Amblyopia training device control method, device, equipment and medium

Abstract

The invention discloses an amblyopia training device control method and device, equipment and a medium, and belongs to the technical field of medical scientific research, and the method comprises the steps: before a training target starts training, controlling an amblyopia training device to sequentially display a plurality of first images generated according to a preset parameter combination, and enabling the training target to execute a corresponding exercise operation; wherein the image displayed by the amblyopia training device comprises a target stimulation object; collecting correct rate data after the training target executes a plurality of practice operations, and fitting a correlation coefficient between the crowdedness degree of the target stimulation object and the parameter combination according to the correct rate data; after the training target starts training, controlling the amblyopia training device to display a second image, and enabling the training target to execute corresponding training operation; and adjusting the crowding degree of the target stimulation object in the second image according to the training result of each training operation, the ladder algorithm and the correlation coefficient. The problem that the accuracy of the target stimulation object displayed by the amblyopia training device is low can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical scientific research, and in particular to a control method and device for amblyopia training apparatus, equipment and medium. BACKGROUND

[0002] Amblyopia is a common visual developmental disorder in children, which is characterized by the best corrected visual acuity of one or both eyes being lower than the normal level. Therefore, it is necessary to develop corresponding training devices for amblyopia problems.

[0003] For amblyopia problems, the prior art has developed various training devices, and the existing training devices mostly rely on disinhibition training, fusion training, stereoscopic training and other training methods. The above devices do not consider the influence of fixation stability on amblyopia recovery. Based on previous findings, controlling fixation microsaccades through retinal image motion can increase the stability of central fixation, and also improve the contrast sensitivity function of the subject, while the crowding effect is closely related to the fixation stability during central fixation. Therefore, the fixation stability of the training target can be improved by presenting different target stimuli in the training process. However, different training targets have different visual sensitivities, and the visual sensitivity of different training targets in the training process also changes, which increases the control difficulty of the amblyopia training device.

[0004] Therefore, how to improve the accuracy of the target stimulus presented by the amblyopia training device is a technical problem to be solved at present. SUMMARY

[0005] The present application provides an amblyopia training device control method, device, equipment and medium, which can solve the problem of how to improve the accuracy of the target stimulus presented by the amblyopia training device in the prior art.

[0006] An amblyopia training device control method is provided in an embodiment of the present application, comprising:

[0007] Before the training target starts training, a plurality of first images are generated according to a preset parameter combination, and the amblyopia training device is controlled to display the first images in turn, so that the training target performs corresponding exercise operations according to each displayed first image; wherein the image displayed by the amblyopia training device includes a target stimulus object;

[0008] The accuracy data of the training target after performing a plurality of exercise operations is collected, and the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination is fitted according to the accuracy data;

[0009] After the training target starts training, the amblyopia training device is controlled to display a preset second image, so that the training target performs corresponding training operations;

[0010] Based on the training results of each training operation, and in conjunction with the ladder algorithm and the correlation coefficient, the crowding level of the target stimulus object in the second image is adjusted.

[0011] Compared with the prior art, the above embodiments have the following beneficial effects: First, practice operations are performed on each training target. Based on the results of the practice operations performed on each training target, the corresponding correlation coefficients are calculated for training targets with different visual sensitivities. This provides a basis for adjusting the second image when subsequent training targets perform training operations, thereby improving the control accuracy of the amblyopia training device. In addition, since the training process is dynamic, the visual sensitivity of the training target may change dynamically during the training process. Therefore, based on the results of each training session of the training target, the crowding degree of the generated second image is further adjusted by combining the ladder algorithm and accurate correlation coefficients. This allows for real-time tracking of the current trainee's visual sensitivity, thereby improving the control accuracy of the amblyopia training device in a dynamic training environment.

[0012] Further, fitting the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data includes:

[0013] The practice operation performed when the interfering stimulus object is present in the first image is taken as the first practice operation, and the practice operation performed when the interfering stimulus object is not present in the first image is taken as the second practice operation; the accuracy data includes a first accuracy rate after performing the first practice operation and a second accuracy rate after performing the second practice operation; each parameter combination corresponds to a first accuracy rate and a second accuracy rate;

[0014] Based on the first accuracy rate and the second accuracy rate, calculate the degree of congestion corresponding to each of the parameter combinations;

[0015] The correlation coefficient is fitted by nonlinear curve fitting, combining the parameter combinations and the crowding degree corresponding to each parameter combination.

[0016] Compared with the prior art, the above embodiments have the following beneficial effects: by distinguishing between the presence and absence of interfering stimuli, the crowding degree of the target stimulus object in the actual visual scene is more realistically reflected, avoiding the limitations of a single scene; furthermore, by capturing the complex relationship between parameter combination and crowding degree through nonlinear fitting, a more accurate basis is provided for the subsequent dynamic updating of the second image through the step algorithm, thereby improving the control accuracy of the amblyopia training device.

[0017] Further, the step of calculating the congestion level corresponding to each parameter combination based on the first accuracy and the second accuracy includes: calculating the difference between the second accuracy and the first accuracy, and using the ratio of the difference to the second accuracy as the congestion level.

[0018] Compared with the prior art, the above embodiments have the following beneficial effects: normalization is performed based on non-interference stimuli, thereby eliminating the influence of differences in the basic accuracy of different training targets, making the degree of crowding more comparable horizontally, and the ratio of the difference between the second accuracy and the first accuracy to the second accuracy can more directly reflect the weakening ratio of the accuracy by interference stimuli, thereby improving the accuracy of crowding degree assessment and providing more accurate data support for subsequent correlation coefficient fitting.

[0019] Further, adjusting the crowding level of the target stimulus object in the second image based on the training results of each training operation, combined with the ladder algorithm and the correlation coefficient, includes:

[0020] When the training result is correct for the first preset number of consecutive times, the parameter combination is adjusted according to the correlation coefficient and the QUEST algorithm to increase the crowding degree of the target stimulus object in the second image;

[0021] When the training result is a second preset number of consecutive errors, the parameter combination is adjusted according to the correlation coefficient and the QUEST algorithm to reduce the crowding of the target stimulus object in the second image.

[0022] Compared with the prior art, the above embodiments have the following beneficial effects: by implementing a step-by-step algorithm to achieve progressive difficulty adjustment, the training challenge and feasibility are balanced, the training difficulty is always maintained within a range suitable for the visual sensitivity of the training target, and the parameter combination is adjusted according to the real-time training results of different training targets, thereby improving the versatility of the amblyopia training device for different training targets.

[0023] Furthermore, adjusting the parameter combination based on the correlation coefficient and the QUEST algorithm includes:

[0024] The parameter combination includes the eccentricity of the target stimulus object in the image displayed by the amblyopia training device, the distance between the target stimulus object and the interference stimulus object, and the size of the target stimulus object;

[0025] According to the QUEST algorithm, select the parameter to be adjusted from the parameter combination;

[0026] Based on the correlation coefficient, the adjustment amount of the parameter is determined, and the parameter is adjusted according to the adjustment amount.

[0027] Compared with existing technologies, the above embodiments have the following beneficial effects: By using the QUEST algorithm, Bayesian inference is used to quickly converge to the optimal parameter combination, reducing trial and error costs; by providing a basis for subsequent parameter adjustments based on the accurately fitted correlation coefficient, the subsequent adjustment amount is determined to be consistent with actual needs, improving the control accuracy of the amblyopia training device; finally, by optimizing the eccentricity of the target stimulus object, the distance between the target stimulus object and the interference stimulus object, and the size of the target stimulus object, the spatial layout and visual complexity of the target stimulus are comprehensively controlled, thereby improving the scientific nature of the control of the amblyopia training device.

[0028] Furthermore, the amblyopia training device includes: an eye-tracking instrument; the control method for the amblyopia training device further includes:

[0029] The eye-tracking instrument is calibrated before generating the plurality of first images;

[0030] When performing the exercise or training operation, the eye position data of the training target collected by the eye-tracking instrument is read.

[0031] Based on the eye position data, the gaze stability of the training target is calculated;

[0032] When the gaze stability does not meet the preset conditions, a prompt message is output.

[0033] Compared with existing technologies, the above embodiments have the following beneficial effects: by using an eye-tracking instrument, the gaze stability of the training target can be tracked in real time to ensure that the training target performs training operations in the best state; when the gaze stability of the training target is poor, prompts are provided to help the training target correct eye position and avoid ineffective training; at the same time, by collecting gaze stability data, data support is provided for subsequent research on amblyopia training schemes.

[0034] Furthermore, the training operations include detection training operations, identification training operations, and reading training operations;

[0035] Wherein, the target stimulus object in the second image corresponding to the detection training operation is a fixed-direction number or letter optotype;

[0036] The target stimulus in the second image corresponding to the discrimination training operation is a random-direction number or letter visual target;

[0037] The target stimulus in the second image corresponding to the reading training operation is a word visual target with a fixed orientation.

[0038] Compared with the prior art, the above embodiments have the following beneficial effects: by setting training operations of different difficulty, different training needs can be met, thereby improving the versatility of the amblyopia training device for different training objectives; at the same time, it can also provide a training process from simple to complex training operations for the same training objective, thereby improving the overall training effect of the amblyopia training device.

[0039] Another embodiment of this application provides a control device for an amblyopia training device, including: a practice operation module, a correlation coefficient fitting module, a training operation module, and a crowding degree adjustment module;

[0040] The practice operation module is used to generate several first images according to a preset parameter combination before the training target starts training, and control the amblyopia training device to display the first images sequentially, so that the training target can perform corresponding practice operations according to each displayed first image; wherein the images displayed by the amblyopia training device include: target stimulus objects;

[0041] The correlation coefficient fitting module is used to collect the accuracy data after the training target performs several of the training operations, and to fit the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data.

[0042] The training operation module is used to control the amblyopia training device to display a preset second image after the training target starts training, so that the training target can perform corresponding training operations.

[0043] The crowding adjustment module is used to adjust the crowding level of the target stimulus object in the second image based on the training results of each training operation, combined with the ladder algorithm and the correlation coefficient.

[0044] Another embodiment of this application provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the amblyopia training device control method of this application.

[0045] Another embodiment of this application also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the visual impairment training device control method of this application. Attached Figure Description

[0046] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating a control method for an amblyopia training device provided in some embodiments of this application;

[0048] Figure 2 This is a schematic diagram of the structure of an amblyopia training device provided in some embodiments of this application;

[0049] Figure 3 This is a schematic diagram of an amblyopia training result provided in some embodiments of this application;

[0050] Figure 4 This is a schematic diagram of a user interface for a detection training operation provided in some embodiments of this application;

[0051] Figure 5 This is a schematic diagram of a detection training operation corresponding to a second image provided in some embodiments of this application;

[0052] Figure 6 This is a schematic diagram of a user interface for identifying training operations provided in some embodiments of this application;

[0053] Figure 7 This is a schematic diagram of a second image corresponding to a discrimination training operation provided in some embodiments of this application;

[0054] Figure 8 This is a schematic diagram of a user interface for a reading training operation provided in some embodiments of this application;

[0055] Figure 9 This is a schematic diagram of a reading training operation corresponding to a second image provided in some embodiments of this application;

[0056] Figure 10 This is a schematic diagram of the operation process of an amblyopia training device provided in some embodiments of this application;

[0057] Figure 11 This is a schematic diagram of the structure of a control device for an amblyopia training device provided in some embodiments of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0060] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0061] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0062] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0063] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0064] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0065] To address amblyopia, various training devices have been developed, most of which rely on methods such as disinhibition training, fusion training, and stereoscopic training. However, none of these devices consider the impact of fixation stability on amblyopia recovery. Previous findings suggest that controlling fixation microsaccades through retinal image motion to further increase central fixation stability can improve contrast sensitivity, and the crowding effect is closely related to fixation stability during central fixation. Therefore, presenting different target stimuli during training can improve fixation stability. However, different training targets correspond to different visual sensitivities, and the training process is dynamic, with varying changes in visual sensitivity for different targets. These issues increase the difficulty of controlling amblyopia training devices.

[0066] Please refer to Figure 1 To address the problem of improving the accuracy of target stimuli presented by amblyopia training devices in existing technologies, this application provides a control method for an amblyopia training device, including steps S101 to S104, specifically including:

[0067] S101: Before the training target begins training, several first images are generated according to a preset parameter combination, and the amblyopia training device is controlled to display the first images in sequence, so that the training target can perform corresponding practice operations according to each displayed first image; wherein, the images displayed by the amblyopia training device include: target stimulus objects.

[0068] Furthermore, in some embodiments of this application, reference is made to... Figure 2 The amblyopia training device includes a display and an eye tracker, as well as a chin rest and a forehead rest to fix the head of the training target; the display is used to show images.

[0069] Furthermore, in some embodiments of this application, the parameter combination includes the eccentricity of the target stimulus object in the image displayed by the amblyopia training device, the distance between the target stimulus object and the interference stimulus object, and the size of the target stimulus object.

[0070] Preferably, in some embodiments of this application, since the size of the visual target and the distance from the visual target to the center are determined by the number of pixels, after setting the initial visual target size (i.e., the number of pixels), it needs to be converted into a visual field angle using a visual field angle formula. In these embodiments, the visual target includes, but is not limited to, target stimuli and interfering stimuli. Therefore, whenever the display in the amblyopia training device changes, the visual field angle needs to be converted using the following steps to ensure the consistency of the visual target's visual field angle:

[0071] (1) Calculate the physical dimensions of the monitor screen using the following formula:

[0072]

[0073] Among them, W phys H is the physical width of the monitor screen, measured in centimeters. phys a:b represents the physical height of the monitor screen in centimeters; S represents the aspect ratio of the monitor screen; and S represents the screen size in feet.

[0074] (2) Calculate the physical size of each pixel using the following formula:

[0075]

[0076] Among them, Pixel w The width of a single pixel, in centimeters; Pixel h W×H represents the height of a single pixel in centimeters; W×H represents the screen resolution of the display.

[0077] (3) Calculate the field of view angle of the target using the following formula:

[0078] Assuming the target size is N×M pixels and the distance between the training target and the display screen is D centimeters, then:

[0079]

[0080] Where, θ h θ is the horizontal field of view angle. v This refers to the vertical field of view angle.

[0081] (4) Calculate the viewing angle from the edge of the screen to the center using the following formula:

[0082]

[0083]

[0084] in, A horizontal edge perspective; This is a vertical edge viewpoint.

[0085] It should be noted that in some embodiments of this application, the size of the quantitative target, the distance between the interfering stimulus and the target stimulus, the distance from the target stimulus to the center point, and the eccentricity of the target stimulus all need to be converted from pixels to visual field angles using the above-mentioned visual field angle calculation formula.

[0086] Preferably, in some embodiments of this application, for the central fixation target and the screen edge viewing angle, the central fixation target is set as a red cross with a size of 1×1°, with a white 0.25° dot at the center of the cross, and a viewing angle of 20° to the screen edge. The resulting target pixel width varies depending on the screen size, resolution, and viewing distance. Larger screens have wider pixels at the same resolution, requiring a reduction in the number of pixels to maintain the same viewing angle. Here, the central fixation target is independent of the interference stimulus and symmetrical to the target stimulus, located in the center of the screen, and is used to determine the eccentricity of each target stimulus. The eccentricity of each target stimulus is the distance between the central fixation target and the target stimulus, which is also expressed as a visual field angle, typically adjusted between 5 and 15°.

[0087] Preferably, in some embodiments of this application, before performing the practice operation, a fixation operation is also performed, specifically: a central fixation target is displayed in the amblyopia training device, with a flashing black and white pixel pattern as the background, so that the training target fixates on the central fixation target. When the training target's fixation is stable, the background is presented as a gray background. When the gray background disappears, it means that the training target's eyeball is moving. At this time, the training target is prompted to move its eyeball and to fixate on the central fixation target. Each fixation operation lasts for 5 seconds, then rests for 5 seconds and performs the fixation operation again, for a total of 5 cycles.

[0088] Preferably, in some embodiments of this application, for peripheral fixation targets, letters, numbers, or Chinese characters with a height of x degrees (x-wide, where x is the degree of visual field) are set, and the target size is between 0.5° and 5°. The target size is increased or decreased by 0.5° each time, so the target size is divided into 0.5°, 1°, 1.5°, 2°, 2.5°, 3°, 3.5°, 4°, 4.5°, and 5°; with the center of the target as the mass point, the spacing between the interference stimulus and the target stimulus is divided into 6 values, namely 0.8x, 1x, 1.2x, 1.4x, 1.6x, and 1.8x.

[0089] Preferably, in some embodiments of this application, the smaller the spatial distance between the interfering stimulus and the target stimulus, the greater the impact of the interfering stimulus on the recognition of the target stimulus. Therefore, the spatial distance at which the interfering stimulus just does not affect the recognition of the target stimulus is called the critical distance. According to Bouma's law, the critical distance is proportional to the eccentricity of the target stimulus, approximately equal to 0.5 times the eccentricity. At this point, the critical distance is independent of factors such as the size and category of the target stimulus. However, in the study of Chinese characters, it has been found that the spacing between Chinese characters does not follow Bouma's law; the relationship between the two is U-shaped. Both excessively small and excessively large spacings interfere with reading. Only when the spacing is 0.23 to 0.37 times the eccentricity is the crowding minimized. Since the crowding effect does not exist when the critical spacing is equal to 0.5 times the eccentricity under the conditions of English, numbers and Garber optotypes, and the crowding effect does not exist when the critical spacing is equal to 0.23 to 0.37 times the eccentricity under the conditions of Chinese characters, the embodiment of this application, in executing the control method of the amblyopia training device, will prevent the spacing between the target stimulus object and the interference stimulus object in the image displayed on the screen from reaching the critical spacing, so as to eliminate the phenomenon of no crowding effect that occurs when the critical spacing is reached, thus weakening the training effect.

[0090] Preferably, in some embodiments of this application, in order to accurately simulate the critical spacing and eliminate slight differences in target size, eccentricity, and spacing caused by screen distance, parameters, etc., the critical spacing is determined by the following steps:

[0091] (1) First, based on the following accurate simulation formula, adjust the asymptotic value, scale factor, and x-intercept using the nonlinear least squares fitting method and the trust region algorithm provided in the Matlab curve fitting toolbox:

[0092] pc=α(1-e (-s(d-i)) )

[0093] Where pc is the correct proportion; α is the asymptote; s is the proportion factor; d is the distance between the target stimulus and the interference stimulus; and i is the x-intercept.

[0094] (2) Further calculate the critical spacing using the following formula:

[0095] c = i - ln(0.1) / s

[0096] Where c is the critical spacing.

[0097] Preferably, in some embodiments of this application, generating a plurality of first images according to a preset combination of parameters includes:

[0098] First, a first image containing interfering stimuli is generated based on a preset combination of parameters. Then, the eccentricity and size parameters of the target stimuli remain unchanged, and the interfering stimuli are removed to generate a first image without interfering stimuli.

[0099] S102: Collect the accuracy data of the training target after performing several of the training operations, and fit the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data.

[0100] Furthermore, in some embodiments of this application, fitting the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data includes:

[0101] The practice operation performed when the interfering stimulus object is present in the first image is taken as the first practice operation, and the practice operation performed when the interfering stimulus object is not present in the first image is taken as the second practice operation; the accuracy data includes a first accuracy rate after performing the first practice operation and a second accuracy rate after performing the second practice operation; each parameter combination corresponds to a first accuracy rate and a second accuracy rate;

[0102] Based on the first accuracy rate and the second accuracy rate, calculate the degree of congestion corresponding to each of the parameter combinations;

[0103] The correlation coefficient is fitted by nonlinear curve fitting, combining the parameter combinations and the crowding degree corresponding to each parameter combination.

[0104] Furthermore, to more clearly illustrate the correspondence between accuracy data, crowding levels, and parameter combinations, the following section details the process of performing training on the training objective:

[0105] Since subsequent training operations include detection, discrimination, and reading-related training, the practice phase requires generating first images for these training operations. First, each training iteration is defined as a phase of 10 repetitions. Then, 30 repetitions of each of the three training operations are performed, one with interfering stimuli and one without. Therefore, a total of 180 repetitions are needed, resulting in 18 phases and 18 first images. Nine phases involve performing the first training operation with interfering stimuli, and the other nine phases involve performing the second training operation without interfering stimuli. Thus, with a known parameter combination, one first image with interfering stimuli and one without can be generated. That is, a known parameter combination allows the training target to achieve a first accuracy rate after 10 repetitions of the first training operation and a second accuracy rate after 10 repetitions of the second training operation. Based on these first and second accuracy rates, the crowding level corresponding to this known parameter combination can be calculated. Therefore, 180 training repetitions require nine known and distinct parameter combinations to obtain nine crowding level results.

[0106] This application, by distinguishing between the presence and absence of interfering stimuli, more realistically reflects the crowding level of the target stimulus object in the actual visual scene, avoiding the limitations of a single scene; furthermore, it captures the complex relationship between parameter combinations and crowding level through nonlinear fitting, providing a more accurate basis for subsequent dynamic updates of the second image through a step algorithm, thereby improving the control accuracy of the amblyopia training device.

[0107] Furthermore, in some embodiments of this application, the step of calculating the congestion level corresponding to each parameter combination based on the first accuracy and the second accuracy includes: calculating the difference between the second accuracy and the first accuracy, and using the ratio of the difference to the second accuracy as the congestion level.

[0108] Specifically, based on the first accuracy rate and the second accuracy rate, the formula for calculating the congestion level corresponding to each parameter combination is as follows:

[0109] C effect =(AA c ) / A

[0110] Among them, C effect A represents the degree of congestion; A represents the second highest accuracy rate; A c This is the highest accuracy rate.

[0111] Furthermore, in some embodiments of this application, fitting the correlation coefficient by nonlinear curve fitting, combining each of the parameter combinations and the crowding degree corresponding to each parameter combination, includes:

[0112] (1) First, define the relationship between the parameter combination and the degree of congestion:

[0113]

[0114] f(e)=(1+e / α) β

[0115] β≈1

[0116] Where k is a proportionality constant reflecting the degree of crowding under specific individual or experimental conditions, which is also one of the correlation coefficients. The larger the value of k, the weaker the crowding effect of a given spacing and size in the parameter combination; s is the size of the target stimulus; d is the spacing between the target stimulus and the interference stimulus; e is the eccentricity of the target stimulus. It should be noted that the size s, spacing d, and eccentricity e are parameters in the parameter combination; f(e) is the eccentricity function; α is a constant that controls the rate of increase of eccentricity crowding, which is also one of the correlation coefficients; β is a constant that controls the magnitude of increase of eccentricity crowding, which is usually set to 1; exp() is an exponential function with the real number e as the base.

[0117] (2) Based on the combination of multiple parameters and the degree of crowding corresponding to each parameter combination, the optimal correlation coefficient is obtained by fitting α and k in the above relationship through nonlinear curve fitting.

[0118] As can be seen from the above embodiments, this application uses non-interference stimuli as a benchmark and performs normalization processing to eliminate the influence of differences in the basic accuracy of different training targets, making the degree of crowding more comparable horizontally. Moreover, the ratio of the difference between the second accuracy and the first accuracy to the second accuracy can more directly reflect the weakening ratio of the accuracy by interference stimuli, thereby improving the accuracy of crowding degree assessment and providing more accurate data support for subsequent correlation coefficient fitting.

[0119] S103: After the training target starts training, the amblyopia training device is controlled to display a preset second image so that the training target can perform the corresponding training operation.

[0120] Furthermore, in some embodiments of this application, the training operation includes a detection training operation, a discrimination training operation, and a reading training operation;

[0121] Wherein, the target stimulus object in the second image corresponding to the detection training operation is a fixed-direction number or letter optotype;

[0122] The target stimulus in the second image corresponding to the discrimination training operation is a random-direction number or letter visual target;

[0123] The target stimulus in the second image corresponding to the reading training operation is a word visual target with a fixed orientation.

[0124] Preferably, in some embodiments of this application, the target stimulus object in the second image corresponding to the detection training operation is a fixed-directional number or letter optotype, including:

[0125] The training operation was performed 150 times. Each training session required the training target fixation center to fixate on a visual target. The second image generated in each training session met the following criteria: the target stimulus was presented radially or tangentially at eccentricities of 2°–10° around the periphery of the visual field, with intervals of 30° circumference. Two to eight interfering stimuli surrounded the target stimulus. Both the target stimulus and the interfering stimuli were letters or numbers. (Reference) Figure 4 as well as Figure 5 As shown, the training objective can be derived from... Figure 4 In the interface shown, select the target stimulus to be set. For example, if the target stimulus is the letter D, the following will be generated: Figure 4 The second image shown. Wherein Figure 3 , Figure 4 , Figure 6 , Figure 8 The Crowding Effect Training System in this context refers to a system for training the degree of crowding. Figure 4 as well as Figure 8 The Others option refers to selecting other target stimuli. Figure 3 It will display the accuracy data of the training target for each exercise and each training task.

[0126] Preferably, in some embodiments of this application, the target stimulus object in the second image corresponding to the discrimination training operation is a random-direction number or letter optotype, including:

[0127] The discrimination training operation was performed 150 times. Each training session required the training target fixation center to fixate on a visual target. The second image generated in each discrimination training operation met the following requirements: the target stimulus was radially distributed at eccentricities of 1°–10° around the periphery of the visual field, with intervals of 30° in a circle. There were 2–8 interfering stimuli surrounding the target stimulus. The target and interfering stimuli included c, E, T, and Garber visual targets. The visual targets had eight orientations: up, down, left, right, upper right, lower right, upper left, and lower left. (Reference) Figure 6 as well as Figure 7 As shown, the training objective can be derived from... Figure 6 In the interface shown, select the target stimulus to be set. Assuming the target stimulus is selected as 'c', the following will be generated: Figure 7 The second image shown.

[0128] Preferably, in some embodiments of the present application, the target stimulus object in the second image corresponding to the reading training operation is a vocabulary visual target in a fixed direction, including:

[0129] The reading training operation is performed 150 times. During each training, the training target fixation center needs to fixate on the visual target. At the same time, the second image generated by each reading training operation satisfies: presenting target stimuli in a tangent distribution at an eccentricity of 1° to 10° in the peripheral visual field and presenting them at intervals of 45° in circular degrees. The target stimuli are composed of simple English words for primary school students (words with 3 to 8 letters) and Chinese words (words with 3 to 6 Chinese characters). Refer to Figure 8 and Figure 9 As shown, the training target can select the target stimulus object to be set this time from the interface shown in Figure 8 As shown. Assuming that the selected target stimulus object is the Chinese character "合", the second image shown in Figure 9 will be generated.

[0130] Preferably, in some embodiments of the present application, when the training operation is performed, the execution order of the training operation is: first detect the training operation, then distinguish the training operation, and finally perform the reading training operation.

[0131] By setting training operations with different difficulties to adapt to different training needs, the versatility of the amblyopia training device for different training targets is improved; at the same time, a training process from simple to complex training operations can also be provided for the same training target, improving the comprehensive training effect of the amblyopia training device.

[0132] S104: According to the training result of each training operation, in combination with the staircase algorithm and the correlation coefficient, adjust the crowding degree of the target stimulus object in the second image.

[0133] Furthermore, in some embodiments of the present application, the adjusting the crowding degree of the target stimulus object in the second image according to the training result of each training operation, in combination with the staircase algorithm and the correlation coefficient, includes:

[0134] When the training result is correct for a continuous first preset number of times, adjust the parameter combination according to the correlation coefficient in combination with the QUEST algorithm to increase the crowding degree of the target stimulus object in the second image;

[0135] When the training result is incorrect for a continuous second preset number of times, adjust the parameter combination according to the correlation coefficient in combination with the QUEST algorithm to reduce the crowding degree of the target stimulus object in the second image.

[0136] Preferably, in some embodiments of this application, adjusting the crowding level of the target stimulus object in the second image based on the training results of each training operation, combined with the ladder algorithm and the correlation coefficient, specifically involves:

[0137] Each second image disappears after 150ms. The training target needs to answer the question corresponding to the target stimulus object shown in the second image within 2 seconds. If the answer is correct for three consecutive second images with the same level of crowding, the crowding level of the target stimulus object in the next second image is increased; if the answer is incorrect, the crowding level of the target stimulus object in the next second image is decreased. Each time the crowding level of the target stimulus object in the second image is adjusted, the combination parameters are adjusted according to the QUEST algorithm to adjust the crowding level of the target stimulus object in the second image.

[0138] Preferably, in some embodiments of this application, when adjusting the crowding level of the target stimulus object in the second image, the method further includes: if the correct answer is given three times consecutively while keeping the crowding level of the second image unchanged, the corresponding parameter combination of the second image remains unchanged, but the contrast between the target stimulus object and the interference stimulus object is increased, specifically by 0.1 log unit (log unit is a logarithmic operation, i.e., log 0.1). If the correct answer is given three times consecutively while keeping the system parameter combination unchanged, the crowding level of the target stimulus object in the second image is increased; if the incorrect answer is given three times consecutively while keeping the system parameter combination unchanged, the contrast is reduced by 0.1 log unit for each incorrect answer until the correct answer is given three times consecutively, then the contrast at this point is used, and the crowding level of the target stimulus object in the second image is increased. The adjustment range of the contrast is between 0.5 and 1.5 log units.

[0139] Preferably, in some embodiments of this application, when adjusting the crowding level of the target stimulus object in the second image, the method further includes: after the training target answers incorrectly twice in a row, without changing the crowding level of the target stimulus object in the second image, and adding a shaking cue for the target stimulus object in the current second image, that is, shaking the target stimulus object as a cue to increase the training target's recognition and discrimination ability. When using peripheral visual reading, the peripheral target stimulus object is obscured due to the crowding effect. The training target's attention can be attracted by the shaking, thereby improving the training target's attention. The advantage of the shaking motion is that when the target stimulus object is a letter stimulus, the spatial attributes of the letter stimulus remain basically unchanged. Based on the training target's performance, five shaking amplitudes are selected for training, ranging from 0% (static) to 10% of the target stimulus's width. Specifically, these consist of five shaking levels: 0%, 2.5%, 5%, 7.5%, and 10%, with the shaking occurring in a left-right direction. If the training target still answers incorrectly once even with increased shaking amplitude, the shaking amplitude for the next training session is increased. If the target answers correctly three times consecutively, the training session proceeds to the next session, at which point the shaking prompt is turned off, and the crowding level of the target stimulus in the second image is increased by one degree.

[0140] As can be seen from the above embodiments, this application achieves progressive difficulty adjustment through a step algorithm, balancing the challenge and feasibility of training, always keeping the training difficulty within a range suitable for the visual sensitivity of the training target, and adjusting the parameter combination according to the real-time training results of different training targets, thereby improving the versatility of the amblyopia training device for different training targets.

[0141] Furthermore, in some embodiments of this application, adjusting the parameter combination based on the correlation coefficient and the QUEST algorithm includes:

[0142] The parameter combination includes the eccentricity of the target stimulus object in the image displayed by the amblyopia training device, the distance between the target stimulus object and the interference stimulus object, and the size of the target stimulus object;

[0143] According to the QUEST algorithm, select the parameter to be adjusted from the parameter combination;

[0144] Based on the correlation coefficient, the adjustment amount of the parameter is determined, and the parameter is adjusted according to the adjustment amount.

[0145] Preferably, in some embodiments of this application, the step of selecting the parameter to be adjusted from the parameter combination according to the QUEST algorithm includes: selecting one of the three parameters of the combined parameters for adjustment according to the QUEST algorithm. For example, when selecting the distance between the target stimulus and the interference stimulus, if it is necessary to increase the crowding level, the distance is decreased, and vice versa; when selecting the size of the target stimulus, if it is necessary to increase the crowding level, the size is increased, and vice versa; when selecting the eccentricity of the target stimulus, if it is necessary to increase the crowding level, the eccentricity is increased, and vice versa. The QUEST (Quick Psychoacoustic Threshold Estimation) algorithm is an existing algorithm in psychophysical methods, and will not be described in detail here.

[0146] Preferably, in some embodiments of this application, determining the adjustment amount of the parameter based on the correlation coefficient, and adjusting the parameter based on the adjustment amount, includes:

[0147]

[0148] f(e)=(1+e / α) β

[0149] β≈1

[0150] Based on the above relationship, adjust the selected parameters and ensure that after the parameters are adjusted, the crowding degree of the target stimulus in the second image changes according to the specified order of magnitude.

[0151] As can be seen from the above embodiments, this application uses the QUEST algorithm and Bayesian inference to quickly converge to the optimal parameter combination, reducing trial and error costs; by providing a basis for subsequent parameter adjustments based on the accurately fitted correlation coefficient, it ensures that the subsequent adjustment amount is consistent with the actual needs, thereby improving the control accuracy of the amblyopia training device; finally, by optimizing the eccentricity of the target stimulus, the distance between the target stimulus and the interference stimulus, and the size of the target stimulus, it comprehensively controls the spatial layout and visual complexity of the target stimulus, thereby improving the scientific nature of the control of the amblyopia training device.

[0152] Furthermore, in some embodiments of this application, such as Figure 2 As shown, the amblyopia training device includes: an eye-tracking instrument; the control method for the amblyopia training device further includes:

[0153] The eye-tracking instrument is calibrated before generating the plurality of first images;

[0154] When performing the exercise or training operation, the eye position data of the training target collected by the eye-tracking instrument is read.

[0155] Based on the eye position data, the gaze stability of the training target is calculated;

[0156] When the gaze stability does not meet the preset conditions, a prompt message is output.

[0157] Preferably, in some embodiments of this application, calculating the gaze stability of the training target based on the eye position data includes: quantifying gaze stability using the global bivariate contour ellipse area (BCEA) (wherein, Figure 3 , Figure 4 , Figure 6 as well as Figure 8 In this context, BCEA is also the area of ​​the global bivariate contour ellipse, and the formula is: Where χ 2 This is the chi-square value (2 degrees of freedom), corresponding to a probability value of 0.682 (i.e., ±1SD); σ x and σ y ρ represents the standard deviation of the horizontal and vertical components of the eye position coordinates. Eye position is acquired by an eye tracker, which can collect the microsaccades of the training target when fixating on the central target, with the origin (0,0) as the central fixation point; ρ corresponds to the Pearson correlation coefficient between the horizontal and vertical components of the eye position. BCEA provides the area of ​​the ellipse covering 68% of the eye position in the experiment.

[0158] Preferably, in some embodiments of this application, during fixation, practice, and training operations, the eye-tracking device records the fixation point of the training target's eye movement in real time. If the fixation point exceeds 2°, it is determined to be saccade rather than fixational microsaccade. At this time, the amblyopia training device will play an audio prompt that the training target has no fixation center. The tester's remote device will also receive the fixation stability value and prompt of the training target in real time, while saccade caused by blinking will be ignored.

[0159] Further, refer to Figure 10 , Figure 10 The process shown is as follows Figure 2 The operation procedure of the amblyopia training device shown includes:

[0160] Step 1: Activate the amblyopia training device (including the main unit, monitor, remote control device, and eye tracker);

[0161] Step 2: Connect the device to the same local area network as the host computer via remote control (specifically, via Bluetooth).

[0162] Step 3: Fix the head of the training target to the chin rest and forehead rest, and set the viewing distance to 50cm;

[0163] Step 4: Train one eye and cover the opposite eye of the training target;

[0164] Step 5: Calibrate and adjust the eye tracker;

[0165] Step 6: The monitor screen displays the operation procedure of the amblyopia training device (including how to practice and train).

[0166] Step 7: Perform the fixation operation. If the training objective is to perform the training for the first time, proceed to step 8; otherwise, proceed to step 9.

[0167] Step 8: Perform the practice exercises;

[0168] Step 9: Execute the detection training operation, the identification training operation, and the reading training operation in sequence. After training ends, determine whether the training objective has ended. If yes, proceed to step 10; otherwise, end the training and generate the accuracy and maximum congestion level of each operation in this training.

[0169] Step 10: Determine whether the training target should continue training a single eye. If yes, proceed to step 4; otherwise, remove the occlusion and begin step 5.

[0170] It should be noted that the amblyopia training device, amblyopia training device control method, and amblyopia training device control system mentioned in the embodiments of this application are also applicable to strabismus training.

[0171] In summary, the amblyopia training device control method provided in this application has the following advantages compared to the prior art: First, practice operations are performed on each training target. Based on the results of the practice operations performed on each training target, the corresponding correlation coefficients are calculated for training targets with different visual sensitivities. This provides a basis for adjusting the second image when subsequent training targets perform training operations, thereby improving the control accuracy of the amblyopia training device. In addition, since the training process is dynamic, the visual sensitivity of the training target may change dynamically during the training process. Therefore, based on the results of each training session of the training target, the crowding degree of the generated second image is further adjusted by combining the ladder algorithm and accurate correlation coefficients. This allows for real-time tracking of the current trainee's visual sensitivity, thereby improving the control accuracy of the amblyopia training device in a dynamic training environment.

[0172] like Figure 11 As shown, based on the above-described method embodiments, an embodiment of this application provides a control device for amblyopia training device, including: a practice operation module 201, a correlation coefficient fitting module 202, a training operation module 203, and a crowding degree adjustment module 204.

[0173] Further, in some embodiments of this application, the practice operation module 201 is used to generate several first images according to a preset parameter combination before the training target starts training, and control the amblyopia training device to display the first images sequentially, so that the training target can perform corresponding practice operations according to each displayed first image; wherein, the images displayed by the amblyopia training device include: target stimulus objects; the correlation coefficient fitting module 202 is used to collect the accuracy data after the training target performs several of the practice operations, and fit the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination according to the accuracy data; the training operation module 203 is used to control the amblyopia training device to display a preset second image after the training target starts training, so that the training target can perform corresponding training operations; the crowding degree adjustment module 204 is used to adjust the crowding degree of the target stimulus object in the second image according to the training result of each training operation, combined with the ladder algorithm and the correlation coefficient.

[0174] Further, in some embodiments of this application, the correlation coefficient fitting module 202 includes: a practice operation classification unit, a crowding degree calculation unit, and a curve fitting unit; the correlation coefficient fitting module 202 is used to fit the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data, including: the practice operation classification unit is used to take the practice operation performed when there is an interfering stimulus object in the first image as the first practice operation, and the practice operation performed when there is no interfering stimulus object in the first image as the second practice operation; the accuracy data includes a first accuracy rate after performing the first practice operation and a second accuracy rate after performing the second practice operation; each parameter combination corresponds to a first accuracy rate and a second accuracy rate; the crowding degree calculation unit is used to calculate the crowding degree corresponding to each parameter combination based on the first accuracy rate and the second accuracy rate; the curve fitting unit is used to fit the correlation coefficient by nonlinear curve fitting, combining each parameter combination and the crowding degree corresponding to each parameter combination.

[0175] Furthermore, in some embodiments of this application, the congestion calculation unit is used to calculate the congestion level corresponding to each parameter combination based on the first accuracy and the second accuracy, including: calculating the difference between the second accuracy and the first accuracy, and using the ratio of the difference to the second accuracy as the congestion level.

[0176] Further, in some embodiments of this application, the crowding adjustment module 204 includes: a first crowding adjustment unit and a second crowding adjustment unit; the crowding adjustment module 204 is used to adjust the crowding degree of the target stimulus object in the second image according to the training result of each training operation, combined with the ladder algorithm and the correlation coefficient, including: the first crowding adjustment unit is used to adjust the parameter combination according to the correlation coefficient and the QUEST algorithm to increase the crowding degree of the target stimulus object in the second image when the training result is correct for a first preset number of consecutive times; the second crowding adjustment unit is used to adjust the parameter combination according to the correlation coefficient and the QUEST algorithm to decrease the crowding degree of the target stimulus object in the second image when the training result is incorrect for a second preset number of consecutive times.

[0177] Furthermore, in some embodiments of this application, adjusting the parameter combination based on the correlation coefficient and the QUEST algorithm includes: the parameter combination includes the eccentricity of the target stimulus object in the image displayed by the amblyopia training device, the distance between the target stimulus object and the interference stimulus object, and the size of the target stimulus object; selecting the parameter to be adjusted from the parameter combination according to the QUEST algorithm; determining the adjustment amount of the parameter according to the correlation coefficient, and adjusting the parameter according to the adjustment amount.

[0178] Furthermore, in some embodiments of this application, the amblyopia training device includes: an eye-tracking instrument; the amblyopia training device control device further includes a stability detection module; the stability detection module includes: an eye-tracking instrument calibration unit, an eye position data reading unit, a gaze stability calculation unit, and a prompt information output unit; wherein, the eye-tracking instrument calibration unit is used to calibrate the eye-tracking instrument before generating the plurality of first images; the eye position data reading unit is used to read the eye position data of the training target collected by the eye-tracking instrument when performing the practice operation or the training operation; the gaze stability calculation unit is used to calculate the gaze stability of the training target based on the eye position data; and the prompt information output unit is used to output a prompt information when the gaze stability does not meet a preset condition.

[0179] Furthermore, in some embodiments of this application, the training operation includes a detection training operation, a discrimination training operation, and a reading training operation; wherein, the target stimulus object in the second image corresponding to the detection training operation is a fixed-direction number or letter visual target; the target stimulus object in the second image corresponding to the discrimination training operation is a random-direction number or letter visual target; and the target stimulus object in the second image corresponding to the reading training operation is a fixed-direction word visual target.

[0180] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement the amblyopia training device control method provided by any of the above-described method embodiments of this application.

[0181] In summary, the amblyopia training device control device provided in this application has the following advantages compared with the prior art: First, a practice operation is performed on each training target. Based on the results of the practice operation performed on each training target, the corresponding correlation coefficient is calculated for training targets with different visual sensitivities. This provides a basis for adjusting the second image when subsequent training targets perform training operations, thereby improving the control accuracy of the amblyopia training device. In addition, since the training process is dynamic, the visual sensitivity of the training target may change dynamically during the training process. Therefore, based on the results of each training session of the training target, the crowding degree of the generated second image is further adjusted by combining the step algorithm and accurate correlation coefficients. This allows for real-time tracking of the current trainee's visual sensitivity, thereby improving the control accuracy of the amblyopia training device in a dynamic training environment.

[0182] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0183] Based on the above-described embodiments of the amblyopia training device control method, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the amblyopia training device control method of any embodiment of this application.

[0184] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0185] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0186] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0187] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the amblyopia training device control method described in any of the above-described method embodiments of this application.

[0188] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

Claims

1. A control method for an amblyopia training device, characterized in that, include: Before the training target begins training, several first images are generated according to a preset combination of parameters, and the amblyopia training device is controlled to display the first images sequentially, so that the training target can perform corresponding practice operations based on each displayed first image; wherein, the images displayed by the amblyopia training device include: target stimulus objects; Collect accuracy data after the training target performs several of the aforementioned practice operations, and fit the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data; Once the training target begins training, the amblyopia training device is controlled to display a preset second image so that the training target can perform the corresponding training operation. Based on the training results of each training operation, and in conjunction with the ladder algorithm and the correlation coefficient, the crowding level of the target stimulus object in the second image is adjusted.

2. The control method for an amblyopia training device as described in claim 1, characterized in that, The fitting of the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data includes: The practice operation performed when the interfering stimulus object is present in the first image is taken as the first practice operation, and the practice operation performed when the interfering stimulus object is not present in the first image is taken as the second practice operation; the accuracy data includes a first accuracy rate after performing the first practice operation and a second accuracy rate after performing the second practice operation; each parameter combination corresponds to a first accuracy rate and a second accuracy rate; Based on the first accuracy rate and the second accuracy rate, calculate the degree of congestion corresponding to each of the parameter combinations; The correlation coefficient is fitted by nonlinear curve fitting, combining the parameter combinations and the crowding degree corresponding to each parameter combination.

3. The control method for an amblyopia training device as described in claim 2, characterized in that, The step of calculating the congestion level corresponding to each parameter combination based on the first accuracy and the second accuracy includes: calculating the difference between the second accuracy and the first accuracy, and using the ratio of the difference to the second accuracy as the congestion level.

4. The control method for an amblyopia training device as described in claim 2, characterized in that, The step of adjusting the crowding level of the target stimulus object in the second image based on the training results of each training operation, combined with the ladder algorithm and the correlation coefficient, includes: When the training result is correct for the first preset number of consecutive times, the parameter combination is adjusted according to the correlation coefficient and the QUEST algorithm to increase the crowding degree of the target stimulus object in the second image; When the training result is a second preset number of consecutive errors, the parameter combination is adjusted according to the correlation coefficient and the QUEST algorithm to reduce the crowding of the target stimulus object in the second image.

5. The control method for an amblyopia training device as described in claim 4, characterized in that, The step of adjusting the parameter combination based on the correlation coefficient and the QUEST algorithm includes: The parameter combination includes the eccentricity of the target stimulus object in the image displayed by the amblyopia training device, the distance between the target stimulus object and the interference stimulus object, and the size of the target stimulus object; According to the QUEST algorithm, select the parameter to be adjusted from the parameter combination; Based on the correlation coefficient, the adjustment amount of the parameter is determined, and the parameter is adjusted according to the adjustment amount.

6. The control method for an amblyopia training device as described in claim 1, characterized in that, The amblyopia training device includes: an eye-tracking instrument; the control method for the amblyopia training device further includes: The eye-tracking instrument is calibrated before generating the plurality of first images; When performing the exercise or training operation, the eye position data of the training target collected by the eye-tracking instrument is read. Based on the eye position data, the gaze stability of the training target is calculated; When the gaze stability does not meet the preset conditions, a prompt message is output.

7. The control method for an amblyopia training device as described in claim 1, characterized in that, The training operations include detection training operations, identification training operations, and reading training operations. Wherein, the target stimulus object in the second image corresponding to the detection training operation is a fixed-direction number or letter optotype; The target stimulus in the second image corresponding to the discrimination training operation is a random-direction number or letter visual target; The target stimulus in the second image corresponding to the reading training operation is a word visual target with a fixed orientation.

8. A control device for an amblyopia training device, characterized in that, include: The module includes a practice operation module, a correlation coefficient fitting module, a training operation module, and a crowding adjustment module. The practice operation module is used to generate several first images according to a preset parameter combination before the training target starts training, and control the amblyopia training device to display the first images sequentially, so that the training target can perform corresponding practice operations according to each displayed first image; wherein the images displayed by the amblyopia training device include: target stimulus objects; The correlation coefficient fitting module is used to collect the accuracy data after the training target performs several of the training operations, and to fit the correlation coefficient between the crowding degree of the target stimulus object and the parameter combination based on the accuracy data. The training operation module is used to control the amblyopia training device to display a preset second image after the training target starts training, so that the training target can perform corresponding training operations. The crowding adjustment module is used to adjust the crowding level of the target stimulus object in the second image based on the training results of each training operation, combined with the ladder algorithm and the correlation coefficient.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a control method for an amblyopia training device as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a control method for an amblyopia training device as described in any one of claims 1 to 7.

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