Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar

Ding, C., Hong, H., Zou, Y., Chu, H., Zhu, X., Fioranelli, F. , Le Kernec, J. and Li, C. (2019) Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar. IEEE Transactions on Geoscience and Remote Sensing, 57(9), pp. 6821-6831. (doi:10.1109/TGRS.2019.2908758)

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Abstract

Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Le Kernec, Dr Julien
Authors: Ding, C., Hong, H., Zou, Y., Chu, H., Zhu, X., Fioranelli, F., Le Kernec, J., and Li, C.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Geoscience and Remote Sensing
Publisher:IEEE
ISSN:0196-2892
ISSN (Online):1558-0644
Published Online:23 April 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Transactions on Geoscience and Remote Sensing 57(9):6821-6831
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy

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