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A non-contact heart rate estimation method using video magnification and neural networks
IEEE Instrumentation & Measurement Magazine ( IF 2.1 ) Pub Date : 2020-06-01 , DOI: 10.1109/mim.2020.9126072
Ernesto Moya-Albor , Jorge Brieva , Hiram Ponce , Lourdes Martinez-Villasenor

Heart rate (HR) monitoring is a significant task in many medical, sports and aged care in assisted living applications, among other disciplines. In the literature, several works have reported effectiveness in addressing the measurement of HR using contact sensors such as adhesive or dry electro-conductive electrodes. However, there are several issues associated with contact sensors like portability problems, skin irritation, discomfort and body movement constraints. In this regard, this paper presents a non-contact HR estimation method using vision-based methods and neural networks. This work uses a bio-inspired Eulerian motion magnification approach to highlight the blood irrigation process of the cardiac pulse, which is later inputted to a feed-forward neural network trained to estimate the HR. For experimental analysis, we compare two magnification procedures, based on Gaussian and Hermite decomposition, over video recordings collected from the wrists of five subjects. Results show that the Hermite-based magnification method is robust under noise analysis (4.24 bpm of root mean squared-error in the worst case scenario). Furthermore, our results demonstrate that the Hermite-based method is competitive in the state-of-the-art (1.86 bpm in average of root mean squared-error) and can be implemented using a single camera for contactless HR estimation.

中文翻译:

一种使用视频放大和神经网络的非接触式心率估计方法

心率 (HR) 监测是辅助生活应用中的许多医疗、运动和老年护理等学科中的一项重要任务。在文献中,有几项工作报告了使用接触传感器(如粘合剂或干导电电极)测量 HR 的有效性。然而,接触式传感器存在一些问题,例如便携性问题、皮肤刺激、不适和身体运动限制。在这方面,本文提出了一种使用基于视觉的方法和神经网络的非接触式 HR 估计方法。这项工作使用受生物启发的欧拉运动放大方法来突出心脏脉搏的血液冲洗过程,然后将其输入到经过训练以估计 HR 的前馈神经网络。对于实验分析,我们比较了两种放大程序,基于高斯和 Hermite 分解,对从五个受试者手腕收集的视频记录进行比较。结果表明,基于 Hermite 的放大方法在噪声分析下是稳健的(最坏情况下的均方根误差为 4.24 bpm)。此外,我们的结果表明,基于 Hermite 的方法在现有技术中具有竞争力(均方根误差的平均值为 1.86 bpm),并且可以使用单个摄像头实现非接触式 HR 估计。
更新日期:2020-06-01
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