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基于深度学习和微多普勒雷达数据的人类活动识别.

Tan-Hsu Tan1, Jia-Hong Tian2, Alok Kumar Sharma3

  • 1Innovation Frontier Institute of Research for Science and Technology, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用雷达数据和深度学习模型的新型人类活动识别 (HAR) 系统. 该系统达到98.2%的准确性,通过物联网 (IoT) 提高了安全和生活质量.

关键词:
跨道运营的跨道运营深度学习是一种深度学习.人类活动的认可 人类活动的认可微多普勒效应的产生.雷达 雷达传感器 雷达传感器

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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:

  • 人类活动识别 (HAR) 对物联网 (IoT) 应用至关重要,改善生活质量和安全.
  • 现有的HAR系统往往在准确性和现实世界的适用性方面面临限制.
  • 基于雷达的传感为HAR提供了一个有希望的,保护隐私的方法.

研究的目的:

  • 开发一个先进的HAR系统,利用雷达数据的微多普勒效应.
  • 增强特征提取和模型性能,以准确识别活动.
  • 用公开可用的雷达数据集来验证拟议系统的有效性.

主要方法:

  • 采用了一个双流一维卷积神经网络 (1D-CNN) 结合一个双向门循环单元 (BiGRU).
  • 短时间里埃转换 (STFT) 将雷达数据处理成时间频率表示.
  • 一个新的跨道操作 (CCO) 促进了平行卷积层之间的特征交换.

主要成果:

  • 拟议的1D-CNN+CCO-BiGRU模型在Rad-HAR数据集上实现了98.2%的高精度.
  • 该系统有效地提取了空间和时间特征,以实现强大的活动识别.
  • 与使用雷达传感器的现有 HAR 系统相比,其表现优越.

结论:

  • 开发的HAR系统显示了对现实世界物联网应用的巨大潜力.
  • 1D-CNN,BiGRU和CCO的整合为基于雷达的活动识别提供了一个强大的方法.
  • 这一进步有助于在物联网框架内的HAR更广泛的领域.