使用神经网络从视频中远程估计血氧
Remote Blood Oxygen Estimation From Videos Using Neural Networks
AI总结：
本文提出首个基于卷积神经网络的非接触式智能手机血氧估计方案，通过分析手部视频实现生理感知，其模型优于现有接触式方法，并分析了肤色与手部侧别的影响。
摘要：
血氧饱和度（SpO$_2$）是呼吸功能的重要指标，在COVID-19大流行期间受到越来越多的关注。临床发现表明，COVID-19患者在出现任何明显症状之前，可能会出现显著偏低的SpO$_2$。摄像头的普及促使研究人员探索使用视频监测SpO$_2$的方法。大多数涉及智能手机的现有方案是基于接触式的：它们需要指尖覆盖手机摄像头和附近光源，以捕获从被照亮组织中重新发出的光。在本文中，我们提出了首个基于卷积神经网络的使用智能手机摄像头的非接触式SpO$_2$估计方案。该方案分析参与者手部的视频进行生理感知，这既方便又舒适，还能保护隐私并允许佩戴口罩。我们受SpO$_2$测量的光生理学模型启发设计了神经网络架构，并通过可视化通道组合的权重展示了可解释性。我们提出的模型优于为基于接触式SpO$_2$测量而设计的最先进模型，展示了我们提出的方法为公共卫生做出贡献的潜力。我们还分析了肤色和手部侧别对SpO$_2$估计性能的影响。
英文摘要：
Blood oxygen saturation (SpO$_2$) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic. Clinical findings show that it is possible for COVID-19 patients to have significantly low SpO$_2$ before any obvious symptoms. The prevalence of cameras has motivated researchers to investigate methods for monitoring SpO$_2$ using videos. Most prior schemes involving smartphones are contact-based: They require a fingertip to cover the phone's camera and the nearby light source to capture re-emitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO$_2$ estimation scheme using smartphone cameras. The scheme analyzes the videos of a participant's hand for physiological sensing, which is convenient and comfortable, and can protect their privacy and allow for keeping face masks on. We design our neural network architectures inspired by the optophysiological models for SpO$_2$ measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO$_2$ measurement, showing the potential of our proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO$_2$ estimation performance.
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