Parametric Study of Performance of Remote Photopletysmography System

Remote photoplethysmography (RPPG) is a technique in which we measure sub-cutaneous variations in blood flow, usually through a camera, to obtain physiological signals. Studies involving RPPG have increased in the past few years due to its numerous applications including remote healthcare, anti-spoofing, among others. While there have been many studies on how to increase RPPG's accuracy of bio-markers predictions in a variety of settings, most of them are usually done using workstation computers, yet some of the most promising applications of RPPG probably would be on limited resources, low-power embedded systems. Therefore, we did an extensive study on the effects of one of the most important design parameters in RPPG systems, sliding window (SW) size, for a variety of algorithms, in order to quantify the trade-off between computational cost in time and accuracy in root-mean-squared-error (RMSE), using a standardized public database. We also studied how different face detection and region-of-interest selection affected these results. Finally, based on these, we came up with a new and simple metric that takes into account both computation and accuracy, as a means to design dynamic systems which make the best out of the available resources With correct tuning, we can use this metric to reduce computational costs by up to 47%.