Method of analyzing eye tracking data for estimating user's cognitive and emotional level of consumption of visual information. A training machine learning model is trained using a data set containing gaze information of known training users, their known cognitive levels and their EEG signal measurements. A calibrating machine learning model is trained using a data set of calibrating visual information displayed to a user, calibrating gaze tracks of that user, calibrating actions data of that user, and calibrating session data related to the session environment. The device displays to that user a target visual information and records target eye tracking data of that user in response to consuming the target information. The recorded target eye tracking data is calibrated via the calibrating machine learning model. The calibrated target eye tracking data is fed into the training machine learning model, which estimates the cognitive levels of consumption of the target visual information of that user.

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

Techniques described herein include detecting a degree of motion sickness experienced by a user within a vehicle. A suitable combination of physiological data (heart rate, heart rate variability parameters, blood volume pulse, oxygen values, respiration values, galvanic skin response, skin conductance values, and the like), eye gaze data (e.g., images of the user), vehicle motion data (e.g., accelerometer, gyroscope data indicative of vehicle oscillations) may be utilized to identify the degree of motion sickness experienced by the user. One or more autonomous actions may be performed to prevent an escalation in the degree of motion sickness experienced by the user or to ameliorate the degree of motion sickness currently experienced by the user.