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Details of Grant 

EPSRC Reference: EP/R020922/1
Title: Data Analytics for Health-Care Profiling using Smart Meters
Principal Investigator: Hurst, Dr W
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computing and Mathematical Sciences
Organisation: Liverpool John Moores University
Scheme: First Grant - Revised 2009
Starts: 01 June 2018 Ends: 31 July 2019 Value (£): 99,892
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Mobile Computing
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
27 Nov 2017 EPSRC ICT Prioritisation Panel Nov 2017 Announced
Summary on Grant Application Form
Through the completion of this research, we will demonstrate that a small simple change, using existing infrastructure technologies, can have a large impact with significant benefits for society and academia. As such, the premise of this research is to investigate whether data analysis of smart meter electricity readings can be used to support social care that meets a person's individual needs, maximises independence and promotes a sense of security for those living alone.

By the end of 2020 it is expected that 55% of global electricity meters will be smart meters. Within the UK, Energy suppliers and the government are funding the cost of the smart meter roll out and ongoing maintenance. We envision that by investigating advanced machine learning and load disaggregation techniques of this highly accurate sensing network, detailed habits of an individual's interactions with electrical devices can be mathematically modelled.

This research is needed to support and enable a larger number of people to remain independent whilst living with long-term health conditions, such as Alzheimer's. For example, in the UK, around one in five adults are registered disabled and more than one million of those currently live alone. These conditions place significant demands on healthcare services globally.

Existing monitoring services (such as motion sensors, cameras, fall detectors and communication hubs, wearable body networks) are intrusive, expensive and are met with patient resistance. Additionally, current technical solutions are tailored to a specific application and do not meet the ongoing changing requirements of a patient; whereas our approach would require minimal installation, and builds on the smart meter infrastructure, without the need for user interaction.

Analytics are tailored to an individual's health condition for monitoring, early intervention, detection and prediction of self-limiting conditions. If abnormal behaviour is detected, an alert could then be sent to a carer or family member. Specifically, the research will allow us to devise a system that can detect when an Alzheimer's patient has left an oven on or remained awake at night.

The technology creates a personalised profile of the user's behaviour at home. Our system is a disruptive technological solution within tele-health/tele-medicine. Uniquely, there is no requirement for the deployment of sensors around the home. We are employing an existing highly advanced sensor system, which is readily deployed, to provide peace of mind and remote patient care, compared to current technologies available on the market today.

This research complements other recently funded EPSRC projects conducted on smart meter analytics. However, this research is unique in that it is the first project to propose using the smart grid for health analytics, as other projects are concerned primarily with load balancing and energy reduction practices.

The successful completion of the proposed project will involve research in the areas of computer science, specifically big data analytics, and healthcare. Our collaborators from Mersey Care NHS Trust are supporting the research by providing medical advice on Alzheimer's disease profiling and providing patient trials.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.livjm.ac.uk