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 issue

6 Citations (Scopus)
89 Downloads (Pure)

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

Fingerprint

Channel state information
Monitoring
Doppler effect
Marketing
Signal processing
Servers
Cameras
Hardware
Assisted living

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

Cite this

Tan, B., Chen, Q., Li, W., Chetty, K., Woodbridge, K., & Piechocki, R. J. (2018). Exploiting WiFi Channel State Information for Residential Healthcare Informatics. IEEE Communications Magazine, 56(5), 130-137. https://doi.org/10.1109/MCOM.2018.1700064

Exploiting WiFi Channel State Information for Residential Healthcare Informatics. / Tan, Bo; Chen, Qingchao; Li, Wenda; Chetty, Kevin; Woodbridge, Karl; Piechocki, Robert J.

In: IEEE Communications Magazine, Vol. 56, No. 5, 17.05.2018, p. 130-137.

Research output: Contribution to journalSpecial issue

Tan, B, Chen, Q, Li, W, Chetty, K, Woodbridge, K & Piechocki, RJ 2018, 'Exploiting WiFi Channel State Information for Residential Healthcare Informatics' IEEE Communications Magazine, vol. 56, no. 5, pp. 130-137. https://doi.org/10.1109/MCOM.2018.1700064
Tan, Bo ; Chen, Qingchao ; Li, Wenda ; Chetty, Kevin ; Woodbridge, Karl ; Piechocki, Robert J. / Exploiting WiFi Channel State Information for Residential Healthcare Informatics. In: IEEE Communications Magazine. 2018 ; Vol. 56, No. 5. pp. 130-137.
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