# WiPIHT: A WiFi-Based Position-Independent Passive Indoor Human Tracking System

## TL;DR

WiPIHT is a WiFi-based system that tracks human movement indoors without needing cameras or special equipment, using WiFi signals to detect motion and direction.

## Contribution

WiPIHT introduces a position-independent WiFi-based system for passive indoor human tracking using channel state information and motion analysis.

## Key findings

- WiPIHT accurately reconstructs human activity trajectories without knowing the initial positions of WiFi devices or the human body.
- The system uses WiFi channel state information and autocorrelation to extract movement speed and direction features.
- Experiments show WiPIHT outperforms existing methods in tracking accuracy and real-time performance.

## Abstract

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost.