The invention is related to an eye tracking system for determining reference gaze data of a user in a scene exposing a pupil of the user. The eye tracking system comprising processing circuitry configured to obtain a first eye image comprising the pupil of the user, the first eye image being captured during a first time period; determine, based on the first eye image, a first pupil size; obtain a second eye image comprising the pupil of the user, the second eye image being captured during a second time period; determine, based on the second eye image, a second pupil size; obtain scene information of the scene exposing the pupil of the user, the scene information comprising at least the first luminance level, the second luminance level and spatial information of the second region during the second time period; determine a pupil size change between the first pupil size and the second pupil size, the pupil size change indicating that the user is looking at the second region; determine reference gaze data of the user during the second time period, if the pupil size change is larger than a pupil size change threshold. The invention further relates to a head-mounted device, a method, a computer program and a carrier.

In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

A device for measuring eye movement in a human subject comprises a housing, at least one stimulator mounted to the housing, and a sensor. The at least one stimulator is configured to provide stimulus to one or both eyes of the subject. The sensor is configured to collect information related to movement of one or both eyes of the subject. The device also includes a user interface that is configured to control the at least one stimulator and display information collected by the camera.

A refraction device includes a main body, a spherical power lens coupled to the main body, an astigmatic power lens movably coupled to the main body, and a visual display coupled to the main body and oriented toward an optical pathway extending through the spherical power lens and the astigmatic power lens. The visual display is configured to display an image for testing visual acuity.

A method of screening a pupil of a subject to determine whether the pupil reflex resembles a canonical pupil reflex is disclosed. The method comprises the steps of stimulating the pupil with a stimulus source, such as a pupilometer and determining whether one of various pupillary response conditions is met.

A method to optimize learning based upon ocular information of a subject includes providing a video camera for recording a close-up view of a subject's eye. A first electronic display shows a plurality of educational subject matter to the subject. A second electronic display shows an output to an instructor. Changes in ocular signals of the subject are processed through the use optimized algorithms. A cognitive state model determines a low to a high cognitive load experienced by the subject. The cognitive state model is evaluated based on the changes in the ocular signals for determining a probability of the low to the high cognitive load experienced by the subject. The probability of the low to the high cognitive load experienced by the subject is displayed to the instructor.

A system for calibrating an equipment, the system including a beam splitter; a first reticle configured to be removably attached to the equipment; and an image capture device including an image plane, wherein an image of the first reticle is configured to be received by way of the beam splitter at the image plane along an optical axis of the beam splitter, wherein the orientation as indicated by the first reticle is compared to an orientation of the image plane and if the orientation as indicated by the first reticle differs from the orientation of the image plane, the equipment is rotated about the optical axis of the beam splitter such that the orientation as indicated by the first reticle matches the orientation of the image plane.

Systems and methods are disclosed for assessing the function of parts of one or more sensory fields of a subject. Pupillary responses to at least two clustered ensembles of stimuli presented to predetermined portions of the sensory fields to be tested are measured. Each cluster comprises individual stimuli presented at locations across the sensory field, where the locations are defined on appropriate axes for the tested sensory fields. The method comprises: presenting statistically independent sequences of selected individual stimuli from the two or more clustered stimulus ensembles to a sensory field of a subject, thereby evoking pupillary responses in at least one pupil of the subject; detecting responses of the pupil or pupils evoked by the stimuli using at least one sensor; and processing the detected responses to relate the detected response to the sensory function of each component part of the sensory field. The sensory fields may be, but are not limited to, the visual fields of the two eyes of a subject.

An ophthalmological apparatus includes a photographing mode selection processor that selects any one of a plurality of photographing modes including a color photographing mode and a fluorescent photographing mode, a photographic optical system that photographs a subject's eye in a photographing mode selected by the photographing mode selection processor, a vision fixation optical system including a vision fixation target display that displays a vision fixation target, the vision fixation optical system projecting an image of the vision fixation target displayed in the vision fixation target display on the subject's eye, and a control processor that changes an amount of light of the vision fixation target in accordance with the photographing mode selected by the photographing mode selection processor.