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Assignees
MemberUniversity of MiamiUniversity of MiamiThe University of Miami, established in 1925 and based in Coral Gables, Florida, is a private research university recognized for its comprehensive academic offerings, robust research infrastructure, and strong interdisciplinary focus. Home to more than 19,000 students and with more than 400 acres of campuses across the Miami region, its mission encompasses education, research, innovation, and community service. The institution supports clinical, biomedical, marine, and atmospheric research initiatives, delivers diverse undergraduate and graduate programs, and maintains numerous research centers and institutes dedicated to scientific, medical, and societal advancements.
The University of Miami, established in 1925 and based in Coral Gables, Florida, is a private research university recognized for its comprehensive academic offerings, robust research infrastructure, and strong interdisciplinary focus. Home to more than 19,000 students and with more than 400 acres of campuses across the Miami region, its mission encompasses education, research, innovation, and community service. The institution supports clinical, biomedical, marine, and atmospheric research initiatives, delivers diverse undergraduate and graduate programs, and maintains numerous research centers and institutes dedicated to scientific, medical, and societal advancements.
Publication Number
US-12329453-B2
Publication Date
2025-06-17
Expiration Date
Abstract
A method is disclosed for improving accuracy of visual field testing in head-mounted displays. The method includes retrieving a visual field testing pattern for a head-mounted display, the visual field testing pattern including stimuli displayed at respective locations in a visual field of the head-mounted display. The visual field testing pattern is generated on the head-mounted display. Data is retrieved from a tilt sensor, located at the head-mounted display, for detecting degrees of head tilt of a user wearing the head-mounted display and the degree of head tilt is determined. A comparison is made between the degree of head tilt of the user to a first threshold degree. In response to the degree of head tilt of the user meeting or exceeding the first threshold degree, a recommendation to the user is generated for display on the head-mounted display.
Core Innovation
The invention relates to visual-field testing and calibration in a head-mounted display (HMD), including addressing cyclotorsion and incorrect head tilt. A tilt sensor located on the HMD detects head tilt, compares the detected head tilt to head tilt thresholds, and provides a recommendation and/or automatically adjusts icon or stimulus positions based on the tilt direction and magnitude.
The invention also enables accurate replication of Humphrey-like curved-field testing when the HMD surface is flat or has variable curvature. Using a coordinate transformation approach, viewing angles on a curved reference surface are mapped to equivalent stimulus locations on the display.
For HMD calibration, the invention generates edge calibration stimuli and collects edge eye tracking data. Based on the collected edge eye tracking data, it computes a projective transform matrix between a virtual plane of expected stimulus locations and a display plane of actually seen locations, and then applies the projective transform matrix to center eye tracking data to determine gaze location.
The invention produces a calibration score from a difference between the center stimulus location and the gaze location, including boundary-based scoring based on a boundary around the center stimulus. It also supports adaptive boundary enlargement and includes optional checks for sustained center-looking using spatial deviation threshold and temporal deviation threshold concepts.
Claims Coverage
The provided set includes three independent claims: a system claim, a method claim, and a non-transitory machine-readable media claim. Each independent claim centers on computing and applying a projective transform matrix from edge eye tracking data to determine gaze location, and generating a calibration score based on a difference between a center location and the gaze location.
Projective transform matrix calibration using edge and center eye tracking data
A head-mounted display calibration system that stores a plurality of icons for respective visual field locations, generates a plurality of edge icons on the head-mounted display, receives edge eye tracking data during a plurality of edge calibration periods, calculates a projective transform matrix based on the edge eye tracking data, generates a center icon at a center location, receives center eye tracking data during a center calibration period, applies the projective transform matrix to the center eye tracking data to determine a gaze location, and generates a calibration score based on a difference between the center location and the gaze location.
Coordinate transformation between planes for gaze location determination and calibration scoring
A method that receives edge eye tracking data during a plurality of first calibration periods for edge stimuli displayed on the head-mounted display, calculates based on the edge eye tracking data a projective transform matrix indicating coordinate transformations between a first plane and a second plane, receives center eye tracking data during a second calibration period for a center stimulus displayed on the head-mounted display, applies the projective transform matrix to the center eye tracking data to determine a gaze location, and generates a calibration score based on a difference between a center location and the gaze location.
Non-transitory machine-readable instructions for projective-transform-based gaze location and calibration scoring
Non-transitory machine-readable media that, when executed, receive edge eye tracking data during a plurality of first calibration periods for edge stimuli displayed on the head-mounted display, calculate a projective transform matrix based on the edge eye tracking data, receive center eye tracking data during a second calibration period for a center stimulus displayed on the head-mounted display, apply the projective transform matrix to the center eye tracking data to determine a gaze location, and generate a calibration score based on a difference between a center location and the gaze location.
Across the independent claims, the core coverage is the use of edge eye tracking data to calculate a projective transform matrix, applying that matrix to center eye tracking data to determine gaze location, and generating a calibration score based on a difference between a center location and the gaze location.
Stated Advantages
Documented Applications
No documented applications found
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