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Details of Grant 

EPSRC Reference: EP/N024966/1
Title: Intelligent Wearable Sensors for Predictive Patient Monitoring
Principal Investigator: Tarassenko, Professor L
Other Investigators:
Clifton, Professor DA De Vos, Professor M
Researcher Co-Investigators:
Project Partners:
Equivital Technology Microsoft Oxford University Hospitals NHS Trust
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research
Starts: 01 October 2016 Ends: 30 September 2019 Value (£): 972,972
EPSRC Research Topic Classifications:
Med.Instrument.Device& Equip.
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
16 Feb 2016 Healthcare Impact Partnerships 2015/2016 Announced
Summary on Grant Application Form
There is an urgent, unmet need for reliable, continuous patient monitoring, both in hospitalised patients, and in home settings. Delays in recognition of the changes in vital signs associated with physiological deterioration worsen outcomes and increase healthcare costs. In hospital wards patients are mainly mobile and unmonitored by electronic systems. Manual observations of vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation and level of consciousness) are made by a nurse every 4 - 12 hours, resulting in late recognition of deterioration. Over 40,000 patients deteriorate on UK wards each year until they are so unwell that they require admission to an Intensive Care Unit (ICU). Care is delayed in over a third of these patients, and the mortality rate for such emergency admissions is around one quarter.

Wearable monitoring systems are required to address the needs of ambulatory patients in hospital. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for vital-sign monitoring; (ii) a lack of robustness in the estimates of the vital signs that wearable sensors produce; (iii) very limited battery life that requires batteries to be re-charged at a rate that prevents their use on a large scale; and (iv) limited subsequent use of the data for clinical decision support.

We propose to develop a range of "intelligent" wearable sensors, with smart algorithms embedded within them, to overcome these limitations. Our disruptive sensor system will build on major EPSRC-funded grants that have demonstrated proof-of-concept: the "Hospital of the Future" Grand Challenge in Healthcare IT (2009-2013) and the "Centre of Excellence in Medical Engineering" (2009-2015, funded jointly by the Wellcome Trust).
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Organisation Website: http://www.ox.ac.uk