Abstract: A method comprising receiving, as input, physiological parameters data measured in a human subject in response to an administered test question protocol comprising (a) a plurality of test question segments, each comprising at least one test question, and (b) a recovery period following each of the test question segments; determining a stress signal associated with the test question protocol, based, at least in part, on one or more states of stress detected in the physiological parameters data; temporally associating values of the stress signal with the plurality of test question segments and the recovery periods; and calculating, for at least some of the test question segments, a segment psychophysiological response score associated with the responses by the subject, based on an analysis of the temporally associated values of the stress signal.

Abstract: A method comprising: receiving, as input, a video sequence comprising a plurality of frames and depicting a scene; detecting, in each of said frames, one or more regions of interest (ROI) associated with an object in said scene; extracting, from each of said ROIs with respect to all of said frames, a feature set representing spectral reflectance; at a training stage, train a machine learning classifier on a training set comprising: (i) all of said feature sets, and (ii) labels indicating whether each of said ROIs depicts living skin tissue; and at an inference stage, applying said trained machine learning classifier to a target feature set extracted from a target ROI detected in a target video sequence, to determine whether said ROI depicts living skin tissue.

Abstract: A method comprising receiving, as input, a plurality of PPG waveform signal segments; extracting, from each of the segments, a feature set representing the PPG waveform signal; at a training stage, training a machine learning model on a training set comprising: (i) the feature sets, and (ii) labels indicating a quality parameters associated with the PPG waveform signal in each of the PPG waveform signal segments; and at an inference stage, applying the trained machine learning model to at least one feature set extracted from at least one target PPG waveform signal segment, to determine a quality parameter of the at least one target PPG waveform signal.

Abstract: A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, a video image stream of a bodily region of a subject, continuously extract from said video image stream at least some of: (i) facial parameters of said subject, (ii) skin-related features of said subject, and (iii) physiological parameters of said subject, and apply a first trained machine learning classifier selected from a group of trained machine learning classifiers, based, at least in part, on a detected combination of said facial parameters, skin-related features, and physiological parameters, to determine one or more states of stress in said subject.

Abstract: A method comprising receiving, as input, physiological parameters data measured in a human subject in response to an administered test question protocol comprising (a) a plurality of test question segments, each comprising at least one test question, and (b) a recovery period following each of the test question segments; determining a stress signal associated with the test question protocol, based, at least in part, on one or more states of stress detected in the physiological parameters data; temporally associating values of the stress signal with the plurality of test question segments and the recovery periods; and calculating, for at least some of the test question segments, a segment psychophysiological response score associated with the responses by the subject, based on an analysis of the temporally associated values of the stress signal.

Abstract: A method comprising: receiving, as input, a video sequence comprising a plurality of frames and depicting a scene; detecting, in each of said frames, one or more regions of interest (ROI) associated with an object in said scene; extracting, from each of said ROIs with respect to all of said frames, a feature set representing spectral reflectance; at a training stage, train a machine learning classifier on a training set comprising: (i) all of said feature sets, and (ii) labels indicating whether each of said ROIs depicts living skin tissue; and at an inference stage, applying said trained machine learning classifier to a target feature set extracted from a target ROI detected in a target video sequence, to determine whether said ROI depicts living skin tissue.