Device and method for non-invasive and non-contact physiological well being monitoring and vital sign estimation

A non-contact, non-invasive physiological monitoring device, consisting of:

• a camera configured to capture real-time facial image data of a subject as a sequence of frames under lighting conditions maintained at a fixed illuminance level within a range of 300 to 500 lux via artificial lighting;
• a signal processing unit, operatively coupled to the camera, wherein said signal processing unit receives the sequence of frames from the camera, wherein the signal processing unit comprises:
• a video processor implemented as a dedicated hardware circuitry, configured to:
• detect facial landmarks using a predefined facial landmark detection algorithm;
• and
• isolate one or more regions of interest (ROIs) based on the detected landmarks to build a time-series sequence of ROI data, wherein each ROI corresponds to a specific anatomical region;
• a machine learning accelerator, implemented as a specialized integrated circuit, configured to:
• receive the time-series sequence of ROI data from the video processor;
• and
• apply a trained neural network model, trained using semi-supervised methods on annotated video datasets correlated with ground-truth physiological signals selected from the group consisting of, electrocardiogram (ECG), and pulse oximetry data, wherein the network architecture comprises transformer blocks with self-attention and positional-encoding mechanisms, enabling it to capture the spatio-temporal features of the ROI sequence and, in turn, detect subtle changes in pixel intensity linked to blood-volume pulsations and micro-motions of facial tissue that arise from temporal and spatial variations within the ROI time-series, wherein from these features, the model extracts imaging photoplethysmography (iPPG) and imaging ballistocardiography (iBCG) signals;
• a feature construction module following the principles of optical coherence tomography (OCT) for feature preparation, providing improved background noise and motion reduction allowing better stability of feature extraction in dynamic environments of constant motion, wherein the feature construction module is implemented in hardware logic, configured to:
• combine the extracted iPPG and iBCG signals from the machine learning accelerator, with facial image features derived from the time-series sequence of ROI data, wherein said facial image features comprise localized pixel intensity gradients calculated by comparing pixel values within and around the ROI landmarks, thus capturing spatial patterns of intensity variation;
• and
• construct a high-dimensional feature representation in the form of a volumetric tensor, where:
• one dimension corresponds to the temporal progression across the frames, effectively treating time as a “depth” dimension;
• two dimensions represent the spatial coordinates of the ROI, capturing height and width;
• and
• one or more additional feature channels represent iPPG, iBCG, alongside the localized pixel intensity gradients;
• a prediction unit, comprising a hardware-based inference engine interfaced to the signal processing unit configured to:
• receive the high-dimensional feature representation in the form of a volumetric tensor from the feature construction module;
• and
• apply a second trained neural network model, the second trained neural network model is trained on facial video data and corresponding ground-truth physiological measurements, and is made up of a combination of Convolutional layers, and Transformer with self-attention with positional encoding, specialized in analysing the volumetric tensor, combining temporal, spatial, and physiological feature relationships of the iPPG, iBCG and ROI data of volumetric tensor, to compute at least one physiological metric, with significant improvement in performance with error percentage less than 5% that aligns with medically validated criteria;
• an output unit comprising:
• a hardware-based display interface configured to display the predicted at least one physiological metric from the prediction unit, in real-time to a user, providing immediate feedback on the subject's health status;
• and
• a hardware-based probabilistic inference component configured to estimate an uncertainty metric associated with the predicted at least one physiological metric by applying a statistically grounded method, and inference using a trained ensemble of models that provides variance estimates, to yield a quantifiable confidence measure indicating the reliability of the prediction;
• and
• a communication interface, implemented as a hardware module, configured to:
• transmit the predicted at least one physiological metric and its associated uncertainty metric to an external device or networked system via a standardized communication protocol;
• and
• format the transmitted data following standard healthcare data interchange protocols for ensuring compatibility with electronic health record systems or cloud-based analytics platforms, wherein said sequence of frames is recorded under controller lighting conditions maintained at a fixed illuminance level within a range of 300 to 500 lux to ensure consistent pixel intensity values by using artificial lighting, wherein said predefined facial landmark detection algorithm is selected from a group consisting of, a Haar cascade classifier, and a deep learning based facial landmark model, stored in on-chip memory, the algorithm identifying reference points such as corners of the eyes, edges of the nostrils, and corners of the mouth which is used to select ROIs from regions including the forehead, cheeks, and nose, which are known to exhibit minute pixel intensity fluctuations correlated to blood perfusion and micro-movements induced by the cardiovascular and respiratory activity, to optimize detection of hemodynamic variation, wherein said standardized communication protocol is selected from a group consisting of, Wi-Fi, Bluetooth, or Ethernet, wherein said standard healthcare data interchange protocols are selected from a group consisting of, HL7, FHIR protocols, ensuring compatibility with electronic health record systems or cloud-based analytics platforms.