Liu Lingling, Zhao Yuejin, Kong Lingqin, Liu Ming, Dong Liquan, Ma Feilong, Pang Zongguang
Beijing Institute of Technology, School of Optoelectronics, Beijing Key Laboratory of Precision Photoelectric Measuring Instrument and Technology, Beijing, China.
J Med Imaging (Bellingham). 2018 Apr;5(2):024503. doi: 10.1117/1.JMI.5.2.024503. Epub 2018 Jun 29.
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 . 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.
对重要生理体征进行远程监测能够对个人健康状况进行不引人注意、无限制且非接触式的评估。我们展示了一种针对多受试者的基于图像光电容积脉搏波描记法的简单却强大的心率（HR）估计方法。与之前的研究不同，我们的研究开发了一种技术的自学习程序。我们改进了压缩跟踪算法以从视频序列中跟踪感兴趣区域，并使用支持向量机滤除由面部反射光变化引起的潜在误搏动。对40名受试者的实验结果表明，平均误差的绝对值从3.6降至……我们进一步对10名受试者同时进行实验，无论视频分辨率为600×800，均可实时预测心率，结果显示对心率预测有适度但显著的影响。
