  Estimating heart rate is important for monitoring users in various
situations. Estimates based on facial videos are increasingly being researched
because it makes it possible to monitor cardiac information in a non-invasive
way and because the devices are simpler, requiring only cameras that capture
the user's face. From these videos of the user's face, machine learning is able
to 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 articles review. We searched Derwent Innovation,
IEEE Xplore, Scopus, and Web of Science knowledge bases and identified 7 patent
filings, 11 datasets, and 20 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 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 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.
