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Details of Grant 

EPSRC Reference: EP/R041679/1
Title: Intelligent RF Sensing for Falls and Health Prediction - INSHEP
Principal Investigator: Fioranelli, Dr F
Other Investigators:
Researcher Co-Investigators:
Project Partners:
CENSIS Health and Social Care Alliance Scotland
Department: School of Engineering
Organisation: University of Glasgow
Scheme: Standard Research - NR1
Starts: 01 February 2018 Ends: 31 January 2020 Value (£): 253,001
EPSRC Research Topic Classifications:
Med.Instrument.Device& Equip.
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:  
Summary on Grant Application Form
The proportion of elderly people is increasing worldwide. In the UK, the Office for National Statistics estimates that "The number of people aged 75 and over is projected to rise by 89.3%, to 9.9 million, by mid-2039; the number of people aged 85 and over is projected to more than double, to reach 3.6 million by mid-2039; and the number of centenarians is projected to rise nearly 6 fold, from 14,000 at mid-2014 to 83,000 at mid-2039". Consequently, conditions such as diabetes, obesity, dementia, Parkinson's disease are expected to increase their incidence, with more and more people affected by multiple conditions at the same time (multimorbidity).

Furthermore, statistics in the UK show that "falls and fractures in people aged 65+ account for over 4 million hospital bed days each year in England alone, and the healthcare cost associated with fragility fractures is estimated at £2bn a year". Physical consequences of fall events (fractures, contusions, open wounds, abrasions, strain, and concussions) often require treatment at A&E departments if not hospitalisation, but they also lead to anxiety and loss of independence. All these reduce the quality of life of the people affected and of their families, as well as generate public costs for healthcare provision.

Our project will investigate how radar technologies will help vulnerable individuals (older people and people with cognitive or physical impairments, or with multi-morbidity conditions) preserve their independence and quality of life, and provide caregivers and health professionals with individualised information on each patient. In practical terms, our system will monitor activity levels over longer periods of time to detect early signs of cognitive and functional decline, providing not only prompt detection of critical events (e.g. falls, strokes), but also predicting these events from indicators in the data that will enable individualised prompt treatment and intervention from health professionals.

Radar snsors transmit and receive electromagnetic waves similar to those used by common devices such as Wi-Fi routers, and the analysis of the received echoes can provide information on how and where a person moves. Radar offers the advantage of providing contactless and non-intrusive monitoring, with no need for the end-users to carry or interact with devices, or alter their behaviour, and no need to record direct optical images of them. This makes these sensors attractive as a potential alternative to wearable sensors and conventional video-cameras, or as a complementary sensor to those ones.

Our project will combine cutting-edge research in the field of electronic engineering and machine learning, with end-users engagement from the very early stages (older people, caregivers, health professionals, community members). We will take into account their inputs, requirements, issues, attitudes in relating with our technology, and inform the design and technical choices while developing our system. This will enable to address potential users' acceptance issues and barriers to the development and adoption of the technology, an element of strength to maximise the impact of our proposal.

Key Findings
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Potential use in non-academic contexts
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Summary
Date Materialised
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Organisation Website: http://www.gla.ac.uk