Gesture-Radar: a Dual Doppler Radar Based System for Robust Recognition and Quantitative Profiling of Human Gestures

Zhu Wang, Zhiwen Yu, Xinye Lou, Bin Guo, Liming (Luke) Chen

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33 Citations (Scopus)
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Gesture recognition is key to enabling natural human-computer interactions. Existing approaches based on wireless sensing focus on accurate identification of arm gesture types. It remains a challenge to recognize and profile the details of arm gestures for precise interaction control. In addition, current approaches have strict positioning requirements between radars and users, making them difficult for real-world deployment. In this article, we adopt the multisensor approach and present gesture-radar - a dual Doppler radar-based gesture recognition and profiling system, which can capture subtle arm gestures with less positioning or environmental dependence. Gesture-radar uses two vertically placed Doppler radars to collect complementary sensing data of gestures, based on which cross-analysis can be performed for gesture recognition and profiling. Specifically, we first propose a two-stage classification model and enhance the signal proximity matching method by applying constraint functions to the DTW algorithm, aiming to improve the accuracy of gesture type recognition. Afterward, we establish and analyze unique features from the time-frequency spectrogram, which can be used to characterize in-depth gesture details, e.g., the angle or range of an arm movement. Experimental results show that gesture-radar achieves up to 93.5% average accuracy for gesture type recognition, and over 80% precision for profiling gesture details. This proves that the proposed approach is viable and can work in real-world environments.

Original languageEnglish
Article number9265263
Pages (from-to)32-43
Number of pages12
JournalIEEE Transactions on Human-Machine Systems
Issue number1
Publication statusPublished (in print/issue) - 20 Nov 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Major Program for Technological Innovation 2030–New Generation Artificial Intelligence under Grant 2018AAA0100500, in part by the National Natural Science Foundation of China under Grant 61960206008 and Grant 62072375, and in part by the Fundamental Research Funds for the Central Universities under Grant 3102019AX10. This article was recommended by Associate Editor J. Wachs.
(Corresponding author: Zhu Wang.) Zhu Wang, Zhiwen Yu, Xinye Lou, and Bin Guo are with the School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China (e-mail:;;;

Publisher Copyright:
© 2013 IEEE.

Copyright 2021 Elsevier B.V., All rights reserved.


  • Doppler radar
  • dual channel information
  • gesture recognition
  • human-computer interaction
  • multiple sensors
  • wireless sensing


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