  Passive sensing techniques based on Wi-Fi signals have emerged as a promising
technology in advanced wireless communication systems due to their widespread
application and cost-effectiveness. However, the proliferation of low-cost
Internet of Things (IoT) devices has led to dense network deployments,
resulting in increased levels of noise and interference in Wi-Fi environments.
This, in turn, leads to noisy and redundant Channel State Information (CSI)
data. As a consequence, the accuracy of human activity recognition based on
Wi-Fi signals is compromised. To address this issue, we propose a novel CSI
data signal extraction method. We established a human activity recognition
system based on the Intel 5300 network interface cards (NICs) and collected a
dataset containing six categories of human activities. Using our approach,
signals extracted from the CSI data serve as inputs to machine learning (ML)
classification algorithms to evaluate classification performance. In comparison
to ML methods based on Principal Component Analysis (PCA), our proposed
High-Dimensional Factor Model (HDFM) method improves recognition accuracy by
6.8%.
