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使用智能手机内置摄像头系统通过视频成像提取心率的验证。

Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone.

作者信息

Kwon Sungjun, Kim Hyunseok, Park Kwang Suk

机构信息

Interdisciplinary Program of Bioengineering, Seoul National University, Seoul, Korea.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2174-7. doi: 10.1109/EMBC.2012.6346392.

Abstract

As a smartphone is becoming very popular and its performance is being improved fast, a smartphone shows its potential as a low-cost physiological measurement solution which is accurate and can be used beyond the clinical environment. Because cardiac pulse leads the subtle color change of a skin, a pulsatile signal which can be described as photoplethysmographic (PPG) signal can be measured through recording facial video using a digital camera. In this paper, we explore the potential that the reliable heart rate can be measured remotely by the facial video recorded using smartphone camera. First, using the front facing-camera of a smartphone, facial video was recorded. We detected facial region on the image of each frame using face detection, and yielded the raw trace signal from the green channel of the image. To extract more accurate cardiac pulse signal, we applied independent component analysis (ICA) to the raw trace signal. The heart rate was extracted using frequency analysis of the raw trace signal and the analyzed signal from ICA. The accuracy of the estimated heart rate was evaluated by comparing with the heart rate from reference electrocardiogram (ECG) signal. Finally, we developed FaceBEAT, an iPhone application for remote heart rate measurement, based on this study.

摘要

随着智能手机变得非常普及且其性能迅速提升,智能手机展现出作为一种低成本生理测量解决方案的潜力,该方案准确且可在临床环境之外使用。由于心脏脉搏会导致皮肤微妙的颜色变化,一种可被描述为光电容积脉搏波(PPG)信号的搏动信号可以通过使用数码相机记录面部视频来测量。在本文中,我们探索了通过使用智能手机摄像头记录的面部视频远程测量可靠心率的潜力。首先,使用智能手机的前置摄像头记录面部视频。我们使用面部检测在每一帧图像上检测面部区域,并从图像的绿色通道生成原始跟踪信号。为了提取更准确的心脏脉搏信号,我们将独立成分分析(ICA)应用于原始跟踪信号。通过对原始跟踪信号和ICA分析信号进行频率分析来提取心率。通过与参考心电图(ECG)信号的心率进行比较来评估估计心率的准确性。最后,基于这项研究,我们开发了FaceBEAT,一款用于远程心率测量的iPhone应用程序。

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