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 PhO 2 , 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 PhO 2 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 PhO 2 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 τ coefficient.

中文翻译：

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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

呼吸系统疾病患者需要频繁和准确的血氧水平监测。然而，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计系统，称为 PhO2，使用当今现成的智能手机上随时可用的相机和手电筒功能。由于手机的相机和手电筒不是为此目的而制造的，因此将它们用于氧气水平估计会带来很多困难。我们引入了具有成本效益的附加组件以及一组用于空间和光谱光信号调制的算法，以放大感兴趣的光信号，同时最大限度地减少噪声。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。我们还推导出了一个非线性参考模型，该模型采用异常值去除技术，允许 PhO2根据智能手机摄像头产生的颜色强度比准确估计氧气含量。对六名受试者的 COTS 智能手机的评估表明，PhO2与 FDA 批准的金标准脉搏血氧仪相比，可以在 3.5% 的误差率内估计氧饱和度。此外，我们在医院的评估与由 0.83/1.0 Kendall τ 系数限定的地面实况具有高度相关性。