Radar for healthcare: recognising human activities and monitoring vital signs

Fioranelli, F. , Le Kernec, J. and Shah, S. A. (2019) Radar for healthcare: recognising human activities and monitoring vital signs. IEEE Potentials, 38(4), pp. 16-23. (doi: 10.1109/MPOT.2019.2906977)

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Although typically associated with large-scale, defense -related use to monitor ships and aircraft, radar has been employed in the past few years for a number of short-range, civilian applications. We have discussed and presented some examples of radar used to support health-care provisions, to help monitor vital signs of patients at risk and their daily activities, a useful proxy for their more general physical and cognitive well-being. Unlike cameras and wearables, radar does not collect sensitive images of the people monitored or require users to wear, carry, or interact with new devices that may be perceived as intrusive; it can, therefore, have significant advantages in terms of users' perception and compliance. We have shown a few experimental results of the radar signatures for different human activities as well as an example of radar data tracking the respiratory rate of a monitored subject. The collection and full understanding of these data will be key to developing innovative signal processing and machine-learning algorithms to automate monitoring and consequently timely and proactive diagnostics for future healthcare provision.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Shah, Mr Syed and Le Kernec, Dr Julien
Authors: Fioranelli, F., Le Kernec, J., and Shah, S. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Potentials
ISSN (Online):1558-1772
Published Online:01 August 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Potentials 38(4):16-23
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy