  In this article we present SHARP, an original approach for obtaining human
activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi)
devices. SHARP grants the possibility to discern the activities of different
persons, across different time-spans and environments. To achieve this, we
devise a new technique to clean and process the channel frequency response
(CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at
a radio monitor device. The Doppler shift reveals the presence of moving
scatterers in the environment, while not being affected by
(environment-specific) static objects. SHARP is trained on data collected as a
person performs seven different activities in a single environment. It is then
tested on different 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, it 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 way. The collected
CFR dataset and the code are publicly available for replicability and
benchmarking purposes.
