Technology Category
Intelligent Robots: Localization, Mapping, and NavigationPlanning, Routing, and Scheduling: Activity and Plan RecognitionComputer Vision: Motion & Tracking
WPTrack: A Wi-Fi and Pressure Insole Fusion System for Single Target Tracking
🤖 AI Summary
Single-link Wi-Fi indoor human tracking suffers from difficulties in acquiring initial position and blind spots in tangent-direction Doppler frequency shift (DFS) estimation. Method: This paper proposes the first continuous localization framework fusing Wi-Fi channel state information (CSI) with plantar pressure sensing. Footwear-embedded pressure sensors precisely determine walking direction and initialize position, while CSI phase differences and Doppler velocity analysis, 90-node pressure distribution modeling, and adaptive walking-speed estimation jointly enable tight CSI–pressure data fusion. Contribution/Results: Simulation yields initial localization errors of 0.02–42.55 cm; real-world trajectory tracking closely matches ground-truth paths. The framework significantly improves accuracy, robustness, and directional awareness for single-link Wi-Fi tracking, overcoming fundamental limitations of conventional approaches.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Tracking indoor targets with single Wi-Fi link limitations
Overcoming initial position and blind spot challenges in tracking
Fusing Wi-Fi and pressure data for accurate human tracking
Innovation
Methods, ideas, or system contributions that make the work stand out.
Fuses Wi-Fi CSI and pressure insole data
Calculates phase difference and Doppler velocity
Uses CSI-pressure fusion for precise tracking
🔎 Similar Papers
2024-03-17Citations: 0
