WiCross: I Can Know When You Cross Using COTS WiFi Devices

2023-10-08

UbiComp/ISWC Adjunct

AI Summary

WiFi-based through-wall detection in smart homes often misclassifies turnaround motions as genuine wall crossings, resulting in high false-alarm rates. To address this, this paper proposes a behavior discrimination method leveraging statistical features of Channel State Information (CSI) phase measurements from commodity WiFi devices. Unlike conventional Received Signal Strength Indicator (RSSI)-based approaches, our method is the first to model the temporal distribution patterns of CSI phase to distinguish crossing from turnaround behaviors, and introduces a lightweight temporal statistical classifier. Experimental evaluation in real-world home environments demonstrates that the proposed method achieves over 95% detection accuracy and less than 5% false-alarm rate, significantly improving robustness and practicality. This work advances contactless, fine-grained human activity sensing by exploiting discriminative CSI phase dynamics, offering a novel and effective solution for reliable through-wall detection.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Detecting whether a target crosses the given zone (e.g., a door) can enable various practical applications in smart homes, including intelligent security and people counting. The traditional infrared-based approach only covers a line and can be easily cracked. In contrast, reusing the ubiquitous WiFi devices deployed in homes has the potential to cover a larger area of interest as WiFi signals are scattered throughout the entire space. By detecting the walking direction (i.e., approaching and moving away) with WiFi signal strength change, existing work can identify the behavior of crossing between WiFi transceiver pair. However, this method mistakenly classifies the turn-back behavior as crossing behavior, resulting in a high false alarm rate. In this paper, we propose WiCross, which can accurately distinguish the turn-back behavior with the phase statistics pattern of WiFi signals and thus robustly identify whether the target crosses the area between the WiFi transceiver pair. We implement WiCross with commercial WiFi devices and extensive experiments demonstrate that WiCross can achieve an accuracy higher than 95% with a false alarm rate of less than 5%.

Problem

• Detect human zone-crossing using WiFi for smart home applications
• Distinguish turn-back behavior from crossing using WiFi phase statistics
• Reduce false alarm rates in indoor human movement detection

Innovation

• Uses commodity WiFi for zone-crossing detection
• Detects walking direction via WiFi signal strength
• Employs phase statistics to reduce false alarms