Radar-Based Hierarchical Human Activity Classification

Li, X., Fioranelli, F. , Yang, S. , Romain, O. and Le Kernec, J. (2021) Radar-Based Hierarchical Human Activity Classification. In: IET International Radar Conference 2020, Chongqing City, China, 4-6 Nov 2020, pp. 1373-1379. ISBN 9781839535406 (doi:10.1049/icp.2021.0566)

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Abstract

Worldwide the ageing population is increasing, and there are new requirements from governments to keep people at home longer. As a consequence assisted living has been an active area of research, and radar has been identified as an emerging technology of choice for indoor activity monitoring. Activity classification has been investigated, but is often limited by the classification accuracy in the most challenging yet realistic cases. This paper aims to evaluate and improve the accuracy in classifying six commonly performed indoor activities from the University of Glasgow open dataset. For activity classification, the selection of features to discriminate between activities is paramount. Activity classification is usually done as one vs all strategy with one classifier and a set of features to distinguish between all the activities. In this paper, we propose to optimise the feature selection and classifier choice per activity using a hierarchical classification structure. This strategy reached 95.4% accuracy for all activities and about 100% for walking, opening the field for personnel recognition.

Item Type:Conference Proceedings
Additional Information:The authors would like to thank the British Council 515095884 and Campus France 44764WK – PHC Alliance France-UK for their financial support.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Yang, Dr Shufan and Fioranelli, Dr Francesco and Le Kernec, Dr Julien and Romain, Professor Olivier
Authors: Li, X., Fioranelli, F., Yang, S., Romain, O., and Le Kernec, J.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781839535406
Copyright Holders:Copyright © 2021 The Institution of Engineering and Technology
First Published:First published in IET International Radar Conference 2020: 1373-1379
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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