  In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g.,
software-defined radios (SDR)-based methods have emerged as promising
candidates for intelligent remote sensing of human vitals, and could help in
containment of contagious viruses like covid19. To this end, this work utilizes
the universal software radio peripherals (USRP)-based SDRs along with classical
machine learning (ML) methods to design a non-contact method to monitor
different breathing abnormalities. Under our proposed method, a subject rests
his/her hand on a table in between the transmit and receive antennas, while an
orthogonal frequency division multiplexing (OFDM) signal passes through the
hand. Subsequently, the receiver extracts the channel frequency response
(basically, fine-grained wireless channel state information), and feeds it to
various ML algorithms which eventually classify between different breathing
abnormalities. Among all classifiers, linear SVM classifier resulted in a
maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner,
data was collected by doing real-time experiments on 4 subjects in a lab
environment. For label generation purpose, the breathing of the subjects was
classified into three classes: normal, fast, and slow breathing. Furthermore,
in addition to our proposed method (where only a hand is exposed to RF
signals), we also implemented and tested the state-of-the-art method (where
full chest is exposed to RF radiation). The performance comparison of the two
methods reveals a trade-off, i.e., the accuracy of our proposed method is
slightly inferior but our method results in minimal body exposure to RF
radiation, compared to the benchmark method.
