  Proximity detection in indoor environments based on WiFi signals has gained
significant attention in recent years. Existing works rely on the dynamic
signal reflections and their extracted features are dependent on motion
strength. To address this issue, we design a robust WiFi-based proximity
detector by considering gait monitoring. Specifically, we propose a gait score
that accurately evaluates gait presence by leveraging the speed estimated from
the autocorrelation function (ACF) of channel state information (CSI). By
combining this gait score with a proximity feature, our approach effectively
distinguishes different transition patterns, enabling more reliable proximity
detection. In addition, to enhance the stability of the detection process, we
employ a state machine and extract temporal information, ensuring continuous
proximity detection even during subtle movements. Extensive experiments
conducted in different environments demonstrate an overall detection rate of
92.5% and a low false alarm rate of 1.12% with a delay of 0.825s.
