Method for providing personalized brain training game service for elderly user and system thereof

Abstract

A method for providing a brain training game service tailored to an elderly user is disclosed. According to one aspect of the present invention, a method for providing a brain training game service tailored to an elderly user is provided, comprising: a providing unit providing a brain training game service to an elderly user through a terminal; a collecting unit collecting user data while the user plays the game; a determining unit determining the user's cognitive state based on the user data; and the providing unit changing the method of providing the game service according to the user's cognitive state.

Description

Technology Field

[0001] The present invention relates to a method and system for providing a brain training game service tailored to elderly users. Background Technology

[0003] With the recent increase in the elderly population, brain training services aimed at preventing or alleviating conditions such as dementia and mild cognitive impairment are being provided in various forms. Accordingly, brain training game services utilizing smartphones, tablets, etc., to support the improvement of memory, attention, calculation ability, or spatial cognitive ability may be proposed. However, since the cognitive state of the elderly can vary depending on the individual or situation, if game services are provided in a uniform manner, the difficulty level may not be appropriate or the cognitive burden may increase, potentially leading to reduced participation and training effectiveness. Therefore, there is a need for technology capable of providing user-customized brain training game services by adaptively changing the method of game service delivery to reflect the user's condition. Prior art literature

[0005] Republic of Korea Published Patent Application No. 10-2020-0075529 (Published June 26, 2020) The problem to be solved

[0006] The present invention provides a method or system for providing a brain training game service tailored to elderly users that can reduce cognitive burden and improve game participation and brain training effects. means of solving the problem

[0008] According to one aspect of the present invention, a method for providing a brain training game service tailored to an elderly user is provided, comprising the steps of: a providing unit providing a brain training game service to an elderly user through a terminal; a collecting unit collecting user data while the user plays the game; a determining unit determining the user's cognitive state based on the user data; and the providing unit changing the method of providing the game service according to the user's cognitive state.

[0009] User data includes voice data obtained from the user's voice, gaze data obtained by tracking the user's gaze, and operation data obtained from the user's operation log. The step of determining the user's cognitive state involves calculating a voice recognition score based on speech fluency measured in the voice data, calculating a gaze recognition score based on gaze fixation time and gaze deviation frequency measured in the gaze data, calculating an operation recognition score based on the frequency of selection reversal measured in the operation data, calculating a cognitive state score by weighting the voice recognition score, the gaze recognition score, and the operation recognition score, and determining the user's cognitive state according to the cognitive state score.

[0010] After the step of determining the user's cognitive state, the recommendation unit may further include a step of recommending welfare services for the user based on the cognitive state score and the guardian's asset information and dependent family information received from the user's guardian.

[0011] The step of recommending welfare services includes a step of calculating a self-determination index that quantifies the cognitive ability of the user to select welfare services themselves based on a cognitive state score.

[0012] It may include the step of calculating a support capacity index that indexes the economic ability of a guardian to accept the cost of welfare services based on the guardian's asset information and dependent family information received in advance from the user's guardian, and the step of providing a list of recommended welfare services according to a recommendation index calculated by weighting the self-determination index and the support capacity index.

[0013] The step of providing a list of recommended welfare services may be: if the self-determination index is greater than or equal to the first set value, the weight assigned to the self-determination index is increased, and leisure or cultural services are recommended first; if the self-determination index is less than the second set value, the weight assigned to the support capacity index is increased, and medical or care services are recommended first; if the support capacity index is greater than or equal to the third set value, welfare services requiring relatively high costs are included in the list of recommended welfare services; and if there is a transmission of an emergency notification, the weight assigned to the self-determination index is decreased to 0, and medical or care services optimized for emergency situations are recommended first.

[0014] According to another aspect of the present invention, a brain training game service provision system tailored for an elderly user is provided, comprising a providing unit that provides a brain training game service to an elderly user through a terminal, a collecting unit that collects user data while the user plays a game, and a determining unit that determines the user's cognitive state based on the user data, wherein the providing unit changes the method of providing the game service according to the user's cognitive state. Effects of the invention

[0016] According to the present invention, a method or system for providing a brain training game service tailored to elderly users can be provided, which can reduce cognitive burden and improve game participation and brain training effects. Brief explanation of the drawing

[0018] FIG. 1 is a flowchart illustrating a method for providing a brain training game service tailored to elderly users according to an embodiment of the present invention. FIG. 1 is a schematic diagram showing a system for providing a brain training game service tailored to elderly users according to another embodiment of the present invention. Specific details for implementing the invention

[0019] The present invention is capable of various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that it includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the present invention. In describing the present invention, detailed descriptions of related prior art are omitted if it is determined that such detailed descriptions may obscure the essence of the present invention.

[0020] Terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another.

[0021] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0022] Hereinafter, embodiments of the method and system (100) for providing a brain training game service tailored to elderly users according to the present invention will be described in detail with reference to the attached drawings. In describing with reference to the attached drawings, identical or corresponding components are given the same reference numerals, and redundant descriptions thereof will be omitted.

[0024] First, a method for providing a brain training game service tailored to elderly users according to one embodiment of the present invention will be described.

[0025] According to the present embodiment, as illustrated in FIG. 1, a method for providing a brain training game service tailored to an elderly user is presented, comprising the steps of: a providing unit (110) providing a brain training game service to an elderly user through a terminal (S110); a collecting unit (120) collecting user data while the user plays the game (S120); a determining unit (130) determining the user's cognitive state based on the user data (S130); and the providing unit (110) changing the method of providing the game service according to the user's cognitive state (S140).

[0026] According to the present embodiment, while providing game services to elderly users, the user's cognitive state is determined based on user data, and the method of providing game services is changed accordingly. By providing customized game services to elderly users, the cognitive burden can be reduced and game participation and brain training effects can be improved.

[0027] Hereinafter, each step of the method for providing a brain training game service tailored to elderly users according to the present embodiment will be described with reference to FIGS. 1 and FIGS. 2.

[0029] The method according to the present embodiment can be performed by a computing device.

[0030] Step 110 allows the providing unit (110) to provide a brain training game service to an elderly user through a terminal.

[0031] By providing game services to elderly users, opportunities for brain training to improve cognitive function can be provided.

[0032] Game services may be provided through a terminal, where the terminal may be a smartphone, tablet PC, etc.

[0033] The game service provided to the user includes brain training game content for improving cognitive function, such as dementia prevention, improvement of mild cognitive impairment, cognitive rehabilitation training, and cognitive health management for the elderly, and the game content may include background music, sound effects, visual interfaces, etc.

[0034] Depending on the user's cognitive domain, the game content may include at least one of a memory training game group, a concentration training game group, a language training game group, a reflex training game group, a spatial perception training game group, an observation training game group, an attention training game group, and a thinking training game group.

[0035] A group of memory training games is designed to improve the user's memory and recall abilities and may include picture matching games, matching number games, etc.

[0036] The concentration training game group is designed to improve the user's concentration and working memory abilities and may include sequence memory games, color matching games, etc.

[0037] The language training game group is intended to improve the user's language comprehension and expression abilities and may include word chain games, object matching games, etc.

[0038] The group of speed training games is designed to improve the user's reaction speed and physical coordination ability, and may include fruit catching games, whack-a-mole games, musical instrument playing games, etc.

[0039] A group of spatial perception training games is intended to improve the user's spatial awareness and visual reasoning abilities, and may include maze games, puzzle games, etc.

[0040] The observation training game group is designed to improve the user's visual exploration ability and detailed information recognition ability, and may include location-memory games, coloring games, etc.

[0041] The attention training game group is designed to improve the user's selective attention and error detection abilities, and may include hidden number finding games, spot the difference games, etc.

[0042] The thinking skills training game group is intended to improve the user's problem-solving and reasoning abilities and may include riddle games, hidden word search games, etc.

[0043] The game service can evaluate the user's cognitive ability level based on the user's game performance results and adjust the game difficulty or provide recommended games according to the evaluation results.

[0044] Step 120 allows the collection unit (120) to collect user data while the user is playing the game.

[0045] User data may be data regarding the user's state, behavior, reactions, etc. that occur during the game.

[0046] Such user data can be collected using a camera, microphone, memory, etc. equipped in the terminal.

[0047] Step 130 allows the judgment unit (130) to determine the user's awareness state based on user data.

[0048] User cognitive state is a cognitive state of the user that may comprehensively represent concentration, responsiveness, judgment ability, game performance status, etc.

[0049] Step 140 allows the providing unit (110) to change the method of providing the game service according to the user's perception state.

[0050] By changing the method of dynamically providing game services based on the user's cognitive state, the user's cognitive load can be reduced, and game engagement and brain training effects can be improved.

[0051] User data may include voice data obtained from the user's voice, gaze data obtained by tracking the user's gaze, and operation data obtained from the user's operation log.

[0052] Voice data can be collected by acquiring the user's voice through a microphone equipped in the terminal.

[0053] Eye gaze data can be collected by tracking the user's gaze through a camera equipped in the terminal.

[0054] Operation data can be collected by obtaining operation logs generated by user operation through a touchscreen provided on the terminal and stored in a storage medium such as memory provided on the terminal.

[0055] The step of determining the user's cognitive state (S130) can determine the user's cognitive state based on the cognitive state score calculated using user data.

[0056] Here, the cognitive state score can be calculated by weighting the voice recognition score calculated from voice data, the gaze recognition score calculated from gaze data, and the operation recognition score calculated from operation data.

[0057] At this time, the weights applied to each cognitive score can be set based on the importance of each cognitive score calculated according to the results of the artificial intelligence model learning the correlation between each cognitive score and the user's cognitive state.

[0058] Here, the artificial intelligence model may be a linear regression model, a logistic regression model, a decision tree model, a Random Forest model, an XGBoost model, or a combination thereof.

[0059] The speech recognition score can be calculated based on speech fluency measured from speech data.

[0060] Speech fluency may be a feature value that measures the frequency or duration of stuttering, repetitive speech, and periods of silence occurring in interjections, interjections, self-talk, and response voices uttered by the user during a game in speech data.

[0061] Speech recognition scores can be calculated higher as speech fluency increases; for example, as the frequency of stuttering, the frequency of repeated speech, or the frequency or duration of silence intervals decreases, the speech recognition score can be calculated higher.

[0062] The gaze recognition score can be calculated based on the gaze fixation time and gaze deviation frequency measured from the gaze data.

[0063] Gaze fixation time can be a feature value in gaze data that measures the time a user's gaze continuously stays on a specific location or object.

[0064] The frequency of gaze deviation can be a feature value that measures the number of times the focus is disrupted in gaze data as the user's gaze fails to remain on the object being looked at and moves to another location.

[0065] The gaze recognition score can be calculated higher the longer the fixed gaze time and the lower the frequency of gaze deviation.

[0066] The manipulation recognition score can be calculated based on the frequency of choice reversal measured in the manipulation data.

[0067] The frequency of selection reversal may be a feature value measuring the number of times hesitation occurs in the manipulation data, such as canceling a user-selected item or changing it to another item.

[0068] The manipulation recognition score can be calculated higher as the frequency of choice reversal decreases.

[0069] Speech recognition scores, gaze recognition scores, and manipulation recognition scores can each be calculated using an artificial intelligence model, wherein the artificial intelligence model can receive feature values ​​corresponding to each recognition score as input to calculate the corresponding recognition score, and specifically, it may be a linear regression model, a decision tree model, a Random Forest model, an XGBoost model, an artificial neural network model, or a combination thereof.

[0070] The step of determining the user's cognitive state (S130) can determine the user's cognitive state as a normal state, i.e., a state in which the game can be performed smoothly, if the cognitive state score is greater than or equal to the first set score.

[0071] In addition, if the cognitive state score is less than the first set score but greater than or equal to the second set score (< first set score), the user's cognitive state can be determined as an attention state, that is, a state of reduced concentration or increased cognitive load during game performance.

[0072] In addition, if the cognitive state score is less than the second set score, the user's cognitive state can be determined to be in an overloaded state, that is, a state where the cognitive load has increased to the point where it is difficult to perform the game.

[0073] The first setting score and the second setting score may be set based on expert evaluation results, statistical analysis results, or the learning results of an artificial intelligence model, wherein the artificial intelligence model may specifically be a linear regression model, a logistic regression model, a decision tree model, a Random Forest model, an XGBoost model, or a combination thereof.

[0074] The step of changing the game service provision method (S140) can lower the game difficulty if the user's perception state is determined to be an attention state.

[0075] For example, in the case of a picture matching game, the difficulty of the game can be lowered by reducing the number of pairs.

[0076] Additionally, the step of changing the method of providing the game service (S140) may provide the game service primarily through a visual interface when the user's perception state is determined to be overloaded.

[0077] By changing the method of providing game services to match the user's cognitive state in this way, it is possible to reduce the user's cognitive load and improve game engagement and brain training effects.

[0078] Specifically, the step of changing the method of providing the game service (S140) can simplify the visual interface by adjusting the size of the selection button on the screen, etc., when the user's perception state is determined to be overloaded, and can remove background music and sound effects and output only voice in a specific frequency band with high clarity.

[0079] After the step of changing the method of providing the game service (S140), the step of sending an emergency notification (S150) may be further included.

[0080] The step of transmitting an emergency notification (S150) is that if the user's response time after the game service provision method is changed is longer than the set time, the judgment unit (130) determines it to be an emergency situation and transmits an emergency notification to the user's guardian.

[0081] In addition, if the user's response time is less than the set time after the game service provision method is changed, it is determined that the user's cognitive state has been recovered, and the game service provision method can be restored to the original method.

[0082] Here, the setting time can be dynamically set per user based on the average response time calculated from the user's past response history.

[0083] Meanwhile, after the step of determining the user's cognitive state (S130), a step of recommending welfare services (S160) may be further included.

[0084] In the step of recommending welfare services (S160), the recommendation unit (160) can recommend welfare services for the user based on the user's cognitive state score, and the guardian's asset information and dependent family information.

[0085] The guardian's asset information and dependent family information can be entered in advance by requesting them via the guardian's device.

[0086] Specifically, the step of recommending welfare services (S160) may include a step of calculating a self-determination index based on the user's cognitive state score (S161), a step of calculating a support capacity index based on the guardian's asset information and dependent family information (S162), and a step of providing a list of recommended welfare services according to a recommendation index calculated by weighting the self-determination index and the support capacity index (S163).

[0087] The self-determination index is a characteristic value that quantifies a user's cognitive ability to select welfare services independently, and it can be calculated as higher as the cognitive state score increases.

[0088] The support capacity index is a characteristic value that quantifies a caregiver's economic ability to accept the costs of welfare services; it can be calculated to be higher when the caregiver has more assets and fewer dependents.

[0089] The self-determination index and the support capacity index can each be calculated using an artificial intelligence model, wherein the artificial intelligence model can calculate the corresponding index by receiving feature values ​​corresponding to each index as input, and specifically, it may be a linear regression model, a decision tree model, a Random Forest model, an XGBoost model, an artificial neural network model, or a combination thereof.

[0090] The step of providing a list of welfare services (S163) may calculate a recommendation index by weighting the self-determination index and the support capacity index, and provide a list of recommended welfare services to the user and / or guardian according to the recommendation index.

[0091] At this time, the weights applied to each index may be set based on the importance of each index calculated according to the results of the artificial intelligence model learning the correlation between each index and the welfare service recommendation results (whether the recommended welfare service is used, whether it is selected, satisfaction, suitability, etc.), and the artificial intelligence model may be a linear regression model, a logistic regression model, a decision tree model, a Random Forest model, an XGBoost model, or a combination thereof.

[0092] A set index is pre-labeled for each welfare service, and welfare services with a set index greater than or equal to the recommendation index may be included in the recommended welfare service list. In this case, the set index may be set based on expert evaluation results, statistical analysis results, or the learning results of an artificial intelligence model. Specifically, the artificial intelligence model may be a linear regression model, a logistic regression model, a decision tree model, a Random Forest model, an XGBoost model, or a combination thereof.

[0093] The step of providing a list of recommended welfare services (S163) increases the weight assigned to the self-determination index when the self-determination index is greater than or equal to the first set value, and recommends welfare services according to the recommendation index calculated accordingly, while considering the user's preferences, it may recommend leisure or cultural services relatively first.

[0094] In addition, if the self-determination index is less than the second set value, the weight assigned to the support capacity index is increased, and welfare services are recommended according to the recommendation index calculated accordingly, but medical or care services may be recommended relatively preferentially in consideration of the user's health.

[0095] In addition, if the support capacity index is above the third setting value, welfare services requiring relatively high costs can be included in the recommended welfare service list.

[0096] Meanwhile, if an emergency notification is transmitted, the weight assigned to the self-determination index is reduced to 0, and welfare services are recommended according to the recommendation index calculated accordingly, and in this case, medical or care services optimized for the emergency situation may be recommended first.

[0097] The first, second, and third settings may be set based on expert evaluation results, statistical analysis results, or the learning results of an artificial intelligence model, wherein the artificial intelligence model may specifically be a linear regression model, a logistic regression model, a decision tree model, a Random Forest model, an XGBoost model, or a combination thereof.

[0099] Next, a system (100) providing a brain training game service tailored to elderly users according to another embodiment of the present invention will be described.

[0100] According to the present embodiment, as illustrated in FIG. 2, a brain training game service provision system (100) customized for elderly users is presented, comprising a providing unit (110) that provides a brain training game service to an elderly user through a terminal, a collecting unit (120) that collects user data while the user plays the game, and a determining unit (130) that determines the user's cognitive state based on the user data, wherein the providing unit (110) changes the method of providing the game service according to the user's cognitive state.

[0101] According to the present embodiment, while providing game services to elderly users, the user's cognitive state is determined based on user data, and the method of providing game services is changed accordingly. By providing customized game services to elderly users, the cognitive burden can be reduced and game participation and brain training effects can be improved.

[0102] With reference to FIGS. 1 and FIGS. 2, each component of the system (100) providing a brain training game service tailored to an elderly user according to the present embodiment will be described below, and the specific details thereof may follow the content of the method for providing a brain training game service tailored to an elderly user described above.

[0104] The providing unit (110) can provide a brain training game service to an elderly user through a terminal.

[0105] The collection unit (120) can collect user data while the user is playing the game.

[0106] The judgment unit (130) can determine the user's awareness status based on user data.

[0107] The providing unit (110) can change the method of providing game services according to the user's perception state.

[0108] User data may include voice data obtained from the user's voice, gaze data obtained by tracking the user's gaze, and operation data obtained from the user's operation log.

[0109] The judgment unit (130) can calculate a voice recognition score based on speech fluency measured in voice data, calculate an eye recognition score based on gaze fixation time and gaze deviation frequency measured in gaze data, calculate an operation recognition score based on the frequency of selection reversal measured in operation data, calculate a recognition state score by weighting the voice recognition score, the eye recognition score, and the operation recognition score, and determine the user's recognition state according to the recognition state score.

[0110] The judgment unit (130) can determine the user's perception state to be normal if the perception state score is greater than or equal to the first set score, determine the user's perception state to be at attention if the perception state score is less than the first set score but greater than or equal to the second set score, and determine the user's perception state to be overloaded if the perception state score is less than the second set score.

[0111] The providing unit (110) can lower the game difficulty when the user's perception state is determined to be an attention state, and can provide the game service mainly through a visual interface when the user's perception state is determined to be an overload state.

[0112] The providing unit (110) can provide a simplified visual interface when the user's perception state is determined to be overloaded, remove background sounds and sound effects, and output only voice in a specific frequency band with high clarity.

[0113] The judgment unit (130) can determine that if the user's reaction time after the game service provision method is changed is less than the set time, the user's awareness state has been restored and the game service provision method is restored to the original method, and if the user's reaction time after the game service provision method is changed is greater than the set time, it can determine that it is an emergency situation and send an emergency notification to the user's guardian.

[0114] A recommendation unit (160) that recommends welfare services for the user based on the cognitive state score, and the guardian's asset information and dependent family information received from the user's guardian may be further included.

[0115] The recommendation unit (160) can calculate a self-determination index that indexes the cognitive ability of the user to choose welfare services based on a cognitive state score, calculate a support capacity index that indexes the economic ability of the guardian to accept the cost of welfare services based on the guardian's asset information and dependent family information received in advance from the user's guardian, and provide a list of recommended welfare services according to the recommendation index calculated by weighting the self-determination index and the support capacity index.

[0116] The recommendation unit (160) can increase the weight assigned to the self-determination index when the self-determination index is greater than or equal to the first set value, and recommend leisure or cultural services first; when the self-determination index is less than the second set value, it can increase the weight assigned to the support capacity index, and recommend medical or care services first; when the support capacity index is greater than or equal to the third set value, it can include welfare services that incur relatively high costs in the recommended welfare service list; and when there is a transmission of an emergency notification, it can decrease the weight assigned to the self-determination index to 0, and recommend medical or care services optimized for emergency situations first.

[0118] Meanwhile, the components of the aforementioned embodiments can be easily identified from a process perspective. That is, each component can be identified as a separate process. Furthermore, the processes of the aforementioned embodiments can be easily identified from the perspective of the device components.

[0119] In addition, the technical details described above may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiments, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media 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 ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. Hardware devices may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

[0121] Although an embodiment of the present invention has been described above, those skilled in the art may modify and change the present invention in various ways by adding, changing, deleting, or adding components, etc., without departing from the spirit of the present invention as described in the claims, and such modifications and changes are also to be included within the scope of the rights of the present invention. Explanation of the symbols

[0123] 100: System for Providing Customized Brain Training Game Services for Elderly Users 110: Provider 120: Collection Department 130: Judgment Department 160: Recommendation Section

Claims

Claim 1 A providing unit provides a brain training game service to an elderly user through a terminal; a collecting unit collects user data while the user plays the game; a determining unit determines the user's cognitive state based on the user data; The providing unit includes a step of changing the game service provision method according to the user perception state, and the user data includes voice data obtained from the user's voice, gaze data obtained by tracking the user's gaze, and operation data obtained from the user's operation log. The step of determining the user perception state includes calculating a voice perception score based on speech fluency measured in the voice data, calculating a gaze perception score based on gaze fixation time and gaze deviation frequency measured in the gaze data, calculating an operation perception score based on the frequency of selection reversal occurrence measured in the operation data, calculating a perception state score by weighting the voice perception score, the gaze perception score, and the operation perception score, and determining the user perception state according to the perception state score, wherein if the perception state score is greater than or equal to a first set score, the user perception state is determined to be a normal state; if the perception state score is less than the first set score but greater than or equal to a second set score lower than the first set score, the user perception state is determined to be an attention state; and if the perception state score is less than the second set score, the user perception state is determined to be an overload state. The step of determining and changing the game service provision method comprises, if the user perception state is determined to be the attention state, lowering the game difficulty; if the user perception state is determined to be the overload state, adjusting the size of the selection button on the screen to simplify and provide the visual interface; removing background music and sound effects and outputting only voice in a specific frequency band with high clarity; and after the step of changing the game service provision method, the determination unitA method for providing a brain training game service tailored to an elderly user, comprising the additional step of determining that the user's cognitive state has been recovered and restoring the game service provision method to the original method if the user's response time is less than a set time after the game service provision method is changed, and determining that an emergency situation has been detected if the response time is greater than or equal to the set time and sending an emergency notification to the user's guardian, wherein the set time is dynamically set for each user based on the average response time calculated from the user's past response history. Claim 2 delete Claim 3 A method for providing a brain training game service tailored to an elderly user, comprising, in addition to the step of determining the user's cognitive state, a recommendation unit recommending a welfare service for the user based on the cognitive state score and the guardian's asset information and dependent family information received from the user's guardian, after the step of determining the user's cognitive state. Claim 4 In paragraph 3, the step of recommending the welfare service comprises: a step of calculating a self-determination index that indexes the cognitive ability of the user to select the welfare service themselves based on the cognitive state score; a step of calculating a support capacity index that indexes the economic ability of the guardian to accept the cost of the welfare service based on the guardian's asset information and dependent family information received in advance from the user's guardian; and a step of providing a list of recommended welfare services according to a recommendation index calculated by weighting the self-determination index and the support capacity index. Claim 5 A method for providing a brain training game service tailored to an elderly user, wherein, in paragraph 4, the step of providing the recommended welfare service list comprises: increasing the weight assigned to the self-determination index when the self-determination index is greater than or equal to a first set value while prioritizing the recommendation of leisure or cultural services; increasing the weight assigned to the support capacity index when the self-determination index is less than a second set value while prioritizing the recommendation of medical or care services; including the welfare service requiring relatively high costs in the recommended welfare service list when the support capacity index is greater than or equal to a third set value; and, when there is a transmission of the emergency notification, decreasing the weight assigned to the self-determination index to 0 while prioritizing the recommendation of medical or care services optimized for emergency situations.

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