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31 December 2019 Hand gesture recognition based on convolutional neural network using a bistatic radar system
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Abstract
Recently, hand gesture recognition based-on radar has attracted many researchers in the field of human–computer interfaces. However, the number of kinds of hand gestures and recognition accuracy can be still increased. In this paper, we propose a hand gesture recognition approach based on convolutional neural network (CNN) using a bistatic radar system. Firstly, we build a bistatic radar system which consists of two pulse radars and define an active area of hand gesture called gesture desktop. Then, two time-distance maps are obtained by signal pre-processing, and we build a Bistatic-CNN with two branches of convolutional layers as a classifier to recognize 14 hand gestures. The bistatic radar system can offer us much more information of hand gesture from different perspectives and achieve much higher hand gesture recognition accuracy than single radar. The experimental results based on the measured data show that the proposed approach can recognize 14 hand gestures with average accuracy over 98%.
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Kaixuan He, Zhaocheng Yang, Luntao Zhuang, and Xinbo Zheng "Hand gesture recognition based on convolutional neural network using a bistatic radar system", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113840Q (31 December 2019); https://doi.org/10.1117/12.2558416
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