Translation Resilient Opportunistic WiFi Sensing

Mohammud Junaid Bocus*, Wenda Li, Jonas Paulavicius, Ryan Mcconville, Raul Santos-Rodriguez, Kevin Chetty, Robert J Piechocki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Passive wireless sensing using WiFi signals has become a very active area of research over the past few years. Such techniques provide a cost-effective and non-intrusive solution for human activity sensing especially in healthcare applications. One of the main approaches used in wireless sensing is based on fine-grained WiFi Channel State Information (CSI) which can be extracted from commercial Network Interface Cards (NICs). In this paper, we present a new signal processing pipelines required for effective wireless sensing. An experiment involving five participants performing six different activities was carried out in an office space to evaluate the performance of activity recognition using WiFi CSI in different physical layouts. Experimental results show that the CSI system has the best detection performance when activities are performed half-way in between the transmitter and receiver in a line-of-sight (LoS) setting. In this case, an accuracy as high as 91% is achieved while the accuracy for the case where the transmitter and receiver are co-located is around 62%. As for the case when data from all layouts is combined, which better reflects the real-world scenario, the accuracy is around 67%. The results showed that the activity detection performance is dependent not only on the locations of the transmitter and receiver but also on the positioning of the person performing the activity.
Original languageEnglish
Title of host publication25th International Conference on Pattern Recognition (ICPR2020)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication statusAccepted/In press - 22 Jun 2020

Structured keywords

  • Digital Health

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