CN114067955A

CN114067955A - Cognitive ability training method and device based on action and electronic equipment

Info

Publication number: CN114067955A
Application number: CN202210026243.7A
Authority: CN
Inventors: 黄超; 张跃曦
Original assignee: Beijing Wujiang Naozhi Technology Co ltd
Current assignee: Beijing Wujiang Naozhi Technology Co ltd
Priority date: 2022-01-11
Filing date: 2022-01-11
Publication date: 2022-02-18

Abstract

The disclosure provides a cognitive ability training method and device based on actions and electronic equipment. The method comprises the following steps: providing a training task, wherein the training task is used for improving the target cognitive ability and is related to the action; acquiring physiological data of a user when the user executes a training task; based on the physiological data, a training score is determined for the user to perform a training task. The method can promote the improvement of cognitive ability by utilizing movement, and effectively improve the efficiency and effect of cognitive ability training.

Description

Cognitive ability training method and device based on action and electronic equipment

Technical Field

The embodiment of the disclosure relates to the technical field of cognitive ability training, in particular to a method and a device for cognitive ability training based on actions and electronic equipment.

Background

Cognitive ability refers to the ability of an individual to receive, process and process information, including attention, memory, thinking, reaction, and voluntary control. Cognitive ability has a great influence on people's life, learning and work. Therefore, the method has important value and significance for targeted intervention training of cognitive ability. However, existing cognitive ability training schemes are still not ideal in terms of training efficiency and effectiveness.

Therefore, there is a need for a new cognitive ability training method.

Disclosure of Invention

The embodiment of the disclosure provides a cognitive ability training method and device based on actions, an electronic device and a storage medium.

In a first aspect, the present disclosure provides a method for cognitive ability training based on actions, including:

providing a training task, wherein the training task is used for improving the target cognitive ability and is related to the action;

acquiring physiological data of a user when the user executes the training task;

and determining a training score for the user to perform the training task based on the physiological data.

In some optional embodiments, before providing the training task, the method further includes a step of obtaining the training task, including:

acquiring a training task appointed by a user; or

And determining the training task according to the cognitive test result and the physical ability test result of the user.

In some optional embodiments, the determining the training task according to the cognitive test result and the physical ability test result of the user includes:

determining a corresponding cognitive training task according to the cognitive test result;

determining a corresponding physical ability training task according to the physical ability test result;

and obtaining the training task according to the cognitive training task and the physical training task.

In some optional embodiments, the determining a corresponding cognitive training task according to the cognitive test result includes:

determining the target cognitive ability required to be improved according to the cognitive test result;

and determining a cognitive training task corresponding to the target cognitive ability based on a preset cognitive ability knowledge graph, wherein the cognitive ability knowledge graph comprises cognitive ability nodes, cognitive training task nodes and correlation coefficients between the cognitive ability nodes and the corresponding cognitive training task nodes.

In some optional embodiments, the determining the corresponding fitness training task according to the fitness test result includes:

determining the target motion capability required to be improved according to the physical fitness test result;

and determining a physical ability training task corresponding to the target motion ability based on a preset physical ability knowledge graph, wherein the physical ability knowledge graph comprises motion ability nodes, physical ability training task nodes and correlation coefficients between the motion ability nodes and the physical ability training task nodes.

In some optional embodiments, the physiological data includes at least one of physiological state data and motion amount data.

In some alternative embodiments, the physiological data is obtained based on at least one of motion depth image data, acceleration data, heart rate data, electrodermal data, and blood oxygen data.

In some alternative embodiments, a cognitive training task of the above-described training tasks responds by an action.

In some optional embodiments, the determining a training score for the user to perform the training task based on the physiological data includes:

determining a basic score for the user to execute the training task according to a response result of the user to the cognitive training task in the training task;

determining a weight value corresponding to the basic score according to the physiological data;

and obtaining the training score according to the basic score and the weight value.

In some optional embodiments, the determining, according to the physiological data, a weight value corresponding to the base score includes:

determining the weight value as a first weight value when the physiological data is first physiological data;

determining the weight value as a second weight value when the physiological data is second physiological data;

the human activity level corresponding to the first physiological data is higher than the human activity level corresponding to the second physiological data, and the first weight value is greater than the second weight value.

In a second aspect, the present disclosure provides a cognitive ability improving device based on actions, comprising:

the training task providing unit is used for providing a training task, wherein the training task is used for improving the cognitive ability of a target and is related to actions;

the physiological data acquisition unit is used for acquiring physiological data of a user when the user executes the training task;

and the training score determining unit is used for determining the training score of the user for executing the training task based on the physiological data.

In some optional embodiments, the apparatus further includes a training task obtaining unit, configured to:

acquiring a training task appointed by a user; or

In some optional embodiments, the training task obtaining unit is further configured to:

In some optional embodiments, the training score determining unit is further configured to:

In a third aspect, the present disclosure also provides an electronic device, including:

one or more processors;

a storage device having one or more programs stored thereon,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described in any embodiment of the first aspect of the disclosure.

In a fourth aspect, the present disclosure also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, implements the method as described in any one of the embodiments of the first aspect of the present disclosure.

In the cognitive ability training method, the cognitive ability training device, the electronic equipment and the storage medium, based on the positive correlation between the movement and the cognitive ability, the corresponding training score is determined according to the physiological data of the user during the training task, the improvement of the cognitive ability can be promoted by using the movement, and the efficiency and the effect of the cognitive ability training are effectively improved.

Drawings

Other features, objects, and advantages of the disclosure will become apparent from a reading of the following detailed description of non-limiting embodiments which proceeds with reference to the accompanying drawings. The drawings are only for purposes of illustrating the particular embodiments and are not to be construed as limiting the disclosure. In the drawings:

FIG. 1 is an exemplary system architecture diagram in which embodiments of the present disclosure may be applied;

FIG. 2 is a flow diagram of a method of motion-based cognitive ability training in accordance with an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a cognitive ability knowledge graph according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a motion-based cognitive improvement device according to an embodiment of the present disclosure;

FIG. 5 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present disclosure.

Detailed Description

The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the action-based cognitive ability training method, apparatus, electronic device, and storage medium of the present disclosure may be applied.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as a cognitive enhancement application based on actions, a voice recognition application, a web browser application, etc., may be installed on the

terminal devices

101, 102, 103.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When the

terminal devices

101, 102, and 103 are hardware, they may be various electronic devices with a display screen, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg Audio Layer 4), laptop portable computers, desktop computers, and the like. When the

terminal apparatuses

101, 102, 103 are software, they can be installed in the above-listed terminal apparatuses. It may be implemented as multiple software or software modules (e.g., to provide an action-based cognitive improvement service), or as a single software or software module. And is not particularly limited herein.

In some cases, the method for training cognitive abilities based on actions provided by the present disclosure may be performed by the

terminal devices

101, 102, 103, and accordingly, the cognitive ability improving apparatus based on actions may be disposed in the

terminal devices

101, 102, 103. In this case, the system architecture 100 may not include the server 105.

In some cases, the motion-based cognitive ability training method provided by the present disclosure may be performed by the

terminal devices

101, 102, 103 and the server 105 together, for example, the steps of "providing a training task" and "acquiring physiological data of the user when performing the training task" may be performed by the

terminal devices

101, 102, 103, and the steps of "determining a training score of the user performing the training task based on the physiological data" may be performed by the server 105. The present disclosure is not limited thereto. Accordingly, the cognitive ability improvement means based on the motion may be provided in the

terminal devices

101, 102, and 103 and the server 105, respectively.

The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow 200 of one embodiment of a method of motion-based cognitive ability training according to the present disclosure is shown, the method being implemented by, for example, the terminal device shown in fig. 1, or by both the terminal device and the server shown in fig. 1. As shown in fig. 2, the method for training cognitive abilities based on actions includes the following steps:

step 201, providing a training task.

In this embodiment, the training task is used to improve the target cognitive ability and is associated with an action.

In some alternative embodiments, the target cognitive ability may be a memory, such as a sensory memory ability, a short-term memory ability, a semantic memory ability, a contextual memory ability, or a technical memory ability. Here, the sensory memory ability may refer to an ability of a person to simultaneously memorize a plurality of target objects within a short time (e.g., 1 second). Short-term memory ability may refer to a person's ability to temporarily store a certain amount of material in a mental working memory. Semantic memory ability may refer to a person's ability to retain semantic knowledge for a long period of time (e.g., 1 minute). Contextual memory may refer to a person's ability to remember a particular event. Skill memory can refer to a person's ability to quickly learn, based on rules, to react to different stimuli with the correct actions based on feedback.

In some alternative embodiments, the target cognitive ability may be attention, such as attentional capacity ability, selective attention ability, continuous attention ability, autonomic ability, and responsiveness. Here, note that capacity capability may refer to a person's ability to remember multiple targets at the same time. Selecting attention capacity may refer to a person's ability to focus attention on a particular target among multiple targets. Continuous attention capacity may refer to a person's ability to continuously focus attention on a target. Self-control capability may refer to the ability of a person to control impulsive behavior. Responsiveness may refer to the ability of a person to respond quickly to an external stimulus.

In this embodiment, for different target cognitive abilities, the cognitive training task may be improved through corresponding cognitive training tasks. The following illustrates the correspondence between the target cognitive abilities and the cognitive training tasks.

For sensory memory, the task of partial reporting may be augmented by a picture, i.e. multiple different target objects are presented on the display device and disappear quickly (e.g. within 1 second), requiring the user to remember each target object and report out the specific target object on demand in subsequent training.

For short-time memory ability, the position memory breadth task can be used for improving, namely, a plurality of target objects are presented on a display device simultaneously or sequentially, a user is required to remember each target object sequentially, and the target objects presented once are reported in a subsequent training according to a required sequence, a reverse sequence or other specific sequences.

For semantic memory ability, the semantic memory ability can be improved through a picture naming task, namely a series of images are continuously presented on a display device, and a user is required to quickly speak the name of a target object corresponding to the images in subsequent training. These objects may be, for example, designated objects, animals, plants, humans, etc.

The scene memory ability can be improved through a human brain-picture contact memory task, namely two different images are presented on the display device in pairs or in sequence, the user is required to remember the contact between the two different images, and the clue text is presented after a certain time, so that the user can remember the image matched with the clue according to the prompt of the clue text. These images may be images indicating different visual, auditory, tactile, olfactory, etc. channels of perception or different object types.

For the skill memory ability, the probability learning task can be used for improving, namely, the target object is presented on the display equipment, the user is required to make a certain action response to the target object, and the optimization behavior is adjusted according to the error feedback in the subsequent training, so that the accuracy of the action response is gradually improved.

For the attention capacity capability, the attention capacity type task can be improved, namely a plurality of different target objects are presented on a display device, a user is required to remember the appearance and the position of each target object, and the corresponding target object is accurately recalled or selected in subsequent training. The target object may be, for example, a color patch of different colors and/or shapes, an arrow of different colors and/or orientations, an object commonly seen in life, a human face, and the like.

For the attention selecting capability, the attention selecting capability can be improved through a multi-target tracking type task, namely, a plurality of irregularly moving targets are presented on a display device, a user is required to memorize a specific target in the plurality of irregularly moving targets, and the previous specific target is identified in subsequent training. The target may be an image of a firefly, a bee, or a butterfly, for example.

For continuous attention capacity, it can be promoted by a continuous job task, i.e. a series of targets are continuously presented on a display device, requiring the user to continuously react to the targets for a period of time. These objects may be designated numbers, text, graphics, and the like.

The self-control capability can be improved through an impulse control type task, namely, a rolling image is presented on the display equipment, the image comprises different channel images and a target object, and a user is required to control the target object to pass through a channel corresponding to a specified channel image according to a preset requirement, but not pass through a channel corresponding to the same channel image according to an inertia habit.

For the reaction capability, it can be promoted by a reaction speed type task, i.e. presenting the target object on the display device, asking the subject to make a specified action when the target object appears, and calculating the time interval between the target object appearing and the subject completing the specified action.

In this embodiment, the training task is associated with an action. In some embodiments, the training task may be responded to by an action. For example, in the human brain-picture contact memory task, the user selects an image matching the clue text by jumping left or right. For another example, in a probabilistic learning task, a user reacts to a target object by making some action. In some embodiments, the training task described above may require the user to perform a cognitive training task while performing a particular action. For example, the user performs a picture portion reporting task while running. As another example, the user performs an impulse-control type task while jumping.

In this embodiment, the training task may be obtained by combining a cognitive training task and a physical training task. The cognitive training task may include the picture part reporting task, the position memory extent task, the picture naming task, the human brain-picture association memory task, the probability learning task, the attention capacity type task, the multi-target tracking type task, the continuous operation task, the impulse control type task, the reaction speed type task, and the like described above. Physical fitness training tasks may include strength tasks (e.g., archery squats), speed tasks (e.g., highlift, jogging), endurance tasks (e.g., on-site intermittent highlift), coordination tasks (e.g., cross leg turns, recumbent braces, or cross arm wraps), flexibility tasks (e.g., leg presses, bow step presses, or bend over to touch the foot), and agility tasks (e.g., back running or repeated jumping), among others.

In one example, a cognitive training task and a physical training task may be organically fused to obtain a corresponding training task. For example, the human brain-picture association memory task and the strength task can be organically fused, and a user is required to select an image matched with the clue text in a way of opening and closing and jumping or squatting deeply.

In another example, the cognitive training task and the physical training task may be performed in parallel to obtain the corresponding training tasks. For example, the picture part reporting task and the endurance task may be performed in parallel, so that the user memorizes a target object presented on the display device while jogging and reports a specific target object as required.

In some embodiments, the training task may be specified by a user. For example, information such as the names, contents, and difficulties of a plurality of candidate training tasks may be displayed on a display device, and the candidate training tasks designated by the user may be set as target training tasks in response to a selection operation by the user.

In other embodiments, the execution subject of the method in this embodiment may determine the target cognitive ability and the target motor ability that need to be improved according to the cognitive test result and the physical ability test result of the user, and then combine the cognitive training task corresponding to the target cognitive ability and the physical ability training task corresponding to the target motor ability to obtain the training task.

Here, the target motion ability is, for example, strength ability, speed ability, endurance ability, coordination ability, flexibility ability, or sensitivity ability. The exercise capacity can be improved through physical training tasks such as strength tasks, speed tasks, endurance tasks, coordination tasks, flexibility tasks or sensitive tasks.

In this embodiment, the cognitive test task and the physical fitness test task may be provided to the user in advance, and the cognitive test result and the physical fitness test result of the user may be obtained according to the completion condition of the user on the test task.

For example, corresponding cognitive test tasks can be provided for cognitive abilities such as sensory memory ability, short-term memory ability, semantic memory ability, situational memory ability, skill memory ability, attention capacity ability, selective attention ability, continuous attention ability, self-control ability, reaction ability and the like, so as to obtain scores of the cognitive abilities (namely cognitive test results), and the cognitive ability with low score is determined as the target cognitive ability to be improved.

For another example, a fitness test task may be provided for each athletic ability, such as strength ability, speed ability, endurance ability, coordination ability, flexibility ability, and agility ability, so as to obtain a score (i.e., a fitness test result) of each athletic ability, and determine the athletic ability with a low score as a target athletic ability to be improved.

In this embodiment, the cognitive training task corresponding to the target cognitive ability may be determined based on a preset cognitive ability knowledge graph.

Fig. 3 is a schematic diagram of a cognitive ability knowledge graph according to an embodiment of the present disclosure. As shown in fig. 3, the cognitive ability knowledge graph includes cognitive ability nodes, cognitive training task nodes, correlation coefficients between different cognitive ability nodes, and correlation coefficients between a cognitive ability node and a corresponding cognitive training task node. The cognitive capacity node comprises cognitive capacity A, cognitive capacity B and cognitive capacity C. The cognitive training task nodes corresponding to the cognitive ability A comprise a training task 1 and a training task 2. The cognitive training task nodes corresponding to the cognitive competence B comprise a training task 3 and a training task 4. The cognitive training task nodes corresponding to the cognitive competence C comprise a training task 5 and a training task 6. The correlation coefficient between cognition a and cognition B was 0.53, the correlation coefficient between cognition B and cognition C was 0.71, and the correlation coefficient between cognition C and cognition a was 0.64. The correlation coefficients between cognitive ability a and training task 1 and training task 2 were 0.7 and 0.8, respectively. The correlation coefficients between cognitive ability B and training task 3 and training task 4 were 0.6 and 0.5, respectively. The correlation coefficients between cognitive ability C and training task 5 and training task 6 were 0.7 and 0.9, respectively. The larger the correlation coefficient between two nodes, the higher the degree of correlation between the two nodes.

In the example shown in fig. 3, assuming that the test score of cognitive ability a is the lowest or the test score of cognitive ability a is lower than a certain threshold, cognitive ability a may be determined as the target cognitive ability. Based on the cognitive ability knowledge graph shown in fig. 3, the cognitive training tasks corresponding to the cognitive ability a, namely training task 1 and training task 2, can be found. On the basis, the cognitive training task with the largest correlation coefficient with the cognitive ability A, namely the training task 2, can be selected to generate a final training task.

Similarly, the physical fitness training task corresponding to the target athletic ability can be determined based on a preset athletic ability knowledge map. The structure of the exercise ability knowledge graph and the selection process of the physical ability training task can be referred to the above-described examples, and are not described herein again.

In some embodiments, the training task may be provided to the user by means of hardware such as a display screen or a speaker, for example, by means of images or sounds, for example, to provide information such as cognitive training topics, action examples, or prompt voices.

Step 202, acquiring physiological data of a user during a training task.

In the present embodiment, the physiological data may include at least one of physiological state data and motion amount data. The physiological status data may be a current physiological index of the user, such as heart rate data, skin electricity data, or blood oxygen data. The motion amount data may be energy consumed by the user during the training task, and may be calculated based on position information (e.g., displacement, speed, acceleration, or the like) of the user's body collected by a visual sensor (e.g., a depth camera) and physiological index information (e.g., weight, height, or the like) of the user.

Taking running as an example, caloric expenditure (kcal) during running can be calculated by body weight (kg) x exercise time (hours) x index K, where the index K is 30 ÷ speed (minutes/400 meters). Assuming that a person weighs 60 kg, runs for 1 hour, and runs at a speed of 3 minutes/400 meters or 8 km/hour, the amount of heat consumed during his running is 60 × 1 × 30/3kcal (kilocalories) =600kcal (kilocalories). The calculation method covers a part of heat consumed by the increase of the basal metabolic rate after the exercise, namely a part of heat generated by the increase of the body temperature after the exercise.

In some embodiments, the heart rate data, the skin electrical data and the blood oxygen data of the user can be collected through a wearable device (such as a smart bracelet), the position data of the body of the user and the processed displacement data, speed data and acceleration data are collected through a visual sensor (such as a depth camera), and physiological data of the user in the training task is obtained on the basis of the position data, the speed data and the acceleration data.

Based on the physiological data, a training score for the user to perform the training task is determined, step 203.

Numerous studies have demonstrated the relationship of exercise to cognitive ability. Wherein the results of both lateral and longitudinal observational studies indicate that a positive correlation of exercise with cognitive ability is found in people of all ages, i.e. higher exercise is often accompanied by a relatively higher level of cognitive ability. Researchers have found that aerobic motor intervention can significantly increase the level of tasks requiring highly executed control (Executive control). Low intensity exercise (including daily activity) can affect cognitive ability and neuroplasticity, and self-reported total exercise (independent of exercise intensity) is positively correlated with gray matter volume in the Prefrontal and cingulate cortex of the brain. And if stretching exercises are carried out, the attention task level of middle-aged and elderly people can be effectively improved. Low intensity exercises, such as coordination training and resistance exercises, also contribute to the improvement of cognitive abilities, such as memory ability.

Based on the rules revealed by the above research results, in the present embodiment, the evaluation of the training score considers the physiological data of the user performing the training task in addition to the response of the user to the cognitive training task.

In some embodiments, the training score may be obtained by: determining a basic score of a user for executing a training task according to a response result of the user to a cognitive training task in the training task; determining a weight value corresponding to the basic score according to the physiological data; and obtaining a training score according to the basic score and the weight value.

In some embodiments, the weight value may be determined to be a first weight value in the case where the physiological data is first physiological data. The weight value may be determined as the second weight value in a case where the physiological data is the second physiological data. The human activity level corresponding to the first physiological data is higher than the human activity level corresponding to the second physiological data, and the first weight value is larger than the second weight value.

In one example, assume that the user has correctly completed 80% of the topics in the cognitive training task, i.e., the base score is 80. On this basis, assuming that the heart rate of the user when performing the training task is 120 times per minute, the corresponding weight value may be determined to be 1.2. Assuming that the heart rate of the user when performing the training task is 150 times per minute, the corresponding weight value may be determined to be 1.5. Since a heart rate of 150 beats/minute generally corresponds to a higher level of human activity relative to a heart rate of 120 beats/minute, the corresponding weight value is also greater. Thus, where the user's heart rate is 120 beats per minute, the user's training score is 801.2=96 points. In the case of a user with a heart rate of 150 beats per minute, the user's training score is 801.5=120 points. Through the mode, on the one hand, positive correlation between the movement and the cognitive ability is considered, so that the evaluation on the cognitive training effect is more accurate and reasonable, on the other hand, the cognitive ability training of the user under a higher movement level can be promoted, and a better cognitive training effect is favorably achieved.

In some embodiments, information such as training scores, exercise amount, physical health data indexes, cognitive ability improvement indexes and the like can be displayed on a display device in real time, so that the cognitive ability training condition can be fed back to the user in time.

In the cognitive ability training method based on the movement provided by the embodiment of the disclosure, based on the positive correlation between the movement and the cognitive ability, the corresponding training score is determined according to the physiological data of the user when the user executes the training task, so that the improvement of the cognitive ability can be promoted by using the movement, and the efficiency and the effect of the cognitive ability training can be effectively improved.

With further reference to fig. 4, as an implementation of the method described above, the present disclosure provides an embodiment of a motion-based cognitive performance improving apparatus, which corresponds to the method embodiment shown in fig. 2, and which may be applied in various electronic devices.

As shown in fig. 4, the motion-based cognitive performance improving apparatus 400 of the present embodiment includes: a training task providing unit 401, configured to provide a training task, where the training task is used to improve target cognitive ability and is related to an action; a physiological data acquisition unit 402, configured to acquire physiological data of a user during a training task; a training score determining unit 403, configured to determine a training score for the user to perform a training task based on the physiological data.

In this embodiment, for specific processing of the training task providing unit 401, the physiological data obtaining unit 402, and the training score determining unit 403 of the motion-based cognitive performance improving apparatus 400 and the technical effects thereof, reference may be made to the related descriptions of step 201, step 202, and step 203 in the corresponding embodiment of fig. 2, which are not described herein again.

In some optional embodiments, the apparatus further includes a training task obtaining unit (not shown in fig. 4) configured to: acquiring a training task appointed by a user; or determining the training task according to the cognitive test result and the physical ability test result of the user.

In some optional embodiments, the training task obtaining unit is further configured to: determining a corresponding cognitive training task according to the cognitive test result; determining a corresponding physical ability training task according to the physical ability test result; and obtaining the training task according to the cognitive training task and the physical training task.

In some optional embodiments, the training task obtaining unit is further configured to: determining the target cognitive ability required to be improved according to the cognitive test result; and determining a cognitive training task corresponding to the target cognitive ability based on a preset cognitive ability knowledge graph, wherein the cognitive ability knowledge graph comprises cognitive ability nodes, cognitive training task nodes and correlation coefficients between the cognitive ability nodes and the corresponding cognitive training task nodes.

In some optional embodiments, the training task obtaining unit is further configured to: determining the target motion capability required to be improved according to the physical fitness test result; and determining a physical ability training task corresponding to the target motion ability based on a preset physical ability knowledge graph, wherein the physical ability knowledge graph comprises motion ability nodes, physical ability training task nodes and correlation coefficients between the motion ability nodes and the physical ability training task nodes.

In some alternative embodiments, the physiological data is obtained based on the location data and at least one of processed displacement data, velocity data, acceleration data, heart rate data, electrodermal data, and blood oxygenation data.

In some optional embodiments, the training score determining unit 403 is further configured to: determining a basic score for the user to execute the training task according to a response result of the user to the cognitive training task in the training task; determining a weight value corresponding to the basic score according to the physiological data; and obtaining the training score according to the basic score and the weight value.

In some optional embodiments, the training score determining unit 403 is further configured to: determining the weight value as a first weight value when the physiological data is first physiological data; determining the weight value as a second weight value when the physiological data is second physiological data; the human activity level corresponding to the first physiological data is higher than the human activity level corresponding to the second physiological data, and the first weight value is greater than the second weight value.

It should be noted that details of implementation and technical effects of each unit in the cognitive performance improving device based on actions provided in the embodiments of the present disclosure may refer to descriptions of other embodiments in the present disclosure, and are not described herein again.

Referring now to FIG. 5, a block diagram of a computer system 600 suitable for use in implementing the electronic device of the present disclosure is shown. The computer system 500 shown in fig. 5 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.

As shown in fig. 5, computer system 500 may include a processing device (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage device 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the computer system 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, and the like; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the computer system 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates a computer system 500 having various means of electronic equipment, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the method for motion-based cognitive ability training as illustrated by the embodiment shown in fig. 2 and its alternative embodiments.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, a training task providing unit may also be described as a "unit for providing training tasks".

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims

1. A method of motion-based cognitive ability training, comprising:

providing a training task, wherein the training task is used for improving target cognitive ability and is related to action;

based on the physiological data, a training score for the user to perform the training task is determined.

2. The method of claim 1, wherein prior to said providing a training task, said method further comprises the step of obtaining said training task, comprising:

acquiring a training task appointed by a user; or

3. The method of claim 2, wherein the determining the training task based on the cognitive test results and physical fitness test results of the user comprises:

and obtaining the training task according to the cognitive training task and the physical ability training task.

4. The method of claim 3, wherein the determining a corresponding cognitive training task from the cognitive test results comprises:

determining a cognitive training task corresponding to the target cognitive ability based on a preset cognitive ability knowledge graph, wherein the cognitive ability knowledge graph comprises cognitive ability nodes, cognitive training task nodes and correlation coefficients between the cognitive ability nodes and the corresponding cognitive training task nodes.

5. The method of claim 3, wherein determining the corresponding fitness training task based on the fitness test result comprises:

6. The method of claim 1, wherein the physiological data comprises at least one of physiological state data and motion amount data.

7. The method of claim 1, wherein the physiological data is obtained based on at least one of motion depth image data, acceleration data, heart rate data, electrodermal data, and blood oxygen data.

8. The method of claim 1, wherein a cognitive training task of the training tasks responds with an action.

9. The method of claim 1, wherein the determining a training score for a user to perform the training task based on the physiological data comprises:

determining a basic score of the user for executing the training task according to a response result of the user to the cognitive training task in the training task;

and obtaining the training score according to the basic score and the weighted value.

10. The method of claim 9, wherein said determining a weight value corresponding to the base score from the physiological data comprises:

determining the weight value as a first weight value in the case that the physiological data is first physiological data;

determining the weight value as a second weight value in the case that the physiological data is second physiological data;

11. A motion-based cognitive improvement device, comprising:

a training task providing unit for providing a training task, wherein the training task is used for improving the target cognitive ability and is related to the action;

the physiological data acquisition unit is used for acquiring physiological data of a user during the training task;

and the training score determining unit is used for determining a training score of the user for executing the training task based on the physiological data.

12. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-10.

13. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by one or more processors, implements the method of any one of claims 1-10.