WPTrack: A Wi-Fi and Pressure Insole Fusion System for Single Target Tracking

TL;DR

WPTrack introduces a novel fusion system combining Wi-Fi CSI and pressure insoles to enable accurate single-target indoor tracking with minimal equipment, addressing key challenges in initial position detection and blind spots.

Contribution

This work presents the first fusion system combining Wi-Fi CSI and pressure insoles for single target indoor tracking, improving initial position accuracy and trajectory estimation.

Findings

Initial position accuracy ranges from 0.02 cm to 42.55 cm.

Trajectory tracking closely matches actual movement in real-world tests.

Fusion of CSI and pressure data enhances tracking precision.

Abstract

As the Internet of Things (IoT) continues to evolve, indoor location has become a critical element for enabling smart homes, behavioral monitoring, and elderly care. Existing WiFi-based human tracking solutions typically require specialized equipment or multiple Wi-Fi links, a limitation in most indoor settings where only a single pair of Wi-Fi devices is usually available. However, despite efforts to implement human tracking using one Wi-Fi link, significant challenges remain, such as difficulties in acquiring initial positions and blind spots in DFS estimation of tangent direction. To address these challenges, this paper proposes WPTrack, the first Wi-Fi and Pressure Insoles Fusion System for Single Target Tracking. WPTrack collects Channel State Information (CSI) from a single Wi-Fi link and pressure data from 90 insole sensors. The phase difference and Doppler velocity are computed from the CSI, while the pressure sensor data is used to calculate walking velocity. Then, we propose the CSI-pressure fusion model, integrating CSI and pressure data to accurately determine initial positions and facilitate precise human tracking. The simulation results show that the initial position localization accuracy ranges from 0.02 cm to 42.55 cm. The trajectory tracking results obtained from experimental data collected in a real-world environment closely align with the actual trajectory.