Key result
Photoplethysmography-based heart rate estimation using compressive tracking and SVM cuts mean error by ~64%.
Why the study?
Does an image photoplethysmography-based method using compressive tracking and support vector machine improve heart rate prediction accuracy from facial video?
Does an image photoplethysmography-based method using compressive tracking and support vector machine improve heart rate prediction accuracy from facial video?
Absolute Event Rate: 1.3% vs 3.6%
A novel image photoplethysmography-based method using compressive tracking and support vector machine enables robust, real-time heart rate prediction for multiple subjects from facial video.
May support contactless HR monitoring; leaves open prospective validation in clinical populations.
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.
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Liu et al. (2018) studied this question. Image photoplethysmography-based heart rate estimation using compressive tracking and support vector machine vs. Baseline or previous method was evaluated on Absolute value of mean error in heart rate prediction. A photoplethysmography-based heart rate estimation method using compressive tracking and support vector machine reduced the absolute mean error from 3.6 to 1.3 beats/min.
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