A Diplopia Detection System Based on Virtual Reality Technology and Its Usage Method

Abstract

This invention provides a diplopia detection system and its usage method based on virtual reality technology, belonging to the field of virtual reality technology. The invention includes a cloud server, a detection device, a software terminal, and a communication module; the communication module is used to realize data exchange; the detection device includes VR glasses and a control handle, with the VR glasses and control handle exchanging data wirelessly. This invention constructs a novel diplopia detection method by detecting a 3D panoramic virtual environment, achieving the goal of accurately quantifying the severity and range of diplopia, while simultaneously solving the detection error problem caused by head rotation compensation, ensuring the accuracy and precision of diplopia detection results.

Description

Technical Field

[0001] This invention relates to a diplopia detection system, specifically a diplopia detection system and its usage method based on virtual reality technology, belonging to the field of virtual reality technology. Background Technology

[0002] Like most visual impairments, diplopia is a subjective sensation. Fusion, a crucial component of level three visual function, is a prerequisite for normal binocular vision. Abnormal binocular fusion results in diplopia, impairs stereoscopic vision, and reduces depth perception, significantly impacting fine motor skills. Therefore, accurately quantifying the severity and extent of diplopia is of significant clinical importance for diagnosis and treatment evaluation. Currently, commonly used diplopia detection methods in clinical practice include the red-glass test, Hess screen test, and Goldmann perimeter test. However, the red-glass test is qualitative and cannot quantify the severity of diplopia; furthermore, head rotation compensation significantly affects the interpretation of the results. The Hess screen test and Goldmann perimeter test are semi-quantitative and cannot accurately measure the severity and extent of diplopia. To accelerate the diagnosis and treatment evaluation of diplopia in the clinical field, accurately quantifying the severity and extent of diplopia is a problem that must be solved.

[0003] The emergence of VR technology provides theoretical support for solving the aforementioned problems. VR technology, also known as virtual reality technology, is a computer simulation system that can create and allow users to experience virtual worlds. Its basic implementation involves using electronic devices to simulate virtual environments, thus providing a sense of immersion. With the rapid development of technology, virtual imaging technologies, represented by VR, have broken down the boundaries between the digital virtual world and the physical real world. Developing brain-computer interface applications in a virtual reality environment can effectively improve the user's immersion and stereoscopic perception, while also providing timely and accurate information and solutions based on specific scenarios. These characteristics demonstrate the enormous potential of applying VR technology in the medical field.

[0004] Therefore, addressing the current bottlenecks hindering the diagnosis and treatment of diplopia—namely, the lack of effective and accurate quantitative detection methods and intelligent equipment for patients with diplopia, and the absence of a cloud-based data storage system for diplopia detection information acquisition—this invention aims to overcome the shortcomings of existing technologies by providing a VR-based interactive clinical diplopia detection system. The system is designed to accurately quantify the severity and range of diplopia and utilize big data storage, providing a more effective, accurate, and highly operable new approach for the diagnosis of diplopia. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] The purpose of this invention is to provide a dual vision detection system and its usage method based on virtual reality technology in order to solve the above-mentioned problems and address the issues in the prior art.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a dual vision detection system based on virtual reality technology, comprising a cloud server, a detection device, a software terminal, and a communication module;

[0009] The communication module is used to realize data exchange;

[0010] The detection device includes VR glasses and a control handle, and the VR glasses and the control handle exchange data through a wireless connection.

[0011] The software terminal includes an information acquisition module, a target type selection module, a diplopia severity detection module, a binocular single vision range detection module, and a detection result output and storage module.

[0012] The diplopia severity detection module is used to detect and quantify the severity of diplopia at a fixed location;

[0013] The binocular single vision range detection module is used to detect and quantify the binocular single vision range, binocular single vision distance, and the severity of diplopia.

[0014] The test result output storage module is used to store the test results to the cloud server and output the test results to the corresponding items in the report by retrieving key information from the cloud database.

[0015] A method of using a diplopia detection system includes the following steps:

[0016] Step 1: After logging into the software, the examiner can input the subject's basic information through the VR control handle. The diplopia detection system automatically stores the input information and outputs it to the cloud server.

[0017] Step 2: The inspector uses the VR controller to select the visual target display graphics during the inspection process;

[0018] Step 3: Fix the detection plane in front of the center of the visual field, and then proceed to the diplopia severity detection module and the binocular single vision range detection module in sequence.

[0019] Step 4: Based on the test results, in the diplopia severity detection module, the diplopia detection system outputs positive diplopia points and data reflecting the severity of diplopia at those points, and automatically determines and outputs the diplopia type to the cloud server through a logic algorithm program; in the binocular single vision range detection module, the diplopia detection system outputs data that reflects the size of the diplopia range to the cloud server.

[0020] After the test is completed, the database reads and retrieves the corresponding data to generate a test report and outputs it to the local database.

[0021] Preferably, before the test, a suitable target size is selected according to the test requirements; the subject controls the movement of the target, so that the target moves from the center of the visual field to the specified direction. When the target moves to the position where diplopia occurs or the boundary of the visual field in that direction, the subject confirms the actual position of the target and enters the next test direction. The above operation is repeated until the test is completed.

[0022] When the confirmed position is the center point of the field of view, the BSVF output of the current detection plane is 0 and the next detection plane is entered. Selecting the return key can reconfirm the position of the target in the previous detection direction.

[0023] After the test is completed, the output data, which reflects the size of the binocular single visual range, namely the BSVF and BSVD values, is sent to the cloud server.

[0024] Preferably, when the diplopia severity detection module is in use, a detection target appears in the detection plane. The subject controls the movement of the target. When the subject moves the target to coincide with the detection target, he presses the confirmation button to confirm the actual position of the target corresponding to the detection point and moves to the next detection point. The above operation is repeated until the detection is completed.

[0025] When the target and the target being tested are on the same horizontal plane, the diplopia detection system automatically determines the difference in the center point coordinates of the target and the target being tested through a logical algorithm program and records it as a positive diplopia point or a negative diplopia point.

[0026] For positive diplopia points, the software records and stores the values ​​of |x-detector-x-detected| and |y-detector-y-detected|.

[0027] For negative diplopia points, the software records and stores the output values ​​of |x-x-tested| and |y-y-tested| as 0.

[0028] Preferably, the number of detection planes and the detection distance of each plane are determined as needed before detection, and the detection is carried out in sequence from near to far during detection.

[0029] This invention provides a double vision detection system based on virtual reality technology, which has the following beneficial effects:

[0030] 1. This application applies VR technology to the field of medical diplopia detection, filling a gap in the clinical application of virtual reality technology for diplopia detection. Furthermore, compared to traditional diplopia detection equipment, VR equipment is moderately priced, making it easier to promote and apply in primary care hospitals. Additionally, the portability of VR equipment reduces the dependence of examiners on the testing location, enabling long-distance, large-scale testing within limited spaces, while reducing the need for assistance from clinical professionals, promoting the downward flow of medical resources, and meeting the demand for high-quality medical services from patients visiting primary care hospitals.

[0031] 2. This application uses a 3D panoramic virtual environment to accurately quantify the severity and range of diplopia, while also solving the detection errors caused by head rotation, thus ensuring the accuracy and precision of diplopia detection results. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the information collection interface of the present invention;

[0033] Figure 2 This is a schematic diagram of the visual target of the present invention;

[0034] Figure 3 This is a flowchart illustrating the severity detection module for diplopia of the present invention.

[0035] Figure 4 This is a schematic diagram of the diplopia severity detection module of the present invention;

[0036] Figure 5 This is a schematic diagram of the diplopia report of the present invention;

[0037] Figure 6 This is a schematic diagram of the principle of the binocular single vision range detection module of the present invention;

[0038] Figure 7 This is a schematic diagram of the binocular single vision range detection module of the present invention;

[0039] Figure 8 This is a schematic diagram of the binocular single vision range detection report of the present invention;

[0040] Figure 9 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0041] This invention provides a diopter detection system and its usage method based on virtual reality technology.

[0042] Please see Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 This includes cloud servers, detection devices, software terminals, and communication modules;

[0043] The communication module is used to realize data exchange between the cloud server and the detection device, between the detection device and the software terminal, and between the software terminal and the cloud server;

[0044] The cloud server includes: the source program files of the software terminal, the data algorithm script program, and the running data of the detection system;

[0045] The detection device includes VR glasses and a control handle. The VR glasses and the control handle exchange data wirelessly. The VR glasses provide a hardware and software integrated computing environment that matches the performance requirements of the detection system, including a 3D panoramic virtual environment presentation device and a sensing device. The 3D panoramic virtual environment presentation device presents a real-time virtual scene during the operation of the detection system. The sensing device tracks and captures the movements of the inspector and the subject, and synchronously calculates and interacts with the 3D panoramic virtual environment presentation device to provide real-time feedback on the movements of the inspector and the subject.

[0046] The software terminal includes an information acquisition module, a target type selection module, a diplopia severity detection module, a binocular single vision range detection module, and a detection result output and storage module;

[0047] The diplopia severity detection module is used to detect and quantify the severity of diplopia at fixed locations;

[0048] The binocular single vision range detection module is used to detect and quantify the binocular single vision range, binocular single vision distance, and the severity of diplopia.

[0049] The test result output and storage module is used to output and store the test result documents to the cloud server, and to output the corresponding test results to the corresponding items on the report after retrieving key information from the cloud database.

[0050] A method of using a diplopia detection system includes the following steps:

[0051] Please refer to the appendix again. Figure 1 After logging into the software, the examiner can input the subject's basic information through the VR controller. The diplopia detection system automatically stores the input information and outputs it to the cloud server. Clicking "Enter Detection" will take you to the next module, and clicking "Exit" will exit the diplopia detection system.

[0052] Please refer to the appendix again. Figure 2 The inspector uses the VR controller to select the visual target display graphic during the detection process, and proceeds to the next module after selection; clicking exit will exit the diplopia detection system.

[0053] Please refer to the appendix again. Figure 3 Appendix Figure 4 and attached Figure 5 The detection process for the diplopia severity detection module is as follows: Figure 3 The detection principle of the diplopia severity detection module is described in [link to module]. Figure 4 The actual testing process can be seen in the following figure. Figure 5 .

[0054] The detection plane is fixed in front of the center of the field of vision, meaning that head rotation will not affect the position of the target in front of the person's eyes, thus eliminating the deviation caused by head rotation during the detection process.

[0055] Clicking the back button will re-detect the previous detection point; clicking exit will exit the double vision detection system.

[0056] Based on the test results, in the diplopia severity detection module, the diplopia detection system outputs positive diplopia points and data reflecting the severity of diplopia at those points, and automatically determines and outputs the diplopia type to the cloud server through a logical algorithm program; in the binocular single vision range detection module, the diplopia detection system outputs data that reflects the size of the diplopia range to the cloud server.

[0057] After the test is completed, the database reads and calls the corresponding data to generate a test report and outputs it to the local database. Clicking "Print" will print the report.

[0058] It is important to emphasize that the test target and the target being tested are on the same horizontal plane. The test target appears in the test plane. The subject controls the movement of the target being tested (which is the same size, shape, and material as the test target). When the subject moves the target being tested to coincide with the test target, the subject presses the confirmation button to confirm the actual position of the target being tested at that test point and moves to the next test point. The above operation is repeated until the test is completed. The diplopia detection system automatically determines the difference in the center point coordinates of the test target (x test, y test, z test) and the target being tested (x target, y target, z target) through a logical algorithm program and records it as a positive or negative diplopia point.

[0059] For positive diplopia points, the software records and stores the values ​​of |x-detector-x-detected| and |y-detector-y-detected|.

[0060] For negative diplopia points, the software records and stores the output values ​​of |x-x-tested| and |y-y-tested| as 0.

[0061] When using the binocular single vision range detection module, the detection plane needs to be fixed in front of the center of the visual field, that is, head rotation will not affect the position of the visual target in front of the person's eyes, thus eliminating the deviation caused by head rotation during the detection process;

[0062] Before the test, select an appropriate target size according to the test requirements; the subject controls the movement of the target, moving it from the center of the visual field to the specified direction. When the target moves to the position where diplopia occurs or the boundary of the visual field in that direction, the subject confirms the actual position of the target and moves to the next test direction. Repeat the above operation until the test is completed.

[0063] When the confirmed position is the center point of the field of view, the BSVF output of the current detection plane is 0 and the next detection plane is entered. Selecting the return key will reconfirm the position of the target in the previous detection direction. If the current detection direction is the first detection direction, selecting the return key will return to the previous detection plane. If the current detection direction is the first detection direction of the first detection plane, selecting the return key will return to the previous detection module.

[0064] After the detection is completed, the BSVF and BSVD values ​​of the plane are output and stored, and the next detection plane is entered. If it is the last detection plane, the detection ends and the BSVF and BSVD values ​​of all detection planes are output and stored to the cloud server.

[0065] It's important to clarify that binocular single vision refers to the image of an object simultaneously falling on corresponding points on the retinas of both eyes and being integrated into a single image by the visual center. Diplopia, on the other hand, occurs when the image of an object falls on non-corresponding points on the retinas of both eyes. Specifically, one image is on the fovea of ​​the fixating eye, while the other falls on the peripheral retina of the deviating eye. This prevents the information from being fused by the visual center, resulting in diplopia. Therefore, the size of the binocular single vision range can indirectly reflect the size of the diplopia range.

[0066] As explained here, the number of detection planes and the detection distance of each plane are determined as needed before detection, and detection is performed in sequence from near to far during detection.

[0067] This application utilizes a 3D panoramic virtual environment to accurately quantify the severity and range of diplopia, while simultaneously addressing the detection errors caused by head rotation, thus ensuring the accuracy and precision of diplopia detection results.

[0068] This application applies VR technology to the field of medical diplopia detection, filling a gap in the clinical application of virtual reality technology for diplopia detection. Furthermore, compared to traditional diplopia detection equipment, VR equipment is moderately priced, making it easier to promote and apply in primary care hospitals. Additionally, the portability of VR equipment reduces the dependence of examiners on the testing location, enabling long-distance, large-scale testing within limited spaces, while reducing the need for assistance from clinical professionals, promoting the downward flow of medical resources, and meeting the demand for high-quality medical services from patients visiting primary care hospitals.

[0069] This application outputs the diplopia test results as data information and stores them on a cloud server, providing technical support for the establishment of a nationwide diplopia database and the sharing of resource information in the context of the big data era.

[0070] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A diopter detection system based on virtual reality technology, characterized in that: Includes cloud servers, detection devices, software terminals, and communication modules; The communication module is used to realize data exchange; The detection device includes VR glasses and a control handle, and the VR glasses and the control handle exchange data through a wireless connection. The software terminal includes an information acquisition module, a target type selection module, a diplopia severity detection module, a binocular single vision range detection module, and a detection result output and storage module. The diplopia severity detection module is used to detect and quantify the severity of diplopia at a fixed location; The binocular single vision range detection module is used to detect and quantify the binocular single vision range, binocular single vision distance, and the severity of diplopia. The test result output storage module is used to store the test results to the cloud server, and to output the test results to the corresponding items on the report by retrieving key information from the cloud database; Before the test, select an appropriate target size according to the test requirements; the subject controls the movement of the target, moving it from the center of the visual field to the specified direction. When the target moves to the position where diplopia occurs or the boundary of the visual field in that direction, the subject confirms the actual position of the target and moves to the next test direction. Repeat the above operation until the test is completed. When the confirmed position is the center point of the field of view, the binocular single vision range of the current detection plane is output as 0 and the next detection plane is entered. The return key can be selected to reconfirm the position of the target in the previous detection direction; wherein, the binocular single vision range refers to the area of ​​the polygon enclosed by the actual positions of each target. After the test is completed, the data that reflects the size of the binocular single vision range, namely the binocular single vision range and binocular single vision distance, is output and stored to the cloud server; wherein, the binocular single vision distance refers to the distance from the center of the visual field to the actual position of the target. When using the diplopia severity detection module, a detection target appears in the detection plane. The subject controls the movement of the target. When the subject moves the target to coincide with the detection target, he / she presses the confirmation button to confirm the actual position of the target corresponding to the detection point and moves to the next detection point. The above operation is repeated until the detection is completed. When the target and the target being tested are on the same horizontal plane, the diplopia detection system automatically determines the difference in the center point coordinates of the target and the target being tested through a logical algorithm program and records it as a positive diplopia point or a negative diplopia point. For positive diplopia points, the software records and stores the values ​​of |x-detector-x-detected| and |y-detector-y-detected|. For negative diplopia points, the software records and stores the output values ​​of |x-x-tested| and |y-y-tested| as 0.

2. A method of using a diplopia detection system, applicable to the diplopia detection system based on virtual reality technology as described in claim 1, characterized in that, Includes the following steps: Step 1: After logging into the software, the examiner can input the subject's basic information through the VR control handle. The diplopia detection system automatically stores the input information and outputs it to the cloud server. Step 2: The inspector uses the VR controller to select the visual target display graphics during the inspection process; Step 3: Fix the detection plane in front of the center of the visual field, and then proceed to the diplopia severity detection module and the binocular single vision range detection module in sequence. Step 4: Based on the test results, in the diplopia severity detection module, the diplopia detection system outputs positive diplopia points and data reflecting the severity of diplopia at those points, and automatically determines and outputs the diplopia type to the cloud server through a logic algorithm program. In the binocular single vision range detection module, the diplopia detection system outputs data that reflects the size of the diplopia range to the cloud server; After the test is completed, the database reads and retrieves the corresponding data to generate a test report and outputs it to the local database. Before the test, select an appropriate target size according to the test requirements; the subject controls the movement of the target, moving it from the center of the visual field to the specified direction. When the target moves to the position where diplopia occurs or the boundary of the visual field in that direction, the subject confirms the actual position of the target and moves to the next test direction. Repeat the above operation until the test is completed. When the confirmed position is the center point of the field of view, the binocular single vision range of the current detection plane is output as 0 and the next detection plane is entered. The return key can be selected to reconfirm the position of the target in the previous detection direction; wherein, the binocular single vision range refers to the area of ​​the polygon enclosed by the actual positions of each target. After the test is completed, the data that reflects the size of the binocular single vision range, namely the binocular single vision range and binocular single vision distance, is output and stored to the cloud server; wherein, the binocular single vision distance refers to the distance from the center of the visual field to the actual position of the target. When using the diplopia severity detection module, a detection target appears in the detection plane. The subject controls the movement of the target. When the subject moves the target to coincide with the detection target, he / she presses the confirmation button to confirm the actual position of the target corresponding to the detection point and moves to the next detection point. The above operation is repeated until the detection is completed. When the target and the target being tested are on the same horizontal plane, the diplopia detection system automatically determines the difference in the center point coordinates of the target and the target being tested through a logical algorithm program and records it as a positive diplopia point or a negative diplopia point. For positive diplopia points, the software records and stores the values ​​of |x-detector-x-detected| and |y-detector-y-detected|. For negative diplopia, the software records and stores the output values ​​of |x-x-tested| and |y-y-tested| as 0; Before detection, the number of detection planes and the detection distance of each plane are determined as needed. During detection, the planes are detected in sequence from near to far.

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