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WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users' bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.
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http://dx.doi.org/10.1109/RBME.2022.3156810 | DOI Listing |
Sci Data
August 2025
Department of Law and Economics, UnitelmaSapienza, Piazza Sassari 4, Rome, RM 00161, Italy.
Wi-Fi sensing is an innovative technology that enables numerous human-related applications. Among these, Wi-Fi based person re-identification (Re-ID) is an emerging research topic aiming to address well-known challenges related to traditional vision-based methods, such as occlusions or illumination changes. This approach can serve as either an alternative or a supplementary solution to those conventional techniques.
View Article and Find Full Text PDFSensors (Basel)
August 2025
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
In recent years, indoor user identification via Wi-Fi signals has emerged as a vibrant research area in smart homes and the Internet of Things, thanks to its privacy preservation, immunity to lighting conditions, and ease of large-scale deployment. Conventional deep-learning classifiers, however, suffer from poor generalization and demand extensive pre-collected data for every new scenario. To overcome these limitations, we introduce SimID, a few-shot Wi-Fi user recognition framework based on identity-similarity learning rather than conventional classification.
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August 2025
Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
As the demand for context-aware services in smart environments continues to rise, Indoor Positioning Systems (IPSs) have evolved from auxiliary technologies into indispensable components of mission-critical infrastructure. This paper presents a comprehensive, multidimensional evaluation of IPSs through the lens of critical infrastructure, addressing both their technical capabilities and operational limitations across dynamic indoor environments. A structured taxonomy of IPS technologies is developed based on sensing modalities, signal processing techniques, and system architectures.
View Article and Find Full Text PDFNatl Sci Rev
September 2025
School of Electronic Engineering, Xidian University, Xi'an 710071, China.
The increasing demand for public safety has created an urgent need for high-performance technologies capable of detecting hazardous liquids with high accuracy, efficiency, and cost-effectiveness. Conventional liquid detection methods often fall short in addressing these requirements due to limitations in precision, operational complexity, and scalability. This study introduces a wireless intelligent system for the detection of suspicious liquids, leveraging advancements in programmable metasurface and software defined radio technologies.
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July 2025
Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan.
Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications.
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