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In-home behavioral monitoring as a tool for confident, independent living (updated 30/05 - User Testing and Final Deck)

Curo has built a monitoring product that is tracking data which may be indicative of a potential fall.

Photo of Rob Deeming
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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?

Curo is designed for the care hierarchies that exist around older adults. Our product provides behavioral data that we hope can be indicative of a range of challenges at home, including falls. If we focus on behavioral patterns that often proceed a fall, it may be possible to look for and identify these behaviors and alert the family/caregivers when a fall is likely to occur in the near future.


Changes in behavior, particularly in seniors can indicate an increased likelihood of a fall. These changes can be very subtle can take place over an extended period of time, which make them difficult to detect through human observation alone. If an elderly person is living alone, it is even more likely that these signs will be missed. 

By using passively collected data from easy to install sensors in the home, Curo can identify those key signals associated with an increased likelihood of falls.

This information can be shared with family and caregivers in a timely manner, along with critical information about the cause of the decline. With this information, family members or caregivers can respond appropriately to reduce the risk of falls.


According to the World Health Organization (WHO) there are 4 areas which influence the likelihood of a fall:

1. Biological

2. Behavioral (Our area of focus)

3. Environmental (Secondary area of focus)

4. Socioeconomic

Furthermore, WHO have established a three-stage prevention model to lower the risks of falls:

1. Build awareness of the importance of fall prevention

2. Improve identification and assessment of risk factors. (Our area of focus)

3. Identify and implement realistic and effective intervention. (Secondary area of focus)

Curo will address this by:

Relating to “Improve identification and assessment of risk factors” (2) in the prevention model:

1. Determining (and defining exactly) which our data points have the strongest correlation with increased risk. Based on WHO, these are likely to include:

- Decline in movement (Physical activity)

- Medication usage

- Temperature in the home (Environmental)

- Food preparation (Healthy eating)

- Leaving the house (Social interaction)

2. Create a risk algorithm based on the occurrence (or otherwise) of the above behaviors (none, low, medium high).  

1. None - no behaviors associated with increased risk of fall have been observed

2. Low - one of the behaviors has been observed

3. Medium - either; one of the behaviors more than once OR two distinct behaviors have been observed

4.High - combination of multiple behaviors and multiple frequencies of events have been observed 

Relating to “Identify and implement realistic and effective intervention” (3) in the prevention model:

3. Inform / Alert caregiver and/or family of increased risk.  

4. To ensure that we address the WHO’s need for “realistic & effective” intervention, we will not just alert caregivers, but also provide information on what behaviors specifically have to lead to the increased risk.  Therefore allowing for more informed care decisions to take place.

5. Post intervention, Curo can monitor to determine success or failure of integration and care provision (i.e falls risk will decrease if successful)



Curo is an established health-tech startup who have spent the last two years developing this product to keep elderly people in their homes longer. The team of 7 people have a background in digital consulting, development, product management, and entrepreneurship and are based in Melbourne & Sydney Australia and Ann Arbor, Michigan.


There are a number of competitors in the senior health monitoring space.  However, the balance of costs/usability and insight from each system varies greatly.  The existing options typically have three shortcomings with Curo does not:

1) Fixed 'hardwired' systems. This makes them difficult to install (i.e requiring an electrician) & difficult to redeploy.  Therefore competitors to Curo are typically found in facilities where the system can be left in place semi-permanently.  Curo's major differentiator here is that the system can easily be installed by anyone in the home environment.  The home environment is key, as this is where early prevention can best occur.  

2) Hardware-focused companies.  Unlike Curo's competitors, Curo makes use of existing off-the-shelf IoT sensors, making it hardware agnostic. This means as the technology changes/improves, Curo will continue to use modern equipment produced by the largest IoT companies in the world (i.e Samsung, Amazon, Belkin etc).

REFINEMENT PHASE - Kickoff Session

To kick off the Refinement Phase of the Challenge, we met up in the Curo offices in Melbourne. We went through the existing Curo product and brainstormed ways in which we could use the existing data collected by Curo to identify and reduce falls risk. 



We decided to focus on family members using the product (Curo is aimed at both family members and professional care providers). The reason family members were chosen in this instance was to simplify the experience and allow us to prototype and test part of the solution quickly and easily. 


We went through our User Journey and used the "Determining What to Prototype" tool to help us prioritize what to prototype for this challenge. We began by looking at each step of the User Journey and asking "What is the Most Important Question to Answer" for each stage, followed by ideas on "How We Might Test It".

This lead us to focus on two key questions: "How might family members be informed of an increase in falls risk?" followed by "How might they react?" and "How would we want them to react?"

To test this we decided to mock up of a fall risk indicator on top of the existing Curo app. We decided this could then be shown and discussed with family members to ascertain how they might react and gauge how effective the falls risk indicator is at communicating the risk while not causing panic and distress.


We refined our user journey and added two personas, John and his daughter Mary, to better explain the concept. These can be viewed in the slide deck below.


Using the existing Curo app as a base, we mocked up two different versions of fall risk indicators, one in which falls risk was communicated more directly and in graduated stages and one with more general recommendations based on the severity of the falls risk. Our hypothesis was that by communicating falls risk on a scale of 1-4 for example, a family member might be more distressed than if that information was presented more as general recommendations (while still alerting them when an urgent intervention needed to be made.) This was one of the key assumptions we wished to test with the prototype.


Questions we want to test
1. How do family members react when seeing a change notification. How are they responding? Are we creating unnecessary stress? What do they think each notification means?

2. What is the best metric to communicate change?
Do you call it falls risk, or just notifying that something is changing?
What is the best language to communicate change?

How will we test this?
Put the prototype in front of someone. “If you saw this warning, what would this evoke for you? What would you want to do in response?”

Test Overview
We conducted tests with two participants in the Curo offices.
We took both versions of the prototype and presented it to the participants, asking them to talk through their thoughts as they interacted with each one. 

Participant 1

Participant 2


Both participants drastically preferred prototype A. Overall they both found it much clearer and easier to understand.

Both participants during the testing told unprompted stories about grandparents who had recently passed away and expressed the wish that they had had Curo.  

  • In Rebecca’s case, they pro-actively removed the family member from their home (against will) because of concern specifically about a fall. 
  • In Elias’s case, they were unaware of the difficulties the family member was having in the lead up to passing away.  Elias believed this ‘fall prevention feature’ that we are testing within Curo would have certainly helped them better understand what he was going through.


Our internal discussions had focused largely on Prototype B. We had believed that a softer approach (i.e Education rather than alerts) would be more meaningful to the user.  However, the testing showed a clear bias in favor of prototype A.

We had overestimated the ‘panic’ that might be caused by displaying a fall risk and underestimated users desire to get straight to the point (i.e there is a low risk of fall). 

Although the user testing didn’t cover the recommendations, both confirmed the importance of making relevant recommendations after having made an assessment. For example “if meals are identified as a problem, what are some tips to help specifically with that”.

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

If we better understand behaviors that tend to come before a fall, we can create specific tasks in our existing product to spot those behaviors in the homes of older adults. We can correlate that data to reports of whether/when a fall has happened, in turn testing the validity of using behavioral data to predict falls. If this is validated, we can set up notifications to alert an older adult's care hierarchy that a fall may happen, and actions that can be taken to help prevent it.

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

We would value guidance from behavioural specialists and researchers who have proof points or theories around the behaviours that typically proceed falls. We have the ability to test lots of things, we would benefit from insight on what to look for!

How long has your idea existed?

  • Over 1 year

This idea emerged from

  • A group brainstorm

Tell us about your work experience:

Curo is an established health-tech startup who have spent the last two years developing this product. We have a background in digital consulting, development, product management, and entrepreneurship and have offices in Melbourne, Australia and Ann Arbor, Michigan.

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

Today, Curo already passively collects behavioral data from the homes of seniors. The next step is to identify which parts of our data set can specifically indicate the increased likelihood of a fall in the future. By identifying and isolating key influences of falls within our data, we can inform caregivers earlier not just about the increased likelihood, but also the key areas to focus on.

How does your idea demonstrate our Criteria of Affordability?

Curo exists as a commercially available product both in the United States and in Australia. It uses a combination of affordable IoT sensors and the Curo software platform to bring remote monitoring to the masses. Furthermore, by sharing critical wellbeing data directly with family, we reduce the need for expensive 24/7 call centers or response teams. This challenge is about adding a very valuable piece to Curo (fall prevention) without changing the underlying cost of the solution.

How does your idea demonstrate or plan to demonstrate scalability?

Curo currently partners with the 3rd largest Health Insurer in Australia (HCF). The ability to scale our solution through their membership is a key channel to market which can be replicated across other health insurers.

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

Curo is about to launch its next pilot in Melbourne, Australia involving 500 senior participants. This project will be measuring the outcomes which are important to us including: volume, severity and cause of hospitalisations. Our intention is to validate that using the Curo solution improves each of those key metrics. Importantly, if we were to roll out this fall prevention project within that pilot, we will be able to statistically validate the lower number of falls during the pilot.

What are your immediate next steps after the challenge?

Further user testing will be helpful to refine the user experience around Prototype A, in particular, finalizing the recommendations specific to the identified fall influencers will support users in their chosen intervention. Once the user experience is finalized, the next step is to determine the efficacy of the algorithm that we have built.
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Team (4)

Sarah's profile
Sarah Halliday

Role added on team:

"Sarah is an OpenIDEO Community Prototyper who is mentoring the team during the Refinement Phase of the Challenge. She has a background in interaction and visual design and 9 years experience as a Registered Nurse. Her LinkedIn profile can be viewed here:"

Rob's profile
Tim's profile
Tim McDougall

Role added on team:

"Tim is Co-founder of Curo. He has spent the last 10 years as an entrepreneur and digital consultant, predominately helping private businesses and government take advantage of the changing digital landscape. His LinkedIn profile can be viewed here:"

Matt's profile
Matt McDougall

Role added on team:

"Matt is Co-founder of Curo. He brings 2 years of experience as an industry-leading developer. He previously set up and sold a development agency to Australia’s premier Drupal agency. His LinkedIn profile can be viewed here:"

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Attachments (6)

Curo - User experience map.pdf

This is the first version of the user experience map developed by the Curo team (relating to falls prevention.)

Curo - Determining What to Prototype.pdf

This OpenIDEO tool was used to help the Curo team determine what to prototype and prioritize certain sections of the user experience.


FRAT (Falls Risk Assessment Tools) are used across all Australian hospitals and aged care providers to assess falls risk in patients. Assessments are generally made on patient admission and are reviewed regularly by allied health staff (often weekly in acute care scenarios). This specific FRAT tool was developed by the Peninsula Health Falls Prevention Service (Victoria). This tool has been used by the Curo team to help define specific indicators that increase falls risk.

WHO Global Report on Falls Prevention in Older Age.pdf

This World Health Organization report includes international and regional perspectives on falls prevention issues and strategies. It is the product of conclusions and recommendations made at the WHO Technical Meeting on Falls Prevention in Older Age in Victoria, Canada in February 2007. This article was used by the Curo team to help define specific indicators that increase falls risk.

Curo Overview IDEO.pdf

This is an example of a Curo pitch deck, explaining the original concept.

Curo Demo Day Presentation IDEO.pdf

This is an example of a Curo pitch deck, explaining the original concept.


Join the conversation:

Photo of Kate Rushton

Hi Rob

Thank you for all your hard work in this challenge and dedication.

All of the ideas posts are locked but the comments section is still open, so please feel free to look at other ideas and comment on them, seek feedback on your idea etc.

Photo of Kate Rushton

Hi Rob and Tim,

We are half-way through the refinement phase and I can’t wait to see continued updates on your idea. More information can be found in the refinement toolkit which can be found at the top of the refinement phase page.

An easy first step is to complete the refinement questions which can be found by logging into your OpenIDEO account and selecting the ‘Edit Contribution’ button on the top left hand corner.

If you scroll down to the bottom, you can see the five added questions with a character limit (including spaces) to help you focus your answer. The questions start with "How would you describe this idea while in an elevator with someone? (what's the elevator pitch for this idea?) - 400 character limit"

In addition to answering these questions it would be helpful if you could mention how your solution fits in the market in the ‘full description’ section of your post. Who are your competitors and how is your idea unique?

If you have any questions, please feel free to reach out to me, my email address is

Hope to see you on the refinement call this Friday at 9 am PST.

Photo of Angel Landeros

I think that this sensor, once it's fully developed could be a good add to Curo. It would help detect everyday activities that could predict a fall risk or actually detect a fall. You could develop develop 2nd and 3rd order synthetic sensors to track showering/bathroom routines, cooking, going up and down stairs, opening up pill bottles, and even detect when someone/something falls. If something does not match the standard routine an alert to follow up directly.

Photo of Kate Rushton

Hi Rob,

Welcome to the refinement phase. I look forward to learning more about Curo.

It would be great to know more about how Curo sits in the monitoring space - are you aware of any competitors in the market or under development?

Also, if you could share news articles or maybe presentations from conferences about Curos that would be very helpful and feel free to share offline, my email address is

I have been looking through previous OpenIDEO challenge ideas and there are two you may be interested in looking at because of the way they display information:

"Sweat Smart” - a sweatband from our previous healthy lives challenge (and finalist idea) - It also has links to two really good user profiles and a great user journey.

BrightTap - a smart water meter mentioned in the Impact section of our Water and Sanitation challenge has a really user-friendly display -

Photo of eldy wullur

Hi Rob,
A very interesting idea, monitoring the behavior of the elderly should be done carefully and its development from day to day. There are some of our elderly who even fall while sitting relaxed. And because the head is weak, we do not even have time to hold it.

Photo of Gregg

Rob: This is an intriguing concept--early detection and alerts about potential hazards. But your presentation here on OpenIDEO and on your website does not give enough specifics about the sensors and the data analysis to convince me that you can actually pull it off. The judges of this Challenge might also be dubious unless you provide more details.
I have heard several care managers and occupational therapists advise older people with mild physical impairments to do the following to minimize the risk of falls: 1) Exercise regularly; 2) Install grab bars in the bathroom and use them when getting in/out of the bath/shower and on/off the toilet; 3) Have more night-lighting on paths traversed in middle of the night than sufficient during younger years; 4) If you need a walker, use it everywhere except where you can steady yourself with grab bars; 5) Tighten up or replace rickety chairs and stools; 6) Stow away throw rugs; 7) Don't leave stuff on the floor where you might trip on it; 8) Limit alcohol consumption to a two ounces per day per 100 pounds of body weight, and sip it slowly while consuming food. To that list, I would add don't turn around quickly, avoid low blood pressure, and don't use stairs in the middle of the night. Can your system monitor several of these?
The fact that accommodations have been made for some of these things, doesn't mean that they are retained. I have watched couples agree to have their throw rugs and rickety furniture stored away, only to have them a later retrieve and replace them.
If you can make the system work at a cost that is affordable to many, I can see it becoming a breakthrough in fall avoidance. That is because one of the major causes of falls by older people is their denial of slowly lost strength, balance, and visual accuity, but often are in denial of that, and your system could provide them with objective early warning feedback that supplements observations of family members and physicians.
So I hope that you use the Refinement period to provide more details about how your system would work.

Photo of Kate Rushton

Hi Rob!

I am just posting the link to your company's website here - - so the community can find out more about what you are doing.

Photo of Katlyn Green

Hi Rob,

This is a cool idea. We are developing Path Feel - an insole to improve balance with the hope the data collected from the insoles during walking and standing that can also highlight risk factors for falls, such as gait and balance abnormalities. This could certainly augment the data you are thinking of collecting to give a more robust prediction of falls.

Photo of Breisi Brito

This is awesome! I was thinking about this challenge and solving it as a preventative app... so.. building on top of this and David's Socks/Shoe idea for gathering balance data about person as the go about their day, you could extend this app to gather more information and use AI to predict and thus help prevent falls... so... I saw an elderly person walk off a bus the other day and as he hit the footpath there was a slight lift in the pavement and he tripped, luckily he regained his balance, but if your app had a GPS mapping feature and was able to log a balance issue at that point (day/time) plus all the other great info you guys have logged, and add that to a big data repository (imagine all the data that these devices could generate), you can start to correlate it all (using AI) to create preventative information... you could then add it to Siri or whatever that Google AI is called, and make them warn people (vibration or sound) based on where they are and number of occurrences of falls that have happened in that area or at that time, or due to what they are doing at the time, etc etc etc AND... adding to this, even if people saw that there was a potential area that could cause an accident to happen they could log it (sort of the way Waze works, with crowd sourcing information)... you can then even use this information to get councils to fix these trouble spots...

Photo of Rob Deeming

Thanks for the comments, Breisi, there is so much potential. We are definitely interested to find ways to compare patterns in data to help better understand what is happening in any particular home.

The voice-based warning system is a nice addition too!

Photo of Anne-Laure Fayard

Hi Rob, very interesting idea! It reminds me of a similar product I heard about this fall during a workshop. I was not able to ask for the specific information and could not find it. Here is what I found and posted during the research phase:

You might also want to check an idea that some NYU students developed during a Design Jam:

Good luck with this idea which I think has a lot of potential.

Photo of Rob Deeming

Thanks Anne-Laure, this is super interesting. I keep hearing about gait and the significance it has as an indicator of all kinds of things.

We are basically agnostic to sensors at Curo - we are eager to capture data from anything that meaningfully adds to the mix of information we are able to gather on a person's behaviour.

I will follow up with the guys at CMU.

Photo of Zandri Kuun

Hi Rob,
I really like this idea. It would be interesting to discover which behaviours precede falls and how noticeable they are. A few things may include: fatigue, hunger, dizziness and disorientation.

Photo of Rob Deeming

Absolutely Zandri. Those all seem like key indicators, and the good news is that we can already assess (at least by interpolation) the first two. The more I understand about the behavioural science, the more I am led to believe that gait has an important role to play. It seems that changes in gait are a key indicator of broader changes in health status.

Photo of Kate Rushton

Hi Rob!

It is good to see you in the ideas phase.

Would you say Curo is a 'most viable' or 'most promising' idea?

Photo of Rob Deeming

Hi Kate - I would say 'most viable'. The monitoring kit is effectively ready to go, we just need to better understand the behavioural data piece, and set up a pilot that allows us to specifically look for/assess those behaviours and then test some hypotheses! Rob

Photo of Devendra Natekar


Quick question. How are the activities tracked? Are they manually entered by the person or their caregiver or is it through sensors?

Photo of Rob Deeming

Hi Devandra:

All through sensors placed around the home. We specifically pick up medication activity, meals, going to bed, getting up, visits to the bathroom, general movement around the house, ambient conditions and arrival/departure from the house.