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