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Device-free human presence detection is a crucial technology for variousapplications, including home automation, security, and healthcare. Whilecamera-based systems have traditionally been used for this purpose, they raiseprivacy concerns. To address this issue, recent research has explored the useof wireless channel state information (CSI) extracted from commercial WiFiaccess points (APs) to provide detailed channel characteristics. In this paper,we propose a device-free human presence detection system for multi-roomscenarios using a time-selective conditional dual feature extract recurrentnetwork (TCD-FERN). Our system is designed to capture significant time featureson current human features using a dynamic and static data preprocessingtechnique. We extract both moving and spatial features of people anddifferentiate between line-of-sight (LoS) and non-line-of-sight (NLoS) cases.Subcarrier fusion is carried out in order to provide more objective variationof each sample while reducing the computational complexity. A voting scheme isfurther adopted to mitigate the feature attenuation problem caused by roompartitions, with around 3% improvement of human presence detection accuracy.Experimental results have revealed the significant improvement of leveragingsubcarrier fusion, dual-feature recurrent network, time selection and conditionmechanisms. Compared to the existing works in open literature, our proposedTCD-FERN system can achieve above 97% of human presence detection accuracy formulti-room scenarios with the adoption of fewer WiFi APs.

Time-Selective RNN for Device-Free Multi-Room Human Presence Detection Using WiFi CSI