Hand gesture recognition has long been a hot topic in human computer interaction. Traditional camera-based hand gesture recognition systems cannot 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 Doppler radar sensor with dual receiving channels at 5.8GHz 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.
@article{arxiv.1711.02254,
title = {Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks},
author = {Jiajun Zhang and Jinkun Tao and Jiangtao Huangfu and Zhiguo Shi},
journal= {arXiv preprint arXiv:1711.02254},
year = {2017}
}
Comments
Best Paper Award of International Conference on Communications, Signal Processing, and Systems 2017