This dataset comprises Channel State Information (CSI) data collected from WiFi signals in six indoor environments, specifically designed for research in indoor intrusion detection. The dataset captures fine-grained variations in wireless signals caused by human, which are indicative of potential intrusions. CSI data, extracted from commercial WiFi chipsets, provides detailed amplitude and phase information across subcarriers, enabling robust detection of subtle environmental changes.
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 data port, we build the first comprehensive WiFi dataset of motion from various sources in real-world contexts. It includes WiFi data of humans, pets, cleaning robots, and fans.