Gaze analysis method and device using virtual reality content for identifying children with developmental disabilities

Abstract

The present invention relates to a method performed by a gaze movement analysis device and, specifically, to a gaze analysis method and device using virtual reality content for identifying children with developmental disabilities, the method comprising the steps of: receiving VR content from a VR content provision device; collecting gaze movement information of a user watching the VR content; correcting the collected gaze movement information; collecting time data for an object at which the gaze is fixed; and calculating gaze movement data on the basis of the gaze movement information and the time data for the object at which the gaze is fixed, wherein a machine learning model is trained using the gaze movement data, and children with developmental disabilities are identified by means of the machine learning model.

Description

A method and device for analyzing gaze using virtual reality content for screening children with developmental disabilities.

[0001] The present invention relates to a method and device for analyzing gaze using virtual reality content for screening children with developmental disabilities, and more specifically, to a method and device for screening whether a child has a developmental disability by collecting gaze and facial expression data related to interaction while an infant or child performs a given task through a virtual reality device.

[0002] South Korea has a total fertility rate of 0.8, far below the 1.3 standard for countries with extremely low birth rates. Meanwhile, the elderly population is increasing, surpassing the aging society and projected to enter a super-aged society by 2025. This significant demographic shift is further emphasizing the importance of the health of future generations, and there is growing recognition that social responsibility for childbirth and childrearing must be strengthened.

[0003] Accordingly, in order to promote health in infancy and early childhood, which is the foundation of lifelong health, the government implemented a nationwide infant and toddler health checkup for children under 6 years of age who are eligible for health insurance starting November 15, 2007, and from January 1, 2008, a total of 5 national checkups were conducted for all infants and toddlers under 6 years of age before entering school, including those eligible for medical aid. Afterwards, on January 1, 2010, a 42-48 month checkup cycle was added, and from April 1, 2012, a 66-71 month checkup was added, so that infant and toddler health checkups are conducted twice before the first birthday after birth and once a year thereafter, for a total of 7 health checkups. From January 1, 2021, an early infancy checkup was added from 14 to 35 days after birth, so that checkups are conducted three times until the first year of life and once a year thereafter, for a total of 8 health checkups.

[0004] Infants and toddlers experience extremely rapid growth and development. Therefore, assessing whether growth and development are normal requires assessing against age-appropriate norms. While each child's growth and development patterns vary, individual assessments are necessary. However, there are specific criteria for certain movements and behaviors that must be performed at certain times. Early intervention and appropriate intervention for infants and toddlers with developmental delays often allow them to catch up. Therefore, parents who assume they will be fine and delay professional evaluations can have a negative impact on their child's development.

[0005] Therefore, there is a need for discussion on how to determine whether infants and toddlers have developmental disabilities and take preemptive measures using more convenient methods.

[0006] The background technology described above is technical information that the inventor possessed for the purpose of deriving the present invention or acquired during the process of deriving the present invention, and cannot necessarily be said to be technology known to the general public prior to the filing of the present invention.

[0007] The problem to be solved by the disclosure of the present invention is a method for screening developmental disorders by collecting data related to interaction characteristics.

[0008] In addition, the problem to be solved through the disclosure of the present invention is to use a machine learning model to determine whether a child has a developmental disability and to provide results thereon.

[0009] In a method performed by a gaze movement analysis device with respect to a problem to be solved through some embodiments of the present invention, the method includes the steps of receiving VR content from a VR content providing device, collecting gaze movement information of a user viewing the VR content, correcting the collected gaze movement information, collecting gaze-fixed object time data, and calculating gaze movement data based on the gaze movement information and the gaze-fixed object time data; wherein the gaze movement data is utilized to train a machine learning model, and a child with a developmental disability can be selected through the machine learning model.

[0010] In one embodiment, the step of correcting the collected eye movement information may further include the step of checking information on the time at which an eye blink occurred.

[0011] In one embodiment, the step of correcting the collected eye movement information may further include the step of calculating eye position coordinates at a time immediately before and after an eye blink occurs.

[0012] In one embodiment, the step of correcting the collected eye movement information may further include a step of correcting the eye movement path lost due to eye blinking by applying an interpolation method.

[0013] In one embodiment, the step of generating gaze movement data based on gaze movement information and gaze-fixed object time data may further include a step of preprocessing the gaze movement data.

[0014] In one embodiment, the step of generating gaze movement data based on gaze movement information and gaze-fixed object time data may further include a step of performing a labeling task on gaze movement data of children with developmental disabilities and children with normal development.

[0015] In one embodiment, the step of generating gaze movement data based on gaze movement information and gaze-fixed object time data may further include a step of training a machine learning model by utilizing the labeling data to screen children with developmental disabilities.

[0016] In one embodiment, the step of generating gaze movement data based on gaze movement information and gaze-fixed object time data may further include a step of generating a developmental disability screening result by utilizing a learned machine learning model.

[0017] According to the problem solving means of the present invention described above, while an infant or child performs a given task through a virtual reality device, gaze and facial expression data related to interaction can be collected, an index for evaluating interaction can be calculated, and developmental disorders can be screened through comparison / analysis.

[0018] In addition, according to the problem-solving means of the present invention, it is possible to provide an accessible method and device that can be utilized in the actual medical or educational fields by screening whether a child has a developmental disability through a more convenient and faster method.

[0019] FIG. 1 illustrates an exemplary environment in which a gaze movement analysis device according to some embodiments of the present disclosure can be applied.

[0020] FIG. 2 is a flowchart of an operation for screening children with developmental disabilities by providing VR content that can be performed in a gaze movement analysis device according to some embodiments of the present disclosure and calculating gaze movement information of a user who has viewed the VR content.

[0021] FIG. 3 is a flowchart specifically illustrating steps for correcting gaze movement information according to some embodiments of the present disclosure.

[0022] FIG. 4 is a flowchart illustrating another embodiment of utilizing eye movement data according to some embodiments of the present disclosure.

[0023] FIG. 5 is a diagram of an exemplary computing device that may implement a device and / or system according to various embodiments of the present disclosure.

[0024] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the attached drawings. The advantages and features of the present disclosure, and methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the attached drawings. However, the technical idea of ​​the present disclosure is not limited to the following embodiments and may be implemented in various different forms. The following embodiments are provided only to complete the technical idea of ​​the present disclosure and to fully inform those skilled in the art of the present disclosure of the scope of the present disclosure, and the technical idea of ​​the present disclosure is defined only by the scope of the claims.

[0025] When assigning reference numerals to components in each drawing, it should be noted that identical components are assigned the same numerals whenever possible, even if they appear on different drawings. Furthermore, when describing the present disclosure, if a detailed description of a related known configuration or function is deemed likely to obscure the gist of the present disclosure, such detailed description will be omitted.

[0026] Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used in a meaning that can be commonly understood by a person of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in commonly used dictionaries shall not be interpreted ideally or excessively unless explicitly specifically defined. The terminology used herein is for the purpose of describing embodiments and is not intended to limit the present disclosure. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase.

[0027] Additionally, terms such as first, second, A, B, (a), (b), etc. may be used to describe components of the present disclosure. These terms are only intended to distinguish the components from other components, and the nature, order, or sequence of the components are not limited by the terms. When it is described that a component is "connected," "coupled," or "connected" to another component, it should be understood that the component may be directly connected or connected to the other component, but another component may also be "connected," "coupled," or "connected" between each component.

[0028] The terms "comprises" and / or "comprising" as used in the specification do not exclude the presence or addition of one or more other components, steps, operations and / or elements.

[0029] Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0030] In addition, when describing the components of the present invention, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only to distinguish the components from other components, and the nature, order, or sequence of the components are not limited by the terms. Throughout the specification, when a part is said to "include" or "have" a component, this does not mean that other components are excluded, but rather that other components can be further included, unless specifically stated otherwise. In addition, terms such as "part" and "module" described in the specification mean a unit that processes at least one function or operation, and this can be implemented by hardware, software, or a combination of hardware and software.

[0031]

[0032] FIG. 1 illustrates an exemplary environment in which a gaze movement analysis device according to some embodiments of the present disclosure may be applied. A system including a VR content providing device (100) and an gaze movement analysis device (200) illustrated in FIG. 1 allows the VR content providing device to provide VR content and analyze the gaze movements of a user while viewing the content, thereby determining whether the user has a developmental disability.

[0033] Below, the operations of the components illustrated in Fig. 1 related to the judgment operation of a developmentally disabled child of the eye movement analysis device (200) through the above-described system will be described in more detail.

[0034] FIG. 1 illustrates an example in which a VR content providing device (100) and a gaze movement analysis device (200) are connected via a network, but this is only for convenience of understanding, and the number of devices that can be connected to the network may vary.

[0035] Meanwhile, Fig. 1 merely illustrates a preferred embodiment for achieving the purpose of the present disclosure, and some components may be added or deleted as needed. Below, the components illustrated in Fig. 1 will be described in more detail.

[0036] The eye movement analysis device (200) can collect and analyze various information generated from the VR content providing device (100). The various information can include all data generated from the VR content providing device (100), and the eye movement analysis device (200) can receive VR content provided by the VR content providing device (100). In this case, the VR content refers to virtual reality content for evaluating children's interactions, and can include any content for inducing the user's eye movement.

[0037] The above VR content can be implemented in the form of task-based content such as picking up a directed object or stacking blocks, in which the user understands given instructions and performs the task.

[0038] The VR content providing device (100) illustrated in FIG. 1 may be implemented with one or more computing devices. For example, all functions of the VR content providing device (100) may be implemented in a single computing device. As another example, the first function of the VR content providing device (100) may be implemented in a first computing device, and the second function may be implemented in a second computing device. Here, the computing device may be, but is not limited to, a notebook, a desktop, a laptop, etc., and may include all types of devices equipped with computing functions. In addition, the VR content providing device (100) may provide VR content stored in a database within the device to the eye movement analysis device (200).

[0039] Meanwhile, the eye movement analysis device (200) may be implemented with one or more computing devices. For example, all functions of the eye movement analysis device (200) may be implemented with a single computing device. As another example, the first function of the eye movement analysis device (200) may be implemented with a first computing device, and the second function may be implemented with a second computing device. Here, the computing device may be, but is not limited to, a notebook, a desktop, a laptop, etc., and may include all types of devices equipped with computing functions. However, the eye movement analysis device (200) may preferably be implemented with a high-performance, server-level computing device. An example of a computing device will be described with reference to FIG. 5.

[0040] To avoid redundant explanations, the various operations performed by the eye movement analysis device (200) will be described in more detail later with reference to the drawings below, FIG. 2.

[0041] In addition, the function that can be implemented in the eye movement analysis device (200) may also be implemented by utilizing the electronic device mounted on the VR content providing device (100). Therefore, in FIG. 1, the eye movement analysis device (200) and the VR content providing device (100) are illustrated separately, but according to one embodiment, the eye movement analysis device (200) is mounted on the VR content providing device (100), so that a corresponding device within the VR content providing device (100) can implement the first function, the second function, etc. It should be noted that the present invention is not limited to the embodiment in which the VR content providing device (100) and the eye movement analysis device (200) are externally separated as illustrated in FIG. 1.

[0042] In this specification, for the convenience of explanation, a situation in which a VR content provision device (100) and a gaze movement analysis device (200) implement functions separately will be described.

[0043] In some embodiments, components included in an environment to which the eye movement analysis device (200) is applied may communicate via a network. The network may be implemented as any type of wired / wireless network, such as a local area network (LAN), a wide area network (WAN), a mobile radio communication network, or Wibro (Wireless Broadband Internet).

[0044] Meanwhile, the environment illustrated in FIG. 1 illustrates a connection via a network via a VR content providing device (100) and a gaze movement analysis device (200), but it should be noted that the scope of the present disclosure is not limited thereto, and the VR content providing device (100) may also be connected to the gaze movement analysis device (200) via P2P (Peer to Peer).

[0045] So far, with reference to FIG. 1, an exemplary environment in which a corresponding device (200) according to some embodiments of the present disclosure can be applied has been described. Hereinafter, with reference to the drawings including FIG. 2 and below, methods according to various embodiments of the present disclosure will be described in detail.

[0046] Each step of the methods described below may be performed by a computing device. In other words, each step of the methods may be implemented by one or more instructions executed by a processor of the computing device. All steps included in these methods may be performed by a single physical computing device, but first steps of the method may be performed by a first computing device, and second steps of the method may be performed by a second computing device.

[0047] In the following Figure 2, the explanation will continue assuming that each step of the methods is performed by the eye movement analysis device (200) illustrated in Figure 1. However, for the convenience of explanation, the description of the operating entity of each step included in the methods may be omitted.

[0048]

[0049] FIG. 2 is a flowchart of an operation for screening children with developmental disabilities by providing VR content that can be performed in a gaze movement analysis device according to some embodiments of the present disclosure and calculating gaze movement information of a user who has viewed the VR content.

[0050] In step S100, the eye movement analysis device (200) can receive VR content from the VR content providing device (100). The VR content can be virtual reality content for evaluating children's interactions, and can be configured as content for inducing the user's eye movement. It can also be content implemented in a form of understanding given instructions and performing tasks, such as task-based content such as picking up an instructed object or stacking blocks.

[0051] In step S200, the eye movement analysis device (200) provides VR content to the user, and when the user views the VR content, the user's eye movement information can be collected. At this time, when viewing the VR content, the user's eye movement information can include data that can be used to determine task performance by recording task performance and task completion times.

[0052] In addition, the eye movement information may include coordinate values ​​of the user's gaze within a two-dimensional plane (x, y values ​​of the left and right gaze), coordinates of objects within content implemented in a three-dimensional space, and eye movement path information converted into a two-dimensional space. In addition, the eye movement information may include center values ​​of the left and right gazes (center values ​​of the x, y coordinates), and time information when the left and right eyes blinked.

[0053] In step S300, the gaze movement analysis device (200) can correct the acquired gaze movement information. Hereinafter, the operation of correcting the gaze movement information in the gaze movement analysis device (200) will be described in detail with reference to FIG. 3.

[0054]

[0055] FIG. 3 is a flowchart specifically illustrating steps for correcting gaze movement information according to some embodiments of the present disclosure.

[0056] In step S310, the eye movement analysis device (200) can confirm the time information when the eye blink occurred. In addition, in step S320, the eye movement analysis device (200) can calculate the gaze position coordinates at the time immediately before and after the eye blink occurred. Furthermore, in step S330, the eye movement analysis device (200) can apply an interpolation method to correct the gaze movement path lost due to the eye blink.

[0057] Returning to FIG. 2, in step S400, the eye movement analysis device (200) can collect time data of an object within the VR content on which the user's gaze is fixed when viewing VR content. The eye movement analysis device (200) can identify eye movement data to be analyzed by collecting eye movement and time data of an object on which the gaze is fixed when the user views the VR content. At this time, the eye movement analysis device (200) can collect data on the fixed gaze movement of a fixed object included in the VR content. At this time, the eye movement analysis device (200) can collect eye movement and time data of the fixed object by utilizing the identification of dispersion threshold technique (I-DT technique).

[0058] The above-described technique may be a technique for an algorithm that determines whether an eye movement is located within a limited range for the minimum time required to be classified as a fixation. In this case, the condition for classifying it as a fixational eye movement may be to calculate a moving average value by moving a set window considering the time of eye movement and the sampling rate of the device collecting eye movement data, and to calculate a value D to be compared with the moving average value in order to classify it as a fixational eye movement.

[0059] The above D value can be calculated by adding the difference between the maximum / minimum values ​​of the coordinate values ​​x and y of the line of sight. -> D = [max(x) - min(x)] + [max(y) - min(y)]

[0060] Furthermore, by comparing the moving average and D, if the moving average is less than D, it can be classified as fixational eye movement. Furthermore, after moving the window to the next gaze path data and calculating the moving average, the aforementioned multiple procedures can be repeated. At this time, if the moving average can no longer be calculated, the multiple procedures can be terminated.

[0061] At this time, the I-DT technique is used to identify fixational eye movement, and the following indices are calculated for analysis of gaze movement data. The definitions of the calculated indices can be as follows.

[0062] Eye gaze movement time (EMT): Gaze movement that is not classified as fixational eye movement (mean and standard deviation) due to saccadic eye movement, Fixation duration (FD): Gaze movement time classified as fixation (mean and standard deviation), First fixation duration (FFD): Time of the first fixation, Last fixation (LF): Last fixation, Time to first fixation (TFF): Time to the first confirmed fixation immediately after the start of the task, Number of fixations (NF): Number of fixations confirmed from the start to the end of the task.

[0063] In step S500, the gaze movement analysis device (200) can generate gaze movement data based on gaze movement information and gaze-fixed object time data. At this time, the gaze movement data includes gaze movement information and gaze-fixed object time data, and may be different from the gaze movement information. The specific details of step S500 will be described in detail with reference to FIG. 4.

[0064]

[0065] FIG. 4 is a flowchart illustrating another embodiment of utilizing eye movement data according to some embodiments of the present disclosure.

[0066] In step S510, the gaze movement analysis device (200) can preprocess the gaze movement data. The preprocessing process performs data preprocessing on the collected data (EMT mean, EMT standard deviation, FD, FFD, LF, NF), and may include missing data processing (using the K-Nearest Neighbor (KNN) algorithm) and data standardization (zero mean, unit variance).

[0067] In step S520, the eye movement analysis device (200) may perform labeling on eye movement data of children with developmental disabilities and children with normal development. In this case, the labeling process may refer to the process of generating learning data for training a machine learning model using the labeled data.

[0068] At this time, the labeling task can label eye movement data collected from children with developmental disabilities and children with normal development, and label eye movement data by dividing them into children diagnosed with one type of developmental disability (DDC: Developmentally Delayed Child) and children not diagnosed with one type of developmental disability (TDC: Typically Developed Child).

[0069] More specifically, after this labeling process, data on eye movement data for children with developmental disabilities and children with typical development may be labeled as 1 for children with developmental disabilities, and 0 for children with typical development. The following labeling process is merely an example and should not be interpreted as being limited to this.

[0070] In step S530, the eye movement analysis device (200) can utilize the labeling data to train a machine learning model to identify children with developmental disabilities. The machine learning model may be an artificial intelligence model or an AI model algorithm, and the process of training the machine learning model may be identical to the process of training a supervised learning algorithm.

[0071] At this time, the machine learning model may include KNN, SVM (Support Vector Machine), RF (Random Forest), LightGBM (Light Gradient-Boosting Machine), Logistic Regression (LR), K-means, and Least Absolute Shrinkage and Selection Operator (LASSO) models, and the machine learning model may be trained using labeled data. Accordingly, the machine learning model may continuously learn about the labeled data, and when eye movement data is input in the future, the output data may be output as a probability value for which labeling information is produced.

[0072] In step S540, the eye movement analysis device (200) can produce a developmental disorder screening result by utilizing the learned machine learning model.

[0073] At this time, the trained machine learning model can interpret the output values ​​to determine whether a person has a developmental disability. The machine learning model of the present invention can perform soft voting ensembling on each output value of each labeled category to output a developmental disability screening result.

[0074] Below, an exemplary computing device in which a gaze movement analysis device can be implemented is described using FIG. 5.

[0075]

[0076] FIG. 5 is a diagram of an exemplary computing device that may implement a device and / or system according to various embodiments of the present disclosure.

[0077] A computing device (1500) may include one or more processors (1510), a bus (1550), a communication interface (1570), a memory (1530) for loading a computer program (1591) to be executed by the processor (1510), and a storage (1590) for storing the computer program (1591). However, only components related to the embodiment of the present disclosure are illustrated in FIG. 5. Therefore, a person skilled in the art to which the present disclosure pertains may recognize that other general components may be included in addition to the components illustrated in FIG. 5.

[0078] The processor (1510) controls the overall operation of each component of the computing device (1500). The processor (1510) may include a central processing unit (CPU), a microprocessor unit (MPU), a microcontroller unit (MCU), a graphics processing unit (GPU), or any other type of processor well known in the art of the present disclosure. In addition, the processor (1510) may perform operations for at least one application or program for executing a method according to embodiments of the present disclosure. The computing device (1500) may include one or more processors.

[0079] The memory (1530) stores various data, commands, and / or information. The memory (1530) may load one or more programs (1591) from the storage (1590) to execute a method according to embodiments of the present disclosure. The memory (1530) may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

[0080] The bus (1550) provides communication between components of the computing device (1500). The bus (1550) may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0081] The communication interface (1570) supports wired and wireless Internet communication of the computing device (1500). Furthermore, the communication interface (1570) may support various communication methods other than Internet communication. To this end, the communication interface (1570) may be configured to include a communication module well known in the technical field of the present disclosure.

[0082] According to some embodiments, the communication interface (1570) may be omitted.

[0083] Storage (1590) can non-temporarily store one or more programs (1591) and various data.

[0084] Storage (1590) may be configured to include non-volatile memory such as Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which the present disclosure pertains.

[0085] The computer program (1591) may include one or more instructions that, when loaded into the memory (1530), cause the processor (1510) to perform methods / operations according to various embodiments of the present disclosure. That is, the processor (1510) may perform the methods / operations according to various embodiments of the present disclosure by executing the one or more instructions.

[0086] Various embodiments of the present disclosure and effects according to the embodiments have been described with reference to FIGS. 1 through 5 so far. The effects according to the technical concept of the present disclosure are not limited to the effects described above, and other effects not mentioned will be clearly understood by those skilled in the art from the description in the specification.

[0087] The technical idea of ​​the present disclosure described with reference to FIGS. 1 to 5 so far can be implemented as a computer-readable code on a computer-readable medium. The computer-readable recording medium can be, for example, a removable recording medium (CD, DVD, Blu-ray disc, USB storage device, removable hard disk) or a fixed recording medium (ROM, RAM, computer-attached hard disk). The computer program recorded on the computer-readable recording medium can be transmitted to another computing device via a network such as the Internet and installed on the other computing device, thereby allowing it to be used on the other computing device.

[0088] Although all components constituting the embodiments of the present disclosure have been described as being combined or operated in combination as one, the technical concept of the present disclosure is not necessarily limited to such embodiments. That is, within the scope of the present disclosure, all components may be selectively combined and operated one or more times.

[0089] Although operations are depicted in the drawings in a particular order, this should not be understood to imply that the operations must be performed in the particular order depicted, or in any sequential order, or that all depicted operations must be performed to achieve the desired results. In certain circumstances, multitasking and parallel processing may be advantageous. Furthermore, the separation of the various components in the embodiments described above should not be understood to imply that such separation is absolutely necessary, and it should be understood that the program components and systems described may generally be integrated together into a single software product or packaged into multiple software products.

[0090] Although the embodiments of the present disclosure have been described with reference to the attached drawings, those skilled in the art will appreciate that the present disclosure can be implemented in other specific forms without changing the technical idea or essential features thereof. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. The scope of protection of the present disclosure should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included in the scope of rights of the technical ideas defined by the present disclosure.

Claims

1. In a method performed by a gaze movement analysis device, A step of receiving VR content from a VR content providing device; A step of collecting eye movement information of a user viewing the above VR content; A step of correcting the collected eye movement information; A step of collecting object time data with fixed gaze; and A step of generating gaze movement data based on gaze movement information and gaze-fixed object time data; including; By utilizing the above eye movement data, a machine learning model is trained, and children with developmental disabilities are selected through the machine learning model. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

2. In paragraph 1, The step of correcting the collected eye movement information further includes the step of checking the time information when the eye blink occurred. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

3. In paragraph 2, The step of correcting the collected eye movement information further includes the step of calculating the gaze position coordinates at the time immediately before and after the eye blink occurs. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

4. In paragraph 3, The step of correcting the collected eye movement information further includes a step of correcting the eye movement path lost due to eye blinking by applying an interpolation method. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

5. In paragraph 1, Based on the gaze movement information and the gaze-fixed object time data, the step of producing gaze movement data further includes a step of preprocessing the gaze movement data. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

6. In paragraph 5, The step of producing gaze movement data based on gaze movement information and gaze-fixed object time data further includes a step of performing labeling work on gaze movement data of children with developmental disabilities and children with normal development. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

7. In paragraph 6, The step of generating gaze movement data based on gaze movement information and gaze-fixed object time data further includes a step of training a machine learning model by utilizing the labeling data to screen children with developmental disabilities. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

8. In paragraph 7, Based on the gaze movement information and the gaze-fixed object time data, the step of producing gaze movement data further includes a step of producing a developmental disorder screening result by utilizing the learned machine learning model. A gaze analysis method using virtual reality content for screening children with developmental disabilities.

9. Processor; network interface; memory; and A computer program loaded into the above memory and executed by the above processor, The above processor, Instructions for receiving VR content from a VR content providing device; Instructions for collecting eye movement information of a user viewing the above VR content; Instructions for correcting the collected eye movement information; Instructions for collecting object time data with fixed gaze; and An instruction for producing gaze movement data based on gaze movement information and gaze-fixed object time data; including performing the following; By utilizing the above eye movement data, a machine learning model is trained, and children with developmental disabilities are selected through the machine learning model. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

10. In paragraph 9, The instruction to correct the collected eye movement information is performed by including an instruction to check the time information when the eye blink occurred. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

11. In paragraph 10, The instructions for correcting the collected eye movement information further include instructions for calculating the gaze position coordinates at the time immediately before and after the blink occurs. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

12. In paragraph 11, The instruction to correct the collected eye movement information is performed by applying an interpolation method, and further includes an instruction to correct the eye movement path lost due to eye blinking. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

13. In paragraph 9, Based on the gaze movement information and the gaze-fixed object time data, the instruction for producing gaze movement data further includes an instruction for preprocessing the gaze movement data. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

14. In paragraph 13, The instruction for producing eye movement data based on eye movement information and gaze-fixed object time data further includes an instruction for performing labeling work on eye movement data of children with developmental disabilities and children with normal development. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

15. In paragraph 14, Based on the gaze movement information and the gaze-fixed object time data, the instruction for producing gaze movement data further includes an instruction for training a machine learning model by utilizing the labeling data to screen children with developmental disabilities. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

16. In paragraph 15, Based on the gaze movement information and the gaze-fixed object time data, the instruction for producing gaze movement data further includes an instruction for producing a developmental disorder screening result by utilizing the learned machine learning model. A gaze analysis device utilizing virtual reality content for screening children with developmental disabilities.

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