Prediction of blood volume pulse waveform features using remote PPG

Master thesis (2022)

Authors

R.A. Sangers Electrical Engineering, Mathematics and Computer Science

Contributors

M. Bittner Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (supervisor 1)
J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Ruben Sangers
More Info
expand_more

Published Date

01-07-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Contactless measurement of changes in blood volume by exploiting the color fluctuations in the face is a technique commonly referred to as remote photoplethysmography (rPPG). Recent developments show promising results for heart rate estimation from low-cost cameras, making applications in remote healthcare possible. Remote PPG applications in at home diagnostics focus predominantly on heart rate estimation, while other features of the blood volume pulse can provide valuable information as well, but have scarcely been studied using rPPG. In this work, we aim to lay a foundation for rPPG feature prediction. We study pulse wave prediction using a variety of input representations, model architectures and datasets to thereby investigate which combined approaches are the most promising. Our results show which input representation is most suitable based on the feature of interest and demonstrate the ability to predict pulse waveform features using rPPG. These results take the first steps towards including the prediction of a wide range of waveform properties to make more remote health monitoring possible.