Smart meters trialled as remote care monitoring system for elderly and disabled people
Smart meters are being trialled as a form of remote monitoring to flag up possible causes for concern for elderly and disabled people living in their own homes.
The trial is being led by the University of Edinburgh’s School of Informatics in partnership with The Data Lab and Blackwood Homes and Care, a specialist housing provider offering accessible homes for disabled people.
Through the Smart Meters for Independent Living (SMILE) project the group are developing and testing artificial intelligence (AI) methods to analyse energy usage data from consenting residents’ smart meters, creating a view of their daily routines and spotting unusual changes in behaviour that might indicate problems.
The trial began in November 2019 and is currently analysing energy usage data in several homes across Scotland.
The system works by machine learning algorithms using energy usage patterns to identify the timing of people's relevant activities in the home and looking for changes that should be flagged up.
The system will then alert the individual, their loved one or carer, enabling a decision on the best course of action to be made.
Individuals and their families or carers can set specific rules for the system, telling it which changes in routine are a cause for concern, such as a shower lasting longer than usual or a change to normal cooking schedules, which could indicate that an incident has occurred.
The aim is for the new predictive digital technology will provide an additional service to complement the traditional push button personal alarm worn by residents – particularly aiding people with dementia and those who may be confused, may forget or be unable to activate their current alarm.
The technology also has the potential to be used as a decision support tool, so that if it detects a resident getting up frequently during the night, health and care professionals can review whether they need changes in their support.
The project is also supported by CareBuilder, Hildebrand, Mydex CIC & Smart Energy GB.
Findings of the trial are expected to be published in autumn 2021.
Dr Lynda Webb from the School of Informatics at the University of Edinburgh said: “It is very exciting to be working collaboratively with Blackwood Homes and the industry partners on this project.
“It provides an opportunity to apply the machine learning outputs from our previous EPSRC (Engineering and Physical Sciences Research Council) research project, IDEAL, in a new real world setting for social good.
“The fact that we are also co-designing the service with Blackwood customers means we can take forward the research in a way that is adapted to people's true needs.”
Colin Foskett, head of innovation at Blackwood Homes, said: “At Blackwood we are always looking for ways of enabling our customers to live more independently.
“The UK smart meter rollout programme presents an opportunity to use energy usage data for good.
“If we can prove the principle of the technology with this project, then we have an opportunity to provide a safety net for vulnerable people, to identify patterns of decline and provide early intervention, potentially saving lives and reducing hospital admissions.”
Gillian Docherty, CEO of The Data Lab, said: “This project has the potential to shape the way we view machine learning and AI in social care settings by empowering individuals to go about their daily routines without worry and only receive carer intervention when necessary.
“Scotland has an aging population, and in the next few decades we need to find new ways to deliver the best possible social care against a backdrop of stretched resources and falling carer numbers.
“Machine learning and AI can be a non-invasive way to do this and will also encourage greater personalisation of care based on an individual’s data.
“The SMILE project is funded as part of The Data Lab Collaborative Innovation programme and further strengthens the relationship between Blackwood Homes and The Data Lab, cementing the relationship for further support in terms of skills, network access and external funding support in the years ahead.
“We’re proud to be involved in such a forward-thinking project and look forward to receiving the initial findings soon.
“It is another fantastic example of data being used as a force for good.”