Exploiting WiFi Channel State Information for Residential Healthcare Informatics

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

Research output: Contribution to journalArticle (Academic Journal)peer-review

28 Citations (Scopus)
186 Downloads (Pure)

Abstract

Detection and interpretation of human activities have 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 evolving 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, and the unobtrusive nature of WiFi-based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-The-shelf devices or bespoke hardware. This includes phase variations, frequency shifts, and signal levels. In this article, we describe the healthcare application of Doppler shifts in the WiFi CSI caused by human activities that take place in the signal coverage area. The technique is shown to recognize different types of human activities and behavior and be very suitable for applications in healthcare. Three experimental case studies 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 realworld scenarios.

Original languageEnglish
Pages (from-to)130-137
Number of pages8
JournalIEEE Communications Magazine
Volume56
Issue number5
Early online date17 May 2018
DOIs
Publication statusPublished - May 2018

Structured keywords

  • Digital Health

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