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Details of Grant 

EPSRC Reference: EP/R018677/1
Title: IRC Next Steps Plus : OPERA - Opportunistic Passive Radar for Non-Cooperative Contextual Sensing
Principal Investigator: Piechocki, Professor RJ
Other Investigators:
Craddock, Professor IJ Lane, Professor NDA Chetty, Dr K
Woodbridge, Professor K Santos-Rodriguez, Dr R Tan, Dr B
Researcher Co-Investigators:
Project Partners:
DecaWave Ltd Toshiba
Department: Electrical and Electronic Engineering
Organisation: University of Bristol
Scheme: Standard Research
Starts: 01 January 2019 Ends: 31 March 2023 Value (£): 1,363,228
EPSRC Research Topic Classifications:
Artificial Intelligence Information & Knowledge Mgmt
Networks & Distributed Systems
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
15 Feb 2018 HIPs 2017 and IRC Next Steps Plus Panel Announced
Summary on Grant Application Form
Physical activity and behaviour is a very large component in an array of long-term chronic health conditions such as diabetes, dementia, depression, COPD, arthritis and asthma, and the UK currently spends 70% of its entire health and social care budget on these types of conditions. All aspects of self-care, new therapies or management conditions, require novel non-intrusive technologies able to capture the salient data on causes and symptoms over long periods of time.

The OPERA Project - Opportunistic Passive Radar for Non-Cooperative Contextual Sensing - will investigate a new unobtrusive sensing technology for CONTEXUAL SENSING - defined as concurrent physical activity recognition and indoor localisation - to facilitate new applications in e-Healthcare and Ambient Assisted Living (AAL). The OPERA platform will be integrated into the "SPHERE long term behavioural sensing machine" to gather information alongside various other sensors around the home so as to monitor and track the signature movements of people.

The OPERA system will be built around passive sensing technology: a receiver-only radar network that detects the reflections of ambient radio-frequency signals from people - in this case, principally, the WiFi signals in residential environments. These opportunistic signals are transmitted from common household WiFi access points, but also other wireless enabled devices which are becoming part of the Internet of Things (IoT) home ecosystem.

The project will make use of cutting-edge hardware synchronisation techniques, and recent advances in direction finding techniques to enable accurate device-free (non-cooperative) localisation of people. It will also employ the latest ideas in micro-Doppler radar signal processing, bio-mechanical modelling and machine/deep learning for automatic recognition of both everyday activities e.g. tidying and washing-up, to events which require urgent attention such as falling. OPERA is expected to overcome some of the key barriers associated with the state-of-the-art contextual sensing technologies. Most notably non-compliance with wearable devices, especially amongst the elderly, and the invasion of privacy brought about by the intrusive nature of video based technologies.

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