  This paper presents a two-dimensional phase extraction system using passive
WiFi sensing to monitor three basic elderly care activities including breathing
rate, essential tremor and falls. Specifically, a WiFi signal is acquired
through two channels where the first channel is the reference one, whereas the
other signal is acquired by a passive receiver after reflection from the human
target. Using signal processing of cross-ambiguity function, various features
in the signal are extracted. The entire implementations are performed using
software defined radios having directional antennas. We report the accuracy of
our system in different conditions and environments and show that breathing
rate can be measured with an accuracy of 87% when there are no obstacles. We
also show a 98% accuracy in detecting falls and 93% accuracy in classifying
tremor. The results indicate that passive WiFi systems show great promise in
replacing typical invasive health devices as standard tools for health care.
