AU2024292037B2

AU2024292037B2 - A digital display device field of vision testing system

Info

Publication number: AU2024292037B2
Application number: AU2024292037A
Authority: AU
Inventors: Yu Xiang Kong; Algis Vingrys
Original assignee: Glance Optical Pty Ltd
Current assignee: Glance Optical Pty Ltd
Priority date: 2023-07-20
Filing date: 2024-07-16
Publication date: 2025-06-26
Anticipated expiration: 2044-07-16
Also published as: WO2025015374A1; AU2024292037A1

Abstract

The present system is designed for home-based, self-administered visual field testing without the need for expert involvement, utilising common computing devices. The system adapts to differences in various technology platforms, ensuring accuracy and reliability of the tests by using calibration for screen size and brightness, real-time viewing distance monitoring, adjustments for display curvature, gaze stability and eye occlusion detection using a front-facing camera according to various described embodiments.

Description

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A Digital Display Device Field of Vision Testing System

Field of the Field of theInvention Invention

[0001] The invention is a system designed to assess and monitor visual field

thresholds using common digital display devices enabling accurate testing and even

self-testing. The system is designed to replace traditional specialised bowl-type visual

field-testing machines whilst offering accurate vision field testing and progression

monitoring using a wide range of digital display consumer electronic devices.

Background of the Invention

[0002] Glaucoma, a leading cause of blindness globally, is characterised by the

degeneration of the optic nerve, often associated with cupping of the optic disc and

elevated intraocular pressure (IOP). Glaucoma typically damages vision starting in

the peripheral regions. Thus, visual field (VF) tests, covering a wide area (e.g., 30

degrees), are standard for diagnosing glaucoma. These tests, known as perimetry or

automated perimetry, use machines with a bowl-shaped surface projecting varying

intensity light spots. The patient focuses on a central spot and responds to peripheral

light spots, with the machine measuring retinal sensitivity and creating a graphical

representation of deviations from normal values, aiding in diagnosing and monitoring

glaucoma. glaucoma.

[0003] Alternative digital display-based methods for eye testing have been proposed

including US20230165460A1 (Shousha, 2023-06-01) which uses a wearable device

which displays stimuli at various field locations of the visual field and use spatial

information to select appropriate display locations to collect feedback based on the

eye's response using sensors, thereby assisting in diagnosing ocular anomalies.

[0004] US20130155376A1 (Huang et al., 2013-06-20) involves a video game

designed to map a test subject's peripheral vision. The game includes a moving visual

fixation point confirmed by the subject's action and requires the subject to locate a

briefly presented visual stimulus. The game runs on a platform comprising a video

display, user input device, and video camera to map the subject's visual perception

thresholds, thresholds, comparable comparable with with age-stratified age-stratified normative normative data. data.

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[0005] However, there is a pressing need for home-based vision testing that can be

self-administered without professional supervision on conventional computing

devices. However, variations in display characteristics, such as screen resolution and

brightness, as well as improper use and variable environmental factors, can render

vision testing on these devices relatively inaccurate.

Summary of the Disclosure

[0006] The described field of vision testing system is designed for home-based, self-

administered visual field testing using common digital display devices. In

embodiments, it employs calibration steps to accommodate different screen sizes and

brightness levels, as well as methods to ensure accurate testing, including stabilising

viewing distance, detecting improper use, and accounting for digital display curvature.

[0007] Similar to standard perimetry, the test subject focuses on a fixation point and

responds to stimuli displayed at peripheral locations.

[0008] The system may account for screen size variability by displaying calibration

markers and offset adjustment controls to adjust the distance between them to match

a fixed physical object. This allows the system to calculate a screen size scaling

factor according to the distance and subsequently scale the screen size accordingly.

This procedure can be performed accurately by non-specialist home users on

conventional computer devices.

[0009] To account for screen brightness variability, the system may display a first

stimulus at a brightness below known visual threshold (lowest contrast level which

can just be detectable by human eye) and a second stimulus at a brightness just

above above the the threshold, threshold, calculating calculating screen screen brightness brightness based based on on the the frequency frequency of of

responses to these stimuli. The system determines the screen brightness by

analysing the number of responses received for each stimulus.

[0010] The system may ensures proper viewing distance through viewing distance

calibration, which involves positioning the digital display a set distance from the

user's face and analysing image data obtained from a camera to set a reference

calibrated facial metric. Real-time viewing distance monitoring is achieved by

continually analysing image data to determine a real-time facial metric and comparing wo 2025/015374 PCT/AU2024/050758 it to the calibrated metric to determine the real-time viewing distance. The system can warn the user if the viewing distance deviates from the calibrated distance.

[0011] For smaller screens, the system may adjust the position of the fixation point to

the four corners of the screen to enable testing of viewing distances greater than from

the centre. The system may also reduce threshold variability by applying a scaling

factor to spot size to account for the tangent effect of digital displays, particularly at

peripheral locations.

[0012] Gaze stability may be is monitored by using a front-facing camera to capture

images of the eye and analyse pixel intensity of key features to assess gaze stability,

ensuring reliable visual field testing. The system may also verifies the occlusion of

the non-tested eye by capturing images of the eyes and ensuring the correct eye is

occluded, with warning messages provided if the incorrect eye is occluded.

[0013] In embodiments, the system translates a stimulus position from flat screen

geometry to curved screen geometry and detect background environmental

brightness by analysing general and localised brightness within image data obtained

from the camera.

[0014] For vision field testing, the system may employ Bayesian probability prediction

to perform a binary search for the threshold contrast at each location, using a

population probability density function (PDF) that includes individuals with normal

eyes and those with disease. This allows for fast and accurate estimation of

thresholds based on the subject's response matrix. Neighbourhood logic checks for

errors in responses and recovers from potential corruptions in threshold logic, refining

the prediction of initial seeding intensity for each test location based on neighbouring

location endpoints. Furthermore, the system may employ adaptive responses by

adjusting the speed of the test "wait-window" based on the average speed of response

to stimuli for each individual subject. If a subject responds quickly, the software

reduces the interval between stimuli, and if they respond slowly, it increases the

interval. The algorithm dynamically adjusts the timing based on a running average of

past responses.

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[0015] Overall, the present system allows for visual field testing to be performed

conveniently and efficiently on commonly available devices, providing accurate

results while addressing technical considerations associated with the transition from

curved bowl surfaces to digital displays.

[0016] Other aspects of the invention are also disclosed.

Brief Description of the Drawings

[0017] Notwithstanding any other forms which may fall within the scope of the present

invention, preferred embodiments of the disclosure will now be described, by way of

example only, with reference to the accompanying drawings in which:

[0018] Figure 1 shows the key components and functionality of a field of vision testing

system, which includes a personal computing device with a digital display used for

displaying a field of vision testing user interface.

[0019] Figure 2 illustrates the processing carried out by the screen size calibration

controller.

[0020] Figure 3 shows a screen size calibration user interface presented by the screen

size calibration controller. The interface includes a fixation point and stimuli that are

scaled according to the calculated screen size scaling factor.

[0021] Figure 4 demonstrates the processing performed by the screen brightness

calibration controller. The figure shows three scenarios: calibrated screen brightness

detection, excessive brightness detection, and too dark brightness detection.

[0022] Figure 5 depicts the three screen brightness detections: calibrated screen

brightness detection, excessive brightness detection, and too dark brightness

detection.

[0023] Figure 6 illustrates the processing conducted by the gaze stability detection

controller. It shows the analysis of eye image data to detect gaze stability.

[0024] Figure 7 shows the operation of the gaze stability detection controller,

detecting image properties across the eye region to assess gaze stability.

[0025] Figure 8 represents the processing carried out by the eye occlusion detection

controller. controller.

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[0026] Figure 9 shows the operation of the eye occlusion detection controller,

detecting an occluded eye region and a non-occluded eye region in the facial image

data. data.

[0027] Figure 10 displays the processing performed by the viewing distance

calibration controller.

[0028] Figures 11A - D illustrates the exemplary image processing conducted by the

viewing viewing distance distance calibration calibration controller, controller, including including the the detection detection of of facial facial width width and and

boundary analysis.

[0029]

[0029] Figure Figure 12 12 illustrates illustrates mapping mapping of of flatscreen flatscreen geometry geometry to to curved curved screen screen

geometry.

[0030] Figure 13 shows processing for bright spot detection.

Description of Embodiments

[0031] Figure 1 shows some key components 101 and functionality 102 of a field of

vision testing system 100, which comprises a personal computing device (such as a a tablet tablet PC) PC) having having aa digital digital display display 103 103 configured configured to to display display aa field field of of vision vision testing testing

user interface thereon. In accordance with a preferred embodiment, the present

system 100employs system 100 employsa a WebWeb server server architecture architecture wherein wherein the personal the personal computing computing

device is in operable communication with a Web server via the Internet and the

personal computing device has a web browser application 104 configured to send

HTTP requests to the Web server and to render webpages served by the Web server

in response thereto. The personal computing device comprises a processor for

processing digital data which is in operable communication with a memory device,

the the memory memory device device configured configured to to store store digital digital data data including including computer computer program program code code

instructions. In use, the processor fetches these computer program code instructions

and associated data from the memory device for interpretation and execution of the

functionality functionality herein. herein. The The computer computer program program code code instructions instructions may may be be logically logically divided divided

into into aa plurality plurality of of computer computer program program code code instruction instruction controllers controllers 105 105 as as will will be be

described described in in further further detail detail below. below. The The computer computer program program code code instruction instruction controllers controllers

may comprise a stimulus testing controller which is configured to perform an

assessment assessment of of visual visual field field function function 106 106 by by displaying displaying aa fixation fixation point point and and peripheral peripheral

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stimuli on the digital display 103 and recording responses to the peripheral stimuli. In

embodiments, the system 100 may comprise a front-facing camera 107 wherein

computer vision analysis 108 is performed on image data obtained by the camera 107

for for various various purposes purposes as as will will be be described described in in further further detail detail below. below. The The controllers controllers 105 105

may comprise a screen size calibration controller 109 which calculates a screen size

scaling factor to ensure that the actual distance between the stimuli and the fixation

point is uniform across different types of digital displays irrespective of their screen

size and/or screen resolution.

[0032] Figure 2 shows processing 110 by the screen size calibration controller and

Figure 3 shows a screen size calibration user interface displayed by the controller. At

step 111, the controller 109 is configured to display calibration markers 115 (in this

case calibration lines) and offset adjustment controls 116 (in this case decrement and

increment buttons) which are controllable to adjust the distance 117 between the

markers 115. The user is instructed to place a physical object of set size, such as a

ruler 118, between the markers 115 and to use the offset adjustment controls 116 to

increase or decrease the display distance 117 between the markers 115 until the

distance between the markers 115 matches a set length of the ruler 118, such as 5

cm. The system 100 calculates the screen size scaling factor according to the

distance between the markers 115 and thereafter scales the screen size according to

the screen size scaling factor to ensure that the actual distance between the stimuli

and and the the fixation fixation point point is is uniform uniform irrespective irrespective of of screen screen size size and/or and/or screen screen resolution resolution

variability of different types of digital displays 103. At step 113, the controller 109

may calculate the distance between the markers 115 as pixels per unit length to take

into account screen resolution.

[0033] Figure 3c shows the field of vision testing user interface 119 having the fixation

point 120 and stimuli 121 which are scaled from a small scale 122 to a larger scale

123 according to the calculated screen size scaling factor. It should be noted that the

scaling factor may be calibrated both across the X axis and the Y axis of the digital

display 103 to account for differences in X and Y screen resolution accordingly. With

reference to Figure 1, the controllers 105 may further comprise a screen brightness wo 2025/015374 PCT/AU2024/050758 PCT/AU2024/050758 calibration controller 124 configured to calibrate the brightness of the digital display

103 to account for any screen brightness variability between different types of

personal computing devices. The screen brightness calibration controller 124 may

display instructions to increase or decrease the brightness of the screen or may

alternatively automatically adjust the brightness of the screen.

[0034] Figure 4 shows processing 125 by the screen brightness calibration controller

124 resulting in calibrated screen brightness detection 126, excessive brightness

detection 127, and inadequate brightness detection 128. Specifically, the screen

brightness calibration controller 124 is configured to display a less bright stimulus

129A at a brightness below a threshold and a brighter stimulus 129B at a brightness

above the threshold. The stimuli 129 may take the form of greyscale dots displayed

adjacently on the screen. The user is instructed to respond to the display of the stimuli

129A and 129B at step 130 depending on their visibility. For example, the user would

respond to both stimuli 129A and 129B if both were visible, only to the brighter

stimulus 129B if only the brighter stimulus 129B was visible, and not respond if neither

stimulus 129 were visible.

[0035] Figure 4 shows detection of a high-frequency response 131A to the less bright

stimulus 129A (e.g., greater than 50%) and a low-frequency response 131B (i.e., less

than 50%) to the brighter stimulus 129B. Calibrated screen brightness 126 is detected

when both responses 131A and 131B are equal as shown in Figure 5. Excessive

screen brightness 127 is detected when a high-frequency response 131A to the low

brightness stimulus 129A is detected, and the user may be instructed to lower the

brightness of the screen. Inadequate screen brightness (too dark) 128 is detected

when a low-frequency response 131B is detected for the brighter stimulus 129B.

Similarly, the user may be instructed to increase the brightness of the screen.

[0036] With reference to Figure 1, the controllers 105 may further comprise a viewing

distance controller 133 configured for viewing distance calibration by analysing image

data obtained from the camera to determine a reference calibrated facial metric when

the digital display 103 is positioned a set distance from a user's face. The user may

be be instructed instructed to to position position the the digital digital display display 103 103 appropriately appropriately using using an an object object of of set set wo 2025/015374 PCT/AU2024/050758 length, such as a 30 cm ruler. Then, during use, the viewing distance controller 133 may be configured for real-time viewing distance monitoring by continually monitoring image data obtained from the camera to determine a real-time facial metric and comparing the calibrated reference and real-time facial metrics to determine a real- time viewing distance. Viewing distance controller 133 may be configured to warn the user if the display 103 is too close or too far away. The viewing distance controller

133 may be configured to calculate the calibrated facial metric measured as a pixel

width, which may represent facial width, distance between the eyes, nose width, and

jawline width. Furthermore, the viewing distance controller 133 may be configured for

employing at least one of boundary analysis, contrast differential analysis, and colour

differential analysis to determine the calibrated facial metric.

[0037] Figure 10 shows processing 135 by the viewing distance controller 133 and

Figures 11A - D shows exemplary image processing by the viewing distance controller

133, which may be individualised to each user's facial features to allow for small or

large faces to be used by the viewing distance controller 133. At step 137, the viewing

distance controller 133 is configured to capture an image 143 of the user's face as

shown in Figure 11A and, at step 138, perform computer vision analysis 108 thereon.

The computer vision analysis 108 may comprise detecting a facial width 144 as shown

in Figure 11B. The facial width 144 may be calculated in pixels. The facial width 144

may comprise detection of the width of a visually detected facial boundary 145. The

boundary 145may boundary 145 maybebe determined determined using using boundary boundary detection detection analysis, analysis, including including

intensity and colour differential analysis.

[0038] Intensity differential analysis detects variations in pixel intensity to detect

edges. For instance, in the facial image, the boundary 145 between the face and the

background often exhibits significant changes in brightness. By analysing these

intensity gradients, the viewing distance controller 133 can effectively outline the

facial boundary 145. Colouring differential analysis assesses differences in colour

between adjacent pixels are assessed. In a facial image, the skin tone may contrast

with the background or other facial features, such as the eyes or hair. By evaluating wo 2025/015374 PCT/AU2024/050758 PCT/AU2024/050758 these colour variations, the viewing distance controller 133 can accurately identify the edges of the face.

[0039] The computer vision analysis 108 may employ edge detection algorithms, such

as the Canny edge detector. This method uses a multi-stage process, including

gradient calculation, non-maximum suppression, and edge tracking by hysteresis, to

produce a precise edge map of the image. Additionally, boundary detection by the

viewing distance controller 133 can be enhanced with machine learning approaches.

Convolutional neural networks (CNNs) can be trained to recognise and delineate

boundaries by learning from annotated datasets.

[0040] In alternative embodiments, the computer vision analysis 108 is configured to

detect the width between the outer edges of both eyes. The computer vision analysis

108 may detect the width between the eyes in a facial image using Haar Cascades,

which are pre-trained classifiers that detect specific facial features based on edge or

line detection. This approach involves using a series of stages, each containing a set

of Haar-like features, to identify the eyes within the image. Once the eyes are

detected, the distance between their centres can be measured in pixels by the viewing

distance controller 133.

[0041] The computer vision analysis 108 may alternatively use convolutional neural

networks (CNNs) specifically trained for facial landmark detection to accurately locate

key points on the face, such as the corners of the eyes. For example, the facial

landmark detection model might identify the coordinates of the inner and outer

corners of each eye, allowing for precise calculation of the inter-eye distance. This

approach benefitsfrom approach benefits from thethe robustness robustness and accuracy and accuracy of learning of deep deep learning models, models,

especially when trained on large datasets of annotated facial images.

[0042] The computer vision analysis 108 may alternatively use a Histogram of

Oriented Gradients (HOG) feature descriptor for eye detection which focuses on the

structure of local gradients, which are indicative of edges and textures. By extracting

HOG features from a facial image and applying a sliding window technique, the

viewing distance controller 133 can detect the presence and location of eyes. Once

identified, the width between the detected eye regions can be computed.

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[0043] The viewing distance controller 133 may be calibrated at step 139 wherein the

user is instructed to hold the digital display 103 a set distance from the face, such as

30 cm. During such calibration, the viewing distance controller 133 calculates a

reference width 144 representing a calibrated distance, whereafter the real-time

viewing distance is thereafter calculated at step 140 according to the real-time width

144 and the calibrated reference width 144. At step 141, the viewing distance

controller 133 is configured to determine if the actual viewing distance exceeds the

reference distance by a threshold, such as 10%, and, if so, at step 142, the viewing

distance controller 133 may be configured to display a warning to the user to hold the

digital display 103 closer or further away as the case may be. Figure 11C shows

wherein the user is too far away from the digital display 103 and therefore the visually

detected facial boundary 145 is smaller than the calibrated reference width 144

whereas Figure 11E shows wherein the user is too close to the digital display 103

such that the visually detected facial boundary is wider than the calibrated width 144.

[0044] In embodiments, the stimulus testing controller is configured to translate

eccentricities and angular locations of the stimuli 121 to equivalent Cartesian X-Y

values on the digital display 103. Furthermore, the stimulus testing controller may be

configured to apply a scaling factor to the display of stimuli 121 to account for a visible

tangent effect of the display of the stimuli on the digital display. In other words, the

stimuli 121 may be displayed larger and with elliptical distortion further away from the

fixation point 120.

[0045] With reference to Figure 1, the controllers 105 may further comprise a gaze

stability detection controller 146 configured to detect gaze stability by analysing eye

image data obtained by the camera 107. Figure 6 shows processing 134 by the gaze

stability detection controller 146 and Figure 7 illustrates exemplary operation thereof.

At step 148, the gaze stability detection controller 146 performs gaze detection on

eye image data obtained from the eye region 152 of the user captured at step 149.

At step 150, the gaze stability detection controller 146 detects gaze stability and, if

detecting gaze instability, displays a warning message of unstable gaze to the user

at step 151.

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[0046] Figure 7 shows that the gaze stability detection controller 146 may be

configured to detect image properties 153 across the eye region 152. These image

properties properties 153 153 typically typically represent represent changes changes in in image image brightness, brightness, with with large large brightness brightness

changes detected at the edge of the iris and/or pupil. Alternatively, the image

properties 153 may be colour properties. As shown, the image properties 153 may

comprise image properties 153A detected across an X axis of the eye region 152 and

image properties 153B across a Y axis of the eye region

[0047]15

[0047] 152.Figure Figure 77 illustrates illustrates aahorizontal horizontal gaze gaze change change wherein wherein the image the image properties properties

153A change across the X axis. The gaze stability detection controller 146 may further

detect gaze changes vertically using the image properties 153B across the Y axis of

the eye region 152.

[0048] With reference to Figure 1, the controllers 105 may further comprise an eye

occlusion detection controller 154 configured to detect eye occlusion by analysing

facial image data 143 obtained from the camera 107. The eye occlusion detection

controller 154 may be used by the system 100 to ensure that the non-tested eye is is

closed or occluded. Figure 8 shows processing 156 by the eye occlusion detection

controller 154 and Figure 9 shows exemplary operation thereof. With reference to

Figure 9, the facial image data 143 may have an occluded eye region 152A (which is

occluded with a patch in this embodiment) and a non-occluded eye region 152B. At

step 157, the eye occlusion detection controller 154 captures the eye regions 152

from the facial image data 143 captured by the camera 107. Image processing is

performed thereon at step 158 wherein, at step 159, the eye occlusion detection

controller 154 detects an occluded eye. Detection of the occluded eye may comprise

detection of the colour of the eyepatch or alternatively absence of eye feature

recognition, or alternatively high homogeneity of pixels within the detection area due

to absence of eye feature. In further embodiments, detection of an occluded eye may

comprise the eye recognition only recognising one eye within the facial image data

143. At step 159, the eye occlusion detection controller 154 detects if the eye being

tested is occluded and, if so, at step 160, displays a warning message to the user.

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[0049] In embodiments, the controllers may comprise a screen geometry translation

controller which, with reference to Figure 12, maps a stimulus 121 display from a flat

digital screen to a curved digital screen, specifically focusing on mapping point A on

the flat screen to point B on the curved screen. The setup involves a viewer positioned

at point C, with the viewing distance denoted as d, which is the distance from point C

to the flat screen. The curved screen is represented as an arc with a radius R,

originating originating from from the the centre centre of of curvature. curvature. Two Two primary primary planes planes are are shown: shown: the the plane plane of of

the the flat flat digital digital screen screen where where point point AA is is located located and and the the plane plane of of the the curved curved digital digital

screen where point B is located. The mapping involves calculating the new

coordinates on the curved screen based on the viewing geometry. The line of sight

from point C intersects the flat screen at point A and the curved screen at point B.

The equation y = -d/L1x + d represents the line of sight and x^2 + (y - R)^2 = R^2

describes the curved screen. Solving these equations simultaneously gives the

coordinates of point B on the curved screen.

[0050] In embodiments, the controllers may comprise a bright spot detection controller

configured to implement the process 170 shown in Figure 13, which begins with the

front-facing camera scanning an image of a background scene at step 171. The

system 100 then checks if any pixel's brightness exceeds a predetermined threshold

level at step 172. If such a pixel is detected, the system 100 identifies the beginning

of a cluster of bright pixels at step 173. This cluster is then expanded at step 174 by

including adjacent pixels that also exceed the brightness threshold until the entire

cluster is identified at step 175. The system 100 then assesses whether the cluster

exceeds a specified size at decision 176. If the cluster is larger than the specified

size, it is deemed a bright spot at step 177, and a warning message is displayed to

the user. If the cluster does not exceed the specified size, the system continues

scanning at step 178 for other potential bright spots.

[0051] The foregoing description, for purposes of explanation, used specific

nomenclature to provide a thorough understanding of the invention. However, it will

be be apparent apparent to to one one skilled skilled in in the the art art that that specific specific details details are are not not required required in in order order to to

practise practise the the invention. invention. Thus, Thus, the the foregoing foregoing descriptions descriptions of of specific specific embodiments embodiments of of wo 2025/015374 PCT/AU2024/050758 the the invention invention are are presented presented for for purposes purposes of of illustration illustration and and description. description. They They are are not not intended to be exhaustive or to limit the invention to the precise forms disclosed as obviously obviously many many modifications modifications and and variations variations are are possible possible in in view view of of the the above above details. details.

The embodiments were chosen and described in order to best explain the principles

of the invention and its practical applications, thereby enabling others skilled in the

art art to to best best utilize utilize the the invention invention and and various various embodiments embodiments with with various various modifications modifications

as are suited to the particular use contemplated. It is intended that the following

claims claims and and their their equivalents equivalents define define the the scope scope of of the the invention. invention.

Claims

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Claims 1. 1. A field of vision testing system comprising a digital display and configured for

recording responses to stimuli displayed on the digital display, wherein the system is

configured for screen size scaling comprising:

displaying calibration markers and offset adjustment controls configured to

adjust adjust aa distance distance therebetween; therebetween;

calculating a screen size scaling factor according to the distance; and

scaling a screen size according to the screen size scaling factor.
2. The system as claimed in claim 1, wherein the system is configured to

calculate screen pixels per unit length according to the distance.
3. The system as claimed in claim 2, wherein the system is configured for

calculating a screen size scaling factor for both X and Y axes of the digital display

and scaling the screen size along the X and Y axes according to the screen size

scaling factors respectively.
4. The system as claimed in claim 1, wherein the system is configured for screen

brightness calibration comprising:

displaying a first stimulus at a brightness below a visual threshold and a

second stimulus at a brightness above the threshold; and

calculating a screen brightness according to responses to the stimuli.
5. The system as claimed in claim 4, wherein the screen brightness is classified

as calibrated, inadequate and excessive.
6. The system as claimed in claim 4, wherein excessive screen brightness is

detected by frequency of responses to the first stimulus exceeding a threshold.
7. 7. The system as claimed in claim 4, wherein inadequate screen brightness is

detected by frequency of responses to the second stimulus under a threshold.
8. The system as claimed in claim 4, wherein calibrated screen brightness is

detected by approximately equal responses to both stimuli.

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9. The systemasasclaimed The system claimed in in claim claim 1, wherein 1, wherein the system the system furtherfurther comprises comprises a a

camera andwherein camera and whereinthethe system system is configured is configured for: for:

viewing distance calibration comprising:

positioning the digital display a set distance from a user's face;

analysing image data obtained from the camera to determine a

calibrated facial metric;

real-time viewing distance monitoring comprising:

continually monitoring image data obtained from the camera to

determine a real-time facial metric;

comparing the calibrated and real-time facial metrics to determine a

real-time viewing distance.
10. The system as claimed in claim 9, wherein the calibrated facial metric is

measured in pixel width.
11. The system as claimed in claim 9, wherein the calibrated facial metric

comprises at least one of facial width, distance between the eyes, nose width and

jawline width.
12. The system as claimed in claim 11, wherein system is configured for

configured for employing at least one of boundary analysis, contrast differential

analysis and colour differential analysis to determine the calibrated facial metric.
13. The system as claimed in claim 1, wherein the stimulus testing controller is

configured to adjust a fixation point display location on the digital display.
14. The system as claimed in claim 13, wherein the stimulus testing controller is

configured configured to to adjust adjust the the fixation fixation point point display display location location depending depending on on aa size size of of the the

digital display.
15. The system as claimed in claim 14, wherein the stimulus testing controller is

configured to position the fixation point display location at peripheral or corner

regions of the digital display.

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16. The system as claimed in claim 1, wherein the system is configured for

displaying displaying aa fixation fixation point point and and scaling scaling geometry geometry of of the the stimuli stimuli proportionately proportionately

according to their display distance from the fixation point.
17. The system as claimed in claim 16, wherein the geometry is stimuli are

elliptical distortion.
18. The system as claimed in claim 17, wherein stimuli are displayed with greater

elliptical distortion further away from the fixation point.
19. The systemasasclaimed The system claimed in in claim claim 1, wherein 1, wherein the system the system furtherfurther comprises comprises a a

camera camera and and wherein wherein the the system system is is further further configured configured to to detect detect aa gaze gaze stability stability by by

analysing analysing image image data data of of aa user's user's eye eye obtained obtained from from the the camera. camera.
20. 20. The The system system as as claimed claimed in in claim claim 19, 19, wherein wherein the the system system is is configured configured to to detect detect

image properties across at least one of an X and Y axis of an eye region.
21. 21. The system as claimed in claim 20, wherein the image properties are at least

one of brightness levels, contrast levels and colour values.
22. 22. The The system system as as claimed claimed in in claim claim 20, 20, wherein wherein the the system system is is configured configured to to detect detect

changes in the image properties across the X axis of an eye region to detect gaze

instability. instability.
23. 23. The The system system as as claimed claimed in in claim claim 22, 22, wherein wherein the the system system is is further further configured configured to to

detect changes in the image properties across the Y axis of an eye region to detect

gaze instability. gaze instability.
24. 24. The systemasasclaimed The system claimed in in claim claim 1, wherein 1, wherein the system the system furtherfurther comprises comprises a a

camera and wherein the system is further configured to detect eye occlusion by

analysing facial image data obtained from the camera.
25. 25. The system as claimed in claim 24, wherein the system employs eye image

recognition and recognition of only one eye.
26. 26. The system as claimed in claim 1, wherein the system is configured to

translate a stimulus position from flatscreen geometry to curved screen geometry.

wo 2025/015374 PCT/AU2024/050758
27. 27. The system as claimed in claim 26, wherein the system is configured to

calculate line of sight intersections of the flatscreen geometry and the curved screen

geometry.
28. 28. The systemasasclaimed The system claimed in in claim claim 1, wherein 1, wherein the system the system furtherfurther comprises comprises a a

front facing camera and wherein the system is further configured to detect bright

spots withinimage spots within image data data obtained obtained fromfrom the camera. the camera.
29. 29. The system as claimed in claim 28, wherein the system is configured to:

scan an image of the background scene,

determine if any pixel's brightness exceeds a predetermined threshold,

if if aa bright bright pixel pixel is is detected, detected, identify identify aa start start of of aa cluster cluster of of bright bright pixels; pixels;

expand the cluster by including adjacent pixels exceeding the brightness

threshold threshold until; until;

determining if the cluster exceeds a specified size.
30. 30. The The system system as as claimed claimed in in claim claim 29, 29, wherein wherein the the system system is is further further configured configured to to

display a warning message if the if the cluster exceeds the specified size.