卷积神经网络
计算机科学
人工智能
RGB颜色模型
模式识别(心理学)
计算机视觉
氧饱和度
面子(社会学概念)
语音识别
氧气
物理
社会科学
量子力学
社会学
作者
Yusuke Akamatsu,Yoshifumi Onishi,Hitoshi Imaoka
标识
DOI:10.1109/icassp49357.2023.10096616
摘要
Peripheral blood oxygen saturation (SpO2), an indicator of oxygen levels in the blood, is one of the most important physiological parameters. Although SpO2 is usually measured using a pulse oximeter, non-contact SpO2 estimation methods from facial or hand videos have been attracting attention in recent years. In this paper, we propose an SpO2 estimation method from facial videos based on convolutional neural networks (CNN). Our method constructs CNN models that consider the direct current (DC) and alternating current (AC) components extracted from the RGB signals of facial videos, which are important in the principle of SpO2 estimation. Specifically, we extract the DC and AC components from the spatio-temporal map using filtering processes and train CNN models to predict SpO2 from these components. We also propose an end-to-end model that predicts SpO2 directly from the spatio-temporal map by extracting the DC and AC components via convolutional layers. Experiments using facial videos and SpO2 data from 50 subjects demonstrate that the proposed method achieves a better estimation performance than current state-of-the-art SpO2 estimation methods.
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