Comparative Analysis of Motion-Based Algorithms for Estimating Infant Breathing Rates From an RGB-Camera

Bachelor thesis (2025)

Authors

I.D. Carata Dejoianu Electrical Engineering, Mathematics and Computer Science

Contributors

Jorge Martinez (mentor)
K. Rassels Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (mentor)
C. Lofi Web Information Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
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Published Date

31-01-2025

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Respiratory Rate (RR) is a vital health indicator, especially in infant monitoring, where early detection of abnormalities or variabilities in RR is crucial. Traditionally, the respiratory rate is extracted using contact-based methods, which, although reliable, can be quite intrusive and stressful for long-term monitoring. This study explores the potential of real-time remote RR monitoring on inexpensive hardware, by comparing three motion-based methods of extracting RR from RGB-camera feed: Pixel Intensity Changes (PIC), Optical Flow (OF), and Eulerian Video Magnification (EVM). The three algorithms were benchmarked using the public AIR-125 dataset, which features videos of infants in various positions, with a focus on their accuracy and computational intensity. The results show that the PIC algorithm slightly outperformed the other two algorithms in both accuracy and computational complexity. However, none of the algorithms managed to replicate the performance of the study which initially proposed the dataset as a benchmark.

Files

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