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Details of Grant 

EPSRC Reference: EP/W031892/1
Title: PROTECT: Predictive approaches in managing long-term conditions: from remote monitoring data to digital biomarkers
Principal Investigator: Barnaghi, Professor P
Other Investigators:
Sharp, Professor DJ Nilforooshan, Professor R
Researcher Co-Investigators:
Project Partners:
Department: Brain Sciences
Organisation: Imperial College London
Scheme: Standard Research
Starts: 01 September 2022 Ends: 31 August 2025 Value (£): 1,219,503
EPSRC Research Topic Classifications:
Bioelectronic Devices Med.Instrument.Device& Equip.
Mobile Computing
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
23 Feb 2022 SI Transform health at home Announced
Summary on Grant Application Form
The cost of care for long-term conditions is up to 70% of total health and social care expenditure and account for 50% of GP appointments in the UK. Developing data-driven preventative and predictive measures will enhance the quality of care services and reduce the cost of managing long-term conditions.

Emerging technologies such as the Internet of Things and wearable devices provide new opportunities to collect continuous in-home monitoring data such as movement, daily activity, vital signs and sleep. This data provides new possibilities to monitor the progression of conditions, do risk assessments and predict adverse healthcare events.

This research will develop new information engineering and machine learning methods to integrate and analyse continuous in-home monitoring data and create new digital biomarkers for personalised and preventative care. It will create scientific advances in in-home monitoring technologies and machine learning applied to healthcare and, in particular, managing long-term conditions. It will develop clinically applicable and care informed solutions to utilise remote monitoring data to extract health insights and provide predictive analysis and healthcare risk assessments. The research will contribute to the ambitions outlined in the call to transform care and health at home and enable independence by producing affordable solutions that be applied across a range of long-term health conditions.

The research programme will create a digital platform to integrate environmental and physiological information from in-home observation and measurement technologies safely and securely. It will apply machine learning models and solutions to derive digital biomarkers that can be used to improve healthcare. The overarching aim is to use the system to extract relevant information from complex datasets to allows effective and timely health interventions. To achieve this, the team will build the software infrastructure that allows sensor information to be collected in a safe and secure, and privacy-aware way and create the fundamental building blocks for the analysis of digital biomarkers through the development of person-centred and clinically informed predictive approaches.

Key Findings
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Organisation Website: http://www.imperial.ac.uk