Wi-MoID: Human and Nonhuman Motion Discrimination Using WiFi With Edge Computing

Indoor intelligent perception systems have gained significant attention in recent years. However, accurately detecting human presence can be challenging in the presence of nonhuman subjects, such as pets, robots, and electrical appliances, limiting the practicality of these systems for widespread use. In this article, we propose a novel system ('Wi-MoID') that passively and unobtrusively distinguishes moving human and various nonhuman subjects using a single pair of commodity WiFi transceivers, without requiring any device on the subjects or restricting their movements. Wi-MoID leverages a novel statistical electromagnetic wave theory-based multipath model to detect moving subjects, extracts physically and statistically explainable features of their motion, and accurately differentiates human and various nonhuman movements through walls, even in complex environments. In addition, Wi-MoID is suitable for edge devices, requiring minimal computing resources and storage, and is environment independent, making it easy to deploy in new environments with minimum effort. We evaluate the performance of Wi-MoID in five distinct buildings with various moving subjects, including pets, vacuum robots, humans, and fans, and the results demonstrate that it achieves 97.34% accuracy and 1.75% false alarm rate for identification of human and nonhuman motion, and 95.98% accuracy in unseen environments without model tuning, demonstrating its robustness for ubiquitous use.