CN113317761B - Cognitive dysfunction prevention monitoring device - Google Patents
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Abstract
A cognitive dysfunction prevention monitoring device comprising at least: the cloud server is used for completing correction of the theoretical memory state information and analysis of the change trend of the theoretical memory state information based on the feeling input by a user through the mobile terminal, the actual memory state information and/or external conditions. And, the cloud server records the actual memory state and the theoretical memory state based on iterative analysis of at least one external condition, body signal and/or sensation at each moment. The application can carry out personalized analysis according to the memory state of the user, improves the accurate judgment of the memory state change of the individual, and can provide early warning for the user by combining scene information.
Description
The application is a divisional application of a case with the application number of CN201810218097.1, the application date of 2018, 3 months and 16 days, the application type of the case is an invention patent, and the application name of the case is a cognitive disorder prevention monitoring device.
Technical Field
The invention relates to the field of artificial intelligence, in particular to a cognitive dysfunction prevention and monitoring device.
Background
Senile dementia (also known as Alzheimer's disease) is a neurodegenerative disease that causes cognitive dysfunction of the brain in many ways, such as memory, executive, visual space, linguistic communication, abstract thinking, learning, and computation. At present, senile dementia is mainly evaluated by a cognition-mental related scale after dementia symptoms appear, clinical diagnosis is made by combining with imaging and other examination, but the senile dementia patients diagnosed basically are at middle and late stages when the clinical symptoms are obvious, and the senile dementia has irreversibility in pathology, on the one hand, no effective treatment method is available for the senile dementia at home and abroad, on the other hand, the existing treatment means mainly adopt medicine treatment, but the medicine can only relieve or stabilize the illness state to a limited extent in a specific stage of illness state development, and can not achieve the curative effect, in addition, the medicine treatment is only one link in the senile dementia prevention and treatment measures, only can be implemented after the patients have dementia symptoms, so that the treatment method is difficult to obtain satisfactory curative effect, on the other hand, the clinical information system and auxiliary prevention monitoring device for the senile dementia patients are not available in hospitals, the evaluation and the interpretation of the senile dementia images are also greatly influenced by the personal subjective effect of doctors, the consistency and objectivity are lacked, and the abnormality of cognition-mental scale and the image display can be seen in some other medical treatment methods, the illness state, the mental illness state and the dementia is not serious mental illness state, the mental illness state is caused to the only is serious and the mental illness state of the senile dementia is caused to the patients and the clinical dementia is not really marked to have the clinical and the clinical mental illness state and the clinical symptoms and the senile dementia is difficult to cause the clinical experience and the clinical symptoms.
CN102930286a discloses an image early diagnosis system for senile dementia. The image preprocessing equipment registers the first to-be-detected image to the second to-be-detected image, strips skull in the images, performs tissue segmentation on brain tissue images, and separates left brain from right brain; the image asymmetric feature extraction device extracts the shape and texture features of each tissue from the preprocessed images, so that respective asymmetric feature sets of the two images are obtained and marked as a first image feature set f1 and a second image feature set f2; the merging calculator carries out simple merging operation on the first image feature set f1 and the second image feature set f2 to generate a merging feature set f3; the feature vector selector performs feature screening on the combined feature set f3 by using the optimal feature subset to obtain a new feature set f4; and the optimizing classifier adopts the classifier and optimizing parameters thereof to classify the new feature set f4, and a classification result is obtained. According to the technical scheme, the time sequence asymmetry characteristic of an anatomical structure in the brain MR image is used as a detection standard in early diagnosis of senile dementia, the diagnosis process is long in time consumption and can be executed by a professional technician, and simple and convenient operability cannot be achieved. Meanwhile, due to individual differences of people and lack of monitoring of physical signals, external conditions and objective feelings of the people, the technical scheme cannot implement personalized rehabilitation guidance for the elderly, and can only be relieved through drug treatment.
CN104545899a discloses a senile dementia monitoring system based on mobile internet. The brain electricity monitoring device is used for acquiring brain electricity information of the senile dementia patient or the healthy person in real time and sending the brain electricity information to the intelligent terminal; the intelligent terminal is used for receiving the electroencephalogram information, finishing the cognition-psychological assessment and inputting clinical information, and uploading the electroencephalogram information, the cognition-psychological assessment and the clinical information to the cloud server through the mobile internet; the cloud server is used for receiving MRI image information uploaded by a hospital and information uploaded by the intelligent terminal, so that auxiliary diagnosis is completed, corresponding nursing guide suggestions are generated, and then auxiliary diagnosis results and the nursing guide suggestions are fed back to the intelligent terminal. The technical scheme utilizes the mobile internet technology, and can realize automatic auxiliary diagnosis of senile dementia. However, characteristic data related to senile dementia is so complex that directivity of the characteristic data of senile dementia is not clear. If the diagnosis result and the nursing scheme are given based on the electroencephalogram information only to conduct rehabilitation guidance, opposite effects often occur. Meanwhile, the technical scheme cannot realize differential analysis based on differential characteristic states among individuation so as to give individualized rehabilitation guidance.
CN104902806B discloses methods, systems and devices for assessing susceptibility to neuropathology, disease progression and treatment. The method for providing an assessment related to a neurological or neuropsychiatric disorder comprises: selecting a profile category indicative of one or more aspects of cognitive or sensory functions associated with a neurological or neuropsychiatric disorder, presenting a stimulation sequence to the subject, wherein the stimulation sequence is based on the selected profile category, acquiring physiological signals of the subject before, during and after the presentation of the stimulation sequence to generate physiological data, and processing the physiological data to generate an information set comprising one or more quantitative values associated with the selected profile category. The technical scheme can present the stimulation sequence to the user and collect the feedback of the user receiving the stimulation sequence to form a data set, and adjust the treatment management scheme of the user based on the data. However, the device only evaluates the cognitive state of the client based on simple theoretical data, and external environment condition information which can influence the cognitive condition of the patient is not combined, so that the device is difficult to accurately match with the actual demands of the user when in use.
CN106599582a discloses a look-ahead cognitive function assessment system based on intelligent mobile equipment, a data collection subsystem and a data management subsystem. The data collection subsystem includes a task unit that includes: the task creating module is used for creating tasks by the testee and recording basic information and emotion information; the task waiting to be completed module is used for a tested person to operate the incomplete task; and the completed task module is used for the tested person to operate the completed task. The data management subsystem comprises a task management unit and a look-ahead cognitive function assessment unit. The task management unit comprises a task list module, a task operation statistics module and a reminding clue setting module. The prospective cognitive function evaluation unit comprises a prospective memory capacity evaluation module and an emotion prediction capacity evaluation module. A prospective cognitive function assessment method based on the intelligent mobile device is also provided. The technical scheme can carry out prospective evaluation on the cognitive function of the user, but the prediction does not combine the information of the external environment and the historical data information of the user to evaluate the cognitive function of the user, so that the technical scheme has obvious defects and the generated technical effect is very limited.
CN102319067a discloses a nerve feedback training device based on brain electrical signal for improving brain memory function, which can quantitatively detect the instant state of memory by utilizing scalp brain electrical signal collected during brain activity process, and present brain electrical rhythm wave representing memory level to user, and instruct user to consciously regulate brain electrical rhythm wave, so as to achieve the goal of improving memory level. Firstly, an electroencephalogram signal acquisition module is used for acquiring an electroencephalogram signal of a user under a classical memory task and extracting rhythmic waves representing the level of brain memory; and then, the brain instant memory state is characterized by utilizing the brain electricity analysis module, and is fed back and output to a user in a striking and attractive mode. The user can directionally adjust the brain electric rhythm wave according to the real-time feedback, thereby achieving the purpose of improving the memory. The training instrument of the technical scheme can quantitatively detect the memory state of the user and guide the user to consciously adjust the brain electric rhythm wave, but specific conscious adjusting modes are various, the system can only detect the memory level result after the training of the user, the actual using effect of the adjusting modes cannot be analyzed and personalized adjusted, and the system has limitation in use.
Disclosure of Invention
The present invention stems from the discovery that none of the individuals are able to accurately describe the cognitive or sensory sensations that result from a certain memory state. Although there are methods of brain wave scanning, feature signal detection, and the like, and a large number of algorithms of artificial intelligence such as CNN (convolutional neural network), RNN (recurrent neural network), DNN (deep neural network) are used for the component memory model, the application effect to an individual is only poor.
From a psychologist's perspective, the memory state is manageable, at least guidable. Such management and guidance requires scientific methods and apparatus to assist in achieving the desired effect. The object of the present invention is therefore to provide a device and a method for calibrating and recording the memory state and its associated feeling by the user himself, and thus for specifically training the memory against the threshold range of cognitive dysfunction. According to the invention, training of the cloud server memory analysis algorithm can be completed through a teaching process, and the difference between the actual memory state and the theoretical memory state can be continuously adjusted by the user, so that the device gradually learns the memory training characteristics of the user, and further the memory state of the user is effectively managed.
A cognitive dysfunction prevention monitoring device comprising at least: the mobile terminal is used for automatically acquiring and/or inputting actual memory state information by a user; the detector is used for collecting body signals in a mode of indirectly or directly contacting the body of a user, and the body signals are sent to the cloud server through the mobile terminal; and the cloud server is used for determining theoretical memory state information corresponding to the body signals by using a memory analysis algorithm based on a preset database. The cloud server is characterized in that the cloud server completes correction of the theoretical memory state information and analysis of the change trend of the theoretical memory state information based on the feeling input by the user through the mobile terminal, the actual memory state information and/or external conditions, and the mobile terminal is configured to send an early warning prompt to the current user and push the working mode of the nursing scheme under the condition that the change trend of the theoretical memory state exceeds a critical value at a foreseeable time point. Due to the individual difference and the complexity and diversity of cognitive dysfunction induction factors, when the cognitive dysfunction prevention monitoring device judges the user state, the user can input the mental feeling or external conditions which cannot be directly acquired by the device observed in the daily life, so that the device can more accurately predict the change trend of the memory state of the aged, accurately analyze the current mental state of the aged and more pointedly push a nursing scheme or a training scheme so as to reduce the risk of the user suffering from cognitive dysfunction.
According to a preferred embodiment, the cloud server analyzes a teaching process of the cloud server based on a feeling input by a user, the actual memory state information, body signals and/or external conditions, wherein the cloud server pre-configures parameters of a memory analysis algorithm according to at least two extreme memory state information determined by the teaching process. And teaching the cloud server through the feeling information input by the user and the external condition change information to determine a memory analysis algorithm. After the memory analysis algorithm is determined, the actual memory state is accurately analyzed based on external conditions and body signals, so that the analysis probability of the actual memory state of the individual is improved, and the personalized memory analysis algorithm is facilitated to be formulated. The extreme memory state of the user causes serious memory deterioration of the user, thereby inducing serious consequences such as blurred knowledge of surrounding things, unclear personal consciousness, loss of life self-care ability and the like. In the above case, the memory analysis algorithm is required to have high accuracy to provide early warning information as soon as possible. In the process of actively monitoring a monitoring object to collect body signals, a caretaker of the monitoring object can timely input fine details observed in daily life or mental feelings which cannot be expressed by the monitoring object. The invention can maintain objective and accurate analysis and early warning results as far as possible.
According to a preferred embodiment, the mobile terminal applies test information capable of acquiring memory state information of a user to the user in a text, voice, video and/or graphic mode, and automatically collects test feedback information corresponding to the test information, while the detector collects body signals of the user corresponding to the test feedback information, and the cloud server pre-configures parameters of a memory analysis algorithm based on the test feedback information and/or the body signals in a teaching process and corrects theoretical memory state information corresponding to the body signals. By applying the test information, the corresponding memory state and body signals of the user can be accurately obtained, so that the cloud server is accurately taught. Preferably, through the teaching process, the relevant change trend of the extreme memory with low occurrence probability can be obtained preferentially, so that the memory state information of the cloud server is perfected.
According to a preferred embodiment, the user input experiences include at least an emotion feeling and a sleep feeling, wherein the cloud server stores the emotion feeling and the sleep feeling in a correlated manner, and the mobile terminal is configured to retrieve the actual emotion state information, the actual sleep state information and the associated external conditions and body signals thereof stored in the cloud server by the user in a manner of inputting keywords. Preferably, the mobile terminal stores or provides the feeling input by the user in a text, voice, video and/or graphic manner and the corresponding automatically acquired actual emotion state to the cloud server in an associated form, or records the external condition which causes the actual emotion state of the user and stores or provides the external condition in an associated form with the corresponding actual emotion state to the cloud server, and the cloud server analyzes the correlation between the specific emotion state of the user and the external condition and pre-warns the initiation of the specific emotion state based on the correlation. After the association between the actual emotional state and the external conditions and the body signals are determined, the method is beneficial to guiding in the process of converting the actual emotional state into the specific emotional state, so that the emotion is quickly converted, and the abrupt change of the memory state caused by emotion fluctuation of a user under the extreme emotional condition is avoided, and further the inaccuracy of an analysis result or the untimely of memory training is caused.
According to a preferred embodiment, the mobile terminal records external conditions causing the actual memory state of the user and stores or provides the external conditions to the cloud server in a form associated with the corresponding actual memory state, the cloud server stores the current memory state information input by the user and the external conditions in a manner associated with each other, and the cloud server is configured to analyze the correlation between the specific memory state of the user and the external conditions, and then to perform the working mode of pushing the nursing scheme for the initiation of the specific memory state based on the correlation.
According to a preferred embodiment, the cloud server analyzes and determines a theoretical memory state corresponding to the physical signal acquired by the detector based on the physical signal acquired by the detector and feeds the theoretical memory state back to the mobile terminal, and the mobile terminal outputs stimulation information in the form of text, voice, video and/or graphics to change the external conditions and/or directly provides training stimulation to the user to improve the current memory state of the user.
According to a preferred embodiment, the cloud server stores the current memory state information input by the user and the external conditions related to the actual memory state information provided by the mobile terminal in a manner related to each other, and the mobile terminal is configured to retrieve the actual memory state information stored in the cloud server and/or the mobile terminal in a manner related to the external conditions by the user.
According to a preferred embodiment, the cloud server corrects the theoretical memory state determined based on the analysis of the body signals collected by the detector according to the actual memory state information, and the memory management configuration file composed of the corrected theoretical memory state is stored in the mobile terminal in a manner of being retrievable according to the body signals.
According to a preferred embodiment, the cloud server records the actual memory state and the theoretical memory state based on an iterative analysis of at least one external condition, body signal and/or sensation at each moment in time, and the mobile terminal displays the change trend of the external condition, body signal, sensation change and actual memory state in a manner of displaying the identification with mapping association, reminds the user of the extreme change trend of the actual memory state in a manner of color change, sound and/or vibration, and/or displays advice for changing the actual memory state.
According to a preferred embodiment, the theoretical memory state change trend exceeding the critical value at a predictable time point is classified and stored in a database of a cloud server according to the mode that the feeling input by a user, the actual memory state information, the body signal and the external condition are all related to each other, wherein the cloud server forms a boundary trigger condition that the theoretical memory state change trend exceeds the critical value based on the related data information in the database, and the cloud server is configured to analyze the correlation between the actual memory state information of the user and the boundary trigger condition, and make early warning and improve the external condition and/or the working mode of the care scheme in advance based on the correlation.
The beneficial technical effects of the invention are as follows:
The cognitive dysfunction prevention monitoring device can conduct personalized analysis on the memory state of the user, and improves accurate judgment of the memory state change of individuals. Moreover, the cloud platform can be utilized to carry out large data sharing on the data of the memory state, so that the influence of the change of the external conditions on the memory state of the group can be analyzed by government departments, and the living state of the aged and the disease risk of the whole aged group in the management area can be analyzed. The risk of the aged suffering from the illness can be found as early as possible through sectional or real-time monitoring of the mental state of the aged. Through comprehensive measures such as memory training, active guidance, reasonable nursing schemes and the like, the device can help the old to adjust mental states mainly including memory states so as to prevent cognitive dysfunction.
Drawings
FIG. 1 is a schematic diagram of the logic structure of the present invention;
FIG. 2 is a schematic diagram of a data processing architecture of the present invention;
fig. 3 is a schematic view of a mobile terminal according to the present invention.
List of reference numerals
10: Mobile terminal 20: detector for detecting a target object
30: Cloud server 31: database for storing data
Detailed Description
The following detailed description refers to the accompanying drawings.
Example 1
As shown in fig. 1 and 2, the present invention provides a cognitive dysfunction prevention and monitoring device. The device for preventing and monitoring cognitive dysfunction at least comprises a mobile terminal 10, a detector 20 and a cloud server 30. The mobile terminal 10 is used to automatically collect and/or input actual memory state information by a user. Preferably, the mobile terminal 10 includes an image acquisition device, a video acquisition device, a memory testing device, and a memory training device. The mobile terminal may be an intelligent terminal, such as a mobile device including a notebook, a mobile phone, a smart bracelet, a smart watch, or a camera device. Preferably, the image pickup device includes a general image pickup device and a night image pickup device. Preferably, the automatic collection is to automatically collect a conversation record of a user's normal personal conversation situation, a user's descriptive status of a particular event, and so on. Or the user inputs the memory state of the user in the text description into the mobile terminal.
Preferably, the memory testing device and the memory training device are connected to the mobile terminal by means of wires and/or wirelessly. The memory training device outputs stimulation information in the form of text, speech, video and/or graphics to change the external conditions and/or directly provide training stimulation to the user to improve the user's current memory state. The memory testing device applies test information capable of acquiring memory state information of a user to the user in a text, voice, video and/or graphic mode, and automatically acquires test feedback information corresponding to the test information. The cloud server pre-configures parameters of a memory analysis algorithm based on test feedback information and/or body signals in a teaching process, and corrects theoretical memory state information corresponding to the body signals. Preferably, the memory testing device and the memory training device may be VR devices worn by the user. The VR device includes at least a sensing unit. The sensing unit is capable of collecting information related to brain activity of a user and generating an output signal during a memory test or a memory training. The output signal may be transmitted to the mobile terminal wirelessly and/or via a wire.
The detector of the present invention is used to acquire body signals in a manner that indirectly or directly contacts the body of a user. The detector comprises a plurality of modules for collecting physiological signals of human bodies. The detection module of the detector at least comprises one or more of a pulse sensor, a heartbeat sensor, a blood pressure sensor, a respiratory frequency sensor, a sound acquisition module, a hand vibration module and a step frequency sensor. Preferably, a microwave signal sensor can be additionally arranged on the detection module in the detector according to the requirement and used for detecting the change of brain waves. The detection module in the detector can also be added with electrodes arranged on the head according to the requirements. Preferably, the body signals collected by the probe 20 are sent to the cloud server 30 via the mobile terminal 10.
The cloud server 30 of the present invention is used for determining theoretical memory state information corresponding to a body signal by using a memory analysis algorithm based on a preset database. Preferably, the cloud server 30 is provided with a database 31 storing several memory analysis algorithms. The cloud server 30 analyzes and determines the theoretical memory state corresponding to the physical signal collected by the detector 20 and feeds back the theoretical memory state to the mobile terminal 10. Preferably, the memory analysis algorithm comprises a Bayesian classification algorithm, a neural network, a support vector machine, a decision tree, learning based on case reasoning, association rule learning and other machine learning algorithms.
For example, the memory analysis algorithm of the cloud server is as follows.
In this embodiment, the classification calculation result of the memory is classified into a healthy state, a sub-healthy state of the memory and a severely degraded state of the memory. The method comprises the steps of taking a healthy state, a memory sub-healthy state and a memory severely-declined state as three clustering centers, and adopting a fuzzy C-means algorithm to perform clustering analysis on the three clustering centers. Specifically, it is assumed that the set of memory state levels to be classified is x= { X 1,x2,...xn}∈Rd, and the set is a set of limited data, that is, body signal data, external condition data, feeling characteristic data, and the like. Element x is a d-dimensional vector, and a fuzzy partition matrix (u ij)c×n and c cluster center points v= { V 1,v2,...vn } are found so that the objective function:
The method meets the following conditions:
Wherein, m is [1, + ] is called fuzzy weighting coefficient, u ij is called membership, x j represents the degree to which the vector is attached to the center point, and d (x j,vi) is the Euclidean distance of the objective functions x j and vi.
Preferably, the cloud server 30 completes the teaching process of the cloud server 30 based on the operation of the mobile terminal 10 by the user. Preferably, the cloud server at least comprises one or more of a CPU, a processor, a microprocessor, a server and a server group. First, the cloud server 30 requires user cooperation to complete the teaching process. The teachings in this disclosure are directed to exemplary artificial intelligence programming.
For example, during the teaching of the user using the device of the present invention, it is necessary to input emotion feeling, sleep feeling, and current memory state information through the mobile terminal. During the process of using the device of the present invention, the mobile terminal 10 actively collects the current external conditions of the user. External conditions include weather conditions, noise figures, the current mental state of the user, and the like. Preferably, the user inputs the current emotion feeling and the sleep feeling, and enters the teaching process after inputting the current emotion feeling and the sleep feeling after the moderation adjustment again in the specified time range interval. In the teaching process, the cloud server forms a memory analysis algorithm matched with the user based on the external conditions, sleep feeling and emotion feeling of the user and the memory state change trend. While correcting the theoretical memory state.
For example: the method steps of the teaching process comprise:
S1: starting a teaching mode;
S2: user selection: selecting the teaching object according to the user name, and if the teaching object is the user already recorded in the data storage module, directly selecting; if the object taught at this time is a user which is not yet recorded, recording the memory state information of the user through the mobile terminal and then selecting, wherein the memory information of the user comprises a user name, a user face photo, a user sample memory and external conditions and feelings related to the user name and the user face photo;
S3: sample selection: if the sample memory content and the teaching matching content of the teaching are already stored under the user name, directly selecting the stored sample memory content and teaching matching content; if the sample memory content and the teaching matching content of the teaching are not contained under the user name, reading the teaching matching content of the teaching through a mobile terminal, storing the sample memory content and the teaching matching content of the teaching under the user name, and then selecting the stored sample memory content and teaching matching content;
S4: memory state transition: the memory state of the user is not exactly the same for different periods of time. And at least acquiring physical signals, external conditions and memory test feedback results after repeated memory training for a plurality of times of a user within a period of one month, and carrying out theoretical memory state analysis. Forming a plurality of memory state samples by the change of the memory state in different time periods;
S5: effect evaluation: and the cloud server corrects a memory analysis algorithm by comparison according to the memory state analyzed by the theoretical memory state and the memory state information of the user recorded in the teaching process, and records the external conditions and feelings related to the memory state.
Preferably, the user's sample memory is the optimal memory state information associated with physical signals, external conditions, emotional feelings, and sleep feelings and saved in the form of video, audio, text, and/or pictures, which are formed in the user's performing of a plurality of memory tests over a certain time frame.
Preferably, when the actual memory state, the body signal and the external condition of the user are stored in the cloud server in an associated form, the cloud server 30 writes the association relationship between the actual memory state, the body signal and the external condition and the memory analysis algorithm into the memory through the teaching process, so as to form the personalized memory analysis algorithm for the user. The more the number of times the user and the cloud server 30 pass through the mobile terminal 10, the more accurate the memory analysis algorithm of the cloud server 30. For example, the weight parameters of various information are adjusted according to the sample information input by the user a plurality of times, so that the theoretical memory state of the memory analysis algorithm is as consistent or similar as possible to the actual memory state of the user.
Wherein, the cloud server 30 pre-configures parameters of a memory analysis algorithm according to at least two types of extreme memory state information determined by the teaching process.
Preferably, during teaching, the mobile terminal 10 applies stimulus information capable of inducing a memory state to a user, and the actual memory state information corresponding to the stimulus information is automatically acquired and/or input by the user. While the detector 20 collects body signals of the user corresponding to the stimulation information. The cloud server pre-configures parameters of a memory analysis algorithm based on at least two of the actual memory state information and the body signals in a teaching process. Preferably, the cloud server 30 corrects theoretical memory state information corresponding to the body signal based on the actual memory state.
Preferably, the stimulus information includes video, picture, text information, sound, and the like, which can cause the user to recall the response. The stimulus information causes a recall response by the user through the information carried by the stimulus information about a particular event, a particular scene, a memory training topic, and the like. For example, video information records continuous memory information of the occurrence and end of a certain event by a user within a certain time range. After the mobile terminal displays a certain segment, judging whether the user can obtain detailed information of the whole event according to the segment in a questioning mode, or classifying the memory state of the user based on the completion degree of recall of the whole event.
Preferably, the cloud server 30 also forms a personalized memory state change curve based on the change trend of the actual memory state in the teaching process. For example, the memory state changes to: ambiguity of consciousness, clear intermittent consciousness, normal consciousness but only capable of memorizing simple events, capable of memorizing partial details of complex events, and capable of memorizing all details of the whole event. Under normal conditions, the user does not instantaneously switch from the conscious fuzzy state to the memory normal state.
By continuously analyzing the theoretical memory state of the current user, the cognitive dysfunction prevention and monitoring device can determine the change trend of the memory state by using the cloud server 30. Preferably, the cloud server or the mobile terminal analyzes the change trend of the actual memory state based on the body signal, the actual memory state information and/or the external condition information. In the test process, the memory test device outputs test information to the test object in a picture, video, audio and/or text mode and receives the feedback result of the test object. And obtaining the memory state change trend of the user in a certain time period by summarizing and analyzing the multiple test results in the certain time period. Preferably, under the condition that the memory state of the test object is extremely poor when the test object is tested for the first time, the cloud server and/or the mobile terminal test the current emotion, the physical condition and the sleep quality of the test object and adjust the emotion and the sleep quality of the test object according to the detection result, or train the memory of the test object. The adjusting of the emotion of the test object may be performed by preferably, in the case that the mobile terminal 10 determines that the trend of the change of the actual memory state of the current user exceeds the threshold value for at least one continuous time interval according to the analysis of the actual memory state information, and/or in the case that the trend of the change of the theoretical memory state of the current user exceeds the threshold value for the analysis of the cloud server, the cloud server sends an early warning prompt to the current user through the mobile terminal. Specifically, the mobile terminal 10 or the cloud server 30 analyzes the trend of the actual memory state of the current user, and confirms that the trend of the actual memory state exceeds the threshold at a predictable time point. At this time, the cloud server 30 sends various prompts such as vibration, sound, color change, etc. to the user through the mobile terminal 10, so as to remind the user of abnormal situations in the memory state, and start to perform memory training on the user or output a nursing scheme and guide a caretaker to perform rehabilitation care on the user.
Preferably, the mobile terminal 10 stores or provides the feeling input by the user in text, voice, video and/or graphics and the corresponding automatically acquired actual memory state to the cloud server 30 in a form of association. Or the mobile terminal 10 records the external condition that causes the actual memory state of the user and stores or provides the external condition to the cloud server 30 in a form associated with the corresponding actual memory state. The cloud server 30 analyzes the correlation between the specific memory state of the user and the external relationship, and gives an early warning of the initiation of the specific memory state based on the correlation. Preferably, the external conditions are, for example, weather conditions, noise figures, the current mental state of the user, etc.
The mobile terminal 10 or the cloud server 30 stores the external condition, the physical signal and the actual memory state at the same time in an associated manner, and records the memory state change trend in the previous limiting time and the memory state change trend in the latter limiting time of the actual memory state. The cloud server 30 analyzes the correlation of the extremely poor memory state and the external condition, the emotional state, and the sleep state of the user with each other, and gives an early warning of the initiation of the extremely poor memory state based on the correlation.
Preferably, the cloud server 30 stores the current memory status information input by the user and the external conditions related to the actual memory status information provided by the mobile terminal 10 in a manner of associating with each other, so as to facilitate information retrieval. The mobile terminal 10 is arranged to retrieve by a user the actual memory state information stored at the cloud server 30 and/or the mobile terminal 10 in a manner related to the external conditions.
Preferably, the theoretical memory state change trend exceeding the critical value at the foreseeable time point is classified and stored in a database of a cloud server according to the mode that the theoretical memory state change trend is related to the feeling input by the user, the actual memory state information, the body signal and the external condition, wherein the cloud server forms a boundary trigger condition that the theoretical memory state change trend exceeds the critical value based on the related data information in the database, and the cloud server is set to analyze the correlation between the actual memory state information of the user and the boundary trigger condition and make early warning and improve the external condition and/or the working mode of the nursing scheme in advance based on the correlation. The sub-health state and the severely reduced state of memory can be classified into a category in which the trend of the memory state is beyond a threshold. The cloud server records physical signals, external conditions, user input feelings and actual memory state information in a memory sub-health state or a memory severely-declined state. For example, based on multiple monitoring within a certain time period, in the first monitoring, the weather is clear, the weather is warm, the sleep quality is poor, the emotion is dysphoria, the actual memory state is a total forgetting object when going out, in the second monitoring, the weather is clear, the weather is warm, the sleep quality is good, the emotion is dysphoria, the actual memory state is clear in thought and quick in response, and under the condition that both are in the memory sub-health state, the emotion is dysphoria and the sleep quality is poor, and the emotion is poor, which is one of boundary triggering conditions that causes the memory state change trend to exceed a critical value. The cloud server analyzes and obtains that the actual memory state has close correlation with the sleep quality and the emotion state to a greater extent under the conditions. Based on the correlation, the cloud server may provide suggestions for the current situation of the user, such as improving living environment, adjusting work and rest time, performing proper exercise, participating in community activities, and the like. Based on the mode, the device provided by the invention can learn the memory state adjustment characteristics of the user, further guide the memory state of the user in advance in a simple and effective mode, and avoid the trend of state deterioration.
Memory state retrieval is an important tool in the memory training process. The user can retrieve the actual memory state information stored at the cloud server by retrieving the body signal that triggered a certain poor memory state. The actual memory status information may preferably be some kind of mental feeling of the user recorded in text, picture and/or video, or may be sound, video or image information collected by the mobile terminal 10. Through searching, a user can determine the history of external conditions, emotional states, sleep states and actual memory states affecting the memory states of the user, thereby being beneficial to rehabilitation training of the user or a caretaker.
Preferably, the step of inputting, by the user, the actual memory state by the mobile terminal 10 includes: the user selects the current external condition, the type and level of emotion, the sleep type and level, the memory level in a click mode, and/or the user inputs the actual memory state change trend in a text, voice, video or graphic mode for a period of time.
Preferably, the step of automatically collecting the actual memory state of the user by the mobile terminal 10 includes: the mobile terminal 10 tests the user through the memory testing device and collects the actual memory state in an audio and/or video recording manner, and compares the collected actual memory state with the current external condition, emotion type and level, sleep type and level, and memory level selected by the user in a click manner, thereby correcting the collected actual memory state.
In particular, the mobile terminal 10 may provide the actual memory status information as the search result in a manner related to the external condition. For example, if the input and search are performed under the external condition "cloudy day-winter" or body signals, several related actual memory state change trends are searched, and so on. The user can learn about the influence of external conditions and recall the current situation.
While the cloud server 30 is capable of analyzing body signals and actual memory states, they are all based on theoretical studies. Each individual has different characters, and some people enjoy the cloudy day, and the memory state is better in the cloudy day. Some people are disliked in overcast days, and the memory state is poor in overcast days. Therefore, a teaching process is required to correct the theoretical memory state, enabling the analysis of the cloud server 30 to adapt to individual individuals. Preferably, the cloud server 30 corrects the theoretical memory state determined based on the analysis of the body signals collected by the probe according to the actual memory state information. The memory management configuration file composed of the corrected theoretical memory state is stored in the mobile terminal in a mode of searching according to the body signal. Preferably, the corrected theoretical memory state can be retrieved by customizable keywords. Preferably, the retrieved theoretical memory state is capable of delivering the retrieved results in a manner provided along with the approximated actual memory state. The theoretical memory state and the approximate actual memory state are provided together, so that a user can determine the wanted retrieval information according to the retrieval result, and the accuracy of information retrieval is improved.
Preferably, the step of correcting the theoretical memory state according to the actual memory state comprises: the actual memory state is compared to the theoretical memory state and the theoretical memory state is qualitatively and/or quantitatively adjusted to generate a user memory profile associated with the current user. Preferably, the user memory profile also accounts for external conditions associated with each of the actual memory state information. Such a configuration is advantageous in eliminating the influence of adverse external conditions and guiding the user to adapt to the external conditions in an appropriate manner, and in promoting the development of his memory state in a better direction while providing memory training or rehabilitation care, thereby reducing the risk of cognitive dysfunction to a greater extent. Specifically, when the theoretical memory state is adjusted, personalized conditions related to the theoretical memory are set in the user memory configuration file. The personalized conditions include external conditions and body signals that match the user. When the user is in an extreme memory state and needs to be guided, the user memory profile adjusts the nursing scheme and the memory training scheme of the mobile terminal based on the personalized conditions related to the theoretical memory state, or changes the external conditions of the user to gradually adjust the slow change of the memory state of the user.
Preferably, the display of the mobile terminal 10 includes an external condition identifier, a physical characteristic identifier, a memory state identifier, and a feeling identifier. Wherein the changes in the external condition identifier, the physical characteristic identifier, the memory state identifier and the feeling identifier are related to each other, and wherein at least one external condition qualitatively and/or quantitatively changes the physical characteristic. At least one physical characteristic qualitatively and/or quantitatively triggers a change in the memory state identification. At least one user experience qualitatively and/or quantitatively triggers a change in the user's memory state. Preferably, when the user inputs the feeling, the cloud server analyzes the actual memory state through the corresponding external conditions, the body signals and the feeling sent by the mobile terminal and displays the actual memory state as a memory state identifier. Preferably, the external condition identifier, the physical characteristic identifier, the memory state identifier and the feeling identifier are displayed on the mobile terminal in real time in a synchronous change mode. The user can simultaneously check the external condition identifier, the physical characteristic identifier, the memory state identifier and the feeling identifier and other identifier changes caused by the change of one identifier at the mobile terminal.
For example, the mobile terminal 10 stores and displays external condition information, body signal information, actual memory state information, and feeling information in such a manner that the mapped association of external condition, body signal, actual memory state, and feeling increases gradually. Wherein each external condition is associated with at least one body signal map. Each body signal is associated with at least one actual memory state map. Each actual memory state is associated with at least one sense map.
Preferably, the cloud server records the actual memory state and the theoretical memory state based on an iterative analysis of at least one external condition, body signal and/or sensation at each moment in time. The mobile terminal 10 displays the change trend of the external condition, the change of the body signal, the sense change and the actual memory state in a manner of displaying the mark with the mapping association, alerts the user of the extreme change trend of the actual memory state in a manner of color change, sound and/or vibration, and/or displays advice for changing the actual memory state. The suggestions may be, for example: the user is advised to conduct sports, travel, exploratory, shopping to change external conditions or user experience.
The mobile terminal 10 displays the perception of user input, the actual memory state associated therewith, external conditions and body signals in a circular array of at least two circles. Wherein each circle is divided into a plurality of spaces for recording information.
As shown in fig. 3, the mobile terminal 10 stores and displays the feeling of the user input, the actual memory state associated therewith, the external conditions and the body signals in the form of a circular list consisting of four circles. Wherein. The circular list includes an outer circle identifier and an inner circle identifier. The inner ring marks comprise a first inner ring mark 11 and a second inner ring mark 12 with a radius larger than that of the first inner ring mark. The outer ring identifiers comprise a first outer ring identifier 13 and a second outer ring identifier 14 with a radius larger than that of the first outer ring identifier.
Each space of the first inner ring mark 11 is used for storing an external condition associated with an actual memory state. Each space of the second inner circle logo 12 is used to store physical characteristics associated with an actual memory state. Each space of the first outer ring identification 13 is used for storing the actual memory state. Each space of the second outer ring identification 14 is used to store the perception of user input. The change in each type of information may cause a change in the other information.
For example, the noise figure in the external condition increases, each space of the external condition mark records clockwise and changes color, and each space of the physical feature mark records the physical signal clockwise and changes corresponding color. After the noise figure increases beyond the personality threshold, each space of the sensation mark records the sensation clockwise and a corresponding color change occurs. For example, the perception of user input is level 2 annoyance. The memory state indicator changes based on the change in external conditions, the change in physical characteristics and the change in sensations and the level thereof, resulting in a change in the color of the memory state indicator. When the actual memory state of the user is in an extreme state that does not improve or continuously decline in a period of time, the mobile terminal 10 alerts the user to the risk of cognitive dysfunction in a sound, vibration or flashing manner, reminds the user to improve external conditions, actively trains the memory of the user or guides the caretaker to take appropriate care of rehabilitation. Preferably, the cloud server 30 may send a suggestion to the user to guide changing external conditions, such as a sport suggestion, a music song suggestion, an elderly activity suggestion, etc., through the mobile terminal 10.
Example 2
This embodiment is a further improvement of embodiment 1, and the repeated contents are not repeated.
The embodiment provides a memory training method for the elderly, which at least comprises the following steps: automatically collecting and/or inputting actual memory state information by a user; collecting body signals in a mode of indirectly or directly contacting the body of a user, and determining theoretical memory state information corresponding to the body signals by using a memory analysis algorithm based on a preset database; based on the feeling input by the user through the mobile terminal, the actual memory state information and/or external conditions, the cloud server is used for completing correction of the theoretical memory state information and analysis of the change trend of the theoretical memory state information, and in the case that the change trend of the theoretical memory state exceeds a critical value at a predictable time point, the mobile terminal sends an early warning prompt to the current user and pushes the working mode of the nursing scheme.
Preferably, the method further comprises: the cloud server analyzes a teaching process of the cloud server based on a feeling input by a user, the actual memory state information, body signals and/or external conditions, wherein the cloud server pre-configures parameters of a memory analysis algorithm according to at least two types of extreme memory state information determined by the teaching process.
Preferably, the method further comprises: the mobile terminal applies test information capable of acquiring memory state information of a user to the user in a text, voice, video and/or graphic mode, automatically acquires test feedback information corresponding to the test information, simultaneously the detector acquires body signals of the user corresponding to the test feedback information, and the cloud server pre-configures parameters of a memory analysis algorithm based on the test feedback information and/or the body signals in a teaching process and corrects theoretical memory state information corresponding to the body signals.
Preferably, the method further comprises: the user input experiences at least comprise emotion experiences and sleep experiences, wherein the cloud server stores the emotion experiences and the sleep experiences in an associated mode, and the mobile terminal is configured to search actual emotion state information, actual sleep state information and associated external conditions and body signals stored in the cloud server by a user according to a keyword input mode.
Preferably, the method further comprises: the mobile terminal records external conditions for inducing the actual memory state of the user and stores or provides the external conditions to the cloud server in a form of being associated with the corresponding actual memory state, the cloud server stores the current memory state information input by the user and the external conditions in a mutual association mode, and the cloud server is set to a working mode for carrying out a push nursing scheme on the induction of the specific memory state based on the correlation after analyzing the correlation of the specific memory state of the user and the external conditions.
Preferably, the method further comprises: the cloud server analyzes and determines a theoretical memory state corresponding to the physical signal acquired by the detector based on the physical signal, and feeds the theoretical memory state back to the mobile terminal, and the mobile terminal outputs stimulation information in the form of text, voice, video and/or graphics so as to change the external conditions and/or directly provide training stimulation for a user so as to improve the current memory state of the user.
Preferably, the method further comprises: the cloud server stores the current memory state information input by the user and external conditions related to the actual memory state information provided by the mobile terminal in a mutual correlation mode, and the mobile terminal is set to search the actual memory state information stored in the cloud server and/or the mobile terminal by the user according to the external conditions.
Preferably, the method further comprises: the cloud server corrects the theoretical memory state determined based on the body signal analysis acquired by the detector according to the actual memory state information, and a memory management configuration file formed by the corrected theoretical memory state is stored in the mobile terminal in a mode of searching according to the body signal.
Preferably, the method further comprises: the cloud server records the actual memory state and the theoretical memory state based on iterative analysis of at least one external condition, body signal and/or feeling at each moment, and the mobile terminal displays the external condition change, body signal change, feeling change and change trend of the actual memory state in a mode of displaying a mark with mapping association, reminds a user of extreme change trend of the actual memory state in a mode of color change, sound and/or vibration, and/or displays advice for changing the actual memory state.
It should be noted that the above-described embodiments are exemplary, and that a person skilled in the art, in light of the present disclosure, may devise various solutions that fall within the scope of the present disclosure and fall within the scope of the present disclosure. It should be understood by those skilled in the art that the present description and drawings are illustrative and not limiting to the claims. The scope of the invention is defined by the claims and their equivalents.
Claims (8)
1. A cognitive dysfunction prevention monitoring device comprising at least: the mobile terminal (10), the detector (20) and the cloud server (30), wherein,
The mobile terminal (10) is used for automatically acquiring and/or inputting actual memory state information by a user, the detector (20) is used for acquiring body signals in a mode of indirectly or directly contacting the body of the user, the body signals are transmitted to the cloud server (30) through the mobile terminal (10), the cloud server (30) is used for determining theoretical memory state information corresponding to the body signals by utilizing a memory analysis algorithm based on a preset database (31),
It is characterized in that the method comprises the steps of,
The cloud server (30) completes correction of the theoretical memory state information and analysis of the change trend thereof based on the feeling input by the user through the mobile terminal (10), the actual memory state information and the external conditions, and
The cloud server (30) records the actual memory state and the theoretical memory state based on an iterative analysis of at least one external condition, body signal and/or sensation at each moment in time,
The mobile terminal (10) inputting, by a user, an actual memory state comprising: the user selects the current external condition, the type and level of emotion, the sleep type and level, the memory level in a click mode, and/or the user inputs the actual memory state change trend in a text, voice, video or graphic mode for a period of time,
The mobile terminal (10) automatically collects the actual memory state of the user, and the method comprises the following steps: the mobile terminal (10) tests the user through a memory testing device and collects the actual memory state in an audio and/or video recording mode, compares the collected actual memory state with the current external condition, emotion type and level, sleep type and level and memory level selected by the user in a clicking mode,
Thereby correcting the acquired actual memory state,
The mobile terminal (10) provides the actual memory status information as a search result in a manner related to the external condition,
The user inputs the current emotion feeling and the sleep feeling to the mobile terminal (10), and enters the teaching process after inputting the current emotion feeling and the sleep feeling after the moderation adjustment again in the appointed time range interval,
In the teaching process, the cloud server (30) forms a memory analysis algorithm matched with the user based on the external conditions, sleep feeling and emotion feeling of the user and the change trend of the memory state, corrects the theoretical memory state at the same time,
The step of correcting the theoretical memory state based on the actual memory state includes: comparing the actual memory state with the theoretical memory state and qualitatively and/or quantitatively adjusting the theoretical memory state to generate a user memory profile associated with the current user,
The mobile terminal (10) or the cloud server (30) stores the external conditions, the body signals and the actual memory state at the same moment in a correlated way, and records the memory state change trend in the previous limiting time and the memory state change trend in the latter limiting time of the actual memory state.
2. The cognitive dysfunction prevention and monitoring device according to claim 1, characterized in that the cloud server (30) writes the association relation of the actual memory state, the body signal and the external condition and the memory analysis algorithm into the memory through the teaching process to form the personalized memory analysis algorithm for the user.
3. The cognitive dysfunction prevention and monitoring device as set forth in claim 2, characterized in that the cloud server (30) or the mobile terminal (10) is based on body signals, actual memory status
And analyzing the change trend of the actual memory state by the information and/or the external condition information, and obtaining the memory state change trend of the user in a certain time period by summarizing and analyzing the multiple test results in the certain time period.
4. The cognitive dysfunction prevention and monitoring device according to claim 3, characterized in that the mobile terminal (10) analyzes and confirms that the trend of the change of the actual memory state of the current user will exceed a critical value at a predictable time point, and/or the cloud server (30) sends an early warning prompt to the current user when the cloud server (30) analyzes and confirms that the trend of the change of the theoretical memory state of the current user will exceed the critical value at a predictable time point.
5. The cognitive dysfunction prevention and monitoring device as set forth in claim 4, characterized in that said cloud server (30) analyzes a correlation between a user's specific memory state and said external relationship and pre-warns of initiation of said specific memory state based on said correlation.
6. The cognitive dysfunction prevention and monitoring device as set forth in claim 5, characterized in that the cloud server (30) analyzes correlations of the user's poor memory state and the external conditions, emotional states, and sleep states with each other and pre-warns of the initiation of the poor memory state based on the correlations.
7. The cognitive dysfunction prevention and monitoring device as set forth in claim 6, characterized in that the cloud server (30) forms a boundary trigger condition that theoretical memory state change trend exceeds a threshold value based on associated data information in a database (31).
8. The cognitive dysfunction prevention and monitoring device of claim 7, characterized in that the cloud server (30) is configured to analyze a correlation of actual memory state information of a user and the boundary trigger condition and to advance an early warning, improve an external condition and/or an operation mode of a care plan based on the correlation.
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| CN115188447B (en) * | 2022-09-08 | 2022-12-09 | 浙江强脑科技有限公司 | Memory training method and device based on electroencephalogram signals |
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