Fusion of Radar Data Domains for Human Activity Recognition in Assisted Living

Le Kernec, J. , Fioranelli, F. , Romain, O. and Bordat, A. (2022) Fusion of Radar Data Domains for Human Activity Recognition in Assisted Living. In: 14th International conference on Sensing Technology, Chennai, India, 17-19 Jan 2022, pp. 87-100. ISBN 9783030988852 (doi: 10.1007/978-3-030-98886-9_7)

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Radar has long been considered an important technology for indoor monitoring and assisted living. As ageing has become a worldwide problem, it causes a huge burden on the government’s healthcare expenses and infrastructure. Radar-based human activity recognition (HAR) is foreseen to become a widespread sensing modality for health monitoring at home. Conventional radar-based HAR task usually adopts the amplitude of spectrograms as input to a convolutional neural network (CNN), which can limit the achieved performances. A hybrid fusion model is here proposed, which can integrate multiple radar data domains. The result shows that the proposed framework can achieve superior classification accuracy of 92.1% (+2.5% higher than conventional CNN) and a lighter computational load than the state-of-the-art techniques with 3D-CNN.

Item Type:Conference Proceedings
Additional Information:The authors would like to thank the British Council 515095884 and Campus France 44764WK—PHC Alliance France-UK, and PHC Cai Yuanpei—41457UK for their financial support.
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Romain, Professor Olivier and Le Kernec, Dr Julien
Authors: Le Kernec, J., Fioranelli, F., Romain, O., and Bordat, A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Published Online:08 June 2022
Copyright Holders:Copyright © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
First Published:First published in Sensing Technology: Proceedings of ICST 2022: 87-100
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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