Abstract: Systems, methods, apparatuses, and non-transitory computer-readable media are disclosed for non-contact physiological monitoring from facial video. A monocular RGB camera captures facial video of a subject under ambient lighting. One or more processors detect facial skin regions of interest by segmenting facial landmarks and computing a temporal imaging photoplethysmography signal-quality metric to select stable pulsatile regions. Imaging photoplethysmography signals and temporal features are extracted from the selected regions, and a trained convolutional neural network infers OCT-variation feature maps from RGB-video time series without using physical OCT hardware during inference. The processors combine the iPPG features, temporal features, OCT-variation feature maps, and motion-derived imaging ballistocardiography (iBCG)—facial micro-motion features to generate volumetric tensors.

Abstract: A non-contact, non-invasive health monitoring device and method utilizing advanced artificial intelligence (AI) and machine learning techniques. This system captures real-time image data of a user's face using a high-resolution camera and processes the data to extract physiological signals, including Photoplethysmography (PPG, iPPG, and rPPG) and Ballistocardiography (BCG and iBCG). By leveraging facial landmark detection and deep learning models such as Convolutional Neural Networks (CNNs) and Transformers, the device predicts vital signs such as heart rate, respiratory rate, blood pressure, and oxygen saturation, alongside wellness metrics like stress levels and metabolic health. The device employs robust feature construction and signal processing modules to ensure accurate metrics under varying conditions, with error margins below 5%. Outputs are displayed in real time and integrated with external systems using standardized healthcare protocols.

Abstract: The present disclosure pertains to a non-contact, non-invasive health monitoring device and method utilizing advanced artificial intelligence (AI) and machine learning techniques. This system captures real-time image data of a user's face using a high-resolution camera and processes the data to extract physiological signals, including Photoplethysmography (PPG) and Ballistocardiography (BCG). By leveraging facial landmark detection and deep learning models such as Convolutional Neural Networks (CNNs) and Transformers, the device predicts vital signs such as heart rate, respiratory rate, blood pressure, and oxygen saturation, alongside wellness metrics like stress levels and metabolic health. The device employs robust feature construction and signal processing modules to ensure accurate metrics under varying conditions, with error margins below 5%. Outputs are displayed in real time and integrated with external systems using standardized healthcare protocols.