Cognitive training method and system for Alzheimer's disease and medium
By combining evaluation, output, data collection, analysis, and adjustment modules, the problem of the single approach in traditional Alzheimer's disease training methods is solved, realizing a personalized cognitive training system and improving training effectiveness.
Patent Information
- Application Number
- CN202510969523.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-15
- Publication Date
- 2025-10-31
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional cognitive training methods for Alzheimer's disease are limited and cannot effectively train patients with different levels of illness, lacking personalization and adaptability.
A cognitive training system is provided, including an evaluation module, an output module, an acquisition module, an analysis module, and an adjustment module. The system evaluates the patient's condition level through test data, outputs appropriate training tasks, adjusts the difficulty of the training tasks based on the patient's status data, and uses heart rate, voice, eye movement, facial expression, and environmental data to determine the patient's processing ability.
This approach achieves a match between training tasks and patients' symptoms, improves training effectiveness, and adapts to the cognitive ability enhancement needs of different patients.
Smart Images
Figure CN120878083A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of rehabilitation training technology, and in particular to a cognitive training method, system and medium for Alzheimer's disease. Background Technology
[0002] Alzheimer's disease is a neurodegenerative disease with no cure, but early cognitive training can effectively slow its progression.
[0003] Traditional training methods include therapies such as mathematical calculations and color recognition. However, these methods are simplistic and easy to implement, and cannot effectively train patients with varying degrees of Alzheimer's disease.
[0004] Therefore, how to provide patients with training tasks that match their condition is a pressing technical problem that needs to be solved. Summary of the Invention
[0005] In view of the above problems, the present invention provides a cognitive training method, system and medium for Alzheimer's disease that overcomes or at least partially solves the above problems.
[0006] In a first aspect, the present invention provides a cognitive training system for Alzheimer's disease, comprising:
[0007] The assessment module is used to provide a test form to obtain the patient's test data, and to assess the patient's Alzheimer's disease level based on the test data to obtain the assessment result;
[0008] The output module is used to output a corresponding training task based on the evaluation result;
[0009] The data acquisition module is used to collect the patient's status data while performing the training task;
[0010] An analysis module is used to determine the patient's treatment capacity based on the status data;
[0011] An adjustment module is used to adjust the difficulty of the training task based on the patient's processing ability.
[0012] Preferably, the training task includes:
[0013] Memory-based tasks, spatial-language-based tasks, cognitive and thinking-based tasks, and contextual-based tasks.
[0014] Preferably, the status data includes:
[0015] Heart rate data, voice data, eye movement data, facial expression data, blood pressure data, and surrounding environment data.
[0016] Preferably, the analysis module is used for:
[0017] Based on one or more of the heart rate data, voice information, eye movement data, facial expression data, blood pressure data, and surrounding environment data, determine whether the patient's processing ability is higher than the difficulty level of the current training task.
[0018] Preferably, the analysis module is used for:
[0019] Based on the heart rate data, determine whether the heart rate data exceeds the normal range, and obtain the determination result;
[0020] Based on the aforementioned voice information, the speech rate information is determined;
[0021] Based on the eye movement data, the eye movement frequency is determined;
[0022] Based on the facial expression data and blood pressure data, the patient's mental state data is determined;
[0023] Based on the surrounding environmental data, the patient's expressive ability is determined;
[0024] Based on the judgment result, speech rate information, eye movement frequency, mental state data, and any one or more of the patient's expressive ability, determine whether the patient's processing ability is higher than the difficulty level of the current training task.
[0025] Preferably, the analysis module is used for:
[0026] A facial expression recognition model is obtained, which is trained based on historical facial expression data, corresponding historical blood pressure data, and historical patient mental state data.
[0027] The facial expression data and blood pressure data are input into the facial expression recognition model, which outputs the patient's mental state data.
[0028] Preferably, the adjustment module is used for:
[0029] If the patient's ability to handle the situation exceeds the difficulty level of the current training task, increase the difficulty of the training task.
[0030] When a patient's processing ability is lower than the difficulty level of the current training task, reduce the difficulty of the training task.
[0031] When the patient's processing ability is comparable to the difficulty level of the current training task, the difficulty of the training task should not be adjusted.
[0032] Secondly, the present invention also provides a cognitive training method for Alzheimer's disease, comprising:
[0033] Provide a test form to obtain patient test data;
[0034] Based on the test data, the severity of the patient's Alzheimer's disease is assessed, and an assessment result is obtained;
[0035] Based on the evaluation results, an appropriate training task is output;
[0036] Collect patient status data while performing the training task;
[0037] Based on the status data, the patient's treatment capacity is determined;
[0038] The difficulty level of the training tasks is adjusted based on the patient's processing ability.
[0039] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the second aspect.
[0040] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:
[0041] This invention provides a cognitive training system for Alzheimer's disease, comprising: an evaluation module for providing a test form to obtain patient test data and evaluating the patient's Alzheimer's disease level based on the test data to obtain an evaluation result; an output module for outputting an appropriate training task based on the evaluation result; a data acquisition module for acquiring patient state data during the execution of the training task; an analysis module for determining the patient's processing ability based on the state data; and an adjustment module for adjusting the difficulty of the training task based on the patient's processing ability. The system determines the patient's processing ability based on the state data during the execution of the training task and adjusts the difficulty of the training task accordingly, thereby matching the training task with the patient's condition and improving the training effect. Attached Figure Description
[0042] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:
[0043] Figure 1 A schematic diagram of the structure of a cognitive training system for Alzheimer's disease is shown in an embodiment of the present invention;
[0044] Figure 2 A schematic diagram illustrating the correspondence between heart rate data and difficulty level in an embodiment of the present invention is shown;
[0045] Figure 3A flowchart illustrating the steps of a cognitive training method for Alzheimer's disease in an embodiment of the present invention is shown. Detailed Implementation
[0046] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.
[0047] Example 1:
[0048] Embodiments of the present invention provide a cognitive training system for Alzheimer's disease, such as... Figure 1 As shown, it includes:
[0049] The assessment module 101 is used to provide a test form to obtain the patient's test data, and to assess the patient's Alzheimer's disease level based on the test data to obtain the assessment result;
[0050] Output module 102 is used to output an appropriate training task based on the evaluation results;
[0051] The acquisition module 103 is used to acquire the patient's status data when performing training tasks;
[0052] Analysis module 104 is used to determine the patient's treatment capacity based on status data;
[0053] The adjustment module 105 is used to adjust the difficulty of the training task based on the patient's processing ability.
[0054] In a specific implementation, the evaluation module 101 provides a MoCA test form to obtain the patient's initial symptom information.
[0055] The MoCA test form is the Montreal Cognitive Assessment Form, designed to more sensitively detect mild cognitive impairment (MCI) and fill the gaps in the MMSE's early cognitive function assessment. The MoCA test form aims to rapidly and comprehensively assess multiple domains of cognitive function, including attention, executive function, memory, language, visuospatial ability, abstract thinking, computation, and orientation. Through comprehensive assessment of these cognitive domains, it can detect mild cognitive abnormalities at an early stage, providing important evidence for clinical diagnosis, treatment intervention, and disease monitoring.
[0056] Therefore, by providing the MoCA test form, the patient's test data can be obtained, and the patient's Alzheimer's disease level can be judged based on the test data to obtain the judgment result.
[0057] For example, the MoCA test form has a total score of 30 points. A score of 26 or above indicates normal cognitive function; a score between 18 and 25 indicates mild cognitive impairment; a score below 17 indicates moderate cognitive impairment; and a score below 10 indicates severe cognitive impairment.
[0058] Therefore, the assessment results include: normal cognitive function, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment.
[0059] After obtaining the evaluation results, the output module 102 outputs the appropriate training task based on the evaluation results.
[0060] Specifically, when the assessment result is mild cognitive impairment, a training task appropriate to the level of mild cognitive impairment is output.
[0061] The assessment results are ranked from severe cognitive impairment to normal cognitive function, and the corresponding training tasks range from easy to difficult. Naturally, the difficulty level of the training tasks corresponding to the assessment results is precisely designed to improve the patient's cognitive abilities.
[0062] In specific implementations, the training tasks include: memory tasks, spatial language tasks, cognitive and thinking tasks, and contextual tasks.
[0063] Among them, memory-related tasks, such as memory jigsaw puzzles, involve piecing together scene fragments to reconstruct images. After completion, participants recall the names of items or related events, which strengthens short-term memory and episodic recall, thereby activating the hippocampus and frontal lobe and alleviating anxiety through nostalgia.
[0064] Spatial language tasks, such as Orientation Express, involve patients acting as train conductors, interacting with AI agents to select routes according to voice commands, recognizing relationships between seasons, time, and landmarks, and receiving real-time feedback using dynamic 3D maps. This process trains time orientation and spatial cognition, thereby enhancing self-efficacy.
[0065] Cognitive and thinking tasks, such as Produce Match: Improve the matching game, add additional tasks (such as choosing vegetables to make soup), train attention allocation, and enhance the sense of realism with tactile feedback.
[0066] Contextual tasks, such as simulating intelligent dialogue scenarios with real people, generate corresponding dialogue content for everyday situations to obtain patients' responses and thus determine their cognitive level.
[0067] For each type of training task, there are different levels of difficulty.
[0068] After obtaining an appropriate training task based on the assessment of the patient's Alzheimer's disease level, the patient's state data during the execution of the training task is collected through the acquisition module 103.
[0069] These status data specifically include: heart rate data, voice data, eye movement data, facial expression data, blood pressure data, and surrounding environment data.
[0070] Heart rate and blood pressure data can be obtained through the smart bracelet, while voice data, eye movement data, facial expression data, and surrounding environment data can all be obtained through the camera.
[0071] The video information of the patient performing training tasks is obtained through a camera, and voice data, eye movement data, facial expression data and surrounding environment data are extracted from the video information.
[0072] Next, the patient's treatment capacity is determined based on the status data through the analysis module 104.
[0073] Specifically, the analysis module 104 is used to determine whether a patient's processing ability is higher than the difficulty level of the current training task based on one or more of the following: heart rate data, voice information, eye movement data, facial expression data, blood pressure data, and surrounding environment data.
[0074] Specifically, the analysis module 104 is used for:
[0075] Based on heart rate data, determine whether the heart rate data exceeds the normal range and obtain the judgment result;
[0076] Based on voice information, determine speech rate information;
[0077] Determine eye movement frequency based on eye movement data;
[0078] Determine the patient's mental state based on facial expression and blood pressure data;
[0079] Determine the patient's expressive ability based on data from the surrounding environment;
[0080] Based on any one or more of the following factors: judgment result, speech rate information, eye movement frequency, patient's mindset, and patient's expressive ability, determine whether the patient's judgment or processing ability is higher than the difficulty level of the current training task.
[0081] For example, an excessively fast heart rate indicates a problem that is difficult for the patient to manage, meaning their current abilities are insufficient to meet the corresponding training difficulty. Figure 2 The diagram illustrates the correlation between heart rate data and difficulty level. Speaking too quickly also indicates that the patient is struggling to adapt to the current training difficulty. Similarly, high eye movement frequency indicates poor eye concentration, which is also a sign of difficulty adapting to the current training difficulty. Unnatural facial expressions also reflect this. The same applies to expressive ability.
[0082] Conversely, if the heart rate data is normal, the speech rate is normal, the eye movement frequency is normal, the facial expression is natural, and the expression is fluent, it indicates that the current training difficulty can be handled with ease.
[0083] Heart rate and eye movement data can be used to determine if abnormalities are present based on actual data values. Other state data, such as facial expression data combined with blood pressure data, can be identified using appropriate facial expression recognition models. Expressive ability can be identified using appropriate language recognition models.
[0084] Specifically, the analysis module 104 is used for:
[0085] A facial expression recognition model was obtained, which was trained based on historical facial expression data, corresponding historical blood pressure data, and historical patient mental state data.
[0086] Input facial expression data and blood pressure data into the facial expression recognition model, and output the patient's mental state data.
[0087] Therefore, the collected patient facial expression data and blood pressure data are input into the facial expression recognition model to obtain the patient's mental state data.
[0088] This analysis module 104 is also used for:
[0089] A language situation recognition model was obtained, which was trained based on historical interactive speech data and the historical expressive abilities of historical patients.
[0090] The surrounding environment data is input into the facial expression recognition model to output the patient's expressive ability.
[0091] Specifically, the result of expressive ability is a score, and different patients have corresponding expressive ability scores for different levels of illness.
[0092] After obtaining the above-mentioned status data, the number of abnormalities and their corresponding degrees are analyzed and judged. If the number of abnormal states is large and the corresponding degree of abnormality is high, it is determined that the patient's coping ability is low, that is, the corresponding state is difficult to handle; if the number of abnormal states is small and the corresponding degree of abnormality is low, it is determined that the patient's coping ability is adequate; if the number of abnormal states is large and the corresponding degree of abnormality is low, or if the number of abnormal states is small and the corresponding degree of abnormality is high, it is determined that the patient's coping ability is adaptive training, etc.
[0093] The patient's coping ability is thus determined into three categories: difficult to manage, manageable, and adaptable to training. No adjustment to the training task is needed for the adaptable training category, while adjustments are required for the difficult to manage and manageable categories.
[0094] Therefore, the adjustment module 105 is used to adjust the difficulty of the training task based on the patient's processing ability.
[0095] Specifically, adjustment module 105 is used for:
[0096] If the patient's ability to handle the situation exceeds the difficulty level of the current training task, increase the difficulty of the training task.
[0097] When a patient's processing ability is lower than the difficulty level of the current training task, reduce the difficulty of the training task.
[0098] When the patient's processing ability is comparable to the difficulty level of the current training task, the difficulty of the training task should not be adjusted.
[0099] Each type of training task has a corresponding difficulty level. For example, the difficulty levels are divided into beginner, intermediate, and advanced levels.
[0100] Initially, training tasks are typically recommended at an intermediate level of difficulty. After assessing the patient's processing ability, the difficulty level can be adjusted accordingly. If the patient's processing ability exceeds the difficulty level of the training task, the difficulty should be increased to an advanced level. The primary goal of this advanced level is to slow functional decline and maintain basic interactive abilities. This can be achieved through multi-sensory stimulation (combining auditory and color vision) and repetitive positive feedback (such as success cues and visual rewards).
[0101] When a patient's processing ability falls below the difficulty level of the training task, the difficulty of the training task is reduced, i.e., adjusted to a beginner level. The main goal of this beginner level is to relax the mind and consolidate basic cognitive functions. Specifically, training is achieved through single-sensory stimulation (such as visual or auditory stimulation), simple memory tasks (such as card matching, number repetition, etc.), and familiar life scenarios (such as memorizing shopping lists, classifying ingredients, etc.).
[0102] When the patient's processing ability is comparable to the difficulty level of the current training task, the difficulty of the training task will not be adjusted. For this intermediate level of difficulty, the main goal is to strengthen multi-sensory coordination and logical thinking. Specifically, this is achieved through multi-task processing (such as calculating while listening to numbers), short-term memory challenges (such as repeating 5-7 digit numbers), and combining it with hobbies (such as mahjong puzzles or fishing games).
[0103] For scenario-based tasks, such as collecting content from conversations between patients and robots, like a patient wearing smart glasses recording a breakfast scene, the system can automatically extract key objects from the image, such as tableware and other food items. At certain intervals, the robot asks, "Did you have eggs or buns for breakfast?" Then, based on the accuracy of the patient's answer, the system adjusts the level of detail in subsequent questions, such as whether the eggs were fried or boiled.
[0104] Therefore, by adjusting the difficulty of the training tasks, the training tasks can be made more suitable for the patient's coping ability, thereby effectively improving the training effect.
[0105] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:
[0106] This invention provides a cognitive training system for Alzheimer's disease, comprising: an evaluation module for providing a test form to obtain patient test data and evaluating the patient's Alzheimer's disease level based on the test data to obtain an evaluation result; an output module for outputting an appropriate training task based on the evaluation result; a data acquisition module for acquiring patient state data during the execution of the training task; an analysis module for determining the patient's processing ability based on the state data; and an adjustment module for adjusting the difficulty of the training task based on the patient's processing ability. The system determines the patient's processing ability based on the state data during the execution of the training task and adjusts the difficulty of the training task accordingly, thereby matching the training task with the patient's condition and improving the training effect.
[0107] Example 2
[0108] Based on the same inventive concept, this invention also provides a cognitive training method for Alzheimer's disease, such as... Figure 3As shown, it includes:
[0109] S301 provides a test form to obtain patient test data;
[0110] S302, Based on the test data, the patient's Alzheimer's disease level is assessed to obtain the assessment result;
[0111] S303, Based on the evaluation results, output the appropriate training task;
[0112] S304, Collect patient status data while performing the training task;
[0113] S305, Based on the status data, determine the patient's treatment capacity;
[0114] S306, Adjust the difficulty of the training task based on the patient's processing ability.
[0115] In one optional implementation, the training task includes:
[0116] Memory-based tasks, spatial-language-based tasks, cognitive and thinking-based tasks, and contextual-based tasks.
[0117] In one optional implementation, the status data includes:
[0118] Heart rate data, voice data, eye movement data, facial expression data, blood pressure data, and surrounding environment data.
[0119] In one alternative implementation, S305 includes:
[0120] Based on one or more of the heart rate data, voice information, eye movement data, facial expression data, blood pressure data, and surrounding environment data, determine whether the patient's processing ability is higher than the difficulty level of the current training task.
[0121] In one alternative implementation, S305 includes:
[0122] Based on the heart rate data, determine whether the heart rate data exceeds the normal range, and obtain the determination result;
[0123] Based on the aforementioned voice information, the speech rate information is determined;
[0124] Based on the eye movement data, the eye movement frequency is determined;
[0125] Based on the facial expression data and blood pressure data, the patient's mental state data is determined;
[0126] Based on the surrounding environmental data, the patient's expressive ability is determined;
[0127] Based on the judgment result, speech rate information, eye movement frequency, mental state data, and any one or more of the patient's expressive ability, determine whether the patient's processing ability is higher than the difficulty level of the current training task.
[0128] In one optional implementation, based on the facial expression data and blood pressure data, determining the patient's mental state data includes:
[0129] A facial expression recognition model is obtained, which is trained based on historical facial expression data, corresponding historical blood pressure data, and historical patient mental state data.
[0130] The facial expression data and blood pressure data are input into the facial expression recognition model, which outputs the patient's mental state data.
[0131] In one alternative implementation, S306 includes:
[0132] If the patient's ability to handle the situation exceeds the difficulty level of the current training task, increase the difficulty of the training task.
[0133] When a patient's processing ability is lower than the difficulty level of the current training task, reduce the difficulty of the training task.
[0134] When the patient's processing ability is comparable to the difficulty level of the current training task, the difficulty of the training task should not be adjusted.
[0135] Example 3:
[0136] Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described cognitive training method for Alzheimer's disease.
[0137] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages is for the purpose of disclosing the best mode of implementation of the invention.
[0138] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.
[0139] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are explicitly recited in each embodiment. Rather, as reflected in each embodiment, inventive aspects lie in fewer than all features of the single foregoing disclosed embodiment. Therefore, the claims, following the detailed description, are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.
[0140] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.
[0141] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the specific implementation, any of the claimed embodiments can be used in any combination.
[0142] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in a cognitive training system for Alzheimer's disease according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
[0143] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.
Claims
1. A cognitive training system for Alzheimer's disease, characterized in that, include: The assessment module is used to provide a test form to obtain the patient's test data, and to assess the patient's Alzheimer's disease level based on the test data to obtain the assessment result; The output module is used to output a corresponding training task based on the evaluation result; The data acquisition module is used to collect the patient's status data while performing the training task; An analysis module is used to determine the patient's treatment capacity based on the status data; An adjustment module is used to adjust the difficulty of the training task based on the patient's processing ability.
2. The system as described in claim 1, characterized in that, The training tasks include: Memory-based tasks, spatial-language-based tasks, cognitive and thinking-based tasks, and contextual-based tasks.
3. The system as described in claim 1, characterized in that, The status data includes: Heart rate data, voice data, eye movement data, facial expression data, blood pressure data, and surrounding environment data.
4. The system as described in claim 3, characterized in that, The analysis module is used for: Based on one or more of the heart rate data, voice information, eye movement data, facial expression data, blood pressure data, and surrounding environment data, determine whether the patient's processing ability is higher than the difficulty level of the current training task.
5. The system as described in claim 4, characterized in that, The analysis module is used for: Based on the heart rate data, determine whether the heart rate data exceeds the normal range, and obtain the determination result; Based on the aforementioned voice information, the speech rate information is determined; Based on the eye movement data, the eye movement frequency is determined; Based on the facial expression data and blood pressure data, the patient's mental state data is determined; Based on the surrounding environmental data, the patient's expressive ability is determined; Based on the judgment result, speech rate information, eye movement frequency, mental state data, and any one or more of the patient's expressive ability, determine whether the patient's processing ability is higher than the difficulty level of the current training task.
6. The system as described in claim 5, characterized in that, The analysis module is used for: A facial expression recognition model is obtained, which is trained based on historical facial expression data, corresponding historical blood pressure data, and historical patient mental state data. The facial expression data and blood pressure data are input into the facial expression recognition model, which outputs the patient's mental state data.
7. The system as described in claim 4, characterized in that, The adjustment module is used for: If the patient's ability to handle the situation exceeds the difficulty level of the current training task, increase the difficulty of the training task. When a patient's processing ability is lower than the difficulty level of the current training task, reduce the difficulty of the training task. When the patient's processing ability is comparable to the difficulty level of the current training task, the difficulty of the training task should not be adjusted.
8. A cognitive training method for Alzheimer's disease, characterized in that, include: Provide a test form to obtain patient test data; Based on the test data, the severity of the patient's Alzheimer's disease is assessed, and an assessment result is obtained; Based on the evaluation results, an appropriate training task is output; Collect patient status data while performing the training task; Based on the status data, the patient's treatment capacity is determined; The difficulty level of the training tasks is adjusted based on the patient's processing ability.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in claim 8.