DSM-V-based brain cognition assessment method and system, storage medium and computer equipment

Abstract

The invention relates to the technical field of cognitive assessment, and particularly discloses a DSM-V-based brain cognitive assessment method and system, a storage medium and computer equipment. The method comprises the following steps: firstly acquiring recognition feature information and posture feature information of a user, then performing cognitive disorder recognition on the recognition feature information based on DSM-V, endowing a first evaluation score, then acquiring emotion feature information of the user, and acquiring a negative emotion value according to the emotion feature information. Then acquiring an action coordination abnormity index according to the behavior characteristic information, finally acquiring a second evaluation score according to the first evaluation score, the negative emotion value and the action coordination abnormity index, and evaluating the user according to the second evaluation score. Therefore, the accuracy of the DSM-V primary evaluation can be corrected by comprehensively evaluating the user through the second evaluation score, so that the influence of additional factors of the user on cognitive description can be avoided, and the accuracy of cognitive evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cognitive assessment, and in particular to a method, system, storage medium and computer device for brain cognitive assessment based on DSM-V. Background Art

[0002] With the development of society and the intensification of population aging, the incidence of diseases related to brain cognitive dysfunction has gradually increased. Accurate assessment of brain cognitive function is of vital importance for early diagnosis, disease monitoring and the formulation of personalized treatment plans. DSM-V, as the authoritative standard for the diagnosis of mental disorders, provides an important theoretical basis and framework for brain cognitive assessment. DSM-V is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V for short), which is a manual on the classification and diagnostic criteria of mental disorders.

[0003] Currently, brain cognition assessment usually adopts a question-and-answer or test-taking mode to score and judge users. However, this method may not accurately describe the user's cognitive symptoms due to additional factors (such as the user's own emotions and behavioral abnormalities). This single test-taking mode will result in low assessment accuracy. Therefore, a brain cognition assessment method based on DSM-V is needed to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a brain cognitive assessment method, system, storage medium and computer device based on DSM-V to solve the technical problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: Obtaining identification feature information and posture feature information of the user; Based on DSM-V, cognitive impairment is classified based on the identification feature information, and the corresponding degree of brain cognitive impairment is obtained according to the type of cognitive impairment classification; Assigning a score to the user's cognitive impairment level based on the cognitive impairment level to obtain a first evaluation score; Acquire the user's emotional characteristic information, and acquire depression characteristic information and anxiety characteristic information according to the emotional characteristic information, and acquire a negative emotion value according to the depression characteristic information; Acquire posture feature information according to the behavior feature information, and acquire a movement coordination abnormality index according to the posture feature information and the anxiety feature information; A second evaluation score is obtained according to the first evaluation score, the negative emotion value and the movement coordination abnormality index, and the user's brain cognition is evaluated according to the second evaluation score.

[0006] Preferably, the step of acquiring depression characteristic information and anxiety characteristic information according to the emotion characteristic information comprises: Acquiring the user's mental state characteristics according to the emotional characteristic information; Obtaining the duration of the user's low mood within a preset time according to the mental state characteristics; Obtain the user's prescription information based on the drug purchasing system, and screen the prescription information to see whether there is depression-related drug information; If the prescription information contains depression-related drug information, it is determined that the user has potential depression characteristics, and depression characteristic information is obtained based on the potential depression characteristics and the duration of low mood; Acquire the user's physiological response characteristics according to the emotional characteristic information, and acquire the heartbeat characteristics and breathing characteristics according to the physiological response characteristics; Acquire the corresponding abnormal heartbeat frequency and abnormal heartbeat duration according to the heartbeat characteristics, and acquire the abnormal heartbeat frequency according to the abnormal heartbeat times and abnormal heartbeat duration; The duration of shortness of breath and the number of shortness of breath within a preset time are obtained according to the breathing characteristics, and the frequency of shortness of breath is obtained according to the duration of shortness of breath and the number of shortness of breath, and the comprehensive abnormal frequency is obtained according to the abnormal heartbeat frequency and the shortness of breath frequency, and the anxiety state corresponding to the comprehensive abnormal frequency is used as anxiety characteristic information.

[0007] Preferably, the step of obtaining a negative emotion value according to the depression feature information comprises: Acquiring the number of negative characteristics according to the depression characteristic information, wherein the negative characteristics include language negative characteristics, attention negative characteristics and physical negative characteristics; A two-dimensional coordinate system is established according to the negative language features, negative attention features, negative body features and duration of low mood; Acquire multiple overlapping nodes of language negative features, attention negative features and body negative features within the duration of depression according to the two-dimensional coordinate system; The corresponding overlap frequencies are acquired according to the multiple overlap nodes, and the overlap frequencies are used as negative sentiment values.

[0008] Preferably, the step of obtaining the movement coordination abnormality index according to the posture feature information and the anxiety feature information comprises: Acquire a first center of gravity position of the user in an initial static state and a plurality of second center of gravity positions within a preset time according to the posture feature information; Acquire the number of gravity center shifts of the user according to the first gravity center position and the plurality of second gravity center positions; Acquire the posture abnormality frequency according to the center of gravity offset times; The frequency abnormality rate is obtained according to the ratio of the comprehensive abnormal frequency and posture abnormality frequency corresponding to the anxiety feature information to the preset standard frequency, and the frequency abnormality rate is used as the movement coordination abnormality index.

[0009] Preferably, the step of obtaining a second evaluation score according to the first evaluation score, the negative emotion value and the movement coordination abnormality index comprises: Acquire a corresponding first ideal value and a maximum first evaluation value according to the first evaluation score; Acquire a corresponding second ideal value and a maximum negative emotion value according to the negative emotion value; According to the action coordination abnormality index, a corresponding third ideal value and a maximum action coordination abnormality index are obtained; The second evaluation score is calculated according to the first evaluation score, the negative emotion value, the action coordination abnormality index, the first ideal value, the maximum first evaluation value, the second ideal value, the maximum negative emotion value, the third ideal value and the maximum action coordination abnormality index, wherein the calculation formula is: ; Where D(PG) represents the second evaluation score, where the value range of the second evaluation score is [0, +∞], J(PG) represents the first evaluation score, X(XJ) represents the negative emotion value, F(ZH) represents the abnormal coordination index, J ideal represents the first ideal value, J max represents the maximum first evaluation value, X ideal represents the second ideal value, X max Indicates the maximum negative sentiment value, F ideal represents the third ideal value, F max Represents the maximum motor coordination abnormality index.

[0010] Preferably, the step of evaluating the user according to the second evaluation score further includes: Calculating an evaluation difference according to the first evaluation score and the second evaluation score; Determine whether the evaluation difference satisfies a preset threshold interval range; When the evaluation difference does not meet the preset threshold interval range and is less than the minimum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is lower than the current cognitive impairment level; When the evaluation difference satisfies the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is the current level of cognitive impairment; When the evaluation difference does not satisfy the preset threshold interval range and is greater than the maximum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is higher than the current cognitive impairment level.

[0011] Preferably, after the step of evaluating the user according to the second evaluation score, the method further comprises: Obtaining negative emotion keywords according to the negative emotion values, extracting negative emotion factors according to the negative emotion keywords, mapping the negative emotion factors to the coding space according to a preset receiving timing for coding, and obtaining negative emotion coding; Acquire the action abnormality type according to the action coordination abnormality index, extract the abnormal offset factor according to the action abnormality type, map the abnormal offset factor to the coding space according to the preset receiving timing to encode, and obtain the abnormal offset code; Acquire cognitive feature information of the user in the next preset period, and map the cognitive feature information to a coding space for encoding according to a preset receiving timing to obtain cognitive feature coding; Constructing a reference sample of a clustering model according to the negative emotion coding and the abnormal deviation coding to obtain an expanded clustering model; The cognitive feature codes are input into the expansion clustering model, and the cognitive feature codes are grouped and extracted through K-means clustering to obtain the first negative emotion code and the first abnormal deviation code.

[0012] The present application also provides a brain cognitive assessment system based on DSM-V, including: A first acquisition module is used to acquire identification feature information and posture feature information of the user; The first recognition module is used to classify cognitive impairment based on the recognition feature information based on DSM-V, and obtain the corresponding degree of brain cognitive impairment according to the type obtained by the cognitive impairment classification; A second acquisition module is used to assign a score to the user's cognitive impairment level based on the cognitive impairment level to obtain a first evaluation score; A third acquisition module is used to acquire the user's emotional characteristic information, and acquire depression characteristic information and anxiety characteristic information according to the emotional characteristic information, and acquire a negative emotion value according to the depression characteristic information; A fourth acquisition module, acquiring a movement coordination abnormality index according to the posture feature information and the anxiety feature information; The fifth acquisition module is used to obtain a second evaluation score based on the first evaluation score, the negative emotion value and the movement coordination abnormality index, and evaluate the user's brain cognition based on the second evaluation score.

[0013] The present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0014] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0015] The beneficial effects of the present application are as follows: the present application first obtains the identification feature information and posture feature information of the user, then identifies cognitive impairment on the identification feature information based on DSM-V, and assigns a first evaluation score, then obtains the emotional feature information of the user, and obtains a negative emotion value based on the emotional feature information, and then obtains a motion coordination abnormality index based on the behavioral feature information, and finally obtains a second evaluation score based on the first evaluation score, the negative emotion value and the motion coordination abnormality index, and evaluates the user's brain cognition based on the second evaluation score, so that a comprehensive evaluation of the user through the second evaluation score can correct the accuracy of the initial DSM-V evaluation, thereby preventing the user's additional factors (such as their own emotions and behavioral abnormalities) from affecting the cognitive description and improving the accuracy of the cognitive evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present application.

[0017] Figure 2 A schematic diagram of the system structure of an embodiment of the present application.

[0018] Figure 3 A schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0019] Figure 4 A schematic diagram of a two-dimensional coordinate system according to an embodiment of the present application.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] like Figure 1-3 As shown, the present application provides a brain cognitive assessment method based on DSM-V, comprising: S1. Obtaining identification feature information and posture feature information of the user; S2, classifying cognitive impairment based on the identification feature information based on DSM-V, and obtaining the corresponding degree of brain cognitive impairment according to the type of cognitive impairment classification; S3, assigning a score to the user's cognitive impairment level based on the cognitive impairment level to obtain a first evaluation score; S4, obtaining the user's emotional characteristic information, and obtaining depression characteristic information and anxiety characteristic information according to the emotional characteristic information, and obtaining a negative emotion value according to the depression characteristic information; S5, acquiring posture feature information according to the behavior feature information, and acquiring a movement coordination abnormality index according to the posture feature information and the anxiety feature information; S6. Obtain a second evaluation score based on the first evaluation score, the negative emotion value and the movement coordination abnormality index, and evaluate the user's brain cognition based on the second evaluation score.

[0023] As described in the above steps S1-S6, since the current brain cognitive assessment usually adopts a question-and-answer or question-answering method to score and judge the user, this method will not be able to accurately describe the user's cognitive symptoms due to the user's additional factors (such as their own emotions and abnormal behaviors). This single question-answering mode will result in low assessment accuracy. Therefore, the present invention first obtains the user's identification feature information and posture feature information, so that data related to the user can be collected from multiple angles, avoiding the limitations of a single information source and providing a basis for the accuracy of the assessment. Then, based on DSM-V, the identification feature information is classified into cognitive impairments, and the corresponding degree of brain cognitive impairment is obtained according to the type obtained by the cognitive impairment classification. Then, the user's cognitive impairment degree is scored based on the cognitive impairment degree to obtain a first assessment score. In this way, the existing DSM-V is used as the basic assessment basis, and then the user's cognitive impairment level can be preliminarily screened out, and the cognitive impairment level assessment is quantitatively reflected by the scoring method. Since the user will be affected by his own emotions during the cognitive diagnosis process, there will be resistance, randomness, or understanding of emotions caused by academic cognition, which will cause his own emotions to seriously affect the cognitive assessment. Therefore, it is necessary to obtain the user's emotional feature information and, according to the The emotional characteristic information obtains depression characteristic information and anxiety characteristic information, and obtains a negative emotion value according to the depression characteristic information, and then obtains posture characteristic information according to the behavioral characteristic information, and obtains an abnormal movement coordination index according to the posture characteristic information and the anxiety characteristic information. In this way, the abnormal movement coordination index can provide a deeper understanding of the user's psychological state, and the quantitative value of the abnormal movement coordination index can more accurately measure the degree of influence of these emotional problems on the overall state of the user. At the same time, because the posture and movement coordination ability reflect the control and regulation function of the brain on body movement, and are closely related to multiple cognitive fields of the brain, such as the executive function of the frontal lobe, the movement coordination function of the cerebellum, etc., the abnormal movement coordination index can objectively reflect the abnormal degree of the user's movement coordination, and provide an important basis for evaluating the cognitive function of the brain. Finally, the second evaluation score is obtained according to the first evaluation score, the negative emotion value and the abnormal movement coordination index, and the user's brain cognition is evaluated according to the second evaluation score. In this way, the comprehensive evaluation of the user through the second evaluation score can correct the accuracy of the initial evaluation of DSM-V, thereby preventing the user's additional factors (such as their own emotions and behavioral abnormalities) from affecting the cognitive description, and can improve the accuracy of cognitive evaluation.

[0024] In one embodiment, the step S4 of acquiring depression characteristic information and anxiety characteristic information according to the emotion characteristic information includes: S401, obtaining the user's mental state characteristics according to the emotion characteristic information; S402, obtaining the duration of the user's low mood within a preset time according to the mental state characteristics; S403, obtaining the user's prescription information based on the drug purchasing system, and screening whether the prescription information contains depression-related drug information; If the prescription information contains depression-related drug information, it is determined that the user has potential depression characteristics, and depression characteristic information is obtained based on the potential depression characteristics and the duration of low mood; S405, obtaining a physiological response characteristic of the user according to the emotional characteristic information, and obtaining a heartbeat characteristic and a breathing characteristic according to the physiological response characteristic; S406, acquiring the corresponding abnormal heartbeat frequency and abnormal heartbeat duration according to the heartbeat characteristics, and acquiring the abnormal heartbeat frequency according to the abnormal heartbeat times and abnormal heartbeat duration; S407. Obtain the duration of shortness of breath and the number of shortness of breath within a preset time according to the breathing characteristics, and obtain the frequency of shortness of breath according to the duration of shortness of breath and the number of shortness of breath, and obtain the comprehensive abnormal frequency according to the abnormal heartbeat frequency and the shortness of breath frequency, and use the anxiety state corresponding to the comprehensive abnormal frequency as anxiety characteristic information.

[0025] As described in the above steps S401-S407, since the user's emotional characteristics can be comprehensively judged from the appearance and physical index values, the present invention obtains the user's mental state characteristics according to the emotional characteristic information, and then obtains the duration of the user's low mood within a preset time according to the mental state characteristics, wherein the acquisition method is based on shooting a video, and obtaining the user's facial state based on facial recognition, and then extracting the time when the low mood facial state appears within the preset time, so that the severity of the user's depression can be judged by the duration of low mood, wherein short-term low mood may be related to factors such as stress events, while long-term continuous low mood is one of the factors that reflect the depressive characteristics. When obtaining whether the user has depressive characteristics, the user's prescription information can be obtained based on the drug purchase system, and the prescription information can be screened for depression-related drug information. If the prescription information contains depression-related drug information, it is determined that the user has potential depressive characteristics. Among them, combined with the duration of low mood, it can be more accurately judged whether the user has depressive symptoms, and then the depression characteristic information can be comprehensively obtained according to the duration of low mood and the potential depressive characteristics, and then the depression characteristic information can provide an important influencing factor for cognitive judgment and provide an important basis for cognitive evaluation and judgment.

[0026] When people are anxious, their heart rate and breathing will accelerate, which are manifestations of anxiety characteristics. At the same time, when in an anxious state, the body will automatically enter a state of stress, and a large amount of physiological resources will be allocated to the system to deal with potential threats. However, the brain's resources for cognitive processing are relatively limited. In this case, the resources originally allocated to cognitive functions such as attention, memory, and thinking will be partially transferred to deal with the body's stress response, resulting in a relative shortage of resources required for cognitive processing, which will in turn lead to insufficient user cognition. On this basis, it is necessary to first obtain the user's physiological response characteristics based on the emotional feature information, and obtain the heartbeat characteristics and breathing characteristics based on the physiological response characteristics, and then obtain the corresponding abnormal heartbeat frequency and abnormal heartbeat based on the heartbeat characteristics. The normal duration is obtained, and the abnormal heartbeat frequency is obtained according to the number of abnormal heartbeats and the abnormal heartbeat duration. Finally, the duration of shortness of breath and the number of shortness of breath within a preset time are obtained according to the respiratory characteristics, and the shortness of breath frequency is obtained according to the shortness of breath duration and the number of shortness of breath, and the comprehensive abnormal frequency is obtained according to the abnormal heartbeat frequency and the shortness of breath frequency, and the anxiety state corresponding to the comprehensive abnormal frequency is used as the anxiety feature information. In this way, the reactions of two important physiological systems, the cardiovascular system and the respiratory system, are comprehensively considered, so that the judgment of anxiety characteristics is more comprehensive and objective. This method of comprehensive evaluation of multiple physiological indicators can more accurately capture anxiety symptoms, avoid misjudgment caused by fluctuations of a single physiological indicator, and also provide a quantitative basis for the influence of additional factors in the evaluation process.

[0027] In one embodiment, the step S4 of acquiring the negative emotion value according to the depression feature information includes: S408, acquiring the number of negative characteristics according to the depression characteristic information, wherein the negative characteristics include language negative characteristics, attention negative characteristics and physical negative characteristics; S409, establishing a two-dimensional coordinate system according to the negative language characteristics, negative attention characteristics, negative physical characteristics and duration of depression; S4010, acquiring multiple overlapping nodes of language negative features, attention negative features, and body negative features within the duration of depression according to the two-dimensional coordinate system; S4011. Obtain corresponding overlap frequencies according to the multiple overlap nodes, and use the overlap frequencies as negative sentiment values.

[0028] As described in the above steps S408-S4011, since the negative state can be judged from the descriptive words of the language to determine whether there is a negative language feature, and combined with the negative attention features of difficulty in concentrating, easy distraction and excessive attention to negative information, and combined with the negative physical features of listlessness, slow movement and lack of vitality, the negative state of the user can be comprehensively judged. Therefore, the present invention first obtains the number of negative features according to the depression feature information, wherein the negative features include negative language features, negative attention features and negative physical features. In this way, the above-mentioned subdivided features can be used to verify the influence of additional factors of the user on cognitive evaluation. Then, a two-dimensional coordinate system is established according to the negative language features, negative attention features, negative physical features and the duration of low mood, wherein three points on the Y axis of the two-dimensional coordinate system represent the negative language features, negative attention features and negative physical features respectively, and the negative language features count the negative words (such as "despair", "failure", "pain" and so on) within the duration of low mood. The negative attention feature counts the number of times the user is easily distracted and has difficulty concentrating in daily activities (such as reading, working, studying, etc.) during the duration of low mood. The negative body feature records the user's body posture through video and counts the number of body posture (such as hunchback, drooping head and shoulders), body movements (such as slow movements and weakness), and facial expressions (such as frowning and dull eyes). The X-axis represents the duration of low mood. In this way, multiple overlapping nodes of language negative features, attention negative features, and body negative features within the duration of low mood can be obtained according to the two-dimensional coordinate system (such as Figure 4 As shown), then the corresponding overlap frequencies are obtained according to the multiple overlapped nodes, and the overlapped frequencies are used as negative emotion values. In this way, the number and distribution of overlapped nodes reflect the synergy and stability between these features. The more the number and the denser the distribution, the stronger the correlation between these negative features, and the more stable and serious the user's negative emotional state. At the same time, negative emotions can be quantified. This quantification method comprehensively considers multiple depressive features and their temporal relationship. Compared with a single indicator evaluation, it can more accurately reflect the overall negative emotion intensity of the user. For example, if a user frequently experiences negative language, lack of concentration, and physical malaise at the same time over a period of time (many overlapped nodes), his negative emotion value will be higher, indicating that his depression is more serious; conversely, if there are fewer overlapped nodes, the negative emotion value will be relatively low, and the negative emotion value can be used as an important basis for evaluating cognitive judgment.

[0029] In one embodiment, the step S5 of acquiring the movement coordination abnormality index according to the posture feature information and the anxiety feature information comprises: S501, acquiring a first center of gravity position of a user in an initial static state and a plurality of second center of gravity positions within a preset time according to the posture feature information; S502, obtaining the number of gravity center shifts of the user according to the first gravity center position and a plurality of the second gravity center positions; S503, obtaining the posture abnormality frequency according to the number of center of gravity shifts; S504. Obtain a frequency abnormality rate according to the ratio of the comprehensive abnormal frequency and posture abnormality frequency corresponding to the anxiety characteristic information to a preset standard frequency, and use the frequency abnormality rate as a movement coordination abnormality index.

[0030] As described in the above steps S501-S504, since there is a certain degree of overlap and interconnection between the brain regions responsible for motor coordination and cognitive functions, for example, the cerebellum not only plays a key role in regulating the coordination, balance and precision of body movements, but also participates in some cognitive processes, such as attention, language processing and working memory. When motor coordination is abnormal, it may indicate that the function of the cerebellum or its related neural pathways is impaired, which in turn affects the activities of the brain regions connected to it that are involved in cognitive functions, resulting in a decline in cognitive judgment ability. Therefore, the present invention first obtains the first center of gravity position of the user in the initial static state and multiple second center of gravity positions within a preset time according to the posture feature information, and then obtains the number of center of gravity shifts of the user according to the first center of gravity position and the multiple second center of gravity positions. Calculating the number of center of gravity shifts can convert the posture feature into a quantifiable indicator. The more center of gravity shifts, the greater the challenge faced by the user in maintaining a stable posture, which may suggest that there are problems with his motor coordination ability or proprioception. This quantification method can more objectively describe the degree of posture abnormality of the user, avoiding the uncertainty of relying solely on subjective observation. Then, the posture abnormality frequency is obtained according to the number of center of gravity shifts, and then the frequency abnormality rate is obtained according to the comprehensive abnormality frequency corresponding to the anxiety feature information and the ratio of the posture abnormality frequency to the preset standard frequency, and the frequency abnormality rate is used as the motion coordination abnormality index. In this way, the motion coordination abnormality index can provide a unified indicator to measure the degree of abnormality of the user in motion coordination, and can provide comprehensive data support for evaluating the user's cognitive impairment, avoiding the one-sidedness that may be caused by evaluating anxiety or posture features alone.

[0031] In one embodiment, the step S6 of obtaining the second evaluation score according to the first evaluation score, the negative emotion value and the action coordination abnormality index includes: S601, acquiring a corresponding first ideal value and a maximum first evaluation value according to the first evaluation score; S602, acquiring a corresponding second ideal value and a maximum negative emotion value according to the negative emotion value; S603, acquiring a corresponding third ideal value and a maximum action coordination abnormality index according to the action coordination abnormality index; S604: Calculate a second evaluation score according to the first evaluation score, the negative emotion value, the action coordination abnormality index, the first ideal value, the maximum first evaluation value, the second ideal value, the maximum negative emotion value, the third ideal value and the maximum action coordination abnormality index, wherein the calculation formula is: ; Where D(PG) represents the second evaluation score, where the value range of the second evaluation score is [0, +∞], J(PG) represents the first evaluation score, X(XJ) represents the negative emotion value, F(ZH) represents the abnormal coordination index, J ideal represents the first ideal value, J max represents the maximum first evaluation value, X ideal represents the second ideal value, X max Indicates the maximum negative sentiment value, F ideal represents the third ideal value, F max Represents the maximum motor coordination abnormality index.

[0032] As described in the above steps S601-S602, the present invention first obtains the corresponding first ideal value and the maximum first evaluation value according to the first evaluation score, and then obtains the corresponding second ideal value and the maximum negative emotion value according to the negative emotion value, and then obtains the corresponding third ideal value and the maximum movement coordination abnormality index according to the movement coordination abnormality index. Finally, the second evaluation score is calculated according to the first evaluation score, the negative emotion value, the movement coordination abnormality index, the first ideal value, the maximum first evaluation value, the second ideal value, the maximum negative emotion value, the third ideal value and the maximum movement coordination abnormality index. In this way, by calculating the distance between the first evaluation score, the negative emotion value and the movement coordination abnormality index and their respective ideal state values, the overall state of the brain cognition patient is comprehensively evaluated, thereby obtaining the second evaluation score. The larger the distance, the farther the indicator deviates from the ideal state, and the greater the impact on the overall evaluation, so that the user's cognitive state can be more accurately evaluated through the second evaluation score.

[0033] In one embodiment, the step S6 of evaluating the user according to the second evaluation score further includes: S603, calculating an evaluation difference according to the first evaluation score and the second evaluation score; S604, determining whether the evaluation difference satisfies a preset threshold range; When the evaluation difference does not meet the preset threshold interval range and is less than the minimum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is lower than the current cognitive impairment level; When the evaluation difference satisfies the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is the current level of cognitive impairment; When the evaluation difference does not satisfy the preset threshold interval range and is greater than the maximum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is higher than the current cognitive impairment level.

[0034] As described in the above steps S603-S604, the present application first calculates the evaluation difference based on the first evaluation score and the second evaluation score, and then determines whether the evaluation difference satisfies the preset threshold interval range. When the evaluation difference does not satisfy the preset threshold interval range and is less than the minimum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is lower than the current cognitive impairment level. When the evaluation difference satisfies the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is the current cognitive impairment level. When the evaluation difference does not satisfy the preset threshold interval range and is greater than the maximum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is higher than the current cognitive impairment level. In this way, the evaluation of the first evaluation score can be corrected through the evaluation difference, which can avoid the user's additional factors (such as their own emotions and abnormal behaviors) from affecting the cognitive description and can improve the accuracy of cognitive evaluation.

[0035] In one embodiment, after step S6 of evaluating the user according to the second evaluation score, the following steps are included: S7, obtaining negative emotion keywords according to the negative emotion value, and extracting negative emotion factors according to the negative emotion keywords, mapping the negative emotion factors to the coding space according to a preset receiving timing for coding, and obtaining negative emotion coding; S8. Obtaining an abnormal action type according to the abnormal action coordination index, extracting an abnormal offset factor according to the abnormal action type, mapping the abnormal offset factor to a coding space according to a preset receiving timing for coding, and obtaining an abnormal offset code; S9, obtaining cognitive feature information of the user in the next preset period, and mapping the cognitive feature information to the coding space for coding according to the preset receiving timing to obtain cognitive feature coding; S10, constructing a reference sample of a clustering model according to the negative emotion coding and the abnormal deviation coding to obtain an expanded clustering model, S11. Input the cognitive feature code into the expansion clustering model, and extract the cognitive feature code by grouping through K-means clustering to obtain a first negative emotion code and a first abnormal deviation code.

[0036] As described in the above steps S7-S11, after the initial evaluation, the user needs to repeat the first step for evaluation in the evaluation process of the next preset cycle, which takes up time. Therefore, the present invention first obtains negative emotion keywords according to the negative emotion value, and extracts negative emotion factors according to the negative emotion keywords, maps the negative emotion factors to the coding space according to the preset receiving timing for encoding, and obtains negative emotion coding, and then obtains the action abnormality type according to the action coordination abnormality index, and extracts the abnormal offset factor according to the action abnormality type, maps the abnormal offset factor to the coding space according to the preset receiving timing for encoding, and obtains the abnormal offset coding, and then obtains the user's next preset cycle cognitive feature information, and maps the cognitive feature information to the coding space according to the preset receiving timing for encoding, and obtains the cognitive feature ... The coding and the abnormal offset coding are used to construct a reference sample of the clustering model to obtain an expanded clustering model. Finally, the cognitive feature coding is input into the expanded clustering model, and the cognitive feature coding is grouped and extracted through K-means clustering to obtain a first negative emotion coding and a first abnormal offset coding. The specific steps are: the specific steps are to divide the cognitive feature coding into K clusters, and then each cluster is represented by a centroid. The clustering model algorithm continuously optimizes the centroid position in an iterative manner to minimize the distance between the samples in the same cluster and the centroid. For example, for the extraction of cognitive feature coding, the negative emotion coding and the abnormal offset coding are regarded as each cluster with a centroid, and then the cognitive feature coding is regarded as a sample, so that the cognitive feature coding is close to the negative emotion coding and the abnormal offset coding, so that the same coding can be clustered to obtain the first negative emotion coding and the first abnormal offset coding, and then by obtaining the first negative emotion coding and the first abnormal offset coding, it is possible to quickly provide a convenient basis for obtaining additional factors (negative emotion value and action coordination abnormality index) of the user for the next evaluation.

[0037] The present application also provides a brain cognitive assessment system based on DSM-V, including: The first acquisition module 1 is used to acquire the user's identification feature information and posture feature information; The first recognition module 2 is used to classify cognitive impairment based on the recognition feature information based on DSM-V, and obtain the corresponding degree of brain cognitive impairment according to the type obtained by the cognitive impairment classification; A second acquisition module 3 is used to assign a score to the user's cognitive impairment level based on the cognitive impairment level to obtain a first evaluation score; The third acquisition module 4 is used to acquire the user's emotional characteristic information, and acquire depression characteristic information and anxiety characteristic information according to the emotional characteristic information, and acquire a negative emotion value according to the depression characteristic information; A fourth acquisition module 5, acquiring a movement coordination abnormality index according to the posture feature information and the anxiety feature information; The fifth acquisition module 6 is used to obtain a second evaluation score according to the first evaluation score, the negative emotion value and the movement coordination abnormality index, and evaluate the user's brain cognition according to the second evaluation score.

[0038] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned brain cognitive assessment method based on DSM-V when executing the computer program.

[0039] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned brain cognitive assessment method based on DSM-V are implemented.

[0040] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0041] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0042] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A brain cognitive assessment method based on DSM-V, characterized in that: include: Obtaining identification feature information and posture feature information of the user; Based on DSM-V, cognitive impairment is classified based on the identification feature information, and the corresponding degree of brain cognitive impairment is obtained according to the type of cognitive impairment classification; Assigning a score to the user's cognitive impairment level based on the cognitive impairment level to obtain a first evaluation score; Acquire the user's emotional characteristic information, and acquire depression characteristic information and anxiety characteristic information according to the emotional characteristic information, and acquire a negative emotion value according to the depression characteristic information; Acquire a movement coordination abnormality index according to the posture feature information and the anxiety feature information; A second evaluation score is obtained according to the first evaluation score, the negative emotion value and the movement coordination abnormality index, and the user's brain cognition is evaluated according to the second evaluation score.

2. The brain cognitive assessment method based on DSM-V according to claim 1, characterized in that: The step of acquiring depression characteristic information and anxiety characteristic information according to the emotion characteristic information comprises: Acquiring the user's mental state characteristics according to the emotional characteristic information; Obtaining the duration of the user's low mood within a preset time according to the mental state characteristics; Obtain the user's prescription information based on the drug purchasing system, and screen the prescription information to see whether there is depression-related drug information; If the prescription information contains depression-related drug information, it is determined that the user has potential depression characteristics, and depression characteristic information is obtained based on the potential depression characteristics and the duration of low mood; Acquire the user's physiological response characteristics according to the emotional characteristic information, and acquire the heartbeat characteristics and breathing characteristics according to the physiological response characteristics; Acquire the corresponding abnormal heartbeat frequency and abnormal heartbeat duration according to the heartbeat characteristics, and acquire the abnormal heartbeat frequency according to the abnormal heartbeat times and abnormal heartbeat duration; The duration of shortness of breath and the number of shortness of breath within a preset time are obtained according to the breathing characteristics, and the frequency of shortness of breath is obtained according to the duration of shortness of breath and the number of shortness of breath, and the comprehensive abnormal frequency is obtained according to the abnormal heartbeat frequency and the shortness of breath frequency, and the anxiety state corresponding to the comprehensive abnormal frequency is used as anxiety characteristic information.

3. The brain cognitive assessment method based on DSM-V according to claim 1, characterized in that: The step of obtaining a negative emotion value according to the depression feature information comprises: Acquiring the number of negative characteristics according to the depression characteristic information, wherein the negative characteristics include language negative characteristics, attention negative characteristics and physical negative characteristics; A two-dimensional coordinate system is established according to the negative language features, negative attention features, negative body features and duration of low mood; Acquire multiple overlapping nodes of language negative features, attention negative features and body negative features within the duration of depression according to the two-dimensional coordinate system; The corresponding overlap frequencies are acquired according to the multiple overlap nodes, and the overlap frequencies are used as negative sentiment values.

4. The brain cognitive assessment method based on DSM-V according to claim 1, characterized in that: The step of obtaining the movement coordination abnormality index according to the posture feature information and the anxiety feature information comprises: Acquire a first center of gravity position of the user in an initial static state and a plurality of second center of gravity positions within a preset time according to the posture feature information; Acquire the number of gravity center shifts of the user according to the first gravity center position and the plurality of second gravity center positions; Acquire the posture abnormality frequency according to the center of gravity offset times; The frequency abnormality rate is obtained according to the ratio of the comprehensive abnormal frequency and posture abnormality frequency corresponding to the anxiety feature information to the preset standard frequency, and the frequency abnormality rate is used as the movement coordination abnormality index.

5. The brain cognitive assessment method based on DSM-V according to claim 1, characterized in that: The step of obtaining a second evaluation score according to the first evaluation score, the negative emotion value and the action coordination abnormality index comprises: Acquire a corresponding first ideal value and a maximum first evaluation value according to the first evaluation score; Acquire a corresponding second ideal value and a maximum negative emotion value according to the negative emotion value; According to the action coordination abnormality index, a corresponding third ideal value and a maximum action coordination abnormality index are obtained; The second evaluation score is calculated according to the first evaluation score, the negative emotion value, the action coordination abnormality index, the first ideal value, the maximum first evaluation value, the second ideal value, the maximum negative emotion value, the third ideal value and the maximum action coordination abnormality index, wherein the calculation formula is: ； Where D(PG) represents the second evaluation score, where the value range of the second evaluation score is [0, +∞], J(PG) represents the first evaluation score, X(XJ) represents the negative emotion value, F(ZH) represents the abnormal coordination index, J ideal represents the first ideal value, J max represents the maximum first evaluation value, X ideal represents the second ideal value, X max Indicates the maximum negative sentiment value, F ideal represents the third ideal value, F max Represents the maximum motor coordination abnormality index.

6. The brain cognitive assessment method based on DSM-V according to claim 1, characterized in that: The step of evaluating the user according to the second evaluation score further includes: Calculating an evaluation difference according to the first evaluation score and the second evaluation score; Determine whether the evaluation difference satisfies a preset threshold interval range; When the evaluation difference does not meet the preset threshold interval range and is less than the minimum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is lower than the current cognitive impairment level; When the evaluation difference satisfies the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is the current level of cognitive impairment; When the evaluation difference does not satisfy the preset threshold interval range and is greater than the maximum value of the preset threshold interval range, it is determined that the cognitive impairment level corresponding to the first evaluation score is higher than the current cognitive impairment level.

7. The brain cognitive assessment method based on DSM-V according to claim 1, characterized in that: After the step of evaluating the user according to the second evaluation score, the method further comprises: Obtaining negative emotion keywords according to the negative emotion values, extracting negative emotion factors according to the negative emotion keywords, mapping the negative emotion factors to the coding space according to a preset receiving timing for coding, and obtaining negative emotion coding; Acquire the action abnormality type according to the action coordination abnormality index, extract the abnormal offset factor according to the action abnormality type, map the abnormal offset factor to the coding space according to the preset receiving timing to encode, and obtain the abnormal offset code; Acquire cognitive feature information of the user in the next preset period, and map the cognitive feature information to a coding space for encoding according to a preset receiving timing to obtain cognitive feature coding; A reference sample of a clustering model is constructed according to the negative emotion coding and the abnormal deviation coding to obtain an expanded clustering model, The cognitive feature codes are input into the expansion clustering model, and the cognitive feature codes are grouped and extracted through K-means clustering to obtain the first negative emotion code and the first abnormal deviation code.

8. A brain cognitive assessment system based on DSM-V, characterized in that: include: A first acquisition module is used to acquire identification feature information and posture feature information of the user; The first recognition module is used to classify cognitive impairment based on the recognition feature information based on DSM-V, and obtain the corresponding degree of brain cognitive impairment according to the type obtained by the cognitive impairment classification; A second acquisition module is used to assign a score to the user's cognitive impairment level based on the cognitive impairment level to obtain a first evaluation score; A third acquisition module is used to acquire the user's emotional characteristic information, and acquire depression characteristic information and anxiety characteristic information according to the emotional characteristic information, and acquire a negative emotion value according to the depression characteristic information; A fourth acquisition module, acquiring a movement coordination abnormality index according to the posture feature information and the anxiety feature information; The fifth acquisition module is used to obtain a second evaluation score based on the first evaluation score, the negative emotion value and the movement coordination abnormality index, and evaluate the user's brain cognition based on the second evaluation score.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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