Systems, methods, apparatuses, and computer program products for contact-free heart rate monitoring and/or measurement are provided. One method may include receiving video(s) that include visual frame(s) of individual(s) performing exercises, detecting some exposed skin from the video(s), and performing motion compensation to generate color signals for the exposed skin to precisely align frames of the exposed skin. The method may also include generating the color signals by estimating a skin color for each frame by taking a spatial average over pixels of a cheek of the face(s) for R, G, and B channels, respectively, applying an operation to remove remaining motion traces from the frames such that the heart rate traces dominate, and extracting and/or outputting the heart rate of the individuals using a frequency estimator of the skin color signals.

The present invention relates to a device, system and a method for generating a photoplethysmographic image carrying vital sign information of a subject. To provide an increased validity and robustness against motion, in particular against ballistocardiographic motion, the proposed device comprises an input interface (30) for obtaining image data of a skin region of a subject in at least two different wavelength channels, said image data comprising two or more image frames acquired by detecting light transmitted through or reflected from the skin region over time, wherein said image data comprise wavelength-dependent reflection or transmission information in said at least two different wavelength channels, a combination unit (31) for combining, per pixel or group of pixels and per time instant, image data values of said at least two different wavelength channels to obtain a time-variant pulse signal per pixel or group of pixels, and an image generation unit (32) for generating a photoplethysmographic image from a property of the respective pulse signals in a time window including at least two image frames.

A surgical imaging system comprising circuitry configured to accentuate an image characteristic of an area in captured images; identify the change of the image characteristic in the area; and identify the position of a physiological feature relative to the surgical device on the basis of the change in the image characteristic.

An electronic device and method are disclosed herein. The electronic device includes a sensor and a processor. The processor implements the method, including measuring infrared light corresponding to a user using the sensor, and detecting biometric information of the user if the infrared information satisfies a predetermined condition.

An apparatus, method and computer program is described comprising: receiving depth data for a first plurality of pixels of a video image of a scene; determining depth data over time for each of a second plurality of pixels of the video image, wherein the second plurality of pixels comprises at least some of the first plurality of pixels; and processing the determined depth data of successive instances of the second plurality of pixels to generate movement data, wherein each instance of the second plurality of pixels comprises depth data of the second plurality of pixels.

Provided herein are systems and methods for computer monitoring of remote photoplethysmography (rPPG) from camera images based on chromaticity in a converted color space, which reduces motion-induced artifacts in camera images for improved rPPG computer monitoring of physiological parameters. In particular, a rPPG system for monitoring at least one physiological parameter from image data is disclosed herein. A processor subsystem electronically receives a first image data set representative of a series of consecutive images of at least a portion of a living body. The processor subsystem converts the first image data set from a first color space to a second color space to generate a second image data set including first channel data comprising a luminance component and second channel data comprising a chromatic component. The processor subsystem processes the second channel data to monitor the at least one physiological parameter of the living body.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

A physiological signal prediction method includes: collecting a video file, the video file containing long-term videos, and contents of the video file containing data for a face of a single person and true physiological signal data; segmenting a single long-term video into multiple short-term video clips; extracting, by using each frame of image in each of the short-term video clips, features of interested regions for identifying physiological signals so as to form features of interested regions of a single frame; splicing, for each of the short-term video clips, features of interested regions of all fixed frames corresponding to the short-term video clip into features of interested regions of a multi-frame video, and converting the features of the interested regions of the multi-frame video into a spatio-temporal graph; inputting the spatio-temporal graph into a deep learning model for training, and using the trained deep learning model to predict physiological signal parameters.

Systems and methods are disclosed to measure a PPG signal. In some embodiments, a method may include capturing a plurality of frames of a subject; tracking the position of a region of interest of the subject in each of the plurality of frames; creating a first time series signal, a second time series signal, and third time series signal corresponding with respective color channels of the plurality of frames; normalizing the first, second, and third time series signals, combining the normalized first time series signal, the normalized first time series signal, and the normalized first time series signal into a combined signal; creating a spectral signal from the combined signal; and extracting the PPG signal from the spectral signal.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.