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A Survey of Deep Learning-Based Human Activity Recognition in Radar

by Xinyu Li, Yuan He *,† and Xiaojun Jing
Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(9), 1068;
Received: 3 April 2019 / Revised: 23 April 2019 / Accepted: 30 April 2019 / Published: 6 May 2019
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities. View Full-Text
Keywords: human activity recognition; radar; deep learning; human backscattering echoes human activity recognition; radar; deep learning; human backscattering echoes
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MDPI and ACS Style

Li, X.; He, Y.; Jing, X. A Survey of Deep Learning-Based Human Activity Recognition in Radar. Remote Sens. 2019, 11, 1068.

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