Cognitive disorder assessment system and method based on virtual reality

Abstract

The invention discloses a cognitive impairment assessment system and method based on virtual reality, and relates to the technical field of virtual reality, and the method comprises the steps: constructing a plurality of space navigation tasks with different complexities in a preset virtual environment, collecting paths selected by a patient in the process of executing the space navigation tasks and related behavior data, analyzing the behavior data to identify a spatial navigation mode and deviation characteristics of the patient, and regulating and controlling path selection parameters of the patient in a subsequent task based on an identification result to improve evaluation precision; according to the cognitive impairment assessment system and method based on virtual reality, targeted diagnosis suggestions are provided for medical personnel, the assessment efficiency and clinical value are improved, the assessment requirements of different patients and different cognitive impairment types can be met, and the application range and flexibility of the system are expanded.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and particularly to a cognitive impairment assessment system and method based on virtual reality. Background Art

[0002] In recent years, with the development of virtual reality (VR) technology, cognitive assessment methods based on virtual reality have gradually attracted attention. Virtual reality technology can create an immersive three-dimensional virtual environment, enabling patients to perform spatial navigation tasks in a safe and controllable environment, thereby providing a more accurate assessment of cognitive impairment. Through virtual reality technology, diverse spatial scenarios can be simulated to evaluate a patient's path selection ability, spatial memory, and cognitive flexibility in different scenarios.

[0003] Although the application prospect of virtual reality in cognitive impairment assessment is broad, current research still faces some challenges. Especially in spatial navigation assessment, how to ensure the accuracy and sensitivity of the assessment through fine task design and path selection regulation. Existing virtual tasks usually lack effective regulation of patients' path selection, which may lead to deviations in the patient assessment process. For example, patients may deviate from the predetermined path due to simplicity of operation or task design problems, or rely too much on certain fixed path selection strategies, and such assessment results may not accurately reflect their actual spatial navigation ability. Summary of the Invention

[0004] The purpose of the present invention is to provide a cognitive impairment assessment system and method based on virtual reality to regulate the path selection of patients in virtual tasks to solve the problem of inaccurate assessment of spatial navigation ability.

[0005] To achieve the above object, the present invention provides the following technical solution: A cognitive impairment assessment system based on virtual reality, comprising: A virtual environment construction module for constructing multiple spatial navigation tasks with different complexities in a preset virtual environment; A data acquisition module for collecting the paths selected by the patient during the execution of the above spatial navigation tasks and their related behavioral data; A data analysis module for analyzing the collected behavioral data and identifying the patient's spatial navigation pattern and its deviation characteristics, including calculating the deviation of the patient's path length; Constructing a navigation feature matrix; Based on a clustering algorithm, identifying common navigation pattern deviations; A path regulation module for regulating the path selection parameters of the patient in subsequent tasks based on the identification results, thereby improving the accuracy of the assessment, including dynamically adjusting the path complexity based on the patient's navigation deviation; Applying the adjustment result to the virtual environment to update the task; A display output module, which is used to present a virtual environment and task results to a patient and provide real-time feedback on the patient's performance; An evaluation feedback module, which is used to generate a cognitive impairment evaluation report according to the analysis results and provide diagnostic suggestions.

[0006] Preferably, the virtual environment construction module constructs multiple spatial navigation tasks with different complexities in a preset virtual environment, including: Generate a basic three-dimensional scene, including random distributions of obstacles and target points; By adjusting the number of obstacles, the distance of target points, and the complexity of path forks, construct a task complexity parameter, and the specific formula is: d = p×a + q×b + r×c; Where, d represents the task complexity, p, q, and r represent weight factors, a represents the number of obstacles, b represents the distance of target points, and c represents the complexity of path forks.

[0007] Preferably, the data acquisition module acquires the paths selected by the patient and their related behavior data during the execution of the above spatial navigation tasks, including: Record the position trajectory of the patient in the virtual environment in real time, specifically: T(t) = (x, y); Where, T(t) represents the set of coordinates of the patient changing with time during the task execution, t represents time, x represents the abscissa of the patient in the two-dimensional plane of the virtual environment, and y represents the ordinate of the patient in the two-dimensional plane of the virtual environment; Record path selection behaviors, including turning, pausing, and backing; Calculate behavior characteristics, including average speed, path length, and pause duration, and the specific formula is: v = s / u; Where, v represents the average speed, s represents the total moving distance, and u represents the total time.

[0008] Preferably, the display output module presents a virtual environment and task results to the patient and provides real-time feedback on the patient's performance, including rendering the three-dimensional scene and dynamic task elements in the virtual environment, presenting the patient's current position, path trajectory, and completion status in an intuitive graphical interface in real time, and providing behavior prompts in real time in the form of visual, auditory, or tactile feedback according to the patient's task performance, including completion progress, deviation warning, and task goal reminder.

[0009] Preferably, the evaluation feedback module generates a cognitive impairment evaluation report based on the analysis results and provides diagnostic suggestions, including summarizing the patient's behavioral data, including path selection, task completion time, and navigation deviation, classifying and analyzing the patient's navigation ability, generating a detailed evaluation report, including the patient's spatial memory ability, cognitive flexibility, and behavioral deviation, and providing personalized diagnostic suggestions based on the evaluation results, including possible types of cognitive impairment, severity, and targeted training or treatment suggestions.

[0010] Preferably, the virtual environment construction module constructs multiple spatial navigation tasks with different complexities in a preset virtual environment. It also includes generating a basic three-dimensional scene, including obstacles, target points, and navigation paths, creating different navigation tasks by adjusting the obstacle distribution, target point position, and path bifurcation complexity, setting execution goals and completion conditions for each navigation task, and providing task diversity and adaptability.

[0011] Preferably, the data acquisition module collects the paths selected by the patient during the execution of the above spatial navigation tasks and their related behavioral data, including recording the patient's position trajectory in the virtual environment, including spatial coordinates at each time point, collecting the patient's path selection behavioral data, including turning, pausing, and backing operations, and recording the time information, path information, and behavioral characteristics during the task execution for subsequent analysis.

[0012] Preferably, the data analysis module analyzes the collected behavioral data and identifies the patient's spatial navigation pattern and its deviation characteristics. It also includes sorting and classifying the patient's behavioral data, extracting navigation pattern characteristics, identifying navigation deviations and key behavioral characteristics by comparing the actual path with the preset path, and summarizing the common deviation characteristics of the patient in the navigation task, including path selection strategies and task execution performance.

[0013] Preferably, the path regulation module regulates the path selection parameters of the patient in subsequent tasks based on the recognition results, thereby improving the accuracy of the evaluation. It includes determining the patient's performance and ability level in the navigation task according to the analysis results, adjusting the path complexity or target setting of subsequent tasks based on the patient's actual navigation deviation, and dynamically updating the task parameters in the virtual environment to make it more suitable for the patient's actual ability and improve the accuracy of the evaluation.

[0014] A method for evaluating cognitive impairment based on virtual reality, using the above-mentioned virtual reality-based cognitive impairment evaluation system, the method includes: S1: Construct multiple spatial navigation tasks with different complexities in a preset virtual environment; S2: Collect the paths selected by the patient during the execution of the above spatial navigation tasks and their related behavioral data; S3: Analyze the behavioral data to identify the patient's spatial navigation patterns and deviation characteristics; S4: Based on the identification results, regulate the path selection parameters of the patient in subsequent tasks to improve the evaluation accuracy.

[0015] As can be seen from the above technical solutions, the present invention has the following beneficial effects: The virtual reality-based cognitive impairment assessment system constructs multiple spatial navigation tasks with different complexities in a preset virtual environment through a virtual environment construction module. The data acquisition module collects the paths selected by the patient and their related behavioral data during the execution of the above spatial navigation tasks. The data analysis module analyzes the collected behavioral data and identifies the patient's spatial navigation patterns and their deviation characteristics. The path regulation module regulates the path selection parameters of the patient in subsequent tasks based on the identification results, thereby improving the evaluation accuracy. The display output module presents the virtual environment and task results to the patient and provides real-time feedback on the patient's performance. The evaluation feedback module generates a cognitive impairment assessment report based on the analysis results and provides diagnostic suggestions, which can achieve personalized assessment for different patients' cognitive abilities, effectively avoid evaluation biases caused by insufficient task design, dynamically optimize the subsequent task path design by real-time analyzing the patient's path selection behavior and navigation deviation, ensure that the evaluation results are closer to the patient's actual ability level, can evaluate the patient's path selection ability, spatial memory and cognitive flexibility in different scenarios under safe and controllable conditions, comprehensively reflect the characteristics of the patient's cognitive impairment, ensure the accuracy of the evaluation results, and are not interfered by the patient's habitual behavior. By providing real-time feedback on the task performance to the patient through the display output module, and at the same time, the evaluation feedback module generates a detailed cognitive impairment assessment report based on the analysis results, providing targeted diagnostic suggestions for medical staff, improving the evaluation efficiency and clinical value, and can meet the evaluation needs of different patients and different types of cognitive impairment, expanding the scope of application and flexibility of the system. Brief Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the module connection of the system of the present invention; Figure 2 It is a flowchart of the method of the present invention. Detailed Embodiments

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] Such as Figure 1As shown in the figure, the present invention provides a technical solution: a cognitive impairment assessment system based on virtual reality, including: A virtual environment construction module, configured to construct multiple spatial navigation tasks with different complexities in a preset virtual environment; A data acquisition module, configured to acquire the paths selected by the patient during the execution of the above spatial navigation tasks and their related behavior data; A data analysis module, configured to analyze the acquired behavior data and identify the patient's spatial navigation pattern and its deviation characteristics, including calculating the patient's path length deviation, and the specific formula is: e = (l - m) / m × 100%; Wherein, e represents the patient's navigation deviation, l represents the path length, and m represents the preset optimal path length; Construct a navigation feature matrix, specifically: M = [v, t, e]; Wherein, M represents the patient's navigation feature, V represents the average speed, t represents the time, and e represents the patient's navigation deviation; Based on the clustering algorithm, identify common navigation pattern deviations; A path regulation module, configured to regulate the path selection parameters of the patient in subsequent tasks based on the recognition result, so as to improve the accuracy of the assessment, including dynamically adjusting the path complexity based on the patient's navigation deviation, and the specific formula is: c 1 = c × (1 - k × e); Wherein, c 1 represents the adjusted path complexity, k represents the regulation coefficient, c represents the original path complexity, and e represents the patient's navigation deviation; Apply the adjustment result to the virtual environment to update the task; A display output module, configured to present the virtual environment and task results to the patient and provide real-time feedback on the patient's performance; An assessment feedback module, configured to generate a cognitive impairment assessment report based on the analysis result and provide diagnostic suggestions.

[0019] This system creates multiple spatial navigation tasks with different complexities through the virtual environment construction module, uses the data acquisition module to record the path selection and behavior data of the patient in the navigation task, and then quantifies the patient's navigation behavior characteristics through the data analysis module, including the path length deviation calculation formula e = (l - m) / m × 100% and the navigation feature matrix M = [v, t, e], and identifies common navigation pattern deviations through the clustering algorithm. The path adjustment module calculates the adjusted path complexity c 1= c×(1 - k×e), dynamically optimize the path settings of subsequent tasks to ensure that the task experience of the patient in the virtual environment adapts to their ability level. Finally, the system visually presents the navigation results to the patient through the display output module, and the evaluation feedback module generates a detailed cognitive impairment evaluation report to provide a basis for subsequent clinical diagnosis and treatment plans. This system uses virtual reality technology to provide a highly simulated and controllable evaluation environment, enabling the characteristics of cognitive impairment to be accurately quantified in a realistic scenario. Through the navigation feature matrix and path length deviation formula constructed by the data analysis module, it provides a scientific basis for identifying the subtle behavioral deviations of patients. At the same time, combined with the dynamic optimization function of the path adjustment module, it effectively improves the adaptability and accuracy of the evaluation task, avoiding the phenomenon of task settings not matching the patient's abilities. The finally generated evaluation report synthesizes path data and behavioral data, providing comprehensive and quantitative diagnostic references for clinicians, significantly improving the scientific nature and efficiency of cognitive impairment diagnosis. Usually, machine learning algorithms (such as clustering, regression analysis, neural network, etc.) or expert systems are used to train and model a large amount of patient data, so as to correspond a suitable regulation coefficient k to different navigation deviation levels.

[0020] The virtual environment construction module constructs multiple spatial navigation tasks with different complexities in the preset virtual environment, including: Generate a basic three-dimensional scene, including the random distribution of obstacles and target points; By adjusting the number of obstacles, the distance of target points, and the complexity of path forks, construct a task complexity parameter, and the specific formula is: d = p×a + q×b + r×c; Among them, d represents the task complexity, p, q, and r represent weight factors, a represents the number of obstacles, b represents the distance of target points, and c represents the complexity of path forks.

[0021] This implementation mode generates a basic three-dimensional scene through a virtual environment construction module, where obstacles and target points are constructed in a random distribution manner to simulate a complex navigation environment. By setting key parameters that affect navigation difficulty, such as the number of obstacles, the distance to the target point, and the complexity of path bifurcation, the system quantifies the task complexity using the formula d = p×a + q×b + r×c, where the weight factors p, q, and r can be adjusted according to specific evaluation requirements to flexibly control the influence degree of each parameter on the task complexity. This method can precisely adjust the task difficulty to match the patient's navigation ability, thereby realizing personalized assessment of cognitive impairment. Through the random distribution design of the basic three-dimensional scene, this implementation mode makes the virtual environment highly adjustable and realistic, and can comprehensively simulate the complexity in the real navigation environment. The quantitative adjustment of the task complexity is achieved through the formula d = p×a + q×b + r×c, effectively enhancing the flexibility and scientific nature of the assessment task. In addition, by adjusting the weight factors p, q, and r, it is possible to adapt to the assessment needs of different types of patients, optimize the accuracy and applicability of the assessment process, and provide a more comprehensive and personalized solution for cognitive impairment assessment.

[0022] The data acquisition module collects the paths selected by the patient during the execution of the above spatial navigation task and its related behavioral data, including: Record the position trajectory of the patient in the virtual environment in real time, specifically: T(t) = (x, y); Among them, T(t) represents the set of coordinates of the patient changing with time during the task execution, t represents time, x represents the abscissa of the patient in the two-dimensional plane of the virtual environment, and y represents the ordinate of the patient in the two-dimensional plane of the virtual environment; Record path selection behaviors, including turning, pausing, and backing; Calculate behavioral characteristics, including average speed, path length, and pause duration. The specific formulas are: v = s / u; Among them, v represents the average speed, s represents the total moving distance, and u represents the total time.

[0023] This embodiment obtains the position trajectory of the patient in the virtual environment in real time through the data acquisition module, uses the coordinate set T(t) = (x, y) to represent the movement path of the patient over time, and comprehensively captures the behavior patterns of the patient during the execution of the navigation task by recording behavioral events such as turning, pausing, and backing. Behavioral characteristics are calculated based on the path trajectory, including key indicators such as average speed v = s / u, path length, and pause duration, quantitatively analyzing the patient's navigation ability and deviation characteristics from multiple perspectives, and providing a detailed data basis for subsequent analysis and evaluation. This embodiment can comprehensively and meticulously reflect the behavioral characteristics of the patient in the navigation task by recording the position trajectory and path selection behavior of the patient in the virtual environment. Through the quantitative analysis of behavioral characteristics, such as average speed, path length, and pause duration, scientific parametric data are provided, providing an objective basis for the assessment of cognitive impairment. In addition, this method can identify behavioral abnormalities of the patient during the task, such as frequent pauses or path deviations, providing auxiliary support for accurate diagnosis and effectively improving the accuracy and reliability of the assessment.

[0024] The display output module presents the virtual environment and task results to the patient and provides real-time feedback on the patient's performance, including rendering the three-dimensional scene and dynamic task elements in the virtual environment, presenting the patient's current position, path trajectory, and completion status in real time with an intuitive graphical interface, and providing behavioral cues in real time in the form of visual, auditory, or tactile feedback according to the patient's task performance, including completion progress, deviation warnings, and task objective reminders. This embodiment renders the three-dimensional scene and dynamic task elements in the virtual environment through the display output module, constructs an intuitive graphical user interface, and presents the patient's current position, path trajectory, and task completion status in real time, enabling the patient to clearly understand their position in the virtual environment and the task completion status. The display output module generates feedback information in real time according to the patient's task performance and transmits it to the patient in a visual, auditory, or tactile manner. For example, the completion progress bar is displayed through graphical cues, deviation warnings are issued through audio cues, or task objective reminders are given through vibration cues. This multi-dimensional feedback method ensures that the patient can timely understand the deviation of their own behavior or the task completion situation, thereby guiding them to adjust their behavior and complete the task. This embodiment constructs an intuitive graphical interface through three-dimensional scene rendering and dynamic task elements, enabling the patient to understand the progress of the navigation task in real time, improving the immersion of the virtual reality environment and the intuitiveness of task feedback. Through diverse feedback forms of vision, audition, or touch, it can flexibly adapt to the perception abilities of different patients, provide immediate and effective behavioral guidance for the patient, help correct deviant behaviors, and optimize the task completion efficiency. At the same time, this method also enhances the interactivity between the patient and the system, providing a basis for the real-time monitoring and dynamic adjustment of the cognitive impairment assessment task.

[0025] The evaluation feedback module generates a cognitive impairment evaluation report based on the analysis results and provides diagnostic suggestions, including summarizing the patient's behavioral data, such as path selection, task completion time, and navigation deviation, classifying and analyzing the patient's navigation ability, generating a detailed evaluation report, including the patient's spatial memory ability, cognitive flexibility, and behavioral deviation, and providing personalized diagnostic suggestions based on the evaluation results, including possible types of cognitive impairment, severity, and targeted training or treatment suggestions. This implementation method summarizes and analyzes the patient's behavioral data in the virtual reality task through the evaluation feedback module, including key indicators such as path selection, task completion time, and navigation deviation. The system classifies the patient's navigation ability based on the analysis results, such as evaluating their spatial memory ability, cognitive flexibility, and behavioral deviation, and summarizes these classification results into a detailed evaluation report. The evaluation report is presented in the form of data charts and written descriptions, clearly describing the cognitive ability characteristics and deviation situations of the patient. At the same time, the evaluation feedback module combines the patient's specific performance and analysis results to generate personalized diagnostic suggestions, such as possible types of cognitive impairment (such as mild cognitive impairment or early Alzheimer's disease), severity assessment, and targeted training or treatment plan suggestions, providing a scientific basis for clinicians. This implementation method quantifies the patient's behavioral characteristics and converts them into a detailed evaluation report through systematic data summarization and classification analysis, which helps to comprehensively display the cognitive ability characteristics and potential types of disorders of the patient. The generation of personalized diagnostic suggestions provides a targeted training or treatment plan for the patient, which can effectively improve the efficiency and effect of subsequent interventions. In addition, the evaluation report is presented in a data visualization manner, which is convenient for doctors to quickly understand the patient's situation and improves the scientificity, accuracy, and clinical applicability of cognitive impairment evaluation.

[0026] The virtual environment construction module constructs multiple spatial navigation tasks with different complexities in a preset virtual environment. It also includes generating a basic 3D scene, which contains obstacles, target points, and navigation paths. By adjusting the distribution of obstacles, the positions of target points, and the complexity of path bifurcations, different navigation tasks are created. Execution goals and completion conditions are set for each navigation task, providing task diversity and adaptability. In this implementation, the virtual environment construction module generates a basic 3D scene, including randomly distributed obstacles, clearly marked target points, and multiple optional navigation paths, simulating navigation tasks in the real world. By adjusting the number, distribution density of obstacles, the positions of target points, and the complexity of path bifurcations, the system can create various navigation tasks with different difficulties to meet the assessment needs of different patients' cognitive abilities. Each navigation task is assigned a clear execution goal (such as reaching a specified target point) and completion conditions (such as completing path selection within a specified time) to ensure that the task has clear guidance and assessment criteria. Through the above design, this implementation achieves the diversity of task scenarios and dynamically adapts the task complexity according to the patient's performance, optimizing the adaptability and flexibility of the system. This implementation provides a highly customized assessment environment for patients by generating diverse 3D scenes and adjusting the complexity of navigation tasks. The task diversity can cover the assessment needs of different types of cognitive abilities (such as spatial memory, problem-solving ability, and path planning ability), while the dynamic adjustment mechanism of task complexity ensures that patients can complete the assessment under conditions suitable for their cognitive level. This design enhances the flexibility and accuracy of the system for cognitive impairment assessment and provides reliable data support for subsequent diagnosis and treatment recommendations.

[0027] The data acquisition module collects the paths selected by the patient during the execution of the above spatial navigation task and their related behavioral data, including recording the patient's position trajectory in the virtual environment, including the spatial coordinates at each time point, collecting the patient's path selection behavioral data, including turning, pausing, and backing operations, and recording the time information, path information, and behavioral characteristics during the task execution for subsequent analysis. This implementation comprehensively records the patient's navigation behavior in the virtual environment through the data acquisition module. The module records the patient's position information in real time, including the three-dimensional spatial coordinates at each time point, for generating a complete navigation trajectory. It collects the patient's path selection behavioral data, detailedly records key operations such as turning, pausing, and backing during the task execution, and comprehensively captures the behavioral characteristics. At the same time, the system also synchronously records the time information of the task execution (such as the total duration and stage-by-stage time), path information (such as the length and deviation of the selected path), and behavioral characteristics (such as the number of pauses and turning frequency). These data provide a basis for the subsequent analysis module and support the system's comprehensive quantification and classification assessment of the patient's navigation ability. This implementation can comprehensively reflect the patient's navigation ability and cognitive characteristics by detailedly recording the position trajectory and behavioral characteristics of the patient in the virtual environment. The position trajectory data can be used to accurately analyze the patient's navigation path and task completion mode, while the behavioral characteristics (such as turning, pausing, and backing) can reveal potential manifestations of cognitive impairment, such as spatial memory defects or task execution strategy problems. The collected time information and path information provide quantitative indicators for the patient's navigation efficiency and accuracy, and can support the design of subsequent personalized diagnosis and intervention programs. This comprehensive and accurate data acquisition method significantly improves the scientificity and reliability of cognitive impairment assessment.

[0028] The data analysis module analyzes the collected behavioral data, identifies the patient's spatial navigation patterns and their deviation characteristics, and also includes sorting and classifying the patient's behavioral data, extracting navigation pattern features, identifying navigation deviations and key behavioral characteristics by comparing the actual path with the preset path, and summarizing the common deviation characteristics of the patient in the navigation task, including path selection strategies and task execution performance. This implementation method systematically sorts and classifies the collected patient behavioral data through the data analysis module, including key data such as path trajectories, navigation behaviors, and time information. The module first extracts the path selection features and navigation patterns of the patient in the navigation task, calculates the navigation deviation and identifies key behavioral characteristics by comparing with the preset optimal path, such as behaviors like excessive turning, frequent pauses, or deviating from the target point. At the same time, the module summarizes the patient's performance in the task, generalizes their common path selection strategies (such as relying on straight-line selection or repeatedly adjusting directions) and task execution characteristics (such as overly conservative or adventurous behavioral tendencies). These analysis results provide data support for subsequent personalized evaluation and intervention suggestions. Through the systematic analysis of the patient's navigation pattern, this implementation method can comprehensively reveal the characteristics and deviations of their spatial navigation ability. The comparative analysis of the actual path and the preset path provides a quantitative navigation deviation index, enabling the objective description of the patient's cognitive performance. The extraction of key behavioral characteristics, such as path selection strategies and task performance, provides strong support for the accurate assessment and diagnosis of cognitive impairment. The summarized common deviation characteristics can quickly locate the patient's main problem areas, providing a scientific basis for the design of subsequent intervention training or treatment plans, thus significantly improving the efficiency and effectiveness of cognitive impairment assessment.

[0029] The path regulation module regulates the path selection parameters of the patient in subsequent tasks based on the recognition results, so as to improve the accuracy of the evaluation, including determining the performance and ability level of the patient in the navigation task according to the analysis results, adjusting the path complexity or target setting of the subsequent task based on the actual navigation deviation of the patient, and dynamically updating the task parameters in the virtual environment to make it more suitable for the actual ability of the patient and improve the accuracy of the evaluation. In this implementation, through the path regulation module, according to the recognition results of the data analysis module, the performance and ability level of the patient in the navigation task are quantified. For example, indicators such as the patient's navigation deviation, path selection strategy, and task completion efficiency are calculated. Based on this, the module dynamically adjusts the parameters of the subsequent navigation task, including path complexity (such as the number of forks and obstacle density) and target setting (such as the position or number of target points), to ensure that the task difficulty can match the actual ability level of the patient. By updating the task parameters in the virtual environment, the system can provide an adaptive navigation task scenario for the patient, avoiding the task being too simple or too complex, thereby accurately evaluating the patient's cognitive ability and improving the scientific nature and operability of task completion. This implementation improves the adaptability of the task to the actual ability of the patient by dynamically adjusting the path complexity and target setting, ensuring the accuracy and reliability of the evaluation. The path regulation module adjusts according to the real-time analysis results of the patient's navigation performance, which can effectively avoid the evaluation distortion caused by the mismatch of task difficulty. At the same time, by dynamically updating the task parameters in the virtual environment, a personalized evaluation experience is provided for the patient, making the evaluation of cognitive impairment more scientific and targeted. This path regulation mechanism not only improves the effectiveness of the evaluation, but also provides a valuable reference basis for subsequent intervention and treatment design.

[0030] As Figure 2 shown, a method for evaluating cognitive impairment based on virtual reality is also provided. Using the above-mentioned cognitive impairment evaluation system based on virtual reality, the method includes: S1: Construct multiple spatial navigation tasks with different complexities in a preset virtual environment; S2: Collect the paths selected by the patient during the execution of the above spatial navigation tasks and their related behavioral data; S3: Analyze the behavioral data to identify the spatial navigation patterns and deviation characteristics of the patient; S4: Regulate the path selection parameters of the patient in subsequent tasks based on the recognition results to improve the evaluation accuracy.

[0031] This method first generates multiple spatial navigation tasks in a preset scenario through the virtual environment construction module of the virtual reality system (step S1). These tasks have different complexities, which can be achieved by adjusting parameters such as obstacle distribution, target point location, and path bifurcation design. After the patient enters the virtual environment, the data acquisition module records the path trajectory, behavioral characteristics (such as turning, pausing, and backing), and relevant time data during the navigation process in real time (step S2). Subsequently, the data analysis module analyzes the collected behavioral data, extracts the navigation pattern features and identifies spatial navigation deviations, such as path deviation degree, navigation efficiency, and behavioral abnormalities, by comparing the patient's actual path with the optimal path (step S3). Finally, the path regulation module dynamically adjusts the parameters of subsequent navigation tasks, including path complexity and target setting, according to the analysis results, to make the tasks more suitable for the patient's ability level, thereby improving the accuracy and adaptability of the assessment (step S4). By combining virtual reality technology and precise data acquisition and analysis, this method can provide an efficient, comprehensive, and personalized solution for cognitive impairment assessment. By generating navigation tasks with different complexities in the virtual environment (step S1), the method can adapt to patients with different cognitive ability levels. The real-time recording of the data acquisition module (step S2) ensures the integrity and accuracy of the patient's behavioral data, while the precise analysis of the data analysis module (step S3) can reveal the patient's navigation pattern and potential cognitive impairment characteristics. The path regulation module dynamically adjusts the task parameters according to the analysis results (step S4) to ensure that subsequent tasks are more in line with the patient's actual ability, making the assessment results more scientific and reliable. In addition, this method can quickly identify the patient's navigation deviations and cognitive performance, providing strong data support for subsequent intervention or treatment.

[0032] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cognitive impairment assessment system based on virtual reality, characterized in that: include: A virtual environment construction module is used to construct multiple spatial navigation tasks with different complexities in a preset virtual environment; A data collection module, used to collect the path selected by the patient and related behavior data during the execution of the above-mentioned spatial navigation task; A data analysis module for analyzing the collected behavioral data and identifying the patient's spatial navigation pattern and its deviation characteristics, including calculating the patient's path length deviation; Construct navigation feature matrix; Based on clustering algorithm, identify common navigation pattern deviations; A path control module is used to control the path selection parameters of the patient in subsequent tasks based on the recognition results, thereby improving the accuracy of the assessment, including dynamically adjusting the path complexity based on the patient's navigation deviation; Apply the adjustment results to the virtual environment to update the task; A display output module is used to present the virtual environment and task results to the patient and provide real-time feedback on the patient's performance; The assessment feedback module is used to generate a cognitive impairment assessment report based on the analysis results and provide diagnostic suggestions.

2. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The virtual environment construction module constructs a plurality of spatial navigation tasks with different complexities in a preset virtual environment, including: Generate basic 3D scenes, including random distribution of obstacles and target points; By adjusting the number of obstacles, the distance to the target point, and the complexity of the path fork, the task complexity parameter is constructed. The specific formula is: d = p × a + q × b + r × c; Among them, d represents the task complexity, p, q, and r represent weight factors, a represents the number of obstacles, b represents the distance to the target point, and c represents the complexity of the path fork.

3. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The data acquisition module acquires the path selected by the patient during the spatial navigation task and its related behavior data, including: The patient's position trajectory in the virtual environment is recorded in real time, specifically: T(t) = (x, y); Where T(t) represents the set of coordinates that change with time during the task execution of the patient, t represents time, x represents the horizontal coordinate of the patient in the two-dimensional plane of the virtual environment, and y represents the vertical coordinate of the patient in the two-dimensional plane of the virtual environment; Recording path selection behavior, including turns, pauses, and backtracking; Calculate the behavior characteristics, including average speed, path length, and pause duration. The specific formula is: v = s / u; Among them, v represents the average speed, s represents the total distance moved, and u represents the total time.

4. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The display output module presents the virtual environment and task results to the patient, and provides real-time feedback on the patient's performance, including rendering three-dimensional scenes and dynamic task elements in the virtual environment, presenting the patient's current position, path trajectory and completion status in real time with an intuitive graphical interface, and providing behavioral prompts in real time in the form of visual, auditory or tactile feedback based on the patient's task performance, including completion progress, deviation warnings and task goal reminders.

5. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The evaluation feedback module generates a cognitive impairment evaluation report based on the analysis results, and provides diagnostic suggestions including summarizing the patient's behavioral data, including path selection, task completion time, and navigation deviation, classifying and analyzing the patient's navigation ability, generating a detailed evaluation report including the patient's spatial memory ability, cognitive flexibility, and behavioral deviation, and providing personalized diagnostic suggestions based on the evaluation results, including possible types of cognitive impairment, severity, and targeted training or treatment suggestions.

6. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The virtual environment construction module constructs multiple spatial navigation tasks with different complexities in a preset virtual environment, and also includes generating a basic three-dimensional scene, including obstacles, target points and navigation paths, creating different navigation tasks by adjusting the obstacle distribution, target point position and path bifurcation complexity, setting execution goals and completion conditions for each navigation task, and providing task diversity and adaptability.

7. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The data acquisition module collects the path selected by the patient and its related behavior data during the execution of the above-mentioned spatial navigation task, including recording the patient's position trajectory in the virtual environment, including the spatial coordinates of each time point, collecting the patient's path selection behavior data, including turning, pausing and reversing operations, and recording the time information, path information and behavior characteristics during the task execution process for subsequent analysis.

8. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The data analysis module analyzes the collected behavioral data and identifies the patient's spatial navigation pattern and its deviation characteristics, and also includes organizing and classifying the patient's behavioral data, extracting navigation pattern characteristics, identifying navigation deviations and key behavioral characteristics by comparing the actual path with the preset path, and summarizing the common deviation characteristics of patients in navigation tasks, including path selection strategies and task execution performance.

9. The virtual reality-based cognitive impairment assessment system according to claim 1, characterized in that: The path control module controls the path selection parameters of the patient in subsequent tasks based on the recognition results, thereby improving the accuracy of the assessment, including determining the patient's performance and ability level in the navigation task according to the analysis results, adjusting the path complexity or target setting of the subsequent task based on the patient's actual navigation deviation, and dynamically updating the task parameters in the virtual environment to make them more in line with the patient's actual ability, thereby improving the accuracy of the assessment.

10. A method for evaluating cognitive impairment based on virtual reality, using the cognitive impairment evaluation system based on virtual reality according to any one of claims 1 to 9, characterized in that: The method comprises: S1: Construct multiple spatial navigation tasks of different complexity in a preset virtual environment; S2: Collect the path selected by the patient and its related behavior data during the above-mentioned spatial navigation task; S3: analyzing the behavioral data to identify the patient's spatial navigation pattern and deviation characteristics; S4: Based on the recognition results, the path selection parameters of the patient in subsequent tasks are adjusted to improve the evaluation accuracy.

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