计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 322-327.doi: 10.11896/jsjkx.200600122

• 人机交互 • 上一篇    下一篇

基于3D卷积神经网络的CSI跨场景手势识别方法

王炽, 常俊   

  1. 云南大学信息学院 昆明650500
  • 收稿日期:2020-06-21 修回日期:2020-10-11 发布日期:2021-08-10
  • 通讯作者: 常俊(changjun@ynu.edu.cn)
  • 基金资助:
    云南省省教育厅科研基金(2019J0007)

CSI Cross-domain Gesture Recognition Method Based on 3D Convolutional Neural Network

WANG Chi, CHANG Jun   

  1. College of Information,Yunnan University,Kunming 650500,China
  • Received:2020-06-21 Revised:2020-10-11 Published:2021-08-10
  • About author:WANG Chi,born in 1995,undergra-duate student.His main research in-terests include wireless perception and so on.(2110791732@qq.com)CHANG Jun,born in 1970,assistant professor,postgraduate's supervisor,is a member of China Computer Federation.His main research interests include intelligent wireless perception and wireless communication.
  • Supported by:
    Research Fund of Yunnan Province Department of Education(2019J0007).

摘要: 手势识别在人机交互中有着广泛的应用前景,近年来随着无线通信与物联网的飞速发展,几乎任何地方都部署了WiFi设备,并涌现了大批关于WiFi信道状态信息(Channel State Information,CSI)的手势识别方法,目前大多数基于CSI手势识别的研究仅针对了已知场景下的手势识别研究,对于未知场景,需要增加未知场景中的新数据进行额外的学习训练,否则识别精度将会大幅下降,限制了其实用性。针对这一问题,提出了一种基于3D卷积神经网络的CSI跨场景手势识别方法,该系统通过提取与场景无关的特征,并结合3D卷积神经网络学习模型来实现跨场景手势识别,在实验中使用网络公开数据集来验证该方法,结果显示该方法对于6个不同动作手势,在已知场景中的平均识别准确率达到了86.50%,在未知场景中的平均识别准确率达到了84.67%,能够实现跨场景的手势识别。

关键词: 手势识别, WiFi, 信道状态信息, 跨场景, 3D卷积神经网络

Abstract: Gesture recognition has important application prospects in human-computer interaction.In recent years,with the rapid development of wireless communication and the Internet of Things,WiFi devices have been deployed almost anywhere,and a large number of gesture recognition methods have appeared on WiFi channel status information.At present,most researches based on CSI gesture recognition only focus on the research of gesture recognition in known domain.For unknown domain,new data in unknown scenes need to be added for additional learning and training,otherwise the recognition accuracy will be greatly reduced,limiting practicality.To address this problem,a CSI cross-domain gesture recognition method based on 3D convolutional neural network is proposed.The method realizes cross-scene gesture recognition by extracting domain-independent features,and combining with the 3D convolutional neural network learning model.In order to verify the method,experiment uses the public dataest.For 6 different gestures,the results show that the method achieves 86.50% recognition accuracy in known domain,and achieves 84.67% recognition accuracy in unknown scenes,it can achieve cross-scene gesture recognition.

Key words: Gesture recognition, WiFi, Channel state information, Cross-domain, 3D convolutional neural network

中图分类号: 

  • TN99
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