Gesture Recognition System Based on Neural Networks by Using COTS RFID Tag Array

Jiaying Wu, Chuyu Wang, Lei Xie
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Abstract

Nowadays, gesture recognition plays a more and more important role in human-computer interaction. In this regard, contact sensors or computer vision have made some progress, but they also have shortcomings in portability or privacy. In this work, we propose a gesture recognition system which uses RFID tag array and neural networks to recognize gestures. By using an RFID tag array, we can obtain gesture information in a non-contact, non-infringing manner. By combining CNN and LSTM as CNN-LSTM, we can focus on both spatial and temporal features and get better performance. Experiments show that the accuracy of the system on the test set is 92.17%, and it performs well in recognizing different gestures of different users at different speeds.
基于COTS RFID标签阵列的神经网络手势识别系统
如今,手势识别在人机交互中发挥着越来越重要的作用。在这方面,接触式传感器或计算机视觉已经取得了一些进展,但它们在便携性或隐私性方面也存在不足。在这项工作中,我们提出了一种使用RFID标签阵列和神经网络来识别手势的手势识别系统。通过使用RFID标签阵列,我们可以以非接触,非侵权的方式获取手势信息。将CNN和LSTM结合为CNN-LSTM,可以同时关注空间和时间特征,得到更好的性能。实验表明,该系统在测试集上的识别准确率为92.17%,在识别不同用户不同速度的不同手势方面表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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