Recent reviews underscore rapid advancements in remote photoplethysmography (rPPG) combined with deep learning techniques for non-invasive heart rate measurement using standard cameras. Integrating convolutional and recurrent neural networks, these systems overcome motion artifacts and lighting variability, improving accuracy across skin tones and dynamic environments. This technology, capable of real-time, contactless monitoring, represents a major leap forward for telemedicine and digital health applications, enabling continuous vital sign tracking without specialized hardware.