2,885 research outputs found
Contactless WiFi Sensing and Monitoring for Future Healthcare:Emerging Trends, Challenges and Opportunities
WiFi sensing has recently received significant interest from academics, industry, healthcare professionals and other caregivers (including family members) as a potential mechanism to monitor our aging population at distance, without deploying devices on users bodies. In particular, these methods have gained significant interest to efficiently 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 stems from its practical deployments in indoor settings and compliance from monitored persons, unlike other sensors such as wearables, camera-based, and acoustic-based solutions. This paper reviews state-of-the-art research on collecting and analysing channel state information, extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, untapped areas, and related trends.This work aims to provide an overarching view in understanding the technology and discusses its uses-cases from a perspective that considers hardware, advanced signal processing, and data acquisition
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing
In this work we present RAPID, a joint communication and radar (JCR) system
based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz
band. In contrast to most existing approaches for human sensing at
millimeter-waves, which employ special-purpose radars to retrieve the
small-scale Doppler effect (micro-Doppler) caused by human motion, RAPID
achieves radar-level sensing accuracy by retrofitting IEEE 802.11ay access
points. For this, it leverages the IEEE 802.11ay beam training mechanism to
accurately localize and track multiple individuals, while the in-packet beam
tracking fields are exploited to extract the desired micro-Doppler signatures
from the time-varying phase of the channel impulse response (CIR). The proposed
approach enables activity recognition and person identification with IEEE
802.11ay wireless networks without requiring modifications to the packet
structure specified by the standard. RAPID is implemented on an IEEE
802.11ay-compatible FPGA platform with phased antenna arrays, which estimates
the CIR from the reflections of transmitted packets. The proposed system is
evaluated on a large dataset of CIR measurements, proving robustness across
different environments and subjects, and outperforming state-of-the-art sub-6
GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks
multiple subjects, reaching activity recognition and person identification
accuracies of 94% and 90%, respectively.Comment: 16 pages, 18 figures, 4 table
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