Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks

  • Jiajun ZhangEmail author
  • Jinkun Tao
  • Zhiguo Shi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)


Hand gesture recognition has long been a study topic in the field of Human Computer Interaction. Traditional camera-based hand gesture recognition systems can not work properly under dark circumstances. In this paper, a Doppler-Radar based hand gesture recognition system using convolutional neural networks is proposed. A cost-effective Dopper radar sensor with dual receiving channels at 5.8 GHz is used to acquire a big database of four standard gestures. The received hand gesture signals are then processed with time-frequency analysis. Convolutional neural networks are used to classify different gestures. Experimental results verify the effectiveness of the system with an accuracy of 98%. Besides, related factors such as recognition distance and gesture scale are investigated.



This work is supported by Intel under agreement No. CG# 30397855.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Information Science and Electronic EngineeringZhejiang UniversityHangzhouChina

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