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In the post-covid19 era, every new wave of the pandemic causes an increasedconcern 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 thatreflect the status of one's overall health. In this backdrop, this workproposes a deep learning approach to turn a smartphone-the popular hand-heldpersonal gadget-into a diagnostic tool to measure/monitor the three mostimportant body vitals, i.e., pulse rate (PR), blood oxygen saturation level(aka SpO2), and respiratory rate (RR). Furthermore, we propose another methodthat could extract a single-lead electrocardiograph (ECG) of the subject. Theproposed methods include the following core steps: subject records a smallvideo of his/her fingertip by placing his/her finger on the rear camera of thesmartphone, and the recorded video is pre-processed to extract the filteredand/or detrended video-photoplethysmography (vPPG) signal, which is then fed tocustom-built convolutional neural networks (CNN), which eventually spit-out thevitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To beprecise, the contribution of this paper is two-fold: 1) estimation of the threebody vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, visiontransformer, and most importantly by CLIP model; 2) a novel discrete cosinetransform+feedforward neural network-based method that translates the recordedvideo- PPG signal to a single-lead ECG signal. The proposed method isanticipated to find its application in several use-case scenarios, e.g., remotehealthcare, mobile health, fitness, sports, etc.

Your smartphone could act as a pulse-oximeter and as a single-lead ECG