  In the post-covid19 era, every new wave of the pandemic causes an increased
concern among the masses to learn more about their state of well-being.
Therefore, it is the need of the hour to come up with ubiquitous, low-cost,
non-invasive tools for rapid and continuous monitoring of body vitals that
reflect the status of one's overall health. In this backdrop, this work
proposes a deep learning approach to turn a smartphone-the popular hand-held
personal gadget-into a diagnostic tool to measure/monitor the three most
important body vitals, i.e., pulse rate (PR), blood oxygen saturation level
(aka SpO2), and respiratory rate (RR). Furthermore, we propose another method
that could extract a single-lead electrocardiograph (ECG) of the subject. The
proposed methods include the following core steps: subject records a small
video of his/her fingertip by placing his/her finger on the rear camera of the
smartphone, and the recorded video is pre-processed to extract the filtered
and/or detrended video-photoplethysmography (vPPG) signal, which is then fed to
custom-built convolutional neural networks (CNN), which eventually spit-out the
vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be
precise, the contribution of this paper is two-fold: 1) estimation of the three
body vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, vision
transformer, and most importantly by CLIP model; 2) a novel discrete cosine
transform+feedforward neural network-based method that translates the recorded
video- PPG signal to a single-lead ECG signal. The proposed method is
anticipated to find its application in several use-case scenarios, e.g., remote
healthcare, mobile health, fitness, sports, etc.
