Robust real-time heart rate prediction for multiple subjects from facial video using compressive tracking and support vector machine

Remote monitoring of vital physiological signs allows for unobtrusive, nonrestrictive, and noncontact assessment of an individual's health. We demonstrate a simple but robust image photoplethysmography-based heart rate (HR) estimation method for multiple subjects. In contrast to previous studies, a self-learning procedure of tech was developed in our study. We improved compress tracking algorithm to track the regions of interest from video sequences and used support vector machine to filter out potentially false beats caused by variations in the reflected light from the face. The experiment results on 40 subjects show that the absolute value of mean error reduces from 3.6 to 1.3 beats / min. We further explore experiments for 10 subjects simultaneously, regardless of the videos at a resolution of 600 by 800, the HR is predicted real-time and the results reveal modest but significant effects on HR prediction.