Human Activity Classification with Adaptive Thresholding using Radar Micro-Doppler

Li, Z., Fioranelli, F. , Yang, S. , Le Kernec, J. , Abbasi, Q. and Romain, O. (2023) Human Activity Classification with Adaptive Thresholding using Radar Micro-Doppler. In: 2021 CIE International Conference on Radar (CIE Radar 2021), Haikou, Hainan, China, 15 - 19 December 2021, ISBN 9781665468893 (doi: 10.1109/Radar53847.2021.10028630)

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Abstract

Radar systems are increasingly being used for healthcare applications for human activity recognition due to their advantages for privacy compliance, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are often very complex, hence requiring significant computational resources. We propose an adaptive thresholding algorithm used as a ‘mask’ to highlight the region of interest from the micro-Doppler signature. The mask is then applied to spectrogram information. These masked signatures are used for handcrafted feature extraction and classification. A quadratic-SVM classifier is employed based on the features from the information acquired. The preliminary results show that an accuracy of 91.3% is achieved using sequential forward feature selection with feature fusion. Based on our initial result, a Naïve Bayes combiner is used to improve the overall performance further. With this strategy, the accuracy of classification reaches 92.5% for six activities. Additionally, we compare our findings to those of other models utilizing the same database. The results demonstrate that high accuracy can be achieved when adaptive thresholding is used with the SVM method, and computational resources may significantly decrease.

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.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Yang, Dr Shufan and Fioranelli, Dr Francesco and Romain, Professor Olivier and Abbasi, Professor Qammer and Le Kernec, Dr Julien and Li, Zhenghui
Authors: Li, Z., Fioranelli, F., Yang, S., Le Kernec, J., Abbasi, Q., and Romain, O.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISSN:2640-7736
ISBN:9781665468893
Copyright Holders:Copyright © 2023 IEEE
First Published:First published in Proceedings of the 2021 CIE International Conference on Radar (Radar)
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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