  Device-free human presence detection is a crucial technology for various
applications, including home automation, security, and healthcare. While
camera-based systems have traditionally been used for this purpose, they raise
privacy concerns. To address this issue, recent research has explored the use
of wireless channel state information (CSI) extracted from commercial WiFi
access points (APs) to provide detailed channel characteristics. In this paper,
we propose a device-free human presence detection system for multi-room
scenarios using a time-selective conditional dual feature extract recurrent
network (TCD-FERN). Our system is designed to capture significant time features
on current human features using a dynamic and static data preprocessing
technique. We extract both moving and spatial features of people and
differentiate between line-of-sight (LoS) and non-line-of-sight (NLoS) cases.
Subcarrier fusion is carried out in order to provide more objective variation
of each sample while reducing the computational complexity. A voting scheme is
further adopted to mitigate the feature attenuation problem caused by room
partitions, with around 3% improvement of human presence detection accuracy.
Experimental results have revealed the significant improvement of leveraging
subcarrier fusion, dual-feature recurrent network, time selection and condition
mechanisms. Compared to the existing works in open literature, our proposed
TCD-FERN system can achieve above 97% of human presence detection accuracy for
multi-room scenarios with the adoption of fewer WiFi APs.
