Adjustment of training protocols in virtual reality (VR) or augmented reality (AR) environments based on biofeedback are provided. In various embodiments, motion data is collected for a user while the user performs a training protocol in a virtual environment. A biometric measurement is collected for the user while the user performs the training protocol. The motion data and the biometric measurement are provided to a learning system at a remote server. The learning system determines an adjustment to the training protocol based on the motion data and the biometric measurement. The adjustment is provided by the learning system and is applied to the training protocol. In various embodiments, the adjustment, the motion data, and/or the biometric measurement may be logged in an electronic health record.

Pupillometry systems for measuring one or more pupillary characteristics of a patient are shown and described. The puillometry systems include at least one camera for capturing image data of one or more pupils, at least one radiation source configured to project radiation to the one or more pupils, and a computer system in data communication with the at least one camera, the computer system having a processor and a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium includes computer-readable instructions for collecting and time stamping the image data, processing the raw image data in such a way as to bring the images into conformance with standardized image parameters, identifying and measuring the one or more pupils in the image data, processing the image data to produce measurement data of change in the one or more pupillary characteristics, calculating a standardized output of measurement data for the one or more pupillary characteristics, and providing a mechanism to share or store this information with other users via a computer network. Furthermore the pupillometry system has the capability to predict expected values of pupillary indices for a given patient and display to the user the relationship between the expected value and the measured value.

A method for collecting biometric measurement data of an eye on the basis of different measurement modalities, allowing for physiologically correct, representative, and robust biometric measurement data. In the method, the measurement data for individual measurement variables and the dynamic behavior of the eye are recorded continuously at the highest possible repetition rate over the measurement time. The individual phases of the dynamics of the eye which define the limits of the phase for stable vision are analyzed on the basis of the measurement values, and only the measurement data for the individual measurement variables are output which have been detected during the phase for stable vision. Although the proposed method is provided for collecting biometric measurement data in preparation for a cataract operation, the method can also be applied to other areas of ophthalmology to generate error-free measurement data or recordings of the eye.

A diagnostic mask and method of diagnosing are disclosed. The method includes collecting data from a plurality of sensors in a mask placed on a patient's face such that the mask covers the patient's eyes, the data representing a plurality of health signs of the patient, wherein at least one of the health signs relates to the patient's eyes; and transmitting a signal from the mask, the signal representing the data representing the plurality of health signs.

A computer system for determining onset of an acute coronary syndrome (ACS) event in a remote computing environment comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories is provided. The stored program instructions include capturing, using a camera, a first image at a first time of an iris and a pupil of a first eye of a user; following the capturing of the first image, identifying in the first image a first iris information; capturing, using the camera, a second image at a second time of the iris and the pupil of the first eye of the user; following the capturing of the second image, identifying in the second image a second iris information; determining whether the first iris information is within an allowable range of the second iris information; and providing an indication of a likely ACS event based on a determination of whether the first iris information is within the allowable range of the second iris information.

The present invention relates to a method to predict the risk of obtaining a stress related mood disorder or syndrome by a person, comprising a. Measuring at least three parameters comprising at least one sympathetic, one parasympathetic and one hormonal parameter during a stress response, said result of the measurement depicted as RS, RP and RH respectively; b. Estimate the value of one of these parameters by calculating it from the other two parameters; c. Predict the risk on basis of the deviation between calculated and measured value of the parameter that has been estimated in step b).

The invention relates to a computer-assisted method for position determination for an intraocular lens supported by machine learning. The method comprises providing a scan result for an eye. The scan result here represents an image of an anatomical structure of the eye. The method further comprises use of a trained machine learning system for the direct determination of a final location of an intraocular lens to be fitted, wherein digital data of the scan of the eye is used as the input data for the machine learning system.

The present invention relates to sedation assessment. In order to facilitate sedation depth monitoring in an autonomous imaging setting, it is proposed to use the imaging modality itself to measure the response to suitable reflexes in order to determine the depth of sedation wherein suitable reflexes include, but are not limited to, the pupil reflex, so-called superficial reflexes and the withdrawal reflexes. In one embodiment, the pupil reflex may be measured in an MRI system by repeated interleaving of dedicated iris MR imaging with the conventional scan protocol. In another embodiment, superficial reflexes in response to stroking of the skin may be measured. This may involve a dedicated actuator that may be closely integrated with the imaging modality, e.g. an MR receive coil applied to the patient. Alternatively, remote haptic systems may be used. The reflex is then acquired with a suitable diagnostic imaging method. In another embodiment, the withdrawal reflex in response to pain may be measured. This may involve an actuator that induces sudden stitching pain or very local temperature-induced pain and that is closely integrated with the imaging modality, e.g. a pinching device integrated with a patient support or an MR receive coil applied to the patient. The reflex is then acquired with a suitable diagnostic imaging method.

An exemplary system provides a display and a camera on the same side of a device. In some examples, instead of providing a stimulus with a flash of light, the system may utilize the user's eyelids to dark-adapt the pupil and mediate the stimulus using ambient light and/or the light from a display. Use of a front-facing display and front-facing camera further allows the disclosed system to control the ambient lighting conditions during image capture to ensure that additional pupillary stimulation does not occur while measuring the primary pupil response.

Examples of the present subject matter provide techniques for measuring stress levels using a variety of physiological indicators, such as pupil diameter, voice, cortisol levels, skin resistance, etc. Different sensors may be provided to measure the physiological indicators. Those measurements may be collected at a central server, where those measurements may be analyzed to determine the stress level of the user.