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

被引:5
作者
Liu, Lingling [1 ]
Zhao, Yuejin [1 ]
Kong, Lingqin [1 ]
Liu, Ming [1 ]
Dong, Liquan [1 ]
Ma, Feilong [1 ]
Pang, Zongguang [1 ]
机构
[1] Beijing Inst Technol, Sch Optoelect, Beijing Key Lab Precis Photoelect Measuring Instr, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
remote monitoring; image photoplethysmography; heart rate; compress tracking; support vector machine;
D O I
10.1117/1.JMI.5.2.024503
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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. (c) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:6
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