Human Activity Classification Using Radar Signal and RNN Networks

Jiang, H., Fioranelli, F. , Yang, S. , Romain, O. and Le Kernec, J. (2021) Human Activity Classification Using Radar Signal and RNN Networks. In: IET International Radar Conference 2020, Chongqing City, China, 4-6 Nov 2020, pp. 1595-1599. ISBN 9781839535406 (doi: 10.1049/icp.2021.0556)

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223729.pdf - Accepted Version



Radar-based human activities recognition is still an open problem and is a key to detect anomalous behaviour for security and health applications. Deep learning networks such as convolutional neural networks (CNN) have been proposed for such tasks and showed better performance than traditional supervised learning paradigm. However, it is hard to deploy CNN networks to embedded systems due to the limited computational power available. From this point of concern, the use of a recurrent neural network (RNN) is proposed in this paper for human activities classification. We also propose an innovative data argumentation method to train the neural network using a limited number of data. The experiment shows that our network can achieve a mean accuracy of 94.3% in human activity classification.

Item Type:Conference Proceedings
Additional Information:The authors would like to thank the British Council 515095884 and Campus France – PHC Alliance 44764WK France-UK for their financial support.
Glasgow Author(s) Enlighten ID:Romain, Professor Olivier and Yang, Dr Shufan and Fioranelli, Dr Francesco and Le Kernec, Dr Julien
Authors: Jiang, H., 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
Copyright Holders:Copyright © 2021 The Institution of Engineering and Technology
First Published:First published in IET International Radar Conference 2020: 1595-1599
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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