Contactless WiFi Sensing and Monitoring for Future Healthcare: Emerging Trends, Challenges and Opportunities

Yao Ge, Ahmad Taha, S. A. Shah, Kia Dashtipour, Shuyuan Zhu, Jonathan M. Cooper, Qammer Abbasi, Muhammad Imran

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)
    108 Downloads (Pure)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)171-191
    Number of pages21
    JournalIEEE Reviews in Biomedical Engineering
    Volume16
    Early online date7 Mar 2022
    DOIs
    Publication statusPublished - 2023

    Bibliographical note

    This is an Open Access article distributed under the terms of the Creative
    Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
    which permits unrestricted use, distribution, and reproduction in any medium,
    provided the original work is properly cited.

    Funder

    This work is supported in parts by EPSRC grant no.
    EP/T021020/1, EP/T021063/1, and EP/W003228/1.

    Keywords

    • Activity recognition
    • Biomedical monitoring
    • deep learning
    • healthcare detection
    • machine learning
    • Market research
    • Medical services
    • Monitoring
    • Sensors
    • WiFi sensing
    • Wireless fidelity

    ASJC Scopus subject areas

    • Biomedical Engineering

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