CN110584601B - Old man cognitive function monitoring and evaluation system - Google Patents

Abstract

The invention relates to a system for monitoring and evaluating cognitive functions of old people, which comprises: collecting at least one group of attributes of the old, and outputting at least one group of dynamic feature set and/or static feature set to be collected together with the historical attribute set; collecting a dynamic characteristic set and/or a static characteristic set of the old; outputting at least one data set that can be used to assess cognitive function in the elderly based on the dynamic feature set and/or the static feature set; wherein the historical attribute set is correlated with cognitive function, so that the dynamic feature set and/or static feature set required to be acquired can be correlated with the cognitive function. The invention can monitor and evaluate the cognitive function directionally or purposely under the environment of counting medical records by big data.

Description

Old man cognitive function monitoring and evaluation system

Technical Field

The invention relates to the technical field of nursing of old people, relates to a system for monitoring and evaluating cognitive functions of old people, and particularly relates to a system for monitoring and evaluating unknown cognitive impairment types of healthy old people.

Background

Impairment of cognitive function is one of the important symptoms in the early stages of dementia, and studies have shown that: the relative risk of conversion to dementia in a population with cognitive abnormalities is 6.4 times that of a person with cognitive normality. The study indicated that: mild Cognitive Impairment (MCI) is an early manifestation of dementia. Therefore, the impairment of cognitive function of the elderly population can be correctly identified, and mild or suspicious dementia patients can be discovered as soon as possible. The study indicated that: the abnormal cognitive function is an important factor which causes the life quality of the old people to be reduced, depends on the care of others and even causes the survival rate to be reduced. The method has positive significance for measuring and evaluating the cognitive function, and being beneficial to early discovery of senile dementia and improvement of the life quality of the elderly population.

In the research, biochemical changes of cerebrospinal fluid (such as amyloid A beta 42, tau protein level and the like), functional imaging characteristics (such as functional magnetic resonance and nuclear medicine examination), ApoE e4 allele detection and the like have significant value on diagnosis and prediction of MCI, but the examination operations are expensive. Therefore, there is a need to find a suitable method for MCI monitoring and assessment for most elderly populations.

As a result, medical researchers developed various and now widespread cognitive function measurement/assessment tools. For example, the compact mental state scale (MMSE), compiled by Fotstein, is one of the most popular, most commonly used and most influential cognitive impairment screening tools at home and abroad. It includes time and place orientation, language (repeat, name, understand instructions), mental arithmetic, immediate and short-term auditory word memory, structural simulation and other items.

With the development of computer technology, it is favored to apply computer technology to the field of cognitive assessment of the elderly. For example, chinese patent publication No. CN101983613B discloses a computer-aided device for screening Mild Cognitive Impairment (MCI) in the elderly. It includes: MCI test server and inspection client, MCI test server includes: the system comprises a personal information memory unit, a scale memory unit for providing a test scale for objectively judging MCI for an MCI test check client, a scale transmitting unit, a response receiving unit, an MCI degree statistic memory unit, an MCI identification comparison unit and a result transmitting unit. The examination client includes a basic information entry unit, a basic information transmission unit, a scale reception unit, a scale representation unit, a response acquisition unit, a response transmission unit, a result reception unit, and a result display unit. The test questions of the test scale include orientation, memory, attention and alertness, computing power, language naming, delayed recall, word fluency, graphical recognization, verbal understanding, abstract ability, and visual structure skills. The invention actually systematizes the MCI test and needs to be used after logging in by a client.

For example, chinese patent publication No. CN106446566A discloses a method for classifying cognitive functions of elderly people based on random forests. The method firstly adopts MMSE scale score and education degree to divide the cognitive function of the old into three categories. And then extracting key cognitive domains influencing the classification of cognitive function categories of the old by utilizing a cognitive function score relative ratio calculation method and a Pearson linear correlation coefficient calculation method. And (3) constructing a random forest regression model, calculating an attribute importance score of the non-scale attribute, and extracting an external link attribute influencing the cognitive function classification of the old. And finally, based on the extracted key cognitive domain and the external link attribute, equalizing the sample set by adopting an SMOTE (short-cut average) up-sampling method, and constructing a cognitive function classification model of the old by utilizing a random forest method. Compared with a quantitative table classification method, the method provided by the invention has the advantages that the adopted attributes are fewer, the collection is easy, and the convenience is realized; compared with other machine learning algorithms, the method realizes the subdivision of the cognitive function, and is beneficial to realizing the research of the targeted intervention method for the cognitive function of the old.

For example, chinese patent publication No. CN103793593B discloses a method for obtaining an objective quantitative index of brain status. The method comprises the following steps: the brain waves are collected in a wireless mobile mode of the Internet of things, and a plurality of quantitative indexes reflecting old state changes are extracted through calculation of a plurality of mathematical algorithms. The method comprises the steps of collecting blood perfusion of the supraorbital artery of the head by using a wireless mobile method, and extracting a blood perfusion index of the supraorbital artery of the head, which feeds back the water surface quality, by using various mathematical algorithms. And (3) establishing a disease treatment mode under the background of brain inhibition, and controlling the injection pump to inject a sedative drug by closed-loop feedback to automatically control the supraorbital artery.

For example, chinese patent publication No. CN108062878A discloses an apparatus and a method for testing cognitive ability of the elderly. The intelligent apple picker comprises a shell, a controller assembly, a screen assembly and a battery assembly, wherein the controller assembly and the screen assembly are arranged in the shell, the battery assembly is arranged in the shell, and the controller assembly comprises a first ingot module, a second ingot module, a finger biting module, an apple picking module, a grid flashing module and a programmable controller. The shoe-shaped gold ingot robbing module comprises a left shoe-shaped gold ingot storage, a left shoe-shaped gold ingot unit, a left shoe-shaped gold ingot receiver, a right shoe-shaped gold ingot storage, a right shoe-shaped gold ingot unit, a right shoe-shaped gold ingot receiver and a shoe-shaped gold ingot robbing responder. The apple picking module comprises an apple memory, an apple unit and an apple picking responder. The flash grid module comprises a grid display and a grid position memory. The cognitive ability condition acquisition system has the advantages of reasonable structural design, convenience in operation and use and high intelligent degree, can quickly and conveniently acquire the cognitive ability condition of the old people and timely know the cognitive ability condition of the old people, and is relatively high in reliability and relatively wide in applicability.

For example, chinese patent publication No. CN106599542A discloses a method and system for evaluating the comprehensive ability of the elderly. The evaluation method comprises the steps of obtaining grade values of all the ability evaluations based on the comprehensive ability data information of the old, and obtaining the comprehensive ability grade of the old based on the grade values of all the ability evaluations and the weight coefficients thereof; the evaluation system comprises a data module for collecting, processing and storing the comprehensive ability data of the old people and an evaluation module for analyzing and obtaining the comprehensive ability grade of the old people.

For example, chinese patent publication No. CN106599558A discloses a cognitive assessment method and system based on virtual reality. The system comprises: the system comprises an information input module, an intelligent conversion module, a processor module, a face induction module, an electroencephalogram induction module and a VR device module. The method overcomes the problems of the traditional scale in the aspects of application, operation, user experience and the like on the basis of the traditional scale, integrates scale evaluation, micro-expression information and electroencephalogram information to judge the user cognitive condition, provides more comprehensive, detailed and objective evaluation results, has higher credibility, and is a cognitive evaluation method with automatic scale selection, simple operation, good user experience effect and good evaluation effect. The method has wide applicability and low requirement on operators, and is suitable for popularization and application in primary medical institutions lacking of professional one-family personnel.

For example, chinese patent publication No. CN106176009A discloses a multi-modal cognitive detection and rehabilitation system device. The device comprises a host, a display device touch screen with a touch function, a broadcasting device, a key device, a knob device, a rocker device, a video screen recording device, a visual tracking device, an electroencephalogram acquisition device and an IC card reader. When the device is used, data such as electroencephalogram, eye movement, reaction time, fine movement and the like in the testing and training process are collected, a cognitive detection model and a rehabilitation evaluation model are built by means of collecting a medical data mining method based on cloud computing and comprehensively using an artificial neural network, fuzzy logic, a genetic algorithm, a rough set theory, a support vector machine and the like, the cognitive ability of a tester is detected and evaluated, and the detection and evaluation results can be printed by the printing module.

For example, PCT patent publication WO2017/134622 discloses a people monitoring and personal assistance system, particularly for elderly people and people with special and cognitive needs. The system comprises a plurality of sensors for detecting parameters describing the environmental conditions and a plurality of sensors and/or devices for sensing at least the position of a person in the environment and optionally physiological parameters of the person. According to the invention, the system comprises: sensors and devices for monitoring the position of a person at a topological level, which generate tracking signals of the position of the person at events and in the context of a digital topological model of the environment; sensors for monitoring the posture and/or changes in its time and providing status signals corresponding to predetermined postures and/or signals representing event changes of the monitored person's posture, the signals of the sensors being processed by the logic control unit according to software evaluating the position and the time changes of the posture and classifying said signals according to predetermined motion classes and/or posture activities and performing comparisons between parameters such as the rate of change of the position and/or posture and/or the duration of these changes and/or their being sufficient and/or the conformity of the posture with the environment and the same parameters with respect to a predetermined pattern of performing the motion or posture activity.

Based on research and analysis of the prior art, the current cognitive methods for evaluating the elderly have certain blindness: on one hand, the cognitive ability has many associations with the attributes of the elderly, such as degree of academic degree, age, disease history, gender, geographical location, urban and rural locations, whether the elderly live alone or not, and the like, however, none of the existing technologies takes these factors into consideration, but adopts a public evaluation mode, such as inputting a set of data or filling a set of data for cognitive evaluation, and the evaluation result is not combined with the conditions of the elderly themselves, so that the evaluation result is rough due to blindness, and the evaluation result is not beneficial to screening whether the elderly have mild cognitive impairment or not, so that the evaluation result is easy to miss the chance of finding the senile dementia; on the other hand, the cognitive abilities of the elderly mainly include accepting ability, memory and learning ability, thinking ability and expression ability, so that the elderly may have cognitive impairment only in a certain ability or a few abilities, but the existing technology is to evaluate all the abilities, rather than deeply evaluating any ability.

Furthermore, on the one hand, due to the differences in understanding to the person skilled in the art; on the other hand, since the inventor has studied a lot of documents and patents when making the present invention, but the space is not limited to the details and contents listed in the above, however, the present invention is by no means free of the features of the prior art, but the present invention has been provided with all the features of the prior art, and the applicant reserves the right to increase the related prior art in the background.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides an old people cognitive function monitoring and evaluating method, and relates to a method for predicting the type of cognitive function damage which may occur to the old people to be evaluated based on the basic data of the old people and monitoring the capability development trend of the cognitive function corresponding to the cognitive function damage type, so that the old people and the guardian thereof can adopt an intervention scheme in advance, an information combination device generates an attribute group of the old people to be evaluated based on the basic data combination under the condition of receiving the basic data which is acquired by an information acquisition device and can be used for matching the cognitive function damage type of the old people to be evaluated, and the basic data at least comprises one of intrinsic data; the cloud server evaluates at least one cognitive function damage type matched with the attribute information from a knowledge base based on the attribute information and a data set of cognitive functions corresponding to the cognitive function damage type and capable of being used for monitoring the old to be evaluated; the data acquisition terminal acquires the cognitive original data of the old people to be evaluated according to the data group, the data computing equipment generates cognitive characteristic data corresponding to the cognitive function based on the cognitive original data, and the cloud server triggers a cognitive threshold corresponding to the cognitive characteristic data to establish communication connection with the data computing equipment in response to the cognitive characteristic data so that the cloud server can compare the cognitive characteristic data with cognitive historical data to generate cognitive relative data; wherein, in the event that the cognitive relative data triggers a first relative threshold but does not trigger a second relative threshold, the cloud server presents at least a primary care regimen corresponding to the cognitive relative data to the display device in a manner that enables matching of the primary care regimen from the knowledge base; and/or, in the case that the cognitive relative data trigger a second relative threshold, the cloud server at least presents the advanced care plan to the display device and/or sends a rescue signal to a medical institution in a manner that the advanced care plan corresponding to the cognitive relative data can be matched from the knowledge base; and/or, under the condition that the cognitive relative data trigger does not have a first relative threshold, the cloud server can output the cognitive characteristic data to a display device in a manner of prompting the guardian to maintain a primary living scheme; wherein, in the case that the cognitive characteristic data does not trigger its corresponding cognitive threshold, the data computing device may output the cognitive characteristic data to a display device in a manner that prompts the parent to maintain a primary-living scenario.

According to a preferred embodiment, the data acquisition terminal acquires the cognitive raw data in a mode that each cognitive raw data group corresponds to at least one cognitive function monitoring type; wherein each cognitive raw data set comprises at least one cognitive raw data; under the condition that the number of the cognitive original data in each cognitive original data group reaches the corresponding number, the data acquisition terminal sends the cognitive original data group to the data computing equipment, so that the data computing equipment can generate cognitive feature data of a cognitive function monitoring type corresponding to the cognitive original data group based on the cognitive original data group, and the data computing center can send the feature data to the cloud server in a fixed time or an indefinite time, so that the old people can be monitored in different types in time; the cognitive function monitoring type at least comprises at least one of a language monitoring type, a responsiveness monitoring type and a memory monitoring type.

According to a preferred embodiment, the cloud server sends the data set to the display device as follows; the cloud server configures at least one target business of cognitive functions and data elements required to be collected for completing the target business from the knowledge base based on the cognitive functions, and the cloud server is in communication connection with the display device according to a fifth generation mobile communication protocol according to the data group corresponding to the target service, so that the cloud server can present the target service to a display device in at least one of text, picture, sound and video and present the data set to the display device, so that the elderly can perform cognitive ability monitoring on line at least based on information interaction between the cloud server and the display terminal, in the cognitive ability monitoring process, the data acquisition terminal can acquire the cognitive ability original data of the old according to the data group.

According to a preferred embodiment, the display device at least comprises an old people look-up device for the old people to look up and a guardian look-up device for the guardian to look up, wherein the old people look-up device and the guardian look-up device are respectively in communication connection with the cloud server through a fifth generation mobile communication technology, so that the old people look-up device and/or the guardian look-up device can respectively present the relative data in a non-quantitative manner, and the old people and/or the guardian can respectively read the first cognitive monitoring report of the old people in a graphical manner; and/or the monitoring consulting device is in communication connection with the cloud server through a fifth generation mobile communication technology, so that the monitoring consulting device can present the relative data and/or the cognitive characteristic data in a quantitative mode, and therefore the guardian reads a second cognitive monitoring report of the old person in a graphical mode; the old people consulting device and/or the guardianship consulting terminal comprise a touch screen, so that the old people consulting device and/or the guardianship consulting terminal can respectively respond to the touch of old people and/or guardianship to present the first cognitive monitoring report in different non-quantitative modes; the old people consulting device can be in communication connection with the monitoring consulting device at random time in a mode of not performing information interaction through the cloud server under the condition that the old people subscribe, so that the monitoring consulting device can push monitoring suggestions to the old people consulting terminal under the condition that the guardian judges that the first cognitive monitoring report and/or the second cognitive monitoring report are abnormal through professional judgment, and the monitoring suggestions are at least one of videos, sounds, characters and pictures.

According to a preferred embodiment, a first special attribute in the attribute group is mapped to the historical attribute set, so that data elements in the data group can be associated with the cognitive impairment type in a first probability manner based on the mapping relation of the first special attribute to the historical attribute set; wherein the data elements at least comprise dynamic characteristic data and/or static characteristic data of the elderly to be assessed; mapping a second special attribute of the set of attributes to the set of historical attributes, at least if the first probability does not meet an evaluation threshold, to enable data elements of the set of data to be associated with the type of cognitive impairment in a manner that revises the first probability to generate a second probability based on a mapping of the second special attribute to the set of historical attributes.

According to a preferred embodiment, in the case of a first probability greater than the second probability, the order of mapping of the first and second special attributes with the historical attribute set can be interchanged for the purpose of re-matching a new data set with a new type of cognitive impairment and re-probabilistically associating the new data set with the new type of cognitive impairment.

According to a preferred embodiment, the first special attribute and/or the second special attribute can be obtained as follows: the historical attribute set and the cognitive impairment type are associated in a third probability manner, so that the first special attribute and/or the second special attribute can be selected from attribute elements in the attribute group based on the third probability with different weight coefficients; wherein a first weighting factor of the first special attribute is greater than a second weighting factor of the second special attribute.

According to a preferred embodiment, each attribute element in the attribute group is matched with a historical attribute set, so that a cognitive function impairment type corresponding to each attribute element and a probability corresponding to the cognitive function impairment type can be presented, and a weight coefficient corresponding to each attribute element is determined; the historical attribute set at least comprises age, academic degree, gender, living environment, geographical location and whether the elderly live alone, and the cognitive impairment types comprise perception impairment types, memory impairment types, thinking impairment types and speech impairment types.

According to a preferred embodiment, the present invention further provides an elderly cognitive function monitoring and evaluating system, which relates to a method for predicting the type of cognitive function impairment that may occur to the elderly to be assessed based on the basic data of the elderly, and monitoring the ability development trend of cognitive function corresponding to the type of cognitive function impairment, so that the elderly and their guardians can adopt intervention schemes in advance, comprising at least: the method comprises the steps that an information combination device generates an attribute group of an elderly person to be evaluated on the basis of basic data combination under the condition that the basic data, which are collected by an information collection device and can be used for matching cognitive function damage types, of the elderly person to be evaluated, wherein the basic data comprise at least one of inherent data; the cloud server evaluates at least one cognitive function damage type matched with the attribute information from a knowledge base based on the attribute information and a data set of cognitive functions corresponding to the cognitive function damage type and capable of being used for monitoring the old to be evaluated; the data acquisition terminal acquires the cognitive original data of the old people to be evaluated according to the data group, the data computing equipment generates cognitive characteristic data corresponding to the cognitive function based on the cognitive original data, and the cloud server triggers a cognitive threshold corresponding to the cognitive characteristic data to establish communication connection with the data computing equipment in response to the cognitive characteristic data so that the cloud server can compare the cognitive characteristic data with cognitive historical data to generate cognitive relative data; wherein, in the event that the cognitive relative data triggers a first relative threshold but does not trigger a second relative threshold, the cloud server presents at least a primary care regimen corresponding to the cognitive relative data to the display device in a manner that enables matching of the primary care regimen from the knowledge base; and/or, in the case that the cognitive relative data trigger a second relative threshold, the cloud server at least presents the advanced care plan to the display device and/or sends a rescue signal to a medical institution in a manner that the advanced care plan corresponding to the cognitive relative data can be matched from the knowledge base; and/or, under the condition that the cognitive relative data trigger does not reach a first relative threshold, the cloud server can output the cognitive characteristic data to display equipment in a manner of prompting the guardian to maintain a native activity scheme; wherein, in the case that the cognitive characteristic data does not trigger its corresponding cognitive threshold, the data computing device may output the cognitive characteristic data to a display device in a manner that prompts the parent to maintain a primary-living scenario.

According to a preferred embodiment, in the system, the data acquisition terminal acquires the cognitive raw data in a manner that each cognitive raw data group corresponds to at least one cognitive function monitoring type; wherein each cognitive raw data group comprises at least one cognitive raw data; under the condition that the number of the cognitive original data in each cognitive original data group reaches the corresponding number, the data acquisition terminal sends the cognitive original data group to the data computing equipment, so that the data computing equipment can generate cognitive feature data of a cognitive function monitoring type corresponding to the cognitive original data group based on the cognitive original data group, and the data computing center can send the feature data to the cloud server in a fixed time or an indefinite time, so that the old people can be monitored in different types in time; the cognitive function monitoring type at least comprises at least one of a language monitoring type, a responsiveness monitoring type and a memory monitoring type.

Based on the defects of the prior art, the method for monitoring and evaluating the cognitive function of the old has at least the following advantages:

1) a knowledge base which can be used for generating a detection table for the cognitive function of a certain to-be-detected old person is formed through big data statistics, and the knowledge base can also facilitate the tracing of medical records and the big data research of the medical records;

2) compared with blind full-detection, the method can directionally or purposely monitor and evaluate the cognitive function in the environment of big data statistics of medical records based on the special attributes of the old, and screen out a data scheme to be acquired which can be used for finding whether the old has cognitive dysfunction which is strongly associated with the special attributes in a knowledge base based on the special attributes of the old in time, wherein the data scheme to be acquired aims at a certain cognitive dysfunction or certain cognitive dysfunction. Therefore, the data plan to be acquired also has directivity or a shape of purpose while being able to reduce the cost of monitoring and evaluation.

Drawings

FIG. 1 is a schematic flow chart of a method for monitoring and evaluating cognitive function of an elderly person according to the present invention; and

fig. 2 is a schematic block diagram of a system for monitoring and evaluating cognitive functions of an elderly person according to the present invention.

List of reference numerals

100: the knowledge base 500: information acquisition equipment

200: cloud server 600: data acquisition terminal

300: the display device 700: data computing device

400: information combination device

Detailed Description

This is described in detail below with reference to fig. 1-2.

Example 1

Cognition is the process of human cognitive activities, i.e., the process of information processing of individual to sensory signal reception, detection, conversion, reduction, synthesis, encoding, storage, extraction, reconstruction, concept formation, judgment and problem solving.

Cognitive functions generally include receptive functions, cognitive functions including receptive functions, memory and learning functions, mental functions and expression functions.

Cognitive impairment: also known as cognitive disorders. Common types of cognitive disorders include sensory disorders, memory disorders, thought disorders, and speech disorders. Sensory disorders include sensory hypersensitivity, dysesthesia, discomfort, sensory deterioration, sensory deprivation, pathological illusion, hallucinations, and sensory complex disorders. Memory disorders include hypermnesia, memory impairment and memory errors. Thought disorders include abstract generalisation disorders, association process disorders, mental logical disorders and delusions. The speech disorder comprises aphasia, functional dysarthria, motor dysarthria, organic dysarthria, dysarthria caused by hearing disorder, and dysphonia.

The embodiment provides a method for monitoring and evaluating cognitive functions of the old. The method can predict the cognitive function damage type possibly occurring in the old to be evaluated based on the basic data of the old and monitor the cognitive function capability development trend corresponding to the cognitive function damage type, so that the old and a guardian thereof can adopt an intervention scheme in advance.

The method at least comprises the following steps:

s1: the information collecting apparatus 500 collects basic data of the elderly to be evaluated. The basic data includes at least one of the intrinsic data. These underlying data can be intrinsic data associated with cognitive functions. By way of example, intrinsic data may include, but is not limited to, age, academic calendar, place of residence, gender, environment of residence, whether solitary, whether there is a past medical history, and the like.

In the case of receiving the basic data of the elderly to be assessed, which can be used to match the cognitive function impairment type, collected by the information collection device 500, the information composition device 400 generates an attribute group of the elderly to be assessed based on the combination of the basic data. For example, the information composing apparatus 400 forms these basic data into an attribute group of the elderly person to be evaluated in a logical and arithmetic manner. Thus, the information composing device 400 may be a server capable of performing logic and operations.

The cloud server 200 evaluates at least one cognitive impairment type matched with the attribute information from the knowledge base 100 based on the attribute information. And, the cloud server 200 can be used to monitor the cognitive function data set corresponding to the cognitive function impairment type of the elderly to be assessed, and specifically, the cloud server 200 executes the steps as follows:

s2: the historical set of attributes of the elderly are correlated with the type of cognitive impairment. For example, elderly people with impaired cognitive energy supply are investigated from hospitals, welfare hospitals and other institutions. The types of cognitive dysfunction that these elderly have and basic information of these elderly are recorded. Then, a historical attribute set is generated from the part of basic information, and the historical attribute set is matched with the cognitive function impairment type and is stored in the knowledge base 100. The basic information includes age, degree of academic history, medical history, sex, living environment, geographical location and whether or not living alone. According to the method, a plurality of samples with the cognitive impairment of the old are collected firstly, then the collected samples can be classified based on a probability statistics mode, and the historical attribute set and the cognitive impairment type are subjected to probability matching. For example, the historical attribute sets and the types of cognitive impairment in the knowledge base 100 may be generated by the cloud server 200 as follows, but not limited to:

the cognitive function impairment types corresponding to all age groups are counted, wherein the age groups can be 60-65 years old, 65-70 years old, 70-75 years old and above. And/or

The probability change trend of the certain cognitive function impairment type along with the rise of the academic degree. Such as the change trend of the probability of language handicap in the primary school and the following school calendars, junior high school calendars, high school calendars and student calendars. And/or

Probability of some cognitive function damage type for some specific disease. The proportion of memory impairment in patients with gliomas. And/or

And fourthly, the ratio of the single-living old people to the non-single-living old people to the certain cognitive function damage type. For example, the elderly living alone have a proportion of thought disorder and the elderly not living alone have a proportion of thought disorder. And/or

And fifthly, the proportion of different cognitive function impairment types of the old people living in cities and towns of the female with the subject calendar between 75 and 80 years old.

For a specific elderly person to be evaluated, a set of intrinsic data of the elderly person to be evaluated is collected. The intrinsic data includes, but is not limited to, age (X)₁) Degree of learning calendar (X)₂) History of disease (X)_3i) Sex (X)₄) Residential environment (X)₅) Geographic location (X)₆) And whether it is solitary (X)₇). For example, the intrinsic data is set by logical sum operation in the attribute combining device (X)₁、X₂、X_3i、X₄、X₅、X₆、X₇) Is input into the cloud server 200.

S3: and outputting a group of dynamic characteristic data and/or static characteristic data to be acquired based on the matching relation between the acquired attribute group and the historical attribute set. The dynamic characteristic data is data monitored and collected in the motion state of the old, such as dynamic blood pressure, dynamic brain waves, dynamic heart rate, walking pace frequency, speaking speed, speaking volume, facial expression and the like. The static feature data is data monitored and collected in a state where the elderly do not move, such as static blood pressure, static brain waves, static heart rate, static respiratory rate, and the like. The matching of the property groups and the historical property sets may be output by an evaluation unit in the cloud server 200 to the display device 300.

S4: the data acquisition terminal 600 acquires the cognitive original data of the elderly to be evaluated according to the data group. Data acquisition terminal 600 can be wearable equipment, like intelligent bracelet, cervical vertebra appearance, flesh electrical sensor etc.. The cognitive raw data may be dynamic feature data and/or static feature data required for acquisition. The dynamic characteristic data and/or the static characteristic data are collected by corresponding monitoring units. The monitoring unit is used for collecting dynamic characteristic data/static characteristic data to be collected, and is essentially a data collection device, which can be executed by the data collection terminal 600. For example, both a dynamic heart rate and a static heart rate may be collected by the smart band. The speaking speed and the speaking volume can be collected by the voice collecting device.

S5 outputs at least one data set based on the collected dynamic feature data and/or static feature data. The data set is mainly used for evaluating the cognitive function of the old. The data set may be represented by MMSE, for example. The data set includes dynamic data elements corresponding to the dynamic characteristic data and static data elements corresponding to the static characteristic data. Namely: the data elements at least comprise dynamic characteristic data and/or static characteristic data of the elderly to be evaluated.

The cognitive function and assessment method provided by the invention can be carried out as follows: and collecting an attribute group of the old to be evaluated. The attribute group is used for matching at least one group of data groups needing to be collected with the historical information set. And collecting a data group of the old people to be evaluated, wherein the data group can be used for evaluating the cognitive function of the old people to be evaluated. The historical information set is generated according to the mode that the historical attribute set of the old people is correlated with the cognitive function impairment type, so that the matched data group needing to be collected can be correlated with the cognitive function impairment type based on the attribute group. Modern medicine has proven: cognitive ability has many associations with the elderly's own attributes, such as degree of scholarness, age, disease history, gender, geographic location, urban and rural location, whether or not they are solitary, and the like. However, the current cognitive evaluation method is to evaluate the elderly to be evaluated in a universal way, namely, cognitive evaluation is performed by collecting a group of data. This approach ignores the sensitivity of the elderly attributes to this set of data on the one hand and does not combine the results of the assessment with the elderly own attributes on the other hand. The mode in the prior art has certain blindness, and the evaluation data is more, so that the evaluation result is rough, and the evaluation result is not beneficial to screening whether the old has mild cognitive impairment. Therefore, the evaluation results are liable to miss the chance of finding senile dementia. Based on the defects of the prior art, the invention has at least the following advantages: 1. a knowledge base which can be used for generating a detection table aiming at the cognitive function of a certain to-be-detected old person is formed through big data statistics, and the knowledge base can also facilitate the tracing of medical records and the big data research of the medical records; 2. compared with blind full-detection, the method can directionally or purposely monitor and evaluate the cognitive function in the environment of big data statistics of medical records based on the special attributes of the old, and screen out a data scheme to be acquired which can be used for finding whether the old has cognitive dysfunction which is strongly associated with the special attributes in a knowledge base based on the special attributes of the old in time, wherein the data scheme to be acquired aims at a certain cognitive dysfunction or certain cognitive dysfunction. Therefore, the data plan to be acquired also has directivity or a shape of purpose while being able to reduce the cost of monitoring and evaluation. For example, a retired monarch in the academic calendar for a male 65-70 years old who is in good health. The attribute elements whose attribute groups can be decomposed include: good health status, 65-70 years old, male, research student's calendar, retirement and solitary. In this attribute group, the specific attribute of 65-70 years old is selected from the knowledge base 100 based on the keyword search mode. Under the environment of big data statistics, the cognitive dysfunction with the maximum probability corresponding to the age of 65-70 years is perception disorder. Based on the mode of the invention, the scheme of data to be acquired of the perception obstacle with the special attribute of 65-70 is pushed out. Therefore, the male can perform data acquisition according to the data scheme to be acquired so as to acquire a data set for evaluating whether the male has a perception obstacle.

In the present invention, cognitive classification and classification for elderly people is a good desire in the field of care. However, the inventors have earnestly searched the current document library, and have found no feasible way of objectively classifying the cognitive state and cognitive ability change of the elderly by simply using technical measures without departing from the "scale". The motivation for making the present invention stems from how to "break away from scale" to assess the level of visitation and take measures accordingly to deal with the management and control of the cognitive abilities of the elderly, which is undoubtedly facing a well-recognized medical, domestic and social problem. In the invention, the cognitive threshold mainly corresponds to a family care measure. The first relative threshold primarily corresponds to a primary care regimen. While the second relative threshold is primarily an advanced care regimen or medical help.

The data computing device 700 generates cognitive feature data corresponding to cognitive functions based on the cognitive raw data. The data computing device 700 is an existing server with computing functionality. The data computing device 700 is in communication connection with the data acquisition terminal 600 through existing devices such as an amplifier and a noise eliminator, and is used for performing processing such as amplification and noise elimination on the cognitive original data.

S6: under the condition that the cognitive characteristic data exceeds the cognitive threshold value, the cloud server 200 establishes a communication connection with the data computing device 700, and the communication connection is used for transmitting the cognitive characteristic data to the cloud server 200 for relative operation. The cloud server 200 can compare the cognitive characteristic data with the cognitive historical data to generate cognitive relative data, and compare the cognitive relative data with a corresponding relative threshold value to obtain a primary care plan or an advanced care plan. The acquisition of the cognitive relative data can be performed as follows:

(1) for example, the cognitive feature data F_{11i of}Cognitive history data F corresponding to the cognitive history data_12iPerforming relative calculation:

P_1i＝1-|(F_11i-F_12i)|/F_12i*100％

P_1iis the cognitive relative value used for characterizing the relative trend of the ith cognitive state. P_1iThe larger the size, the less the tendency of the change in cognitive state that characterizes the ith elderly.

(2) For example, the cognitive feature data F_11iAnd performing relative calculation with the probability density function of the corresponding cognitive state in the cognitive historical data:

P_1i＝F(F_11i)＝∫f(x_i)dx_i

wherein, f (x)_i) Is a probability density function of the characteristic data representing the ith cognitive state, which is the cognitive characteristic data F before the acquisition cycle_12iDerived based on a convolutional neural network. P_1iThe larger the size, the less the tendency of the change in cognitive state that characterizes the ith elderly.

The cloud server 200 pushes the primary care plan or the advanced care plan to the display device 300 as follows:

(1) in the event that the cognitive relative data triggers the first relative threshold but does not trigger the second relative threshold, the cloud server 200 presents at least the primary care regimen to the display device 300 in a manner that enables matching of the primary care regimen corresponding to the cognitive relative data from the knowledge base 100. And/or the presence of a gas in the atmosphere,

(2) in the case that the cognitive relative data triggers the second relative threshold, the cloud server 200 presents at least the advanced care plan to the display device 300 and/or sends a rescue signal to the medical institution in a manner that the advanced care plan corresponding to the cognitive relative data can be matched out from the knowledge base 100;

(3) and/or, in the case that the cognitive relative data trigger does not reach the first relative threshold, the cloud server 200 may output the cognitive characteristic data to the display device 300 in a manner of prompting the guardian to maintain the primary activity scheme;

s7: the data computing device 700 determines whether the cognitive characteristic data needs to be sent to the cloud server 200 as follows:

(1) in the case that the cognitive feature data does not trigger its corresponding cognitive threshold, the data computing device 700 does not send the cognitive feature data to the cloud server 200, but outputs information on the display device 300 prompting the guardian to maintain the native activity scheme. For example, the prompt message may be, but is not limited to: please keep the existing living status, please keep it, the latest living habits, etc., or a prompt customized by the elderly. The display apparatus 300 may be an electronic product having a display device, such as an Ipad, a smart band, a smart phone, a television, a computer, and the like. The data computing device 700 may be integrated with the television apparatus 300 in a smart phone, computer.

(2) Under the condition that the cognitive characteristic data exceeds the cognitive threshold value, the cloud server 200 establishes a communication connection with the data computing device 700, and the communication connection is used for transmitting the cognitive characteristic data to the cloud server 200 for relative operation.

Preferably, the cognitive threshold, the first relative threshold and the second relative threshold are specific numerical values, which may be defined by a guardian, or defined by a manufacturer after big data analysis, or updated after recording a section of cognitive data of an elderly person and deeply learning by the cloud server 200.

In the invention, different cognitive monitoring schemes can be output according to the acquired cognitive original data: home care protocol, primary care monitoring, and advanced care monitoring. With aging, the cognitive ability of the elderly gradually decreases, and forgetfulness, language expression and the like tend to be weakened. Therefore, the elderly need to be under comprehensive cognitive care to enable the elderly to be in a relatively cognitive healthy state, but intensive cognitive monitoring for the elderly at all times exhausts patience of relatives and friends, so that patience for providing simple cognitive monitoring is finally lost, and physical and mental damage and deterioration of life quality of the elderly are caused. According to the invention, not only is the cloud knowledge base used for the cognitive care scheme, but also the existing cognitive care condition can be used for updating the cognitive historical data, so that the objective grading of the cognitive ability of the old is possible. The method is characterized in that at least three levels of division, namely primary cognitive monitoring and secondary cognitive monitoring (and tertiary cognitive monitoring) are respectively corresponding to the measures of family, primary nursing and advanced nursing (or medical help seeking), so that the method accords with the practical situation that most old people are mainly cared by families. After dividing into three levels, the workload of guardians can be obviously reduced, the psychological burden of nursing personnel and the personnel being nursed is also obviously reduced, because practice proves that higher-strength nursing can bring pressure to the nursing personnel, the pressure inevitably causes adverse effects to the personnel being nursed from two angles of psychological express and hint, and finally causes the nursing relationship to break, so that old people often sink into a puddle and cannot pull out themselves. In the invention, the corresponding primary and advanced care measures are determined by combining the triggering conditions of the emotion relative value under the condition that the cognitive characteristic data exceeds the cognitive threshold, so that the monitoring requirements can not be triggered in a large amount by mistake, and the monitoring intensity of the object to be monitored is reduced. The care scheme not only obviously reduces the labor intensity of care personnel in practice, but also enables the care elderly to have more autonomous activity under the condition of reducing intervention, and is more beneficial to the development of cognitive ability to a healthy state.

Preferably, the data acquisition terminal 600 acquires the cognitive raw data in a manner that each cognitive raw data group corresponds to at least one cognitive function monitoring type.

Preferably, each cognitive data set includes at least one cognitive raw data. Each cognitive data set includes at least one cognitive raw data. Under the condition that the number of the cognitive raw data in each cognitive raw data group reaches the corresponding number of the cognitive raw data group, the data acquisition terminal 600 sends the cognitive raw data group to the data computing equipment 700, so that the data computing equipment 700 can generate cognitive feature data of a monitoring type corresponding to the cognitive raw data group based on the cognitive raw data group, and the data computing equipment 700 can perform primary cognitive monitoring and/or primary cognitive monitoring on the elderly regularly or irregularly. For example, the number N of the cognitive raw data is smaller than the corresponding preset number N_mIn case of (2), the corresponding data acquisition terminal 600 will not recognize the corresponding recognition sourceThe raw data and/or cognitive raw data is sent to the data computing device 700. Only when N ≧ N_mAnd/or P.gtoreq.P_mIn this case, the corresponding cognitive raw data is sent to the data computing device 700. Because the acquisition time of each cognitive raw data is different or non-fixed due to the acquired cognitive raw data, the data computing device 700 is able to perform primary cognitive monitoring of the elderly on a regular or irregular basis. In addition, if the elderly need to be monitored temporarily, the data computing device 700 can be monitored temporarily at times. In this way, the invention has at least the following advantages: on the one hand, the energy consumption of the data computing equipment 700 can be reduced, the data computing equipment 700 does not need to operate continuously, the operation cost is reduced, and the data computing equipment 700 can be miniaturized, lightened and portable; and the technical problem of incorrect characteristic data caused by data missing can be effectively solved. Preferably, the cognitive function monitoring type at least comprises at least one of a language monitoring type, a responsiveness monitoring type and a memory monitoring type. The cloud server 200 and the display device 300 are connected in a communication manner by a fifth generation mobile communication protocol. Therefore, the cloud server 200 can present the target service in the display device 300 in at least one of characters, pictures, sounds and videos and present the data set in the display device 300, so that the old can monitor the cognitive ability at least on line based on information interaction between the cloud server 200 and the display terminal 300, and in the cognitive ability monitoring process, the data acquisition terminal 600 can acquire the original cognitive ability data of the old according to the data set.

Preferably, the cloud server 200 sends the data set to the display device 300 as follows:

the cloud server 200 configures at least one target service of the cognitive function and data elements required to be collected for completing the target service from the knowledge base 100 based on the cognitive function, and generates data groups corresponding to the target service according to the data elements. In the invention, the cognitive function monitoring content (namely the target service) is matched based on the historical condition or the attribute group of the old, and the cognitive function monitoring can be carried out on the old in a personalized way. The cloud server 200 and the display device 300 are in communication connection through a 5G communication protocol, so that the cloud server 200 can present the target service to the display device 300 in at least one of text, picture, sound and video. The old can carry out cognitive function monitoring on line based on the information interaction between cloud server 200 and display device 300 at least, and in the cognitive function monitoring process, data acquisition terminal 600 carries out ability raw data acquisition and/or intelligence raw data acquisition to the old. According to the arrangement of the invention, it has at least the following advantages: the cognitive function detection is customized in an individualized way, the network speed based on the 5G technology, the downloading speed of high-definition videos and pictures or the online chat service are higher in quality, the interactive feeling and the entertainment feeling of the old and the system are facilitated, the old is entertained as if playing games when in detection, the old is attracted to be put into the detection process, and the old and the system can carry out scene interaction and mutual inductance so as to enhance the cognitive function effect.

Preferably, the display device 300 includes at least an elderly review device for elderly review and a guardian review device for guardian review. The elderly consulting device and the monitoring consulting device are respectively in communication connection with the cloud server 200 through a fifth generation mobile communication technology. The cognitive relative data are respectively represented by P_iAnd (5) characterizing. The cognitive relative data are all in the same health metering interval. P is_iHas a value range of [0, 1 ]]. The geriatric review device is capable of presenting the cognitive relative data in a non-digital manner. For example, as shown in FIG. 2, an approximate circle (solid line) displays cognitive relative data on the geriatric reference device. For example, the elderly review device is at least capable of graphically presenting the elderly's cognitive functional status in the form of cognitive relative data in the same cognitive health status image report based on the cognitive relative data. Namely: in the present invention, i may represent different cognitive data of the elderly. In this way, i ═ 1,2,3,4,5,6,7 and 8 represent eight different types of cognitive data, respectively; p₁、P₂、P₃、P₄、P₅、P₆、P₇、P₈Are cognitive relative data relative to cognitive historical data, which are all at [0, 1]Within a range therebetween. For example, the geriatric review device may generate the ring upon receiving the cognitive relative data output by the cloud server 200 as follows: dividing a circle with the radius of 1 into 8 equal parts, and drawing 4 diameters, and dividing P₁、P₂、P₃、P₄、P₅、P₆、P₇、P₈The values are plotted on each radius, and then the arcs are sequentially ended to form an approximate ring, which can be constructed by Matlab. Thus, the elderly can read their health status reports in an unquantized manner. Similarly, in the same way, the monitoring reference device can present the relative data in a non-quantitative way, and the guardians can read the first cognitive monitoring reports of the old people in a graphical way respectively. Circles have a satisfactory meaning in traditional Chinese culture, and a large number of reports in modern psychological monographs indicate that most people have a subconscious pursuit of satisfaction, which can prompt people to perform specific operations spontaneously without thinking. Therefore, the circle + color (especially red) is used for promoting the user to keep healthy living state, which has positive psychological and nursing theoretical basis for the cognitive nursing of the old. In contrast, the deterioration of physicochemical data due to aging or pain often gives patients negative psychological implications, such as the inability of elderly people with senile dementia to see a desire to abandon themselves. The invention skillfully combines red and round of traditional culture to cultivate the user to form good living habits, is more beneficial to prompting the user to adopt a better living mode from the psychological aspect, and greatly reduces the nursing workload. The complex physiological and psychological intervention scheme is arranged behind the circle embodied by the invention, which is the summary of the experience of the inventor of the invention in long-term care research and has obvious clinical effect.

Preferably, the monitoring reference device is in communication connection with the cloud server 200 through a fifth generation mobile communication technology. Thus, the monitoring reference device can present the relative data and/or cognitive characteristic data in a quantitative manner. For example, considerKnowing the characteristic data can be expressed as F_iFor presentation at a monitoring review device. For example, the monitoring review device can at least graphically present different cognitive characteristic data of the elderly person in the same cognitive state image report (second cognitive monitoring report) in absolute value based on the cognitive characteristic data. In this way, the invention also has the following advantages: under the condition that the old people can only qualitatively read the change of the health state of the old people, the guardian can simultaneously and quantitatively read the absolute health value of the old people and the relative health value of the old people, so that the guardian can know the health state and the change of the health state of the old people, and the guardian can conveniently make corresponding intervention decisions. And the guardians respectively read the second cognitive monitoring reports of the old people in a graphical mode.

Preferably, the elderly review device and/or the guardian review terminal includes a touch screen, such that the elderly review device and/or the guardian review terminal are capable of presenting the first cognitive monitoring report in different non-quantitative manners in response to contact by the elderly and/or the guardian, respectively. For example, the elderly may be shown in different non-quantitative ways by saying "show health report" to the elderly review terminal. The different non-quantitative way may also be shown in the form of a colour change, for example a change in colour contrast, such as dividing the disc i equally, the colour being red, the contrast being consistent with the cognitive relative data, i.e. such as a relative health value of 98%, which corresponds to a contrast of 98% for a circular area. By the mode, on one hand, the system provides different health state reading modes for the old, and the old can conveniently select the favorite reading mode; on the other hand, can improve the interactive sense between old person and this system, increase old person's intelligence and experience, enrich its perception and experience, promote its perception ability.

Since the elderly are only able to guide cognitive relative data, not cognitive characteristic data, their actual cognitive state is unknown. In order to effectively pay close attention to the cognitive function, the invention can indirectly inform the elderly of the cognitive characteristic data in the following way. Preferably, the elderly review device can be in communication connection with the guardian review device at an irregular time under the condition of subscription of the elderly, in a manner of not performing information interaction through the cloud server 200. For example, the elderly review device and the monitoring review device may be communicatively connected via a fifth generation mobile communication technology, so that the monitoring review device can push monitoring advice to the elderly review device when the guardian determines, via professional judgment, that the first cognitive monitoring report and/or the second cognitive monitoring report are/is abnormal. The monitoring suggestion is at least one of video, sound, text and picture. Since the elderly consulting device and the monitoring consulting device can be in communication connection through a fifth generation mobile communication technology, occasional high-definition online communication of videos becomes possible.

Example 2

The present embodiment is a modification of embodiment 1 or a supplement to or a replacement for the technical solution.

Preferably, the first special attribute in the attribute group is mapped to the historical attribute set. The mapping relation between the first special attribute and the historical attribute set can predict the relevance of the matched data elements and the cognitive function damage type. Whether the elderly have cognitive impairment is accomplished by monitoring and evaluating data elements. But prediction of big data is a probabilistic event. Therefore, the matched data elements are associated with the cognitive function impairment type in a first probability manner based on the mapping relation between the first special attribute and the historical attribute set. Preferably, the first probability presents its confidence level at the same time as presenting it, so as to evaluate the accuracy of the first probability, and facilitate the decision making of the monitoring and evaluating personnel. For example, a retired monarch in the academic calendar for a male 65-70 years old who is in good health. The attribute elements whose attribute groups can be decomposed include: good health status, 65-70 years old, male, research student's calendar, retirement and solitary. In this attribute group, the specific attribute of 65-70 years old is selected from the knowledge base 100 based on the keyword search mode. Under the environment of big data statistics, the most likely cognitive dysfunction corresponding to the ages of 65-70 is cognitive dysfunction, and the data sets matched based on the relation between the special attributes and the attribute sets of the ages of 65-70 are (dynamic brain waves, memory power, and the like,Photo identification and motion simulation). The first probability of the perception disorder being respectively identified with dynamic brain waves, memory, photos and motion imitation is P₁₁、P₁₂、 P₁₃、P₁₄Respectively corresponding confidence degrees are alpha₁、α₂、α₃、α₄. In this way, the invention also has the following advantages: 1. the special attribute is a representative attribute element which is screened from the collected attribute group and is used as the attribute group, so that the matching calculation amount of the attribute group and the cognitive function impairment type is reduced, a group of data groups can be rapidly matched, and the cognitive function impairment type assessment of the old can be conveniently carried out in time; for example, if an old person has performed a surgery in a brain language region once, the medical history can be used as a special attribute, based on statistics of big data, the big probability of the cognitive function damage type corresponding to the surgery in the brain language region once performed is language cognitive disorder, and the matched data elements are (language follow-up, language repeat, speech rate, language definition); 2. in order to enable the data elements output through the special attributes to be used for evaluating the corresponding cognitive function damage types, the method also judges whether the group of data elements can accurately predict cognitive damage or not through statistics in a probability mode and an evaluation mode of confidence coefficient of the data elements, so that the data elements matched by the method have statistical relevance with the cognitive function damage types, and a monitoring party can conveniently make a next decision.

The first probability that the first special property may result and its confidence level are not high. For example, a retired monarch in the academic calendar for a male 65-70 years old who is in good health. The first special attribute is 65-70 years old, and the first probability that the corresponding perception disorder is respectively identified and imitated with the corresponding dynamic brain waves, memories, photos and actions is P₁₁、P₁₂、P₁₃、P₁₄Respectively corresponding confidence degrees are alpha₁₁、α₁₂、α₁₃、α₁₄. The accuracy rate of dynamic brain wave, memory, photo recognition and motion simulation for evaluating the perception disorder of 65-70 years old people is P₁(P₁₁、P₁₂、P₁₃、P₁₄) Confidence of the accuracy is alpha (alpha)₁₁、α₁₂、α₁₃、α₁₄). But the accuracy is P based on the big data environment₁Not high (e.g., 55%, less than the evaluation threshold of 75%), with a confidence level of α₁Is 0.90 (less than 0.95). The first special attribute of 65-70 years old is described as a representative attribute having a high evaluation error. To be able to effectively overcome this error, it is preferred that the second special attribute in the attribute group is mapped to the historical attribute set at least in case the first probability does not meet the evaluation threshold. The second special attribute is also another representative attribute in the set of attributes. For example, a retired monarch in the academic calendar for a male 65-70 years old who is in good health. The first probability is P₁55%, less than the evaluation threshold 75%. Then the second special attribute in the attribute group is mapped to the historical attribute set. For example, the first special attribute is "65-70 years old", and the second special attribute is "solitary". Then, based on the mapping relationship between the second special attribute "solitary" and the historical attribute set, the first probability is modified according to the first probability that the "solitary" will result, and a second probability is generated. That is to say P₂＝βP₁(P₁₁、P₁₂、P₁₃、 P₁₄) Or P₂＝P₁(β₂₁P₁₁、β₂₂P₁₂、β₂₃P₁₃、β₂₄P₁₄). Since the special attribute corresponds to a constraint, the accuracy with which the data elements corresponding to the first special attribute and the second special attribute are used to evaluate the corresponding cognitive impairment may vary under a plurality of constraints. Such changes can be used as a decision basis for the monitoring party.

Preferably, in the case that the first probability is greater than the second probability, the mapping order of the first special attribute and the second special attribute with the historical attribute set can be interchanged. For example, the first probability P₁55% and the second probability is 45%, then the first special attribute may be the second special attribute in the second operation and the second special attribute in the first operation may be the second operationThe first special attribute in the calculation. This sequential interchange is primarily used to re-match new data sets and new types of cognitive impairment. For example, a retired monarch in the academic calendar for a male 65-70 years old who is in good health. In the second probability operation, "solitary" is used as the first special attribute, and "65-70 years old" is used as the second special attribute. In a big data environment, the big probability of the cognitive disorder type corresponding to the "solitary" is speech disorder, the corresponding data elements are (language follow-up, language repeat, speech rate, language definition). And the corresponding language handicap is respectively corresponding to the first probability P' of language reading, language repeating, speech speed and language definition₁₁、P`<sub>12</sub>、P`₁₃、P`<sub>14</sub>. Probability of speech disorder for evaluating speech disturbance is P' by language follow-up, language repeat, speech speed and speech definition<sub>1</sub>(P`₁₁、P`<sub>12</sub>、P`₁₃、P`₁₄). In this manner, the present invention is able to retrieve new data sets for evaluation of new types of cognitive impairment. Based on this, the invention can obtain at least two different data sets for evaluating different types of cognitive impairment. In addition, the monitoring party can modify the knowledge base 100 based on the test results. For example, in the case that the first probability is greater than the second probability, the monitoring party evaluates the first group of data elements for the elderly person and also evaluates the second group of data elements for the elderly person, and the final result is: the confidence level of the first set of matching sequences (first special attributes) is higher than the confidence level of the second set of matching sequences (first second special attributes), the data stored in the knowledge base 100 can be modified based on the test results.

Preferably, if the second probability is greater than the first probability, the monitoring party may evaluate the corresponding data type by collecting the corresponding data element.

Preferably, the first special attribute and/or the second special attribute can be obtained as follows: the historical attribute set and the cognitive function impairment type are associated in a third probabilistic manner. Statistical principle based on big data, historical attribute set and cognitive powerAn association of the impairment type is essentially a probabilistic association. In this manner, the particular attribute is selected based on a third probability of association between the historical set of attributes and the type of cognitive impairment. Based on this, the invention also has the following advantages: 1. the method can select factors possibly causing cognitive impairment of the old to be evaluated under a big data environment based on the attribute set input by the old to be evaluated; 2. The selection is carried out by depending on the probability principle, and the screened factors can determine the weight coefficients thereof according to the probability, for example, the first weight coefficient of the first special attribute is larger than the second weight coefficient of the second special attribute, and the weight coefficients can be determined in a mode of averaging according to the probability; 3. since the attribute sets entered into the elderly are numerous, the cognitive impairment types are screened in the attribute set mode instead of the attribute elements, which causes difficulty in data calculation. Preferably, each attribute element in the attribute group is matched with a historical attribute set, so as to present the cognitive function impairment type corresponding to each attribute element and the probability of having the cognitive function impairment type corresponding to each attribute element, and be used for determining the weight coefficient corresponding to each attribute element. For example, the third probability of the "hydrocephalus" in the historical attribute set corresponding to speech disorder, memory disorder and perception disorder is P₃₁、P₃₂、P_33。For another example, the third probabilities of the speech disorder, the memory disorder and the perception disorder respectively corresponding to the "student study calendar" with the concentrated historical attributes are P₃₄、P₃₅、P₃₆. For another example, the third probabilities of the speech disorder, the memory disorder and the perception disorder corresponding to the age of 60-65 in the historical attribute set are P₃₇、P₃₈、 P₃₉. The first special attribute and/or the second special attribute can be selected from the attribute elements in the attribute group with different weighting coefficients based on the third probability. For example, the medicine is suitable for the aged 60-65 years old in the student's calendar with a medical history of hydrocephalus. In the knowledge base P₃₁Greater than P₃₄And P₃₇，P₃₂Greater than P₃₅And P₃₈，P₃₃Greater than P₃₆And P₃₉Then the first special attributeIt is "hydrocephalus". If in this case, P₃₇Greater than P₃₄，P₃₈Greater than P₃₅、P₃₉Greater than P₃₆Then the second special attribute is "60-65 years old". For example, the corresponding weight coefficient for hydrocephalus may be γ₁＝(P₃₁+P₃₂+P₃₃)/3。

Preferably, the historical attribute set includes at least age, degree of academic, medical history, gender, living environment, geographic location, and whether or not the elderly are solitary. And under the condition that the acquired attribute group of the elderly to be evaluated comprises the medical history, performing logical operation on the medical history and the medical history of the historical attribute set, wherein if the medical history in the attribute group corresponds to the medical history in the historical attribute set one by one, the medical history in the attribute group can be used as a special attribute. Elderly have a history of possible cognitive impairment, but some have. Therefore, the method aims to reduce the operation complexity of the method and improve the screening efficiency. If the medical history is recorded in the attribute group of the elderly to be evaluated, the evaluation unit first performs a logical operation, such as a logical and operation, on the history as a keyword in the history attribute set, and if the result of the logical and operation is the medical history, the medical history may be regarded as a special attribute. If the result of the AND operation is an empty set, the medical history may not be a special attribute. For example, the elderly to be assessed are 63-year-old male elderly with fractured hands. The evaluation unit takes the 'hand fracture' as a key word to perform logic operation with the medical history in the historical attribute set, and if the hand fracture and the medical history in the historical attribute set are matched with each other, the 'hand fracture' can be taken as a special attribute; if the hand fracture does not match the medical history in the historical attribute set, then "hand fracture" may not be a special attribute.

Example 3

The embodiment also discloses a system for monitoring and evaluating the cognitive function of the old people, which is suitable for executing the steps of the method recorded by the invention to achieve the expected technical effect. Relates to a method for predicting the type of cognitive impairment which may occur to the elderly to be assessed based on the basic data of the elderly and monitoring the ability development trend of the cognitive function corresponding to the type of cognitive impairment, so that the elderly and the guardians thereof can adopt an intervention scheme in advance,

as shown in fig. 2, the system at least includes an information combination device 400, an information collection device 500, a cloud server 200, a knowledge base 100, a data collection terminal 600, a data calculation device 700, and a display device 300.

The information combination device 400 generates an attribute group of the elderly to be assessed based on a combination of basic data, which includes at least one of intrinsic data, upon receiving the basic data of the elderly to be assessed, which is acquired by the information acquisition device 500, and which can be used to match the cognitive function impairment type;

the cloud server 200 evaluates at least one cognitive function impairment type matched with the attribute information and a data set of cognitive functions corresponding to the cognitive function impairment type and capable of being used for monitoring the old to be evaluated from the knowledge base 100 based on the attribute information;

the data collecting terminal 600 collects the cognitive original data of the elderly to be evaluated according to the data group,

the data computing equipment 700 generates cognitive characteristic data corresponding to cognitive functions based on the cognitive original data, and the cloud server 200 triggers a corresponding cognitive threshold value to establish communication connection with the data computing equipment 700 in response to the cognitive characteristic data, so that the cloud server 200 can compare the cognitive characteristic data with cognitive historical data to generate cognitive relative data;

wherein, in case the cognitive relative data triggers the first relative threshold but does not trigger the second relative threshold, the cloud server 200 presents at least the primary care plan to the display device 300 in a manner that the primary care plan corresponding to the cognitive relative data can be matched from the knowledge base 100; and/or, in case the cognitive relative data triggers the second relative threshold, the cloud server 200 presents at least the advanced care plan to the display device 300 and/or sends a rescue signal to the medical institution in a manner that the advanced care plan corresponding to the cognitive relative data can be matched from the knowledge base 100; and/or, in the case that the cognitive relative data trigger does not reach the first relative threshold, the cloud server 200 may output the cognitive characteristic data to the display device 300 in a manner of prompting the guardian to maintain the primary activity scheme;

wherein, in case the cognitive characteristic data does not trigger its corresponding cognitive threshold, the data computing device 700 is able to output the cognitive characteristic data to the display device 300 in a manner that prompts the guardian to maintain the primary-living scheme.

Preferably, the data acquisition terminal 600 acquires the cognitive raw data in a manner that each cognitive raw data group corresponds to at least one cognitive function monitoring type;

each cognitive raw data group comprises at least one cognitive raw data; under the condition that the number of the cognitive raw data in each cognitive raw data group reaches the corresponding number, the data acquisition terminal 100 sends the cognitive raw data group to the data computing equipment 700, so that the data computing equipment 700 can generate cognitive feature data of a cognitive function monitoring type corresponding to the cognitive raw data group based on the cognitive raw data group, and the data computing center 200 can send the feature data to the cloud server 200 in a timed or untimed manner, thereby facilitating the timely carrying out different types of monitoring schemes on the old;

the cognitive function monitoring type at least comprises at least one of a language monitoring type, a responsiveness monitoring type and a memory monitoring type.

The cloud server 200 includes an evaluation unit. An evaluation unit which outputs a data set that can be used for evaluating cognitive functions on the basis of the dynamic characteristic data and/or the static characteristic data. The evaluation unit is essentially a data processor with a service of arithmetic function, such as an integrated chip, a CPU, etc., which has hardware modules such as I/O interfaces, processing modules, communication modules, etc. The communication module can adopt a 4G protocol, a 5G protocol, a WIFI protocol, a Bluetooth protocol, an NB-Lot protocol, an EnOcean protocol, a data line and the like to realize communication connection with a peripheral device. The peripheral devices include the information composing apparatus 400, the knowledge base 100, the display apparatus 300, and the monitoring unit.

And the information combination device 400 is used for collecting the attributes of the old to be evaluated. The information combining device 400 may be a keyboard, mouse, voice input device, or the like. For example, the age, medical history, academic history, solitary existence, and other optical attribute elements of a certain aged person to be evaluated are input into the evaluation unit through a keyboard and a mouse.

And the knowledge base 100 is used for storing the association information of the historical attribute set and the cognitive function impairment type of the old.

The display device 300, which may be a printer, may be a video display, may be a cell phone client, and so on.

The cloud server 200 matches the attributes with the historical information sets stored in the knowledge base 100 to form at least one data set to be collected. The historical information set is generated according to the mode that the historical attribute set of the old people is correlated with the cognitive function, so that the data group needing to be collected can be correlated with the cognitive function damage type based on the mutual matching of at least one group of attributes and the historical attribute set.

Preferably, the cloud server 200 can generate the data set according to the following steps: and mapping the first special attribute in the attribute group to the historical attribute set, so that the data element in the data group can be associated with the cognitive function impairment type and output in a first probability mode based on the mapping relation of the first special attribute and the historical attribute set. Wherein the first probability and its corresponding confidence can be presented by the display device 300. The data elements at least comprise dynamic characteristic data and/or static characteristic data of the elderly to be evaluated.

It should be noted that the above-mentioned embodiments are exemplary, and that those skilled in the art, having benefit of the present disclosure, may devise various arrangements that are within the scope of the present disclosure and that fall within the scope of the invention. It should be understood by those skilled in the art that the present specification and figures are illustrative only and are not limiting upon the claims. The scope of the invention is defined by the claims and their equivalents.

Claims (7)

1. An old people cognitive function monitoring and evaluating system relates to a method for predicting the type of cognitive function impairment which may occur to the old people to be evaluated based on the basic data of the old people and monitoring the ability development trend of the cognitive function corresponding to the type of cognitive function impairment, so that the old people and the guardians thereof can directionally take intervention schemes in advance,

at least comprises the following steps:

the information combination device (400) generates an attribute group of the elderly to be assessed on the basis of the basic data combination when receiving the basic data which are acquired by the information acquisition device (500) and can be used for matching the cognitive function impairment type, wherein the basic data at least comprise one of the inherent data;

the cloud server (200) evaluates at least one cognitive function impairment type matched with the attribute information and a data set of cognitive functions corresponding to the cognitive function impairment type and capable of being used for monitoring the old to be evaluated from a knowledge base (100) based on the attribute information;

the data acquisition terminal (600) acquires the cognitive original data of the old people to be evaluated according to the data group,

it is characterized in that the preparation method is characterized in that,

the data computing device (700) generates cognitive characteristic data corresponding to the cognitive function based on the cognitive raw data, and the cloud server (200) triggers a cognitive threshold corresponding to the cognitive characteristic data to establish communication connection with the data computing device (700) in response to the cognitive characteristic data so that the cloud server (200) can compare the cognitive characteristic data with cognitive historical data to generate cognitive relative data;

wherein, in case the cognitive relative data triggers a first relative threshold but not a second relative threshold, the cloud server (200) presents at least a primary care regimen corresponding to the cognitive relative data to a display device (300) in a manner that enables matching of the primary care regimen from the knowledge base (100); and/or, in case the cognitive relative data triggers a second relative threshold, the cloud server (200) presenting at least an advanced care regimen corresponding to the cognitive relative data to the display device (300) and/or issuing a rescue signal to a medical institution in such a way that the advanced care regimen can be matched from the knowledge base (100); and/or, in the event that the cognitive relative data does not trigger the first relative threshold, the cloud server (200) is capable of outputting the cognitive characteristic data to a display device (300) in a manner that prompts the guardian to maintain a primary-living regimen;

wherein, in the event that the cognitive feature data does not trigger its corresponding cognitive threshold, the data computing device (700) is capable of outputting the cognitive feature data to a display device (300) in a manner that prompts the parent to maintain a native-living program;

the cloud server (200) sends the data set to the display device (300) as follows;

the cloud server (200) configures at least one target business of cognitive functions and data elements required to be collected for completing the target business from the knowledge base (100) based on the cognitive functions, and generates the data elements into the data group corresponding to the target business,

the cloud server (200) and the display device (300) are in communication connection through a fifth-generation mobile communication protocol, so that the cloud server (200) can display the target business on the display device (300) in at least one mode of characters, pictures, sounds and videos and display the data group on the display device (300), the old can monitor the cognitive ability on line at least based on information interaction between the cloud server (200) and the display device (300), and in the cognitive ability monitoring process, the data acquisition terminal (600) can acquire original cognitive ability data of the old according to the data group.

2. The system according to claim 1, wherein the data acquisition terminal (600) acquires the cognitive raw data in a manner that each cognitive raw data group corresponds to at least one cognitive function monitoring type;

wherein each cognitive raw data set comprises at least one cognitive raw data; under the condition that the number of the cognitive raw data in each cognitive raw data group reaches the corresponding number, the data acquisition terminal (600) sends the cognitive raw data group to the data computing equipment (700), so that the data computing equipment (700) can generate cognitive feature data of a corresponding cognitive function monitoring type based on the cognitive raw data group, and the data computing equipment (700) can send the feature data to the cloud server (200) in a timing or non-timing mode, so that the old people can be monitored in different types in a timing mode;

the cognitive function monitoring type at least comprises at least one of a language monitoring type, a responsiveness monitoring type and a memory monitoring type.

3. The system according to claim 2, wherein said display device (300) comprises at least an elderly review device for review by said elderly person and a guardian review device for review by said guardian,

wherein the elderly review device and the guardian review device are respectively in communication connection with the cloud server (200) through a fifth generation mobile communication technology, so that the elderly review device and/or the guardian review device can respectively present the relative data in a non-quantitative manner and a manner of promoting subconscious satisfaction thereof, and therefore the elderly and/or the guardian can respectively read the first cognitive monitoring report of the elderly in a manner of being graphical and promoting a user to tend to generate a positive psychological state; and/or

The monitoring and consulting device is in communication connection with the cloud server (200) through a fifth generation mobile communication technology, so that the monitoring and consulting device can present the relative data and/or the cognitive characteristic data in a quantitative mode, and therefore the guardians read second cognitive monitoring reports of the old people in a graphical mode respectively;

wherein the elderly review device and/or the guardian review device includes a touch screen such that the elderly review device and/or the guardian review device are capable of presenting the first cognitive monitoring report in different non-quantitative manners in response to contact by an elderly person and/or a guardian, respectively;

the old people consulting device can be in communication connection with the monitoring consulting device at random time in a mode of not performing information interaction through the cloud server (200) under the condition that the old people subscribe, so that the monitoring consulting device can push a monitoring suggestion to the old people consulting device under the condition that the monitoring people judge that the first cognitive monitoring report and/or the second cognitive monitoring report are abnormal through professional judgment, and the monitoring suggestion is at least one of video, sound, characters and pictures.

4. The system of claim 3, wherein a first special attribute in the set of attributes is mapped to a set of historical attributes such that data elements in the set of data can be associated with the type of cognitive impairment in a first probabilistic manner based on a mapping of the first special attribute to the set of historical attributes; wherein the data elements at least comprise dynamic characteristic data and/or static characteristic data of the elderly to be assessed;

mapping a second special attribute of the set of attributes to the set of historical attributes, at least if the first probability does not meet an evaluation threshold, to enable data elements of the set of data to be associated with the type of cognitive impairment in a manner that revises the first probability to generate a second probability based on a mapping of the second special attribute to the set of historical attributes.

5. The system of claim 4, wherein the mapping order of the first and second special attributes to the historical set of attributes is interchangeable for re-matching a new data set and a new cognitive impairment type and re-probabilistically associating the new data set and the new cognitive impairment type where the first probability is greater than the second probability.

6. The system according to claim 5, characterized in that the first special attribute and/or the second special attribute can be obtained as follows:

the historical attribute set and the cognitive impairment type are associated in a third probability manner, so that the first special attribute and/or the second special attribute can be selected from attribute elements in the attribute group based on the third probability with different weight coefficients;

wherein a first weighting factor of the first special attribute is greater than a second weighting factor of the second special attribute.

7. The system according to claim 6, wherein each attribute element in the attribute group is matched with a historical attribute set so as to present a cognitive impairment type corresponding to each attribute element and a probability corresponding to the cognitive impairment type for determining a weight coefficient corresponding to each attribute element;

the historical attribute set includes at least age, academic degree, gender, living environment, geographic location and whether the elderly are solitary,

the cognitive impairment types include a perception impairment type, a memory impairment type, a thinking impairment type and a speech impairment type.

Images