Machine Learning Models and Videos of Facial Regions for Estimating Heart Rate: A Review on Patents, Datasets, and Literature

Abstract

Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because they allow the monitoring of cardiac information in a non-invasive way and because the devices are simpler, as they require only cameras that capture the user’s face. From these videos of the user’s face, machine learning can estimate heart rate. This study investigates the benefits and challenges of using machine learning models to estimate heart rate from facial videos through patents, datasets, and article review. We have searched the Derwent Innovation, IEEE Xplore, Scopus, and Web of Science knowledge bases and identified seven patent filings, eleven datasets, and twenty articles on heart rate, photoplethysmography, or electrocardiogram data. In terms of patents, we note the advantages of inventions related to heart rate estimation, as described by the authors. In terms of datasets, we have discovered that most of them are for academic purposes and with different signs and annotations that allow coverage for subjects other than heartbeat estimation. In terms of articles, we have discovered techniques, such as extracting regions of interest for heart rate reading and using video magnification for small motion extraction, and models, such as EVM-CNN and VGG-16, that extract the observed individual’s heart rate, the best regions of interest for signal extraction, and ways to process them.