Ocular system to optimize learning

Inventors

Zakariaie, David • McNeil, Kathryn • Rowe, Alexander • Brown, Joseph • Herrmann, Patricia • Bowden, Jared • Anabtawi, Taumer • Sommerlot, Andrew R. • Weisberg, Seth • Choi, Veronica

Assignees

Senseye Inc

Abstract

A method to measure a cognitive load based upon ocular information of a subject includes the steps of: providing a video camera configured to record a close-up view of at least one eye of the subject; providing a computing device electronically connected to the video camera and the electronic display; recording, via the video camera, the ocular information of the at least one eye of the subject; processing, via the computing device, the ocular information to identify changes in ocular signals of the subject through the use of convolutional neural networks; evaluating, via the computing device, the changes in ocular signals from the convolutional neural networks by a machine learning algorithm; determining, via the machine learning algorithm, the cognitive load for the subject; and displaying, to the subject and/or to a supervisor, the cognitive load for the subject.

Core Innovation

The invention provides a method to measure cognitive load, short-term memory, and long-term memory encoding based on ocular information of a subject. The method consists of recording a close-up view of at least one eye of a subject using a video camera connected to a computing device and an electronic display. The ocular information is processed using convolutional neural networks to identify changes in ocular signals.

A machine learning algorithm evaluates the changes in ocular signals obtained from the neural networks, determining either the cognitive load or the state of memory encoding for the subject. The results are then displayed to the subject and/or to a supervisor on an electronic display. The method explicitly details a broad spectrum of ocular signals that can be used, including but not limited to eye movement, various gaze metrics, saccade parameters, pupil and iris characteristics, and a range of blink and segmentation features.

The problem being addressed is the need for improved ocular systems capable of objectively measuring cognitive states, memory encoding, and emotional or operational risk factors in an efficient, non-invasive manner. This method addresses limitations of prior systems, enabling greater accuracy and flexibility in determining and displaying cognitive and memory-related metrics using advanced image processing and machine learning techniques.

Claims Coverage

There are two independent claims, each focusing on a method to measure cognitive load or memory encoding using ocular information and advanced processing techniques.

The independent claims cover methods for measuring cognitive load and memory encoding states using real-time ocular information processed by convolutional neural networks and machine learning, with results delivered to the user or supervisor via electronic display.

Stated Advantages

Documented Applications