Activity recognition with cooperative radar systems at C and K band

Shrestha, A., Li, H., Fioranelli, F. and Le Kernec, J. (2019) Activity recognition with cooperative radar systems at C and K band. Journal of Engineering, 2019(20), pp. 7100-7104. (doi: 10.1049/joe.2019.0559)

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Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused.

Item Type:Articles
Additional Information:A. Shrestha is supported for his PhD by the UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Award to the School of Engineering. The authors acknowledge support from the UK EPSRC through grant EP/R041679/1 INSHEP.
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Le Kernec, Dr Julien and Shrestha, Mr Aman and Li, Haobo
Authors: Shrestha, A., Li, H., Fioranelli, F., and Le Kernec, J.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Journal of Engineering
ISSN (Online):2051-3305
Published Online:11 July 2019
Copyright Holders:Copyright © The Institution of Engineering and Technology 2019
First Published:First published in Journal of Engineering 2019(20):7100-7104
Publisher Policy:Reproduced under a Creative Commons license

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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