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Abstract
This study presents a machine learning model to improve the accuracy of remote photoplethysmography (rPPG) signals extracted from videos. The model compares rPPG signals with traditional photoplethysmogram (PPG) signals from sensors, evaluated across various datasets and conditions (rest and movement). Evaluation metrics, including dynamic time warping and correlation coefficients, demonstrated significant improvements in accuracy, achieving results comparable to direct-contact heart signal measurements. This approach offers a promising tool for enhancing remote healthcare applications.
Publisher
Communications Medicine
Published On
Jun 07, 2024
Authors
Rodrigo Castellano Ontiveros, Mohamed Elgendi, Carlo Menon
Tags
machine learning
remote photoplethysmography
healthcare
accuracy
signal measurement
heart monitoring

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