Cognitive function evaluation method and device, robot and program product

Abstract

The invention relates to the field of cognitive function evaluation, in particular to a cognitive function evaluation method, device, equipment and program product. The method comprises the following steps: obtaining individual information of a subject, and determining a difficulty score of the subject according to the individual information of the subject; determining a question corresponding to the difficulty score of the subject according to a preset corresponding relationship between the difficulty score and the question; and performing man-machine interaction evaluation according to the determined question, determining an adaptive auxiliary strategy according to the psychological state of the subject in the man-machine interaction evaluation process until the man-machine interaction evaluation of the determined question is completed, and obtaining an evaluation result of the cognitive function of the subject. According to the method, the questions are selected for man-machine interaction evaluation based on the difficulty score determined by the individual information, the method can effectively adapt to individual differences, and the auxiliary strategy is determined according to the psychological state in the evaluation process, so that the accuracy of the evaluation result can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of cognitive function assessment, and in particular to cognitive function assessment methods, devices, equipment and program products. Background Art

[0002] Cognitive function refers to an individual's ability to process information, understand, remember, and use knowledge. Cognitive function is the basis of human intelligence and behavior, and has a vital impact on daily life, learning, and work. Therefore, in recent years, the medical and brain science communities have increasingly conducted research on human cognitive function.

[0003] From the perspective of cognitive impairment evidence research, key research directions include cognitive behavior, imaging and molecular biology. Among them, when cognitive function is assessed through cognitive behavior, the subjects are given a set of scale questions, and a scoring or losing score system is used. Combined with big data clinical statistics, the threshold of the scale assessment is determined to distinguish the cognitive functions of the population. Limited by the judgment scale of the evaluators and the individual differences of the subjects, the reliability of cognitive assessment is affected, which is not conducive to determining more accurate assessment results. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a cognitive function assessment method, device, equipment and program product to solve the problem in the prior art that it is not conducive to determining more accurate assessment results when assessing subjects.

[0005] A first aspect of an embodiment of the present application provides a method for evaluating cognitive function, the method comprising:

[0006] Acquiring individual information of a subject, and determining a difficulty score of the subject according to the individual information of the subject;

[0007] Determining the question corresponding to the difficulty score of the subject according to the preset correspondence between the difficulty score and the question;

[0008] A human-computer interaction assessment is performed according to the determined topic, and an adaptive auxiliary strategy is determined according to the psychological state of the subject during the human-computer interaction assessment, until the human-computer interaction assessment of the determined topic is completed, and an assessment result of the cognitive function of the subject is obtained.

[0009] In combination with the first aspect, in a first possible implementation manner of the first aspect, the individual information further includes individual sensory motor function information of the subject;

[0010] After obtaining the individual information of the subject, the method further comprises:

[0011] Determining the evaluation method of the human-computer interaction evaluation according to the individual sensory-motor function information of the subject;

[0012] Conduct human-computer interaction assessment based on the determined topics, including:

[0013] Conduct human-computer interaction evaluation based on the determined topic and the determined evaluation method.

[0014] In combination with the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the individual sensory-motor function information includes at least one of individual hearing function information, individual visual function information, individual language function information, and individual limb function information;

[0015] Determining the evaluation means of the human-computer interaction evaluation according to the individual sensory-motor function information of the subject includes:

[0016] When the individual sensory motor function information of the subject includes normal hearing function, determining that the evaluation means of the human-computer interaction evaluation of the subject includes voice output interaction;

[0017] When the individual sensory motor function information of the subject including visual function is normal, determining that the evaluation means of the human-computer interaction evaluation of the subject includes image output interaction;

[0018] When the individual sensory motor function information of the subject including language function is normal, determining that the evaluation means of the human-computer interaction evaluation of the subject includes voice input interaction;

[0019] When the individual sensory motor function information of the subject includes normal limb function, it is determined that the evaluation means for the human-computer interaction evaluation of the subject includes limb movement interaction.

[0020] In combination with the first aspect, in a third possible implementation manner of the first aspect, obtaining individual information of a subject, and determining a difficulty score of the subject according to the individual information of the subject includes:

[0021] Obtaining one or more of the subject's gender information, age information, education level information, and living habit information;

[0022] The difficulty score of the subject is determined according to the gender information and one or more of the age information, educational level information and living habit information.

[0023] In combination with the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, determining the difficulty score of the subject according to one or more of the gender information, the age information, the education level information, and the living habit information includes:

[0024] A scoring base is determined according to the gender information, a scoring influence coefficient is determined according to the age information, cultural level information and living habit information, and a difficulty score of the subject is determined according to the scoring base and the scoring influence coefficient.

[0025] In combination with the first aspect, in a fifth possible implementation of the first aspect, determining an auxiliary strategy for adaptation according to the psychological state of the subject during the human-computer interaction evaluation process includes:

[0026] Obtaining the psychological state of the subject during the human-computer interaction evaluation process;

[0027] When the psychological state does not meet the preset requirements, the subject is assisted in adjusting the psychological state through at least one of a voice guidance strategy and an action guidance strategy.

[0028] In combination with the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect, obtaining the psychological state of the subject during the human-computer interaction evaluation process includes at least one of the following:

[0029] Acquiring a skin image of the subject, determining changes in the subject's heart rate and / or blood pressure based on the skin image, and determining the subject's psychological state based on the changes in the heart rate and / or blood pressure;

[0030] Collecting the subject's body movements and voice, inputting the body movements and voice into a pre-trained state analysis model, and outputting the subject's psychological state;

[0031] A pupil image of the subject is collected, and the psychological state of the subject is determined according to pupil change information in the pupil image.

[0032] A second aspect of an embodiment of the present application provides a cognitive function assessment device, characterized in that the device comprises:

[0033] A difficulty score determination unit, used to obtain individual information of a subject and determine a difficulty score of the subject according to the individual information of the subject;

[0034] A question determination unit, used to determine the question corresponding to the difficulty score of the subject according to the preset correspondence between the difficulty score and the question;

[0035] The auxiliary evaluation unit is used to perform a human-computer interaction evaluation according to the determined topic, and determine an adaptive auxiliary strategy according to the psychological state of the subject during the human-computer interaction evaluation process until the human-computer interaction evaluation of the determined topic is completed to obtain an evaluation result of the cognitive function of the subject.

[0036] A third aspect of an embodiment of the present application provides a robot, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the robot implements the method described in any one of the first aspects.

[0037] A fourth aspect of the embodiments of the present application provides a computer program product, which, when executed on a computer, enables the computer to execute the method in the first aspect or its various implementations.

[0038] A fifth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0039] The sixth aspect of the embodiment of the present application provides a chip for implementing the methods in each implementation of the first aspect. Specifically, the chip includes: a processor for calling and running a computer program from a memory, so that a device equipped with the chip executes the method in the first aspect or its implementation.

[0040] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain individual information of the subject, determine the difficulty score of the subject based on the individual information, and determine the corresponding questions for human-computer interaction evaluation based on the difficulty score of the subject, so that the evaluation content can effectively adapt to individual differences, provide accurate evaluation questions for the subject, and improve the accuracy of the evaluation results; during the test process, the auxiliary strategy is determined according to the psychological state of the subject during the human-computer interaction evaluation process, so that the subject can stably perform human-computer interaction during the human-computer interaction evaluation process, thereby further improving the accuracy of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art 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 any creative labor.

[0042] Figure 1 It is a schematic diagram of an implementation scenario of a cognitive function evaluation method provided in an embodiment of the present application;

[0043] Figure 2 It is a schematic diagram of the implementation flow of a method for evaluating cognitive function provided in an embodiment of the present application;

[0044] Figure 3is a schematic diagram of the changes in brain function with age provided by an embodiment of the present application;

[0045] Figure 4 This is a representation of the impact of difficulty score provided by the embodiment of the present application;

[0046] Figure 5 is a schematic diagram of a human-computer interaction evaluation provided in an embodiment of the present application;

[0047] Figure 6 A schematic diagram of a cognitive function evaluation device provided in an embodiment of the present application;

[0048] Figure 7 It is a schematic diagram of a robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0050] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0051] Cognitive function covers an individual's ability to process, understand, remember, and apply knowledge. It is the core of human intelligence and behavior, and plays an extremely important role in daily activities, learning, and work. Because of this, in recent years, the research investment in human cognitive function in the fields of medicine and brain science has continued to increase.

[0052] The current research focuses include cognitive behavior and imaging molecular biology.

[0053] Cognitive behavioral science uses a set of scale questions for the subjects, a scoring or losing score system based on answering questions, and a combination of big data statistical results to determine the evaluation threshold to evaluate the cognitive function of the population. Cognitive behavioral science is limited by three major external factors, including: First, the judgment scales of different staff members for the subjects are often affected by the staff members' personalization, which often affects the subjects' real evaluation scores; second, the subjects' cultural level, clinical tension, and disguise psychology lead to increased abnormal deviations in the scores of the subjects; third, the questions used for jade are often insufficient in the perception, responsiveness, and sensitivity dimensions of brain cognition due to the single technical means. For example, paper questions cannot evaluate the subjects' dynamic judgment of the speed of objects moving, resulting in an incomplete assessment of the cognitive dimensions of the human brain, and the scope of the question assessment is not enough to determine the results of the brain cognitive assessment.

[0054] Imaging uses resting-state functional MRI (rs-fMRI) as a non-invasive neuroimaging technology. It is based on blood oxygen level-dependent imaging and reflects the dynamic activities of neurons and brain regions through changes in magnetic resonance signals of blood flow, blood volume, and the ratio of oxygenated hemoglobin to deoxygenated hemoglobin. rs-fMRI can play a certain role in the assessment of early cognitive function, but the time point for early judgment is still relatively lagging and the cost is relatively high.

[0055] In terms of molecular biology, existing studies have shown that cognitive impairment is related to the accumulation of proteins in the brain, including Tau protein and β-amyloid protein (Aβ). The functional status of the cognitive system of the population can be determined by the detection of toxic proteins. Combined with imaging findings, the accumulation of toxic proteins is not a typical accumulation in a single brain region, but rather accumulates over time in multiple regions. Using toxic protein detection and quantitative data, a large amount of toxic proteins is often found when the population has already suffered cognitive impairment. Therefore, the detection of toxic proteins cannot currently be quantitatively used to diagnose cognitive impairment at an early stage, and is often used as a supplementary diagnostic indicator as a basis for cognitive assessment.

[0056] To solve the above problems, the present application embodiment proposes a cognitive function assessment method, such as Figure 1The figure shows a schematic diagram of the implementation scenario of the cognitive function evaluation method. In the implementation scenario, the robot can obtain the individual information of the subject, including the individual attributes of the subject and the sensory motor function information of the subject. The individual attributes include information such as gender, age, cultural level and living habits. The difficulty score of the subject is determined based on the individual attribute information, and the questions used for human-computer interaction evaluation can be determined through the difficulty score, so that the cognitive function evaluation of the cognitive level differences of different individuals can be effectively adapted. Based on the individual sensory motor function information in the individual information, the means for human-computer interaction evaluation can be determined, including at least one of voice output interaction, voice input interaction, image output interaction and body movement interaction, so that the robot can adapt to the differences in sensory motor function information of different individuals. In the process of human-computer interaction evaluation according to the determined questions and means, the psychological state of the subject can be detected in real time. When the psychological state deviates, the corresponding auxiliary strategy is adapted so that the subject can complete the evaluation in a normal psychological state, obtain a more accurate evaluation result of cognitive function, and output it to the evaluation end where the staff is located.

[0057] Figure 2 A schematic diagram of a method for evaluating cognitive function provided in an embodiment of the present application is provided, wherein the method comprises:

[0058] In S201, individual information of a subject is obtained, and a difficulty score of the subject is determined according to the individual information of the subject.

[0059] The cognitive function of the subject in the embodiments of the present application refers to the various psychological processes involved in the individual's processing, understanding and response to external information. The cognitive function of the subject may include at least one of the subject's perception, memory, logic, calculation and action execution. Among them:

[0060] Perception refers to an individual's ability to receive and interpret information about the outside world through the senses (such as vision, hearing, touch, etc.). Perception involves recognizing and understanding sensory input such as shapes, colors, sounds, and tactile patterns. Perception functions underlie cognitive processes and provide essential information about the environment.

[0061] Memory refers to an individual's ability to store, retain, and recall information. Memory can be divided into short-term memory (or working memory) and long-term memory. Short-term memory involves temporarily holding and manipulating information, while long-term memory involves the permanent storage of information. Memory is essential for learning, decision-making, and daily functioning.

[0062] Logic refers to an individual's ability to make sound inferences and judgments. This includes both inductive reasoning (extracting general patterns from specific examples) and deductive reasoning (deriving specific conclusions from general principles). Logical functions enable individuals to analyze problems, evaluate arguments, and make evidence-based decisions.

[0063] Computation usually refers to mathematical operations, but it can be understood more broadly here as the ability to process and manipulate information. This includes numerical calculations, conceptual manipulation, and abstract thinking. Computational ability enables individuals to solve complex problems and make effective decisions.

[0064] Motor execution refers to an individual's ability to plan and execute movements. This involves motor planning, coordination, and control to achieve a specific goal or task. Motor execution is important for physical activities in daily life, motor skills, and hand-eye coordination.

[0065] These cognitive functions interact with each other to support an individual's learning, adaptation, and interaction with the environment. In cognitive function assessments, these aspects are often examined in detail to understand the subject's cognitive status.

[0066] The individual information in the embodiments of the present application includes the individual attributes of the subject. The individual attributes are used to reflect the average cognitive level of the group to which the subject belongs. The individual attributes include at least one of age information, gender information, education level information and living habit information.

[0067] In general, the human brain will show certain changes with age, such as Figure 3 As shown in the figure, with the increase of age, the average T scores of various cognitive aspects begin to decline after the age of 55. Among them, with the increase of age, the perceptual speed basically declines linearly, and language memory, inductive reasoning and spatial orientation decline in three plateaus at the ages of 55, 65 and 75. The decay rate of spatial orientation is slower than that of language memory and inductive reasoning. Digital calculation reaches maturity at the peak of 45 years old and then gradually declines. Language ability varies greatly among individuals, and is the best maintained in the cognitive dimension.

[0068] The living habit information in the embodiment of the present application may include at least one of the following information: whether the person lives alone, exercise frequency, medical history information, and whether the person has a smoking habit.

[0069] An example of determining a difficulty score based on gender information, age information, education level information, and lifestyle information can be as follows: Figure 4 As shown in FIG. 1 , in the difficulty score impact table, the base number used to calculate the difficulty score and the impact coefficient used to calculate the difficulty score can be determined. Figure 4 As shown, the base for calculating the difficulty score is the gender information. If the subject's gender is male, the base for determining the difficulty score is 1.0. If the subject's gender is female, the base for determining the difficulty score is 0.9.

[0070] After determining the base of the difficulty score, the influence coefficient of the difficulty score can be further determined based on the subject's cultural level information, age information, and living habits information. Figure 4 As shown, when the subject is younger than 55 years old, the influence coefficient is set to 1.2, between 55 and 65 years old, the influence coefficient is 1.0, between 65 and 75 years old, the influence coefficient is 0.9, and the influence coefficient is 0.8 for those over 75 years old. For the educational level of the subject, including below primary school, primary school or junior high school, high school, college and above, the corresponding influence coefficients are 0.85, 1.0, 1.1 and 1.2 respectively. For the living habits of the subject, including habit information such as whether living alone, exercise frequency, medical history information, and whether smoking habits, the influence coefficients corresponding to various habit information can be set separately.

[0071] Based on the individual information of the subjects, combined Figure 4 The difficulty score impact table shown can determine the difficulty score of the questions used by the subjects for the human-computer interaction assessment.

[0072] For example, a subject is a male with an age of 60 years old and an education level of primary school or below. His lifestyle includes living alone, exercising regularly (exercise frequency is greater than a predetermined frequency threshold), having no medical history and no smoking habit. The difficulty score for this subject is: 1.0*1.0*0.85*0.8*1.1*1.0*1.0=0.748.

[0073] In the embodiment of the present application, the individual information may also include the subject's individual sensory motor function information. The subject's individual sensory motor function information may include at least one of individual hearing function information, individual visual function information, individual language function information, and individual limb function information.

[0074] The individual information in the embodiments of the present application can be determined by the input information of the staff, or can also be determined based on the input information of the subject, including form filling information, voice interaction information and other input information.

[0075] When determining the evaluation method of human-computer interaction evaluation based on the individual emotional motor function information in the individual information, it can include:

[0076] When the individual sensory-motor function information of the subject includes normal hearing function, it is determined that the evaluation means for the human-computer interaction evaluation of the subject includes voice output interaction, that is, the evaluation means may include interacting with the subject using voice, such as describing the content of the question through voice, or guiding the subject to adjust his psychological state through voice, etc.

[0077] When the individual sensory-motor function information of the subject includes normal visual function, it is determined that the evaluation means for human-computer interaction evaluation of the subject includes image output interaction, that is, the evaluation means may include interacting with the subject using images, such as outputting schematic diagrams of operation prompts or action prompts.

[0078] When the individual sensory-motor function information of the subject includes normal language function, it is determined that the evaluation means for the human-computer interaction evaluation of the subject includes voice input interaction, that is, the evaluation means may include interacting with the subject in the form of receiving voice, such as parsing the content of the subject's answer voice.

[0079] When the individual sensory motor function information of the subject includes normal limb function, it is determined that the evaluation means of the human-computer interaction evaluation of the subject includes limb movement interaction. That is, the evaluation means may include interacting with the subject in the form of receiving limb movements, such as collecting the subject's movement images and parsing relevant information of the movement images.

[0080] In S202, the question corresponding to the difficulty score of the subject is determined according to the preset correspondence between the difficulty score and the question.

[0081] When determining the correspondence between the difficulty score and the question, a plurality of difficulty score intervals may be set to determine the correspondence between the difficulty score intervals and the question.

[0082] for example Figure 4 In the difficulty score impact table shown, the highest difficulty score is determined to be 1.74 and the lowest is 0.40. The difficulty score can be divided into three intervals, namely greater than 1.0, 0.8-1.0 and less than 0.8, and the corresponding question difficulty is level 3 difficulty, level 2 difficulty and level 1 difficulty. The subject can be selected for human-computer interaction assessment according to the difficulty of the question. For example, if the subject's difficulty score is determined to be 1.1 through the difficulty score impact table, the corresponding question difficulty is level 3 difficulty, and level 3 difficulty questions can be selected to evaluate the subject's cognitive function.

[0083] In a possible implementation, after determining the difficulty of the question, the evaluation method of the question can be further determined based on the individual sensory-motor function information of the subject, including one or more of the interactive methods such as voice output interaction, image output interaction, voice input interaction, and body movement interaction for evaluation.

[0084] like Figure 5 In the schematic diagram of the human-computer interaction assessment shown, based on the subject's characteristics, that is, the subject's individual sensory-motor function information, including the subject's hearing (individual hearing function information), vision (individual visual function information), speech (individual language function information) and limb behavior (individual limb function information), the subject's cognitive function can be evaluated by adapting the corresponding assessment means according to the subject's difficulty score. This includes evaluating the subject's perception function, memory function, logic function, calculation function and action execution function.

[0085] For example, for the deaf and mute, the questions provided can be eye movement questions and body behavior questions to conduct human-computer interaction assessment.

[0086] In S203, a human-computer interaction assessment is performed according to the determined topic, and an adaptive auxiliary strategy is determined according to the psychological state of the subject during the human-computer interaction assessment, until the human-computer interaction assessment of the determined topic is completed, and an assessment result of the cognitive function of the subject is obtained.

[0087] After determining the topics used in the human-computer interaction assessment and the assessment methods for the determined topics, the human-computer interaction assessment can be conducted on the subjects one by one. During the human-computer interaction assessment, the psychological state of the subjects is monitored in real time. According to the psychological state of the subjects, the subjects are assisted to complete the human-computer interaction assessment in a normal state. When the psychological state of the subjects is in an abnormal state, the relevant auxiliary strategies are used to assist the subjects to adjust their psychological state to a normal state.

[0088] In an embodiment of the present application, a skin image of the subject, such as a facial image of the subject, can be captured by a camera, changes in the subject's heart rate and / or blood pressure can be determined based on the skin image, and the subject's psychological state can be determined based on the changes in the heart rate and / or blood pressure.

[0089] There is a close relationship between psychological state and blood pressure level and the psychological state of the human body. When the human body is in a negative emotion such as tension, anxiety, depression, etc., the human body will release a large amount of stress hormones, such as adrenaline and norepinephrine, which will cause blood vessels to contract and the heart rate to increase, thereby causing blood pressure to rise. Therefore, the changes in heart rate and blood pressure can reflect the psychological state of an individual. For example, when an individual is in a state of tension or anxiety, the heart rate usually increases and blood pressure may also increase. When it is determined that the subject is in a state of tension, anxiety or depression, the robot's body movements, such as hugging action guidance strategies, can be used to adjust the subject to a relaxed state. Alternatively, the subject can be adjusted to a relaxed state for answering questions and evaluation by playing voice, including soothing words, or soothing music or voice materials for emotional guidance.

[0090] In the embodiment of the present application, the subject's body movements can be collected by a camera, the subject's voice can be collected by a microphone, the collected body movements and voice can be analyzed, and information such as body movement features and voice features can be extracted to determine the subject's psychological state. For example, a trained state analysis model can be used to extract and calculate features of the subject's voice and / or body movements, and the subject's psychological state can be output, or the subject's psychological state can be determined based on the speech speed features and trembling features of the collected voice and / or body movements, based on the pre-statistical correspondence.

[0091] For example, a nervous or anxious individual may show specific body language and voice characteristics, such as trembling, faster speech, or a tense voice. These characteristics can be input into a pre-trained model to analyze and output the individual's psychological state. When it is determined that the subject is in an anxious or nervous state, the robot's body movements, such as hugging, can be used to guide the subject to a relaxed state. Alternatively, the subject can be adjusted to a relaxed state for answering questions by playing voice, including soothing words, or soothing music.

[0092] In a possible implementation, the embodiment of the present application can collect the pupil image of the subject and determine the psychological state of the subject through the change information of the pupil image.

[0093] The dilation or contraction of pupils is closely related to the individual's psychological state, such as attention, emotional arousal, and cognitive load. For example, when an individual is curious or focused, the pupil may dilate. This pupil reaction can be used to infer the individual's psychological state. When the subject's pupil shrinks to a predetermined value, it indicates that the subject's psychological state may be distracted, and the subject can be prompted to concentrate through voice to obtain more accurate evaluation results.

[0094] The embodiment of the present application obtains individual information of the subject, determines the difficulty score of the subject based on the individual information, and determines corresponding questions for human-computer interaction evaluation based on the difficulty score of the subject, so that the evaluation content can effectively adapt to individual differences, provide accurate evaluation questions for the subject, and help improve the accuracy of the evaluation results; and, during the test process, the robot can determine auxiliary strategies according to the psychological state of the subject during the human-computer interaction evaluation process, so that the subject can stably perform human-computer interaction during the human-computer interaction evaluation process, thereby further improving the accuracy of the evaluation results.

[0095] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0096] Figure 6 A schematic diagram of a cognitive function evaluation device provided in an embodiment of the present application, the device comprising:

[0097] A difficulty score determination unit 601 is used to obtain individual information of a subject and determine a difficulty score of the subject according to the individual information of the subject;

[0098] A question determination unit 602, configured to determine a question corresponding to the difficulty score of the subject according to a preset correspondence between the difficulty score and the question;

[0099] The auxiliary evaluation unit 603 is used to perform a human-computer interaction evaluation according to the determined topic, and determine an adaptive auxiliary strategy according to the psychological state of the subject during the human-computer interaction evaluation process until the human-computer interaction evaluation of the determined topic is completed to obtain an evaluation result of the cognitive function of the subject.

[0100] Figure 6 The cognitive function assessment device shown is Figure 2 The evaluation method of cognitive function shown corresponds to this.

[0101] Figure 7 is a schematic diagram of a robot provided in an embodiment of the present application. Figure 7 As shown, the robot 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70, such as a cognitive function evaluation program. When the processor 70 executes the computer program 72, the steps in the above-mentioned cognitive function evaluation method embodiments are implemented. Alternatively, when the processor 70 executes the computer program 72, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0102] Exemplarily, the computer program 72 may be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 72 in the robot 7.

[0103] The robot 7 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The robot may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will appreciate that Figure 7 It is only an example of a robot 7 and does not constitute a limitation of the robot 7. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the robot may also include input and output devices, network access devices, buses, etc.

[0104] The processor 70 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0105] The memory 71 may be an internal storage unit of the robot 7, such as a hard disk or memory of the robot 7. The memory 71 may also be an external storage device of the robot 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the robot 7. Further, the memory 71 may include both an internal storage unit and an external storage device of the robot 7. The memory 71 is used to store the computer program and other programs and data required by the robot. The memory 71 may also be used to temporarily store data that has been output or is to be output.

[0106] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0107] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0109] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0113] In addition, an embodiment of the present application also provides a computer program product, which, when executed on a computer, enables the computer to execute the methods in the above-mentioned implementation modes.

[0114] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for evaluating cognitive function, characterized in that: The method comprises: Acquiring individual information of a subject, and determining a difficulty score of the subject according to the individual information of the subject; Determining the question corresponding to the difficulty score of the subject according to the preset correspondence between the difficulty score and the question; A human-computer interaction assessment is performed according to the determined topic, and an adaptive auxiliary strategy is determined according to the psychological state of the subject during the human-computer interaction assessment, until the human-computer interaction assessment of the determined topic is completed, and an assessment result of the cognitive function of the subject is obtained.

2. The method according to claim 1, characterized in that The individual information also includes the individual sensory motor function information of the subject; After obtaining the individual information of the subject, the method further comprises: Determining the evaluation method of the human-computer interaction evaluation according to the individual sensory-motor function information of the subject; Conduct human-computer interaction assessment based on the determined topics, including: Conduct human-computer interaction evaluation based on the determined topic and the determined evaluation method.

3. The method according to claim 2, characterized in that The individual sensory motor function information includes at least one of individual hearing function information, individual visual function information, individual language function information and individual limb function information; Determining the evaluation means of the human-computer interaction evaluation according to the individual sensory-motor function information of the subject includes: When the individual sensory motor function information of the subject includes normal hearing function, determining that the evaluation means of the human-computer interaction evaluation of the subject includes voice output interaction; When the individual sensory motor function information of the subject including visual function is normal, determining that the evaluation means of the human-computer interaction evaluation of the subject includes image output interaction; When the individual sensory motor function information of the subject including language function is normal, determining that the evaluation means of the human-computer interaction evaluation of the subject includes voice input interaction; When the individual sensory motor function information of the subject includes normal limb function, it is determined that the evaluation means for the human-computer interaction evaluation of the subject includes limb movement interaction.

4. The method according to claim 1, characterized in that: Acquiring individual information of a subject, and determining a difficulty score of the subject according to the individual information of the subject, including: Obtaining one or more of the subject's gender information, age information, education level information, and living habit information; The difficulty score of the subject is determined according to the gender information and one or more of the age information, cultural level information and living habit information.

5. The method according to claim 4, characterized in that Determining the difficulty score of the subject according to one or more of the gender information, the age information, the education level information and the living habit information includes: A scoring base is determined according to the gender information, a scoring influence coefficient is determined according to the age information, cultural level information and living habit information, and a difficulty score of the subject is determined according to the scoring base and the scoring influence coefficient.

6. The method according to claim 1, characterized in that The auxiliary strategy for adaptation is determined according to the psychological state of the subject during the human-computer interaction evaluation process, including: Obtaining the psychological state of the subject during the human-computer interaction evaluation process; When the psychological state does not meet the preset requirements, the subject is assisted in adjusting the psychological state through at least one of a voice guidance strategy and an action guidance strategy.

7. The method according to claim 6, characterized in that Obtaining the psychological state of the subject during the human-computer interaction assessment process includes at least one of the following: Acquiring a skin image of the subject, determining changes in the subject's heart rate and / or blood pressure based on the skin image, and determining the subject's psychological state based on the changes in the heart rate and / or blood pressure; Collecting the subject's body movements and voice, inputting the body movements and voice into a pre-trained state analysis model, and outputting the subject's psychological state; A pupil image of the subject is collected, and the psychological state of the subject is determined according to pupil change information in the pupil image.

8. A cognitive function assessment device, characterized in that: The device comprises: A difficulty score determination unit, used to obtain individual information of a subject and determine a difficulty score of the subject according to the individual information of the subject; A question determination unit, used to determine the question corresponding to the difficulty score of the subject according to the preset correspondence between the difficulty score and the question; The auxiliary evaluation unit is used to perform a human-computer interaction evaluation according to the determined topic, and determine an adaptive auxiliary strategy according to the psychological state of the subject during the human-computer interaction evaluation process until the human-computer interaction evaluation of the determined topic is completed to obtain an evaluation result of the cognitive function of the subject.

9. A robot comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the robot is caused to implement the method according to any one of claims 1 to 7.

10. A computer program product comprising computer program instructions, characterized in that When the computer program is executed, the method according to any one of claims 1 to 7 is performed.

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