论文标题
引入集成图像深度学习解决方案以及如何为所有人带来实验室水平的心率和血氧。
Introduction of Integrated Image Deep Learning Solution and how it brought laboratorial level heart rate and blood oxygen results to everyone
论文作者
论文摘要
公众和医疗专业人士认识到在19009年大流行期间准确测量和储存血液氧和心率的重要性。对准确的无接触式设备的需求是由于需要减少交叉感染和传统的Oximeter的短缺的激励，部分原因是全球供应链问题。本文与其他可穿戴设备相比，评估了无接触式的无接触式Min-Program Healthypai心率（HR）和氧饱和度（SPO2）测量。在对185个样本的人力资源研究（在实验室环境中有81个，在现实生活中的104个）中，平均绝对错误（MAE）$ \ pm $ $标准偏差为$ 1.4827 \ pm 1.7452 $在实验室中，$ 6.9231 \ 6.9231 \ pm 5.6426 $在现实生活环境中。在对24个样本的SPO2研究中，测量的平均绝对错误（MAE）$ \ pm $标准偏差为$ 1.0375 \ pm 0.7745 $。我们的结果验证了使用集成图像深度学习解决方案（IIDLS）框架可以准确测量HR和SPO2的HealthyPai，从而提供了至少与市场上FDA批准的可穿戴设备相当的测试质量，并超过了消费级和研究级可穿戴标准。
The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contact-less devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contact-less mini-program HealthyPai's heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) $\pm$ standard deviation was $1.4827 \pm 1.7452$ in the lab, $6.9231 \pm 5.6426$ in the real-life setting. In the SpO2 study of 24 samples, the mean absolute error (MAE) $\pm$ standard deviation of the measurement was $1.0375 \pm 0.7745$. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.
