A machine learning-based approach for constructing remote | ResearchBunny

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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

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