基于智能手机摄像头的非接触式手部视频 SpO$_2$ 监测的多通道比值法
A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO$_2$ Monitoring Using Smartphone Cameras
AI总结：
本文提出一种基于智能手机摄像头手部视频的非接触式SpO2监测方法，利用RGB三通道和自适应窄带带通滤波器提取心搏相关分量，平均绝对误差达1.26%，优于传统RoR方法25%。
摘要：
血氧饱和度（SpO$_2$）是反映肺部和呼吸功能的重要指标。关于 COVID-19 的临床发现表明，许多患者在病情恶化前不久出现了危险的低血氧水平。因此，建议特别是易感人群定期监测血氧水平以作预防。近期的研究探讨了如何利用无处不在的智能手机摄像头来推断 SpO$_2$。这些工作大多基于接触式，要求用户用手指覆盖手机摄像头及其附近光源，以捕获从照亮组织中重新发射的光。接触式方法可能导致皮肤刺激和卫生问题，尤其是在大流行病期间。在本文中，我们提出了一种使用智能手机获取的手部视频进行 SpO$_2$ 监测的非接触式方法。考虑到智能手机摄像头红（R）、绿（G）和蓝（B）颜色通道的光学宽带特性，我们利用 RGB 传感的全部三个通道来提取 SpO$_2$ 信息，超越了仅使用两个波长的传统比值法。为了进一步促进准确的 SpO$_2$ 预测，我们设计了基于准确估计心率的自适应窄带通滤波器，以获取每个颜色通道中与心脏最相关的交流（AC）分量。实验结果表明，当以脉搏血氧仪为参考时，我们提出的血氧估计方法可达到 1.26% 的平均绝对误差，比传统 RoR 方法性能提升了 25%。
英文摘要：
Blood oxygen saturation (SpO$_2$) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO$_2$. Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO$_2$ monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO$_2$ information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO$_2$ prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
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