KR102435234B1 - An Educational system for providing customized contents based on artificial intelligence and method thereof - Google Patents

Abstract

The present invention provides an educational system through provision of user-customized content based on artificial intelligence and a method thereof. The method comprises the steps of: (a) providing a guide to select cognitive function rehabilitation training content; (b) providing, when one of the content is selected, the selected content to a user through an HMD terminal and a controller or a hand tracking terminal; and (c) adaptively changing the content output through the HMD terminal by increasing or decreasing the difficulty level of the currently selected content according to user input information input through the controller or the hand tracking terminal.

Description

An Educational system for providing customized contents based on artificial intelligence and method thereof

The present invention relates to a cognitive function rehabilitation training method and system, and more particularly, to a cognitive function rehabilitation training method and system capable of improving a user's dementia status through the provision of user-customized content based on artificial intelligence.

In addition, the present invention is the project number 10316877 managed by Kyungpook National University Technopark as a research institution as a project of the Ministry of SMEs and Startups, and is a "Development of a Bio Signal Interface Cave System to improve cognitive function of the elderly by interworking with VR stereoscopic images and biosignals". , which was conducted from May 22, 2020 to March 21, 2021, during the research period of GL Co., Ltd. as the lead institution.

As the average life expectancy increases, the number of patients due to cognitive impairment such as dementia is increasing. It is expected that the number will exceed this, and the resulting social cost is increasing.

In addition, many people agree that cognitive dysfunction such as dementia suffers not only the patient but also the family, and it is no longer a family problem, but a social problem.

In particular, in the case of mild cognitive impairment, which has a relatively low degree of cognitive impairment, it is necessary to manage and monitor it so that it does not further develop into dementia.

Drugs have been conventionally used to diagnose, treat, and relieve symptoms of such cognitive impairment, but in the case of therapeutic drugs, there is a risk of side effects and the treatment efficiency is low.

In addition, the existing cognitive function test and diagnosis is based on only a multi-item questionnaire, so the efficiency as a screening test is insufficient.

Accordingly, there is an increasing demand for more effective content provision and treatment technologies that can improve overall cognitive dysfunction.

Korean Patent No. 10-1935876 (announced on January 07, 2019)

The present invention reflects the problems and needs as described above, and an object of the present invention is to provide an educational system and method that can improve the dementia status of the user through the provision of user-customized content based on artificial intelligence.

In addition, the present invention analyzes the user's problem performance in real time based on artificial intelligence when performing each mission and can adjust the difficulty level according to the user's level through real-time analysis, so that the content is efficiently provided according to the user's mild cognitive impairment state. It aims to provide an educational system and method that can adaptively change

To this end, an aspect of the present invention provides an artificial intelligence-based education method through the provision of customized content, comprising the steps of: (a) guiding to select cognitive function rehabilitation content; (b) if any one of the contents is selected, providing the selected contents to a user through an HMD terminal and a controller or a hand tracking terminal; and (c) adaptively changing the content output through the HMD terminal by adjusting the level upward or downward from the difficulty level of the currently selected content according to user input information input through the controller or the hand tracking terminal; may include.

Another aspect of the present invention provides a system for education through artificial intelligence-based user-customized content provision, comprising: a Head Mount Display (HMD) terminal for outputting cognitive function rehabilitation training contents; a controller or hand tracking terminal providing user's hand tracking information; and guide the selection of the cognitive function rehabilitation training contents, and when any one of the contents is selected, the selected contents are provided to the user through the HMD terminal and the controller or the hand tracking terminal, and input through the controller or the hand tracking terminal A cognitive function rehabilitation control apparatus for adaptively changing the content output through the HMD terminal by adjusting the level upward or downward from the difficulty level of the currently selected content according to the user input information to be selected; may include.

In addition, the total number of difficulties provided by the mission in the selected content is n,
According to the difficulty increase setting method, if the number of correct answer selections accumulated in the mission is greater than the minimum number of correct answer selections required for difficulty increase, the difficulty level is raised,
Depending on the difficulty setting method, if the accumulated number of incorrect answer selections in the mission is greater than the minimum number of incorrect answers required to lower the difficulty, the accumulated number of correct answer selections in the mission will be reduced, or if the number of correct answers selected is 0, the difficulty level may be lowered. .

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In addition, the method of setting the minimum number of correct answer selections (b) for increasing the difficulty is characterized in that it is determined by Equation 1 below.

[Equation 1]

Here, each x is the input difficulty, b is the minimum number of correct answers that require an increase in difficulty in x level difficulty, e (constant) is the number of correct answers required to increase the difficulty in the lowest difficulty level, and f (constant) is the number of correct answers required to increase the difficulty in the highest difficulty level. The number of correct answers required to increase the difficulty level, n (constant), refers to the total number of difficulty levels. In this case, b is the absolute value of an integer rounded to the first decimal place.

In addition, the method of setting the minimum number of incorrect answer selections (a) for lowering the difficulty is characterized in that it is determined by the following Equation (2).

[Equation 2]

Here, x is the input difficulty, a is the minimum number of incorrect answer selections required to lower the difficulty in x level difficulty, g (constant) is the number of incorrect answer selections required to lower the difficulty in the lowest difficulty level, and h (constant) is the difficulty in the highest difficulty level. The number of incorrect answer selections required for downgrade, n (constant), refers to the total number of difficulty levels. Here, a is the absolute value of an integer rounded to the first decimal place.

The present invention relates to a system and method for education through the provision of user-customized content based on artificial intelligence, and it is possible to improve the dementia state of the user or the cognitive function of the elderly and infirm.

In addition, when performing each mission, the user's problem performance is analyzed in real time based on artificial intelligence and the level of difficulty can be adjusted according to the user's level through real-time analysis. By making it possible to change to , it is possible to maximize the effect of cognitive function rehabilitation training.
In addition, the present invention can be extended and applied to various types of educational systems and programs for improving learning effects in addition to educational systems and programs for improving the cognitive function of the elderly or the dementia of the user.

1 is a block diagram of a user-customized cognitive function rehabilitation training system according to a preferred embodiment of the present invention.
FIG. 2 is a diagram illustrating a hand tracking terminal or controller as a user input device in the system of FIG. 1 .
3 is a view showing a detailed configuration diagram of each part of the cognitive function rehabilitation system of FIG. 1 .
FIG. 4 is a flowchart illustrating an artificial intelligence-based user-customized content providing process using the cognitive function rehabilitation training system of FIG. 1 .
5 is a flowchart illustrating a difficulty adjustment process according to a user's problem performance in the AI-based user-customized content provision process of FIG. 4 .
FIG. 6 is a graph showing an example of a difficulty increasing process in the difficulty adjustment process of FIG. 5 , and FIG. 7 is a graph showing an example of a difficulty decreasing process in the difficulty adjusting process of FIG. 5 .
8 to 26 are diagrams showing examples of 'User A' who needs to increase the difficulty level.
27 to 50 are diagrams showing examples of 'User B' who needs to lower the difficulty level.

Hereinafter, for the convenience of explanation, it is summarized as "dementia" as a comprehensive concept that can include all of the cognitive dysfunctions such as mild cognitive impairment, Alzheimer's disease, and dementia.

In addition, the present invention can be extended and applied to various types of educational systems and programs for improving learning effects in addition to educational systems and learning programs for improving the dementia status of users or improving cognitive functions of the elderly.
Hereinafter, with reference to the accompanying drawings, using artificial intelligence-based virtual reality technology according to a preferred embodiment of the present invention, the cognitive function rehabilitation training method and system that can improve the dementia state of the user through the provision of user-customized content will be described in detail. Explain.

<Cognitive function rehabilitation training system through the provision of customized contents>

1 is a block diagram of a user-customized cognitive function rehabilitation training system according to a preferred embodiment of the present invention. Referring to FIG. 1 , the user-customized cognitive function rehabilitation training system includes a cognitive function rehabilitation control apparatus 100 , an HMD terminal 200 , and a controller 300 .

Here, the content provided is mainly described in the virtual reality (VR (Virtual Reality)) method, but is not limited thereto, and XR ( Rehabilitation of cognitive functions in the method of eXtended Reality (extended reality) content can be provided. In this case, the cognitive function rehabilitation training contents will be mainly described with user-customized cognitive function rehabilitation training contents to be described later. Such user-customized cognitive function rehabilitation training content may include interactive content that changes an object output through the HMD terminal 200 in response to user input information input through the controller 300 .

In addition, the HMD (Head Mount Display) terminal 200 is a display device that enables viewing of a virtual object and reality together while being worn on a user's head like glasses. That is, the HMD terminal 200 is formed in a shape that covers the entire face of the eye and head, and 100% virtual reality is VR depending on the environment, MR including some real information, and AR operating based on the real environment. allows users to view

The controller 300 detects the user's hand movement while providing cognitive function rehabilitation training content, generates input information, and provides the input information to the control device 100 . In this case, a hand tracking terminal or a controller may be employed as the user input device, which will be described in detail with reference to FIG. 2 .

Figure 2 (a) illustrates the hand tracking process, determining the coordinates of the interactable object based on the central coordinates in virtual reality, and based on the user's hand position obtained through the depth measurement sensor, the user Detects hand input information of The hand tracking terminal for this purpose includes a depth measuring sensor for detecting the user's hand position.

Figure 2 (b) is an example of a controller, a joystick or a tool specially created for interaction, the user holds in his hand and presses or moves a button to interact with the object of the XR-type interactive cognitive rehabilitation training content In this way, it is composed of a receiver that receives a value sensed by the controller, a motion capture sensor for recognizing the position of the controller, and a module for processing it.

The cognitive function rehabilitation control apparatus 100 provides content for improving dementia through the HMD terminal 200 and the controller 300 , and analyzes user input information input through the controller 300 in real time. At this time, the control device 100 analyzes the problem performance of the user through the controller 300 in real time for each mission of the content provided through the artificial intelligence-based learning program, and evaluates the user's level through real-time analysis will do In addition, the content may include m experiential missions, and each m missions may be divided into n levels of difficulty. to provide content that is level-up or down-leveled according to the user's mild cognitive impairment state by evaluating it, and this will be described in more detail through the cognitive rehabilitation training process to be described later.

In this way, it is possible to adjust the difficulty level according to the user level in each mission of the content output through the HMD terminal 200, so that it is possible to respond customized to the user in cognitive rehabilitation treatment. That is, the level (level) of the content output through the HMD terminal 200 can be adaptively changed through difficulty adjustment according to the user's mild cognitive impairment state, thereby maximizing the effect of dementia improvement or cognitive rehabilitation treatment. .

With reference to FIG. 3 showing a detailed configuration diagram of each part of the cognitive rehabilitation training system as described above, each part of the cognitive rehabilitation training system will be described in more detail.

Figure 3 (a) is a block diagram of the cognitive function rehabilitation control apparatus 100 of the cognitive function rehabilitation training system according to a preferred embodiment of the present invention. Referring to (a) of FIG. 3 , the cognitive function rehabilitation control apparatus 100 includes a first control unit 102 , a first memory unit 104 , a first user interface unit 106 , and a second It is composed of one display 108 and a first communication module 110 .

The first control unit 102 controls each part of the cognitive function rehabilitation training system and provides user-customized cognitive function rehabilitation training contents through the HMD terminal 200 and the controller 300 . At this time, the first communication module 110 receives the user input information input through the third communication module 110 of the controller 300 in real time, and the first control unit 102 is the first communication module ( 110) and analyzes the user's problem performance for each mission of the content through an AI-based learning program in real time. Then, the user's level is evaluated through real-time analysis, and the first control unit 102 analyzes the user's problem performance in real time and evaluates the user's level (level) to determine the level according to the user's mild cognitive impairment state. Either up or down the level. Then, the level-adjusted content matching the n levels of difficulty is read in each mission of the content stored in the first memory unit 104 and data transmission/reception between the first communication module 110 and the second communication module 210 is performed. It is possible to provide the content with the user-customized difficulty level adjusted to the HMD terminal 200 .

In addition, by driving the neural optical stimulation module 212 of the HMD terminal 200, direct optical stimulation is provided to cognitive function rehabilitation trainees, or indirect stimulation of the brain through visual stimulation through an indirect stimulation wave generator (not shown) or an LED device. can be provided to maximize the effect.

The first memory unit 104 stores various information including the control program of the first control unit 102, and stores information for user-customized cognitive function rehabilitation training contents. In this case, the difficulty adjustment matching table formed based on the artificial intelligence-based learning program may be further stored, and the difficulty adjustment matching table may store content for each mission divided into n levels of difficulty in each mission of the stored content. .

The first user interface unit 106 is in charge of the interface between the user and the first control unit 102 . The first display 108 displays information according to the control of the first controller 102 to guide the user. The first communication module 110 is responsible for communication between the HMD terminal 200 as an external device, the controller 300 and the first control unit 102 .

3 (b) is a block diagram of the HMD terminal 200 of the cognitive function rehabilitation training system. Referring to FIG. 3B , the HMD terminal 200 includes a second control unit 202 , a second memory unit 204 , a second user interface unit 206 , and a second display 208 . ), and a second communication module 212 and a speaker 214 .

The second control unit 202 not only controls the HMD terminal 200 as a whole, but also matches n levels of difficulty in each mission as user-customized content according to the control of the external cognitive function rehabilitation control device 100 . The level-adjusted content is received through data transmission/reception between the first communication module 110 and the second communication module 210 . Then, the video and audio of the interactive cognitive function rehabilitation training contents of the XR method with the difficulty adjusted are outputted through the second display 208 and the speaker 214 . Through this, it is possible to provide user-customized content through difficulty adjustment according to the degree of cognitive impairment of the user.

The second memory unit 204 stores various information including a control program of the second control unit 202 . The second user interface unit 206 is in charge of an interface between a user and the second control unit 202 . The second display 208 outputs video and audio information according to the interactive cognitive function rehabilitation content of the XR method for both eyes of the user. The second communication module 210 is in charge of communication between the second control unit 202 and the cognitive function rehabilitation control apparatus 100, which is an external device. The speaker 214 outputs audio of user-customized cognitive function rehabilitation training contents under the control of the second control unit 202 .

Additionally, the neurophotostimulation module 212 provides a photostimulation that projects light having a color of a specific wavelength to the user's head for a specified time in a gamma frequency band. In addition, for indirect brain stimulation through visual stimulation, an indirect stimulation wave generator (not shown) and a light emitting device such as an LED may be further provided, and the indirect stimulation wave generator may be integrated into the second control unit 202 . have. Through this photostimulation, the HMD terminal 200 induces the user's brain activity. For indirect brain stimulation through visual stimulation, an indirect stimulation wave generator (not shown) and a light emitting device such as an LED may be further provided, and the indirect stimulation wave generator may be integrated into the second control unit.

For example, the second control unit 202 outputs video and audio of the content through the second display 208 and the speaker 214 according to the control of the external cognitive function rehabilitation control apparatus 100, and the neural light The stimulation module 212 may be driven to provide photostimulation to the user.

In addition, in the case of VR/MR, the second control unit 202 may generate a gamma band wave of 45 Hz through an indirect stimulation wave generator (not shown) according to the control of the external cognitive function rehabilitation control device 100 . let there be Accordingly, when the second control unit 202 projects 45Hz of a predetermined color generated through the indirect stimulation wave generator (not shown) through the second display 208, the specified color and black with nothing in 1 second The colors alternate 45 times each and are displayed on the second display 208 . When the user looks at the flickering screen, a visual stimulus of 45 Hz, that is, a gamma band, is indirectly stimulated to the brain, thereby providing a reduction effect of beta-amyloid.

In addition, in the case of AR, the second control unit 202 drives an LED (not shown) under the control of the external cognitive function rehabilitation control device 100 to cause the LED to blink at 45 Hz, so that the user controls the light. When you look at it, you receive a visual stimulus in the gamma band, and this stimulus can indirectly stimulate the brain to provide the effect of reducing beta-amyloid.

In addition, the cognitive function rehabilitation control apparatus 100 may control the HMD 200 according to the degree of cognitive impairment of the user. For example, in the case of severe cognitive impairment, it may be desirable to provide indirect brain stimulation through visual stimulation in addition to direct photostimulation of the user's head. It is judged that sufficient effects can be realized even with indirect brain stimulation through visual stimulation without stimulation. Therefore, through this photostimulation, the HMD terminal induces the brain activity of the cognitive rehabilitation trainee.

Figure 3 (c) is a block diagram of the controller 300 of the cognitive function rehabilitation training system according to a preferred embodiment of the present invention. Referring to FIG. 3C , the controller 300 includes a third control unit 302 , a third memory unit 304 , a third user interface unit 206 , a sensor 308 , Consists of a third communication module (110). The third control unit 302 controls each part of the controller 300 , in particular, the user's motion detected through the sensor 308 , that is, hand tracking information and user input information from the third user interface unit 306 . is provided to the cognitive function rehabilitation control apparatus 100 through the third communication module 110 . The third memory unit 304 stores various information including the processing program of the third control unit 302 . The third user interface unit 306 provides various types of information input by the user through a joystick or a button to the third control unit 302 . The sensor 308 generates sensor information for tracking the position of the user's hand who operates the controller 300 using motion capture or the like, and provides it to the third controller 302 .

<Cognitive function rehabilitation training process through the provision of customized contents>

Hereinafter, a method for improving the user's dementia state through the artificial intelligence-based user-customized content providing process using the cognitive function rehabilitation training system described with reference to FIGS. 1 to 3 will be described in detail below.

FIG. 4 is a flowchart illustrating an artificial intelligence-based user-customized content providing process using the cognitive function rehabilitation training system of FIG. 1 .

Referring to FIG. 4 , the cognitive function rehabilitation control apparatus 100 guides the user log-in through the first display (step 500), and the user receives the log-in information with reference to the guide and performs the log-in. When the login is successful (step 502), the cognitive function rehabilitation control apparatus 100 guides the user-customized cognitive function rehabilitation training contents that can be provided to the user when the user login is performed, and also selects any one content. Guide to select (step 504). In this case, the user-customized cognitive function rehabilitation training contents may be composed of m plurality of experiential missions, and each experiential mission may be divided into n levels of difficulty. Accordingly, it may be composed of a plurality of level-adjusted contents matching n levels of difficulty in each mission of the contents. In addition, the setting of the n levels of difficulty has n levels divided up and down, at least according to the artificial intelligence-based learning program described in FIG. is set

Then, when any one of the user-customized cognitive function rehabilitation training contents is selected (step 506), the cognitive function rehabilitation control apparatus 100 guides the wearing of the HMD terminal 200 and the controller 300 (508). step).

When the HMD terminal 200 and the controller 300 are worn, the cognitive function rehabilitation control apparatus 100 performs pairing with the HMD terminal 200 and the controller 300 to form a communication path (step 510) ). Then, the cognitive function rehabilitation control apparatus 100 executes the selected user-customized cognitive function rehabilitation training content (step 512), while outputting media information of the rehabilitation training content through the HMD terminal 200 (step 514,516), the controller The interactive content for progressing the content is provided by receiving the hand tracking information or input information provided from the 300 (steps 518 and 520).

In the reproduction of these cognitive function rehabilitation training contents, the user's problem performance is determined through an artificial intelligence-based learning program (step 522), and customized cognitive function rehabilitation training contents are provided by adjusting the difficulty level according to the user's level (step 524). ), this difficulty adjustment process will be described in detail with reference to FIG. 5 .

These user-customized cognitive function rehabilitation training contents are executed until the reproduction is completed (step 528), and when the reproduction of the rehabilitation training contents is completed, the cognitive function rehabilitation training control apparatus 100 increases the number of training times of the logged-in user and ends (step 530).

Additionally, the improvement of dementia can be improved through neuro-photostimulation rehabilitation training. For example, the cognitive function rehabilitation control apparatus 100 checks whether the number of rehabilitation training times of the logged-in user is greater than a predetermined number of neuro-optical stimulation required. Then, if the number of rehabilitation training of the logged-in user is less than the predetermined number of required neuro-optical stimulation, the cognitive function rehabilitation control apparatus 100 provides a light irradiation command for neuro-optical stimulation to the HMD terminal 200 . . Then, the HMD terminal 200 receiving the light irradiation command for the neural light stimulation provides a light stimulus that projects light having a color of a specific wavelength to the gamma frequency band for a specified time on the head of the cognitive function rehabilitation trainee. .

In this case, the cognitive function rehabilitation control apparatus 100 may control the HMD 200 to directly photostimulate the user's head or indirectly stimulate the brain through visual stimulation according to the degree of cognitive impairment of the user. For example, in the number of times required for neural photostimulation, the number of times required for direct photostimulation of the head and the number of times for indirect brain stimulation through visual stimulation can be set in detail. In this case, in the case of severe cognitive impairment, it may be desirable to provide direct photostimulation to the user's head as well as indirect brain stimulation through visual stimulation. It is judged that sufficient effects can be realized even with indirect brain stimulation through visual stimulation without direct stimulation of the head. Through this photostimulation, the HMD terminal induces the brain activity of the cognitive rehabilitation trainee.

5 is a flowchart illustrating a difficulty adjustment process according to a user's problem performance in the AI-based user-customized content provision process of FIG. 4 .

Referring to FIG. 5 , the process of adjusting the difficulty level upward or downward according to the user's problem performance provided through the artificial intelligence-based learning program according to the present invention is performed. That is, by analyzing the user's problem performance (input) in the mission experience being performed, the level can be raised/lowered by adjusting the difficulty in the next mission performed.

For example, it is assumed that one mission is performed among a plurality of m missions provided for user-customized content, and in this case, it is assumed that each experiential mission has a section divided by n difficulty levels. At this time, each stage includes the following conditions for changing the difficulty to a mission experience stage with low difficulty (low level) or an experience stage with high difficulty (high level).

For example, assuming the current difficulty level is X, the minimum number of incorrect answer selections to lower the difficulty (a), the minimum number of correct answers to increase the difficulty level (b), the number of times the user selects an incorrect answer (c), the user may be composed of the number of times (d) that the correct answer is selected.

At this time, the problem performance (input) is a task of analyzing the number of times the user selects an incorrect answer (c) and the number of times the user selects the correct answer (d) to determine whether the difficulty level of x set in the current mission is at a level suitable for the user say

In FIG. 5, 1) As a first step, a mission is performed.

First, when the mission is started (S540), a and b are set through the set difficulty (x), and when the mission starts, the accumulated c in the current mission becomes 0, and when the difficulty of the mission is adjusted, the accumulated c in the current mission is d becomes 0 (S542, S544), at this time, each mission stores a respective d value.

Next, when the user evaluates the judgment performance for the performed mission (S546),

2) The second stage is the process of lowering the difficulty level. For example, if the user selects an incorrect answer, c increases, and when c is greater than or equal to a in the mission currently being performed, when c is greater than or equal to 1, c is decreased. At this time, when c is less than 0, the difficulty is lowered (S560, S562, S564, S566, S570).

3) The third step is to increase the difficulty level. For example, if the user selects the correct answer, d increases. At this time, when d is more than b, the difficulty level is increased (S550, 552, S554, S556).

In addition, the formula for setting the minimum number of correct answers (b) to increase the difficulty in 1 to n difficulties prepared in each mission is as shown in Equation 1 and FIG. 6 below.

Here, each x is the input difficulty between 1 and n, b is the minimum number of correct answers that require an increase in difficulty in x level difficulty, and e (constant) is the correct answer required to increase the difficulty in the current mission's lowest difficulty (1). The number of times, f (constant) is the number of correct answers required to increase the difficulty from the highest difficulty (n) of the current mission, and n (constant) is the total number of difficulty levels. In this case, b is the absolute value of an integer rounded to the first decimal place.

6 is a graph illustrating a difficulty increase process in the difficulty adjustment process of FIG. 5 .

Referring to FIG. 6 , for example, in the current experience stage, the total number of difficulties (n) is 5, and in the lowest difficulty level (x=1), if the number of correct answer selections (d) is accumulated more than once (e), the difficulty is raised At the highest difficulty level (x=5) in the current experience stage, if the number of correct answer selections (d) is accumulated more than 3 times (f), the difficulty level may be increased. At this time, when the current level of difficulty (x) is 3, the minimum number of correct answer selections (b) required to increase the difficulty according to Equation 1 is 2. Therefore, the difficulty level is increased only when the number of correct answer selections (d) is accumulated more than twice (b). In addition, in the highest difficulty (n), the difficulty does not increase even if the difficulty increase condition is met. However, only the number of correct answer selections (d) and incorrect answer selection times (c) accumulated in the mission are initialized.

In addition, the formula for setting the minimum number of incorrect answer selections (a) to lower the difficulty in 1 to n difficulty levels of each mission is shown in Equation 2 and FIG. 7 below.

Here, x is the input difficulty between 1 and n, a is the minimum number of incorrect answer selections required to lower the difficulty in x level difficulty, g (constant) is the number of incorrect answer selections required to lower the difficulty in the lowest difficulty (1) of the current mission, h (constant) is the number of incorrect answers required to lower the difficulty in the highest difficulty level (n) of the current experience level, and n (constant) is the total number of difficulty levels. Here, a is the absolute value of an integer rounded to the first decimal place.

7 is a graph illustrating a difficulty lowering process in the difficulty adjustment process of FIG. 5 .

Referring to FIG. 7 , for example, in the current experience stage, the total number of difficulties (n) is 5, and in the current experience stage, the number of incorrect answer selections (c) is 3 times (g) in the lowest difficulty level (x=1). If more than one is accumulated, the number of correct answer selections (d) for the mission is reduced by one, or if the number of correct answer selections (d)r is 0, the difficulty level is lowered. In addition, at the highest difficulty level (x=5) in the current experience stage, if the number of incorrect answer selections (c) is accumulated more than once (h), the number of correct answer selections (d) for the mission is reduced by one, or the number of correct answer selections (d) is reduced by one. If it is 0, it is assumed that the difficulty is lowered. At this time, when the level (x) of the current difficulty is 3, the minimum number of incorrect answer selections (a) required to lower the difficulty according to the above formula is 2. Therefore, if the number of incorrect answer selections (c) is accumulated two or more times (a), the number of correct answer selections (d) of the corresponding mission is reduced by one, or if the number of correct answer selections (d) is 0, the difficulty level is lowered. In addition, in the lowest difficulty level (1), even if the difficulty lowering condition is met, the difficulty level is not lowered. However, only the number of correct answer selections (d) and incorrect answer selection times (c) accumulated in the mission are initialized.

Hereinafter, the difficulty adjustment process in FIGS. 5 to 7 will be described in more detail with reference to FIGS. 8 to 50 , taking the mission 'making gimbap' as an example.

For example, the mission is to experience making gimbap in the market. Then, when you start the mission, guests start to come. At this time, the 'basic kimbap' is gimbap with all the ingredients added one by one. Each person may ask for an order other than the 'basic kimbap'. For example, for 'Basic Gimbap', all the listed ingredients (optional) must be put one by one, and depending on the customer, orders may come in such as to remove spinach or add one more ham.
In addition, in the result of performing the 'Make Gimbap' mission, when the number of satisfied customers is more than half, it is recognized as 'Correct answer selection' and the number of times (d) of selecting the correct answer is accumulated. The selected number of times (c) is accumulated.
At this time, it is assumed that the highest difficulty (n) prepared in the 'Make Gimbap' mission is 5.

In addition, the setting of the content constant value is as follows.

- In the lowest difficulty level (1) of the current mission, the number of correct answer selections (e) required to increase the difficulty level is assumed to be 1.

- In the highest difficulty (5) of the current mission, the number of correct answer selections (f) required to increase the difficulty is assumed to be 3. However, even if the difficulty increase condition is met at the highest difficulty level, the actual difficulty level does not increase, and only the accumulated number of correct answer selections (d) and incorrect answer selection times (c) are initialized.

- In the lowest difficulty (1) of the current mission, the number of incorrect answer selections (g) required to lower the difficulty is assumed to be 3. However, even if the difficulty lowering condition is met at the lowest difficulty level, the actual difficulty level is not lowered, and only the accumulated number of correct answer selections (d) and incorrect answer selection times (c) are initialized.

- In the highest difficulty (5) of the current mission, the number of incorrect answer selections (h) required to lower the difficulty is assumed to be 1.

8 to 26 show examples of 'User A' who needs to increase the difficulty level.

8 to 26 , in the current difficulty level (x) 3, the number of satisfied guests from the result of performing the mission is more than half, so the mission is processed as 'correct', and the number of times the user selects the correct answer (d) is accumulated once. If d is more than b, the difficulty is raised and the current difficulty (x) is raised from 3 to 4 in the next mission.

27 to 50 show an example of 'User B' who needs to lower the difficulty level.

Referring to FIGS. 27 to 50 , in the current difficulty level (x) 5, the number of satisfied guests in the result of performing the mission is less than a majority, so an 'wrong answer' is processed, and the number of times (c) the user selects an incorrect answer is 1 time. Cumulatively, if c is more than a, the number of correct answer selections (d) is reduced, or if d is less than 0, the difficulty is lowered, and the current difficulty (x) is lowered from 5 to 4 in the next mission.

As described above, the technical ideas described in the embodiments of the present invention may be implemented independently, or may be implemented in combination with each other. In addition, although the present invention has been described through the embodiments described in the drawings and detailed description of the invention, these are merely exemplary, and those of ordinary skill in the art to which the present invention pertains may make various modifications and equivalent other embodiments therefrom. It is possible. Accordingly, the technical protection scope of the present invention should be defined by the appended claims.

100: cognitive function rehabilitation training control device
200: HMD terminal
300: controller

Claims (8)

In the cognitive function rehabilitation training method,
(a) guiding to select cognitive function rehabilitation training contents;
(b) if any one of the contents is selected, providing the selected contents to a user through an HMD terminal and a controller or a hand tracking terminal;
(c) In the reproduction of the cognitive function rehabilitation training contents according to user input information input through the controller or hand tracking terminal, the user's problem performance is determined through an artificial intelligence-based learning program, and the user's current level is evaluated and evaluated adaptively changing the content output through the HMD terminal by adjusting the level upward or downward in the difficulty level of the selected content according to the dementia state of the user; and
(d) According to the dementia state of the user in step (c), in severe cases, direct photostimulation to the user's head and indirect brain stimulation through visual stimulation are provided, and in mild cases, separate Cognitive function rehabilitation training method through the provision of artificial intelligence-based user-customized content comprising; providing indirect brain stimulation through visual stimulation without direct stimulation to the head. According to claim 1,
In step (c),
The total number of difficulties provided by the mission in the selected content is n,
(c-1) According to the difficulty increase setting method, if the number of correct answer selections accumulated in the mission is more than the minimum number of correct answer selections required for difficulty increase, the difficulty level is adjusted upward;
(c-2) According to the difficulty setting method, if the accumulated number of incorrect answer selections in the mission is greater than the minimum number of incorrect answer selections required to lower the difficulty, reduce the accumulated number of correct answer selections in the mission, or if the number of correct answer selections is 0, the difficulty level Cognitive function rehabilitation training method through the provision of user-customized content based on artificial intelligence, characterized in that this downward adjustment. According to claim 1,
In the step (c), the method of setting the minimum number of correct answer selections (b) for increasing the difficulty level is cognitive function rehabilitation training through providing user-customized content based on artificial intelligence, characterized in that it is determined by the following Equation 1 Way.
[Equation 1]

Here, each x is the input difficulty in the corresponding mission, b is the minimum number of correct answers required to increase the difficulty in x level difficulty, e (constant) is the number of correct answers required to increase the difficulty in the lowest difficulty (1), f (constant) ) is the number of correct answers required to increase the difficulty in the highest difficulty (n), and n (constant) is the total number of difficulty stages prepared for the mission. In this case, b is the absolute value of an integer rounded to the first decimal place. According to claim 1,
In the step (c), the method of setting the minimum number of incorrect answer selections (a) to lower the difficulty level is cognitive function rehabilitation training through the provision of user-customized content based on artificial intelligence, characterized in that the following Equation 2 is determined Way.
[Equation 2]

Here, x is the input difficulty in the corresponding mission, a is the minimum number of incorrect answer selections required to lower the difficulty in x level difficulty, g (constant) is the number of incorrect answer selections required to lower the difficulty in the lowest difficulty (1), h (constant) is the number of incorrect answers required to lower the difficulty from the highest difficulty (n), and n (constant) refers to the total number of difficulty stages prepared for the mission. Here, a is the absolute value of an integer rounded to the first decimal place. In the cognitive function rehabilitation training system,
In addition to outputting cognitive function rehabilitation training contents, it provides indirect brain stimulation through visual stimulation and a neuro-optical stimulation module that provides optical stimulation that projects light with a specific wavelength to the user's head for a specified time in the gamma frequency band. HMD (Head Mount Display) terminal having an indirect stimulation wave generator;
a controller or hand tracking terminal providing user's hand tracking information; and
Guide to select the cognitive function rehabilitation training contents,
When any one of the contents is selected, the selected contents are provided to the user through the HMD terminal and the controller or hand tracking terminal,
In the reproduction of the cognitive function rehabilitation training contents according to user input information input through the controller or hand tracking terminal, the user's problem performance is determined through an artificial intelligence-based learning program, and the user's current level is evaluated. Adaptively change the content output through the HMD terminal by adjusting the level up or down in the difficulty level of the selected content according to the dementia status,
Depending on the user's dementia status, in severe cases, direct photostimulation is provided to the user's head through the neurophotostimulation module, and indirect brain stimulation through visual stimulation through the indirect stimulation wave generator is provided, and in mild cases In this case, a cognitive function rehabilitation control device that provides indirect brain stimulation through visual stimulation through the indirect stimulation wave generator without direct stimulation to a separate head through the neuro-optical stimulation module. Cognitive rehabilitation training system through the provision of user-customized content. 6. The method of claim 5,
The total number of difficulties provided by the mission in the selected content is n,
According to the difficulty increase setting method, if the number of correct answer selections accumulated in the mission is greater than the minimum number of correct answer selections required for difficulty increase, the difficulty level is raised,
According to the difficulty setting method, if the accumulated number of incorrect answer selections in the mission is greater than the minimum number of incorrect answer selections required to lower the difficulty, reduce the accumulated number of correct answer selections in the mission, or if the number of correct answers is 0, the difficulty level is lowered. A cognitive function rehabilitation training system through the provision of customized contents based on artificial intelligence. 6. The method of claim 5,
A method of setting the minimum number of correct answer selections (b) for increasing the difficulty is a cognitive function rehabilitation training system through the provision of artificial intelligence-based user-customized content, characterized in that it is determined by the following equation (1).
[Equation 1]

Here, each x is the input difficulty for the corresponding mission, b is the minimum number of correct answers that require an increase in difficulty in the x level difficulty, e (constant) is the number of correct answers required to increase the difficulty in the lowest difficulty (1), f (constant) ) is the number of correct answers required to increase the difficulty in the highest difficulty (n), and n (constant) is the total number of difficulty stages prepared for the mission. In this case, b is the absolute value of an integer rounded to the first decimal place. 6. The method of claim 5,
A method of setting the minimum number of incorrect answer selections (a) to lower the difficulty is a cognitive function rehabilitation training system through the provision of artificial intelligence-based user-customized content, characterized in that it is determined by Equation 2 below.
[Equation 2]

Here, x is the input difficulty for the mission, a is the minimum number of incorrect answer selections required to lower the difficulty in the x level difficulty, g (constant) is the number of incorrect answer selections required to lower the difficulty in the lowest difficulty (1), h (constant) is The number of incorrect answer selections required to lower the difficulty from the highest difficulty (n), n (constant), refers to the total number of difficulty stages prepared for the mission. Here, a is the absolute value of an integer rounded to the first decimal place.

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