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Estimating heart rate is important for monitoring users in varioussituations. Estimates based on facial videos are increasingly being researchedbecause it makes it possible to monitor cardiac information in a non-invasiveway and because the devices are simpler, requiring only cameras that capturethe user's face. From these videos of the user's face, machine learning is ableto estimate heart rate. This study investigates the benefits and challenges ofusing 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 patentfilings, 11 datasets, and 20 articles on heart rate, photoplethysmography, orelectrocardiogram data. In terms of patents, we note the advantages ofinventions related to heart rate estimation, as described by the authors. Interms of datasets, we discovered that most of them are for academic purposesand with different signs and annotations that allow coverage for subjects otherthan heartbeat estimation. In terms of articles, we discovered techniques, suchas extracting regions of interest for heart rate reading and using VideoMagnification for small motion extraction, and models such as EVM-CNN andVGG-16, that extract the observed individual's heart rate, the best regions ofinterest for signal extraction and ways to process them.

Machine learning models and facial regions videos for estimating heart rate: a review on Patents, Datasets and Literature