Nuralogix Corporation

Pu Zheng

Kang Lee

Si Wu

Marzio Pozzuoli

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.

System and method for camera-based stress determination

A system and method for health diagnostics and more specifically to an image-capture based system and method for detecting physiological state for a subject. The system provides a remote and non-invasive approach by which to detect physiological state with a high confidence. The system enables monitoring of hemoglobin concentration changes by optical imaging and related detection systems.

System and method for detecting physiological state

A system for detecting invisible human emotion in a retail environment is provided. The system comprises a camera and an image processing unit. The camera is configured in a retail environment to capture an image sequence of a person before and during when a price of a product or service becomes visible. The image processing unit is trained to determine a set of bitplanes of a plurality of images in the captured image sequence that represent the hemoglobin concentration (HC) changes of the person, and to detect the person's invisible emotional states based on HC changes. The image processing unit is trained using a training set comprising a set of subjects for which emotional state is known.

System and method for detecting invisible human emotion in a retail environment

A system for detecting deception is provided. The system comprises a camera, an image processing unit, and a notification device. The camera is configured to capture an image sequence of a person of interest. The image processing unit is trained to determine a set of bitplanes of a plurality of images in the captured image sequence that represent the hemoglobin concentration (HC) changes of the person, and to detect the person's invisible emotional states based on HC changes. The image processing unit is trained using a training set comprising a set of subjects for which emotional state is known. The notification device provides a notification of at least one of the person's detected invisible emotional states.

Deception detection system and method

A system and method for detecting subliminal facial responses of a human subject to subliminal stimuli. The method includes: receiving captured first facial response data approximately time-locked with a presentation of subliminal target stimuli to a plurality of human subjects; receiving captured second facial response data approximately time-locked with a presentation of subliminal foil stimuli to the plurality of human subjects; receiving captured unidentified facial response data to a subliminal stimulus from the target human subject; determining a target probability measure that the unidentified facial response data of the target human subject is in response to the subliminal target stimuli using a machine learning model trained with a subliminal response training set, the subliminal response training set comprising the first captured facial response data and the captured second facial response data; and outputting the target probability measure.

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Toronto, Canada

Pu Zheng