Exploiting WiFi Channel State Information for Residential Healthcare Informatics

Bo Tan, Qingchao Chen, Wenda Li, Kevin Chetty, Karl Woodbridge, Robert J. Piechocki

    Research output: Contribution to journalSpecial issuepeer-review

    55 Citations (Scopus)
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    Abstract

    Detection and interpretation of human behaviors has emerged as a challenging healthcare problem in areas such as assisted living and remote monitoring. Besides traditional approaches that rely on wearable devices and camera systems, WiFi based technologies are materialising as a promising solution for indoor monitoring and activity recognition. This is, in part, due to the pervasive nature of WiFi in residential settings such as homes and care facilities. Moreover, WiFi based sensing is unobtrusive and minimally impinges on privacy. Advanced signal processing techniques are able to accurately extract WiFi channel status information (CSI) using commercial off-the-shelf (COTS) devices or bespoke hardware. This includes phase variations, frequency shifts and signal levels. In this paper we describe a methodology that identifies Doppler shifts in the WiFi CSI, caused by human activities which take place in the signal coverage area. The technique is shown to recognize different types of human activities and behaviours, and subsequently facilitate applications in healthcare. Three experimental case studies based on empirical data are presented to illustrate the capabilities of WiFi CSI Doppler sensing in assisted living and residential care environments. We also discuss the potential opportunities and practical challenges for real-world scenarios.

    Publisher Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Original languageEnglish
    Pages (from-to)130-137
    Number of pages7
    JournalIEEE Communications Magazine
    Volume56
    Issue number5
    DOIs
    Publication statusPublished - 17 May 2018

    Bibliographical note

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Keywords

    • WiFi
    • CSI
    • Behaviour Recognition
    • Healthcare
    • Sensing

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    • A WiFi-Based Passive Sensing System for Human Presence and Activity Event Classification

      Li, W., Tan, B. & Piechocki, R., Dec 2018, In: IET Wireless Sensor Systems. 8, 6, p. 276-283

      Research output: Contribution to journalArticlepeer-review

      15 Citations (Scopus)
    • SPHERE: A sensor platform for healthcare in a residential environment

      Woznowski, P., Burrows, A., Diethe, T., Fafoutis, X., Hall, J., Hannuna, S., Camplani, M., Twomey, N., Kozlowski, M., Tan, B., Zhu, N., Elsts, A., Vafeas, A., Paiement, A., Tao, L., Mirmehdi, M., Burdhardt, T., Damen, D., Flach, P., Piechocki, R. & 2 others, Craddock, I. & Oikonomou, G., 6 Dec 2016, Designing, Developing, and Facilitating Smart Cities: Urban Design to IoT Solutions. Angelakis, V., Tragos, E., Pöhls, H. C., Kapovits, A. & Bassi, A. (eds.). Switzerland: Springer, p. 315-333 19 p.

      Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

      47 Citations (Scopus)

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