RGANet:一种人类活动识别模型,用于从WiFi频道中提取时间和空间特征的状态信息
Jianyuan Hu1, Fei Ge1, Xinyu Cao1
1School of Computer Science, Central China Normal University, Wuhan 430070, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了RGANet,这是一种基于Wi-Fi的人类活动识别 (HAR) 系统. 使用修改后的ResNet和GRU模型,RGANet有效地提取空间和时间特征,在基准数据集上实现高精度.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 无线网络和Wi-Fi技术正在迅速发展,推动对先进应用的需求.
- 使用Wi-Fi通道状态信息 (CSI) 的人类活动识别 (HAR) 是一个重要的研究领域.
- 现有的深度学习HAR模型往往忽略空间信息或未充分利用它.
研究的目的:
- 为基于Wi-Fi的HAR开发一个增强的深度学习模型.
- 从CSI数据中有效利用空间和时间特征.
- 为了提高HAR系统的准确性和性能.
主要方法:
- 拟议的RGANet模型,修改剩余网络 (ResNet) 进行空间特征提取.
- 使用修改后的门式循环单元 (GRU) 模型进行时间序列学习.
- 来自Wi-Fi信号的使用频道状态信息 (CSI),用于活动识别.
主要成果:
- 在UT_HAR数据集上实现了99.4%的准确性.
- 在NTU-FI HAR数据集上实现了99.24%的准确性.
- 与现有的HAR模型相比,已经证明了性能改进.
结论:
- 拟议的RGANet模型有效地提取和利用CSI.的空间和时间特征.
- 在基于Wi-Fi的人类活动识别方面,RGANet提供了显著的进步.
- 该模型显示了对基准数据集的高精度和卓越性能.
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