Exploiting WiFi Channel State Information for Residential Healthcare Informatics

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.

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