A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models

TL;DR

This paper introduces a high-dimensional factor model for Wi-Fi-based human activity recognition, significantly improving classification accuracy by effectively handling noisy CSI data in dense IoT environments.

Contribution

The paper presents a novel High-Dimensional Factor Model (HDFM) for extracting signals from noisy CSI data, enhancing Wi-Fi human activity recognition accuracy over traditional PCA-based methods.

Findings

HDFM improves recognition accuracy by 6.8% compared to PCA-based methods.

The system successfully classifies six human activity categories.

CSI signal extraction with HDFM enhances robustness in noisy Wi-Fi environments.

Abstract

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%.