Target-Oriented WiFi Sensing for Respiratory Healthcare: From Indiscriminate Perception to In-Area Sensing

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i.e.$ </tex-math></inline-formula>, beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing.

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Publication Details

Journal
IEEE Network
Published
2024-12-16
DOI
https://doi.org/10.1109/mnet.2024.3518514
Citations
3
Primary Topic
Wireless Networks and Protocols
Type
article
Field-Weighted Citation Impact
1.05
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article

Target-Oriented WiFi Sensing for Respiratory Healthcare: From Indiscriminate Perception to In-Area Sensing

Mianxiong Dong, Xiang Zhang, Xiao Sun, Meng Wang et al.
3 citations
IEEE Network
Wireless Networks and Protocols
1.05
article

Target-Oriented WiFi Sensing for Respiratory Healthcare: From Indiscriminate Perception to In-Area Sensing

Mianxiong Dong, Xiang Zhang, Xiao Sun, Meng Wang, Meng Li, Peng Zhao, Zhi Liu, Jinyang Huang, Huan Yan
article en
3 citations

Abstract

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i.e.$ </tex-math></inline-formula>, beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing.

IEEE NetworkVol. 39(5)
Guizhou Normal University (CN), Hefei University of Technology (CN), University of Electro-Communications (JP), Muroran Institute of Technology (JP)
Reduced inequalities
Openalex Percentile: Top 20%
Wireless Networks and Protocols
1.05
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