Strabismus Detection Method, Strabismus Detection Device and Wearable Device

Abstract

The present application relates to a strabismus detection method, a strabismus detection device and a wearable device. Among them, the strabismus detection method includes: obtaining the eye movement fluctuation information of each eye of a detection object based on a first detection rule; when the eye movement fluctuation information of each eye of the detection object is lower than a first threshold, obtaining the first eye movement information of each eye of the detection object when performing strabismus detection based on the first detection rule; wherein, the eye movement fluctuation information and the first eye movement information are associated with the change between the mean values of the eye movement direction vectors of the eyes during each eye movement plateau period when performing strabismus detection based on the first detection rule; when the first eye movement information of any eye is greater than a second threshold, it is determined that the detection object has an eye position deviation. It realizes an objective and unified eye position deviation measurement process and improves the efficiency of strabismus detection.

Description

Technical Field

[0001] This application relates to the technical field of eye tracking, and particularly to a strabismus detection method, a strabismus detection device, and a wearable device. Background Art

[0002] Strabismus refers to the inability of both eyes to simultaneously focus on the same target. When one eye fixates on the target, the line of sight of the other eye will deviate, resulting in asymmetry in the positions of the two eyes. For children in the developmental stage, strabismus is one of the risk factors for amblyopia. In addition, strabismus may also affect the development of visual function, motor function, and fine hand, brain, and eye coordination, causing significant negative impacts on the physical and mental health of patients. Qualitative and quantitative measurements of strabismus can evaluate whether the detected object has strabismus, as well as the type and degree of strabismus, so as to formulate a targeted treatment plan.

[0003] In current strabismus detection, it is often the case that clinicians subjectively detect and judge strabismus based on strabismus measurement devices, such as synoptophore, prism, penlight, and Maddox rod, in the clinic. This process depends on the measurement techniques, empirical judgment, and equipment accuracy of the clinicians, lacking an objective and unified measurement process, thus resulting in low efficiency of strabismus detection.

[0004] Regarding the problem of low efficiency of strabismus detection in the related art, no effective solution has been proposed yet. Summary of the Invention

[0005] In this embodiment, a strabismus detection method, a strabismus detection device, and a wearable device are provided to solve the problem of low efficiency of strabismus detection in the related art.

[0006] In a first aspect, in this embodiment, a strabismus detection method is provided, including:

[0007] Obtaining the eye movement fluctuation information of each eye of the detected object based on a first detection rule;

[0008] When the eye movement fluctuation information of each eye of the detected object is lower than a first threshold, obtaining the first eye movement information of each eye of the detected object when performing strabismus detection based on the first detection rule;

[0009] Wherein, the eye movement fluctuation information and the first eye movement information are associated with the change between the mean eye movement direction vectors of the eyes in each eye movement plateau period when performing strabismus detection based on the first detection rule;

[0010] When the first eye movement information of any eye is greater than a second threshold, determining that the detected object has an eye position deviation; wherein: determining the eye movement plateau period of the eye includes:

[0011] Collecting the eye movement direction vectors of the eye, and generating an eye movement waveform diagram based on the eye movement direction vectors;

[0012] Determine the effective eye movement waveform in the eye movement waveform diagram, and determine the eye movement plateau period of the eye according to the extreme points in the effective eye movement waveform diagram.

[0013] In some embodiments, it further includes:

[0014] Based on the second detection rule, obtain the second eye movement information of at least one eye of the detection object, and based on the second eye movement information, determine the type of eye position deviation of the detection object; the second eye movement information is associated with the change between the mean values of the eye movement direction vectors of the eye during each eye movement plateau period when detecting the strabismus type based on the second detection rule.

[0015] In some embodiments, the second detection rule is the cover-uncover detection rule; the obtaining of the second eye movement information of at least one eye of the detection object and the determination of the type of eye position deviation of the detection object based on the second eye movement information include:

[0016] Cover the first eye of the detection object.

[0017] Based on the second eye movement information of the second eye, determine whether the type of eye position deviation of the detection object is manifest strabismus, wherein the second eye movement information of the second eye is associated with the change between the mean values of the eye movement direction vectors of the second eye during the eye movement plateau period before and after covering.

[0018] If not, uncover the first eye, and based on the second eye movement information of the first eye, determine whether the type of eye position deviation of the detection object is latent strabismus, wherein the second eye movement information of the first eye is associated with the change between the mean values of the eye movement direction vectors of the first eye during the eye movement plateau period before and after uncovering.

[0019] If it is impossible to determine whether the type of eye position deviation of the detection object is latent strabismus based on the second eye movement information of the first eye, then cover the second eye of the detection object.

[0020] Based on the second eye movement information of the first eye, determine whether the type of eye position deviation of the detection object is manifest strabismus, wherein the second eye movement information of the first eye is associated with the change between the mean values of the eye movement direction vectors of the first eye during the eye movement plateau period before and after covering.

[0021] [[ID=

[0022] In some of these embodiments, the first detection rule is the alternate covering rule. Based on the first detection rule, eye movement fluctuation information of one eye of the detection object is obtained, including:

[0023] Based on the difference between the average values of the eye movement direction vectors of one eye during each eye movement plateau period in M alternate coverings, the eye movement fluctuation information of this eye is obtained; where M is greater than or equal to 2.

[0024] In some of these embodiments, the first detection rule is the alternate covering rule. When obtaining strabismus detection based on the first detection rule, the first eye movement information of one eye of the detection object includes:

[0025] Taking the absolute value of the average value of the differences between the average values of the eye movement direction vectors of one eye during each eye movement plateau period in M alternate coverings as the first eye movement information of this eye; where M is greater than or equal to 2.

[0026] In some of these embodiments, determining the valid eye movement waveform diagram in the eye movement waveform diagram includes:

[0027] Filtering the eye movement waveform diagram to remove invalid eye movement signals in the eye movement waveform diagram, and obtaining the valid eye movement waveform diagram.

[0028] In some of these embodiments, the method further includes:

[0029] If the detection object has eye position deviation;

[0030] Cover the first eye of the detection object;

[0031] Adjust the display position of the target object based on the moving direction and step size;

[0032] Uncover the first eye and cover the second eye of the detection object;

[0033] Judge whether the third eye movement information of the second eye is less than or equal to a third threshold; if so, determine the strabismus angle based on the current display position of the target object, the interpupillary distance of the detection object, and the distance from the eyes of the detection object to the target object; the third eye movement information of the second eye is associated with the change between the average values of the eye movement direction vectors of the second eye during the eye movement plateau period before and after covering the second eye; if not, repeat the above steps until the third eye movement information of the second eye is less than or equal to the third threshold to determine the strabismus angle.

[0034] In some of these embodiments: the step size includes an initial step size and an adaptive step size; wherein, the adaptive step size is associated with the third eye movement information corresponding to each covering and uncovering of the second eye;

[0035] The moving direction includes an initial moving direction and a target moving direction, and the target moving direction is associated with the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after each occlusion of the second eye.

[0036] In a second aspect, a strabismus detection device is provided in this embodiment, including: a fluctuation information acquisition module, a first eye movement information acquisition module, and a detection module; where:

[0037] The fluctuation information acquisition module is configured to acquire the eye movement fluctuation information of each eye of the detection object based on a first detection rule;

[0038] The first eye movement information acquisition module is configured to acquire the first eye movement information of each eye of the detection object when performing strabismus detection based on the first detection rule when the eye movement fluctuation information of each eye of the detection object is lower than a first threshold; wherein, the eye movement fluctuation information and the first eye movement information are associated with the change between the average eye movement direction vectors of the eyes during each eye movement plateau period when performing strabismus detection based on the first detection rule;

[0039] The detection module is configured to determine that the detection object has an eye position deviation when the first eye movement information of any eye is greater than a second threshold; where: determining the eye movement plateau period of the eye includes:

[0040] Collect the eye movement direction vectors of the eye, and generate an eye movement waveform diagram based on the eye movement direction vectors;

[0041] Determine the effective eye movement waveform diagram in the eye movement waveform diagram, and determine the eye movement plateau period of the eye according to the extreme points in the effective eye movement waveform diagram.

[0042] In a third aspect, a wearable device is provided in this embodiment, including a display component;

[0043] The display component is configured to display a target according to a received display control signal; the display control signal is generated according to the eye movement direction vector of the detection object when observing the target and an eye position deviation detection rule.

[0044] In some embodiments, the wearable device further includes a data acquisition component; where:

[0045] The data acquisition component is configured to acquire the eye movement direction vector of the detection object when observing the target.

[0046] In some embodiments, the wearable device further includes a strabismus detection processor; the strabismus detection processor is connected to the display component and the data acquisition component;

[0047] The strabismus detection processor is used to receive the eye movement direction vectors collected by the data acquisition component and send the display control signal to the display component; and send the strabismus detection result to the display component.

[0048] In some embodiments thereof, the wearable device is a head-mounted device.

[0049] Compared with the related art, in this embodiment, a strabismus detection method, a strabismus detection device and a wearable device are provided. The strabismus detection method therein obtains the eye movement fluctuation information of each eye of the detection object based on the first detection rule; when the eye movement fluctuation information of each eye of the detection object is lower than the first threshold, the first eye movement information of each eye of the detection object is obtained when performing strabismus detection based on the first detection rule; wherein, the eye movement fluctuation information and the first eye movement information are associated with the change between the mean values of the eye movement direction vectors of the eyes in each eye movement plateau period when performing strabismus detection based on the first detection rule; when the first eye movement information of any eye is greater than the second threshold, it is determined that the detection object has eye position deviation; wherein: determining the eye movement plateau period of the eye includes: collecting the eye movement direction vector of the eye, generating an eye movement waveform diagram based on the eye movement direction vector; determining the effective eye movement waveform diagram in the eye movement waveform diagram, and determining the eye movement plateau period of the eye according to the extreme points in the effective eye movement waveform diagram. The above processes can all be executed and completed by an electronic device with data operation capabilities, realizing the qualitative analysis of eye position deviation. Compared with the related art, this application eliminates the dependence on the professional's own experience, making the detection result more objective, more accurate, and the detection process will also be more efficient.

[0050] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. Description of the Drawings

[0051] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0052] Figure 1 is the hardware structure block diagram of the terminal of the strabismus detection method according to the embodiment of the present application;

[0053] Figure 2 is the flowchart of the strabismus detection method according to the embodiment of the present application;

[0054] Figure 3 is a schematic diagram of an adaptive filter according to an embodiment of the present application;

[0055] Figure 4 is a schematic diagram of strabismus angle calculation according to an embodiment of the present application;

[0056] Figure 5 is a flowchart of the strabismus detection method according to some embodiments of the present application;

[0057] Figure 6 is a structural block diagram of the strabismus detection device according to an embodiment of the present application;

[0058] Figure 7 is a schematic structural diagram of the wearable device according to an embodiment of the present application. Detailed Embodiments

[0059] To more clearly understand the purpose, technical solution, and advantages of the present application, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments.

[0060] Unless otherwise defined, the technical terms or scientific terms involved in the present application shall have the general meaning understood by those with ordinary skills in the technical field to which the present application belongs. In the present application, words such as "a", "one", "a kind of", "the", "these", etc. do not indicate a limitation in quantity, and they can be singular or plural. The terms "including", "comprising", "having" and any variants thereof involved in the present application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device including a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent in these processes, methods, products, or devices. The terms "connected", "coupled", etc. involved in the present application do not limit to physical or mechanical connections, but may include electrical connections, whether directly or indirectly. The term "plurality" involved in the present application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. Usually, the character " / " indicates that the associated objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in the present application are only used to distinguish similar objects and do not represent a specific sorting of the objects.

[0061] In the method embodiment provided in this embodiment, it can be executed on a terminal, a computer, or a similar computing device. For example, running on a terminal, Figure 1 is a hardware structural block diagram of the terminal of the strabismus detection method in this embodiment. As Figure 1 shown, the terminal may include one or more ( Figure 1A processor 102 (only one is shown in the figure) and a memory 104 for storing data, wherein the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 The structure shown is only schematic and does not limit the structure of the above terminal. For example, the terminal may further include more or fewer components than those Figure 1 shown in the figure, or have a different configuration from that Figure 1 shown in the figure.

[0062] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the strabismus detection method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0063] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by the communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0064] In this embodiment, a strabismus detection method is provided. Figure 2 is a flowchart of the strabismus detection method of this embodiment, as Figure 2 shown in the figure, and the process includes the following steps:

[0065] Step S210, based on the first detection rule, obtain the eye movement fluctuation information of each eye of the detection object.

[0066] First of all, it should be noted that all steps of the strabismus detection method provided in this embodiment can be implemented by a processor with data operation capabilities in a wearable device equipped with a display component, or by a terminal or server associated with the display component and independent of the display component. The detection object is the object that needs to be detected for eye position deviation. During the detection process, the display control can be performed on the display component worn by the detection object to form the occlusion and removal of occlusion of the detection object's field of view. Generally speaking, split-screen (left screen and right screen) display is achieved through the display component, that is, a target object is displayed in the center of the left screen and the right screen respectively. When the left and right screens are turned on simultaneously, based on the display of the left and right screens, the target object appears directly in front of the binocular field of view. Taking the example that the strabismus detection method runs on the processor in the wearable device, if the wearable device is a VR or AR glasses, when the strabismus detection method is executed, the detection object wears the VR glasses and sees the left screen and the right screen in the virtual space, as well as the presented target object through the VR glasses. The target object can be: a visual target, a geometric figure, or a light spot, etc., which are simple in shape, clear in structure, and easy to fixate on. The eye movement direction vector of the detection object when fixating on the target object can be collected by using the data acquisition component integrated in the wearable device or associated with the wearable device, such as an eye tracker.

[0067] This first detection rule can be specifically set according to the strabismus qualitative detection method. For example, the display of the display component fixated by the detection object is controlled based on the first detection rule, or the display presented in the virtual space is controlled based on the first detection rule.

[0068] In this embodiment, the first detection rule can be an alternating occlusion detection rule set based on the alternating occlusion detection method, or other occlusion rules that can play the function of strabismus qualitative detection like the alternating occlusion detection method. Exemplarily, the first detection rule can be a display control rule for the display component that can alternately form a field of view occlusion for the left and right eyes of the detection object. Based on this first detection rule, this step controls the display component to display the target object, and on this basis, collects the eye movement direction vector of the detection object when fixating on the target object.

[0069] Among them, the eye movement direction vector can be the difference in the eye ball direction between two moments, and the specific eye movement direction at any moment can be represented by a quaternion, which can effectively reduce the storage of the device and improve the calculation performance. Among them, the quaternion is a simple hypercomplex number, generally represented as q=(ω, x, y, z), where ω is the real part, and x, y, z are the imaginary parts.

[0070] After obtaining the quaternion representation of the above-mentioned eye movement direction vector, the Euler angles including three free variables (yaw, pitch, roll) can be obtained through coordinate system conversion. In this embodiment, only the eye position deviation in the horizontal direction can be considered, so only the rotation of the x-axis (roll angle) can be considered. Thus, the eye movement direction vector that can be used for eye movement information statistics can be obtained.

[0071] In most cases, for people with strabismus problems, the two eyes are coordinated, but there are also a few uncoordinated strabismus categories, such as restrictive strabismus and paralytic strabismus. Therefore, for coordinated strabismus, the strabismus can be judged based on only one eye. In this step, in order to take into account the uncoordinated strabismus situation as well to improve the screening sensitivity, the eye movement direction vectors of both eyes of the detection object are collected.

[0072] The above-mentioned eye movement plateau periods refer to: the eye movement plateau period when the left eye is covered (the right eye is not covered); the eye movement plateau period when the right eye is covered (the left eye is not covered).

[0073] Taking the first detection rule, the alternate cover test, as an example, here the fluctuation values of the left and right eyes of the detection object when the alternate cover is performed once are defined:

[0074] Assume the fluctuation value of the right eye is , and the fluctuation value of the left eye is . At this time, R 1. R 2. L 1. L 2 is specifically:

[0075] When the right eye is not covered (for example, the right screen of the head-mounted display component is turned on and the left screen is turned off): when the right eye is in the eye movement plateau period, the average value of the right eye movement direction vector R 1; when the left eye is in the eye movement plateau period, the average value of the left eye movement direction vector L 1.

[0076] When the right eye is covered (for example, the right screen of the head-mounted display component is turned off and the left screen is turned on): when the right eye is in the eye movement plateau period, the average value of the right eye movement direction vector R 2, and when the left eye is in the eye movement plateau period, the average value of the left eye movement direction vector L 2.

[0077] The process of obtaining the eye movement fluctuation information of each eye of the detection object under the first detection rule according to the change between the average values of the eye movement direction vectors of both eyes of the detection object during the eye movement plateau period can specifically be: calculating the variance of the above-mentioned fluctuation values of the left eye multiple times, and calculating the variance of the above-mentioned fluctuation values of the right eye multiple times., so as to obtain the eye movement fluctuation conditions of each eye of the detection object, that is, variance is the eye movement fluctuation information of the left eye, and variance is the eye movement fluctuation information of the right eye.

[0078] In addition, in addition to statistically calculating the fluctuation values based on variance to obtain the eye movement fluctuation information, the autocorrelation function, information entropy, etc. of the above-mentioned fluctuation values can also be calculated to statistically obtain the eye movement fluctuation information of each eye of the detection object.

[0079] Therefore, in step S210, under the first detection rule, the eye movement fluctuation information of each eye of the detection object can be obtained by collecting the eye movement direction vectors of each eye of the detection object multiple times. Specifically, the difference between the means of the eye movement direction vectors in the eye movement plateau period of any one eye collected before and after each occlusion can be used as the fluctuation value of one alternating occlusion; then, statistical calculations (such as calculating variance) for measuring the stable state are performed on the fluctuation values corresponding to multiple alternating occlusions, so as to obtain the eye movement fluctuation information to determine whether the eye movement of the detection object has reached a stable state.

[0080] Step S220, when the eye movement fluctuation information of each eye of the detection object is lower than the first threshold, obtain the first eye movement information of each eye of the detection object when performing strabismus detection based on the first detection rule; wherein, the eye movement fluctuation information and the first eye movement information are associated with the change between the means of the eye movement direction vectors of the eyes in each eye movement plateau period when performing strabismus detection based on the first detection rule.

[0081] Step S230, when the first eye movement information of any eye is greater than the second threshold, determine that the detection object has eye position deviation.

[0082] In this step, for the accuracy of strabismus detection, the subsequent judgment of whether there is eye position deviation (i.e., strabismus) can be performed on the premise that the eye movement fluctuations of both eyes of the detection object are stable. When the eye movement fluctuation information in step S210 is variance, the above first threshold can be a variance threshold. The variance of the fluctuation values of the left eye can be calculated M times , and the variance of the fluctuation values of the right eye can be calculated M times , when and are both less than the first threshold, it is confirmed that the eye movement fluctuation of the detection object has reached a stable state. Next, determine the first eye movement information of the detection object. Continuing to take the detection of the detection object using the head-mounted display component and taking the alternating occlusion detection rule as the first detection rule as an example, the determination process of the first eye movement information is described:

[0083] First, in the initial state, both the left and right screens of the head-mounted display device are turned on, and the target objects are respectively located in the middle of the two screens. For one alternation:

[0084] When the left screen is closed and the right screen is turned on, the average value of the eye movement direction vectors of the right eye during its eye movement plateau period is obtained respectively. R 1, and the average value of the eye movement direction vectors of the left eye during the eye movement plateau period. L 1.

[0085] Among them, R 1 is obtained by summing the vectors of the eye movement direction vectors of the right eye during the eye movement plateau period in the horizontal dimension and then dividing by N . ( , T is the duration of the eye movement plateau period, t f is the time required to record an eye movement direction vector, that is, in this embodiment, it is taken at a certain frame rate N , for example , that is, 90 points are continuously taken within one second).

[0086] L The calculation method of 1 is the same as that of R 1, and will not be elaborated here.

[0087] When the right screen is closed and the left screen is turned on, the average value of the eye movement direction vectors of the left eye during its eye movement plateau period is obtained respectively L 2, and the average value of the eye movement direction vectors of the right eye during its eye movement plateau period R 2.

[0088] Calculate , respectively, and take the calculated difference as the change between the average values of the eye movement direction vectors of the left and right eyes corresponding to one alternating cover during the eye movement plateau period.

[0089] After performing M alternating covers, for the left eye: M ( ) values are obtained, and for the right eye, M ( ) values are obtained. Calculate the variance of M ( ) or the variance of M ( ) , and judge the eye movement situation when and are both less than the first threshold.

[0090] Calculate the average value of M ( ) L a (this value is used to measure the eye movement situation of the left eye, and its absolute value is used as the first eye movement information of the left eye), or calculate the average value of M ( ) R a(This value is used to measure the eye movement of the right eye, and its absolute value serves as the first eye movement information of the right eye). If L a the absolute value of or R a the absolute value of is greater than the second threshold, it is confirmed that the detected object has eye position deviation. In particular, it can also be based on L a or R a the positive and negative signs of to determine whether the strabismus of the detected object is esotropia or exotropia. Generally speaking: for the left eye, L a being negative is exotropia, L a being positive is esotropia; for the right eye, R a being negative is esotropia, R a being positive is exotropia. It should be noted that the positive and negative in this embodiment are relative concepts and need to be determined according to the definition when the eye movement tracker outputs the eye movement vector. In this embodiment, for the left eye, from the nasal side to the temporal side, the value of the eye movement direction vector decreases; for the right eye, from the nasal side to the temporal side, the value of the eye movement direction vector increases.

[0091] The above eye movement direction vector can be specifically implemented based on eye movement tracking technology. An eye movement tracking detector can be used to obtain the eye movement direction vector. Among them, based on the principle of the above eye movement tracking technology, different components can be combined to implement a data acquisition component for the eye movement direction vector to collect the eye movement direction vector. Or a display device that itself integrates an eye movement tracking detector can be directly used, such as a virtual reality (VR) head-mounted display device. When implementing the strabismus detection method of this embodiment based on the VR head-mounted display device, the eye movement direction vector can be directly obtained based on the application programming interface (API for short) provided by the display device itself. In this way, based on an electronic device such as a computer with data operation and processing capabilities, qualitative analysis of the eye position deviation of the detected object can be realized based on the collected eye movement direction vector.

[0092] In related technologies, usually professionals such as physicians subjectively detect and judge strabismus manually according to the strabismus measurement process. That is to say, there is currently no fixed, unified, and standardized strabismus detection workflow in clinical practice. Currently, common strabismus measurement methods include qualitative detection using the cover-uncover method and the alternate cover test, as well as the corneal reflection method and the prism corneal reflection method. The existing strabismus measurement methods in related technologies lack objective means and rely on the experience of professional physicians and the accuracy of professional detection equipment, resulting in low efficiency of strabismus detection.

[0093] Through the above steps S210 to S230, this embodiment provides an objective and standardized workflow for detecting eye position deviation. From the data collection of the eye movement direction vector, data processing to the final data analysis to obtain the detection result of eye position deviation, the entire process can be executed and completed by an electronic device with data operation capabilities, realizing the qualitative analysis of eye position deviation. Compared with related technologies, it eliminates the dependence on the experience of professionals, making the detection result more objective and accurate, and the detection process will also be more efficient.

[0094] Therefore, in the above steps S210 to S230, based on the first detection rule, the eye movement fluctuation information of each eye of the detection object is obtained; when the eye movement fluctuation information of each eye of the detection object is lower than the first threshold, the first eye movement information of each eye of the detection object is obtained when performing strabismus detection based on the first detection rule; among them, the eye movement fluctuation information and the first eye movement information are related to the change between the mean values of the eye movement direction vectors of the eyes during each eye movement plateau period when performing strabismus detection based on the first detection rule; when the first eye movement information of any eye is greater than the second threshold, it is determined that the detection object has eye position deviation. It realizes an objective and unified strabismus detection process and improves the efficiency of strabismus detection.

[0095] Among them, in one embodiment, the above strabismus detection method may further include:

[0096] Based on the second detection rule, the second eye movement information of at least one eye of the detection object is obtained, and based on the second eye movement information, the type of eye position deviation of the detection object is determined; the second eye movement information is related to the change between the mean values of the eye movement direction vectors of the eyes during each eye movement plateau period when performing strabismus type detection based on the second detection rule.

[0097] The above second detection rule may be a cover-uncover detection rule for identifying the type of eye deviation of a detection object when it is determined that the detection object has eye deviation. Similarly, the second detection rule is also a display control rule that acts on a display component to cover and uncover the visual field of the detection object. Specifically, the second detection rule may be a detection rule set based on the cover-uncover detection method (a rule for covering and uncovering one eye of at least one eye of the detection object), or other cover rules that can also achieve the identification of the type of eye deviation.

[0098] Among them, in order to achieve independent covering of the binocular visual fields of the detection object, the binocular visual fields of the detection object can be separated. In this embodiment, the methods for separating binocular visual fields include but are not limited to: split-screen display, providing polarized glasses for the detection object, and providing red-green glasses for the detection object. Among them, split-screen display specifically means that the display component includes two independent display screens, corresponding to the left eye and the right eye of the detection object respectively. These two display screens can display the same or different images. The turning off and turning on of the two display screens are independent of each other. The detection object wears polarized glasses to achieve the separation of binocular visual fields. Specifically, the polarization directions of the polarized lenses corresponding to the left and right eyes are different. By using the display component to provide only the polarized light with the polarization direction corresponding to the left eye or the polarization direction corresponding to the right eye, the separation of the visual field of one eye is achieved. The detection object wears red-green glasses to achieve the separation of binocular visual fields. Specifically, one lens is set as a red lens and the other lens is set as a green lens. When the target object on the display component is red, the visual field of the eye corresponding to the red lens is equivalent to being covered. Similarly, when the target object on the display component is green, the visual field of the eye corresponding to the green lens is equivalent to being covered. Those skilled in the art can understand that different methods can be set according to the requirements of the actual application scenario to achieve the separation of binocular visual fields, so as to achieve independent covering of binocular visual fields according to different covering rules.

[0099] The above type of eye deviation may characterize the ability of fusion control. Manifest strabismus cannot be controlled by the binocular fusion mechanism, while latent strabismus can maintain binocular single vision under the control of the fusion reflex without eye deviation. However, when the brain fusion is blocked or out of control, this potential eye deviation will appear. Therefore, based on the difference between manifest strabismus and latent strabismus, the above second detection rule can be set to achieve the display control of the display component, and then the data acquisition and processing of the eye movement direction vector are realized under this display control to obtain the second eye movement information, and the type of eye deviation of the detection object is determined according to the second eye movement information.

[0100] In this embodiment, the identification of the type of eye deviation is realized, and the qualitative detection of eye deviation can be realized in a standardized and unified process, thereby improving the accuracy and efficiency of eye deviation detection.

[0101] Among them, in one embodiment, the second detection rule is the cover-uncover detection rule; obtaining the second eye movement information of at least one eye of the detection object, and based on the second eye movement information, determining the type of eye position deviation of the detection object, including:

[0102] Cover the first eye of the detection object; based on the second eye movement information of the second eye, determine whether the type of eye position deviation of the detection object is manifest strabismus. Among them, the second eye movement information of the second eye is associated with the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after covering; if not, uncover the first eye, and based on the second eye movement information of the first eye, determine whether the type of eye position deviation of the detection object is latent strabismus. Among them, the second eye movement information of the first eye is associated with the change between the average eye movement direction vectors of the first eye during the eye movement plateau period before and after uncovering; if it is impossible to determine whether the type of eye position deviation of the detection object is latent strabismus based on the second eye movement information of the first eye, then cover the second eye of the detection object; based on the second eye movement information of the first eye, determine whether the type of eye position deviation of the detection object is manifest strabismus. Among them, the second eye movement information of the first eye is associated with the change between the average eye movement direction vectors of the first eye during the eye movement plateau period before and after covering; if it is impossible to determine whether the type of eye position deviation of the detection object is manifest strabismus based on the second eye movement information of the first eye after covering the second eye of the detection object, uncover the second eye, and based on the second eye movement information of the second eye, determine whether the type of eye position deviation of the detection object is latent strabismus. Among them, the second eye movement information of the second eye is associated with the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after uncovering.

[0103] Next, taking the use of a head-mounted display device to perform split-screen display on the detection object and adopting the cover-uncover rule as the second detection rule as an example, the process of determining the type of eye position deviation will be described:

[0104] In the initial state, both the left and right screens are turned on, and the target objects are respectively located in the middle of the two screens.

[0105] Calculate the average value of the right eye movement direction vector when the right eye is in the eye movement plateau period R 3. Turn off the left screen and calculate the average value of the right eye movement direction vector when the right eye is in the eye movement plateau period R 4 (wherein, when detecting, either the left eye can be covered first or the right eye can be covered first. Here, the example of covering the left eye first is used for illustration).

[0106] Calculate the absolute value of The absolute value of is the current second eye movement information of the right eye. If the absolute value of is greater than the preset threshold, it is manifest strabismus;

[0107] Otherwise, turn on the left screen and calculate the average value of the left eye's eye movement direction vector when the left eye is in the eye movement plateau phase. L 4;

[0108] Calculate the average value of the left eye's eye movement direction vector when the left eye is in the eye movement plateau phase before the left screen is turned on (i.e., when the left screen is turned off). L 3;

[0109] Calculate the absolute value of The absolute value of is the second eye movement information of the current left eye.

[0110] If the absolute value of is greater than the preset threshold, it is latent strabismus;

[0111] If the absolute value of is less than or equal to the preset threshold, turn off the right screen and calculate the average value of the left eye's eye movement direction vector when the left eye is in the eye movement plateau phase. L 5;

[0112] Before turning off the right screen (i.e., when the left screen was turned on in the previous step), the average value of the left eye's eye movement direction vector when the left eye is in the eye movement plateau phase is L 4;

[0113] Calculate the absolute value of The absolute value of is the second eye movement information of the current left eye. If the absolute value of is greater than the preset threshold, it is manifest strabismus;

[0114] If the absolute value of is less than or equal to the preset threshold, turn on the right screen and calculate the average value of the right eye's eye movement direction vector when the right eye is in the eye movement plateau phase. R 6;

[0115] Calculate the average value of the right eye's eye movement direction vector when the right eye is in the eye movement plateau phase before the right screen is turned on (i.e., when the right screen is turned off). R 5;

[0116] Calculate the absolute value of The absolute value of is the second eye movement information of the current right eye. If the absolute value of is greater than the preset threshold, it is latent strabismus.

[0117] In particular, if the calculated is less than or equal to the preset threshold, suspicious strabismus can be reported and a prompt message for suggesting follow-up in the strabismus and amblyopia specialist department can be generated. At this time, there are cases where the detection object does not cooperate well or extremely rare special strabismus, such as intermittent strabismus with excellent control ability, which requires further intervention by a physician for confirmation.

[0118] In this embodiment, according to the second detection rule, when it is determined that the detected object has eye position deviation, the recognition of the type of eye position deviation is further realized, so that more accurate eye position deviation detection can be provided.

[0119] In one embodiment, the first detection rule is the alternating cover test rule. Based on the first detection rule, the eye movement fluctuation information of one eye of the detected object is obtained, including:

[0120] Based on the difference between the average values of the eye movement direction vectors of one eye during each eye movement plateau period after M times of alternating cover test, the eye movement fluctuation information of this eye is obtained; where M is greater than or equal to 2. In practical applications, M can be 3. Specifically, the process of obtaining the eye movement fluctuation information of each eye after M times of alternating cover test can refer to the above process of obtaining multiple fluctuation values and calculating the variances of the multiple left-eye fluctuation values and the variances of the multiple right-eye fluctuation values The process is not described in detail here.

[0121] In one embodiment, the first detection rule is the alternating cover test rule. When obtaining the strabismus detection based on the first detection rule, the first eye movement information of one eye of the detected object includes:

[0122] The absolute value of the mean of the differences between the average values of the eye movement direction vectors of one eye during each eye movement plateau period after M times of alternating cover test is the first eye movement information of this eye; where M is greater than or equal to 2. In practical applications, M can be taken as 3. Specifically, the process of obtaining the first eye movement information of one eye after M times of alternating cover test can refer to the above process of obtaining the average value L a 、 R a The process is not described in detail here.

[0123] In addition, in one embodiment, determining the eye movement plateau period of the eye may include:

[0124] Collect the eye movement direction vector of the eye, generate an eye movement waveform diagram based on the eye movement direction vector; remove the invalid eye movement signals in the eye movement waveform diagram to obtain an effective eye movement waveform diagram; determine the extreme points in the effective eye movement waveform diagram; determine the eye movement plateau period of this eye based on the extreme points in the effective eye movement waveform diagram.

[0125] In this embodiment, specifically:

[0126] In the process of generating an eye movement waveform diagram, the collected eye movement direction vectors can be preprocessed first. The preprocessing can include denoising and coordinate transformation. That is, denoise the collected eye movement direction vectors, and then transform the coordinate system of the denoised eye movement direction vectors. Use the eye movement direction vectors with the transformed coordinate system to generate the eye movement waveform diagram. At this time, the abscissa of the eye movement waveform diagram can be time or frame number; the ordinate can be the Euler angle of the eye movement direction vector obtained after coordinate transformation of the eye movement direction vector. It should be noted that in this embodiment, the eye movement direction vector is transformed into the Euler angle as the vertical axis of the eye movement waveform diagram. In practical applications, the eye movement direction vector can be transformed into the required coordinate system according to needs to generate the eye movement waveform diagram.

[0127] Next, remove the invalid eye movement signals in the eye movement waveform diagram to obtain a valid eye movement waveform diagram; among them, the invalid eye movement signals can include: eye movement direction vectors collected due to the user closing their eyes or nystagmus. Specifically, the invalid eye movement signals can be removed by filtering the eye movement waveform diagram. Determine the extreme points of the above-mentioned valid eye movement waveform diagram; the extreme points refer to the peaks or valleys of the valid eye movement waveform diagram.

[0128] Finally, determine the eye movement plateau period based on the extreme points of the valid eye movement waveform diagram. As an implementable manner, the peaks or valleys of the valid eye movement waveform diagram can be found first. After determining the peaks or valleys, the flat curves composed of the points near the peaks or the points near the valleys are confirmed as the curves where the eye movement plateau period is located. The points near the peaks or valleys need to satisfy that the standard deviation of the eye movement direction vectors corresponding to these points is less than a preset value. That is, judge the peaks or valleys of the signal through the feature extraction of the eye movement signal, and then determine the corresponding plateau period according to the peaks or valleys. It should be noted that the method for determining the eye movement plateau period can be diverse. For example, when determining the points near the peaks or valleys, it can also be determined based on probability expectation. Therefore, the above method for determining the eye movement plateau period should not be used as a limitation to the technical solution of the present invention.

[0129] In this embodiment, determining the eye movement information and the eye movement information of the eyes based on the change between the mean values of the eye movement direction vectors in the eye movement plateau period can improve the accuracy of strabismus detection to a certain extent.

[0130] Furthermore, when removing the invalid eye movement signals in the eye movement waveform diagram (denoising the eye movement waveform diagram), considering that there are large differences in the eye movement waveforms among individuals, it is difficult to use a fixed filter parameter to denoise the eye movement waveform diagrams of different people. Therefore, in order to achieve high-robustness filtering of the eye movement waveform diagram, this embodiment can use an adaptive filtering method to filter the eye movement waveform diagram to remove the invalid eye movement signals in the eye movement waveform diagram.

[0131] Specifically, during the filtering process, the filter parameters obtained at the previous moment can be utilized to achieve the filtered result after filtering, and then the filter parameters used for filtering at the current moment can be adjusted so that the filtered eye movement waveform signal adapts to the statistical characteristics in the case of the noise position or the statistical characteristics changing with time, thereby achieving the purpose of optimal filtering of the eye movement waveform diagram.

[0132] More specifically, Figure 3 is a schematic diagram of an adaptive filtering for this embodiment. As Figure 3 shown, the eye movement waveform signal corresponding to the eye movement waveform diagram x ( n ) is used as the input signal. After being input into the digital filter with adjustable parameters, an output signal y ( n ) filtered with the current filter parameters is generated. The output signal y ( n ) is compared with the reference signal d ( n ) to obtain the error signal e( n ):

[0133]

[0134]

[0135] Based on the adaptive algorithm, the above-mentioned filter parameters are dynamically adjusted until the root mean square of the above-mentioned error signal is minimized. The adjustment of the filter parameters can be expressed as:

[0136]

[0137] Wherein, x ( n ) is the input eye movement waveform signal, w ( n ) is the current filter parameter, y ( n ) is the output signal, d ( n ) is the reference signal, e( n ) is the error signal, w ( n+ 1) is the filter parameter for the next iteration, μ is the learning factor.

[0138] In this embodiment, according to the requirements of the actual application scenario, time-domain filtering or frequency-domain filtering of the eye movement waveform can be combined with the above-mentioned adaptive filtering to achieve more flexible and efficient extraction of eye movement information in a dynamic and complex signal environment. Among them, time-domain filtering can be implemented based on moving average filtering, median filtering, etc. Taking moving average filtering as an example, the output feedback of the adaptive filter is used to adjust the window size or coefficients of the moving average filter. Moving average filtering aims to smooth time series data by calculating the sliding average of several consecutive data points in time, so as to reduce random fluctuations. The specific working principle is as follows: for the data point at the target moment, the average value of the data within the sample window of the preset time length at the target moment is calculated as the output value of the data point at the target moment. When the sample window moves along the time dimension, the average value is calculated based on the data falling into the moved sample window as the output value of the new target moment data point until the smoothing process of all data points in the time series is completed, and the finally smoothed time series data is obtained. The mathematical expression of the moving average filter can be:

[0139]

[0140] Among them, is the eye movement waveform signal input to the moving average filter, N is the length of the sample window of the moving average filter, and this sample window length can determine the length of the data sequence used for averaging processing; n is the target moment, and the output signal of the moving average filter at the target moment is ; m is the m th data point within the sample window.

[0141] Among them, moving average filtering also includes: unweighted moving average filtering and weighted moving average filtering. Among them, unweighted moving average filtering assigns the same weight to all data within the sample window for averaging; weighted moving average filtering assigns different weights to different data points within the sample window and makes the sum of the weights equal to 1. In particular, for the exponentially weighted moving average filter, higher weights are assigned to the data points within the preset range of the eye movement waveform data points to be smoothed currently, while lower weights are assigned to the data points outside the preset range but also falling within the sample window, and the weights decrease exponentially with the degree of deviation from the data points to be balanced currently in time.

[0142] In this embodiment, by adopting moving average filtering, the measurement error and noise components in the eye movement waveform diagram can be reduced, and the accuracy of the final eye position deviation detection can be improved.

[0143] An adaptive filter is designed in the frequency domain, and the gain of the frequency domain filter is adjusted to adapt to the spectral characteristics of the signal. An adaptive algorithm is used to adjust the filter parameters in the frequency domain. For example, frequency domain filtering can be implemented based on Fourier frequency domain filtering. A moving average filter performs smoothing filtering by convolving data in the time domain. Time domain convolution is equivalent to frequency domain multiplication. Unlike the aforementioned moving average filter, which performs convolution in the time domain, the purpose of Fourier frequency domain filtering is to obtain the required eye movement waveform signal in a specific frequency range through frequency domain filtering based on the different frequency characteristics of different eye movement waveform signals, such as the frequency range of the eye movement waveform signal during the plateau period. When using Fourier frequency domain filtering, a low-pass filter or a band-pass filter can be selected to implement frequency domain filtering based on the requirements of the actual application scenario.

[0144] Among them, the low-pass filter only allows low-frequency signals to pass through while blocking high-frequency signals. Therefore, by designing a low-pass filter, high-frequency noise with a frequency higher than a preset frequency in the eye movement waveform signal can be removed. Here, noise caused by electronic noise or rapid tremor can be removed. The bandpass filter can allow eye movement waveform signals within a specific frequency range to pass through. Therefore, low-frequency components with a frequency lower than a preset first frequency and high-frequency components with a frequency higher than a preset second frequency can be removed simultaneously. For example, slow drift signals and rapid tremor signals in the eye movement waveform signal can be filtered out based on the bandpass filter. Those skilled in the art should understand that a low-pass filter or a bandpass filter can be selected according to the requirements of the actual application scenario.

[0145] Next, we will take the Fourier frequency domain filtering with a low-pass filter as an example. x ( t ), the corresponding Fourier transform for:

[0146]

[0147] in, f Represents the frequency of the eye movement waveform signal in Hertz. t Represents the time dimension of the eye movement waveform signal.

[0148] In an ideal state, the frequency response of the low-pass filter is for:

[0149]

[0150] in, is the cut-off frequency, which is determined according to the distributions of the desired signal and the eye movement noise signal in the frequency domain. is multiplied by the frequency response of the filter to obtain the frequency representation of the filtered signal :

[0151]

[0152] After that, the above is subjected to an inverse Fourier transform to obtain the filtered output signal in the time domain . Since is 0 at , the integral processing of the above inverse Fourier transform can be simplified to:

[0153]

[0154] In particular, in actual application scenarios, the impulse response of the function of an ideal low-pass filter will result in an infinite time delay. For this, an approximate method can be used, such as using a suitable window function to design an actual frequency-domain filter, and an adaptive filter can adjust the parameters of the frequency-domain filter in real time.

[0155] Based on this, signals outside the desired frequency domain can be filtered out based on Fourier frequency-domain filtering, and the eye movement waveform signals for effectively realizing eye position deviation detection can be retained, thereby improving the accuracy of subsequent eye movement deviation detection. Additionally, based on adaptive filtering, it is also possible to perform adaptive digital filtering processing in a more robust manner for the differences in eye movement waveforms of different detection objects, thereby improving the accuracy of the filtering result and further improving the accuracy of strabismus detection.

[0156] In addition to the above adaptive filtering, other filtering algorithms, such as the least mean square algorithm, the Kalman filter, etc., can also be used to denoise the eye movement waveform diagram. The specific filtering method is not limited in this embodiment.

[0157] In one embodiment, a strabismus detection method is provided. After determining that the detection object has an eye position deviation by using the above steps, the strabismus angle can be further determined. The strabismus detection method further includes the following steps:

[0158] If there is an eye deviation in the detection object; cover the first eye of the detection object; adjust the display position of the target object based on the moving direction and the step size; uncover the first eye and cover the second eye of the detection object; determine whether the third eye movement information of the second eye is less than or equal to the third threshold; if so, determine the strabismus angle based on the current display position of the target object, the interpupillary distance of the detection object, and the distance from both eyes to the target object; the third eye movement information of the second eye is related to the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after covering the second eye; if not, repeat the above steps until the third eye movement information of the second eye is less than or equal to the third threshold to determine the strabismus angle.

[0159] In this embodiment, the above step size includes an initial step size and an adaptive step size; wherein, the adaptive step size is related to the corresponding third eye movement information before and after covering the second eye each time; the moving direction includes an initial moving direction and a target moving direction, and the target moving direction is related to the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after covering the second eye each time.

[0160] It can be understood that, similar to the determination of whether there is strabismus in the previous steps, when detecting the strabismus angle, it is still displayed in split screen through the display component, that is, a target object is displayed in the center of the left screen and the right screen respectively. When the left and right screens are turned on at the same time, based on the display of the left and right screens, the target object will appear directly in front of the binocular vision. When detecting the strabismus angle, the healthy eye of the detection object can be covered as the first eye first. In addition, the detection of the strabismus angle is usually also carried out by alternately covering with the default state that both eyes of the detection object are open, and the alternate covering can correspond to the closing of the left and right screens of the display device. For example: covering the right eye corresponds to closing the right screen, and covering the left eye corresponds to closing the left screen.

[0161] Taking the left eye as the first eye as an example, the determination of the strabismus angle in this embodiment will be described below.

[0162] Cover the left eye of the detection object, that is, close the left screen and turn on the right screen. At this time, the target object on the right screen appears in the center of the right screen.

[0163] Calculate the average value of the right eye movement direction vector of the right eye during the eye movement plateau period when the left screen is closed and the right screen is turned on .

[0164] Before turning on the left screen, the position of the target object on the left screen needs to be determined first. In this embodiment, when initially moving the target object on the left screen, the initial moving direction and the initial step size can be determined in the following way:

[0165] For the initial moving direction, it can be determined according to whether the detection object is esotropia or exotropia. As can be seen from the above, whether the left eye is esotropia or exotropia is determined by La The symbol of a determines whether the right eye is esotropic or exotropic, which is determined by R a The symbol of a , which will not be elaborated here. For esotropia, the target usually moves inward, that is, in the direction towards the nasal side. For exotropia, the target usually moves outward, that is, in the direction towards the temporal side.

[0166] For the initial step it can be obtained by the following formula:

[0167]

[0168] where is the distance from the eye to the screen, PD is the interpupillary distance, is the convergence angle (which can be calculated according to the interpupillary distance PD and specifically:

[0169]

[0170] Additionally, is the eye movement angle, which can be: the mean of the differences between the mean eye movement direction vectors of the left eye during each eye movement plateau period in M alternating cover tests L a the corresponding angle (such as Euler angle) or the mean of the differences between the mean eye movement direction vectors of the right eye during each eye movement plateau period in M alternating cover tests R a the corresponding angle (such as Euler angle). For this embodiment, since the left eye is taken as the first eye for illustration, therefore, the here is R a the corresponding angle.

[0171] In other embodiments, the initial step can also be determined according to actual needs.

[0172] After knowing the initial step and the initial movement direction, adjust the target on the left screen located at the center of the left screen to the corresponding position (display position) according to the initial step and the initial movement direction. This position is the current display position of the target when the left screen is turned on.

[0173] Turn on the left screen and turn off the right screen (at this time, the target on the left screen has moved to the corresponding position), and calculate the mean of the right eye movement direction vectors of the right eye during the eye movement plateau period after the right screen is turned off .

[0174] Calculate Whether the absolute value of is less than or equal to the third threshold; if so, determine the display position of the target on the left screen according to the initial step size and the initial moving direction, and determine the strabismus angle based on the current display position of the target, the interpupillary distance of the detection object, and the distance from the eyes of the detection object to the target.

[0175] Figure 4 It is a schematic diagram of the calculation of the strabismus angle in an embodiment of the present application. Refer to Figure 4 , Figure 4 In, O refers to the center of the display screen, which is the center of the left screen for the left screen and the center of the right screen for the right screen. The current display position of the target refers to the position of the target relative to the center point O. The strabismus angle is composed of and . Specifically: The strabismus angle can be obtained through the following formula:

[0176]

[0177] where PD is the interpupillary distance, is the distance from the eyes to the target, dt is the distance between the current position of the target and the center point O (that is, the absolute value of the current position coordinates of the target).

[0178] For , since the target is presented on the display screen, the distance from the eyes to the target is also the distance from the eyes to the display screen. For the virtual space, the distance from the eyes to the target is the distance from the eyes to the virtual screen.

[0179] If the absolute value of (the current third eye movement information of the right eye) is greater than the third threshold, then it is possible to: adjust the position of the target on the left screen according to the adaptive step size and the target movement direction. To ensure that when the left screen is turned on next time, the target has moved to the corresponding position.

[0180] In this embodiment, the adaptive step size can be output by PID, and the size of the adaptive step size is related to the third eye movement information corresponding to before and after covering the right eye this time. In other embodiments, after the target is initially moved through the initial step size and the initial moving direction, a fixed step size can also be used to move the target subsequently, and the size of the fixed step size can be determined according to actual experience.

[0181] The target movement direction can be determined according to the sign of. In this embodiment, when it is positive, the target moves in the temporal direction (away from the center point O), when it is negative, the target moves in the nasal direction (towards the center point O).

[0182] Since the absolute value of is greater than the third threshold, therefore, repeat the above-mentioned alternate occlusion one more time, that is:

[0183] Close the left screen and turn on the right screen; calculate the average value of the right eye movement direction vector of the right eye during the eye movement plateau period after the left screen is closed and the right screen is turned on .

[0184] Turn on the left screen and close the right screen, and calculate the average value of the right eye movement direction vector of the right eye during the eye movement plateau period after the left screen is turned on . (Before the left screen is turned on, the target has moved to a new display position based on the previous display position, according to the adaptive step and the target movement direction).

[0185] Calculate whether the absolute value of (the third eye movement information of the second eye during this alternate occlusion process) is less than or equal to the third threshold. If so, determine the strabismus angle according to the current display position of the target display, the interpupillary distance of the detection object, and the distance from the detection object's eyes to the target (see the aforementioned strabismus angle calculation formula).

[0186] If the absolute value of is still greater than the third threshold, then continue to determine the position to which the target will move during the next alternate occlusion according to the adaptive step and the target movement direction. Similarly, continue to execute the alternation of "close the left screen and turn on the right screen; turn on the left screen and close the right screen" until:

[0187] the absolute value of is less than or equal to the third threshold (here n is greater than or equal to 1).

[0188] Among them, is the average value of the right eye movement vector of the right eye during the eye movement plateau period after the left screen is turned on and the right screen is closed during the th n alternate occlusion; then is the average value of the right eye movement direction vector of the right eye during the eye movement plateau period after the left screen is closed and the right screen is turned on during the th n alternate occlusion.

[0189] It should be noted that the above takes the initial state where both the left and right screens are turned on (which can be understood as the state where the eyes of the detection object are open) as an example of alternately covering the left and right eyes to detect the strabismus angle. When performing the alternate cover test, the strabismus angle can also be calculated in the order of "turn off the right screen, turn on the left screen, turn on the right screen, and turn off the left screen". Therefore, the order of alternate cover test should not be regarded as a limitation on the technical solution of the present invention. In addition, in practical applications, the strabismus angle detection method of this embodiment can also be used to perform multiple tests on the strabismus angle, and the multiple measured strabismus angles are statistically averaged, and the average value is used as the final strabismus angle.

[0190] In this solution, the inventor considered that the measurement of the strabismus angle can be abstracted as a process of gradually approaching from a certain initial point to a target point. Among them, the initial point can correspond to the third eye movement information when it is determined that the detection object has eye position deviation. The target point can be the third eye movement information when the eyes return to the orthotropic position, which can be specifically determined according to the above or to determine. The measurement process of the strabismus angle can be: a process of gradually approaching from the initial position (eye movement at the strabismus position) to the target position (eye movement returning to the orthotropic position). Based on this, the inventor proposed that a PID controller can be used to dynamically and adaptively adjust the step size of the target object, so as to find the best moment when the eye movement stops under alternate cover during multiple alternate cover tests (that is, when the third eye movement information corresponding to the current alternate cover is less than or equal to the third threshold). The PID controller can quickly and accurately perform dynamic and adaptive adjustment on the step size of the target object movement, and then can adjust the position of the target object during the strabismus angle detection according to the adaptive step size. When the third eye movement information is less than or equal to the third threshold during a certain alternate cover test, the strabismus angle of the detection object can be determined.

[0191] In this embodiment, the adaptive step size can be calculated by the following formula:

[0192]

[0193] Among them, is the adaptive step size, , is the difference between the average eye movement direction vector of the second eye in the eye movement plateau period at the current moment and the average eye movement direction vector of the second eye in the eye movement plateau period at the initial moment; among them, the initial moment refers to the moment when the healthy eye of the detection object is first covered during the detection of the strabismus angle, and the other eye is in the orthotropic position without being covered; Specifically, it can be based on the above or to represent; is the proportional coefficient, is the integral coefficient, is the differential coefficient. In the formula, t refers to each iteration process, the t th control period.

[0194] For the process of detecting the strabismus angle, in the case of alternate occlusion with "closing the left screen and opening the right screen, opening the left screen and closing the right screen":

[0195]

[0196] Among them, is when performing the n th alternate occlusion, when the right eye is occluded (left screen open, right screen closed), the average value of the right eye movement direction vector of the right eye during the eye movement plateau period. For example, the corresponding to when the right eye is occluded during the 1st alternate occlusion, and the .

[0197] For the process of detecting the strabismus angle, in the case of alternate occlusion with "closing the right screen and opening the left screen, opening the right screen and closing the left screen":

[0198]

[0199] Among them, is the average value of the left eye movement direction vector of the left eye during the eye movement plateau period when the right screen is initially closed and the left screen is open. is when performing the n th alternate occlusion, when the left eye is occluded (right screen open, left screen closed), the average value of the left eye movement direction vector of the left eye during the eye movement plateau period.

[0200] In this embodiment, , , , in practical applications, according to actual needs, such as the detection accuracy and detection timeliness of the strabismus angle, etc., , , can be selected to dynamically output an adaptive step size, and then adjust the position of the target object based on this adaptive step size.

[0201] The following is a brief description of each link in the above calculation of the adaptive step size:

[0202] The first link is the proportional link P (Proportion), and the component force generated by this link is: ,

[0203] The magnitude of is proportional to the error .

[0204] The second link is the differential link D (Differential), and the component force generated by this link is: , which is related to 's rate of change. Adding the component force generated by the differential link on the basis of the first link will produce a damping effect, and thus the change of the eye movement direction vector will always be subject to a "resistance". Therefore, during the process of approaching the target position, the amplitude of left and right swings will gradually decrease and finally converge to a position where the third eye movement information is less than or equal to the third threshold. The differential coefficient can affect the magnitude of this "resistance". In practical applications, the differential coefficient can be adjusted larger to accelerate the convergence process.

[0205] The third link is the integral link I (Integral), and the component force generated by this link is: ,

[0206] The component force generated by the integral link is proportional to the integral of . When persists, the integral component force will gradually increase and attempt to eliminate the error. Adding the integral component force enables the PID to accurately achieve accurate control in the presence of a constant force interference. For example, under the premise of external interferences such as hardware errors, it can more accurately eliminate the errors generated by external interferences.

[0207] So far, by using the PID to output an adaptive step size to continuously adjust the display position of the target object, and then when the third eye movement information is less than or equal to the third threshold, the strabismus angle can be determined based on the current display position of the target object, the pupil distance of the detection object, and the distance from the eyes of the detection object to the target object.

[0208] Figure 5 is a flowchart of the eye position deviation detection method for some embodiments. This eye position deviation detection method can be applied to a head-mounted display device with split-screen display, and this head-mounted display device includes a display component. As Figure 5 shown, this eye position deviation detection method includes the following steps:

[0209] Step S501, in the initial display state of the display component, alternately turn on and off the left screen and the right screen, and record the eye movement direction vectors of each eye of the detection object. Among them, the initial display state of the display component can be: the target object is directly in front of the detection object at a preset distance, and both the left screen corresponding to the left eye of the detection object and the right screen corresponding to the right eye are turned on.

[0210] Step S502: After alternately closing the left and right screens for a preset number of times, determine whether the binocular eyes of the detection object reach a preset eye movement stable state. If so, execute Step S503; otherwise, return to execute Step S501. Specifically, it can be based on the eye movement fluctuation information of each eye of the detection object. Among them, the left and right screens are alternately closed in a loop, and the difference in the average value of the eye movement direction vectors in the plateau period corresponding to the opening and closing of the left or right screen in each loop is collected. Then, the variance of the difference in the average value of the eye movement direction vectors in multiple loops (for example, the number of loops is not less than the preset number M) (i.e., the multiple fluctuation values mentioned above) is calculated. When the variance is greater than or equal to the first threshold, the loop continues until the variance is less than the first threshold, and then it is confirmed that the eye movement stable state is reached. After obtaining the above eye movement direction vectors, the eye movement waveform diagram of any one eye can be obtained according to the eye movement direction vectors, and the eye movement waveform diagram is adaptively filtered and feature-recognized, and then the difference in the average value of the eye movement direction vectors corresponding to the opening and closing of the screen in the plateau period is obtained. The specific data processing process can refer to the above embodiments and will not be elaborated here.

[0211] Step S503: Determine whether the first eye movement information of each eye of the detection object is greater than the second threshold. If so, execute Step S504 and Step S516 respectively, and at this time, it is confirmed that the detection object has eye position deviation; otherwise, end the judgment process and confirm that the detection object does not have eye position deviation. The process of calculating the first eye movement information can refer to the process of calculating 、 in the above embodiments.

[0212] Step S504: With both the left and right screens turned on, based on the display of the left and right screens, place the target object directly in front of the detection object's field of view, and obtain the average value of the right eye movement direction vector when the right eye is in the eye movement plateau period , turn off the left screen, and obtain the average value of the right eye movement direction vector when the right eye is in the eye movement plateau period ; Among them, the left and right eyes, and the left and right screens here can be swapped.

[0213] Step S505: Calculate the absolute value of, and obtain the second eye movement information of the right eye. When the right screen is turned off in Step S504, here it is the second eye movement information of the left eye statistically obtained according to the left eye movement direction vector.

[0214] Step S506: Determine whether the second eye movement information in Step S505 is greater than the second threshold. If so, end the judgment process, and at this time, it can be determined that the detection object has manifest strabismus; otherwise, execute Step S507.

[0215] Step S507: Turn on the left screen; among them, when the right screen is turned off in Step S504, here it is to turn on the right screen.

[0216] Step S508: Obtain the second eye movement information of the left eye based on the eye movement direction vector of the left eye before and after the left screen is turned on. The specific calculation process can be obtained by referring to the above embodiment. When the right screen is closed in step S504, the second eye movement information of the right eye is obtained by counting the eye movement direction vector of the right eye.

[0217] Step S509 , determining whether the second eye movement information in step S508 is greater than a second threshold; if so, the determination process ends, and it can be determined that the detected subject has latent strabismus; otherwise, executing step S510 .

[0218] Step S510, close the right screen, and obtain the eye movement direction vector of the left eye when the left eye is in the eye movement plateau phase; wherein, when step S504 is to close the right screen, here it is to close the left screen and obtain the eye movement direction vector of the right eye.

[0219] Step S511: The second eye movement information of the left eye is obtained by counting the eye movement direction vector of the left eye in the eye movement plateau phase before and after the right screen is closed. The specific calculation process can refer to the calculation in the above embodiment. When the right screen is closed in step S504, the second eye movement information of the right eye is obtained by counting the eye movement direction vector of the right eye.

[0220] Step S512, determining whether the second eye movement information in step S511 is greater than a second threshold; if so, the determination process ends, and it can be determined that the detected subject has manifest strabismus; otherwise, executing step S513.

[0221] Step S513, turning on the right screen; when the right screen is turned off in step S504, the left screen is turned on here.

[0222] Step S514: The second eye movement information of the right eye is obtained by counting the eye movement direction vector of the right eye in the eye movement platform period before and after the right screen is turned on. The specific calculation process can be obtained by referring to the above embodiment. When the right screen is closed in step S504, the second eye movement information of the left eye is obtained by counting the eye movement direction vector of the left eye.

[0223] Step S515, determining whether the second eye movement information of step S514 is greater than the second threshold; if so, the determination process ends, and it can be determined that the detected subject has latent strabismus; otherwise, returning to step S501 to re-measure the eye deviation.

[0224] Step S516: Take the alternate closing of the left and right screens once as one cycle iteration. Adjust the stride and moving direction of the target object on the display component according to the third eye movement information of the second eye in the previous iteration round. The healthy eye in the left and right eyes can be regarded as the first eye, and the other eye as the second eye. Alternately cover the first eye and the second eye according to the opening and closing of the left and right screens. For the specific alternation method and stride adjustment method, reference can be made to the above embodiments and will not be elaborated here. Among them, at the initial iteration, the initial stride can be set according to the first eye movement information in step S503, the binocular interpupillary distance, and the distance from the detected object's eyes to the target object. The initial moving direction can be determined according to the situation of esotropia or exotropia.

[0225] Step S517: After controlling the target object to move according to the above-mentioned stride and moving direction, alternately close the left and right screens.

[0226] Step S518: Obtain the eye movement direction vector of the second eye and determine the third eye movement information of the second eye. The process of calculating the third eye movement information of the second eye can refer to the calculation process in the above embodiments. process.

[0227] Step S519: Determine whether the third eye movement information of the second eye in the current iteration round is less than or equal to the third threshold; if so, execute step S520; otherwise, return to execute step S516 in the next iteration round.

[0228] Step S520: Determine the strabismus angle. Specifically, the strabismus angle calculation formula in the above embodiments can be referred to determine the strabismus angle.

[0229] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here. For example, steps S516 to S520 and steps S504 to S515.

[0230] The above steps S501 to S520 implement a fully automatic qualitative and quantitative detection process for eye position deviation based on split-screen display and eye movement tracking. Among them, by tracking the eye movements of the detection object, the opening and closing of the split-screen display and the position change of the target object are controlled to determine whether the detection object has eye position deviation, esotropia or exotropia, manifest strabismus or latent strabismus. If there is eye position deviation, a quantitative measurement result of the deviation angle is given. Based on the principle of eye movement for eye position deviation, the positions of the target objects on the screens of the left and right eyes and the opening and closing of the screens are controlled, the eye movement conditions are monitored, and the eye position deviation of the detection object is automatically detected. In the specific data processing process of detecting the eye position deviation angle, the eye movement direction vectors of the left and right eyes in the split-screen display are obtained and recorded in real time, the left and right eye movement waveform diagrams are generated through digital filtering of the eye movement direction vectors, and the eye movement characteristics are extracted from the waveform diagrams to determine whether a new round of split-screen display is required or the inspection is aborted. Specifically, in this embodiment, a step-by-step progressive algorithm is introduced, that is, the initial moving step and moving direction are set based on the average eye movement information of the left and right eyes collected in the previous step. After the target object moves, the moving step and moving direction of the next iteration are dynamically adjusted according to the average eye movement information recorded in the split-screen display in the new round of iteration to quickly achieve convergence and output the eye position deviation angle.

[0231] In this embodiment, a strabismus detection device is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated here. The following terms "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0232] Figure 6 is the structural block diagram of the strabismus detection device 60 in this embodiment, as Figure 6 shown. The strabismus detection device 60 includes: a fluctuation information acquisition module 62, a first eye movement information acquisition module 64, and a detection module 66; where:

[0233] The fluctuation information acquisition module 62 is used to acquire the eye movement fluctuation information of each eye of the detection object based on the first detection rule; the first eye movement information acquisition module 64 is used to acquire the first eye movement information of each eye of the detection object when the eye movement fluctuation information of each eye of the detection object is lower than the first threshold during the strabismus detection based on the first detection rule; wherein, the eye movement fluctuation information and the first eye movement information are related to the change between the average eye movement direction vectors of the eyes during each eye movement plateau period during the strabismus detection based on the first detection rule; the detection module 66 is used to determine that the detection object has eye position deviation when the first eye movement information of any eye is greater than the second threshold.

[0234] It should be noted that each of the above modules can be a functional module or a program module, and can be implemented either by software or by hardware. For the modules implemented by hardware, each of the above modules can be located in the same processor; or each of the above modules can also be located in different processors in any combined form.

[0235] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated in this embodiment.

[0236] In this embodiment, a wearable device is also provided. Figure 7 It is a schematic structural diagram of the wearable device 70 of this embodiment, as [[ID=ON]]Figure 7 shown. The wearable device 70 may include a display component 72; the display component 72 is used to display a target according to the received display control signal; the display control signal is generated according to the eye movement direction vector and the eye position deviation detection rule when the detection object observes the target. It can be understood that the strabismus detection result can be displayed on the same display component, or on the terminal display associated with this wearable device, such as a computer, a mobile phone, or another display of the wearable device.

[0237] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated in this embodiment.

[0238] Specifically, in one embodiment, the above wearable device 70 may further include a data acquisition component 74, and the data acquisition component 74 is used to acquire the eye movement direction vector when the detection object observes the target. The data acquisition component 74 may specifically be an eye tracking detector.

[0239] In addition, in one embodiment, the above wearable device 70 further includes a strabismus detection processor 76, and the strabismus detection processor 76 is communicatively connected to the display component 72 and the data acquisition component 74; the strabismus detection processor 76 is used to receive the eye movement direction vector acquired by the data acquisition component 74, and send a display control signal to the display component 72; and, send the calculated eye position deviation angle to the display component 72.

[0240] In some of these embodiments, the above wearable device 70 may be head-mounted.

[0241] In addition, the setting of various thresholds, expected signals, and the setting of the occlusion duration for one alternation involved in the above embodiments can all be obtained based on clinical experience or measured data obtained from multiple tests.

[0242] It should be understood that the specific embodiments described herein are for explaining this application rather than limiting it. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in this application without creative efforts fall within the protection scope of this application.

[0243] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0244] Obviously, the attached drawings are only some examples or embodiments of this application. For those of ordinary skill in the art, this application can also be applied to other similar situations based on these drawings without creative efforts. Additionally, it can be understood that although the work done during this development process may be complex and time-consuming, for those of ordinary skill in the art, certain design, manufacturing, or production changes based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient disclosure of this application.

[0245] The term "embodiment" in this application means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The phrase appears at various positions in the specification and does not necessarily mean the same embodiment, nor does it mean being independent or alternative to other embodiments and mutually exclusive. Those of ordinary skill in the art can clearly or implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict.

[0246] The above-described embodiments merely represent several implementation manners of this application. Their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of patent protection. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all fall within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A strabismus detection method, characterized in that, Including: Obtain the eye movement fluctuation information of each eye of the detection object based on the first detection rule; When the eye movement fluctuation information of each eye of the detection object is lower than the first threshold, obtain the first eye movement information of each eye of the detection object when performing strabismus detection based on the first detection rule; Wherein, the eye movement fluctuation information and the first eye movement information are associated with the change between the average eye movement direction vectors of the eyes during each eye movement plateau period when performing strabismus detection based on the first detection rule; When the first eye movement information of any eye is greater than the second threshold, determine that the detection object has eye position deviation; wherein: determining the eye movement plateau period of the eye includes: Collect the eye movement direction vector of the eye, and generate an eye movement waveform diagram based on the eye movement direction vector; Determine the effective eye movement waveform diagram in the eye movement waveform diagram, and determine the eye movement plateau period of the eye according to the extreme points in the effective eye movement waveform diagram; The first detection rule is the alternating cover test rule. Obtaining the first eye movement information of one eye of the detection object when performing strabismus detection based on the first detection rule includes: Taking the absolute value of the mean of the differences between the average eye movement direction vectors of one eye covered alternately M times during each eye movement plateau period as the first eye movement information of the eye; where M is greater than or equal to 2.

2. The strabismus detection method according to claim 1, wherein It also includes: Obtain the second eye movement information of at least one eye of the detection object based on the second detection rule, and determine the type of eye position deviation of the detection object based on the second eye movement information; The second eye movement information is associated with the change between the average eye movement direction vectors of the eyes during each eye movement plateau period when performing strabismus type detection based on the second detection rule.

3. The strabismus detection method according to claim 2, wherein The second detection rule is the cover-uncover test rule; obtaining the second eye movement information of at least one eye of the detection object and determining the type of eye position deviation of the detection object based on the second eye movement information includes: Cover the first eye of the detection object; Based on the second eye movement information of the second eye, judge whether the type of eye position deviation of the detection object is manifest strabismus. Wherein, the second eye movement information of the second eye is associated with the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after covering; If not, uncover the first eye, and based on the second eye movement information of the first eye, judge whether the type of eye position deviation of the detection object is latent strabismus. Wherein, the second eye movement information of the first eye is associated with the change between the average eye movement direction vectors of the first eye during the eye movement plateau period before and after uncovering; If it is impossible to determine whether the type of eye position deviation of the detection object is latent strabismus based on the second eye movement information of the first eye, then cover the second eye of the detection object; Based on the second eye movement information of the first eye, judge whether the type of eye position deviation of the detection object is manifest strabismus. Wherein, the second eye movement information of the first eye is associated with the change between the average eye movement direction vectors of the first eye during the eye movement plateau period before and after covering; If, after covering the second eye of the detection object, it is impossible to determine whether the type of eye position deviation of the detection object is manifest strabismus based on the second eye movement information of the first eye, uncover the second eye, and based on the second eye movement information of the second eye, determine whether the type of eye position deviation of the detection object is latent strabismus, where the second eye movement information of the second eye is related to the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after uncovering.

4. The strabismus detection method according to claim 1, wherein The first detection rule is the alternate cover test rule. Based on the first detection rule, obtain the eye movement fluctuation information of one eye of the detection object, including: Obtain the eye movement fluctuation information of this eye based on the difference between the average eye movement direction vectors of one eye during each eye movement plateau period in M alternate cover tests; where M is greater than or equal to 2.

5. The strabismus detection method according to claim 1, wherein Determine the valid eye movement waveform diagram in the eye movement waveform diagram, including: Filter the eye movement waveform diagram to remove the invalid eye movement signals in the eye movement waveform diagram to obtain the valid eye movement waveform diagram.

6. The strabismus detection method according to any one of claims 1 to 5, characterized in that, The method further includes: If the detection object has eye position deviation; Cover the first eye of the detection object; Adjust the display position of the target object based on the moving direction and step size; Uncover the first eye and cover the second eye of the detection object; Determine whether the third eye movement information of the second eye is less than or equal to a third threshold; if so, determine the strabismus angle based on the current display position of the target object, the interpupillary distance of the detection object, and the distance from the eyes of the detection object to the target object; the third eye movement information of the second eye is related to the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after covering the second eye; if not, repeat the above steps until the third eye movement information of the second eye is less than or equal to the third threshold to determine the strabismus angle.

7. The strabismus detection method according to claim 6, wherein: The step size includes an initial step size and an adaptive step size; where the adaptive step size is related to the corresponding third eye movement information before and after covering the second eye each time; The moving direction includes an initial moving direction and a target moving direction, and the target moving direction is related to the change between the average eye movement direction vectors of the second eye during the eye movement plateau period before and after covering the second eye each time.

8. An esotropia detection device, characterized in that Includes: A fluctuation information acquisition module, a first eye movement information acquisition module, and a detection module; where: The fluctuation information acquisition module is used to obtain the eye movement fluctuation information of each eye of the detection object based on the first detection rule; The first eye movement information acquisition module is used to obtain the first eye movement information of each eye of the detection object when performing strabismus detection based on the first detection rule when the eye movement fluctuation information of each eye of the detection object is lower than a first threshold; where the eye movement fluctuation information and the first eye movement information are related to the change between the average eye movement direction vectors of the eyes during each eye movement plateau period when performing strabismus detection based on the first detection rule; The detection module is used to determine that the detection object has eye position deviation when the first eye movement information of any eye is greater than a second threshold; where: determining the eye movement plateau period of the eye includes: Collect the eye movement direction vector of the eye, and generate an eye movement waveform diagram based on the eye movement direction vector; Determine the effective eye movement waveform diagram in the eye movement waveform diagram, and determine the eye movement plateau period of the eye according to the extreme points in the effective eye movement waveform diagram; The first detection rule is the alternating cover test rule. When obtaining the first eye movement information of one eye of the detection object during the strabismus detection based on the first detection rule, it includes: The absolute value of the mean of the differences between the mean values of the eye movement direction vectors of one eye during each eye movement plateau period in M times of alternating cover tests is used as the first eye movement information of the eye; where M is greater than or equal to 2.

9. A wearable device, characterized in that, Apply the strabismus detection method according to any one of claims 1 to 7, and the wearable device includes a display component; The display component is configured to display a target according to the received display control signal; the display control signal is generated according to the eye movement direction vector of the detection object when observing the target and the eye position deviation detection rule.

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