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Exploiting WiFi Channel State Information for Residential Healthcare Informatics

Tan, B; Chen, Q; Chetty, K; Woodbridge, K; Li, W; Piechocki, R; (2018) Exploiting WiFi Channel State Information for Residential Healthcare Informatics. IEEE Communications Magazine , 56 (5) pp. 130-137. 10.1109/MCOM.2018.1700064. Green open access

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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 the healthcare application of 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.

Type: Article
Title: Exploiting WiFi Channel State Information for Residential Healthcare Informatics
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/MCOM.2018.1700064
Publisher version: https://doi.org/10.1109/MCOM.2018.1700064
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: WiFi, CSI, Behavior Recognition, Healthcare, Sensing
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10039523
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