How can sensors help?
Explore case studies from real-life examples.
The cat and the latch
Housing association staff noticed high humidity readings in a home – over 83% in the resident's bedroom.
These conditions were of extra concern because the resident was an elderly lady with asthma.
When the housing association’s care team visited, they found an unexpected cause: a broken latch meant that the resident couldn’t open the window, as her house cat would escape.
The lack of ventilation was the cause of the high humidity, which had in turn led to damp spores forming on the bedroom walls.
The solutions were straightforward but impactful. The mould was treated and the window was repaired, so it could be opened without the cat escaping.
Shortly afterwards, the resident commented on improvements to her physical health: “I was coughing and needing my blue inhaler more. I was also getting itchy eyes and they would puff up. That has stopped.”
“Thank you so much for your help – my health has improved as a result.”
Warming a cold home
Sensor data showed a disturbing drop in temperature in a family home.
Conversations with the residents revealed they were now in fuel poverty due to changes in their circumstances and a loss of income and benefits.
The home had a high-rated EPC, however the housing team found that there wasn’t enough loft insulation, resulting in higher energy costs.
The team was able to help the family find financial support and a local charity supplied a heated blanket and energy vouchers. A change in energy supplier and a Warm Home Discount also helped towards bills. The insulation was upgraded to improve warmth in the home.
“Insert quote from housing provider.”
What about the future? Housing and care organisations that help to support older adults often need to know how people are coping at home.
Most people have a routine that is consistent over time, cooking, cleaning, washing, and keeping warm at similar times each day.
For example, a person might wake up at 7:30am and make a cup of tea. Their carer might arrive a little later, and help them have a shower. Around midday, they might have lunch, followed by a few hours watching television in the living room, before the evening routine of dinner and popping the heating on before bed.
By combining data on humidity, electricity use, temperature, and carbon dioxide, indoor sensors can help to keep track of these activities.
Crucially, they can also use a technique called machine learning to spot patterns that can represent a typical day at home, and flag breaks in a person’s routine that might be a sign of changes in their health or living conditions.
Using this kind of data analysis could allow organisations who support independent living to notice when a resident misses a shower, has a house that is too cold, or isn’t sleeping or cooking regularly.
Alerting care teams about possible concerns could help them to intervene early, and solve a problem before it becomes serious.
