27 Sep 2021

Elderly Non-intrusive Remote Monitoring

Guest Blog: Boruo Xu, Head of Data Solutions Team at Informetis Europe Ltd discusses efficient and cost-effective adult care on day one of techUK's Data Analytics Week.

One of the biggest challenges in an ageing society is to provide efficient and cost-effective adult care, whilst ensuring that the “caree” (the person receiving the care) is fully happy with the it too. As residents age, they progress from informal in-home care to formal care homes and hospitals. If we could delay the transition from in-home care to care homes without sacrificing elderly person's Quality of Life (QoL), this on its own will save on health tens of thousands of pounds per person per year on their health care provision costs. In addition, the fact the Caree is still on their own home means their overall QoL is further improved.  

The issue with providing high-quality health care for people living at home is to know when they need help. There are a few common solutions. One is to provide regular visits to residents to collect information and provide the necessary help. The issue with this approach is that some residents may not need frequent visits while others may need more urgent intervention when accidents occur. The other issue to consider is that quite a lot of elderly people do not like the idea of being visited by “strangers” and consider  it too intrusive. Another common approach is to deploy intrusive sensors around the home to monitor movements and appliances. Solutions with cameras, motion sensors, door sensors and smart plugs could provide an accurate picture of what is happening in the house. However, this comes at the price of the Caree’s privacy, as much of this highly sensitive data is analysed by third parties. 

Since 2015, Informetis has been developing a new solution for non-intrusively monitoring residents’ behaviours within their own home. By installing a single Informetis Smart Sensor meter inside the fuse box and using machine learning techniques such as non-intrusive load monitoring, we are able to identify the status of key appliances in real time. For example, when a kettle or an oven is being used and for how long. From the usage data of key appliances, we have been able to develop machine learning based behaviour models to correlate with these usage patterns. Notifications and alerts are provided when unusual behaviours happen, such as the kettle not being used for too long or the oven being left ON for too long. These notifications and alerts allow remote carers to gain insights about the Caree without infringing their privacy. Carers could also provide effective health care when they notice behaviour changes which are often associated with long term health deterioration.  

A commercial service of this solution has been available in Japan for more than 2 years. In the UK, it is being actively trialled with several partners. Both in Japan and in the UK, this service has received a lot of positive feedback. Relatives and children are able to monitor, in an unobtrusive and 24/7 manner, the activities of their parents and notice any significant deviations in their behaviour pattern. Quite often, the lack of change in the caree’s behaviour pattern provides a sense of relief for their carers. By using this solution, carers can be sure that the careers are healthy and safe or if they require any help without the need for asking awkward questions about their well being which sometimes carees are not keen to answer in a truthful manner. 

Hopefully this non-intrusive monitoring service would provide a new solution to in-home health care and provide greater peace of mind for the carers and the families. 



Boruo Xu, Head of Data Solutions Team at Informetis Europe Ltd


Katherine Holden

Katherine Holden

Head of Data Analytics, AI and Digital ID, techUK

Katherine joined techUK in May 2018 and currently leads the Data Analytics, AI and Digital ID programme. 

Prior to techUK, Katherine worked as a Policy Advisor at the Government Digital Service (GDS) supporting the digital transformation of UK Government.

Whilst working at the Association of Medical Research Charities (AMRC) Katherine led AMRC’s policy work on patient data, consent and opt-out.    

Katherine has a BSc degree in Biology from the University of Nottingham.

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