Ge Fei, Dai Zhenyang, Yang Zhimin, Wu Fei, Tan Liansheng
School of Computer Science, Central China Normal University, Wuhan 430070, China.
Sensors (Basel). 2024 Dec 5;24(23):7793. doi: 10.3390/s24237793.
In recent years, the research on human activity recognition based on channel state information (CSI) of Wi-Fi has gradually attracted much attention in order to avoid the deployment of additional devices and reduce the risk of personal privacy leakage. In this paper, we propose a hybrid network architecture, named VBCNet, that can effectively identify human activity postures. Firstly, we extract CSI sequences from each antenna of Wi-Fi signals, and the data are preprocessed and tokenised. Then, in the encoder part of the model, we introduce a layer of long short-term memory network to further extract the temporal features in the sequences and enhance the ability of the model to capture the temporal information. Meanwhile, VBCNet employs a convolutional feed-forward network instead of the traditional feed-forward network to enhance the model's ability to process local and multi-scale features. Finally, the model classifies the extracted features into human behaviours through a classification layer. To validate the effectiveness of VBCNet, we conducted experimental evaluations on the classical human activity recognition datasets UT-HAR and Widar3.0 and achieved an accuracy of 98.65% and 77.92%. These results show that VBCNet exhibits extremely high effectiveness and robustness in human activity recognition tasks in complex scenarios.
近年来，基于Wi-Fi信道状态信息（CSI）的人类活动识别研究逐渐受到广泛关注，旨在避免额外设备的部署并降低个人隐私泄露风险。在本文中，我们提出了一种名为VBCNet的混合网络架构，它能够有效地识别人类活动姿势。首先，我们从Wi-Fi信号的每个天线提取CSI序列，并对数据进行预处理和分词。然后，在模型的编码器部分，我们引入了一层长短期记忆网络，以进一步提取序列中的时间特征，并增强模型捕捉时间信息的能力。同时，VBCNet采用卷积前馈网络而非传统前馈网络，以增强模型处理局部和多尺度特征的能力。最后，模型通过分类层将提取的特征分类为人的行为。为验证VBCNet的有效性，我们在经典的人类活动识别数据集UT-HAR和Widar3.0上进行了实验评估，准确率分别达到了98.65%和77.92%。这些结果表明，VBCNet在复杂场景下的人类活动识别任务中展现出了极高的有效性和鲁棒性。
