CN114847949A - Cognitive fatigue recovery method based on electroencephalogram characteristics and relaxation indexes - Google Patents
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Abstract
The application provides a cognitive fatigue rehabilitation method based on electroencephalogram characteristics and relaxation indexes, which comprises the steps of determining an electroencephalogram signal set of a target user, wherein the electroencephalogram signal set comprises a plurality of target electroencephalogram signals within a preset electroencephalogram signal threshold range; extracting emotion data reflecting the cognitive fatigue rehabilitation degree and the rehabilitation state of a target user from the target electroencephalogram signal based on the brain cognitive features; determining the starting and ending time of the target user in the corresponding cognitive fatigue recovery state and the overall cognitive fatigue recovery degree in the starting and ending time according to the emotion data; and determining the cognitive fatigue rehabilitation quality of the target user according to the change condition of the total cognitive fatigue rehabilitation degree in a preset period. Implementation of the method can reduce the misinterpretation of the cognitive fatigue state and stage.
Description
Technical Field
The application relates to the technical field of cognitive fatigue rehabilitation, in particular to a cognitive fatigue rehabilitation method based on electroencephalogram characteristics and relaxation indexes.
Background
Cognitive load is a multi-dimensional structure representing the load placed on a learner's cognitive system when processing a particular task. This structure consists of a causal dimension reflecting the interaction between tasks and learner characteristics and an assessment dimension reflecting the concept of measurability such as psychological load, psychological effort and performance. Currently, the prior art has adopted three types of monitoring data, namely sound, body movement and heart rate, to monitor the cognitive fatigue mental state of a user. However, the accuracy of both sound, body movement and heart rate is not high, and although the monitoring data is artificially corrected to a certain extent by a related fitting algorithm, the finally judged cognitive fatigue state and stage have great misjudgment due to the inaccuracy of the acquisition of the monitoring data.
Disclosure of Invention
The embodiment of the application aims to provide a cognitive fatigue recovery method based on electroencephalogram characteristics and relaxation indexes, and the misjudgment of the cognitive fatigue state and stage can be reduced.
The embodiment of the application also provides a cognitive fatigue recovery method based on the electroencephalogram characteristics and the relaxation indexes, which comprises the following steps:
determining an electroencephalogram signal set of a target user, wherein the electroencephalogram signal set comprises a plurality of target electroencephalograms within a preset electroencephalogram signal threshold range;
extracting emotion data reflecting the cognitive fatigue rehabilitation degree and the rehabilitation state of a target user from the target electroencephalogram signal based on the brain cognitive features;
determining the starting and ending time of the target user in the corresponding cognitive fatigue recovery state and the overall cognitive fatigue recovery degree in the starting and ending time according to the emotion data;
and determining the cognitive fatigue rehabilitation quality of the target user according to the change condition of the total cognitive fatigue rehabilitation degree in a preset period.
Therefore, according to the cognitive fatigue recovery method based on the electroencephalogram characteristics and the relaxation indexes, the emotion data reflecting the cognitive fatigue recovery degree and the recovery state of the target user are extracted from the target electroencephalogram signals based on the brain cognitive characteristics, and the cognitive fatigue recovery degree and the recovery state of the target user can be better analyzed for the target electroencephalogram signals based on the determined brain cognitive characteristics, so that the required emotion data can be extracted, the analysis effect is improved, and the identification accuracy of the emotion data is ensured. According to the emotion data, the starting and ending time of the target user in the corresponding cognitive fatigue recovery state and the overall cognitive fatigue recovery degree in the starting and ending time are determined, the cognitive fatigue recovery state of the target user is judged according to the change condition of the overall cognitive fatigue recovery degree in a preset period, and the recognition accuracy of the cognitive fatigue state and the cognitive fatigue stage is improved.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a cognitive fatigue recovery method based on electroencephalogram characteristics and relaxation indexes provided by an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a cognitive fatigue recovery method based on electroencephalogram characteristics and relaxation indexes in some embodiments of the present application. The method is applied to a computer device (the computer device may be a terminal or a server, the terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, or includes smart glasses, a main control box, and a set of movable trolley boxes with rollers, which integrate the devices such as the smart glasses, the main control box, a control computer host, a UPS uninterruptible power supply, a power switch and a network interface, the server may be an independent server or a server cluster composed of a plurality of servers), for example, for description, the method includes the following steps:
step S100, determining an electroencephalogram signal set of a target user, wherein the electroencephalogram signal set comprises a plurality of target electroencephalograms within a preset electroencephalogram threshold range.
Specifically, after acquiring a plurality of initial electroencephalograms, the computer device determines values corresponding to the initial electroencephalograms. And then, the computer equipment determines the value of the corresponding initial electroencephalogram signal based on the preset electroencephalogram signal threshold range, retains the initial electroencephalogram signal when the value is within the electroencephalogram signal threshold range, and outputs the initial electroencephalogram signal as a target electroencephalogram signal. Otherwise, the initial electroencephalogram signal is taken as an irrelevant electroencephalogram signal to be removed, and an electroencephalogram signal set is constructed based on the determined target electroencephalograms.
In one embodiment, the preset electroencephalogram threshold range may be [ -2.4e6,2.4e6 ]. 100, and the specific values thereof are not limited in the embodiments of the present application.
In one embodiment, the computer device can transmit the acquired initial electroencephalograms to the cloud service computing center, the cloud service computing center eliminates irrelevant electroencephalograms outside a preset electroencephalogram threshold range, and an electroencephalogram signal set is constructed based on the remaining target electroencephalograms (i.e., electroencephalograms within the preset electroencephalogram threshold range).
And S200, extracting emotional data reflecting the cognitive fatigue rehabilitation degree and the rehabilitation state of the target user from the target electroencephalogram signal based on the brain cognitive characteristics.
In one embodiment, the computer device may transmit the acquired electroencephalogram signal to a cloud service computing center, and perform FFP conversion on the target electroencephalogram signal based on the cloud service computing center to obtain an energy amplitude with basic data as frequencies of α, β waves, and the like. And then, performing regression calculation on the basic data by the brain cognitive characteristics determined by the cloud service computing center to obtain required emotional data reflecting the cognitive fatigue rehabilitation degree and the rehabilitation state of the target user.
And step S300, determining the starting and ending time of the target user in the corresponding cognitive fatigue recovery state and the overall cognitive fatigue recovery degree in the starting and ending time according to the emotion data.
Specifically, the computer device determines start and stop times of the target user in the corresponding cognitive fatigue recovery state and time periods of each cognitive fatigue recovery state based on the time of the emotion data fed back by the cloud service computing center, and then performs comprehensive calculation on the cognitive fatigue recovery degrees determined at different time points within the start and stop times. For example, averaging, weighting calculations, etc., determine the overall degree of cognitive fatigue recovery over the start-stop time.
And S400, determining the cognitive fatigue rehabilitation quality of the target user according to the change condition of the overall cognitive fatigue rehabilitation degree in a preset period.
According to the cognitive fatigue recovery method based on the electroencephalogram characteristics and the relaxation indexes, the emotional data reflecting the cognitive fatigue recovery degree and the recovery state of the target user are extracted from the target electroencephalogram signals based on the brain cognitive characteristics, the cognitive fatigue recovery degree and the recovery state of the user can be better analyzed from the target electroencephalogram signals, the required emotional data can be extracted, the analysis effect is improved, and the identification accuracy of the emotional data is guaranteed. According to the emotion data, the starting and ending time of the target user in the corresponding cognitive fatigue recovery state and the overall cognitive fatigue recovery degree in the starting and ending time are determined, the cognitive fatigue recovery state of the target user is judged according to the change condition of the overall cognitive fatigue recovery degree in a preset period, and the recognition accuracy of the cognitive fatigue state and the cognitive fatigue stage is improved.
In one embodiment, the target brain electrical signal is acquired by an acquisition device for acquiring brain electrical signals generated at a predetermined brain location, the predetermined brain location including at least one of forehead, forehead and temple.
Specifically, the acquisition device is arranged at the brain position of the target user to ensure the smooth acquisition of the target electroencephalogram signals.
In one embodiment, the acquisition device may be a sensor integrated with a brain electrical chip that reads brain signals of a target user using dry electrodes, filters surrounding noise, and converts the read brain signals into digital signals.
In one embodiment, the collecting device can also be an electrode cap integrated with an electroencephalogram signal collecting electrode, the electroencephalogram signal is collected from the outside of the cerebral cortex through the electrode cap and is transmitted to the computer device through a lead, and the computer device carries out cognitive fatigue monitoring based on the received electroencephalogram signal.
According to the embodiment, the problems of inconvenience in wearing, difficulty in operation and difficulty in wearing caused by other indexes (such as body position/abdominal breathing, blood oxygen and the like) are solved, the balance between accuracy and popularization is achieved, and the method and the device have good convenience and universality.
In one embodiment, in step S100, the target electroencephalogram signal is determined based on the following steps:
and S1000, acquiring an electroencephalogram original signal, and filtering the electroencephalogram original signal to obtain a corresponding electroencephalogram filtering signal.
And S1001, converting the obtained electroencephalogram filtering signal into a corresponding digital electroencephalogram signal, and determining a target electroencephalogram signal based on the digital electroencephalogram signal.
According to the embodiment, the original electroencephalogram original signals are filtered and subjected to digital conversion processing, the quality of the electroencephalogram signals is improved, the influence of external interference signals on the detection precision of the electroencephalogram signals is avoided, and the detection accuracy is improved.
In one embodiment, in step S200, extracting emotion data reflecting the cognitive fatigue recovery degree and the recovery state of the target user from the target electroencephalogram signal based on the brain cognitive features includes:
step S2000, FFP conversion is carried out on the electroencephalogram signal set to obtain corresponding electroencephalogram data, and the electroencephalogram data comprise at least one of preset electroencephalogram frequency band energy ratio and preset electroencephalogram data of brain channels.
Specifically, the preset brain electrical band comprises at least one of a brain electrical alpha band, a brain electrical beta band, a brain electrical theta band, a brain electrical delta band and a brain electrical gamma band, and the preset brain channel comprises at least one of a left brain channel and a right brain channel.
Step S2001, performing regression calculation on the brain wave data based on the brain cognitive features to obtain emotion data reflecting the cognitive fatigue rehabilitation degree and the rehabilitation status of the target user.
Specifically, the computer device may perform computational analysis on the obtained electroencephalogram signal based on a preset feature analysis algorithm to obtain brain cognitive features carried in the electroencephalogram signal. For example, time domain feature analysis, spectrum feature analysis, nonlinear spectrum feature analysis, and the like may be used, and the present embodiment does not limit the specific feature analysis manner.
In one embodiment, the smaller the value of the cognitive fatigue recovery degree is, the deeper the user is. The cognitive fatigue recovery state may be 0 or 1 in different implementation environments, where 0 indicates that the user is not aware and 1 indicates that the user is aware. Of course, the value thereof may be other data, which is not limited in the embodiment of the present application.
In one embodiment, the computer device can also acquire detailed description information of the brain cognitive features, and calculate the emotion data based on the comprehensive brain cognitive features and the corresponding detailed description information, so that the calculation accuracy of the emotion data is improved under the condition of increasing the detailed description information of the brain cognitive features.
According to the embodiment, based on the determined brain cognitive characteristics, the cognitive fatigue rehabilitation degree and the rehabilitation state of the user can be better analyzed for the target electroencephalogram signals, so that the required emotion data can be extracted, the analysis effect is improved, and the identification accuracy of the emotion data is ensured.
In one embodiment, the emotion data further includes mental data, the mental data including at least one of attention data for determining a degree of attention of the target user and relaxation data for determining a degree of mental relaxation of the target user, the method further comprising:
and step S500, determining mental relaxation degree of the target user in the stages before, during and after cognitive fatigue rehabilitation through the attention data and the relaxation degree data.
Specifically, the computer device may determine initial mental relief degrees of the target user in the stages before, during and after the cognitive fatigue recovery according to the attention data and the relief degree data, respectively, and determine a final target mental relief degree by integrating the obtained initial mental relief degrees.
In one embodiment, the computer device may also determine the mental relief of the target user in the corresponding cognitive fatigue recovery stage based on the attention data and the degree of correlation between the relief data and the target user cognitive fatigue recovery degree and recovery state.
In one embodiment, the computer device may further obtain current internal and external influence factors that may influence mental relaxation of the user, for example, a change condition of a noise level of a surrounding environment, a change condition of a temperature, a change condition of a physical function, and the like, and judge accuracy of the mental relaxation of the target user at the corresponding stage, which is obtained in real time, so as to ensure accuracy of the mental relaxation.
In one embodiment, the higher the value of the attention data, the higher the attention of the user, and the higher the value of the degree of looseness data, the higher the degree of looseness of the user.
And S600, determining the cognitive input efficiency, the waking efficiency and the cognitive fatigue recovery efficiency of the target user according to the value change trends of the mental relaxation degree in different cognitive fatigue recovery stages.
Specifically, the computer device performs mobile weighting calculation on historical emotion data used by the target user to obtain changes of the emotion data in a certain period, such as mental relaxation. In the corresponding cognitive fatigue recovery stage, if the value of the mental relaxation degree is high, the recovery state in the current stage is good, otherwise, the recovery state is bad.
Step S700, carrying out comprehensive evaluation of rehabilitation effect based on the cognitive efficiency, the waking efficiency and the cognitive fatigue rehabilitation efficiency of the target user, and determining corresponding rehabilitation suggestions according to the comprehensively obtained rehabilitation effect evaluation results, wherein: the rehabilitation suggestion comprises a first rehabilitation suggestion which is based on the rehabilitation effect evaluation result, determines a descending step length according to the interval difference when the interval difference between the rehabilitation effect and the preset standard rehabilitation level is smaller than a preset threshold value, and gradually reduces rehabilitation time according to the descending step length; the rehabilitation recommendation further comprises a second rehabilitation recommendation of the first rehabilitation recommendation, which is used for determining an increasing step length according to the interval difference and gradually increasing the rehabilitation time according to the increasing step length when the interval difference between the rehabilitation effect and the preset standard rehabilitation level is larger than a preset threshold value.
Specifically, the computer device will be divided into two phases, single reporting and multiple reporting, for comprehensive evaluation. In the single reporting stage, the value of the movement weighted relaxation degree calculated by the computer equipment is more than 60, and the movement weighted relaxation degree accounts for more than 60% of the report rate, so that the rehabilitation effect is considered to be good, and the rehabilitation time can be gradually reduced. If the rate is lower than 30%, the rehabilitation effect is poor, and the rehabilitation time needs to be increased; the intermediate result is kept unchanged for the single use time. In the multiple-reporting stage, if the calculated relaxation value 60 and above ratio curve is more than 60%, the use can be stopped. If the concentration is lower than 30%, the use is stopped, and other means are considered for rehabilitation.
In one embodiment, the computer device may perform a comprehensive assessment of the healing effect based on the heart rate data of the target user.
In one embodiment, the computer device will acquire electrocardiographic data of the target user and perform a time-frequency domain analysis on the electrocardiographic data to acquire the heart rate variability parameter. And then, the computer equipment carries out quantitative evaluation by combining the cognitive efficiency, the waking efficiency, the cognitive fatigue rehabilitation efficiency and the heart rate variation parameters of the target user, and determines the rehabilitation effect based on the obtained quantitative evaluation result.
According to the embodiment, the comprehensive evaluation of the rehabilitation effect is carried out from multiple aspects, and the evaluation reliability and effectiveness can be improved.
In one embodiment, in step S400, determining the cognitive fatigue rehabilitation quality of the target user according to the change of the overall cognitive fatigue rehabilitation degree in a preset period includes:
and step S4000, determining a cognitive fatigue rehabilitation stage corresponding to the target user according to the change condition of the total cognitive fatigue rehabilitation degree in a preset period, wherein the cognitive fatigue rehabilitation stage comprises a shallow cognitive fatigue rehabilitation stage and a deep cognitive fatigue rehabilitation stage.
Step S4001, determining the cognitive fatigue rehabilitation quality of the target user according to the starting and ending time and the transition cycle period of the transition from the shallow cognitive fatigue rehabilitation stage to the deep cognitive fatigue rehabilitation stage, and the proportion of the shallow cognitive fatigue rehabilitation stage to the deep cognitive fatigue rehabilitation stage in the total cognitive fatigue rehabilitation stage.
In one embodiment, the method further comprises: when the target user is determined to finish the cognitive fatigue rehabilitation, quantitatively displaying the cognitive fatigue rehabilitation condition determined in the corresponding cognitive fatigue rehabilitation stage in a preset statistical form; and/or determining the cognitive fatigue rehabilitation efficiency of the target user by integrating the cognitive fatigue rehabilitation conditions determined in different cognitive fatigue rehabilitation stages and the posture form of the target user.
Specifically, for different application scenarios, the computer device may analyze rehabilitation indexes such as a cognitive fatigue rehabilitation state based on the acquired brain wave signal. For example, in a cognitive fatigue rehabilitation scene, the computer device may determine, according to current electroencephalogram data of the user, a degree of relaxation of the user, a cognitive fatigue rehabilitation state, a start-stop time of each cognitive fatigue rehabilitation state, and a time period in which each cognitive fatigue rehabilitation state is located, and provide a data visualization analysis and provide a corresponding suggestion scheme. For another example, in a meditation scene, the computer device may further determine the degree of relaxation and concentration of the user according to the current electroencephalogram data of the user, and provide a data visualization for analysis and provide a corresponding proposal. For another example, in an educational scenario, the computer device may also determine the cognitive fatigue rehabilitation status, concentration level, pressure value, and emotional arousal level of the user according to the current electroencephalogram data of the user, and provide a digitalized visual analysis and provide a corresponding suggestion scheme. In the stress relieving scene, the computer equipment can judge the relaxation degree, the pressure value and the passive state condition of the user according to the current electroencephalogram data of the user, and provide the data visualization analysis and provide the corresponding suggestion scheme. In the behavior correction scene, the computer equipment can judge the concentration degree, the tolerance degree, the emotional awakening state and the passive emotional state of the user according to the current electroencephalogram data of the user, provide the data visualization analysis and provide the corresponding proposal. In the emotion persuasion scene, the computer equipment can also judge the pressure value, the relaxation degree, the tolerance, the emotion awakening state and the passive emotion state of the user according to the current electroencephalogram data of the user, and provide digitalized visual analysis and a corresponding suggestion scheme.
Above-mentioned embodiment, on the one hand, compare with cognitive fatigue rehabilitation monitoring index such as rhythm of the heart, body movement and breathing, the brain wave has very big promotion to user's cognitive fatigue rehabilitation state and cognitive fatigue rehabilitation stage's precision, accuracy, has left out the inconvenience of wearing that other indexes (body position/abdominal respiration, blood oxygen etc.) brought simultaneously, does benefit to user's use at ordinary times. On the other hand, the dilemma of difficult operation and difficult wearing in a medical scene is reduced, and the balance between accuracy and popularization is achieved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A cognitive fatigue recovery method based on electroencephalogram characteristics and relaxation indexes is characterized by comprising the following steps:
determining an electroencephalogram signal set of a target user, wherein the electroencephalogram signal set comprises a plurality of target electroencephalograms within a preset electroencephalogram signal threshold range;
extracting emotion data reflecting the cognitive fatigue rehabilitation degree and the rehabilitation state of a target user from the target electroencephalogram signal based on the brain cognitive features;
determining the starting and ending time of the target user in the corresponding cognitive fatigue recovery state and the overall cognitive fatigue recovery degree in the starting and ending time according to the emotion data;
and determining the cognitive fatigue rehabilitation quality of the target user according to the change condition of the total cognitive fatigue rehabilitation degree in a preset period.
2. The method of claim 1, wherein the target brain electrical signal is acquired by an acquisition device for acquiring brain electrical signals generated at a predetermined brain location, the predetermined brain location including at least one of forehead, forehead and temple.
3. The method of claim 1, wherein the target brain electrical signal is determined based on the steps of:
acquiring an electroencephalogram original signal, and filtering the electroencephalogram original signal to obtain a corresponding electroencephalogram filtering signal;
and converting the obtained electroencephalogram filtering signal into a corresponding digital electroencephalogram signal, and determining a target electroencephalogram signal based on the digital electroencephalogram signal.
4. The method of claim 1, wherein the extracting, from the target electroencephalogram signal, emotion data reflecting the degree and state of recovery from the cognitive fatigue of the target user based on the brain-cognitive features comprises:
FFP conversion is carried out on the electroencephalogram signal set to obtain corresponding electroencephalogram data, and the electroencephalogram data comprise at least one of preset electroencephalogram frequency band energy ratio and preset electroencephalogram data of brain channels;
and performing regression calculation on the brain wave data based on the brain cognitive features to obtain emotion data reflecting the cognitive fatigue rehabilitation degree and the rehabilitation state of the target user.
5. The method of claim 4, wherein the preset brain electrical band comprises at least one of brain electrical alpha band, brain electrical beta band, brain electrical theta band, brain electrical delta band, and brain electrical gamma band, and the preset brain channel comprises at least one of brain left channel and brain right channel.
6. The method of claim 1, wherein the emotion data further comprises mental data, the mental data comprising at least one of attention data for determining a level of attention of the target user and relaxation data for determining a level of mental relaxation of the target user, the method further comprising:
determining mental relaxation degrees of the target user in the stages before, during and after the cognitive fatigue rehabilitation through the attention data and the relaxation data;
determining the cognitive input efficiency, the waking efficiency and the cognitive fatigue rehabilitation efficiency of a target user according to the value change trend of the mental relaxation degree in different cognitive fatigue rehabilitation stages;
carrying out comprehensive evaluation on the rehabilitation effect based on the cognitive efficiency, the waking efficiency and the cognitive fatigue rehabilitation efficiency of the target user, and determining a corresponding rehabilitation suggestion according to a rehabilitation effect evaluation result obtained comprehensively, wherein:
the rehabilitation suggestion comprises a first rehabilitation suggestion which is based on the rehabilitation effect evaluation result, determines a descending step length according to the interval difference when the interval difference between the rehabilitation effect and a preset standard rehabilitation level is smaller than a preset threshold value, and gradually reduces rehabilitation time according to the descending step length;
and the rehabilitation suggestion also comprises a second rehabilitation suggestion of the first rehabilitation suggestion which determines an increasing step length according to the interval difference and gradually increases the rehabilitation time according to the increasing step length when the interval difference between the rehabilitation effect and the preset standard rehabilitation level is larger than a preset threshold.
7. The method according to claim 1, wherein the determining the cognitive fatigue rehabilitation quality of the target user according to the change of the overall cognitive fatigue rehabilitation degree in a preset period comprises:
determining a cognitive fatigue rehabilitation stage corresponding to a target user according to the change condition of the total cognitive fatigue rehabilitation degree in a preset period, wherein the cognitive fatigue rehabilitation stage comprises a shallow cognitive fatigue rehabilitation stage and a deep cognitive fatigue rehabilitation stage;
and determining the cognitive fatigue rehabilitation quality of the target user according to the starting and stopping time and the transition cycle of the transition from the shallow cognitive fatigue rehabilitation stage to the deep cognitive fatigue rehabilitation stage, and the proportion of the shallow cognitive fatigue rehabilitation stage and the deep cognitive fatigue rehabilitation stage in the total cognitive fatigue rehabilitation stage.
8. The method according to any one of claims 1-7, further comprising:
when the target user is determined to finish the cognitive fatigue rehabilitation, quantitatively displaying the cognitive fatigue rehabilitation condition determined in the corresponding cognitive fatigue rehabilitation stage in a preset statistical form;
and/or determining the cognitive fatigue rehabilitation efficiency of the target user by integrating the cognitive fatigue rehabilitation conditions determined in different cognitive fatigue rehabilitation stages and the posture form of the target user.
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| CN115153554B (en) * | 2022-08-17 | 2023-05-12 | 国家康复辅具研究中心 | Cognitive load assessment method and system |
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