A cognitive function assessment and intervention method based on spatial navigation video games

Abstract

The present application relates to a kind of cognitive function evaluation and intervention method based on spatial navigation video game, the method includes: obtaining user identity information to establish file;Present three-dimensional virtual environment with first person view, execute route repetition and backtracking task and record direction selection accuracy;According to weighted formula, calculate comprehensive score, compare with preset threshold and output cognitive state discrimination result;According to the real-time performance of user, dynamically adjust path complexity and moving speed, form personalized training;After completing training, evaluate again, generate post-intervention discrimination result;Record and show score change trend and difficulty progression trajectory.The present application integrates screening and intervention, through objective behavior data and adaptive algorithm, can effectively distinguish healthy elderly people and mild cognitive impairment elderly people, and improve cognitive function through personalized training, provides efficient digital solution for elderly cognitive health management.

Description

Technical Field

[0001] This invention relates to the intersection of digital health technology and cognitive science, and more specifically, to a method for cognitive function assessment and intervention based on spatial navigation video games. Background Technology

[0002] With the increasing aging of the global population, mild cognitive impairment (MCI), as an important precursor stage of Alzheimer's disease (AD), requires early identification and intervention. Spatial navigation ability is a crucial component of cognitive function, and its decline often precedes abnormalities in traditional memory tests, making it a sensitive behavioral indicator of early cognitive impairment. However, current technologies have the following limitations: First, traditional cognitive assessment tools (such as MMSE and MoCA) have limited sensitivity to early decline in spatial navigation ability and struggle to detect subtle cognitive deficits. Furthermore, existing spatial navigation assessments often rely on expensive virtual reality equipment or complex laboratory paradigms, which are costly, complex to operate, and difficult to implement in communities and homes.

[0003] Secondly, traditional cognitive training methods (such as paper-and-pen exercises and single computer tasks) are boring and lack interest, resulting in insufficient training motivation and poor compliance among the elderly. Third, existing tools struggle to organically combine standardized spatial navigation capability assessments with personalized, gamified cognitive intervention training to form an effective closed-loop management path of "screening-intervention-reassessment". Summary of the Invention

[0004] The technical problem this invention aims to solve is how to develop a low-cost, easily promoted, and integrated method that combines accurate assessment and effective intervention, which is of great significance for the early management and proactive intervention of cognitive health in the elderly.

[0005] This invention provides a method for cognitive function assessment and intervention based on spatial navigation video games, characterized by comprising the following steps: S1: Obtain user identity information and create personal profiles; S2: In evaluation mode, a three-dimensional virtual navigation environment is provided to the user from a first-person perspective, and a preset number of route repetition and backtracking tasks are performed. The user's direction selection accuracy is recorded for each task. The route repetition and backtracking tasks adopt the difficulty parameters under preset path complexity and movement speed. S3: Calculate the overall score based on the direction selection accuracy of each task, compare the overall score with the preset cognitive state distinction threshold, and output the user's current cognitive state; S4: If the user's current cognitive state is mild cognitive impairment, then enter the training model for intervention training; in the training mode, the user performs route repetition and backtracking tasks, and dynamically adjusts the difficulty parameters of subsequent tasks according to the direction selection accuracy under the current difficulty parameters, and continuously updates the direction selection accuracy. S5: After completing the preset training cycle, the evaluation mode is entered again. The user performs route repetition and backtracking tasks, collects the direction selection accuracy, calculates the comprehensive score after intervention training, compares the comprehensive score with the cognitive state distinction threshold, and outputs the cognitive state after intervention training. S6: Store the user's overall score, difficulty parameters, and cognitive state before and after the intervention training process, generate visual charts, and show the trend of overall score changes, the progression trajectory of difficulty parameters, and the comparison results of cognitive state.

[0006] Compared with existing technologies, this application has the following advantages: by utilizing a three-dimensional virtual navigation environment to simulate real navigation scenarios and automatically recording behavioral data through a program, the evaluation results are more objective and accurate; it effectively distinguishes between healthy elderly people and elderly people with mild cognitive impairment; by adopting a first-person perspective three-dimensional virtual navigation environment to simulate real-life scenarios, it has greater ecological validity than traditional paper-and-pencil tests; by automatically recording behavioral data such as the accuracy rate of direction selection through a program, subjective scoring errors are eliminated, resulting in more objective and accurate evaluation results; and by dynamically adjusting the path complexity and movement speed according to the user's real-time task performance, it forms a personalized training path that varies from person to person, ensuring that the training intensity is always within the user's "zone of proximal development," which can both maintain training motivation and optimize intervention effects.

[0007] In one possible implementation, the user's identity information in step S1 includes age, gender, and years of education.

[0008] In one possible implementation, in step S2, the user's direction selection is received through the terminal screen of the mobile terminal in the three-dimensional virtual navigation environment, and the user moves along the path specified by the direction selection in a passive movement manner in the three-dimensional virtual navigation environment.

[0009] Compared with existing technologies, passive movement simplifies user operations and avoids the complex operation of simultaneously knowing how to move and choose a direction. Simplified operation reduces invalid data caused by technical difficulties, ensuring that the evaluation results reflect true cognitive ability rather than operational proficiency. Passive movement ensures that all users move at the same speed, eliminating the interference of individual differences in operation speed on task performance and making the evaluation results more comparable.

[0010] In one possible implementation, the S2 step of recording the user's direction selection accuracy for each task includes: First, obtain the preset standard path for each task, where the standard path is an absolute direction sequence; Next, the user's current orientation in the 3D virtual navigation environment is obtained in real time; The correct relative direction is calculated based on the current orientation and the absolute direction in the standard path; Then, the direction selection input by the user through the terminal device is compared with the correct relative direction. If the comparison results match, the direction selection is determined to be correct; otherwise, the direction selection is determined to be incorrect. Finally, count the number of times the direction was correctly selected, and calculate the direction selection accuracy rate: Direction selection accuracy = (Number of times the direction was selected correctly / Total number of times the direction was selected) × 100%.

[0011] Compared with existing technologies, this technology automatically converts the preset absolute direction into the relative direction from the user's current perspective, eliminating the need for the user to have the ability to judge absolute direction and reducing cognitive load. Regardless of how the user's orientation changes in the virtual environment, the correct relative direction can be calculated in real time, ensuring the objectivity and consistency of each judgment.

[0012] In one possible implementation, the formula for calculating the overall score based on the direction selection accuracy in step S3 is as follows: ; In the formula, This represents the overall score. This represents the average direction selection accuracy after a preset number of route repetitions. This represents the average direction selection accuracy after a preset number of route backtracking steps. These are the weighting coefficients. and Weighting coefficient Based on the analysis of the user's working characteristic curve, the optimization objective is predetermined to maximize the area under the curve.

[0013] Compared with existing technologies, route tracing is more sensitive to early cognitive decline and is given higher weight, making the overall score more reflective of the early risk of mild cognitive impairment and improving screening sensitivity.

[0014] In one possible implementation, the preset cognitive state differentiation threshold in step S3 is determined through the following steps: First, the cognitive status of each individual in the sample group was determined using the Montreal Cognitive Assessment Scale. The cognitive status included a healthy state and a mild cognitive impairment state. Each individual performed a preset number of route repetition and backtracking tasks in a three-dimensional virtual navigation environment. The comprehensive score of each individual was calculated based on the direction selection accuracy. Secondly, using the overall score as the test variable and the cognitive state determined by the Montreal Cognitive Assessment Scale as the state variable, we plotted the working characteristic curve of the subjects. Next, the optimal cutoff value is selected as the cognitive state differentiation threshold based on the principle of maximizing the Yoden index.

[0015] Compared with existing technologies, state labeling using the Montreal Cognitive Assessment Scale establishes a correlation between the overall score and clinical diagnosis, enhancing the clinical credibility of cognitive screening tools. Selecting the cutoff value based on the maximum Youden index balances sensitivity and specificity, avoiding extreme cases of missed or misdiagnosis.

[0016] In one possible implementation, the cognitive state output in step S3 includes a healthy state and a state with mild cognitive impairment.

[0017] In one possible implementation, the dynamic adjustment of the path complexity and movement speed of subsequent tasks based on the user's real-time task performance in step S4 specifically includes: When the user's directional selection accuracy in the current task is greater than or equal to the first preset threshold, increase the path complexity and / or movement speed of subsequent tasks. When the user's directional selection accuracy in the current task is less than or equal to the second preset threshold, the path complexity and / or movement speed of subsequent tasks are reduced. When the user's directional selection accuracy in the current task is between the second preset threshold and the first preset threshold, the path complexity and movement speed of subsequent tasks remain unchanged.

[0018] Compared with existing technologies, this method dynamically adjusts the difficulty based on the user's real-time performance, ensuring that each user can train at a level that suits their ability, thus avoiding a one-size-fits-all training approach.

[0019] In one possible implementation, the path complexity of a task is characterized by the number of turns in the path. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the process of this application; Figure 2 The ROC curve for subjects in Experiment 1 of this application who were distinguished between healthy status and mild cognitive impairment by comprehensive score; Figure 3 This is a pre- and post-test comparison chart of the MMSE scale scores between the experimental group and the control group in Experiment 2 of this application. Figure 4 This is a pre- and post-test comparison chart of the MoCA scale scores between the experimental group and the control group in Experiment 2 of this application. Figure 5This is a pre- and post-test comparison of the Santa Barbara Direction Scale (SBSOD) scores between the experimental group and the control group in Experiment 2 of this application. Figure 6 This is a pre- and post-test comparison chart of the spatial anxiety scale scores between the experimental group and the control group in Experiment 2 of this application. Figure 7 This is a before-and-after comparison graph of the experimental group and the control group in Experiment 2 of this application regarding the time taken to complete the connection test A; Figure 8 This is a pre- and post-test comparison of the scores of the experimental group and the control group on the Cauchy block task in Experiment 2 of this application. Detailed Implementation

[0021] First, those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the embodiments of this application and are not intended to limit the scope of protection of the embodiments of this application. Those skilled in the art can make adjustments as needed to adapt to specific application scenarios.

[0022] In the description of the embodiments of this application, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application based on the specific circumstances.

[0023] In the embodiments of this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0024] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0025] See Figure 1 As shown in the illustration, this application discloses a method for cognitive function assessment and intervention based on a spatial navigation video game, implemented through an application installed on an Android mobile tablet. The application includes core functional modules such as user management, game engine, assessment logic, training logic, and data analysis. The game engine is developed using the C# language and runs on the Android platform. The game environment is a 3D modeled virtual city community, including three-dimensional buildings and intersections. All turns are 90°, and there are road signs along the route. To simplify the operation, a "passive movement" method is adopted, where the character moves automatically in a specified direction. Users only need to select the direction by tapping the direction buttons (←, ↑, →) at the bottom of the screen at each intersection.

[0026] The spatial navigation video game designed in this application includes route repetition and backtracking tasks, wherein: Route repetition is used to assess egocentric spatial navigation ability with the user as the frame of reference. In route repetition, a route from the starting point to the destination is presented from a first-person perspective. After watching and learning, the user must start from the starting point and choose the correct direction of travel at each intersection to reproduce the route. The route backtracking item is used to evaluate non-egocentric spatial navigation capabilities with the environment as the frame of reference. In the route backtracking item, after the user is familiar with the route, the user is required to start from the destination location and navigate back to the starting point by selecting a direction.

[0027] This application provides an embodiment of a cognitive function assessment and intervention method based on a spatial navigation video game, comprising the following steps: S1: Obtain user identity information and create personal profiles; Specifically, the identity information includes age, gender, years of education, etc., which are used for subsequent group analysis or as corrective factors for cognitive assessment.

[0028] S2: In evaluation mode, a three-dimensional virtual navigation environment is provided to the user from a first-person perspective, and a preset number of route repetition and backtracking tasks are performed. The user's direction selection accuracy is recorded for each task. The route repetition and backtracking tasks adopt the difficulty parameters under preset path complexity and movement speed. Specifically, the preset number of times is preferably 3, with route repetition items and route backtracking items each executed 3 times, for a total of 6 tasks. Each path is fixed to contain 5 turns, the movement speed is 3m / s, and each task has a time limit of 60 seconds, which will automatically end if the time limit is exceeded.

[0029] The terminal device is preferably a mobile terminal device (such as an Android tablet) that supports touch screen operation. The user adopts a "passive movement" method, where the character moves automatically in the specified direction. The user only needs to select the direction at each intersection by clicking the direction button (left turn, straight, right turn) on the touch screen, thereby reducing the user's workload.

[0030] The method for determining the direction selection accuracy includes: First, obtain the preset standard path for each task, where the standard path is an absolute direction sequence; Next, the user's current orientation in the 3D virtual navigation environment is obtained in real time; When a user reaches an intersection, the correct relative direction is calculated through coordinate transformation based on the current orientation and the absolute direction of the next node in the standard path; in this embodiment of the application: If the target's absolute direction is left-handed (rotated -90°) relative to the current orientation, then the correct relative direction is "left-handed". If the target's absolute direction is straight (rotated 0°) relative to the current orientation, then the correct relative direction is "straight". If the target's absolute direction is right (rotation +90°) relative to the current orientation, then the correct relative direction is "right turn".

[0031] Then, the direction selection input by the user through the terminal device is compared with the correct relative direction. If the comparison results match, the direction selection is determined to be correct; otherwise, the direction selection is determined to be incorrect. Finally, count the number of times the direction was correctly selected, and calculate the direction selection accuracy rate: Direction selection accuracy = (Number of times the direction was selected correctly / Total number of times the direction was selected) × 100%.

[0032] S3: Calculate the overall score based on the direction selection accuracy of each task, compare the overall score with the preset cognitive state distinction threshold, and output the user's current cognitive state; The formula for calculating the overall score based on the accuracy of direction selection is as follows: ; In the formula, This represents the overall score. This represents the average direction selection accuracy after a preset number of route repetitions. This represents the average direction selection accuracy after a preset number of route backtracking steps. These are the weighting coefficients. and Weighting coefficient Based on the analysis of the user's work characteristic curve, the optimization objective is predetermined to maximize the area under the curve; in this embodiment of the application, , .

[0033] The threshold for distinguishing cognitive states is determined through the following steps: First, the cognitive status of each individual in the sample group was determined using the Montreal Cognitive Assessment Scale. The cognitive status included a healthy state and a mild cognitive impairment state. Each individual performed a preset number of route repetition and backtracking tasks in a three-dimensional virtual navigation environment. The comprehensive score of each individual was calculated based on the direction selection accuracy. Secondly, using the overall score as the test variable and the cognitive state determined by the Montreal Cognitive Assessment Scale as the state variable, we plotted the working characteristic curve of the subjects. Next, the optimal cutoff value is selected as the cognitive state differentiation threshold based on the principle of maximizing the Yoden index.

[0034] In a preferred embodiment, the cognitive state includes a healthy state and a mild cognitive impairment state, and the cognitive state differentiation threshold is 13 points. When the comprehensive score is lower than 13 points, it is output that there is a risk of mild cognitive impairment.

[0035] S4: If the user's current cognitive state is mild cognitive impairment, then enter the training model for intervention training; in the training mode, the user performs route repetition and backtracking tasks, and dynamically adjusts the difficulty parameters of subsequent tasks according to the direction selection accuracy under the current difficulty parameters, and continuously updates the direction selection accuracy. Specifically, the dynamic adjustment includes: When the user's task execution accuracy in the current training is greater than or equal to the first preset threshold (preferably 80%), increase the path complexity and / or movement speed of subsequent tasks. When the accuracy of the task execution is less than or equal to the second preset threshold (preferably 60%), the path complexity and / or movement speed of subsequent tasks are increased. When the accuracy of the task execution is between the second preset threshold and the first preset threshold, the path complexity and movement speed of subsequent tasks remain unchanged.

[0036] The path complexity is characterized by the number of turns in the path, with the number of turns ranging from 1 to 10; the moving speed ranges from 3 m / s to 4 m / s, including three levels: 3 m / s, 3.5 m / s, and 4 m / s.

[0037] S5: After completing the preset training cycle, the evaluation mode is entered again. The user performs route repetition and backtracking tasks, collects the direction selection accuracy, calculates the comprehensive score after intervention training, compares the comprehensive score with the cognitive state distinction threshold, and outputs the cognitive state after intervention training. Specifically, the preset cycle is preferably 12 weeks, with one training session per week and each training session lasting 30 minutes; the user uses preset difficulty parameters including: the number of turns on the path is fixed at 5, and the movement speed is fixed at 3 m / s.

[0038] S6: Store the user's overall score, difficulty parameters, and cognitive state before and after the intervention training process, generate visual charts, and show the trend of overall score changes, the progression trajectory of difficulty parameters, and the comparison results of cognitive state.

[0039] Specifically, the overall score trend is displayed as a line graph, and the progression trajectory of the difficulty parameters is displayed as a ladder graph or bar chart. The cognitive state discrimination comparison results are output in text. Optionally, the user's overall score trend can also be compared with the average trend of the same age group and education level group.

[0040] In addition, all users' evaluation history (overall score, accuracy of each subtask) and various data during the intervention training process (daily training duration, difficulty change curve, accuracy change) are stored in a cloud database.

[0041] The data management and analysis dashboard displays individual users' cognitive assessment score trends, training difficulty progression curves, and score comparisons on scales such as MMSE and MoCA before and after training in the form of charts.

[0042] Experiment 1: Screening efficacy verification A total of 123 participants were recruited from the community and hospitals, including healthy older adults and older adults with mild cognitive impairment (MCI). Among them, 62 were healthy older adults and 61 were older adults with mild cognitive impairment. All participants signed informed consent forms. The diagnosis of mild cognitive impairment was based on the Montreal Cognitive Assessment (MoCA) score, with 26 points as the cutoff value. Participants with a score ≥26 were considered healthy older adults, and those with a score <26 were considered older adults with mild cognitive impairment.

[0043] All participants were tested using the evaluation module of this invention. Test parameters were set as follows: each path contained 5 fixed turns, the moving speed was 3 m / s, route repetition and route backtracking were performed 3 times each, for a total of 6 tasks. Each task had a time limit of 60 seconds. The system automatically recorded the user's direction selection accuracy in each task and calculated a comprehensive score. ; The experimental results showed that the overall game score of healthy elderly people (47.42 ± 11.29) was significantly higher than that of elderly people with mild cognitive impairment (32.02 ± 7.50), and the difference was statistically significant (t=7.378, p<0.001).

[0044] Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of the game's overall score distinguishing between healthy older adults and those with mild cognitive impairment was 0.816 (95% confidence interval [0.742, 0.891]). The optimal cutoff value, determined using the Youden index maximization principle, was 13 points. At this cutoff value, the sensitivity was 77.40% and the specificity was 77.00%. The ROC curve is shown below. Figure 2 As shown, the diagonal line is the reference line (AUC=0.5), representing the effectiveness of random classification.

[0045] The above results indicate that the game score of the method of the present invention can effectively distinguish between healthy elderly people and elderly people with mild cognitive impairment, with a moderate to high discrimination power (AUC=0.816), and can be used as a screening tool for mild cognitive impairment.

[0046] Experiment 2: Verification of Intervention Effect To verify the cognitive intervention effect of the method of the present invention, the following randomized controlled experiment was conducted in this embodiment; A randomized controlled trial was conducted, recruiting 66 elderly participants who were randomly assigned to either the experimental or control group using numerical randomization. During the experiment, one participant in the experimental group withdrew due to personal reasons, and three participants in the control group dropped out during the post-test. Ultimately, there were 32 participants in the experimental group and 30 in the control group. There were no significant differences between the two groups in demographic variables such as age, sex, and years of education (p>0.05), making them comparable.

[0047] The experimental group received 12 weeks of training using the method of this invention, with one 30-minute session per week. The training employed an adaptive difficulty adjustment mode, where the system dynamically adjusted path complexity and movement speed based on the user's task execution accuracy in the training levels: difficulty was increased when accuracy was ≥80%, maintained when accuracy was between 60% and 80%, and decreased when accuracy was <60%. The control group received no training intervention.

[0048] To assess the intervention's effectiveness, all participants completed the indicators listed in Table 1 below before training (pre-test) and after training (post-test): Table 1: Indicator Test Table Evaluation Dimensions Assessment tools illustrate Holistic cognitive function Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) Quantifying overall cognitive level Space navigation capability Santa Barbara Direction Scale (SBSOD) and Spatial Anxiety Scale Quantifying subjective spatial navigation ability and navigation-related anxiety Visual space working memory Corsi Block-Tapping Task Quantifying visuospatial working memory capacity Processing speed Trail Making Test Part A Quantitative information processing speed Analysis of variance was used to analyze the experimental data, and the results showed that: Overall cognitive function: The experimental group's MMSE and MoCA scores were significantly higher in the post-test than in the pre-test (p<0.05), while the control group showed no significant change. The interaction between time and group was significant (p<0.01), indicating that the training method of this invention has a significant effect on improving overall cognitive function.

[0049] Spatial navigation ability: The experimental group's SBSOD score was significantly higher in the post-test than in the pre-test (p<0.001), and the spatial anxiety scale score was significantly lower in the post-test than in the pre-test (p<0.001), indicating that subjective spatial navigation ability improved and navigation-related anxiety decreased after training. In the control group, neither indicator showed significant change or a downward trend.

[0050] Visuospatial working memory: The experimental group's scores on the Cauchy block task were significantly higher in the post-test than in the pre-test (p<0.001), indicating that training has a positive impact on visuospatial working memory. The control group showed no significant changes.

[0051] Processing speed: The experimental group's completion time for the connection test A was significantly shorter in the later test than in the earlier test (p<0.001), indicating that training improved information processing speed. The control group's completion time showed no significant change or an increasing trend.

[0052] The above experimental data are as follows Figures 3 to 8 As shown. Among them, Figures 3 to 4 For pre- and post-test comparisons of overall cognitive function (MMSE, MoCA), Figures 5 to 6 For the pre- and post-test comparison of spatial navigation capabilities (SBSOD, spatial anxiety), Figure 7 For the comparison of before and after processing speed (connection test A), Figure 8 Pre- and post-test comparisons for visuospatial working memory (Cauchy block task).

[0053] The above results demonstrate that the method described in this application can effectively improve the overall cognitive function, spatial navigation ability, processing speed, and visuospatial working memory of older adults, and significantly reduce navigation-related spatial anxiety levels. This training effect exhibits multidimensional transfer effects, validating the effectiveness of the method as a cognitive intervention tool.

[0054] In the description of the embodiments of this application, it should be noted that the terms "inner" and "outer" and other terms indicating direction or positional relationship are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or component must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this application.

[0055] In the description of this application, the references to terms such as "an embodiment," "some embodiments," "in this embodiment," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0056] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for cognitive function assessment and intervention based on spatial navigation video games, characterized in that, Including the following steps: S1: Obtain user identity information and create personal profiles; S2: In evaluation mode, a three-dimensional virtual navigation environment is provided to the user from a first-person perspective, and a preset number of route repetition and backtracking tasks are performed. The user's direction selection accuracy is recorded for each task. The route repetition and backtracking tasks adopt the difficulty parameters under preset path complexity and movement speed. S3: Calculate the overall score based on the direction selection accuracy of each task, compare the overall score with the preset cognitive state distinction threshold, and output the user's current cognitive state; S4: If the user's current cognitive state is mild cognitive impairment, then enter the training model for intervention training; in the training mode, the user performs route repetition and backtracking tasks, and dynamically adjusts the difficulty parameters of subsequent tasks according to the direction selection accuracy under the current difficulty parameters, and continuously updates the direction selection accuracy. S5: After completing the preset training cycle, the evaluation mode is entered again. The user performs route repetition and backtracking tasks, collects the direction selection accuracy, calculates the comprehensive score after intervention training, compares the comprehensive score with the cognitive state distinction threshold, and outputs the cognitive state after intervention training. S6: Store the user's overall score, difficulty parameters, and cognitive state before and after the intervention training process, generate visual charts, and show the trend of overall score changes, the progression trajectory of difficulty parameters, and the comparison results of cognitive state.

2. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 1, characterized in that, In step S1, the user's identity information includes age, gender, and years of education.

3. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 1, characterized in that, In step S2, the user's direction selection is received through the mobile terminal screen in the 3D virtual navigation environment. The user moves along the path specified by the direction selection in a passive movement manner in the 3D virtual navigation environment.

4. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 1, characterized in that, The S2 step records the user's direction selection accuracy for each task, including: First, obtain the preset standard path for each task, where the standard path is an absolute direction sequence; Next, the user's current orientation in the 3D virtual navigation environment is obtained in real time; The correct relative direction is calculated based on the current orientation and the absolute direction in the standard path; Then, the direction selection input by the user through the terminal device is compared with the correct relative direction. If the comparison results match, the direction selection is determined to be correct; otherwise, the direction selection is determined to be incorrect. Finally, count the number of times the direction was correctly selected, and calculate the direction selection accuracy rate: Direction selection accuracy = (Number of times the direction was selected correctly / Total number of times the direction was selected) × 100%.

5. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 1, characterized in that, The formula for calculating the overall score in step S3 is as follows: ； In the formula, This represents the overall score. This represents the average direction selection accuracy after a preset number of route repetitions. This represents the average direction selection accuracy after a preset number of route backtracking steps. These are the weighting coefficients. and Weighting coefficient Based on the analysis of the user's working characteristic curve, the optimization objective is predetermined to maximize the area under the curve.

6. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 1, characterized in that, The preset cognitive state differentiation threshold in step S3 is determined through the following steps: First, the cognitive status of each individual in the sample group was determined using the Montreal Cognitive Assessment Scale. The cognitive status included a healthy state and a mild cognitive impairment state. Each individual performed a preset number of route repetition and backtracking tasks in a three-dimensional virtual navigation environment. The comprehensive score of each individual was calculated based on the direction selection accuracy. Secondly, using the overall score as the test variable and the cognitive state determined by the Montreal Cognitive Assessment Scale as the state variable, we plotted the working characteristic curve of the subjects. Next, the optimal cutoff value selected based on the principle of maximizing the Yoden index is used as the threshold for distinguishing cognitive states.

7. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 6, characterized in that, The cognitive states output in step S3 include a healthy state and a state with mild cognitive impairment.

8. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 1, characterized in that, The S4 step dynamically adjusts the path complexity and movement speed of subsequent tasks based on the user's real-time task performance, specifically including: When the user's directional selection accuracy in the current task is greater than or equal to a first preset threshold, increase the path complexity and / or movement speed of subsequent tasks. When the user's directional selection accuracy in the current task is less than or equal to the second preset threshold, the path complexity and / or movement speed of subsequent tasks are reduced. When the user's directional selection accuracy in the current task is between the second preset threshold and the first preset threshold, the path complexity and movement speed of subsequent tasks remain unchanged.

9. The cognitive function assessment and intervention method based on spatial navigation video games according to claim 1, characterized in that, The path complexity of a task is characterized by the number of turns in the path.

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