Device-free near-field human sensing using WiFi signals

Wireless device-free human sensing is an emerging technique of Internet of Things, which holds great potential for ubiquitous location-based services and human-interaction applications. Although existing studies can detect human appearance, they still neglect to further identify whether a user is approaching a sensor or not, which is critical for fine-grained recognition of human behaviors. In this paper, we first conduct comprehensive experiments to measure relationships between signal fading and human positions. The experimental results show that signal fading stepwise changes with different distances of the human to a sensor. Moreover, the signal fading is worse when the human is located closer to an antenna of the sensor. Motivated by these observations, we propose NSee, a novel system for device-free near-field human sensing without site-survey fingerprints. Specifically, we cluster signal fading features of different antennas by a Gaussian mixture model, and further propose a cluster identification algorithm to identify clusters in correspondence to different near-field subareas of human appearance. Based on cluster characteristics, NSee can recognize near-field human presence with online sensing. We implement a prototype of NSee system based on a commercial WiFi card with multiple antennas. Extensive experimental results illustrate that the proposed system can achieve an averaged accuracy of 90% in device-free near-field human recognition.