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

LingLing Liu; Yuejin Zhao; Lingqin Kong; Ming Liu; Liquan Dong; Feilong Ma; Zongguang Pang

doi:10.1117/1.JMI.5.2.024503

29 June 2018 Robust real-time heart rate prediction for multiple subjects from facial video using compressive tracking and support vector machine

LingLing Liu, Yuejin Zhao, Lingqin Kong, Ming Liu, Liquan Dong, Feilong Ma, Zongguang Pang

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 2, 024503 (June 2018). https://doi.org/10.1117/1.JMI.5.2.024503

Abstract

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.

Citation Download Citation

LingLing Liu, Yuejin Zhao, Lingqin Kong, Ming Liu, Liquan Dong, Feilong Ma, and Zongguang Pang "Robust real-time heart rate prediction for multiple subjects from facial video using compressive tracking and support vector machine," Journal of Medical Imaging 5(2), 024503 (29 June 2018). https://doi.org/10.1117/1.JMI.5.2.024503

Received: 28 July 2017; Accepted: 30 April 2018; Published: 29 June 2018

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