A consortium of partners has developed an AI-powered and IoT-enabled device designed to identify when older or vulnerable individuals living alone may require immediate medical support. This device monitors the usage of household appliances and electrical items by analysing data from electricity or smart meters.
CENSIS – Scotland’s innovation centre for sensing, imaging, and IoT technologies – along with the University of Edinburgh, Mydex CIC, Carebuilder, and Blackwood Homes and Care, have built and trialled the technology as part of Blackwood Homes’ Peoplehood project across 19 households in Glasgow, Dundee, and Buckie in Morayshire.
The device connects wirelessly to a smart meter or conventional electric meter, disaggregating the data to identify high-power electrical items such as kettles, microwaves, washing machines, and electric showers. Using machine learning, it tags each item and determines when they are turned on and off, spotting any anomalies.
For instance, if an individual typically boils a kettle to make tea by 8am, the device recognises this as normal behaviour. If the kettle is not turned on by 9am, an automated text message is sent. If there is no response, an alert is sent to their nominated contacts – a family member, carer, neighbour, or response service – who are then notified to check on them.
The device employs a hub process algorithm, allowing data to be processed locally rather than centrally. Combined with an industry-leading personal data store from Mydex, this ensures individuals have full control over who can access their household data.
Stephen Milne, Director of Strategic Projects at CENSIS, said: “This project is all about repurposing energy data to help inform social care and supporting healthy aging. The system learns the typical activity of the individual living in the household and then spots any erratic behaviour, helping to identify when they may have issues. These could be one-off events, like a fall, and with further research, the system may be able to track changes over a longer time period that may indicate gradual, and more difficult to spot health issues, such as the onset of a condition such as dementia.
“While there are other technologies related to monitoring activity, this is the first full service deployment that has been implemented through passively monitoring a property’s smart meter system. The device can also pick out each item being monitored, making it much more likely to spot any anomalies, and is barely noticeable for the householder.
“After these trials, we are looking to develop the technology to the commercial product stage and deploy it at a much bigger scale, and are open to taking this forward with talks with potential long-term partners.”
The machine learning algorithm monitors power usage in 10-second intervals, analysing the power signatures from the household. Throughout the project, the University of Edinburgh compiled a library of these signatures, tagging each high-power item in the homes to identify usage patterns.
Lynda Webb, Senior Researcher in the School of Informatics, the University of Edinburgh, said: “The idea of monitoring electricity use in the home, for spotting if a person might need help, was first conceptualised 10 years ago. A prior project of 250 homes in Edinburgh enabled the development of the algorithms that are used today in this project. It is so exciting to see the application of this idea and the years of algorithm development becoming a service which is already impacting the lives of people in the trial.”
Blackwood Homes and Care’s three-year Peoplehood project aims to create a future-proof model for independent living, enabling people to live healthier and happier lives for longer. Through various initiatives, the project will establish a blueprint for welcoming communities with age-friendly homes supported by advanced technologies, making independent living sustainable as people age.
Lindley Kirkpatrick, Peoplehood Programme Manager at Blackwood Homes, said: “The Peoplehood project has focused on developing activities, techniques and technologies and we are delighted with the progress that has been made across several fronts.
“The development of this new device utilising AI technology could, however, prove to be one of the most exciting that we have seen. For carers and loved ones to get ahead of time notice of potential medical emergencies as well as the onset of conditions of dementia is of huge importance.
“We very much look forward to examining the details that come out of the trial to understand how this has aided participants of the Peoplehood project.”
There’s plenty of other editorial on our sister site, Electronic Specifier! Or you can always join in the conversation by commenting below or visiting our LinkedIn page.
