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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

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

verfasst von : Jiajun Zhang, Jinkun Tao, Zhiguo Shi

Erschienen in: Communications, Signal Processing, and Systems

Verlag: Springer Singapore

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Abstract

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.

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Metadaten
Titel
Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
verfasst von
Jiajun Zhang
Jinkun Tao
Zhiguo Shi
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Communications, Signal Processing, and Systems

Print ISBN: 978-981-10-6570-5

Electronic ISBN: 978-981-10-6571-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-10-6571-2_132

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