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Using smart meter data for remote monitoring of vulnerable residents

Smart meters are being trialed as a form of remote monitoring to flag up possible causes for concern for elderly and disabled people living in their own homes.

We are working on this ground-breaking trial led by The University of Edinburgh’s School of Informatics, in partnership with housing care specialist Blackwood Homes and Care, which provides a range of accessible housing for people with disabilities and older people.

Applying machine learning algorithms to data from smart meters

Through the Smart Meters for Independent Living (SMILE) project the group are developing and testing artificial intelligence 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.

Machine learning algorithms use energy usage patterns to identify the timing of people’s relevant activities in the home, 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.

The ambition is that the new predictive digital technology will provide an additional service to complement the traditional proactive 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.

Colin Foskett, head of innovation at Blackwood Homes, said:

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.

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