Castellano Ontiveros Rodrigo, Elgendi Mohamed, Menon Carlo
Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Commun Med (Lond). 2024 Jun 7;4(1):109. doi: 10.1038/s43856-024-00519-6.
Advancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique.
We developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately.
Our method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements.
This study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare.
健康监测技术的进步越来越依赖于从视频中捕捉心脏信号，这种方法被称为远程光电容积脉搏波描记法（rPPG）。本研究旨在使用一种新颖的计算机技术提高rPPG信号的准确性。
我们开发了一种机器学习模型，通过将rPPG信号与来自传感器的传统光电容积脉搏波（PPG）信号进行比较，来提高rPPG信号的清晰度和准确性。该模型在各种数据集以及不同条件下（如休息和运动）进行了评估。评估指标，包括动态时间规整（用于评估rPPG和PPG之间的时间对齐）和相关系数（用于测量rPPG和PPG之间的线性关联），为验证我们的模型在准确捕捉和复制视频中的生理信号方面的有效性提供了一个强大的框架。
我们的方法在从视频中捕捉的心脏信号准确性方面显示出显著提高，动态时间规整和相关系数证明了这一点。该模型表现出色，证明了其在实现与直接接触心脏信号测量相当的准确性方面的有效性。
本研究引入了一种新颖且有效的机器学习方法来改进从视频中检测心脏信号。结果证明了我们的方法在各种场景下的灵活性及其提高健康监测应用准确性的潜力，使其成为远程医疗保健的一个有前途的工具。
