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人类行走方向检测使用无线信号,机器和深度学习算法.

Hanan Awad Hassan Ali1,2, Shinnazar Seytnazarov1

  • 1Faculty of Computer Science and Engineering, Innopolis University, 420500 Innopolis, Russia.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的无设备方法,用于识别人类活动和室内定位,使用无线信号. 该方法准确地识别了跨越不同环境和个人的行走方向,实现了高准确率.

关键词:
无线网络的信号是Wi-Fi信号.频道状态信息 (CSI) 是指通道状态信息.人类活动的认可 人类活动的认可步行方向检测 步行方向检测

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 信号处理 信号处理

背景情况:

  • 使用无线信号的无设备活动识别和室内定位越来越重要.
  • 现有的检测人类走路方向的方法与环境和个体变化作斗争.

研究的目的:

  • 开发一种强大而适应性的方法,使用无线信号特征识别人类行走方向.
  • 克服当前处理环境变化和不同用户群体的方法的局限性.

主要方法:

  • 使用来自无线信号的通道状态信息 (CSI).
  • 应用了汉佩尔波器来去除异常值,以及离散波纹转换 (DWT) 来减少噪声和特征提取.
  • 采用机器学习和深度学习算法来识别行走方向.

主要成果:

  • 实现了高准确率:92.9% (课堂),95.1% (会议室) 和89% (两间房间) 的未受过训练的数据.
  • 在不同性别,身高和环境中证明了有效性.
  • 验证了该方法的适应性和稳定性.

结论:

  • 拟议的方法有效地检测人类在各种室内环境中的行走方向.
  • 机器和深度学习的整合使得低成本,无设备的人类活动检测.
  • 这种方法为先进的室内传感应用提供了有前途的解决方案.