Cognitive function evaluation method and device, readable storage medium and electronic equipment

Abstract

The invention belongs to the technical field of computers, and particularly relates to a cognitive function evaluation method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring an answering result of a subject for a preset cognitive function evaluation scale, and an answering voice and an answering image of the subject in an answering process; performing voice feature analysis on the answer voice to obtain answer voice feature data of the subject; performing eye movement feature analysis on the answer image to obtain answer eye movement feature data of the subject; determining a first cognitive function evaluation result of the subject according to the answer result; determining a second cognitive function evaluation result of the subject according to the answer voice feature data and the answer eye movement feature data; and determining a comprehensive cognitive function evaluation result of the subject according to the first cognitive function evaluation result and the second cognitive function evaluation result.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and particularly relates to a cognitive function evaluation method, device, computer-readable storage medium, and electronic device. Background Art

[0002] After modern humans reach adulthood, around the age of 45, their cognitive abilities decline with age. The degree and characteristics of cognitive ability decline are significantly different between normal and abnormal populations as they age. In the prior art, a cognitive function assessment scale can be used to construct questions through dimensions such as memory and calculation to evaluate the cognitive abilities of subjects. However, this method highly depends on the cognitive function assessment scale and is easily affected by the cooperation degree of the subjects and the assessment level of the assessors, resulting in a low accuracy rate of the obtained cognitive function assessment results. Summary of the Invention

[0003] In view of this, embodiments of this application provide a cognitive function evaluation method, device, computer-readable storage medium, and electronic device to solve the problem that the existing cognitive function evaluation method is easily affected by the cooperation degree of the subjects and the assessment level of the assessors, resulting in a low accuracy rate of the obtained cognitive function assessment results.

[0004] The first aspect of the embodiments of this application provides a cognitive function evaluation method, which may include:

[0005] Obtain the answering results of the subject for a preset cognitive function assessment scale, as well as the answering voice and answering images of the subject during the answering process;

[0006] Perform voice feature analysis on the answering voice to obtain the answering voice feature data of the subject;

[0007] Perform eye movement feature analysis on the answering images to obtain the answering eye movement feature data of the subject;

[0008] Determine the first cognitive function evaluation result of the subject according to the answering results;

[0009] Determine the second cognitive function evaluation result of the subject according to the answering voice feature data and the answering eye movement feature data;

[0010] Determine the comprehensive cognitive function evaluation result of the subject according to the first cognitive function evaluation result and the second cognitive function evaluation result.

[0011] In a specific implementation manner of the first aspect, the determining the second cognitive function evaluation result of the subject according to the answering voice feature data and the answering eye movement feature data may include:

[0012] Compare the answer voice feature data with the data in a preset voice feature database to obtain a comparison result of the voice feature data;

[0013] Compare the answer eye movement feature data with the data in a preset eye movement feature database to obtain a comparison result of the eye movement feature data;

[0014] Determine the second cognitive function evaluation result of the subject according to the comparison result of the voice feature data and the comparison result of the eye movement feature data.

[0015] In a specific implementation manner of the first aspect, the voice feature database may include multiple voice feature data sub - databases, where each voice feature data sub - database corresponds to a type of evaluation result;

[0016] The step of comparing the answer voice feature data with the data in a preset voice feature database to obtain a comparison result of the voice feature data may include:

[0017] Compare the answer voice feature data with the data in multiple voice feature data sub - databases respectively to obtain multiple comparison sub - results of the voice feature data; where each comparison sub - result of the voice feature data corresponds to a probability of a type of evaluation result;

[0018] Summarize the multiple comparison sub - results of the voice feature data to obtain the comparison result of the voice feature data.

[0019] In a specific implementation manner of the first aspect, the eye movement feature database may include multiple eye movement feature data sub - databases, where each eye movement feature data sub - database corresponds to a type of evaluation result;

[0020] The step of comparing the answer eye movement feature data with the data in a preset eye movement feature database to obtain a comparison result of the eye movement feature data may include:

[0021] Compare the answer eye movement feature data with the data in multiple eye movement feature data sub - databases respectively to obtain multiple comparison sub - results of the eye movement feature data; where each comparison sub - result of the eye movement feature data corresponds to a probability of a type of evaluation result;

[0022] Summarize the multiple comparison sub - results of the eye movement feature data to obtain the comparison result of the eye movement feature data.

[0023] In a specific implementation manner of the first aspect, before obtaining the answer result of the subject for a preset cognitive function evaluation scale, as well as the answer voice and answer image of the subject during the answering process, it may further include:

[0024] Determine the cognitive function assessment mode of the subject;

[0025] Generate a cognitive function assessment scale corresponding to the cognitive function assessment mode for the subject to answer questions.

[0026] In a specific implementation manner of the first aspect, the generating a cognitive function assessment scale corresponding to the cognitive function assessment mode may include:

[0027] Extract cognitive function assessment questions corresponding to the cognitive function assessment mode from a preset question database;

[0028] Generate the cognitive function assessment scale according to the extracted cognitive function assessment questions.

[0029] In a specific implementation manner of the first aspect, after determining the comprehensive cognitive function assessment result of the subject according to the first cognitive function assessment result and the second cognitive function assessment result, it may further include:

[0030] Display the comprehensive cognitive function assessment result through a preset human-computer interaction interface;

[0031] And / or

[0032] Send the comprehensive cognitive function assessment result to a preset target device.

[0033] A second aspect of the embodiments of the present application provides a cognitive function assessment device, which may include:

[0034] A data acquisition module, configured to acquire the answering result of the subject for a preset cognitive function assessment scale, as well as the answering voice and answering image of the subject during the answering process;

[0035] A voice feature analysis module, configured to perform voice feature analysis on the answering voice to obtain the answering voice feature data of the subject;

[0036] An eye movement feature analysis module, configured to perform eye movement feature analysis on the answering image to obtain the answering eye movement feature data of the subject;

[0037] A first assessment result determination module, configured to determine the first cognitive function assessment result of the subject according to the answering result;

[0038] A second assessment result determination module, configured to determine the second cognitive function assessment result of the subject according to the answering voice feature data and the answering eye movement feature data;

[0039] A comprehensive evaluation result determination module, configured to determine the comprehensive cognitive function evaluation result of the subject according to the first cognitive function evaluation result and the second cognitive function evaluation result.

[0040] In a specific implementation manner of the second aspect, the second evaluation result determination module may include:

[0041] A voice feature data comparison unit, configured to compare the answering voice feature data in a preset voice feature database to obtain a voice feature data comparison result;

[0042] An eye movement feature data comparison unit, configured to compare the answering eye movement feature data in a preset eye movement feature database to obtain an eye movement feature data comparison result;

[0043] A second evaluation result determination unit, configured to determine the second cognitive function evaluation result of the subject according to the voice feature data comparison result and the eye movement feature data comparison result.

[0044] In a specific implementation manner of the second aspect, the voice feature database may include multiple voice feature data sub - databases, where each voice feature data sub - database corresponds to an evaluation result type respectively;

[0045] The voice feature data comparison unit may be specifically configured to: respectively compare the answering voice feature data in multiple voice feature data sub - databases to obtain multiple voice feature data comparison sub - results; where each voice feature data comparison sub - result corresponds to the probability of an evaluation result type respectively; and summarize the multiple voice feature data comparison sub - results to obtain the voice feature data comparison result.

[0046] In a specific implementation manner of the second aspect, the eye movement feature database may include multiple eye movement feature data sub - databases, where each eye movement feature data sub - database corresponds to an evaluation result type respectively;

[0047] The eye movement feature data comparison unit may be specifically configured to: respectively compare the answering eye movement feature data in multiple eye movement feature data sub - databases to obtain multiple eye movement feature data comparison sub - results; where each eye movement feature data comparison sub - result corresponds to the probability of an evaluation result type respectively; and summarize the multiple eye movement feature data comparison sub - results to obtain the eye movement feature data comparison result.

[0048] In a specific implementation manner of the second aspect, the cognitive function evaluation device may further include:

[0049] A cognitive function evaluation mode determination module, configured to determine the cognitive function evaluation mode of the subject;

[0050] A cognitive function assessment scale generation module, configured to generate a cognitive function assessment scale corresponding to the cognitive function assessment mode for the subject to answer questions.

[0051] In a specific implementation manner of the second aspect, the cognitive function assessment scale generation module may be specifically configured to: extract cognitive function assessment questions corresponding to the cognitive function assessment mode from a preset question database; and generate the cognitive function assessment scale according to the extracted cognitive function assessment questions.

[0052] In a specific implementation manner of the second aspect, the cognitive function assessment device may further include:

[0053] A cognitive function assessment result display module, configured to display the comprehensive cognitive function assessment result through a preset human-computer interaction interface; and / or send the comprehensive cognitive function assessment result to a preset target device.

[0054] In a third aspect of the embodiments of the present application, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned cognitive function assessment methods are implemented.

[0055] In a fourth aspect of the embodiments of the present application, there is provided an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of any of the above-mentioned cognitive function assessment methods are implemented.

[0056] In a fifth aspect of the embodiments of the present application, there is provided a computer program product, and when the computer program product runs on an electronic device, the electronic device is caused to execute the steps of any of the above-mentioned cognitive function assessment methods.

[0057] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: The embodiments of the present application obtain the answering results of the subjects for a preset cognitive function assessment scale, as well as the answering voices and answering images of the subjects during the answering process; perform voice feature analysis on the answering voices to obtain the answering voice feature data of the subjects; perform eye movement feature analysis on the answering images to obtain the answering eye movement feature data of the subjects; determine the first cognitive function assessment result of the subjects according to the answering results; determine the second cognitive function assessment result of the subjects according to the answering voice feature data and the answering eye movement feature data; determine the comprehensive cognitive function assessment result of the subjects according to the first cognitive function assessment result and the second cognitive function assessment result. Through the embodiments of the present application, cognitive function assessment can be comprehensively considered based on the answering results, answering voice feature data, and answering eye movement feature data of the subjects, effectively improving the accuracy of the obtained cognitive function assessment results. Description of the Drawings

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0059] Figure 1 It is a flowchart of an embodiment of a cognitive function assessment method in the embodiments of the present application;

[0060] Figure 2 It is a schematic flowchart for determining the second cognitive function assessment result of the subject according to the answering voice feature data and the answering eye movement feature data;

[0061] Figure 3 It is a structural diagram of an embodiment of a cognitive function assessment device in the embodiments of the present application;

[0062] Figure 4 It is a schematic block diagram of an electronic device in the embodiments of the present application. Detailed Embodiments

[0063] In order to make the objectives, features, and advantages of the present application more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0064] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their combinations.

[0065] It should also be understood that the terminology used in the specification of this application is for the purpose of describing particular embodiments only and is not intended to limit this application. As used in the specification of this application and the appended claims, unless the context clearly dictates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0066] It should be further understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0067] As used in this specification and the appended claims, the term "if" can be interpreted, depending on the context, as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted, depending on the context, as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]".

[0068] In addition, in the description of this application, the terms "first", "second", "third", etc. are used only for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0069] After modern humans reach adulthood, at around 45 years old, their cognitive ability declines with age. The degree and characteristics of cognitive ability decline are significantly different between the normal population and the abnormal population as they age. In the prior art, a cognitive function assessment scale can be used to construct questions through dimensions such as memory and calculation to evaluate the cognitive ability of the subjects. However, this method is highly dependent on the cognitive function assessment scale and is easily affected by the cooperation degree of the subjects and the assessment level of the assessors, resulting in a low accuracy rate of the obtained cognitive function assessment results.

[0070] In view of this, the embodiments of this application provide a cognitive function assessment method, device, computer-readable storage medium and electronic device to solve the problem that the existing cognitive function assessment method is easily affected by the cooperation degree of the subjects and the assessment level of the assessors, resulting in a low accuracy rate of the obtained cognitive function assessment results.

[0071] In the embodiments of the present application, the cognitive function assessment can be comprehensively considered based on the subject's answer results, answer voice feature data, and answer eye movement feature data, effectively improving the accuracy of the obtained cognitive function assessment results.

[0072] The execution entity of the embodiments of the present application can be an electronic device, including but not limited to computing devices such as mobile phones, tablet computers, desktop computers, notebooks, handheld computers, and servers.

[0073] Please refer to Figure 1 , an embodiment of a cognitive function assessment method in the embodiments of the present application may include:

[0074] Step S101: Obtain the subject's answer results for a preset cognitive function assessment scale, as well as the subject's answer voice and answer images during the answering process.

[0075] Among them, the cognitive function assessment scale can be fixed or dynamically generated according to the actual situation. In a specific implementation manner of the embodiments of the present application, the cognitive function assessment mode of the subject can be first determined, and then a cognitive function assessment scale corresponding to the cognitive function assessment mode can be generated for the subject to answer questions.

[0076] The cognitive function assessment mode can include but not limited to cognitive screening mode, cognitive classification mode, and cognitive typing mode, etc. The cognitive screening mode is mainly used to determine whether the subject's cognitive function is normal, belonging to the screening purpose, usually for the normal elderly population and the population with mild cognitive impairment. The cognitive classification mode is mainly used for the detailed determination of the subject's cognitive function in the cognitive dimension, and the determination mainly focuses on five dimensions: memory, operation, perception, movement, and logic for classification determination. The cognitive typing mode will further subdivide the five dimensions of the classification mode. For example, memory will conduct a detailed typing determination on visual memory and auditory memory, and perception will be divided into spatial perception, time perception, and object-human perception, etc.

[0077] In a specific implementation manner of the embodiments of the present application, a question database can be preset. The question database can include various cognitive function assessment questions for cognitive function assessment. Different cognitive function assessment modes can correspond to different cognitive function assessment questions. During the process of generating a cognitive function assessment scale corresponding to the cognitive function assessment mode, the cognitive function assessment questions corresponding to the cognitive function assessment mode can be extracted from the question database, and a cognitive function assessment scale can be generated based on the extracted cognitive function assessment questions.

[0078] The number of cognitive function assessment questions and the assessment time in the cognitive function assessment scale can both be flexibly set according to the actual situation, and the embodiments of the present application do not make specific limitations in this regard. As an example, in the cognitive screening mode, 10 cognitive function assessment questions can be set, and the assessment time is 3 to 7 minutes; in the cognitive classification mode, 20 cognitive function assessment questions can be set, and the assessment time is 10 to 15 minutes; in the cognitive typing mode, 50 cognitive function assessment questions can be set, and the assessment time is about 30 minutes.

[0079] During the process of the subject answering questions according to the cognitive function assessment scale, the answering results of the subject for the cognitive function assessment scale, as well as the answering voice and answering image of the subject during the answering process, can be obtained.

[0080] Among them, the answering result is the answer of the subject to the cognitive function assessment questions. The subject can answer through interaction methods such as a keyboard, a mouse, a touch screen, etc., or can directly answer through voice. The present application does not make specific limitations on the answering method of the subject. The answering voice is the voice of the subject obtained through a preset voice acquisition device such as a microphone, and the answering image is the image of the subject obtained through a preset image acquisition device such as a camera.

[0081] It should be noted that the information collection process (such as the image collection process, the voice collection process, etc.) and the feature extraction process involved in the embodiments of the present application are executed with the knowledge and permission of the user, that is, the information collection process and the feature extraction process comply with relevant requirements and do not belong to acts that harm the public interest.

[0082] Step S102: Perform voice feature analysis on the answering voice to obtain the answering voice feature data of the subject.

[0083] The answering voice feature data can include, but is not limited to, feature data such as text voice feature values, volume, and speech rate. Specifically, which voice feature analysis method is used to extract the answering voice feature data can be flexibly set according to the actual situation, and can include, but is not limited to, any one of the existing voice feature analysis methods in the prior art. The embodiments of the present application do not make specific limitations in this regard.

[0084] Step S103: Perform eye movement feature analysis on the answering image to obtain the answering eye movement feature data of the subject.

[0085] The answering eye movement feature data can include, but is not limited to, feature data such as eye movement trajectories, pupil positions, time switching, and blink frequencies. Specifically, which eye movement feature analysis method is used to extract the answering eye movement feature data can be flexibly set according to the actual situation, and can include, but is not limited to, any one of the existing eye movement feature analysis methods in the prior art. The embodiments of the present application do not make specific limitations in this regard.

[0086] Step S104. Determine the first cognitive function evaluation result of the subject according to the answer result.

[0087] In the embodiment of the present application, the standard answers corresponding to each cognitive function evaluation question in the cognitive function evaluation scale can be retrieved separately in the question database, and the answer results of the subject for each cognitive function evaluation question in the cognitive function evaluation scale are compared with the corresponding standard answers, so as to obtain the evaluation score of the subject, and it is determined as the first cognitive function evaluation result.

[0088] The data comparison can be carried out separately from five dimensions of memory, operation, perception, action, and logic. Then the final first cognitive function evaluation result can include, but is not limited to, the evaluation scores of the subject in each dimension respectively.

[0089] Step S105. Determine the second cognitive function evaluation result of the subject according to the answer voice feature data and the answer eye movement feature data.

[0090] As Figure 2 shown, step S105 may specifically include the following process:

[0091] Step S1051. Compare the answer voice feature data in the preset voice feature database to obtain the voice feature data comparison result.

[0092] The voice feature database may include multiple voice feature data sub-libraries, where each voice feature data sub-library corresponds to an evaluation result type respectively.

[0093] The evaluation result types may include, but are not limited to, types such as normal, slightly abnormal, and moderately abnormal. Correspondingly, the voice feature database may include, but is not limited to, the first voice feature data sub-library with normal cognitive function, the second voice feature data sub-library with slightly abnormal cognitive function, and the third voice feature data sub-library with moderately abnormal cognitive function, etc.

[0094] The answer voice feature data can be compared in multiple voice feature data sub-libraries respectively to obtain multiple voice feature data comparison sub-results, where each voice feature data comparison sub-result corresponds to the probability of an evaluation result type respectively.

[0095] As an example, the speech feature data of the answers can be compared with the data in the first sub - database of speech features with normal cognitive function, so as to obtain the probability of normal cognitive function, that is, the similarity between the speech feature data of the answers and the speech feature data in the first sub - database of speech features, and determine it as the sub - result of the first speech feature data comparison; the speech feature data of the answers can be compared with the data in the second sub - database of speech features with slightly abnormal cognitive function, so as to obtain the probability of slightly abnormal cognitive function, that is, the similarity between the speech feature data of the answers and the speech feature data in the second sub - database of speech features, and determine it as the sub - result of the second speech feature data comparison; the speech feature data of the answers can be compared with the data in the third sub - database of speech features with moderately abnormal cognitive function, so as to obtain the probability of moderately abnormal cognitive function, that is, the similarity between the speech feature data of the answers and the speech feature data in the third sub - database of speech features, and determine it as the sub - result of the third speech feature data comparison, and so on.

[0096] The specific data comparison method can be flexibly set according to the actual situation, and the embodiments of the present application do not make specific limitations in this regard. As an example, artificial intelligence (AI) technology can be used to perform data comparison to obtain the probability of normal cognitive function, the probability of slightly abnormal cognitive function, and the probability of moderately abnormal cognitive function respectively.

[0097] After obtaining the above - mentioned multiple sub - results of speech feature data comparison, they can be summarized to obtain the speech feature data comparison result, which may include, but is not limited to, the probability of normal cognitive function, the probability of slightly abnormal cognitive function, and the probability of moderately abnormal cognitive function.

[0098] In a specific implementation manner of the embodiments of the present application, the data comparison is performed separately from five dimensions: memory, operation, perception, action, and logic. Then, the final speech feature data comparison result may include, but is not limited to, the probability of normal cognitive function, the probability of slightly abnormal cognitive function, and the probability of moderately abnormal cognitive function of the subject in each dimension respectively.

[0099] Step S1052: Compare the eye movement feature data of the answers with the preset eye movement feature database to obtain the eye movement feature data comparison result.

[0100] The eye movement feature database may include multiple sub - databases of eye movement features, where each sub - database of eye movement features corresponds to a type of evaluation result.

[0101] The types of evaluation results may include, but are not limited to, types such as normal, slightly abnormal, and moderately abnormal. Correspondingly, the eye movement feature database may include, but is not limited to, a first eye movement feature data sub-library with normal cognitive function, a second eye movement feature data sub-library with slightly abnormal cognitive function, and a third eye movement feature data sub-library with moderately abnormal cognitive function, etc.

[0102] The eye movement features data for answering questions can be respectively compared with the data in multiple eye movement feature data sub-libraries to obtain multiple sub-results of eye movement feature data comparison. Among them, each sub-result of eye movement feature data comparison respectively corresponds to the probability of a type of evaluation result.

[0103] As an example, the eye movement features data for answering questions can be compared with the data in the first eye movement feature data sub-library with normal cognitive function, so as to obtain the probability of normal cognitive function, that is, the similarity between the eye movement features data for answering questions and the eye movement features data in the first eye movement feature data sub-library, and determine it as the sub-result of the first eye movement feature data comparison; the eye movement features data for answering questions can be compared with the data in the second eye movement feature data sub-library with slightly abnormal cognitive function, so as to obtain the probability of slightly abnormal cognitive function, that is, the similarity between the eye movement features data for answering questions and the eye movement features data in the second eye movement feature data sub-library, and determine it as the sub-result of the second eye movement feature data comparison; the eye movement features data for answering questions can be compared with the data in the third eye movement feature data sub-library with moderately abnormal cognitive function, so as to obtain the probability of moderately abnormal cognitive function, that is, the similarity between the eye movement features data for answering questions and the eye movement features data in the third eye movement feature data sub-library, and determine it as the sub-result of the third eye movement feature data comparison, and so on.

[0104] The specific data comparison method can be flexibly set according to the actual situation, and the embodiments of the present application do not make specific limitations in this regard. As an example, artificial intelligence (AI) technology can be used to perform data comparison to respectively obtain the probability of normal cognitive function, the probability of slightly abnormal cognitive function, and the probability of moderately abnormal cognitive function.

[0105] After obtaining the above-mentioned multiple sub-results of eye movement feature data comparison, they can be summarized to obtain the eye movement feature data comparison result, which may include, but is not limited to, the probability of normal cognitive function, the probability of slightly abnormal cognitive function, and the probability of moderately abnormal cognitive function.

[0106] The data comparison can be respectively carried out from five dimensions of memory, operation, perception, action, and logic, and the final eye movement feature data comparison result may include, but is not limited to, the probability of normal cognitive function, the probability of slightly abnormal cognitive function, and the probability of moderately abnormal cognitive function of the subject in each dimension respectively.

[0107] Step S1053: Determine the second cognitive function evaluation result of the subject according to the comparison results of the voice feature data and the eye movement feature data.

[0108] In the embodiment of the present application, the comparison results of the voice feature data and the eye movement feature data can be summarized to obtain the second cognitive function evaluation result of the subject, which may include but is not limited to the probabilities of normal cognitive function, slightly abnormal cognitive function, and moderately abnormal cognitive function of the subject in each dimension determined based on the answering voice feature data, and the probabilities of normal cognitive function, slightly abnormal cognitive function, and moderately abnormal cognitive function of the subject in each dimension determined based on the answering eye movement feature data.

[0109] Step S106: Determine the comprehensive cognitive function evaluation result of the subject according to the first cognitive function evaluation result and the second cognitive function evaluation result.

[0110] In a specific implementation manner of the embodiment of the present application, the first cognitive function evaluation result and the second cognitive function evaluation result can be summarized to obtain the comprehensive cognitive function evaluation result of the subject, which may include but is not limited to the evaluation scores of the subject in each dimension, the probabilities of normal cognitive function, slightly abnormal cognitive function, and moderately abnormal cognitive function of the subject in each dimension determined based on the answering voice feature data, and the probabilities of normal cognitive function, slightly abnormal cognitive function, and moderately abnormal cognitive function of the subject in each dimension determined based on the answering eye movement feature data.

[0111] In another specific implementation manner of the embodiment of the present application, normalization processing can also be performed based on the first cognitive function evaluation result and the second cognitive function evaluation result to obtain the final evaluation result. The specific normalization processing method can be flexibly set according to the actual situation, and the embodiment of the present application does not make specific limitations on this.

[0112] After determining the comprehensive cognitive function evaluation result of the subject, the comprehensive cognitive function evaluation result can be displayed through a preset man-machine interaction interface; and / or, the comprehensive cognitive function evaluation result can be sent to a preset target device for further processing and analysis by relevant evaluators.

[0113] In summary, the embodiments of the present application obtain the answering results of the subject for a preset cognitive function assessment scale, as well as the answering voice and answering images of the subject during the answering process; perform voice feature analysis on the answering voice to obtain the answering voice feature data of the subject; perform eye movement feature analysis on the answering images to obtain the answering eye movement feature data of the subject; determine the first cognitive function assessment result of the subject according to the answering results; determine the second cognitive function assessment result of the subject according to the answering voice feature data and the answering eye movement feature data; and determine the comprehensive cognitive function assessment result of the subject according to the first cognitive function assessment result and the second cognitive function assessment result. Through the embodiments of the present application, cognitive function assessment can be performed by comprehensively considering the answering results, answering voice feature data, and answering eye movement feature data of the subject, effectively improving the accuracy of the obtained cognitive function assessment results.

[0114] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0115] Corresponding to the cognitive function assessment method described in the above embodiments, Figure 3 FIG. shows a structural diagram of an embodiment of a cognitive function assessment device provided by an embodiment of the present application.

[0116] In this embodiment, a cognitive function assessment device may include:

[0117] A data acquisition module 301, configured to acquire the answering results of the subject for a preset cognitive function assessment scale, as well as the answering voice and answering images of the subject during the answering process;

[0118] A voice feature analysis module 302, configured to perform voice feature analysis on the answering voice to obtain the answering voice feature data of the subject;

[0119] An eye movement feature analysis module 303, configured to perform eye movement feature analysis on the answering images to obtain the answering eye movement feature data of the subject;

[0120] A first assessment result determination module 304, configured to determine the first cognitive function assessment result of the subject according to the answering results;

[0121] A second assessment result determination module 305, configured to determine the second cognitive function assessment result of the subject according to the answering voice feature data and the answering eye movement feature data;

[0122] The comprehensive evaluation result determination module 306 is configured to determine the comprehensive cognitive function evaluation result of the subject according to the first cognitive function evaluation result and the second cognitive function evaluation result.

[0123] In a specific implementation manner of the embodiment of the present application, the second evaluation result determination module may include:

[0124] The voice feature data comparison unit is configured to compare the answering voice feature data with a preset voice feature database to obtain a voice feature data comparison result;

[0125] The eye movement feature data comparison unit is configured to compare the answering eye movement feature data with a preset eye movement feature database to obtain an eye movement feature data comparison result;

[0126] The second evaluation result determination unit is configured to determine the second cognitive function evaluation result of the subject according to the voice feature data comparison result and the eye movement feature data comparison result.

[0127] In a specific implementation manner of the embodiment of the present application, the voice feature database may include multiple voice feature data sub - databases, where each voice feature data sub - database corresponds to an evaluation result type respectively;

[0128] The voice feature data comparison unit may be specifically configured to: respectively compare the answering voice feature data with multiple voice feature data sub - databases to obtain multiple voice feature data comparison sub - results; where each voice feature data comparison sub - result corresponds to the probability of an evaluation result type respectively; and summarize the multiple voice feature data comparison sub - results to obtain the voice feature data comparison result.

[0129] In a specific implementation manner of the embodiment of the present application, the eye movement feature database may include multiple eye movement feature data sub - databases, where each eye movement feature data sub - database corresponds to an evaluation result type respectively;

[0130] The eye movement feature data comparison unit may be specifically configured to: respectively compare the answering eye movement feature data with multiple eye movement feature data sub - databases to obtain multiple eye movement feature data comparison sub - results; where each eye movement feature data comparison sub - result corresponds to the probability of an evaluation result type respectively; and summarize the multiple eye movement feature data comparison sub - results to obtain the eye movement feature data comparison result.

[0131] In a specific implementation manner of the embodiment of the present application, the cognitive function evaluation device may further include:

[0132] The cognitive function evaluation mode determination module is configured to determine the cognitive function evaluation mode of the subject;

[0133] A cognitive function assessment scale generation module, configured to generate a cognitive function assessment scale corresponding to the cognitive function assessment mode for the subject to answer questions.

[0134] In a specific implementation manner of an embodiment of the present application, the cognitive function assessment scale generation module may specifically be configured to: extract cognitive function assessment questions corresponding to the cognitive function assessment mode from a preset question database; generate the cognitive function assessment scale according to the extracted cognitive function assessment questions.

[0135] In a specific implementation manner of an embodiment of the present application, the cognitive function assessment device may further include:

[0136] A cognitive function assessment result display module, configured to display the comprehensive cognitive function assessment result through a preset human-computer interaction interface; and / or send the comprehensive cognitive function assessment result to a preset target device.

[0137] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices, modules, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0138] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0139] Figure 4 A schematic block diagram of an electronic device provided by an embodiment of the present application is shown. For the convenience of description, only parts related to the embodiment of the present application are shown.

[0140] As Figure 4 shown, the electronic device 4 of this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, the steps in the above-mentioned various cognitive function assessment method embodiments are implemented, such as Figure 1 the steps S101 to S106 shown. Alternatively, when the processor 40 executes the computer program 42, the functions of the various modules / units in the above-mentioned device embodiments are implemented, such as Figure 3 the functions of the modules 301 to 306 shown.

[0141] Exemplarily, the computer program 42 can be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 42 in the electronic device 4.

[0142] The electronic device 4 can include, but is not limited to, computing devices such as mobile phones, tablet computers, desktop computers, notebooks, palmtop computers, robots, and servers. Those skilled in the art can understand that Figure 4 merely examples of the electronic device 4, which do not constitute a limitation on the electronic device 4, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device 4 may further include input / output devices, network access devices, buses, etc.

[0143] The processor 40 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0144] The memory 41 can be an internal storage unit of the electronic device 4, such as the hard disk or memory of the electronic device 4. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 41 can also include both the internal storage unit and the external storage device of the electronic device 4. The memory 41 is used to store the computer program and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules according to needs, 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. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0146] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0148] In the embodiments provided in the present application, it should be understood that the disclosed device / electronic device and method can be implemented in other ways. For example, the device / electronic device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there can be other division methods in actual implementation. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0149] 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 can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] In addition, the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically alone, 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 a software functional unit.

[0151] If the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, all or part of the processes in the above-mentioned embodiment methods of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium may 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, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0152] The above-mentioned embodiments 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various 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: include: Obtaining the subject's answer results for a preset cognitive function assessment scale, as well as the subject's answering voice and answering image during the answering process; Performing voice feature analysis on the answering voice to obtain the answering voice feature data of the subject; Performing eye movement feature analysis on the answering image to obtain the answering eye movement feature data of the subject; Determining a first cognitive function assessment result of the subject according to the answer result; Determining a second cognitive function assessment result of the subject according to the answering voice feature data and the answering eye movement feature data; Determine the comprehensive cognitive function assessment result of the subject based on the first cognitive function assessment result and the second cognitive function assessment result.

2. The cognitive function assessment method according to claim 1, characterized in that: Determining the second cognitive function assessment result of the subject according to the answering voice feature data and the answering eye movement feature data includes: Comparing the answering voice feature data with a preset voice feature database to obtain a voice feature data comparison result; Comparing the eye movement feature data of the answering question in a preset eye movement feature database to obtain an eye movement feature data comparison result; The second cognitive function assessment result of the subject is determined according to the comparison result of the speech feature data and the comparison result of the eye movement feature data.

3. The cognitive function assessment method according to claim 2, characterized in that: The speech feature database includes a plurality of speech feature data sub-databases, wherein each speech feature data sub-database corresponds to a type of evaluation result; The step of comparing the answering voice feature data with a preset voice feature database to obtain a voice feature data comparison result includes: Comparing the answering voice feature data in a plurality of voice feature data sub-databases respectively to obtain a plurality of voice feature data comparison sub-results; wherein each voice feature data comparison sub-result corresponds to a probability of a type of evaluation result; A plurality of speech feature data comparison sub-results are aggregated to obtain the speech feature data comparison result.

4. The cognitive function assessment method according to claim 2, characterized in that: The eye movement feature database includes a plurality of eye movement feature data sub-libraries, wherein each eye movement feature data sub-library corresponds to a type of evaluation result; The step of comparing the eye movement feature data of the question answering with a preset eye movement feature database to obtain the eye movement feature data comparison result includes: Comparing the eye movement feature data of the answer in multiple eye movement feature data sub-databases respectively to obtain multiple eye movement feature data comparison sub-results; wherein each eye movement feature data comparison sub-result corresponds to the probability of one type of evaluation result; A plurality of eye movement feature data comparison sub-results are summarized to obtain the eye movement feature data comparison result.

5. The cognitive function assessment method according to claim 1, characterized in that: Before obtaining the subject's answer results for a preset cognitive function assessment scale, as well as the subject's answering voice and answering image during the answering process, the method further includes: determining a cognitive function assessment pattern for the subject; A cognitive function assessment scale corresponding to the cognitive function assessment model is generated for the subject to answer questions.

6. The cognitive function assessment method according to claim 5, characterized in that: The generating of a cognitive function assessment scale corresponding to the cognitive function assessment mode comprises: Extracting cognitive function assessment questions corresponding to the cognitive function assessment mode from a preset question database; The cognitive function assessment scale is generated according to the extracted cognitive function assessment questions.

7. The method for evaluating cognitive function according to any one of claims 1 to 6, characterized in that: After determining the comprehensive cognitive function assessment result of the subject according to the first cognitive function assessment result and the second cognitive function assessment result, the method further includes: Displaying the comprehensive cognitive function assessment results through a preset human-computer interaction interface; and / or The comprehensive cognitive function assessment result is sent to a preset target device.

8. A cognitive function assessment device, characterized in that: include: A data acquisition module is used to obtain the subject's answer results for a preset cognitive function assessment scale, as well as the subject's answering voice and answering image during the answering process; A voice feature analysis module, used to perform voice feature analysis on the answering voice to obtain the answering voice feature data of the subject; An eye movement feature analysis module, used to perform eye movement feature analysis on the answering image to obtain the eye movement feature data of the subject's answering; A first assessment result determination module, used to determine a first cognitive function assessment result of the subject according to the answer result; A second evaluation result determination module is used to determine a second cognitive function evaluation result of the subject according to the answering voice feature data and the answering eye movement feature data; A comprehensive assessment result determination module is used to determine the comprehensive cognitive function assessment result of the subject based on the first cognitive function assessment result and the second cognitive function assessment result.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the cognitive function assessment method according to any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the cognitive function assessment method according to any one of claims 1 to 7 are implemented.

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