input, or guidance from the OpenIDEO community would be most helpful in building out or refining your idea?

Any and All, please

How long has your idea existed?

  • 4 months - 1 year

This idea emerged from

  • An Individual

How would you describe this idea while in an elevator with someone?

It is a solution to help caregivers monitor their loved ones and or patients. Is it an App? Yes but much more than that - its a caregivers companion

How does your idea demonstrate our Criteria of Affordability?

As the cost of technology reduce so will the 360/365 model. Currently the projected cost for this type of system would be $99 hardware fee - 9.99 a month online storage and subscription fees.

How does your idea demonstrate or plan to demonstrate scalability?

Our solution is an off the shelf product - user should be able to access it through major smartphones as well as brick and motar stores.

How do you plan to measure the impact of your idea?

Determining piece of mind is always a challenging task. In an effort to support our findings with real data. I follow up campaign to first time adopters to help incentivize the initial conversion as well as improve the product.

What are your immediate next steps after the challenge?

Hire the team - build/test/validate the product - find a test pool of willing participants - document iterate, improve - validate.

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