Calibration method, system and equipment for measuring pupil rotation angle and direction and medium

Abstract

The invention provides a calibration method, system and device for measuring the pupil rotation angle and direction and a medium, and relates to the technical field of medical device.The method comprises the steps that after a testee wears nystagmus detection equipment, a reference image containing a scale is shot, and a real-time image containing eyeballs of the testee and a main camera is shot; performing superposition processing on the reference image and the real-time image to generate an actual distance from the eyeball to the main camera; acquiring a preset distance between the main camera and the plurality of LED indicating lamps, and when the LED indicating lamps are lightened in sequence, acquiring an eye image sequence when eyeballs watch the LED indicating lamps; extracting a pixel coordinate position when an eyeball watches the target LED indicator lamp in the eye image sequence; combining a preset distance and the actual distance to calculate a rotation angle when eyeballs watch a target LED indicator lamp; and establishing a calibration coefficient of the pixel displacement and the rotation angle in combination with the pixel coordinate position and the rotation angle. The technical effect of the invention is that the measurement precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical equipment, in particular to a calibration method, system, device and medium for measuring pupil rotation angle and direction. BACKGROUND

[0002] Nystagmus detection is an important diagnostic method for assessing vestibular function and nervous system diseases in clinical medicine, which requires accurate measurement of the rotation angle and motion trajectory of the eyeball of the measured person. Traditional nystagmus detection methods mainly rely on subjective observation by doctors or simple mechanical measurement devices, which are difficult to provide accurate quantitative analysis data and the measurement accuracy is easily affected by individual differences and environmental factors. With the development of digital image processing technology, nystagmus detection methods based on video image analysis have gradually become a research hotspot, but how to establish an accurate eyeball rotation angle measurement standard, especially how to realize personalized calibration correction, is still an important technical challenge in this field.

[0003] Existing nystagmus detection techniques usually use fixed calibration coefficients to convert pixel displacement in images into eyeball rotation angle. This method realizes the standardization of angle measurement by pre-setting uniform conversion parameters. However, due to significant differences in the physiological characteristics of the eyeballs of different measured persons, the use of uniform calibration coefficients cannot accurately reflect the true eyeball movement characteristics of individuals, resulting in insufficient measurement accuracy. SUMMARY

[0004] The present application provides a calibration method, system, device and medium for measuring pupil rotation angle and direction to improve measurement accuracy.

[0005] In a first aspect, the application provides a calibration method for measuring the rotation angle and direction of a pupil, the method comprising: when a subject wears an nystagmus detection device, capturing a reference image containing a scale through a lateral camera arranged on the side of the nystagmus detection device, and capturing a real-time image containing the eyeball of the subject and a main camera of the nystagmus detection device; superimposing the reference image and the real-time image to generate the actual distance from the eyeball of the subject to the main camera; obtaining a preset distance between the main camera and a plurality of LED indicator lights in the nystagmus detection device, when each of the LED indicator lights is lit in turn, collecting an eye image sequence of the subject when the eyeball of the subject gazes at each of the LED indicator lights; extracting the pixel coordinate position of the eyeball of the subject when the eyeball of the subject gazes at a target LED indicator light in the eye image sequence, the target LED indicator light being one of the plurality of LED indicator lights; combining the preset distance and the actual distance to calculate the rotation angle of the eyeball of the subject when the eyeball of the subject gazes at the target LED indicator light; combining the pixel coordinate position and the rotation angle to establish a calibration coefficient of pixel displacement and rotation angle, the calibration coefficient representing the image pixel movement amount corresponding to a unit angle of eyeball rotation.

[0006] By using the above technical solution, the actual distance from the eyeball of the subject to the main camera is measured in real time through the lateral camera, and the individualized eyeball rotation angle is calculated in combination with the preset distance, thereby effectively solving the problem of insufficient measurement accuracy caused by individual differences in the traditional calibration method. By superimposing the reference image and the real-time image to generate an accurate actual distance parameter, the system can establish a dedicated calibration coefficient for each subject, significantly improving the accuracy of the conversion between pixel displacement and rotation angle. The method establishes a complete individualized calibration database through the sequential lighting of the plurality of LED indicator lights and the collection of the eye image sequence, so that the calibration coefficient can accurately represent the image pixel movement amount corresponding to a unit angle of eyeball rotation of a specific subject. This individualized calibration method based on actual distance correction eliminates the influence of device wearing position differences and individual physiological feature changes on measurement accuracy, thereby improving measurement accuracy.

[0007] In a second aspect, the application provides a calibration system for measuring the rotation angle and direction of a pupil, the system comprising: a capturing module, a superimposing module, a collecting module, an extracting module, a first combining module, and a second combining module; wherein, The photographing module is configured to photograph a reference image containing a scale through a lateral camera arranged on a side of the nystagmus detection device when a subject wears the nystagmus detection device, and photograph a real-time image containing eyeballs of the subject and the nystagmus detection device through a main camera; the superposition module is configured to superpose the reference image and the real-time image to generate an actual distance from the eyeballs of the subject to the main camera; the acquisition module is configured to acquire a preset distance between the main camera and a plurality of LED indicator lights in the nystagmus detection device, and acquire an eye image sequence of the subject when the eyeballs of the subject gaze at each of the LED indicator lights when the LED indicator lights are sequentially lighted; the extraction module is configured to extract a pixel coordinate position of the subject when the eyeballs of the subject gaze at a target LED indicator light in the eye image sequence, the target LED indicator light being one of the plurality of LED indicator lights; the first combination module is configured to combine the preset distance and the actual distance to calculate a rotation angle of the eyeballs of the subject when the eyeballs of the subject gaze at the target LED indicator light; and the second combination module is configured to combine the pixel coordinate position and the rotation angle to establish a scaling coefficient of pixel displacement and rotation angle, the scaling coefficient representing an image pixel movement amount corresponding to a unit angle of eyeball rotation.

[0008] In a third aspect, the present application provides an electronic device, which adopts the technical scheme as follows: comprising a processor, a memory, a user interface and a network interface, the memory is used for storing instructions, the user interface and the network interface are used for communication with other devices, and the processor is used for executing the instructions stored in the memory, so that the electronic device executes the computer program of any one of the above-mentioned scaling methods for measuring the rotation angle and direction of the pupil.

[0009] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the technical scheme as follows: storing the computer program capable of being loaded and executed by the processor and executing any one of the above-mentioned scaling methods for measuring the rotation angle and direction of the pupil.

[0010] In summary, the present application includes at least one of the following beneficial technical effects: The actual distance from the eyeball of the measured person to the main camera is measured in real time by the lateral camera, and the personalized eyeball rotation angle is calculated by combining the preset distance, effectively solving the problem of insufficient measurement accuracy caused by individual differences in the traditional calibration method. By superimposing the reference image and the real-time image to generate accurate actual distance parameters, the system can establish a dedicated calibration coefficient for each measured person, significantly improving the accuracy of pixel displacement and rotation angle conversion. This method establishes a complete personalized calibration database by sequentially lighting multiple LED indicator lights and capturing eye image sequences, so that the calibration coefficient can accurately represent the image pixel movement corresponding to a specific measured person's eyeball rotation unit angle. This personalized calibration method based on actual distance correction eliminates the influence of device wearing position differences and individual physiological feature changes on measurement accuracy, improving measurement accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 is a flowchart of a calibration method for measuring pupil rotation angle and direction provided by an embodiment of the present application; Figure 2 is a geometric principle diagram of eyeball rotation angle measurement in an nystagmus detection device provided by an embodiment of the present application; Figure 3 is a flowchart of another calibration method for measuring pupil rotation angle and direction provided by an embodiment of the present application; Figure 4 is a physical diagram provided by an embodiment of the present application; Figure 5 is another physical diagram provided by an embodiment of the present application; Figure 6 is a structural diagram of a calibration system for measuring pupil rotation angle and direction provided by an embodiment of the present application; Figure 7 is a structural diagram of an electronic device provided by an embodiment of the present application.

[0012] Marked with reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0013] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be described clearly and completely in conjunction with the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments.

[0014] In the description of the embodiments of the present application, the words "exemplary", "for example", or "e.g." are used to mean serving as an example, instance, or illustration. Any embodiment or design solution described as "exemplary", "for example", or "e.g." in the embodiments of the present application should not be construed as being more preferred or advantageous than other embodiments or design solutions. In fact, the use of the words "exemplary", "for example", or "e.g." is intended to present related concepts in a particular manner.

[0015] Figure 1 is a flowchart of a calibration method for measuring the rotation angle and direction of the pupil provided by the embodiments of the present application. As shown in the figure, the method comprises S101-S106: Figure 1 S101, after the measured person wears the nystagmus detection device, a reference image containing a ruler is captured by a lateral camera arranged on the side of the nystagmus detection device, and a real-time image containing the eyeball of the measured person and the main camera of the nystagmus detection device is captured.

[0016] After the measured person wears the nystagmus detection device, the first key image acquisition work is performed by the lateral camera arranged on the side of the nystagmus detection device. The purpose of this step is to solve the problem of inaccurate eyeball rotation angle calibration caused by individual anatomical structure differences in the prior art, and to realize personalized accurate calibration by obtaining the real distance from the eyeball of the measured person to the main camera.

[0017] The lateral camera refers to an auxiliary camera installed on the side of the nystagmus detection device, and its optical axis is perpendicular to the optical axis direction of the main camera, which can capture the spatial position relationship between the eyeball of the measured person and the main camera from the side view. In the specific implementation process, the lateral camera first needs to capture a reference image containing a ruler, which is usually a ruler, vertically placed on the same horizontal line as the main camera, and maintains a fixed spatial position relationship with the main camera. The role of this reference image is to establish the conversion relationship between the pixels and the actual physical distance, which is equivalent to providing a known reference standard for subsequent distance measurement.

[0018] Immediately after the measured person correctly wears the nystagmus detection device and maintains a relatively stable posture, the lateral camera immediately captures a second image, i.e. a real-time image containing the eyeball of the measured person and the main camera of the nystagmus detection device. In this real-time image, the lateral camera can capture the spatial position of the eyeball of the measured person and the spatial position of the main camera at the same time, and the spatial relationship of these two key elements is the basic data for calculating the actual distance from the eyeball to the main camera. The main camera refers to the main imaging device in the nystagmus detection device for directly capturing the eye movement of the measured person, which is usually installed at the position directly in front of the eyeball of the measured person.

[0019] ​With this dual-image acquisition design, the system can obtain two important sources of information: the reference image provides the conversion standard from pixels to actual distance, while the real-time image provides the real spatial position relationship between the subject's eyeball and the main camera. Such a design avoids the defect of using a fixed average value to estimate the eyeball-to-camera distance in traditional methods, because there are significant individual differences in the anatomical structures of each subject's facial contour, orbital depth, and nasal ridge height, and using a fixed value will introduce an unavoidable measurement error.

[0020] S102, superimpose the reference image and the real-time image to generate the actual distance from the subject's eyeball to the main camera.

[0021] Superimposition refers to the process of spatial alignment and information integration of two images taken at different times through image registration and fusion algorithms. In specific implementation, the system first performs scale identification and scale information extraction on the reference image, automatically detects the scale lines and numerical markers of the scale in the reference image through image recognition algorithms, establishes the correspondence between the pixel coordinates and the actual physical distance in the image, and generates the pixel scale parameter. This pixel scale parameter represents the actual physical length corresponding to each pixel point under the current lateral camera imaging conditions, providing an accurate conversion reference for subsequent distance measurement.

[0022] At the same time, the system performs feature point recognition and position information extraction on the real-time image, and automatically identifies the feature point position of the subject's eyeball and the geometric center position of the main camera in the real-time image using computer vision technology. The feature points of the eyeball usually choose the pupil center or the center of the corneal reflection spot as the reference point, because these feature points have good contrast and stability in the image, which facilitates accurate positioning by the algorithm. The position information of the main camera is determined by identifying its shell outline or specific marker points to determine its spatial coordinates in the image.

[0023] Next, the system performs image registration to accurately align the reference image and the real-time image in the same spatial coordinate system. Image registration refers to the technical process of finding the spatial transformation relationship between two images to make them geometrically consistent. Since the reference image and the real-time image are both taken by the same lateral camera at similar positions and angles, they have similar perspective transformation characteristics, and the system can determine the accurate mapping relationship between the two images through feature point matching and geometric transformation calculation.

[0024] After image registration is completed, the system overlays and fuses the two images. In the fused image, the scale information, the position information of the eyeball and the main camera are integrated into the same measurement space. At this point, the system can directly measure the pixel distance between the eyeball feature points and the center point of the main camera in the overlaid image. Then, using the previously established pixel scale parameters, this pixel distance is accurately converted into the actual physical distance, that is, the actual distance from the subject's eyeball to the main camera.

[0025] Based on the above embodiments, as an optional implementation, in S102, the reference image and the real-time image are superimposed to generate the actual distance from the subject's eyeball to the main camera, specifically including S21-S24: S21, Identify and extract the scale information of the ruler from the reference image to obtain the pixel scale of the ruler.

[0026] The system identifies and extracts the scale information of the ruler from the reference image to obtain the ruler's pixel scale. The scale information includes the position of the scale lines, numerical markings, and unit information. The system automatically detects these features using image recognition algorithms to establish the correspondence between pixel coordinates and actual lengths. The pixel scale represents the actual physical length corresponding to each pixel, typically in millimeters per pixel. This parameter provides a precise conversion benchmark for subsequent distance measurements, ensuring the accuracy of the measurement results.

[0027] S22 identifies the subject's eye feature points and the position information of the main camera from real-time images.

[0028] The system identifies the subject's eye feature points and the position information of the main camera from real-time images. Eye feature points are typically selected from the center of the pupil or the center of the corneal reflective spot because these locations offer good contrast and stability in the image, facilitating accurate algorithm localization. The position information of the main camera is determined by recognizing its outer contour or specific markers. The system uses computer vision technology to extract the pixel coordinates of these key locations, determining accurate measurement endpoints for distance measurement.

[0029] S23, perform image registration and overlay processing on the reference image and the real-time image to determine the relative position of the eye feature points and the main camera in the same coordinate system.

[0030] The system performs image registration and overlay processing on the reference image and the real-time image to determine the relative position of the eye feature points and the main camera in the same coordinate system. Image registration is a technical process of finding the spatial transformation relationship between two images to keep them consistent in geometry. Since both images are taken by the same lateral camera, they have similar perspective characteristics. The system achieves accurate alignment through feature point matching and geometric transformation calculation, and integrates the ruler information and eye position information in a unified measurement space.

[0031] S24, based on the pixel scale, the pixel distance between the eye feature points and the main camera is converted into the actual physical distance, and the actual distance from the eye of the measured person to the main camera is obtained.

[0032] The system converts the pixel distance between the eye feature points and the main camera into the actual physical distance based on the pixel scale. The system first measures the pixel distance between the eye feature points and the center point of the main camera in the overlaid image, and then multiplies this pixel distance by the pixel scale parameter obtained in step S21 to obtain the corresponding actual physical distance, i.e. the actual distance from the eye of the measured person to the main camera. This image processing-based distance measurement method avoids the use of traditional measurement tools, realizes automatic and high-precision personalized distance measurement, and provides reliable geometric parameters for subsequent calibration calculation.

[0033] S103, obtaining the preset distance between the main camera and the plurality of LED indicator lights in the nystagmus detection device, when each LED indicator light is lit in turn, collecting the eye image sequence of the measured person when the eye gazes at each LED indicator light.

[0034] The preset distance refers to the accurate physical distance between the optical center of the main camera and the geometric center of each LED indicator light, which is determined in advance and fixed in the system parameters by precise measurement tools during the design and manufacturing stage of the nystagmus detection device. These LED indicator lights are usually arranged according to a specific spatial layout inside the nystagmus detection device, forming a fixed geometric configuration centered on the main camera. Common layouts include two LED indicator lights symmetrically distributed in the horizontal direction and one LED indicator light located in the center, constituting the angle calibration reference point in the horizontal direction. Since these distances are determined by high-precision measurement equipment during device manufacturing, they have very high accuracy and stability and will not change during the use of the device.

[0035] After obtaining the preset distance parameter, the system starts to execute the sequential lighting control sequence of the LED indicator lights. Sequential lighting of the LED indicator lights refers to the process of automatically controlling each LED indicator light to emit light one by one according to a preset time interval and order. Usually, the lighting duration of each LED indicator light is 2-3 seconds, which is enough for the subject to complete the eye fixation action and make the eye movement tend to be stable. This sequential lighting design avoids the visual interference that multiple light sources may cause to the subject, ensuring that the subject can clearly identify and accurately fixate on the target LED indicator light that is currently lit.

[0036] When each LED indicator light is sequentially lit, the system continuously collects a sequence of eye images of the subject through the main camera at a high frame rate. The sequence of eye images refers to a series of eye images continuously captured by the main camera at a preset sampling frequency during the lighting period of each LED indicator light. Usually, the sampling frequency is set to 120 frames per second, ensuring that the complete movement process of the subject's eye from one fixation position to another fixation position can be captured. During this acquisition process, the subject needs to fixate on the lit LED indicator light according to the instructions, making the eye rotate from the initial position to the target position and remain relatively stable, so that the system can accurately record the image position information of the eye at different known rotation angles.

[0037] The system records the LED indicator light state and timestamp information corresponding to each image while collecting the sequence of eye images, establishing an accurate correspondence between the image and the specific fixation target. Through image processing algorithms, the system can extract the key feature point positions of the subject's eye from each eye image, such as the pupil center coordinates or the corneal reflection spot position. The pixel coordinate changes of these feature points directly reflect the rotation trajectory of the eye.

[0038] S104, extract the pixel coordinate position of the subject's eye fixation target LED indicator light in the sequence of eye images, the target LED indicator light being one of the plurality of LED indicator lights.

[0039] The target LED indicator light refers to any specific LED indicator light in the sequence of sequentially lit LED indicator lights. Each target LED indicator light corresponds to a specific fixation direction and rotation angle of the subject's eye. In the specific implementation process, the system first needs to perform time synchronization analysis on the sequence of eye images collected in step S103, match the image timestamp with the timestamp of the LED indicator light control signal, and accurately identify the LED indicator light lighting state corresponding to each eye image, thereby grouping and classifying the image sequence according to different target LED indicator lights.

[0040] For each image sub-sequence corresponding to a target LED indicator, the system employs advanced computer vision algorithms for eye feature point detection and localization. The pupil center is usually chosen as the main reference point because it has good contrast features and geometric stability in the image, facilitating accurate positioning by the algorithm.

[0041] The system improves the image quality of the eye region through image preprocessing techniques, including grayscale conversion, noise reduction filtering, and contrast enhancement. In terms of pupil center localization, the system uses an intelligent recognition algorithm based on deep learning, with the following specific implementation process: First, the system pre-collects a large number of eye image samples containing different lighting conditions, different eye states, and different individual differences to construct a training data set. For each image in the training data set, the pixel coordinate position of the pupil center is accurately marked by manual annotation to form standard labeled data. The training data set covers various practical application scenarios such as normal pupil, mydriasis state, partial occlusion, and glasses reflection, ensuring the model's generalization ability. The system uses a convolutional neural network (CNN) architecture to build a pupil center detection model, with network structures including feature extraction layers, attention mechanism layers, and regression prediction layers. The feature extraction layer extracts deep features of eye images through multiple convolution operations, the attention mechanism layer helps the model focus on key features in the pupil region, and the regression prediction layer directly outputs the two-dimensional coordinate values of the pupil center. During model training, the system uses the mean square error loss function to optimize network parameters, and continuously adjusts model weights through the backpropagation algorithm to minimize the error between the predicted pupil center coordinates and the manually annotated true coordinates. After training is complete, the model can automatically identify the pupil region in the input eye image and directly output the accurate pixel coordinate position of the pupil center, with an identification accuracy of sub-pixel level.

[0042] In the process of extracting pixel coordinate positions, the system also needs to consider the stability evaluation of the eye gaze state. Since the subject's eye will go through a transition phase from movement to stillness during the turning process towards the target LED indicator, the system needs to identify the time period when the eye movement has stabilized and accurately gazes at the target LED indicator from the image sub-sequence. This is achieved by analyzing the change speed and amplitude of the pupil center coordinates in consecutive image frames. When the coordinate change speed drops below a pre-set threshold and the change amplitude remains within a stable range, the system determines that the eye has reached a stable gaze state.

[0043] After identifying the stable fixation state, the system extracts the pixel coordinate position of the subject's eye gaze at the target LED indicator light from the corresponding image frame. The pixel coordinate position refers to the two-dimensional coordinate value of the pupil center in the image coordinate system, usually represented by the coordinate system with the top left corner of the image as the origin, the horizontal direction as the x-axis, and the vertical direction as the y-axis. To improve positioning accuracy, the system usually extracts coordinates from multiple consecutive image frames in the stable fixation state, and then calculates the average of these coordinates as the final pixel coordinate position. This averaging method can effectively reduce measurement errors caused by image noise or minor eye tremors.

[0044] For each LED indicator light in the nystagmus detection device, the system repeatedly performs the above pixel coordinate position extraction process, and finally obtains a complete set of pixel coordinate position data, each data point corresponding to the accurate image position of the subject's eye gaze at a specific target LED indicator light. These pixel coordinate position data have high accuracy and repeatability, as they are based on real measurements of the subject's actual eye movement behavior under controlled conditions.

[0045] S105, combining the preset distance and the actual distance, calculates the rotation angle of the subject's eye when gazing at the target LED indicator light.

[0046] The rotation angle refers to the angle of deflection of the eye's optical axis relative to the initial direction when the subject's eye moves from the initial gaze position to the target LED indicator light, usually expressed in degrees. In the specific implementation process, the system establishes a geometric model with the subject's eye center as the vertex, where the line connecting the eye center to the main camera is the reference baseline, and the line connecting the eye center to the target LED indicator light is the target line of sight. The angle between the two lines is the rotation angle to be calculated.

[0047] The system first calls the preset distance parameter obtained in step S103 and the actual distance parameter generated in step S102 for geometric calculation. The preset distance represents the fixed physical distance between the optical center of the main camera and the geometric center of the target LED indicator light, which has been accurately determined during the design phase of the nystagmus detection device and stored in the system parameters. The actual distance represents the individualized physical distance between the subject's eye center and the optical center of the main camera, which is measured in real time through the image superposition processing technique in step S102 and accurately reflects the specific facial anatomical features of the subject.

[0048] Based on the two key distance parameters, the system uses the geometric principle of trigonometric functions to accurately calculate the rotation angle. In the established geometric model, the main camera, the eye center of the measured person and the target LED indicator light form a right triangle, where the actual distance from the eye center of the measured person to the main camera is the adjacent side, the preset distance from the main camera to the target LED indicator light is the opposite side, and the line connecting the eye center to the target LED indicator light is the hypotenuse. According to the tangent theorem of trigonometric functions, the system substitutes the preset distance and the actual distance into the preset formula for calculation, which is α = arctan(D1 / D2), where α represents the rotation angle of the eye of the measured person when gazing at the target LED indicator light, D1 represents the preset distance from the main camera to the target LED indicator light, and D2 represents the actual distance from the eye of the measured person to the main camera.

[0049] During the calculation process, the system needs to consider the influence of the spatial position difference of different target LED indicator lights on the calculation of the rotation angle. For horizontally distributed LED indicator lights, the system mainly calculates the horizontal rotation angle, and for vertically distributed LED indicator lights, the system calculates the vertical rotation angle. When the nystagmus detection device adopts a multi-dimensional LED indicator light layout, the system also needs to perform three-dimensional angle calculation, and calculates the horizontal and vertical rotation angles by vector decomposition.

[0050] After completing the rotation angle calculation, the system also performs rationality verification and precision optimization on the calculation results. Rationality verification includes checking whether the calculated rotation angle is within the normal rotation range of the human eye and whether the rotation angles corresponding to adjacent LED indicator lights meet the expected relationship of the geometric layout. Precision optimization reduces calculation errors by taking the average of multiple calculations or using higher precision numerical calculation methods to ensure the accuracy and stability of the rotation angle value.

[0051] On the basis of the above embodiments, as an optional implementation, in S105, in combination with the preset distance and the actual distance, the rotation angle of the eye of the measured person when gazing at the target LED indicator light is calculated, specifically including: Substituting the preset distance and the actual distance into the preset formula to calculate the rotation angle of the eye of the measured person when gazing at the target LED indicator light; wherein the preset formula is: tan α = D1 / D2; in the formula, α is the rotation angle, D1 is the preset distance, and D2 is the actual distance.

[0052] In the implementation process, the system first calls the preset distance parameter D1 obtained in step S103 and the actual distance parameter D2 generated in step S102, which respectively represent the fixed geometric characteristics of the nystagmus detection device and the individual physiological characteristics of the subject. The preset distance D1 is a fixed value determined by precise measurement during the device manufacturing stage, with high accuracy and stability, while the actual distance D2 is an individualized parameter measured in real time through image processing technology, which can accurately reflect the specific facial anatomy of the subject.

[0053] The system substitutes these two distance parameters into the preset formula for calculation, and obtains the numerical result of the rotation angle a through simple division operation. The advantage of this calculation method is that the mathematical model is simple and clear, and the calculation process is fast and efficient, which can meet the requirement of real-time nystagmus detection system for calculation speed. At the same time, since the parameters used in the formula are accurate values based on actual measurement, the calculation result has good accuracy and reliability.

[0054] In the calculation process, the system also needs to distinguish and process the spatial positions of different target LED indicator lights. For the horizontally distributed LED indicator lights, the system directly applies the preset formula to calculate the horizontal rotation angle; for the vertically distributed LED indicator lights, the system calculates the vertical rotation angle; for the LED indicator lights located at the diagonal position, the system may need to decompose the coordinates and calculate the component angles in the horizontal and vertical directions.

[0055] S106, combining the pixel coordinate position and the rotation angle, a scaling coefficient of pixel displacement and rotation angle is established, which represents the image pixel movement amount corresponding to a unit angle of eyeball rotation.

[0056] The scaling coefficient is a key conversion parameter, which represents the pixel movement amount corresponding to a unit angle of eyeball rotation of the subject, and the unit is usually pixel / degree. The physical meaning of this coefficient is to establish a mathematical bridge between the angle domain of eyeball movement and the pixel domain of image processing, so that the system can accurately extract the eyeball movement angle information with clinical diagnostic value from pure digital image information. Since the physiological parameters such as facial anatomy, eyeball size and orbit depth of each subject are significantly different, the traditional fixed scaling coefficient cannot adapt to such individual differences, and this step effectively solves this technical problem by calculating the individualized scaling coefficient in real time.

[0057] In the implementation process, the system first needs to determine the reference pixel coordinate position as the reference point for pixel displacement calculation. The reference pixel coordinate position is usually selected as the pixel coordinate position when the subject's eyeball gazes at the central LED indicator light, because the central LED indicator light is located at the geometric center of the nystagmus detection device, corresponding to the initial gaze direction of the subject's eyeball, and has good reference characteristics. The system identifies this reference position from the pixel coordinate position data extracted in step S104 and uses it as the origin coordinate for subsequent pixel displacement calculation.

[0058] Next, the system calculates the pixel displacement amount corresponding to each target LED indicator light. The pixel displacement amount refers to the distance difference between the pixel coordinate position when the subject's eyeball gazes at a specific target LED indicator light and the reference pixel coordinate position, which is usually calculated using the Euclidean distance formula. For horizontal LED indicator light layout, the system mainly calculates the horizontal pixel displacement amount, i.e. the difference in x-coordinate; for vertical LED indicator light layout, the system calculates the vertical pixel displacement amount, i.e. the difference in y-coordinate. When two-dimensional eye movement analysis is required, the system will calculate the horizontal and vertical pixel displacement amounts respectively, and establish independent calibration coefficients.

[0059] After obtaining the pixel displacement amount and the corresponding rotation angle, the system establishes the calibration coefficient using the calibration formula. The calibration formula is K = ΔP / α, where K represents the calibration coefficient, ΔP represents the pixel displacement amount, and α represents the corresponding rotation angle. The physical meaning of this formula is to divide the rotation angle by the corresponding pixel displacement amount to obtain the rotation angle corresponding to a unit pixel movement, and then take its inverse to obtain the pixel movement amount corresponding to a unit rotation angle. Through this mathematical transformation, the system establishes a direct conversion relationship from the angle domain to the pixel domain.

[0060] In order to improve the accuracy and stability of the calibration coefficient, the system usually uses the data of multiple target LED indicator lights for comprehensive calculation. Since there are usually multiple LED indicator lights at different positions in the nystagmus detection device, the system can obtain multiple pairs of pixel displacement amount and rotation angle data, and perform fitting analysis on these data using the least squares method or weighted average method to calculate the optimal calibration coefficient value. This multi-point calibration method can effectively reduce the random errors that may exist in single-point measurement and improve the reliability of the calibration result.

[0061] Based on the above embodiments, as an optional implementation, in S106, the calibration coefficient of pixel displacement and rotation angle is established based on the pixel coordinate position and the rotation angle, which specifically includes: The pixel displacement amount between the pixel coordinate position and the reference pixel coordinate position is obtained, and the reference pixel coordinate position is the pixel coordinate position when the eyeball of the measured person gazes at the central LED indicator light; the rotation angle and the pixel displacement amount are substituted into a calibration formula to establish a calibration coefficient of the pixel displacement and the rotation angle; wherein the calibration formula is: K = a / AP; in the formula, K is the calibration coefficient, a is the rotation angle, and AP is the pixel displacement amount.

[0062] The system first needs to determine the reference pixel coordinate position as the reference point for pixel displacement calculation. The reference pixel coordinate position refers to the pixel coordinate position when the eyeball of the measured person gazes at the central LED indicator light, which has special reference significance because the central LED indicator light is located at the geometric center of the nystagmus detection device, corresponding to the initial gaze direction of the eyeball of the measured person and the zero rotation angle state. The system identifies the pixel coordinate value corresponding to the central LED indicator light from the pixel coordinate position data extracted in step S104, and sets it as the origin coordinate for subsequent pixel displacement calculation, ensuring the consistency and accuracy of the displacement measurement.

[0063] Next, the system calculates the pixel displacement amount AP corresponding to each target LED indicator light. The pixel displacement amount refers to the distance difference between the pixel coordinate position when the eyeball of the measured person gazes at a specific target LED indicator light and the reference pixel coordinate position, which reflects the coordinate change in the image when the eyeball rotates from the initial position to the target position. The system uses a coordinate difference calculation method, for LED indicator lights distributed horizontally, the difference in x-coordinate is calculated as the horizontal pixel displacement; for LED indicator lights distributed vertically, the difference in y-coordinate is calculated as the vertical pixel displacement; for cases requiring two-dimensional analysis, the system will calculate the pixel displacement in the horizontal and vertical directions respectively.

[0064] After obtaining the pixel displacement amount and the corresponding rotation angle a, the system substitutes these two parameters into the calibration formula K = a / AP for calculation. K in the calibration formula represents the calibration coefficient, which has the physical meaning of the rotation angle corresponding to a unit pixel displacement, and the unit is usually degree / pixel. This formula establishes a direct conversion relationship from the pixel domain to the angle domain by dividing the rotation angle by the corresponding pixel displacement amount, so that the system can accurately convert any pixel change into the corresponding eyeball rotation angle.

[0065] When applying the calibration formula, the system needs to ensure the accuracy of the data pairing of the rotation angle and the pixel displacement amount. Each target LED indicator light corresponds to a specific data pair of rotation angle and pixel displacement amount, and the system ensures that the correct data combination is used in the calculation process through data indexing and timestamp matching. At the same time, the system also performs a reasonableness test on the calculation result to ensure that the value of the calibration coefficient is within the expected range, avoiding calculation errors caused by abnormal data.

[0066] In order to improve the accuracy and stability of the calibration coefficient, the system usually utilizes the data of multiple target LED indicators for comprehensive calculation. Since multiple LED indicators at different positions are provided in the nystagmus detection device, the system can obtain multiple sets of data pairs of rotation angles and pixel displacement amounts. By applying the calibration formula to these data and calculating the average value, a more reliable calibration coefficient can be obtained. This multi-point calibration method can effectively reduce the random errors that may exist in single-point measurement, and improve the accuracy and reproducibility of the calibration results.

[0067] Figure 2 is a geometric principle diagram for measuring the rotation angle of the eyeball in the nystagmus detection device provided by the embodiment of the present application. The point O in the figure represents the center of the eyeball of the measured person, which is the rotation center point of the eyeball and the key reference position for angle measurement. C1 (Camera) represents the optical center position of the main camera, which is used to collect the eyeball image and perform subsequent image processing and feature extraction. L1 (LED1) and L2 (LED2) represent target LED indicators at different positions, which are used to guide the measured person to direct the gaze and provide known angle reference points for the calibration process.

[0068] Z1 and Z2 points represent the intersection positions of the eyeball optical axis and the screen (Screen) when the measured person gazes at the two LED indicators L1 and L2. When the line of sight of the measured person changes from gazing at L1 to gazing at L2, the eyeball will rotate around the center point O, generating a corresponding rotation angle α. The two α angles shown in the figure represent the deflection angles of the eyeball optical axis when it rotates from the central position to L1 and L2, which are the key parameters that need to be accurately calculated in the calibration process.

[0069] The distance D represents the preset distance from the main camera C1 to the target LED indicator, which is a fixed geometric parameter determined in the design stage of the nystagmus detection device. This preset distance, combined with the actual distance from the eyeball of the measured person to the main camera, can be used to calculate the accurate rotation angle of the eyeball when gazing at different LED indicators through geometric relationship.

[0070] The entire geometric configuration embodies the core idea of the calibration method: by establishing the known spatial geometric relationship, an accurate mathematical correspondence between the physical rotation angle of the eyeball and the observed pixel displacement in the image is established. When the measured person gazes at the LED indicators at different positions according to the instructions, the main camera records the image performance of the eyeball at different rotation angles, and the system calculates the accurate rotation angle by analyzing the pixel coordinate changes of the eyeball feature points in these images, combined with the known geometric parameters, to finally establish a personalized calibration coefficient and achieve high-precision eyeball movement measurement.

[0071] Figure 3is a flowchart of another calibration method for measuring pupil rotation angle and direction provided by the embodiments of the present application, as shown in Figure 3 The method includes S201-S203: S201, when the eyeball of the measured person has individual physiological optical feature differences, an eyeball static image of the measured person under a preset illumination condition is collected, and a physiological optical parameter of the eyeball of the measured person is extracted from the eyeball static image.

[0072] When the eyeball of the measured person has individual physiological optical feature differences, the system collects an eyeball static image of the measured person under a preset illumination condition, and extracts a physiological optical parameter therefrom. The design purpose of this step is to cope with the influence of the physiological structure differences of the eyeballs of different measured persons on the detection accuracy of nystagmus, and to establish a more accurate calibration basis by obtaining individualized optical feature parameters of the eyeball.

[0073] The individual physiological optical feature differences refer to the natural differences in anatomical structure and optical characteristics of the eyeballs of different measured persons, including changes in parameters such as corneal curvature radius, anterior chamber depth, lens thickness, eye axis length, pupil size, iris texture, and scleral reflection characteristics. These differences directly affect the propagation path and reflection characteristics of light inside the eyeball, and further affect the imaging position and optical performance of the feature points of the eyeball in the image. If these individual differences are not compensated, the calibration accuracy will decrease and the measurement error will increase, especially in high-precision nystagmus detection applications, the influence is particularly significant.

[0074] When the system detects that the measured person has significant individual physiological optical feature differences, a special eyeball static image collection process is started. The preset illumination condition refers to a standardized infrared illumination environment built into the device, which is realized by 8 precisely positioned infrared light sources.

[0075] The objective lens structure of the nystagmus detection device is designed as a fully enclosed darkroom environment. When the measured person wears the device, the objective lens is completely isolated from the outside visible light. Before the LED indicator light is turned on, the measured person is in a completely dark visual environment and cannot see any visible light information. However, the 8 infrared light sources built into the device can provide stable near-infrared wavelength illumination. The wavelength of these infrared light sources is usually selected near 850 nanometers or 940 nanometers. This wavelength range is invisible to the human eye and will not cause visual disturbance or pupil response in the measured person, while it can be effectively captured by the infrared sensor of the camera.

[0076] 8 infrared light sources are arranged in a ring or symmetrical layout around the main camera to ensure sufficient and uniform infrared illumination of the eye region. The power, angle of emission and irradiation range of each infrared light source are precisely calculated and adjusted to ensure that the entire eye surface receives uniform infrared illumination, avoiding the generation of local over-brightness or shadow areas. This standardized infrared lighting environment ensures that the camera can obtain clear and high-contrast eye images under any external lighting conditions, creating ideal imaging conditions for accurate extraction of physiological optical parameters.

[0077] The constancy and controllability of the infrared lighting system eliminate the influence of environmental light changes on image quality, allowing the system to collect eye static images under completely controlled lighting conditions, thereby ensuring the accuracy and reproducibility of physiological optical parameter extraction and providing a reliable data foundation for subsequent individual calibration.

[0078] During eye static image acquisition, the system guides the subject to maintain head stability and fixate on a fixed target, ensuring that the eye is in a relatively static state. The advantage of static image acquisition is that it eliminates the interference of eye movement on feature extraction, allowing the system to obtain clear and stable eye structure images for accurate geometric measurement and optical parameter calculation. The main camera continuously acquires multiple frames of eye images in high-resolution mode under pre-set lighting conditions, and the system selects the best quality image frame as the basis for subsequent processing.

[0079] The extraction of physiological optical parameters is the core technical link of this step, and the system uses advanced computer vision algorithms for in-depth analysis of eye static images. Pupil geometric parameters include the center coordinates, radius size and ellipticity of the pupil, which reflect the shape features and spatial position relationship of the pupil in the image. Corneal reflection characteristic parameters include the position, intensity distribution and shape features of the corneal surface reflection spot, which reveal the curvature characteristics and optical properties of the corneal surface. Iris texture feature parameters include the color distribution, texture pattern and boundary sharpness of the iris, which help to accurately identify the boundaries of the iris and pupil and the boundary line between the iris and sclera. Eye contour parameters include the geometric shape, size and direction angle of the visible eye region, which reflect the overall spatial features of the eye in the image.

[0080] The system also establishes a personalized eye optical model during the extraction of physiological optical parameters, which takes into account the geometric and optical features of the subject's eye to provide personalized parameter basis for subsequent angle calculation and calibration processing. By comparing the differences between the standard eye model and the actual parameters of the subject, the system can identify optical features that require special processing and adjust the subsequent image processing algorithms and calibration strategies accordingly.

[0081] S202, correcting the calibration coefficient according to the physiological optical parameter, and generating a target calibration coefficient.

[0082] The necessity of calibration coefficient correction is due to the significant difference in physiological optical characteristics of different subjects, which will cause different pixel changes in the image under the same eyeball rotation angle. If a unified calibration coefficient is used for measurement, systematic errors will inevitably occur. For example, the pixel displacement generated by a larger pupil under the same rotation angle will be larger than that of a smaller pupil, and the difference in corneal curvature radius will also affect the movement characteristics of the corneal reflection spot. These physiological differences will directly affect the accuracy of angle measurement if not corrected. Therefore, the system needs to establish a correction mechanism based on individual physiological optical parameters to make personalized adjustments to the initial calibration coefficient.

[0083] In the specific implementation process, the system first establishes a mathematical relationship model between physiological optical parameters and calibration coefficient correction factors. This model is based on a large amount of experimental data and theoretical analysis, and quantifies the influence degree of different physiological optical parameters on the image performance of eyeball movement. The pupil size correction factor mainly compensates for the influence of pupil diameter change on pixel displacement, and a larger pupil will produce a more obvious edge movement effect. The system realizes accurate correction by establishing a relationship function between pupil diameter and pixel displacement amplification coefficient. The corneal curvature correction factor adjusts the influence of corneal surface curvature change on the movement characteristics of the reflection spot. Different corneal curvatures will change the imaging position and movement trajectory of the reflection spot, and the system establishes the corresponding relationship between the curvature parameter and the correction coefficient through geometric optical calculation.

[0084] The system adopts a multi-parameter fusion correction strategy to combine the correction factors of each physiological optical parameter to generate a comprehensive correction coefficient. The determination of the weighting coefficient is based on the influence weight of each parameter on the overall measurement accuracy. Through machine learning algorithm analysis and training of historical data, the optimal parameter weight distribution is determined. The general form of the correction formula is K_target=K_initial×(1+Σ(wi×Ci)), where K_target represents the target calibration coefficient, K_initial represents the initial calibration coefficient, wi represents the weight coefficient of the i-th parameter, and Ci represents the correction factor corresponding to the i-th parameter.

[0085] In the correction process, the system also considers the interaction effect between different physiological optical parameters. For example, the combined effect of pupil size and corneal curvature may produce a nonlinear influence, and the system processes these complex parameter interaction relationships through the establishment of a multi-dimensional correction model. For special combinations of physiological optical characteristics, the system will enable special correction algorithms to ensure the accuracy and stability of the correction results.

[0086] The target calibration coefficient is the final calibration parameter after personalized correction, which comprehensively considers all relevant physiological and optical characteristics of the measured person, and can accurately reflect the real relationship between the eyeball movement and the image pixel change of the measured person. Compared with the initial calibration coefficient, the target calibration coefficient has higher individual adaptability and measurement accuracy, especially for the measured person with significant physiological and optical characteristic differences, the corrected calibration coefficient can significantly improve the measurement accuracy.

[0087] After generating the target calibration coefficient, the system also performs a verification test to confirm the correction effect. By having the measured person perform a standardized eyeball movement test, the system compares the measurement differences using the initial calibration coefficient and the target calibration coefficient to verify the effectiveness of the correction algorithm. At the same time, the system establishes quality evaluation indicators, including the degree of improvement in measurement accuracy, the effect of improving repeatability, and the enhancement of stability, to provide data support for the continuous optimization of the correction algorithm.

[0088] On the basis of the above embodiment, as an optional implementation, in S202, the physiological and optical parameters include: pupil diameter, curvature radius of corneal reflection spot, and depth from eyeball surface to corneal vertex, and the target calibration coefficient is generated by correcting the calibration coefficient according to the physiological and optical parameters, specifically including: The ratio of the depth from the eyeball surface to the corneal vertex to the preset standard depth is calculated to generate a distance correction coefficient; the ratio of the curvature radius of the corneal reflection spot to the preset standard curvature radius is calculated to generate an optical magnification correction coefficient; the ratio of the pupil diameter to the preset standard pupil diameter is calculated to generate an imaging contrast correction coefficient; the distance correction coefficient, the optical magnification correction coefficient, and the imaging contrast correction coefficient are substituted into the individualized calibration formula to generate the target calibration coefficient; wherein the individualized calibration formula is: K'=K×η1×η2×η3; in the formula, K' is the target calibration coefficient, K is the calibration coefficient, η1 is the distance correction coefficient, η2 is the optical magnification correction coefficient, and η3 is the imaging contrast correction coefficient.

[0089] The system first calculates the ratio of the depth from the eyeball surface to the corneal vertex to the preset standard depth to generate a distance correction coefficient η1. The depth from the eyeball surface to the corneal vertex reflects the front-to-back diameter characteristics of the eyeball, and the eyeball depth differences of different measured persons will affect the projection change amount of the feature points in the image when the eyeball rotates. The eyeball with larger depth produces relatively smaller pixel displacement under the same rotation angle, while the eyeball with smaller depth produces larger pixel displacement. The distance correction coefficient establishes the ratio relationship between the individual depth parameter and the standard depth parameter to realize accurate compensation for this geometric difference.

[0090] Next, the system calculates the ratio of the curvature radius of the corneal reflection spot to the preset standard curvature radius to generate an optical magnification correction coefficient η2. The curvature radius of the corneal reflection spot is an important indicator of the geometric characteristics of the corneal surface, directly affecting the focusing characteristics of the reflected light and the imaging size of the spot in the image. The corneal surface with a larger curvature radius produces a relatively small and clear-edged reflected spot, while the corneal surface with a smaller curvature radius produces a larger reflected spot. This difference affects the accuracy of feature point positioning and the calculation accuracy of pixel displacement. The optical magnification correction coefficient quantifies the influence of curvature differences on imaging features, achieving personalized correction of optical characteristics.

[0091] The system also calculates the ratio of the pupil diameter to the preset standard pupil diameter to generate an imaging contrast correction coefficient η3. The size of the pupil diameter directly affects the sharpness and contrast features of the pupil edge in the image. A larger pupil provides more obvious edge features and higher positioning accuracy, while a smaller pupil can cause edge blurring and increased positioning errors. The imaging contrast correction coefficient establishes a relationship model between pupil size and imaging quality to effectively compensate for measurement errors caused by pupil size differences.

[0092] After obtaining the three correction coefficients, the system substitutes them into the individualized calibration formula K' = K × η1 × η2 × η3 for calculation. This formula uses a product form to integrate the effects of each correction coefficient, where K represents the basic calibration coefficient, η1, η2, and η3 represent the distance correction coefficient, the optical magnification correction coefficient, and the imaging contrast correction coefficient, respectively. Through this multi-parameter joint correction method, the system can consider individual differences in eye geometry, optical characteristics, and imaging quality, and generate a highly personalized target calibration coefficient K'.

[0093] S203, according to the target calibration coefficient, calculating the target rotation angle of the eyeball of the measured person.

[0094] The target rotation angle refers to the angle value of the deflection of the measured person's eyeball relative to the initial position during actual movement. This angle value has been corrected by individualized physiological optical parameters and can accurately reflect the true movement amplitude and direction characteristics of the eyeball. Compared with the rotation angle calculated based on traditional standardized parameters, the target rotation angle has higher individual adaptability and measurement accuracy, especially for measured persons with significant physiological and optical feature differences. The target rotation angle can effectively eliminate measurement deviations caused by individual differences.

[0095] In the implementation process, the system first needs to obtain real-time image data and corresponding pixel coordinate change information of the eyeball movement of the measured person. When the measured person performs nystagmus detection or eyeball movement test, the main camera continuously captures the eyeball image sequence, and the system extracts the pixel coordinate position of the eyeball feature point in real time through the image processing algorithm, and calculates the pixel displacement amount relative to the reference position. This pixel displacement amount reflects the movement trajectory and change amplitude of the eyeball on the image plane, and is the basic data for angle calculation.

[0096] The system combines the real-time measured pixel displacement amount with the target calibration coefficient to calculate the angle, and the calculation formula is θ_target=ΔP×K_target, where θ_target represents the target rotation angle, ΔP represents the pixel displacement amount, and K_target represents the target calibration coefficient. The core of this calculation process is that the calibration parameter corrected by individualization is applied, so that the angle calculation result can accurately reflect the real movement characteristics of the eyeball of the measured person. Compared with the calculation method using the standard calibration coefficient, this individualized calculation method can significantly improve the accuracy and reliability of angle measurement.

[0097] In the angle calculation process, the system also needs to consider the multidimensional characteristics and complexity of the eyeball movement. For the horizontal movement of the eyeball, the system calculates the horizontal rotation angle; for the vertical movement of the eyeball, the system calculates the vertical rotation angle; for the oblique movement, the system calculates the rotation angles of the horizontal and vertical components respectively, and obtains the total rotation angle and direction information through vector synthesis. This multidimensional angle calculation can comprehensively describe the spatial characteristics of the eyeball movement, and provides a complete data basis for the analysis of complex nystagmus patterns.

[0098] The system also performs real-time quality control and data verification when calculating the target rotation angle. By comparing the angle change trend between consecutive frames, detecting abnormal angle jumps or unreasonable movement patterns, and timely identifying and processing possible measurement errors or image interference. At the same time, the system will establish a confidence evaluation mechanism for angle measurement, evaluate the reliability of each angle measurement value according to the image quality, feature point extraction accuracy and stability of the calculation process, and provide a quality reference for subsequent data analysis and diagnosis.

[0099] On the basis of the above embodiment, as an optional implementation, in S203, calculating the target rotation angle of the eyeball of the measured person according to the target calibration coefficient specifically includes: The real-time pixel coordinate position of the eyeball of the measured person in the measurement process is acquired; the target pixel displacement amount between the real-time pixel coordinate position and the reference pixel coordinate position is calculated; the target pixel displacement amount is multiplied by the target scaling coefficient to obtain the target rotation angle of the eyeball of the measured person; wherein the calculation formula of the target rotation angle is: a' = K' x AP'; in the formula, a' is the target rotation angle, K' is the target scaling coefficient, and AP' is the target pixel displacement amount.

[0100] The system first acquires the real-time pixel coordinate position of the eyeball of the measured person in the measurement process, which is the data source of the entire angle calculation process. The real-time pixel coordinate position refers to the specific coordinate value of the eyeball feature point in the current image frame, and the pupil center or corneal reflection spot center is usually selected as the tracking target. The main camera continuously acquires a sequence of eyeball images at a high frame rate, and the system accurately locates the pixel coordinates of the eyeball feature point from each image frame through real-time image processing algorithms. These coordinate data reflect the instantaneous position information of the eyeball on the image plane, providing an accurate positioning basis for subsequent displacement calculation.

[0101] Next, the system calculates the target pixel displacement amount AP' between the real-time pixel coordinate position and the reference pixel coordinate position. The target pixel displacement amount refers to the coordinate change amount of the eyeball in the image when moving from the initial reference position to the current position, and the reference pixel coordinate position is usually the eyeball position when the measured person gazes at the central LED indicator light. Unlike traditional pixel displacement calculation, the calculation process of the target pixel displacement amount has taken into account the influence of individual physiological optical characteristics of the measured person. By applying the physiological optical parameters extracted in the early stage for optimization processing of displacement measurement, the accuracy and reliability of the displacement data are ensured.

[0102] The system multiplies the calculated target pixel displacement amount by the target scaling coefficient to obtain the target rotation angle a' using the calculation formula a' = K' x AP'. This calculation formula embodies the core advantage of the individualized scaling method, where K' is the target scaling coefficient corrected for individual differences, and AP' is the target pixel displacement amount considering the influence of physiological characteristics. Through this combination of individualized parameters, the system can accurately convert pixel changes in the image into corresponding eyeball rotation angles, effectively eliminating the influence of individual differences on the measurement results.

[0103] During the angle calculation process, the system also performs real-time quality assessment and data verification on the calculation results. By analyzing the angle change trend and the continuity of the motion trajectory between consecutive frames, the system can identify and filter abnormal measurement data, ensuring the accuracy and stability of the target rotation angle. At the same time, the system establishes a dynamic evaluation mechanism for measurement accuracy, evaluating the confidence level of each angle measurement value based on image quality, feature point positioning accuracy, and the stability of the calculation process.

[0104] Figure 4 is a physical diagram provided by an embodiment of the present application, as shown in Figure 4 is an internal structure diagram of a nystagmus detection device, as seen from the perspective of the wearer. The device adopts a binocular separation design, with a complete detection system on each side. The core of each side is a black circular main camera for real-time shooting of eye movement pictures. Around each camera are distributed a plurality of green LED indicator lights, which will light up in turn according to the preset program, guiding the eye of the measured person to look in different directions, just like doing eye movement training. Between the camera and the LED lights are also distributed some small white dots, which are infrared light sources, a total of 8, which emit invisible infrared light to illuminate the eyeball, ensuring that the camera can clearly shoot the eyeball image in a completely dark environment. When the measured person wears the device, the eye will be in a completely closed dark room environment, and only when the green LED light is on can the light spot be seen, and the camera continuously monitors the position and movement of the eyeball through infrared illumination. The system can accurately measure the angle and direction of the eyeball rotation by calculating the change of the pupil center position in the image when the eyeball looks at different LED lights, combined with the known distance between the LED lights and the camera, thereby realizing accurate detection and quantitative analysis of eye diseases such as nystagmus.

[0105] Figure 5 is another physical diagram provided by an embodiment of the present application, as shown in Figure 5 is a real-time monitoring interface of the nystagmus detection device, showing the state of the system when it is working. The right side of the screen displays the real-time eyeball image shot by the infrared camera, and the pupil (black circular area) and surrounding iris structure can be clearly seen. There are also some white light spots reflected in the picture, which are the reflection points of the infrared light sources on the eyeball surface, used to help the system accurately locate and track the eyeball position. The left side of the screen is the real-time waveform graph of the eyeball movement, and the green curve records the position change of the eyeball in the horizontal and vertical directions. Each horizontal line represents the angle of the horizontal coordinate, which is convenient for doctors to observe, and is 0°, ±20°, ±40°, respectively. The time is constantly changing, 10s, 20s, 30s... increasing in turn, so that the doctor can directly observe whether the eyeball has abnormal tremor or movement pattern. The blue status bar at the top displays the current test information, including the measured person information and the current test LED indicator light position (LED left). The whole interface design allows the doctor to see the actual image of the eyeball and the quantitative analysis results of the movement data at the same time, which is convenient for real-time judgment of the severity and characteristics of nystagmus. This is the embodiment of the calibration technology mentioned earlier in actual clinical application. The system converts the accurate pixel coordinates into angle measurement, and converts the small movement of the eyeball into quantifiable medical data.

[0106] Based on the above method, the application further discloses a calibration system for measuring the rotation angle and direction of the pupil, as shown in Figure 6 Figure 6 is a structural schematic diagram of a calibration system for measuring the rotation angle and direction of the pupil provided by an embodiment of the application, and the system comprises a shooting module, a superposition module, a collecting module, an extracting module, a first combining module and a second combining module. The shooting module is configured to, after a subject wears an eye jerk detection device, capture a reference image containing a scale through a lateral camera arranged on the side of the eye jerk detection device, and capture a real-time image containing the eyeball of the subject and the eye jerk detection device through a main camera; the superposition module is configured to superimpose the reference image and the real-time image to generate the actual distance from the eyeball of the subject to the main camera; the collecting module is configured to acquire a preset distance between the main camera and a plurality of LED indicator lights in the eye jerk detection device, and collect an eye image sequence of the subject when the eyeball of the subject gazes at each LED indicator light when the LED indicator lights are lit in sequence; the extracting module is configured to extract the pixel coordinate position of the subject when the eyeball of the subject gazes at a target LED indicator light in the eye image sequence, the target LED indicator light being one of the plurality of LED indicator lights; the first combining module is configured to combine the preset distance and the actual distance to calculate the rotation angle of the eyeball of the subject when the eyeball of the subject gazes at the target LED indicator light; and the second combining module is configured to combine the pixel coordinate position and the rotation angle to establish a calibration coefficient of the pixel displacement and the rotation angle, the calibration coefficient representing the image pixel movement amount corresponding to the unit angle of the eyeball rotation.

[0107] It should be noted that, when the system provided in the above embodiments implements its functions, only the division of the above functional modules is used as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0108] Please refer to Figure 7 , which is a structural schematic diagram of an electronic device provided by an embodiment of the application. As shown in Figure 7 , the electronic device 1000 can include at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0109] The communication bus 1002 is configured to realize the connection and communication between the components.

[0110] ​The user interface 1003 can include a display screen and a camera. Optionally, the user interface 1003 can further include a standard wired interface and a wireless interface.

[0111] The network interface 1004 can include a standard wired interface and a wireless interface (e.g., a WI-FI interface). Optionally, the network interface 1004 can further include a WI-FI interface.

[0112] The processor 1001 can include one or more processing cores. The processor 1001 connects various parts of the server through various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005 and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 1001 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU is mainly used to process an operating system, a user interface, and an application program. The GPU is used to render and draw the content to be displayed on the display screen. The modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 1001, but can be implemented by a separate chip.

[0113] The memory 1005 can include a Random Access Memory (RAM) and can also include a Read-Only Memory (ROM). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 1005 can include a program storage area and a data storage area, where the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area can store data related to the various method embodiments described above, etc. The memory 1005 can optionally be at least one storage device located away from the aforementioned processor 1001. As shown in Figure 7 The memory 1005, as a computer storage medium, can include an operating system, a network communication module, a user interface module, and an application program of a method for measuring a pupil rotation angle and direction.

[0114] In the electronic device 1000 shown in Figure 7 In the electronic device 1000 shown in

[0115] An electronic device readable storage medium stores instructions. When executed by one or more processors, the instructions cause an electronic device to perform the method described in one or more of the above embodiments.

[0116] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0117] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0118] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments is merely illustrative, and the units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0120] In addition, the functional units in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0121] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic or optical disk and various program code storage media.

[0122] The above is only exemplary embodiments of the present disclosure, which cannot limit the scope of the present disclosure. Any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the present disclosure. The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional techniques in the art not described in the present disclosure. The specification and embodiments are only considered exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A calibration method for measuring the angle and direction of pupil rotation, characterized in that, The method includes: When the subject wears the nystagmus detection device, a reference image including a scale is captured by a side-facing camera located on the side of the nystagmus detection device, and a real-time image including the subject's eyeball and the main camera of the nystagmus detection device is captured. The reference image is superimposed on the real-time image to generate the actual distance from the subject's eyeball to the main camera; Obtain a preset distance between the main camera and multiple LED indicators in the nystagmus detection device. When each of the LED indicators lights up in sequence, collect a sequence of eye images of the subject when the subject's eyes are focused on each of the LED indicators. Extract the pixel coordinates of the subject's eyeballs when they are focused on the target LED indicator from the eye image sequence; the target LED indicator is one of a plurality of LED indicators. By combining the preset distance and the actual distance, the rotation angle of the subject's eyeball when looking at the target LED indicator is calculated; By combining the pixel coordinate position and the rotation angle, a calibration coefficient for pixel displacement and rotation angle is established. The calibration coefficient represents the amount of image pixel movement corresponding to a unit angle of eyeball rotation.

2. The calibration method for measuring the pupil rotation angle and direction according to claim 1, characterized in that, The step of overlaying the reference image with the real-time image to generate the actual distance from the subject's eyeball to the main camera includes: The scale information of the ruler is identified and extracted from the reference image to obtain the pixel scale of the ruler; Identify the eye feature points of the subject and the position information of the main camera from the real-time images; The reference image and the real-time image are registered and overlaid to determine the relative positions of the eye feature points and the main camera in the same coordinate system. Based on the pixel scale, the pixel distance between the eye feature point and the main camera is converted into the actual physical distance, thus obtaining the actual distance from the subject's eyeball to the main camera.

3. The calibration method for measuring the pupil rotation angle and direction according to claim 1, characterized in that, The calculation of the rotation angle of the subject's eyeball when looking at the target LED indicator, combining the preset distance and the actual distance, includes: Substituting the preset distance and the actual distance into a preset formula, the rotation angle of the subject's eyeball when looking at the target LED indicator is calculated; wherein... The preset formula is: tanα = D1 / D2; In the formula, α is the rotation angle, D1 is the preset distance, and D2 is the actual distance.

4. The calibration method for measuring the pupil rotation angle and direction according to claim 1, characterized in that, The step of establishing calibration coefficients for pixel displacement and rotation angle by combining the pixel coordinate position and the rotation angle includes: The pixel displacement between the pixel coordinate position and the reference pixel coordinate position is obtained, where the reference pixel coordinate position is the pixel coordinate position when the subject's eyeball is focused on the central LED indicator. Substituting the rotation angle and the pixel displacement into the calibration formula, calibration coefficients for pixel displacement and rotation angle are established; where, The calibration formula is: K = α / ΔP; In the formula, K is the calibration coefficient, α is the rotation angle, and ΔP is the pixel displacement.

5. The calibration method for measuring the pupil rotation angle and direction according to claim 1, characterized in that, The method further includes: When there are individual differences in the physiological optical characteristics of the subject's eyeballs, static images of the subject's eyeballs under preset lighting conditions are collected, and the physiological optical parameters of the subject's eyeballs are extracted from the static images of the eyeballs. Based on the physiological optical parameters, the calibration coefficients are corrected to generate target calibration coefficients; The target rotation angle of the subject's eyeball is calculated based on the target calibration coefficient.

6. The calibration method for measuring the pupil rotation angle and direction according to claim 5, characterized in that, The physiological optical parameters include: pupil diameter, radius of curvature of the corneal reflective spot, and depth from the surface of the eyeball to the corneal apex. Based on these physiological optical parameters, the calibration coefficients are corrected to generate target calibration coefficients, including: Calculate the ratio of the depth from the surface of the eyeball to the corneal apex to a preset standard depth, and generate a distance correction coefficient; Calculate the ratio of the radius of curvature of the corneal reflected light spot to the preset standard radius of curvature, and generate an optical magnification correction coefficient; Calculate the ratio of the pupil diameter to the preset standard pupil diameter to generate an imaging contrast correction coefficient; Substituting the distance correction coefficient, the optical magnification correction coefficient, and the imaging contrast correction coefficient into the individualized calibration formula, the target calibration coefficient is generated; wherein... The individualized calibration formula is: K'=K×η1×η2×η3; where K' is the target calibration coefficient, K is the calibration coefficient, η1 is the distance correction coefficient, η2 is the optical magnification correction coefficient, and η3 is the imaging contrast correction coefficient.

7. The calibration method for measuring the pupil rotation angle and direction according to claim 5, characterized in that, The step of calculating the target rotation angle of the subject's eyeball based on the target calibration coefficient includes: Obtain the real-time pixel coordinate position of the subject's eyeball during the measurement process; Calculate the target pixel displacement between the real-time pixel coordinate position and the reference pixel coordinate position; Multiplying the target pixel displacement by the target calibration coefficient yields the target rotation angle of the subject's eyeball; wherein... The formula for calculating the target rotation angle is: α'=K'×ΔP'; In the formula, α' is the target rotation angle, K' is the target calibration coefficient, and ΔP' is the target pixel displacement.

8. A calibration system for measuring the angle and direction of pupil rotation, characterized in that, The system includes: a shooting module, a superposition module, a data acquisition module, an extraction module, a first combining module, and a second combining module; wherein, The shooting module is used to capture a reference image including a scale and a real-time image including the subject's eyeball and the main camera of the nystagmus detection device by using a side-facing camera set on the side of the nystagmus detection device when the subject wears the nystagmus detection device. The overlay module is used to overlay the reference image with the real-time image to generate the actual distance from the subject's eyeball to the main camera; The acquisition module is used to obtain a preset distance between the main camera and multiple LED indicator lights in the nystagmus detection device. When each of the LED indicator lights is lit in sequence, the module acquires a sequence of eye images of the subject when the subject's eyes are focused on each of the LED indicator lights. The extraction module is used to extract the pixel coordinates of the subject's eyeballs when they are looking at the target LED indicator in the eye image sequence, wherein the target LED indicator is one of the multiple LED indicators; The first combining module is used to combine the preset distance and the actual distance to calculate the rotation angle of the subject's eyeball when it is focused on the target LED indicator; The second combining module is used to combine the pixel coordinate position and the rotation angle to establish a calibration coefficient for pixel displacement and rotation angle, wherein the calibration coefficient represents the amount of image pixel movement corresponding to a unit angle of eyeball rotation.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

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