用手机摄像头测心率呼吸率，深度学习让误差降七成以上
Heart rate and respiratory rate prediction from noisy real-world smartphone based on Deep Learning methods
- 用3D卷积神经网络从指尖视频提取生命体征信号
- 心率误差降低68%，呼吸率误差降低75%
- 适合做可穿戴健康监测的算法研究者参考
利用智能手机拍摄指尖视频来估算日常生活中的心率（HR）和呼吸率（RR）已有长期探索。现有文献表明这类估计精度在数次/分钟内，但数据多来自受控实验室环境，其结果能否推广至真实生活尚不明确。为此，研究人员采集了111名参与者在日常生活中的大量手机视频数据，并标注了真实的心率与呼吸率标签。结果显示，传统算法在这些真实场景视频上的表现远差于以往报告：呼吸率误差高出13倍，心率误差高出7倍。值得庆幸的是，近年来深度学习尤其是3D卷积神经网络（CNN）的发展为提升性能提供了可能。本研究提出一种基于新型3D深度卷积网络的全新方法，在实际数据上将心率估计误差减少68%，呼吸率误差减少75%。结果表明，基于回归器的深度学习方法应被优先用于心率与呼吸率的估算。
原文摘要 · Abstract (English)
Using mobile phone video of the fingertip as a data source for estimating vital signs such as heart rate (HR) and respiratory rate (RR) during daily life has long been suggested. While existing literature indicates that these estimates are accurate to within several beats or breaths per minute, the data used to draw these conclusions are typically collected in laboratory environments under careful experimental control, and yet the results are assumed to generalize to daily life. In an effort to test it, a team of researchers collected a large dataset of mobile phone video recordings made during daily life and annotated with ground truth HR and RR labels from N=111 participants. They found that traditional algorithm performance on the fingerprint videos is worse than previously reported (7 times and 13 times worse for RR and HR, respectively). Fortunately, recent advancements in deep learning, especially in convolutional neural networks (CNNs), offer a promising solution to improve this performance. This study proposes a new method for estimating HR and RR using a novel 3D deep CNN, demonstrating a reduced error in estimated HR by 68% and RR by 75%. These promising results suggest that regressor-based deep learning approaches should be used in estimating HR and RR.
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