Method and system for developing a training program to improve symptoms in patients with mild cognitive impairment

Abstract

To provide a method for constructing a training program for improving symptoms of a mild cognitive impairment patient.SOLUTION: A method includes the steps of: arranging, on designated days of the week, direct training algorithms regarding at least one of visualization, fusion, and semantic word frequency directly related to a cognitive function area of a person; controlling the direct training algorithms to be output to a user terminal according to the designated days of the week, and receiving result values for the direct training algorithm from the user terminal; calculating an achievement level for each of the direct training algorithms based on the result values, and determining one of the direct training algorithms based on the achievement level; and rearranging the direct training algorithms on the designated days of the week, together with the determined direct training algorithm, where the rearranging considers both the calculated achievement level and a training algorithm matched to the determined direct training algorithm.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a method and system for constructing a training program to improve symptoms of a patient, and more particularly to a method for constructing a digital dementia treatment agent that can improve symptoms of a patient with mild cognitive impairment, and a system for implementing the method. [Background technology]

[0002] South Korea is considered to have already entered an aging society. As the aging society progresses, the number of dementia patients will increase rapidly, and the social costs incurred in treating and caring for dementia patients will inevitably increase.

[0003] It has already been proven that training that can stimulate cognitive function is effective in preventing dementia and improving the symptoms of dementia that has already developed. Many papers have proven that in the case of patients with mild cognitive impairment, which is the pre-stage of dementia, if they receive training that can improve cognitive function, their cognitive function will improve even more significantly than in patients who already have dementia. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Korean Patent Publication No. 10-2020-0066578 (Published June 10, 2020) Summary of the Invention [Problem to be solved by the invention]

[0005] The technical problem to be solved by the present invention is to provide a digital therapeutic agent for dementia that can improve the symptoms of patients with cognitive impairment. [Means for solving the problem]

[0006] According to one embodiment of the present invention, a method for solving the technical problem is a method for constructing a training program for improving symptoms of a patient with mild cognitive impairment, the method including: allocating a direct training algorithm for at least one of visualization, fusion, and semantic word fluency, which are directly related to a human cognitive function area, on a specified day of the week; controlling the allocated direct training algorithm to be output to a user terminal according to the specified day of the week and receiving a result value for the direct training algorithm from the user terminal; calculating a degree of success for each direct training algorithm based on the result value and determining one of the direct training algorithms based on the calculated degree of success; and re-allocating the direct training algorithm on the specified day of the week, including the determined direct training algorithm, wherein the allocation takes into consideration both the calculated degree of success and a training algorithm matched with the determined direct training algorithm.

[0007] In the method, the direct training algorithms may further include training algorithms for working memory and cognitive agility.

[0008] In the method, the designated day may be determined by information received from the user terminal.

[0009] In the method, the step of distributing the direct training algorithm on a designated day of the week may include distributing different direct training algorithms for morning and afternoon on each day of the week.

[0010] In the method, the step of distributing the direct training algorithms on designated days of the week may include distributing two or more direct training algorithms in the morning and two or more in the afternoon of each day of the week.

[0011] The method may further include a step of arranging indirect training algorithms for word categorization, unusual word search, and past news listening, which are indirectly related to the human cognitive function area, on the specified day of the week so as not to overlap with the direct training algorithms, and the step of receiving the result values ​​may include controlling the output of the arranged direct training algorithms and indirect training algorithms to a user terminal according to the specified day of the week, receiving result values ​​for the direct training algorithms and the indirect training algorithms from the user terminal, and the step of determining one of the direct training algorithms may include calculating a degree of success for each of the direct training algorithms and the indirect training algorithms based on the result values, and determining one of the direct training algorithms based on the calculated degree of success.

[0012] In the method, the direct training algorithm and the indirect training algorithm may be arranged separately in the morning and afternoon of the specified days of the week, and a preset indirect training algorithm among the indirect training algorithms may be fixedly arranged in the afternoon of some of the specified days of the week.

[0013] According to another embodiment of the present invention, a system for solving the technical problem includes a first placement calculation unit that places a direct training algorithm for at least one of visualization, fusion, and semantic word fluency, which are directly related to a human cognitive function area, on a specified day of the week; an output control unit that controls output of the placed direct training algorithm to a user terminal according to the specified day of the week; a communication unit that receives result values ​​for the direct training algorithm from the user terminal; a weak training determination unit that calculates a degree of achievement for each direct training algorithm based on the result values ​​and determines one of the direct training algorithms based on the calculated degree of achievement; and a second placement calculation unit that includes the determined direct training algorithm and places the direct training algorithm again on the specified day of the week, the placement taking into account both the calculated degree of achievement and a training algorithm matched with the determined direct training algorithm.

[0014] In the system, the direct training algorithms may further include training algorithms for working memory and cognitive agility.

[0015] In the system, the designated day of the week may be determined by information received from the user terminal.

[0016] In the system, the first arrangement calculation unit may arrange different direct training algorithms for the morning and afternoon of each day of the week.

[0017] In the system, the first arrangement calculation unit may arrange two or more direct training algorithms in the morning and two or more in the afternoon of each day of the week.

[0018] In the system, the first placement calculation unit places indirect training algorithms for word categorization, unusual word search, and past news listening, which are indirectly related to the human cognitive function area, on the specified day of the week so as not to overlap with the direct training algorithms, the communication unit controls to output the placed direct training algorithms and indirect training algorithms to a user terminal according to the specified day of the week, receives result values ​​for the direct training algorithms and indirect training algorithms from the user terminal, and the weak training determination unit calculates a degree of success for each of the direct training algorithms and the indirect training algorithms based on the result values, and determines one of the direct training algorithms based on the calculated degree of success.

[0019] In the system, the first placement calculation unit may place the direct training algorithm and the indirect training algorithm separately in the morning and afternoon of the specified days of the week, and may place a preset indirect training algorithm among the indirect training algorithms in a fixed manner in the afternoon of some of the specified days of the week.

[0020] One embodiment of the present invention discloses a computer-readable recording medium storing a program for executing the above method. [Effects of the Invention]

[0021] According to the present invention, the symptoms of patients with mild cognitive impairment can be significantly improved.

[0022] According to the present invention, dementia, the most dreaded disease of the elderly, can be prevented or diagnosed at an early stage.

[0023] According to the present invention, the social costs involved in managing dementia patients can be significantly reduced.

[0024] Unlike existing technologies, the present invention can stimulate the memory formation process of patients with cognitive impairment on a memory-by-memory basis through training in imagery, semantics, and fusion, thereby comprehensively improving working memory ability and processing speed. [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a diagram illustrating a schematic view of an entire system for embodying the present invention. [Figure 2] 1 is a block diagram illustrating a management server for implementing a training program construction method according to an embodiment of the present invention. [Figure 3] FIG. 2 is a block diagram showing an example of a processing unit subdivided by function. [Figure 4] 1 is a flowchart illustrating a method according to one embodiment of the present invention. [Figure 5] 10 is a diagrammatic view of a training program according to another embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0026] The present invention can be modified in various ways and can have various embodiments, and specific embodiments are illustrated in the drawings and described in detail in the detailed description. The advantages and features of the present invention, as well as methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the drawings. However, the present invention is not limited to the embodiments disclosed below, and can be embodied in various forms.

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. When describing with reference to the drawings, identical or corresponding components will be given the same reference numerals, and duplicate descriptions thereof will be omitted.

[0028] In the following embodiments, terms such as first and second are not used in a limiting sense but are used to distinguish one component from another.

[0029] In the following embodiments, singular expressions include plural expressions unless the context clearly indicates otherwise.

[0030] In the following embodiments, terms such as "comprise" or "have" mean that the features or components described in the specification are present, and do not preclude the possibility that one or more other features or components may be added.

[0031] When an embodiment can be implemented differently, the order of certain steps may be different from that stated. For example, two steps stated in succession may be performed substantially simultaneously or may be performed in the reverse order from that stated.

[0032] FIG. 1 is a diagram showing a schematic diagram of an entire system for embodying the present invention.

[0033] Referring to FIG. 1, a training program construction system 1 according to the present invention has a structure in which a user terminal group 110 and a management server 200 are connected via a communication network 130 .

[0034] The user terminal group 110 may include at least one user terminal. For example, the number of user terminals included in the user terminal group 110 may be one or n, as shown in Figure 1. In Figure 1, n may be an integer equal to or greater than 1.

[0035] Each user terminal included in the user terminal group 110 is a terminal of a user who uses the training program according to the present invention, and refers to an electronic device equipped with a communication module capable of communicating with the management server 200 .

[0036] A user terminal refers to a smart device that includes an input device for receiving user input, an output device (display) for visually outputting the user terminal input and the terminal processing results, and a communication module capable of communicating with external devices, and is not limited in size or type as long as it includes only the input device, output device, and communication module. For example, in FIG. 1, the user terminal is shown in the form of a smartphone, but the user terminal may also be a PC, laptop, netbook, etc. capable of communicating with the management server 200.

[0037] A user who uses a user terminal refers to a person who uses a training program to improve the symptoms of a patient with cognitive impairment, and may be a patient who has been diagnosed with cognitive impairment or the guardian of such a patient. As another example, the user may be a tester who repeatedly runs the training program to improve performance.

[0038] The management server 200 is a server in which an integrated management program is installed, and refers to a server that manages and controls data flow while communicating with a plurality of user terminals included in the user terminal group 110. An integrated management program (integrated management application) is installed in the management server 200, and according to an embodiment, a portion of the integrated management program may be implemented in the form of a client operated by a user terminal and installed in the user terminals included in the user terminal group 110.

[0039] The communication network 130 functions to connect the user terminals included in the user terminal group 110 to the management server 200, and may include various wired and wireless communication networks such as a data network, a mobile communication network, and the Internet.

[0040] In the present invention, a training program refers to a logical device that is controlled by the management server 200 and can control the screens output from the user terminals included in the user terminal group 110. The training program does not have a physical form and undergoes multiple processes of being optimized for the user who uses the training program. That is, the training program can be continuously updated based on inputs input by the user through the user terminal and commands received from the management server 200. Patients with mild cognitive impairment can improve their symptoms of mild cognitive impairment by repeatedly using the training program operated by the user terminal.

[0041] In the present invention, a training program can include at least one or more direct training algorithms. Alternatively, as another example, a training program can include at least one or more direct training algorithms and at least one or more indirect training algorithms. Hereinafter, unless otherwise specified, a training algorithm is considered to include both direct and indirect training algorithms.

[0042] The training program may be embodied in a form in which direct training algorithms and indirect training algorithms are output through a user terminal in a predetermined order. The order in which the training algorithms are output through the user terminal may vary depending on the user. For example, if user A drives the training program, the contents output from the user terminal may change in the order of direct training algorithm-1, direct training algorithm-2, and direct training algorithm-3. If user B drives the training program, direct training algorithm-2, indirect training algorithm-3, and direct training algorithm-1 may be output in that order to user B's user terminal.

[0043] A user can provide input to a test-type direct or indirect training algorithm that is output to a user terminal, and values ​​calculated from the user's input can be used to update the training program corresponding to that user.

[0044] FIG. 2 is a block diagram illustrating a management server for implementing a training program construction method according to an embodiment of the present invention.

[0045] Referring to FIG. 2, a management server 200 according to an embodiment of the present invention includes a database 210, a communication unit 230, a processing unit 250, and an output unit 270.

[0046] The management server 200 according to an embodiment of the present invention may correspond to or include at least one processor, and thus the management server 200, as well as the communication unit 230, processing unit 250, and output unit 270 included in the management server 200, may be driven in the form of being included in a hardware device such as a microprocessor or a general-purpose computer system.

[0047] The names of the modules included in the management server 200 shown in FIG. 2 are arbitrarily named to intuitively explain the representative functions performed by each module, and when the management server 200 is actually implemented, each module may be given a name different from the name shown in FIG. 2.

[0048] In addition, the number of modules included in the management server 200 of Fig. 2 may vary depending on the embodiment. More specifically, although the management server 200 of Fig. 2 includes a total of three modules, depending on the embodiment, at least two or more modules may be integrated into one module, or at least one or more modules may be separated into two or more modules.

[0049] The database 210 stores various data necessary for the operation of the management server 200. For example, the database 210 stores an integrated management program for controlling the operation of the management server 200, and the database 210 can receive and store data received by the communication unit 230 from a user terminal.

[0050] The communication unit 230 communicates with the user terminals included in the user terminal group 110 .

[0051] The processing unit 250 processes data received and data to be transmitted by the communication unit 230. The data processed by the processing unit 250 includes data received from a user terminal and data to be transmitted to a user terminal.

[0052] In one embodiment, the processing unit 250 may perform the function of combining data received by the communication unit 230 with information stored in the database 210 and processing it, or of issuing instructions to the communication unit 230 and the output unit 270 to operate appropriately in order to implement the method according to the present invention.

[0053] The function performed by the processing unit 250 is not limited to a specific function, and although the processing unit 250 is shown as a single module in Fig. 2, the processing unit 250 may be subdivided into multiple modules depending on the process of the processing unit 250. The processing unit 250 configured from subdivided modules will be described later with reference to Fig. 3. The output unit 270 receives commands from the processing unit 250 and performs the function of calculating and outputting various data.

[0054] FIG. 3 is a block diagram showing an example of a processing unit subdivided by function.

[0055] Referring to FIG. 3, the processing unit 250 includes a first placement calculation unit 251, an output control unit 253, a vulnerability training determination unit 255, and a second placement calculation unit 257.

[0056] According to an embodiment of the present invention, the communication unit 230, the processing unit 250, and the output unit 270 included in the processing unit 250 may be driven in the form of being included in a hardware device such as a microprocessor or a general-purpose computer system. The names of the modules included in the processing unit 250 shown in Fig. 3 are arbitrarily named to intuitively explain the representative functions performed by each module, and when the management server 200 is actually realized, each module may be given a name different from the name shown in Fig. 3.

[0057] The first placement calculation unit 251 performs a function of placing a direct training algorithm for at least one of visualization, fusion, semantic word fluency, working memory, and cognitive agility, which are directly related to the area of ​​human cognitive function, on a specified day of the week. Here, visualization, fusion, semantic word fluency, working memory, and cognitive agility are training algorithms that are structured in different ways. For example, a direct training algorithm for visualization refers to an algorithm that directly stimulates the user's cognitive function by having the user listen to audio that describes a specific situation and visualize the scene in their mind.

[0058] [Table 1] Table 1 shows the direct training algorithms and detailed training algorithms that can improve the symptoms of cognitive impairment. Each of the five algorithms in Table 1 can help improve the symptoms of cognitive impairment patients in different ways.

[0059] Tables 2 to 8 below show examples of direct training algorithms implemented through a user terminal. [Table 2] Table 2 shows an example of mental imagery training. Mental imagery training such as that shown in Table 2 can stimulate the patient's cognition and help restore their identity and self-esteem by using reminiscence therapy, which recalls memories from the patient's youth through voice drama.

[0060] [Table 3] Table 3 shows an example of semantic training. Semantic training such as that in Table 3 can activate the network between the user's long-term memory by having the user freely say words that are associated with the words.

[0061] [Table 4] Table 4 exemplarily shows a training algorithm corresponding to fusion training A. Fusion training A helps users to efficiently utilize their memory through continuous refinement experience.

[0062] [Table 5] Table 5 exemplarily shows a training algorithm corresponding to Fusion Training B. Fusion Training B can help the user experience a refinement process through visual imagery.

[0063] [Table 6] Table 6 shows an example of a training algorithm for span extension training. Through training such as Table 6, the user can experience alternating interactions and reconstruct or stimulate the neural network of the brain.

[0064] [Table 7] Table 7 shows an example of a training algorithm for task training. A user can improve their auditory short-term memory through training such as that shown in Table 7.

[0065] [Table 8] Table 8 shows an example of a training algorithm for processing speed training. Through training such as that shown in Table 8, users can improve their cognitive control, attention, and processing speed.

[0066] There are various methods for directly stimulating human cognitive functions, but in this invention, the algorithms that have been determined to be most effective in improving the symptoms of patients with mild cognitive impairment are the five types mentioned above (imagery, fusion, semantics, working memory, and cognitive agility).

[0067] The first placement calculation unit 251 places the above-mentioned five types of algorithms on designated days of the week. Here, the designated days of the week refer to the days of the week designated by the user or the processing unit 250 among Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday that make up a week, and generally refer to the days of the week on which the user can participate in a training program. For example, the designated days of the week may be weekdays, Monday, Tuesday, Wednesday, Thursday, and Friday, or the user may add or exclude specific days of the week through input.

[0068] The first placement calculation unit 251 can place at least one of five training algorithms on a specified day of the week. For example, direct training algorithm-1 can be placed on Monday, and direct training algorithm-2 can be placed on Tuesday. The initial placement of the training algorithms performed by the first placement calculation unit 251 may be a default placement preset in the first placement calculation unit 251, or may be a randomly selected placement method. As the training program is repeatedly run, the training program is updated according to the characteristics of the user, and therefore the initial placement method of the training algorithms is not particularly limited.

[0069] The output control unit 253 can control the direct training algorithm arranged by the first arrangement calculation unit 251 to be output from the user terminal according to the specified day of the week. Specifically, the output control unit 253 transmits the direct training algorithm, the arrangement method of which has been determined, to the communication unit 230, so that the communication unit 230 can transmit it to the user terminal.

[0070] The user performs training to improve the symptoms of cognitive impairment based on the direct training algorithm output through the user terminal, and the training result values ​​calculated from the user terminal are received by the communication unit 230.

[0071] The weak training determination unit 255 may calculate a success rate for each direct training algorithm based on the received training result value, and may determine one of the direct training algorithms based on the calculated success rate. For example, the weak training determination unit 255 may determine a direct training algorithm corresponding to the lowest success rate, and may execute the determined training algorithm in the next training program. The direct training algorithm determined by the weak training determination unit 255 is a training algorithm for updating the training program in which the user most recently participated, and may replace one of multiple training algorithms included in the training program.

[0072] The second allocation calculation unit 257 rearranges the direct training algorithm to the specified day of the week, including the direct training algorithm determined by the weak training determination unit 255. The second allocation calculation unit 257 rearranges the direct training algorithm to the specified day of the week, taking into consideration both the achievement level calculated just before and the training algorithm matched with the determined direct training algorithm.

[0073] [Table 9] Table 9 shows an example of a rearranged direct training algorithm. Referring to Table 9, the training algorithm that makes up the training program was initially arranged such that Training-1 to Training-5 were arranged sequentially from Monday to Friday, but after rearrangement, Training-5 was removed and Training-1, which showed the lowest level of achievement when the user first participated in the training program, was arranged again, and the order of each training was also changed by day of the week.

[0074] Each direct training algorithm has a matching training algorithm as metadata. A matched training algorithm is placed before a direct training algorithm to improve the training effect and refers to an algorithm that has been experimentally, empirically, and mathematically verified. For example, if a user performs Training-1 and then Training-2 and the result value of Training-2 is higher than when a user performs Training-2 alone, Training-1 can become the training algorithm matched to Training-2. The training algorithm matched to a direct training algorithm will be described in detail with reference to FIG. 5.

[0075] FIG. 4 is a flowchart illustrating a method according to one embodiment of the present invention. The method according to FIG. 4 can be implemented by the management server 200 or the processing unit 250 described with reference to FIGS. 2 and 3, and will be described below with reference to FIG.

[0076] The first placement calculation unit 251 places the direct training algorithm, which is composed of imagery, fusion, and semantics, on the designated day of the week (S410).

[0077] The output control unit 253 controls the arranged direct training algorithm to be output to the user terminal (S420).

[0078] The communication unit 230 receives the result value for the direct training algorithm from the user terminal (S430).

[0079] The vulnerability training determination unit 255 calculates the success rate for each algorithm according to the received result value, and determines the vulnerability training algorithm based on the success rate (S440).

[0080] The second allocation calculation unit 257 takes into consideration both the achievement level and the weak training algorithm, and directly rearranges the training algorithm to the designated day of the week (S450).

[0081] The processing unit 250 determines whether the user's training has been completed (S460), and if not, controls the user terminal to output the rearranged direct training algorithm (S420). In step S420, the user will undergo a second training session.

[0082] FIG. 5 is a diagrammatic view of a training program according to another embodiment of the present invention. In Figure 5, the records of the user's participation in the training program are updated on a weekly basis. For example, a training program that the user participated in from Monday to Friday can be classified as the first training, and a training program that the user participated in from the following Monday to Friday can be classified as the second training.

[0083] As shown in Figure 5, it is assumed that one training program runs only from Monday to Friday. The training program according to Figure 5 is composed of a total of 30 training algorithms, and the training algorithms included in the training program may include direct training algorithms and indirect training algorithms. For convenience of explanation, only a total of 30 training algorithms are shown in Figure 5, but the number of training algorithms arranged to construct a training program may vary depending on the embodiment. For example, as shown in Table 9, a training program may be constructed with five training algorithms.

[0084] When a day of the week is designated, the processing unit 250 can generate a template so that training algorithms for constructing a training program are arranged according to the designated day of the week. In Fig. 5, each day of the week is divided into morning and afternoon, and virtual slots are implemented so that three training algorithms are arranged in each of the morning and afternoon of each day of the week. Depending on the embodiment, the number of slots in the morning and afternoon does not have to be three, and the number of slots in the morning and afternoon may be different from each other.

[0085] Indirect training algorithms are distinct from the direct training algorithms described above and refer to training algorithms that are indirectly related to areas of human cognitive function. Indirect training algorithms can include word categorization, unusual word search, and past news listening.

[0086] Word classification is a training algorithm that shows a user multiple words and classifies them into multiple groups based on their commonalities. Unusual word search is a training algorithm that shows a user several sentences and asks them to find unusual words. Past news listening is a training program that stimulates the user's cognitive functions by structuring events that occurred in the user's 20s and 30s into news stories.

[0087] Indirect training algorithms do not provide as much stimulation to the user's cognitive function as the five direct training algorithms mentioned above, but when used in conjunction with direct training algorithms to create a training program, they can be defined as training algorithms that are effective in improving the symptoms of the user's cognitive dysfunction.

[0088] In addition, an indirect training algorithm can also be a training algorithm recorded in the metadata of a direct training algorithm. For example, the metadata of the integrated training A (storytelling training) described in Table 4 includes word classification. This means that if a user first performs word classification and then creates a story like the one in Table 4, the success rate of storytelling will be further improved.

[0089] As described above, the direct training algorithm includes at least one training algorithm as metadata, and if the user's performance in a specific direct training algorithm is low, the training algorithm included in the metadata is trained immediately before training the direct training algorithm with low performance in order to improve the performance, thereby effectively stimulating the user's cognitive function and inducing recovery of cognitive function, which is a feature of the present invention. In the present invention, the metadata set for each training algorithm is determined based on empirical, mathematical, experimental, and statistical data.

[0090] For ease of explanation, the training that proceeds first will be referred to as the first training, and the training that proceeds next will be referred to as the second training. Also, in Fig. 5, the slot in which the first training algorithm to be performed on Monday morning is placed will be referred to as the first slot 501, and the slot in which the last training algorithm to be performed on Friday afternoon is placed will be referred to as the 30th slot 595. The slots between the first slot 501 and the 30th slot 595 will be referred to in a similar manner.

[0091] The first placement calculation unit 251 can place training algorithms in the morning and afternoon from Monday to Friday. In the first implementation, since there is no data for the user, the first placement calculation unit 251 can place training algorithms in empty slots of the training program according to a default placement, and if there is no default placement, the first placement calculation unit 251 can place training algorithms randomly.

[0092] 5, among the training algorithms arranged in each slot, training algorithms starting with A, B, C, D, or E refer to direct training algorithms, and training algorithms starting with F, G, or H refer to indirect training algorithms. Hereinafter, A to H are referred to as "training codes."

[0093] In Figure 5, the numbers written after the training code refer to the detailed training algorithms described in Table 1. For example, if there are four detailed training algorithms in the direct training algorithm for mental imagery, they are called A1, A2, A3, and A4, respectively. Hereinafter, the numbers written after the training code will be referred to as "training numbers."

[0094] In Figure 5, the number written after the training number indicates the level of the training. Even for training algorithms with the same training number, the higher the level, the greater the stimulation to the user's cognitive function.

[0095] To summarize the above explanation, the second slot 503 in Figure 5 is a training algorithm with training number 3 and training level 2 among the training algorithms for word meaning B. As another example, the fourth slot 511 in Figure 5 is a training algorithm with training number 1 and training level 5 among the training algorithms for mental imagery A.

[0096] When the first placement calculation unit 251 places a training algorithm in each slot included in the training program, construction of the training program is completed. The constructed training program is transmitted to a user terminal, and a user can operate and participate in the training program through the user terminal. The communication unit 230 can receive result values ​​for each training algorithm constituting the training program from the user terminal and transmit them to the weak training determination unit 255.

[0097] The vulnerability training determination unit 255 calculates the success rate for each training algorithm based on the result value, and determines one training algorithm based on the calculated success rate. In this process, the vulnerability training determination unit 255 determines the training algorithm with the lowest success rate as the user's vulnerability training algorithm, and controls the training for that area to be continued intensively.

[0098] The second arrangement calculation unit 257 reconstructs the training program for the second execution, including the direct training algorithm determined by the weak training determination unit 255. The reconstructed training program may be reconstructed so that the weak training algorithm determined by the weak training determination unit 255 is again included, and may also be reconstructed by further considering the degree of success of each training algorithm calculated by the weak training determination unit 255.

[0099] 5 is an example of a training program reconstructed through the above process. First, the training algorithm determined as the weak training algorithm in the first implementation is the A1-2 training algorithm in the 14th slot 543 included in the Wednesday morning training 540. If the user's accuracy rate for the A1-2 training algorithm is 0 and the training level is 2 or higher, the weak training determination unit 255 determines that the user has low achievement in the A1-2 training algorithm and can search for previous training by referring to the metadata of the A1-2 training algorithm.

[0100] As the user repeatedly uses the training program, the performance level continues to be updated, and the arrangement of the training algorithms that make up the training program as a whole will change.

[0101] 5, the pre-training of the A1-2 training algorithm is the F-2 training algorithm, and the F-2 algorithm is placed immediately before the A1-2 training algorithm, which can guide the user's achievement of the A1-2 training algorithm to improve. As mentioned above, the pre-training included in the metadata can be a direct training algorithm or an indirect training algorithm.

[0102] In particular, in the present invention, the first and second placement calculation units 251 and 257 configure a training program so that training can be performed on morning and afternoon days to effectively stimulate the user's cognitive function, and a preset indirect training algorithm can be fixedly set in the afternoon of some days of the week. Referring to Figure 5, the G-3 training algorithm corresponding to "Unusual Word Search Level 3" is set in the 16th slot 551 of the Wednesday afternoon training 550, and the H-2 training algorithm corresponding to "Past News Listening Level 2" is fixedly set in the 30th slot 595 of the Friday afternoon training 590, thereby effectively stimulating the user's cognitive function and inducing symptom relief.

[0103] The second placement calculation unit 257 can simultaneously consider the user's achievement level and training level when rearranging the training algorithms of the remaining slots, excluding the weak training algorithms and the fixedly placed indirect training algorithms.

[0104] For example, in the first implementation, if the level of detailed training achieved by a user in the training belonging to working memory area D is 3 for "word stacking," 4 for "word order," and 5 for "reverse speech," the level of that user's working memory area D is set to 4, and this level value is taken into consideration when rearranging the training algorithm. In this case, the achievement level for the working memory area is 4.

[0105] As another example, if the user's training achievement levels in the first implementation are 1 for imagery training, 1 for fusion training, 4 for working memory training, 2 for vocabulary training, and 2 for cognitive agility training, the training algorithms to be arranged in the training program in the second implementation can be determined by applying the inverse of the achievement level of each training as a weight. According to the above values, the weights for each training are 30%, 30%, 8%, 16%, and 16%, respectively. Therefore, the training algorithms to be arranged in the training program in the second implementation include imagery training and fusion training at the highest rates and working memory training at the lowest rate. The above-mentioned weight calculation can also be applied when determining detailed training algorithms for the same training.

[0106] According to the present invention, the symptoms of patients with mild cognitive impairment can be significantly improved.

[0107] According to the present invention, dementia, the most dreaded disease of the elderly, can be prevented or diagnosed at an early stage.

[0108] According to the present invention, the social costs involved in managing dementia patients can be significantly reduced.

[0109] Unlike existing technologies, the present invention can stimulate the memory formation process of patients with cognitive impairment on a memory-by-memory basis through training in imagery, semantics, and fusion, thereby comprehensively improving working memory ability and processing speed.

[0110] The training program constructed according to the present invention can effectively stimulate the brain areas responsible for the user's cognitive function, improving the thickness of the cerebral cortex and cognitive function. Through DT1 images, the brains of users who experienced the training program according to the present invention were confirmed to have an increase in brain volume, including changes in the entire white matter.

[0111] The above-described embodiments of the present invention may be embodied in the form of a computer program executable by various components on a computer, and the computer program may be recorded on a computer-readable medium, which may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memories.

[0112] On the other hand, the computer program may be one specially designed and constructed for the present invention, or may be one known and available to those skilled in the art of computer software. Examples of computer programs include not only machine language code such as that produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter, etc.

[0113] The specific implementation described in the present invention is one embodiment and does not limit the scope of the present invention in any way. For the sake of brevity, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of the system may be omitted. Furthermore, wire connections or connecting members between components shown in the drawings are illustrative of functional connections and / or physical or circuit connections, and in an actual device, various functional connections, physical connections, or circuit connections may be present that are alternative or additional. Furthermore, unless specifically stated as "essential" or "important," a component is not necessarily required for application of the present invention.

[0114] In the present specification (particularly, the claims), the use of the term "said" and similar indicators applies to both the singular and the plural. Furthermore, when a range is described in the present invention, it encompasses inventions to which individual values ​​within that range are applied (unless otherwise specified), and is equivalent to describing each individual value comprising that range in the detailed description of the invention. Finally, for steps constituting the method of the present invention, unless a clear order is described or a contrary description is provided, the steps are to be performed in any suitable order. The present invention is not necessarily limited by the order in which the steps are described. The use of all examples or exemplary terms (such as, for example, etc.) in the present invention is merely for the purpose of explaining the present invention in detail, and the scope of the present invention is not limited by such examples or exemplary terms unless otherwise limited by the claims. Furthermore, those skilled in the art will recognize that various modifications, combinations, and variations can be made within the scope of the claims or their equivalents, depending on design conditions and factors.

Claims

1. a first configuration calculation unit configuring at least one cognitive training algorithm for training a person's cognitive function domain at a first time point in the morning of a specified day and at a second time point in the afternoon of the specified day; an output control unit controlling the cognitive training algorithms arranged at the first time point and the second time point to be output to a user terminal on the specified day, and a communication unit receiving a result value for the training algorithm from the user terminal; a vulnerability training determination unit calculating a success rate for each of the cognitive training algorithms according to the result value, and determining the cognitive training algorithm with the lowest calculated success rate as a vulnerability training algorithm; a second placement calculation unit places the fragile training algorithm at a first time point in the morning of another day after the specified day or at a second time point in the afternoon of the other day; the second allocation calculation unit determines cognitive training algorithms to be allocated at a first time point in the morning of the other day and a second time point in the afternoon of the other day, so that the reciprocal of the calculated achievement level for each cognitive training algorithm is used as a weighting value to be applied to a proportion of the cognitive training algorithms to be allocated on the other day; The achievement level is continuously updated. How to develop a program to improve symptoms in patients with cognitive impairment.

2. The step of deploying at least one cognitive training algorithm comprises: at least three or more types of cognitive training algorithms are arranged at a first time point in the morning of the specified day and at a second time point in the afternoon of the specified day, and the arranged cognitive training algorithms are different from each other; A method for constructing a program for improving symptoms of a patient with cognitive impairment according to claim 1.

3. A computer-readable recording medium storing a program for executing the method of claim 1.

4. a memory in which at least one program is stored; a processor that operates by executing said at least one program; Including, The processor: deploying at least one cognitive training algorithm for training a cognitive function domain of the person at a first time point in the morning of a specified day and at a second time point in the afternoon of the specified day; Controlling the cognitive training algorithms arranged at the first time point and the second time point to be output to a user terminal on the specified day, and receiving a result value for the training algorithm from the user terminal; Calculating a success rate for each of the cognitive training algorithms based on the result value, and determining the cognitive training algorithm with the lowest calculated success rate as a weak training algorithm; placing the fragile training algorithm at a first time point in the morning of another day after the identified day or at a second time point in the afternoon of the other day; determining cognitive training algorithms to be arranged at a first time point in the morning of the other day and a second time point in the afternoon of the other day, so that the reciprocal of the calculated achievement rate for each cognitive training algorithm is used as a weighting value to be applied to a proportion of cognitive training algorithms to be arranged on the other day; The achievement level is continuously updated. A program building device for improving symptoms in patients with cognitive impairment.

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