Heart Rate Estimation

Heart rate estimation research focuses on developing accurate and efficient methods for measuring heart rate remotely and non-invasively, primarily using video, audio, or wearable sensor data like photoplethysmography (PPG). Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs), often combined with signal processing techniques to improve robustness and accuracy. These advancements hold significant potential for improving healthcare monitoring, particularly in remote patient care and continuous health tracking, by providing convenient and accessible methods for assessing cardiovascular health.