Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Zhang, X., Abbasi, Q. H. , Fioranelli, F. , Romain, O. and Le Kernec, J. (2022) Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps. In: 16th EAI International Conference on Body Area Networks (EAI BODYNETS 2021), Glasgow, UK, 25-26 Oct 2021, pp. 39-51. ISBN 9783030955922 (doi: 10.1007/978-3-030-95593-9_4)

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250637.pdf - Accepted Version



Population ageing has become a severe problem worldwide. Human activity recognition (HAR) can play an important role to provide the elders with in-time healthcare. With the advantages of environmental insensitivity, contactless sensing and privacy protection, radar has been widely used for human activity detection. The micro-Doppler signatures (spectrograms) contain much information about human motion and are often applied in HAR. However, spectrograms only interpret magnitude information, resulting in suboptimal performances. We propose a radar-based HAR system using deep learning techniques. The data applied came from the open dataset “Radar signatures of human activities” created by the University of Glasgow. A new type of hybrid map was proposed, which concatenated the spectrograms amplitude and phase. After cropping the hybrid maps to focus on useful information, a convolutional neural network (CNN) based on LeNet-5 was designed for feature extraction and classification. In addition, the idea of transfer learning was applied for radar-based HAR to evaluate the classification performance of a pre-trained network. For this, GoogLeNet was taken and trained on the newly-produced hybrid maps. These initial results showed that the LeNet-5 CNN using only the spectrograms obtained an accuracy of 80.5%, while using the hybrid maps reached an accuracy of 84.3%, increasing by 3.8%. The classification result of transfer learning using GoogLeNet was 86.0%.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Le Kernec, Dr Julien and Romain, Professor Olivier and Abbasi, Dr Qammer
Authors: Zhang, X., Abbasi, Q. H., Fioranelli, F., Romain, O., and Le Kernec, J.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Published Online:11 February 2022
Copyright Holders:Copyright © 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
First Published:First published in Body Area Networks. Smart IoT and Big Data for Intelligent Health Management: 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings: 39-51
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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