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基于半监督生成对抗网络的Wi-Fi感知手势控制算法.

Chao Wang1,2,3, Yinfan Ding1,2,3, Meng Zhou1,2,3

  • 1Joint Laboratory for International Cooperation of the Special Optical Fiber and Advanced Communication, Shanghai University, Shanghai, China.

PeerJ. Computer science
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

一个新的Wi-Fi传感系统使用通道状态信息 (CSI) 进行智能家居的非接触式手势控制. 增强型生成对抗网络 (GAN) 达到95.67%的准确性,超过使用有限数据的传统方法.

关键词:
在手势识别,手势识别.模式识别 模式识别 模式识别一个Wi-Fi网络的Wi-Fi网络.无线传感传感器是一种无线传感器.

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科学领域:

  • 人与计算机的交互
  • 无线通信无线通信
  • 机器学习 机器学习

背景情况:

  • 现有的智能家居手势控制通常需要专门的硬件或视线,限制可用性.
  • 在家庭环境中,越来越需要无接触的多功能手势识别系统.

研究的目的:

  • 开发和评估用于智能家居的Wi-Fi感应手势控制系统.
  • 通过使用先进的机器学习技术,提高手势识别的准确性和效率.
  • 为了使强大的手势识别,即使有有限的标记数据和在不同的环境.

主要方法:

  • 使用弗雷内尔区域传感模型进行理论研究.
  • 收集和预处理的Wi-Fi通道状态信息 (CSI) 用于手势数据.
  • 采用动态特征提取,格拉米安角总和场 (GASF) 变换,以及带有分类器的增强生成对抗网络 (GAN).
  • 实现了一种半监督学习算法,用于跨场景的手势识别.

主要成果:

  • 通过使用改进的动态双值算法,在手势拦截中实现了98.20%的准确性.
  • 半监督的GAN算法显示平均准确率为95.67%,明显优于LDA,LightGBM和SVM.
  • 该系统在各种场景中保持了超过94%的准确性,在有限的标记数据中表现出高性能.

结论:

  • 开发的Wi-Fi传感系统为智能家居中的非接触式手势控制提供了高度准确和高效的解决方案.
  • 增强的GAN和半监督学习方法与传统方法相比,提供了更高的性能,特别是在数据稀缺的情况下.
  • 这项技术为智能环境中更直观,更无的人与设备的互动铺平了道路.