  Here, we propose an original approach for human activity recognition (HAR)
with commercial IEEE 802.11ac (WiFi) devices, which generalizes across
different persons, days and environments. To achieve this, we devise a
technique to extract, clean and process the received phases from the channel
frequency response (CFR) of the WiFi channel, obtaining an estimate of the
Doppler shift at the receiver of the communication link. The Doppler shift
reveals the presence of moving scatterers in the environment, while not being
affected by (environment specific) static objects. The proposed HAR framework
is trained on data collected as a person performs four different activities and
is tested on unseen setups, to assess its performance as the person, the day
and/or the environment change with respect to those considered at training
time. In the worst case scenario, the proposed HAR technique reaches an average
accuracy higher than 95%, validating the effectiveness of the extracted Doppler
information, used in conjunction with a learning algorithm based on a neural
network, in recognizing human activities in a subject and environment
independent fashion.
