  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 unobtrusive nature of WiFi based sensing. Advanced signal
processing techniques can 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
behaviour 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
real-world scenarios.

Signal Processing