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Details of Grant 

EPSRC Reference: EP/F003714/1
Title: Enabling health, independence and wellbeing for psychiatric patients through personalised ambient monitoring (PAM)
Principal Investigator: Crowe, Professor J
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Sch of Electrical and Electronic Eng
Organisation: University of Nottingham
Scheme: Standard Research
Starts: 10 December 2007 Ends: 09 January 2011 Value (£): 81,553
EPSRC Research Topic Classifications:
Biomechanics & Rehabilitation Digital Signal Processing
Human-Computer Interactions Med.Instrument.Device& Equip.
Networks & Distributed Systems
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
EP/F003684/1 EP/F005091/1
Panel History:  
Summary on Grant Application Form
One in ten of the UK population will suffer a disabling anxiety disorder at some stage in their life whilst 91 million working days are lost per year due to mental health problems and the cost to the country is 32 billion in lost productivity and treatment costs. In addition to this financial burden to the country as a whole there are of course the personal costs to individuals, their family and friends (www.mind.org.uk). Recently Rosie Winterton, the Minister of State for Health Services, announced the Government will help people with long-term conditions such as cancer, or mental health problems, to stay independent and take control of their illness by providing information with medication. PAM (personalised ambient monitoring) will take this even further by allowing patients to select off-the-shelf technology that will monitor their activity signatures ; measurements of behavioural patterns which indicate people's mental health state. PAM will use a set of multiple discreet sensors in a person's home, coupled to a computer system programmed to detect changes in activity signatures. These can then be used to issue automatic alerts to the patient, their family, or their doctor, thus providing the capability to avert debilitating episodes.This pilot study will investigate the feasibility of reducing the incidence of debilitating episodes. This will be achieved by longitudinal monitoring that will permit early detection of deteriorating health and prevent avoidable hospital admissions. This work will build on existing work in this area at the University of Southampton (ISVR and CORMSIS) (that includes an imminent proposal to EPSRC on similar related technology applied to rehabilitation monitoring). University of Nottingham input will extend current work in physiological monitoring in both the conventional and vocational health domains whilst their involvement in the EPSRC funded health technology assessment IMRC will provide a mechanism to potentially assess the project's true value. The University of Stirling already operates in this domain via MATCH and has a stable middleware platform, however at an early stage in this project we will assess the platform, and in particular we will liaise with the EPSRC EQUATOR project regarding use of middleware developed for used in a distributed health monitoring demonstrator.The investigators provide skills and experience across medical devices and sensors, medical signal processing, communications and software services, and Operational Research modelling. This mix is essential to the success of the project, and provides an interesting and distinct interdisciplinary grouping. The IDEAS sandpit has provided a unique opportunity to form such a positive relationship.Deliverables:To healthcare:- An evaluation of ambient monitoring in a mental healthcare context- Improved health, independence and wellbeing status for this patient group- To support concordance with medication and better understanding of the drug(s)To technology:- The ability to simply plug in and turn on a sensor- Low-cost, robust sensors for use in the home- Novel sensors incorporated into, for example, clothing, personal devices (e.g. mobile phones, MP3 players), regular modes of transport. All have the potential to provide data on 'activity'.- Intelligent real-time algorithms to identify behavioural signatures from disparate and sparse data.- Show the potential for a roving gateway on (possibly) a mobile phone working with intermittent connectivity to sensors.
Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.nottingham.ac.uk