A Study on Region of Interest in Remote PPG and an Attempt to Eliminate False Positive Results Using SVM Classification

Abstract

Remote-photoplethysmography (rPPG) is a technique for measuring pulse waves without burdening the person using a remotely installed camera. The pulse waves are estimated by capturing minute color changes in the skin area. From the pulse rate and pulse rate variability metrics estimated from the pulse wave, it is possible to estimate a person's arousal state and emotional response. In this study, the most suitable skin area to accurately detect the pulse using rPPG is verified. The authors also propose a method to automatically remove and correct incorrect pulse detection by introducing machine learning based on the features of the pulse wave waveform obtained from rPPG and demonstrated its effectiveness.