CN113990498B - A user memory status early warning device and method - Google Patents
A user memory status early warning device and method Download PDFInfo
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Abstract
本申请提出了一种用户记忆力状态预警装置，至少包括数据分析模块，其特征在于，所述数据分析模块对用户特定的记忆力状态与外界条件的相关性进行分析，基于所述相关性对所述特定记忆力状态的引发进行推送记忆力训练方案。
The present application proposes a user memory status early warning device, which comprises at least a data analysis module, and is characterized in that the data analysis module analyzes the correlation between the user's specific memory status and external conditions, and pushes a memory training plan based on the correlation to trigger the specific memory status.
Description
The invention relates to a split application of a memory training device for the elderly, which has the application number 201810217913.7, the application date 2018, the application type is the invention, and the application name is 201810217913.7.
Technical Field
The invention relates to the field of artificial intelligence, in particular to a device and a method for early warning a memory state of a user.
Background
Memory training is commonly used in the stages of rapid development of intelligence such as young and teenagers, and is often used to promote the development of intelligence so as to obtain a better level of intelligence development. The memory state of the elderly is in a gliding stage or a stable stage, so that in daily life, the correlation between the memory state and pathology of the elderly is obscured, and the importance of training the memory of the elderly is often ignored. For example, serious memory deterioration of the elderly may induce serious complications such as senile dementia, confusion, listlessness, and dysphoria, further causing the elderly to lose life self-care ability.
Based on the individual differences of the elderly and the differences of external conditions, the same memory training scheme is not maximally applicable to each individual. Meanwhile, compared with young or teenagers, the memory state of the elderly is extremely susceptible to many factors such as illness, weather state, emotional state, sleep state and the like, and the memory of the elderly often has explosive exponential drop characteristics after the occurrence of a sliding trend, so that a series of serious complications such as senile dementia, confusion, unclear mouth and teeth and the like are caused. At this time, the memory training for the elderly has been too late. Therefore, how to realize the individuation of the memory training so as to be suitable for different individuals and early and accurately early warn the aged of the memory deterioration trend, and to develop the memory training in time to improve the memory state of the aged is an urgent problem to be solved.
The prior art publication CN101478912a discloses a method for analyzing an individual for a dementia-type disorder, comprising receiving a plurality of electroencephalogram data associated with the individual, receiving a plurality of cardiovascular risk factor data associated with the individual, receiving a plurality of cognitive data associated with the individual, and determining whether the individual is at an indication of risk for a dementia-type disorder based at least in part on the electroencephalogram data, the cardiovascular risk factor data, and a portion of the cognitive data. The invention may provide systems and methods for analyzing and assessing dementia and dementia-type disorders by integrating electroencephalogram (EEG) applications, neuropsychological testing, and cardiovascular risk factors. Embodiments of the invention may utilize non-linear analysis of EEG data that is not a linear analysis, thereby statistically combining the results of the non-linear EEG data analysis with neuropsychological or cognitive testing and determination of cardiovascular risk factors. The invention may provide more reliable predictive information than linear EEG assays used in conventional systems and methods.
The technical scheme is that the dementia risk is predicted by combining electroencephalogram detection data, psychological test or cognitive test and cardiovascular risk factors and using a statistical principle.
The prior art publication CN107533583a discloses a human user mental impairment monitoring system comprising a measuring system (23) for repeatedly measuring an index of unconscious adjustments made by a human user, the measuring system comprising a processor (6) having access to a memory (7) storing a time-dependent user-specific dataset comprising both time-dependent user-specific physiological datasets and time-dependent user-specific behavioral data, the time-dependent user-specific physiological datasets comprising data representing a physiological parameter of the user, the time-dependent user-specific behavioral datasets comprising data representing a physiological parameter of the user, a processor (6) comprising a calculator (24) for repeatedly analyzing the time-dependent user-specific datasets to repeatedly determine values of the unconscious adjustments made by the human user as a combined index of user-specific physiological data and user-specific behavioral data, a computing system (25) for computing a pattern index and a pre-established structured, the time-dependent user-specific physiological dataset comprising data representing a mental impairment, the time-dependent user-specific-behavioral-parameter comprising data representing a mental impairment, the pre-determined pattern comprising values from a grid pattern of experimental-based-mental impairment model (17) for which the stored values of the mental impairment can be measured in the memory pattern, the grid-based-model of the mental impairment model comprises at least one of the values of the mental impairment (17), and represents a relationship between unconscious adjustment index values implemented by an experimental human user and mental impairment history data of the experimental human user.
The above solutions focus on the quantification of unconscious adjustments by the user as the primary data available to evaluate mental impairment. This data may be taken into account by the physician in diagnosing or treating mental health. The invention focuses on quantification of unconscious adjustments made by the user in daily life.
The prior art publication CN104902806a discloses a method for providing an assessment related to a neurological or neuropsychiatric disorder of a subject, comprising selecting a profiling category indicative of one or more aspects of cognitive or sensory functions associated with the neurological or neuropsychiatric disorder, presenting a stimulation sequence to the subject, the stimulation sequence being based on the selected profiling 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 profiling category.
The above-described technical solutions disclose systems, devices and methods for collecting and using physiological information (e.g., brain signals) to characterize the pathology and/or susceptibility of a subject's nervous system or neuropsychiatric disorder and/or to evaluate the treatment of such diseases. In other words, the above-described technique analyzes information about disorders of the nervous system by detecting physiological data before, during and after a stimulus by applying the stimulus to a subject.
The invention provides a memory training device, which aims at technical problems and achieves technical effects which are completely different from those of the technical schemes.
The technical schemes only relate to judging the disease type or the disease degree of the patient according to the detection data. It does not relate to the technical features of how the push memory training scheme is performed for the initiation of the specific memory state based on the correlation after analyzing the correlation of the specific memory state of the user and the external condition.
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 targeted training of memory. According to the invention, training of the 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 memory training characteristics of the user can be gradually learned, and the memory state of the user can be effectively managed.
A memory training device for the aged at least comprises a data interaction module, a data acquisition module and a storage module, wherein the data interaction module is connected with a data analysis module in a wired and/or wireless mode and is used for automatically acquiring and/or inputting actual memory state information by a user, the data acquisition module is used for acquiring body signals in a mode of indirectly or directly contacting the body of the user, and the storage module is used for storing the data information. Wherein the data analysis module determines theoretical memory state information corresponding to the body signal using a memory analysis algorithm. The data analysis module is used for completing analysis and correction of the theoretical memory state change trend based on the feeling input by a user, the real-time memory state information and/or the external conditions. The theoretical memory state change trend exceeding the critical value at the foreseeable time point is classified and stored in the storage module in a manner of being related to the feeling input by the user, the actual memory state information, the body signal and the external condition. The data analysis module forms boundary trigger conditions of theoretical memory state change trend exceeding a critical value based on the data information related to each other, analyzes the correlation between the actual memory state information of the user and the boundary trigger conditions, and makes early warning, pushing a memory training scheme and/or improving suggestions of external conditions in advance based on the correlation.
Because of individual differences among the elderly and different external conditions, the same memory training scheme is not maximally applicable to each individual. In order to achieve complete intelligence in memory training, it is necessary to train a memory analysis algorithm to continuously narrow the gap between the theoretical memory state and the actual memory state. The memory analysis algorithm can master the memory training characteristics of the current user, so that personalized memory training can be performed. At the same time, it is also particularly important to determine the timing of developing memory training, especially for the elderly. For example, due to many factors such as illness, weather state, emotional state, sleep state, etc., the elderly may have a memory state that decreases exponentially in a continuous environment unsuitable for external conditions, and thus a series of serious complications such as senile dementia, confusion, and unclear mouth and teeth may be caused. At this time, the memory training for the elderly has been too late. Therefore, the invention brings the body signals, feelings and external conditions of the aged into the monitoring test range, and determines the boundary trigger condition by analyzing the correlation between the aged and the memory state change trend under different feelings, different body signals and different external conditions, so that the invention can more accurately determine the development time of the memory training.
According to a preferred embodiment, the memory training device further comprises a memory testing module electrically connected to the data analysis module. The memory test module 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 the data interaction module determines the change trend of the memory state based on the feedback result of the test information. The data analysis module analyzes a teaching process of memory training based on the boundary trigger condition and/or the trend of memory state. Wherein the parameters of the memory analysis algorithm are preconfigured according to at least two types of extreme memory state information determined by the teaching process. After the memory analysis algorithm is determined, the actual memory state is accurately analyzed based on the boundary trigger condition and the change trend of the memory state, 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.
According to a preferred embodiment, the memory test module applies test information to the user in text, speech, video and/or graphics, which enables to obtain memory status information of the user. And the data interaction module automatically collects test feedback information corresponding to the test information. And the data acquisition module acquires body signals, user input experiences and/or external conditions of the user corresponding to the test feedback information. The data analysis module pre-configures parameters of a memory analysis algorithm based on the test feedback information, the external conditions, the user input sensations, and/or the body signals and corrects theoretical memory state information.
According to a preferred embodiment, the data analysis module generates a user memory profile associated with the current user based on continuously qualitatively and/or quantitatively adjusting the theoretical memory state information. And analyzing a teaching process of memory training based on the boundary trigger condition, the trend of change of the memory state, and/or the user memory profile.
According to a preferred embodiment, the data analysis module records external conditions that induce the actual memory state of the user and stores them in the storage module in association with the corresponding actual memory state, and stores the current memory state information entered by the user and the external conditions in association with each other in the storage module. And after analyzing the correlation between the specific memory state of the user and the external condition, pushing a memory training scheme for triggering the specific memory state based on the correlation.
According to a preferred embodiment, the data analysis module analytically determines the theoretical memory state corresponding thereto on the basis of the acquired body signals. And outputting the stimulation information in the form of text, speech, video and/or graphics to change the external conditions and/or to provide memory training directly to the user to improve the user's current memory state.
According to a preferred embodiment, the current memory state information entered by the user and the external conditions associated with the actual memory state information are stored in association with each other to the storage module. And the user searches the actual memory state information through the data interaction module according to the mode related to the external condition.
According to a preferred embodiment, the data analysis module corrects the theoretical memory state determined on the basis of the analysis of the acquired body signals on the basis of the actual memory state information, and a memory management profile consisting of the corrected theoretical memory state is stored in the memory module in a retrievable manner on the basis of the body signals.
According to a preferred embodiment, the data analysis module 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 data interaction module displays the change trend of the external condition, the change of the body signal, the sense change and the actual memory state in a mode of displaying the mark with mapping association, reminds the user of the 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.
The invention also provides a memory training method for the aged, which at least comprises the steps of automatically collecting and/or inputting actual memory state information by a user. The body signal is acquired in a manner that contacts the user's body either indirectly or directly. And determining theoretical memory state information corresponding to the body signals by using a memory analysis algorithm based on a preset database. The analysis and correction of the theoretical memory state change trend is completed based on the feeling input by the user, the real-time memory state information and/or the external condition. The theoretical memory state change trend exceeding the threshold value at a predictable point in time is classified and stored in such a manner that the feeling inputted by the user, the actual memory state information, the body signal, and the external condition are all associated with each other. Wherein, the boundary triggering condition that the change trend of the theoretical memory state exceeds the critical value is formed based on the data information related to each other. And analyzing the correlation between the actual memory state information of the user and the boundary triggering condition, and making early warning, pushing a memory training scheme and/or improving suggestions of external conditions in advance based on the correlation.
The beneficial technical effects of the invention are as follows:
The invention continuously trains the memory analysis algorithm to continuously reduce the gap between the theoretical memory state and the actual memory state, and simultaneously, the memory analysis algorithm learns the memory training characteristics of the current user, thereby being capable of carrying out individualized memory training. Furthermore, the invention brings the body signals, feelings and external conditions of the old people into the monitoring test range, and determines the boundary trigger condition by analyzing the correlation between the old people and the memory state change trend under different feelings, different body signals and different external conditions, so that the invention can more accurately determine the development time of memory training, avoid the memory state of the old people from exponentially dropping in the continuous environment unsuitable for the external conditions, and further cause a series of serious complications such as senile dementia, confusion, unclear mouth teeth and the like.
Drawings
FIG. 1 is a schematic diagram of the connection of the modules of the present invention;
FIG. 2 is a schematic diagram of the logic structure of the present invention, and
Fig. 3 is a schematic diagram of a data interaction module according to the present invention.
List of reference numerals
10, A data interaction module, 20, a data acquisition module, 30, a data analysis module, 31, a database, 40, a storage module and 50, a memory test module.
Detailed Description
The following detailed description refers to the accompanying drawings.
Example 1
As shown in fig. 1, the data analysis module is connected with the data interaction module for automatically acquiring and/or inputting the actual memory state information by the user, the data acquisition module for acquiring the body signals in a way of indirectly or directly contacting the body of the user, the storage module for storing the data information and the memory test module respectively in a wired and/or wireless way. Preferably, the data interaction module may be a mobile terminal. The data acquisition module may be a detector. The data analysis module may be a cloud server. The memory test module can be a memory breadth tester, an intelligent terminal provided with memory software, and the like. The storage module may be a storage chip, a hard disk, a storage server, etc.
Preferably, the data analysis module comprises at least three bi-directional interfaces, a plurality of data input interfaces and at least one data output interface. The data interaction module, the storage module and the database are connected with the data analysis module through different bidirectional interfaces for bidirectional communication. The data acquisition module and the memory test module are connected with the data analysis module through different data input interfaces. Preferably, the data output interface is configured to communicate with a superior or inferior device to transfer data in the memory training apparatus to the superior or inferior device. The data output interface is configured in a one-way communication mode that data only cannot enter, so that the risk of data information in the memory device being stolen is avoided.
As shown in fig. 1,2 and 3, the present invention provides a device for memory training for the elderly. The memory training device at least comprises a data interaction module 10, a data acquisition module 20 and a data analysis module 30. The data interaction module 10 is used for automatically collecting and/or inputting actual memory state information by a user. Preferably, the data interaction module 10 comprises an image acquisition device and a video acquisition device. The data interaction module can be an intelligent terminal, such as a mobile device like a notebook, a mobile phone, a smart bracelet, a smart watch, and the like, and can also be 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 described by the text into the data interaction module.
Preferably, the memory test module is connected to the data interaction module by wire and/or wirelessly. The memory training module outputs stimulation information in the form of text, speech, video and/or graphics to alter the external conditions and/or directly provide training stimulation to the user to improve the user's current memory state. The memory test module 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 data analysis module pre-configures parameters of the memory analysis algorithm based on the test feedback information and/or the body signal during the teaching process and corrects theoretical memory state information corresponding to the body signal. Preferably, the memory test module may be a VR device 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 sent to the data interaction module wirelessly and/or by wire.
The data acquisition module of the present invention is used to acquire body signals in a manner that indirectly or directly contacts the body of a user. The data acquisition module comprises a plurality of modules for acquiring physiological signals of human bodies. The detection module of the data acquisition module 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, the detection module in the data acquisition module can be further provided with a microwave signal sensor according to requirements for detecting the change of brain waves. The detection module in the data acquisition module can also be added with electrodes arranged on the head according to the requirements. Preferably, the body signals acquired by the data acquisition module 20 are sent to the data analysis module 30 via the data interaction module 10.
The data analysis module 30 of the present invention is used to determine theoretical memory state information corresponding to a body signal using a memory analysis algorithm based on a preset database. Preferably, the data analysis module 30 includes a database 31 storing a number of memory analysis algorithms. The data analysis module 30 analyzes and determines the theoretical memory state corresponding to the physical signal acquired by the data acquisition module 20 according to the physical signal, and feeds back the theoretical memory state to the data interaction module 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 data analysis module is as follows.
In this embodiment, the memory state is classified using a minimum home fuzzy support vector machine or an average home fuzzy support vector machine. Memory states can be classified into, for example, a healthy state, a sub-healthy state of memory, and a severely impaired state of memory. Specifically, the classification step of the memory analysis algorithm includes:
A1, defining a fuzzy membership function
To avoid the generation of indistinguishable regions, a fuzzy membership function is introduced, defining:
A2 defining a membership function
There are two definition methods for the attribution degree function, namely, minimum attribution degree and average attribution degree.
Minimum degree of attribution: m i＝Min(mij (x)), j=1, 2,..k, j+.i.
Average degree of attribution:
a3 Classification
After k m i are obtained, x is classified as m i maximum class arg { max (m i (x)) } i=1, 2.
Preferably, the data analysis module 30 completes the teaching process of the data analysis module 30 based on the operation of the data interaction module 10 by the user. Preferably, the data analysis module at least comprises one or more of a CPU, a processor, a microprocessor, a server and a server group. First, the data analysis module 30 requires user interaction to complete the teaching process. The teachings in this disclosure are directed to exemplary artificial intelligence programming.
For example, during the teaching process of the user using the device of the present invention, emotional feeling, sleeping feeling and current memory state information need to be input through the data interaction module. During the use of the device according to the invention, the data interaction module 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 data analysis module 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. While correcting the theoretical memory state.
For example, the method steps of the teaching process include:
S1, starting a teaching mode;
S2, selecting a teaching object according to a user name, and directly selecting if the teaching object is a user which is already recorded in a data storage module, wherein if the teaching object is a user which is not already recorded, recording memory state information of the user through a data interaction module, and then selecting, wherein the memory state information of the user comprises the user name, a user face photo, a user sample memory and external conditions and feelings related to the memory;
s3, sample selection, namely directly selecting stored sample memory content and teaching matching content if the sample memory content and teaching matching content of the teaching are already stored under the user name, reading the teaching matching content of the teaching through a data interaction module if the sample memory content and teaching matching content of the teaching are not contained under the user name, storing the sample memory content and 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 conversion, namely, different time periods, the memory states of the user are not identical. 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;
And S5, effect evaluation, namely, the data analysis module 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 data analysis module in the form of association, the data analysis module 30 writes the association relationship of 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 data analysis module 30 pass through the data interaction module 10, the more accurate the memory analysis algorithm of the data analysis module 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 data analysis module 30 pre-configures parameters of a memory analysis algorithm based on at least two extreme memory state information determined by the teaching process.
Preferably, during the teaching process, the data interaction module 10 applies stimulus information capable of inducing a memory state to the user, and the actual memory state information corresponding to the stimulus information is automatically acquired and/or input by the user. While the data acquisition module 20 acquires the body signals of the user corresponding to the stimulation information. The data analysis module pre-configures parameters of a memory analysis algorithm based on at least two of the actual memory state information, the body signals during teaching. Preferably, the data analysis module 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 data interaction module 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 data analysis module 30 also forms a personalized memory state change curve based on the trend of the actual memory state change during the teaching process. For example, the memory state changes are blurred consciousness, intermittent consciousness clear 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 memory training apparatus of the present invention can determine the trend of the memory state using the data analysis module 30. Preferably, the data analysis module or the data interaction module analyzes the trend of the actual memory state based on the body signal, the actual memory state information and/or the external condition information. For example, the current actual memory state of the user is tested by a memory test module in real-time communication with the data analysis module. The test may be performed in cycles and frequency, for example four sampling tests in a month. In the test process, the memory test module 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 data analysis module and/or the data interaction module 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. Adjusting the emotion of a test subject may alleviate his feeling of autism by talking to the elderly through a voice system, such as Siri.
Preferably, the data analysis module 10 sends a warning prompt to the current user through the data interaction module in case the data interaction module 10 determines that the trend of the change in the actual memory state of the current user will exceed the threshold value at a predictable point in time for at least one continuous time interval of the analysis of the actual memory state information, and/or in case the data analysis module determines that the trend of the change in the theoretical memory state of the current user will exceed the threshold value at a predictable point in time. Specifically, the data interaction module 10 or the data analysis module 30 analyzes the trend of the change of the actual memory state of the current user, and confirms that the trend of the change of the actual memory state will exceed the threshold value at a predictable point in time. At this time, the data analysis module 30 sends various prompts such as vibration, sound, color change, etc. to the user through the data interaction module 10, so as to remind the user of abnormal conditions of the memory state, and start to train the memory.
Preferably, the data interaction module 10 stores or provides the user's perception of textual, voice, video and/or graphical input to the data analysis module 30 in association with the corresponding automatically acquired actual memory state. Or the data interaction module 10 records external conditions that induce the user's actual memory state and stores or provides to the data analysis module 30 in association with the corresponding actual memory state. The data analysis module 30 analyzes a correlation between a specific memory state of a user and the external relationship, and pre-warns of 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.
For example, in overcast and rainy weather, the user is in a geographical position with unfamiliar and noisy living environment, and the actual memory state of the user is influenced by the current emotion state and/or sleep state of the user so that the memory state of the user is lower than the normal state of the user, and at the moment, the data acquisition module acquires the body signals of the user in real time. The data interaction module 10 or the data analysis module 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 data analysis module 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 pre-warns of the initiation of the extremely poor memory state based on the correlation.
Preferably, the data analysis module 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 data interaction module 10 in a manner of being related to each other, so as to facilitate information retrieval. The data interaction module 10 is arranged to retrieve by a user the actual memory state information stored in the data analysis module 30 and/or the data interaction module 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 the data analysis module according to the mode that the feeling input by the user, the actual memory state information, the body signal and the external condition are all related to each other, wherein the data analysis module 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 data analysis module 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 working mode of the external condition and/or the memory training scheme based on the correlation in advance. For example, the classification calculation result of memory can be classified into a healthy state, a sub-healthy state of memory, and a severely degraded state of memory. 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 data analysis module 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 data analysis module analyzes that the actual memory state has a close correlation with the sleep quality and the emotion state to a greater extent under the conditions. Based on the correlation, the data analysis module may make suggestions for the current state of the user, such as improving living environment, adjusting work and rest time, making appropriate movements, participating in community activities, and so on. 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 in the data analysis module by retrieving the body signal that triggered a certain poor memory state. Preferably, the actual memory status information may be some kind of mental feeling recorded by the user in text, picture and/or video mode, or may be sound, video or image information collected by the data interaction module 10. By searching, the user can determine the history of external conditions, emotional states, sleep states and actual memory states affecting the memory states of the user, thereby facilitating the memory training of the user or a caretaker.
Preferably, the step of inputting the actual memory state by the user of the data interaction module 10 includes the user selecting the current external condition, the type and level of emotion, the sleep type and level, the memory level in a click manner, and/or inputting the actual memory state change trend of the user over a period of time in a text, voice, video or graphic manner.
Preferably, the step of automatically acquiring the actual memory state of the user by the data interaction module 10 includes the step of the data interaction module 10 testing the user through a memory test module and acquiring the actual memory state in an audio and/or video recording manner, and comparing the acquired actual memory state with the current external condition, emotion type and level, sleep type and level, memory level selected by the user in a click manner, thereby correcting the acquired actual memory state.
In particular, the data interaction module 10 may provide the actual memory state information as a 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 data analysis module 30 is capable of analyzing body signals and actual memory states, it is 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. Thus, a teaching process is required to correct the theoretical memory state, enabling the analysis of the data analysis module 30 to accommodate individual individuals. Preferably, the data analysis module 30 corrects the theoretical memory state determined based on the analysis of the body signals acquired by the data acquisition module based on the actual memory state information. The memory management configuration file composed of the corrected theoretical memory state is stored in the data interaction module in a manner that the memory management configuration file can be searched 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 based on the actual memory state comprises comparing the actual memory state with the theoretical memory state and qualitatively and/or quantitatively adjusting the theoretical memory state to thereby 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. 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 care scheme and the memory training scheme of the data interaction module based on 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 data interaction module 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 data analysis module analyzes the corresponding external conditions, body signals and feeling transmitted by the data interaction module into the actual memory state and displays the actual memory state as the memory state identification. Preferably, the external condition identifier, the physical characteristic identifier, the memory state identifier and the feeling identifier are displayed in real time on the data interaction module 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 in the data interaction module.
For example, the data interaction module 10 stores and displays external condition information, body signal information, actual memory state information, and sensation information in such a manner that the mapped associations of external conditions, body signals, actual memory states, and sensations gradually increase. 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 data analysis module 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 advice may be, for example, advice the user to conduct sports, travel, feelings, shopping to change external conditions or user experience.
The data interaction module 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 data interaction module 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 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 user's actual memory state is in an extreme state that does not improve or continuously decline over a period of time, the data interaction module 10 alerts the user to the trend of worsening of the user's memory state in the form of sound, vibration or blinking, reminding the user to improve external conditions, actively memory training the user, or guiding caregivers to take appropriate care of their rehabilitation. Preferably, the data analysis module 30 may make suggestions to the user to guide changing external conditions, such as sports suggestions, music songs suggestions, senior event suggestions, etc., through the data interaction module 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 steps of automatically acquiring and/or inputting actual memory state information by a user. The body signal is acquired in a manner that contacts the user's body either indirectly or directly. Theoretical memory state information corresponding to the body signal is determined using a memory analysis algorithm. The analysis and correction of the theoretical memory state change trend is completed based on the feeling input by the user, the real-time memory state information and/or the external condition. The theoretical memory state change trend exceeding the threshold value at a predictable point in time is classified and stored in such a manner that the feeling inputted by the user, the actual memory state information, the body signal, and the external condition are all associated with each other. Wherein, the boundary triggering condition that the change trend of the theoretical memory state exceeds the critical value is formed based on the data information related to each other. And analyzing the correlation between the actual memory state information of the user and the boundary triggering condition, and making early warning, pushing a memory training scheme and/or improving suggestions of external conditions in advance based on the correlation.
Preferably, test information capable of acquiring memory state information of the user is applied to the user in a text, voice, video and/or graphic mode, and the change trend of the memory state is determined based on the feedback result of the test information. And analyzing a teaching process of memory training based on the boundary trigger condition and/or the change trend of the memory state, wherein parameters of a memory analysis algorithm are preconfigured according to at least two types of extreme memory state information determined by the teaching process.
Preferably, test information capable of acquiring memory state information of a user is applied to the user in a text, voice, video and/or graphic mode, and test feedback information corresponding to the test information is automatically acquired. And simultaneously acquiring body signals, user input experiences and/or external conditions of the user corresponding to the test feedback information. Parameters of a memory analysis algorithm are preconfigured based on the test feedback information, the external conditions, the user input sensations and/or the body signals, and theoretical memory state information is corrected.
Preferably, the user memory profile associated with the current user is generated based on continuously qualitatively and/or quantitatively adjusting the theoretical memory state information. Wherein the teaching process of memory training is analyzed based on the boundary trigger condition, the trend of change of the memory state, and/or the user memory profile.
Preferably, external conditions causing the actual memory state of the user are recorded and stored in association with the corresponding actual memory state, and the current memory state information input by the user and the external conditions are stored in association with each other. After analyzing the correlation between the specific memory state of the user and the external condition, pushing a memory training scheme for triggering the specific memory state based on the correlation.
Preferably, the theoretical memory state corresponding thereto is analytically determined on the basis of the acquired body signals. The stimulation information is output in the form of text, speech, video and/or graphics to change the external conditions and/or to provide memory training directly to the user to improve the user's current memory state.
Preferably, the current memory state information input by the user and the external conditions related to the actual memory state information are stored in association with each other. The user retrieves the actual memory state information in a manner related to the external condition.
Preferably, the theoretical memory state determined based on the analysis of the acquired body signals is corrected based on the actual memory state information, and a memory management profile composed of the corrected theoretical memory state is stored in a retrievable manner based on the body signals.
Preferably, the actual memory state and the theoretical memory state are recorded based on an iterative analysis of at least one external condition, body signal and/or sensation at each moment in time. And displaying the change trend of the external condition, the change of the body signal, the feeling change and the actual memory state in a mode of displaying the mark with mapping association, reminding the user of the extreme change trend of the actual memory state in a mode of color change, sound and/or vibration, and/or displaying 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 (4)
1. The user memory state early warning device at least comprises a data analysis module, and is characterized in that the data analysis module analyzes the correlation between a specific memory state of a user and an external condition, the user memory state early warning system further comprises a data interaction module, the data analysis module stores the current memory state information input by the user and the external condition related to the actual memory state information provided by the data interaction module in a mutual correlation manner, the data interaction module is configured to search the actual memory state information stored in the data analysis module and/or the data interaction module according to the correlation manner of the external condition and the external condition by the user, the data analysis module stores the correlation between the external condition, a body signal and the actual memory state at the same moment, and records the memory state change trend within the previous limit time and the memory state change trend within the later limit time of the actual memory state, the data analysis module stores the extremely poor memory state of the user and the external condition, the state and the external condition, the correlation between the state and the sleep state is carried out in a mutual correlation manner, the data analysis module stores the extremely poor memory state and the actual memory state in the correlation manner of the physical state and the physical memory state at the same moment, the physical state change trend is detected by the data analysis module, the correlation between the physical state and the physical state change trend is detected by the user, the correlation of the physical state change in the correlation manner is detected by the correlation of the physical state, and the physical state change in the physical state, the physical state change is detected by the physical state, and the physical state is detected, the data analysis module forms boundary triggering conditions of theoretical memory state change trend exceeding a critical value based on the related data information in the database.
2. The user memory state early warning device according to claim 1, characterized in that the data analysis module is arranged to analyze the correlation of the user's actual memory state information and the boundary trigger condition and to make early warning, improve the external conditions and/or the working mode of the memory training scheme in advance based on the correlation.
3. The user memory state warning device of claim 2, wherein the user is able to retrieve the actual memory state information stored in the data analysis module by retrieving a body signal that triggers a poor memory state.
4. A user memory state early warning method is characterized by at least comprising the following steps:
Analyzing the correlation of the specific memory state of the user and the external condition, pushing the memory training scheme based on the correlation to the initiation of the specific memory state,
Storing the external conditions related to the current memory state information and the actual memory state information inputted by the user in a manner associated with each other,
The user retrieves the actual memory state information in a manner related to the external condition,
The external condition, the body signal and the actual memory state at the same moment are stored in a correlated way, 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 are recorded,
Analyzing 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 giving an early warning on the initiation of the extremely poor memory state based on the correlation,
The theoretical memory state change trend exceeding the threshold value at a predictable point in time is classified and stored in such a manner that it is associated with the feeling input by the user, the actual memory state information, the body signal and the external condition each other,
And forming boundary triggering conditions for theoretical memory state change trend exceeding a critical value based on the data information related to each other.
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| CN108461125B (en) * | 2018-03-16 | 2021-11-05 | 佛山市丈量科技有限公司 | A memory training device for the elderly |
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| CN101478912A (en) * | 2006-06-21 | 2009-07-08 | 莱克西克医疗技术有限公司 | Systems and methods for analyzing and assessing dementia and dementia -type disorders |
| CN104933902A (en) * | 2015-05-10 | 2015-09-23 | 深圳市中幼国际教育科技有限公司 | Infant memory training device and application method thereof |
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| CN113990450B (en) | 2025-02-25 |
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