Cognitive competence assessment method, device, equipment, medium and product

Abstract

The invention discloses a cognitive competence assessment method and device, equipment, a medium and a product, and relates to the field of competence assessment, and the method comprises the steps: obtaining static data of a to-be-tested person through a questionnaire survey mode; obtaining kinematics characteristic data of the to-be-tested person through a motion test; and inputting the static data and the kinematic feature data into a trained machine learning prediction model, and evaluating the cognitive ability of the person to be tested. The method can reduce the influence of subjective factors on the evaluation result, enables the evaluation result to be more accurate, and is convenient, rapid and low in cost.

Description

Technical Field

[0001] The present application relates to the field of ability assessment, and in particular to a cognitive ability assessment method, device, equipment, medium and product. Background Art

[0002] Cognitive ability (cognitive abilities, cognitive ability) refers to the ability of the human brain to process, store and retrieve information, that is, people's ability to grasp the composition of things, the relationship between performance and other things, the driving force of development, the direction of development and the basic laws. It is the most important psychological condition for people to successfully complete activities. The abilities of perception, memory, attention, thinking and imagination are all considered cognitive abilities.

[0003] Existing cognitive ability assessment methods are time-consuming, costly, and require highly trained clinical staff to collect and interpret data, making them less feasible in routine clinical practice. Summary of the invention

[0004] The purpose of this application is to provide a cognitive ability assessment method, device, equipment, medium and product, which can conveniently, quickly and cost-effectively assess the cognitive ability of a subject.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for evaluating cognitive ability, comprising:

[0007] Obtain static data of the subjects through questionnaire survey;

[0008] Obtain kinematic characteristic data of the subject through motion testing;

[0009] The static data and kinematic feature data are input into a trained machine learning prediction model to evaluate the cognitive ability of the subject.

[0010] In a second aspect, the present application provides a cognitive ability assessment device, comprising:

[0011] A static data acquisition module is used to obtain the static data of the test subject through a questionnaire survey;

[0012] A kinematic feature data acquisition module is used to acquire kinematic feature data of a subject through a motion test;

[0013] The cognitive ability assessment module is used to input the static data and kinematic feature data into a trained machine learning prediction model to assess the cognitive ability of the subject.

[0014] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the cognitive ability assessment methods described above.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the cognitive ability assessment methods described above.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any one of the cognitive ability assessment methods described above.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects:

[0018] The present application provides a cognitive ability assessment method, device, equipment, medium and product, which can assess the cognitive ability of the subject by inputting the static data and motion data of the subject into a trained machine learning prediction model. The present application does not require human participation in the assessment process, thereby reducing the influence of subjective factors on the assessment results, and the assessment results are more accurate, convenient, fast and low-cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A flowchart of a cognitive ability assessment method provided in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of functional modules of a cognitive ability assessment device provided in one embodiment of the present application;

[0022] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] In an exemplary embodiment, Figure 1 As shown, a cognitive ability assessment method is provided, which is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps S1 to S3. Among them:

[0026] S1: Obtain static data of the subject through a questionnaire survey. The static data includes: the age and gender of the subject, and the cognitive ability category obtained by the clinician through the questionnaire.

[0027] S2: Obtain the kinematic characteristic data of the subject through the motion test. The kinematic characteristic data includes: the motion test time, the amplitude and mean of the joint data of each joint point of the upper and lower limbs during the motion process; the joint data includes speed, acceleration, angle, smoothness and dynamic energy.

[0028] S3: Input the static data and kinematic feature data into a trained machine learning prediction model to evaluate the cognitive ability of the subject.

[0029] In a specific embodiment, step S2 specifically includes:

[0030] S21: Obtaining the exercise test time of the subject during the exercise test.

[0031] S22: During the motion test, computer vision technology is used to collect the motion coordinates of each joint point of the upper and lower limbs of the subject.

[0032] The movement tests include the hands behind the head movement for upper limbs and the squat and sit-down movements for lower limbs and core.

[0033] In practical applications, motion data is obtained by recording a video of the subject completing the motion test (no longer than 3 minutes, with the longest time for each movement no more than one minute), and using computer vision motion skeleton analysis technology to track the motion coordinates of each joint of the subject's upper and lower limbs.

[0034] S23: Calculate the joint data of each joint point of the upper and lower limbs in each frame during the movement based on the movement coordinates, and calculate the amplitude and mean of the joint data during the entire movement process.

[0035] Joint data includes velocity, acceleration, angle, smoothness, and dynamic energy.

[0036] The speed is calculated as follows:

[0037]

[0038] The acceleration is calculated as follows:

[0039]

[0040] The angle is calculated as follows:

[0041]

[0042] The calculation formula of the smoothness is as follows:

[0043]

[0044] The calculation formula of the dynamic energy is as follows:

[0045] RMS ji =vel ji 2

[0046] The amplitude is calculated as follows:

[0047] AMP j =max(·)-min(·)

[0048] The mean calculation formula is as follows:

[0049]

[0050] Among them, t represents time, vel ji refers to the velocity of the jth joint at the i-th moment; acc ji refers to the acceleration of the jth joint at the i-th moment; x wrist and wrist Represents the coordinate of the wrist; x elbow and elbow Represent the coordinates of the elbow joint, x shoulder and shoulder Represents the coordinates of the shoulder joint, x pelvis and pelvis represents the coordinates of the hip joint, x knee and xknee Represents the coordinates of the knee joint, x ankle and ankle Represents the coordinates of the ankle; Angle elbow Represents the angle of the elbow joint; Angle shoulder Represents the angle of the shoulder joint; Angle pelvis Represents the angle of the hip joint; Angle knee Represents the angle of the knee joint; Jerk ji It refers to the smoothness of the j-th joint point at the i-th moment; AMP refers to the amplitude of the joint data during the entire movement process; · represents the joint data; Mean refers to the mean of the joint data during the entire movement process.

[0051] In a specific embodiment, the training process of the machine learning prediction model specifically includes:

[0052] (1) Obtaining sample data; the sample data includes static sample data, motion sample data and cognitive abilities of multiple users; the users include healthy users and users with cognitive dysfunction.

[0053] In actual applications, a group of healthy users and users with cognitive dysfunction who are of sufficient number and representativeness are gathered, and their static data and motion feature data are collected. The users’ cognitive abilities are obtained by clinicians through questionnaires.

[0054] (2) Selecting the sample data based on feature selection technology to construct a training data set.

[0055] In this embodiment, a training data set is selected from the sample data based on feature selection techniques such as predictor ranking, multicollinearity analysis, and sequential forward selection.

[0056] The prediction factor-based ranking is achieved by training multiple models in different parameter spaces and selecting the top 5% based on their area under the ROC curve (AUC). The final importance ranking is calculated by averaging the importance scores of the best performing models, and then these models are sorted to select the top-k features (i.e. static data and motion feature data).

[0057] Multicollinearity analysis was performed using Spearman rank correlation with hierarchical clustering to remove redundant predictor pairs. In the case of using a fixed threshold, only one predictor was retained in the cluster below the critical value.

[0058] Sequential forward selection newly developed classifiers re-rank the features in the pre-selected subset. Sequential classifiers are developed based on the updated predictor importance ranking and predictors are added sequentially.

[0059] After the above selection, features with significantly high contribution and their corresponding cognitive abilities can be obtained and used as training data sets, so that the final cognitive ability assessment results are more accurate, and the obtained machine learning prediction model is more in line with actual needs and is explainable.

[0060] (3) Training the machine learning prediction model based on the training data set.

[0061] The machine learning prediction model can use common machine learning classification models, such as linear regression, support vector machine, neural network, Gaussian process and random forest, or an integration of several of them. In this embodiment, the machine learning prediction model mainly uses LightGBM.

[0062] This application does not require human participation in the evaluation process, which reduces the impact of subjective factors on the evaluation results. The evaluation results are more accurate, convenient, fast and low-cost.

[0063] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the cognitive ability assessment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more cognitive ability assessment device embodiments provided below can refer to the limitations of the cognitive ability assessment method above, and will not be repeated here.

[0064] In an exemplary embodiment, Figure 2 As shown, a cognitive ability assessment device is provided, comprising the following modules. Among them:

[0065] The static data acquisition module 1 is used to obtain the static data of the test subject through a questionnaire survey.

[0066] The kinematic characteristic data acquisition module 2 is used to acquire the kinematic characteristic data of the subject through a motion test.

[0067] The cognitive ability assessment module 3 is used to input the static data and kinematic feature data into a trained machine learning prediction model to assess the cognitive ability of the subject.

[0068] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program. The computer device may be a server or a terminal, and its internal structure diagram may be as shown in FIG. Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for data to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for evaluating cognitive ability is implemented.

[0069] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0070] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0071] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0072] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0073] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0074] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0075] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for evaluating cognitive ability, characterized in that: include: Obtain static data of the subjects through questionnaire survey; Obtain kinematic characteristic data of the subject through motion testing; The static data and kinematic feature data are input into a trained machine learning prediction model to evaluate the cognitive ability of the subject.

2. The cognitive ability assessment method according to claim 1, characterized in that: The static data include: the age and gender of the subject to be tested.

3. The cognitive ability assessment method according to claim 1, characterized in that: The kinematic characteristic data include: the exercise test time, the amplitude and mean of the joint data of each joint point of the upper and lower limbs during the exercise process; the joint data include speed, acceleration, angle, smoothness and dynamic energy; The kinematic characteristic data of the subject is obtained through the motion test, including: Obtaining the exercise test time of the subject during the exercise test; During the motion test, computer vision technology is used to collect the motion coordinates of each joint point of the upper and lower limbs of the test subject; The joint data of each joint point of the upper and lower limbs in each frame during the movement process are calculated based on the movement coordinates, and the amplitude and mean of the joint data in the entire movement process are calculated.

4. The cognitive ability assessment method according to claim 1, characterized in that: The training process of the machine learning prediction model specifically includes: Acquire sample data; the sample data includes static sample data, motion sample data and cognitive abilities of multiple users; the users include healthy users and users with cognitive dysfunction; Selecting the sample data based on feature selection technology to construct a training data set; The machine learning prediction model is trained based on the training data set.

5. The cognitive ability assessment method according to claim 4, characterized in that: The feature selection techniques include predictor ranking methods, multicollinearity analysis methods, and sequential forward selection methods.

6. The cognitive ability assessment method according to claim 1, characterized in that: The machine learning prediction model is an integration of one or more of linear regression, support vector machine, neural network, Gaussian process and random forest.

7. A cognitive ability assessment device, characterized in that: include: A static data acquisition module is used to obtain the static data of the test subject through a questionnaire survey; A kinematic feature data acquisition module is used to acquire kinematic feature data of a subject through a motion test; The cognitive ability assessment module is used to input the static data and kinematic feature data into a trained machine learning prediction model to assess the cognitive ability of the subject.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cognitive ability assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the cognitive ability assessment method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the cognitive ability assessment method according to any one of claims 1 to 6 is implemented.

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