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

• Published in: IEEE transactions on instrumentation and measurement p. 1
• Main Authors: Shen, Li-Hsiang, Hsiao, An-Hung, Chu, Fang-Yu, Feng, Kai-Ten
• Format: Journal Article
• Language: English
• Published: IEEE 30.12.2023
• Subjects: Attenuation; channel state information (CSI); Data mining; deep learning; device-free; Feature extraction; human presence detection; MIMO communication; OFDM; Systems architecture; Wireless fidelity; Wireless sensing
• DOI: 10.1109/TIM.2023.3348887
• ISSN: 0018-9456

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.