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Smartphone-Based SpO_2 Measurement by Exploiting Wavelengths Separation and Chromophore Compensation

机译:利用波长分离和发色团补偿进行基于智能手机的SpO_2测量

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摘要

Patients with respiratory diseases require frequent and accurate blood oxygen level monitoring. Existing techniques, however, either need a dedicated hardware or fail to predict low saturation levels. To fill in this gap, we propose a phone-based oxygen level estimation system, called PhO2, using camera and flashlight functions that are readily available on today's off-the-shelf smartphones. Since the phone's camera and flashlight were not made for this purpose, utilizing them for oxygen level estimation poses many difficulties. We introduce a cost-effective add-on together with a set of algorithms for spatial and spectral optical signal modulation to amplify the optical signal of interest while minimizing noise. A near-field-based pressure detection and feedback mechanism are also proposed to mitigate the negative impacts of user's behavior during the measurement. We also derive a non-linear referencing model with an outlier removal technique that allows PhO2 to accurately estimate the oxygen level from color intensity ratios produced by the smartphone's camera.An evaluation on COTS smartphone with six subjects shows that PhO2 can estimate the oxygen saturation within 3.5% error rate comparing to FDA-approved gold standard pulse oximetry. In addition, our evaluation in hospitals presents high correlation with ground-truth qualified by the 0.83/1.0 Kendall tau coefficient.
机译:患有呼吸系统疾病的患者需要经常且准确地监测血氧水平。但是,现有技术要么需要专用硬件,要么无法预测低饱和度。为了填补这一空白,我们提出了一种基于电话的氧气水平估算系统,称为PhO2,该系统使用了摄像头和手电筒功能,这些功能在当今的现成智能手机上都可以轻松获得。由于电话的照相机和手电筒不是为此目的而制造的,因此利用它们进行氧含量估算会带来许多困难。我们引入了一种具有成本效益的附加组件,以及用于空间和光谱光信号调制的一组算法,以在减少噪声的同时放大目标光信号。还提出了一种基于近场的压力检测和反馈机制,以减轻测量过程中用户行为的负面影响。我们还使用离群值消除技术导出了一个非线性参考模型,该模型使PhO2可以根据智能手机相机产生的色彩强度比率准确估算氧气含量。对具有六个对象的COTS智能手机进行评估显示,PhO2可以估算出其中的氧气饱和度与FDA批准的金标准脉搏血氧仪相比,误差率为3.5%。此外,我们在医院的评估与以0.83 / 1.0肯德尔tau系数合格的地面真相具有高度相关性。

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