  We present for the first time a novel method that utilizes the chest
movement-modulated radio signals for non-contact acquisition of the
photoplethysmography (PPG) signal. Under the proposed method, a
software-defined radio (SDR) exposes the chest of a subject sitting nearby to
an orthogonal frequency division multiplexing signal with 64 sub-carriers at a
center frequency 5.24 GHz, while another SDR in the close vicinity collects the
modulated radio signal reflected off the chest. This way, we construct a custom
dataset by collecting 160 minutes of labeled data (both raw radio data as well
as the reference PPG signal) from 16 healthy young subjects. With this, we
first utilize principal component analysis for dimensionality reduction of the
radio data. Next, we denoise the radio signal and reference PPG signal using
wavelet technique, followed by segmentation and Z-score normalization. We then
synchronize the radio and PPG segments using cross-correlation method. Finally,
we proceed to the waveform translation (regression) task, whereby we first
convert the radio and PPG segments into frequency domain using discrete cosine
transform (DCT), and then learn the non-linear regression between them.
Eventually, we reconstruct the synthetic PPG signal by taking inverse DCT of
the output of regression block, with a mean absolute error of 8.1294. The
synthetic PPG waveform has a great clinical significance as it could be used
for non-contact performance assessment of cardiovascular and respiratory
systems of patients suffering from infectious diseases, e.g., covid19.
