CN112465139A

CN112465139A - Cognitive training method, system and storage medium

Info

Publication number: CN112465139A
Application number: CN202011337209.9A
Authority: CN
Inventors: 王青; 高妍; 齐永升
Original assignee: Beijing Weiming Brain Technology Co ltd
Current assignee: Beijing Weiming Brain Technology Co ltd
Priority date: 2020-11-25
Filing date: 2020-11-25
Publication date: 2021-03-09

Abstract

The embodiment of the invention provides a cognitive training method, a system and a storage medium. The method comprises the following steps: step S100, determining a plurality of tasks in an initial training set at least according to personal information of a subject, wherein the tasks comprise cognitive training tasks; step S300, providing a plurality of tasks in the current training group for the object according to the execution sequence of the plurality of tasks in the current training group, receiving a feedback signal of the object for executing the tasks, and performing statistical analysis on the feedback signal to obtain a statistical analysis result representing the cognitive ability of the object; and step S500, determining a plurality of tasks in the next training group according to the statistical analysis result, and turning to step S300 until the training is finished when the finishing condition is met. The scheme combines the specificity and variability of cognitive training, carries out individualized adjustment of the cognitive training aiming at the object, covers a wider cognitive network, trains from a global perspective, and improves the cognitive ability of the object.

Description

Cognitive training method, system and storage medium

Technical Field

The invention relates to the technical field of brain training, in particular to a cognitive training method, a cognitive training system and a storage medium.

Background

Cognitive ability refers to the ability of the brain to process, store, and extract information. Human beings recognize the objective world and acquire various knowledge, mainly depending on the cognitive abilities of human beings. Based on the brain neural plasticity principle, a specific cognitive training method can be designed to improve the cognitive ability of an individual.

Cognitive training refers to activities that aim at making people "smarter" and therefore perform better in reasoning ability, solving problems and learning. Many current cognitive training programs are directed to basic cognitive skills such as attention (the ability to selectively focus on relevant information), working memory (the ability to actively remember information related to tasks), or executive functions (involved in a series of processes that control and regulate thoughts and actions). The cognitive ability of the trained object can be improved through cognitive training. The trained objects of cognitive training are very wide, and from children to the elderly, different cognitive training methods and emphasis points are provided for each age group. Besides normal persons desiring to improve cognitive ability, the trained subject may also be a child suffering from attention deficit and hyperactivity disorder, an elderly person suffering from alzheimer's disease, or a patient suffering from brain trauma, and the like.

The existing cognitive training method has low efficiency of improving cognitive ability and is difficult to achieve the targets of users and trained objects.

Disclosure of Invention

The present invention has been made in view of the above problems. According to an aspect of the present invention, there is provided a cognitive training method, including:

step S100, determining a plurality of tasks in an initial training set at least according to personal information of a subject, wherein the tasks comprise cognitive training tasks;

step S300, providing a plurality of tasks in the current training group for the subject according to the execution sequence of the plurality of tasks in the current training group, receiving a feedback signal of the subject for executing the tasks, and performing statistical analysis on the feedback signal to obtain a statistical analysis result representing the cognitive ability of the subject; and

and S500, determining a plurality of tasks in the next training group according to the statistical analysis result, and turning to the step S300 until the training is finished when the finishing condition is met.

Illustratively, the determining the plurality of tasks in the next training set in step S500 includes:

for each cognitive training task in the current training group, determining a corresponding constant mode aiming at the cognitive training task, wherein the constant mode represents the distribution condition of the cognitive ability presented by the sample executing the cognitive training task; determining the ranking position of the object in the determined common involved sample according to the statistical analysis result; and determining the name, the execution sequence, the execution duration and/or the execution intensity of the cognitive training tasks in the next training group according to the ranking position.

Illustratively, the determining the plurality of tasks in the next training set in step S500 further includes:

and determining the name, execution sequence, execution duration and/or execution intensity of the cognitive training tasks in the next training group according to the statistical analysis result of the object executing the tasks of the current training group and the statistical analysis result of the object executing the tasks of the historical training group.

Illustratively, the performing the statistical analysis on the feedback signal in step S300 includes:

performing statistical analysis on the feedback signals in the time window or the feedback frequency window to obtain a statistical analysis result;

exemplarily, step S300 further includes:

and adjusting the execution intensity and/or the execution duration of the current and/or subsequent cognitive training tasks in the current training set based on the statistical analysis result.

Exemplarily, step S300 further includes:

and rewarding the object and adjusting the rewarding of the object for completing subsequent tasks according to the statistical analysis result.

Illustratively, the determining a plurality of tasks in the initial training set in step S100 and/or the determining a plurality of tasks in the next training set in step S500 includes:

determining cognitive training tasks in a training set to obtain the feedback signal; and

determining a training recovery task that is easier to complete than the cognitive training task.

Illustratively, the end condition includes: the execution time of the cognitive training method exceeds the preset time.

Illustratively, the determining the plurality of tasks in the initial training set in step S100 is further based on a result of statistical analysis of the historical cognitive training tasks completed by the subject.

According to another aspect of the present invention, there is provided a cognitive training system comprising a sensor, a processor and a memory, wherein the sensor is configured to acquire the feedback signal from the subject for transmission to the processor; the memory has stored therein computer program instructions for execution by the processor to perform the cognitive training method as described above.

According to a further aspect of the present invention, there is provided a storage medium having stored thereon program instructions for performing, when executed, the cognitive training method as described above.

In the technical scheme of the embodiment of the invention, the cognitive training is executed by taking the training group as a unit and one training group comprises a plurality of tasks in consideration of the specificity of the cognitive training, so that a wider cognitive network is covered; the following training group is determined based on the statistical analysis result of the personal information and the historical cognitive training, so that the variability of the cognitive training is improved, and the personalized customization of the cognitive training is realized. In a word, the scheme realizes the personalized training from the global angle and obviously improves the cognitive ability of the object.

Drawings

The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail embodiments of the present invention with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings, like reference numbers generally represent like parts or steps.

FIG. 1 shows a schematic flow diagram of a cognitive training method according to one embodiment of the present invention;

fig. 2 shows a schematic flow diagram of a cognitive training method according to yet another embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein.

Some existing cognitive training methods only train one cognitive ability independently, and some of the existing cognitive training methods perform combined training within a limited range. For example, some cognitive training methods train on the basis of attention selection, attention distribution, task switching, planning, and the like, or simply combine the above independent training methods, and cannot cover a wider cognitive network, thereby improving cognitive ability comprehensively. And the cognitive ability of the trained object as a whole is improved comprehensively. There is a correlation between individual cognitive abilities. For example, 80% of children at school age of Attention Deficit and Hyperactivity Disorder (ADHD) at clinical referral are also diagnosed with at least one other comorbidity disorder, as are other cognitive deficit disorders. The existing cognitive training method does not consider the limitation that the individual cognitive training or the local cognitive training improves the overall cognitive ability of an individual and optimizes the whole cognitive ability, and possibly causes the weakening or the reduction of other cognitive abilities and even introduces other cognitive side effects.

According to an embodiment of the invention, a cognitive training method is provided. The cognitive training method relates to a plurality of training groups, and at least one training group comprises a plurality of tasks, so that a wider brain cognitive network is covered, and the cognitive ability is comprehensively improved. Fig. 1 shows a schematic flow diagram of a cognitive training method according to one embodiment of the present invention. As shown in fig. 1, the cognitive training method includes the following steps.

Step S100, determining a plurality of tasks in an initial training set at least according to the personal information of the object, wherein the plurality of tasks comprise cognitive training tasks.

Illustratively, the personal information of the object includes: age, gender, native language, vision information, medical history, doctor recommendations, etc. It is understood that the personal information of the subject can be obtained through a human-computer interaction interface of the cognitive training system. For example, through a human-machine interaction interface, personal information of an object may be input.

According to the acquired personal information of the object, an initial evaluation result of the cognitive ability of the object can be obtained, so that a plurality of tasks in an initial training set are determined. The plurality of tasks includes one or more cognitive training tasks. Cognitive training tasks are used to train the cognitive abilities of a trained subject, which correspond to specific brain regions and cognitive abilities. In other words, each cognitive training task has its specificity. The imaging tracking finds that the inside of the brain is modularized when processing a given task, the inside of the brain processes special functions, and the modules process global complex functions. The purpose of each cognitive training task is to train these particular brain module or modules. While the subject is performing the cognitive training task, a feedback signal may be received that the subject is performing the task. By performing statistical analysis on the feedback signal, a statistical analysis result indicating the cognitive ability of the subject can be obtained. The cognitive training tasks in a training set may include cognitive training tasks based on different cognitive paradigms and/or cognitive training tasks based on different manifestations of the same cognitive paradigms. For example, one training set may include cognitive training tasks for working memory and cognitive training tasks for inhibition, both of which belong to different cognitive paradigm-based cognitive training tasks. Through the switching of the cognitive training tasks, resources can be balanced, and more modules can be covered. The higher the brain modularization degree is, the more obvious the training effect is, and the cognitive gain obtained by the individual is also larger.

It is to be appreciated that determining the plurality of tasks in the initial training set includes: the names, execution order, execution time length, execution intensity, and the like of the plurality of tasks are determined. The stimulus attributes may be ranked and combined according to the personal information of the subject, and one or more of the stimulus attributes may be selected to generate one or more cognitive training tasks in the initial training set. The stimulus attributes may include color, semantic, orientation, location, sound, shape, adjacent flanks, and the like. The execution order, execution duration, execution intensity, and the like of the generated cognitive training tasks may be determined still based on the personal information of the subject. For example, the generated plurality of cognitive training tasks may be performed in an easy-to-hard order.

Step S300, providing the plurality of tasks in the current training group to the subject according to the execution sequence of the plurality of tasks in the current training group, receiving a feedback signal of the subject for executing the tasks, and performing statistical analysis on the feedback signal to obtain a statistical analysis result representing the cognitive ability of the subject.

A number of tasks in the initial training set may be determined in step S100, thereby initiating a training process for the subject. Starting from the initial training set, the subjects are provided with the tasks therein on a training set-by-training set basis for completion by the subjects on a per-subject basis. For each training set, when a subject is performing training, it may be referred to as the current training set. For the current training set, the tasks may be provided to the subject in the order in which they were performed for execution by the subject. And receiving a feedback signal of the task executed by the object while the object executes the task, and performing statistical analysis on the feedback signal to obtain a statistical analysis result representing the cognitive ability of the object.

In step S300, the stimulation of the task may be presented to the subject using various playback devices such as a display, a speaker, and the like. The subject may feedback the stimuli according to given rules, depending on the presented stimuli.

For example, in a cognitive training task of response detection, a display is used to present a stimulus to a subject, the stimulus comprising an arrow pointing to the left, the location of the stimulus being to the right in the subject's visual field. The cognitive training task may require the subject to feedback in the direction of the stimulated arrow rather than the position, the feedback may be a left or right key press. The direction indicated by the arrow corresponding to the left key is the left side, and the direction indicated by the arrow corresponding to the right key is the right side. The subject feeds back the stimulus according to the rules given by the cognitive training task, thereby generating a feedback signal.

For another example, in a cognitive training task for racing, an object may control an operable member, such as a joystick, to control an operating object, such as a car, displayed in a screen. The object can control the operation lever to enable the car to cope with random turns, traffic lights at crossroads, avoid obstacles and the like.

For another example, in a cognitive training task of rhythm control, the object may accurately tap the controlled object according to a predetermined music rhythm to feed back a feedback signal and the like.

It will be appreciated that different cognitive training tasks may generate different feedback signals. Statistical analysis of the obtained feedback signal can result in a statistical analysis result that represents the cognitive ability of the subject. Illustratively, the received feedback signal is used as an input, and is statistically analyzed by a signal detection theory method, so as to output variables such as a hit rate, a false alarm rate, a correct reaction time, a miss rate, a discrimination index (discrimination index), and the like. The statistical analysis results may include one or more of these variables. The statistical analysis results represent the personalized cognitive ability of the subject.

It is to be understood that the next training set is relative to the current training set. After the object completes the current training set, a plurality of tasks in the next training set can be determined according to the statistical analysis result of the current training set. Since the subjects performed multiple tasks in the current training set, the statistical analysis of the current training set may represent the cognitive level of the subjects in different cognitive abilities. For example, it may be that for a subject, short-term memory is stronger and suppression is weaker. In determining the tasks in the next training set, cognitive training tasks for weaker cognitive abilities may be enhanced, such as extending their execution duration, enhancing their execution strength, and/or adjusting the execution order to rank the cognitive training task ahead of other cognitive training tasks; accordingly, cognitive training tasks for greater cognitive abilities may be attenuated. Each person's cognitive abilities and levels are different. In the scheme, the following cognitive training task of the subject is determined according to the current personalized cognitive ability of the subject. The cognitive training task is tailored to the current cognitive abilities of the subject. After determining the plurality of tasks in the next training set, turning back to step S300, the determined next training set becomes the current training set. Repeating the operation in step S300 can obtain new statistical analysis results.

If the ending condition is not met currently, the steps are repeated until the ending condition is met, and the training can be ended. In addition, it is understood that the training may be directly ended without performing step S500 again if the ending condition has been satisfied after performing step S300.

Illustratively, the end condition may include: the execution time of the cognitive training method exceeds the preset time. A preset duration may be set for the cognitive training method. When the duration of the cognitive training task performed by the subject reaches or exceeds the preset duration, the training can be ended.

It is contemplated that the nerves may only change due to extensive and long-term practice, and that the changes in the nerves may not be consolidated until later in the training process. Too short training time or times to achieve quantitative to qualitative neural connection changes; if training is continued after the time or times are consolidated, there is no boosting effect and side effects may also occur. The above-mentioned termination conditions ensure the improvement of nerves, and then ensure the cognitive training effect.

In the above technical solution, a plurality of tasks in the initial training set may be determined at least according to the personal information of the subject. The subject may execute the plurality of tasks in the current training set, i.e., the initial training set, in the order of execution of the plurality of tasks and generate the feedback signal. And carrying out statistical analysis on the received feedback signals so as to obtain a statistical analysis result. A number of tasks in the next training set may be determined based on the statistical analysis. Then, the loop is repeated to execute step S300 → step S500 → step S300 … … until the end condition is satisfied, and the training is ended. Thus, the specificity of cognitive training is fully considered, cognitive training is performed by taking a training set as a unit, and one training set comprises a plurality of tasks, so that a wider cognitive network is covered. The next training group is determined based on the statistical analysis results of the personal information and the historical cognitive training, so that the personalized customization of the cognitive training is realized, and the training variability is improved. In a word, the scheme realizes the personalized training from the global angle, comprehensively considers the cognitive ability and carries out balance training, and the cognitive ability of the object is more comprehensively and obviously improved.

Illustratively, one cognitive training task in the training set may include a primary task and/or one or more secondary tasks. Determining the plurality of tasks in the initial training set in step S100 and/or determining the plurality of tasks in the next training set in step S500 includes: a primary task and a secondary task in the training set are determined to receive respective feedback signals. The main task is a task that requires the subject to continuously feedback, such as the cognitive training task of the aforementioned race. An auxiliary task is a task that requires the subject to feedback according to a predetermined rule based on the presented stimulus, such as the cognitive training task of the aforementioned reaction detection.

It is to be appreciated that the primary and secondary tasks can be performed in a cognitive training task in time intervals. Alternatively, the primary and secondary tasks may be executed concurrently in parallel. Through the combination of the main task and the auxiliary task, more brain areas and cognitive networks can be further covered. Thereby more comprehensively improving the cognitive ability of the subject.

Exemplarily, the determining of the plurality of tasks in the initial training set in step S100 and/or the determining of the plurality of tasks in the next training set in step S500 includes determining cognitive training tasks in the training set to obtain the feedback signal; the method can also comprise the following steps: a training recovery task is determined that is easier to complete than the cognitive training task.

As described above, the plurality of tasks include a cognitive training task, and the feedback signal may be obtained by the subject performing the cognitive training task, and will not be described again here. Meanwhile, the plurality of tasks may further include a training recovery task. It is understood that the training recovery task is a task that is easily accomplished by the subject for relaxing the subject. The training recovery task may be a single task. The training recovery task may be an entirely different task than the cognitive training task, or a cognitive training task with significantly reduced speed. Illustratively, the training recovery task may be listening to music, enjoying video, and the like. For example, relaxing music may be played to the subject while a picture of nature is shown, etc. The training recovery task may also be a physiological recovery task, e.g. reminding the subject to stand up to breathe deeply, etc.

From a physiological perspective, glucose in the capillaries is converted to glutamine by glial cells, transported back to neurons, and then lactic acid is produced by glia, a key fuel for neurons to support sustained nerve firing. In the scheme, a proper training recovery task is added to a task of a subject, so that neurons can acquire enough resources in time, and the cognitive training task can better achieve the effect of improving the cognitive ability of the subject.

Illustratively, the determination of the plurality of tasks in the initial training set in step S100 is further based on the results of statistical analysis of the subjects' completion of historical cognitive training tasks.

In some application scenarios, the subject may not be cognitively trained for the first time. The statistical analysis result of the historical cognitive training task completed by the object can be used as a basic parameter, and adaptive methods such as a step method, a Bayes and maximum likelihood method, a magnitude estimation method and the like are adopted, so that one or more tasks in the initial training set are determined.

By introducing the statistical analysis result of the historical cognitive training tasks, the tasks in the initial training set can be determined more pertinently and more accurately, so that the cognitive training method can achieve an ideal training effect at the initial stage, and the overall cognitive ability of the object is smoothly improved.

Exemplarily, the performing the statistical analysis on the feedback signal in step S300 includes: performing statistical analysis on the feedback signals in the time window or the feedback frequency window to obtain a statistical analysis result; the step S300 further includes a step of performing personalized fine tuning on the tasks in the training set. In particular, the execution strength and/or execution duration of the current and/or subsequent cognitive training tasks in the current training set may be adjusted based on the statistical analysis results.

The statistical analysis content is selectively configured according to different tasks. For example, for the current assistance task, may include, but is not limited to: the reaction time deviation, the reaction accuracy and the like of the object fed back in the auxiliary task. For the current primary task, may include, but is not limited to: and evaluating the variable of the performance of the object in a recent time window or a feedback time window. Taking the cognitive training task of the race as an example, the statistical analysis content includes, for example, race distance, number of hit edges, number of step holes, and the like in the last 10 feedback windows.

Optionally, variables such as hit rate, false alarm rate, correct response time, miss rate, discrimination index and the like in a recent period of time window or a feedback frequency window are calculated based on a window calculation method of a signal detection theory. For example, in the last 10 stimuli, the subject should not react to the stimulus, but actually reacted the wrong way. The feedback signals of these false responses are then summed and divided by the total number of times 10, thereby obtaining a real-time false alarm rate. Based on the signal detection theory, the other variables may also be calculated by combining the window calculation method, which is not limited herein.

And taking one or more variables obtained by statistical analysis as parameters, and adjusting the execution intensity and/or execution duration of the next cognitive training task in the current training set by using adaptive methods such as a step method, a Bayes and maximum likelihood method, magnitude estimation and the like. Still taking the cognitive training task of the aforementioned race as an example, the speed of travel, cadence, number and radius of turns, number of traffic lights and/or obstacles, etc. that affect the execution of the main task may be adjusted. For the auxiliary task, one or more combinations of the number, kind, attribute, time point of occurrence, display time after occurrence, timeout upper limit of subject feedback, interval time between stimuli, and the like, among which the stimuli may be adjusted. Illustratively, if the statistical analysis result shows that the real-time false alarm rate is reduced, the execution intensity of the cognitive training task can be adjusted up through a staircase method, for example, the display time after the stimulus appears in the cognitive training task is shortened; the execution duration of the cognitive training task may also be extended.

In the technical scheme, the obtained feedback signals are subjected to statistical analysis in a window mode, so that the execution intensity and/or the execution duration of the current and/or cognitive training tasks in the current training set are adjusted in real time based on the obtained statistical analysis results. Therefore, dynamic personalized real-time adjustment of the cognitive training task can be realized, the high matching degree of the provided task and the current state of the object can be ensured all the time, and the cognitive training quality is ensured.

Illustratively, step S300 further includes a reward step. Specifically, according to the statistical analysis result, the object is rewarded and the reward of the object for completing the subsequent tasks is adjusted. The reward may include virtual items, points, motivational sounds, the subject's own historical training shadow, team training shadow, and the like. The reward can be set according to the statistical analysis result by adopting a ladder method, a Bayes and maximum likelihood method, a magnitude estimation and other self-adapting methods. It is to be understood that multiple cognitive training tasks may be included in a training set. After the current cognitive training task is completed, the subject may be rewarded based on previous reward settings according to the results of statistical analysis of the subject's performance of the cognitive training task. And if the statistical analysis result shows that the cognitive ability of the object is kept stable compared with the prior art, the reward of the object for completing the subsequent cognitive training task can be kept unchanged; if the statistical analysis result shows that the cognitive ability of the subject is reduced or abnormal compared with the prior cognitive ability, the reward of the subject for completing the subsequent cognitive training task can be increased. Thus, the reward remains unchanged, assuming the subject remains in a better training state. Assuming that the subject has difficulty in keeping the training intensity for a long time and has a depressed mood, the training state is poor, the reward type can be increased or the reward intensity can be increased, and the participation degree of the subject can be increased so as to improve the training state of the subject.

Therefore, when the object executes the cognitive training task of one training group, the reward setting is adjusted in real time in a personalized mode according to the execution state of the object. The method not only can dynamically mobilize the participation degree of the subject, but also can prevent the subject from generating depravation, addiction and other side emotions. The dynamic setting and adjustment of the reward can enable the subject to enhance cognitive ability in recognition and achieve the aim of executing a cognitive training task better.

Illustratively, the determining of the plurality of tasks in the next training set in step S500 includes: firstly, for each cognitive training task in a current training group, determining a corresponding normal mode for the cognitive training task; and determining the ranking position of the object in the determined normal involved sample according to the statistical analysis result obtained in the step S300. Then, according to the ranking position, the name, the execution sequence, the execution duration and/or the execution intensity of the cognitive training tasks in the next training group are determined.

The cognitive abilities presented by a large number of samples to perform a current cognitive training task may be analyzed to obtain a corresponding norm. The norm may represent a distribution of cognitive abilities of the sample to perform the cognitive training task. For each cognitive training task, the corresponding norm may be determined. The statistical analysis result obtained in step S300 may represent the current cognitive ability of the subject. After the object completes a certain cognitive training task, the ranking position of the object in the corresponding normal involved sample can be determined according to the cognitive ability represented by the statistical analysis result. From this ranking position, the name, execution order, execution duration, and/or execution strength of the cognitive training tasks in the next training set may be determined. It will be appreciated that the further back the ranking position indicates the poorer cognitive abilities of the subject compared to the sample. On the contrary, the ranking position is superior to the prior one, which shows that the cognitive ability of the object is good. In practical applications, the ranking threshold may be preset. If the ranking position of the object exceeds the ranking threshold value in the normal involved samples, the cognitive ability corresponding to the object is up to the standard. It will be appreciated that the ranking threshold may be set according to the personal information of the subject and the desired training goal, and may be any value between 50% and 70%, for example. Taking 50% as an example, if the ranking position of the object exceeds 50% of the samples in the normal involved samples, it indicates that the cognitive ability of the object has reached the standard. Thus, the execution intensity of the cognitive training task corresponding to the cognitive ability can be kept at the minimum intensity. If the ranking position is lower than the ranking threshold, the corresponding cognitive ability is lower than that of a regular person or even far lower than the normal standard. Then, the execution strength of the cognitive training tasks corresponding to the cognitive training tasks in the next training group can be enhanced and/or the execution duration of the cognitive training tasks corresponding to the cognitive training tasks in the next training group can be prolonged according to the ranking position. The later the ranking position, the lower the corresponding cognitive ability. Thus, the more prioritized the execution order may be. The longer the execution duration may be. The greater the execution strength may be. In this case, the cognitive training task for training the cognitive ability may be added even in the next training set to enhance the training of the cognitive ability. Conversely, the ranking position is more advanced, which indicates that the corresponding cognitive ability is higher. Thus, the execution order may be the later. The shorter the execution time period may be. The smaller the execution intensity may be. Even further, the cognitive training task may be cancelled or replaced with other cognitive training tasks in the next training set.

According to the technical scheme, the tasks in the subsequent cognitive training group can be determined individually according to the current statistical analysis result of the object, the cognitive ability which reaches the standard is kept, and the cognitive ability which does not reach the standard is improved. Therefore, the cognitive training is performed on the object more specifically, and the cognitive ability of the object is comprehensively improved.

Exemplarily, the determining the plurality of tasks in the next training set in step S500 further includes: and determining the name, execution sequence, execution duration and/or execution intensity of the cognitive training tasks in the next training group according to the statistical analysis result of the object executing the tasks of the current training group and the statistical analysis result of the object executing the tasks of the historical training group. Optionally, the statistical analysis results of the tasks of the current training set are compared with the statistical analysis results of the tasks of the historical training set. This makes it possible to obtain the change in cognitive ability of the subject. And determining the cognitive training task in the next training group according to the change situation.

By introducing the statistical analysis result of the tasks of the historical training group, whether the weakening or the reduction of other cognitive abilities is caused or not and whether other cognitive side effects are introduced or not in the process of historical training of a certain cognitive ability can be known. In addition, the overall cognitive ability of the subject can be globally optimized and improved.

Fig. 2 shows a schematic flow diagram of a cognitive training method according to yet another embodiment of the present invention. In step S100, the tasks in the initial training set may be determined according to the personal information of the subject and/or the statistical analysis result of the tasks of the historical cognitive training set completed by the subject. In step S300, the tasks in the current training set are provided to the subject in the order of execution of the tasks in the current training set, while receiving a feedback signal of the subject. The feedback signal may be statistically analyzed to obtain statistical analysis results. According to the statistical analysis result, the execution intensity and/or execution duration of the cognitive training tasks in the current training group can be adjusted, and the reward of the object for completing the subsequent tasks can also be adjusted. In step S500, the name, execution sequence, execution duration and/or execution intensity of the cognitive training tasks in the next training group may be determined according to the statistical analysis result and the statistical analysis result of the historical cognitive training tasks completed by the subject. And then, the process returns to the step S300 until the training is finished when the finishing condition is met. The steps in this embodiment have been described in detail in the previous embodiment, and are not described herein again for brevity.

According to another aspect of the embodiment of the invention, a cognitive training system is also provided. The cognitive training system includes a sensor, a processor, and a memory. The sensor is used for acquiring a feedback signal of the object to execute the task from the object so as to send the feedback signal to the processor. The sensors may include gyroscopes, keys, eye movement devices, posture detection devices, cameras, voice recognition devices, wearable devices for sensing physiological parameters, and the like. The memory has stored therein computer program instructions for execution by the processor to perform the cognitive training method described above.

According to still another aspect of the embodiments of the present invention, there is also provided a storage medium. On which program instructions are stored which, when executed, are adapted to perform the above-mentioned cognitive training method. Illustratively, the storage medium may include a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, or any combination of the above storage media.

Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the foregoing illustrative embodiments are merely exemplary and are not intended to limit the scope of the invention thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present invention should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules in a cognitive training system according to embodiments of the present invention. The present invention may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

The above description is only for the specific embodiment of the present invention or the description thereof, and the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A cognitive training method, comprising:

2. The method of claim 1, wherein the determining the plurality of tasks in the next training set in step S500 comprises:

for each cognitive training task in the current training set,

determining a corresponding constant mode aiming at the cognitive training task, wherein the constant mode represents the distribution condition of the cognitive ability presented by the sample executing the cognitive training task; and

determining the ranking position of the object in the determined normal involved sample according to the statistical analysis result;

and determining the name, the execution sequence, the execution duration and/or the execution intensity of the cognitive training tasks in the next training group according to the ranking position.

3. The method of claim 1 or 2, wherein the determining of the plurality of tasks in the next training set in step S500 comprises:

4. The method of claim 1, wherein,

the performing statistical analysis on the feedback signal in the step S300 includes:

the step S300 further includes:

5. The method of claim 1, wherein the step S300 further comprises:

6. The method of claim 1, wherein the determining the plurality of tasks in the initial training set in step S100 and/or the determining the plurality of tasks in the next training set in step S500 comprises:

7. The method of claim 1, wherein the end condition comprises: the execution time of the cognitive training method exceeds the preset time.

8. The method of claim 1, wherein said determining the plurality of tasks in the initial training set in step S100 is further based on statistical analysis of the subjects' completion of historical cognitive training tasks.

9. A cognitive training system comprising a sensor, a processor, and a memory, wherein,

the sensor is used for acquiring the feedback signal from the object to send to the processor;

the memory has stored therein computer program instructions for execution by the processor to perform the cognitive training method of any one of claims 1 to 8.

10. A storage medium having stored thereon program instructions for performing, when executed, the cognitive training method of any one of claims 1 to 8.