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Details of Grant 

EPSRC Reference: EP/P009824/1
Title: ASPIRE: Automated Sensing & Predictive Inference for Respiratory Exacerbation
Principal Investigator: Clifton, Professor DA
Other Investigators:
Osborne, Dr MA Roberts, Professor S Watkinson, Professor P
Farmer, Professor AJ Tarassenko, Professor L
Researcher Co-Investigators:
Project Partners:
Microsoft Oxehealth Limited Oxford Health NHS Foundation Trust
Oxford Uni. Hosps. NHS Foundation Trust
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research
Starts: 30 March 2017 Ends: 29 March 2021 Value (£): 1,475,512
EPSRC Research Topic Classifications:
Artificial Intelligence Digital Signal Processing
Information & Knowledge Mgmt Software Engineering
EPSRC Industrial Sector Classifications:
Healthcare Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Sep 2016 Intelligent Technologies Prioritisation Panel Announced
Summary on Grant Application Form
There is an urgent, unmet need for reliable, intelligent systems that can monitor patient condition in the home, and which can help patients manage long-term conditions. Delays in recognition of the changes in physiological state worsen outcomes and increase healthcare costs. The ASPIRE programme uses chronic obstructive pulmonary disorder (COPD) as an exemplar, which affects over 210 million people globally. This condition costs the National Health Service over £800 million each year, over half of which is spent treating patients in hospital, rather than caring for them in their homes.

Intelligent monitoring systems are required to address the needs of patients with long-term conditions in their homes. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for 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 helping the patient understand and manage their condition.

We propose to develop an "intelligent" home-based system, with smart algorithms embedded within lightweight healthcare sensors, to overcome these limitations. Our novel work will incorporate next-generation machine learning algorithms to combine information from healthcare sensors with information from GP and hospital visits. This will enable the system to learn "normal" health condition for individual patients, with knowledge of other conditions from which they may be suffering, and which can then make recommendations to the patient concerning self-management of their condition. This work will include close working with world-leading clinicians to ensure that the recommendations provided by the system are correct for the individual patient.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.ox.ac.uk