Brain-muscle combined gesture command recognition method, device and electronic device

Abstract

The embodiment of this specification provides a method for recognizing gesture commands by combining brain and muscle. The method collects EEG signals, EMG signals and images of healthy hand movements in a synchronous free state, determines whether the healthy hand movements meet preset conditions based on the gestures, and stores the signals if they meet the conditions. The display device plays the gestures and limits the state of the healthy hand. The EEG signals and EMG signals in the restricted state are synchronously collected and stored in a storage medium after being timestamped. The EEG signals and EMG signals in the restricted and free states are used to train a gesture prediction model. The EEG signals and EMG signals of stroke patients are collected and the hand movements are predicted using the gesture prediction model. By combining the EEG signals and EMG signals in the restricted and free states, the EEG signals and EMG signals in the idle state are taken into account, and the gesture commands of the patient can be effectively predicted, thereby improving effectiveness.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a method, device, and electronic device for recognizing gesture commands using brain-muscle interaction. Background Art

[0002] Stroke is a disease caused by blocked blood vessels, ischemia, or ruptured blood vessels. It has a high incidence and disability rate, and tends to occur at a younger age. It often results in severe limb motor dysfunction, with hand dysfunction being particularly devastating for patients. The nerves in the hand are densely distributed and closely connected to the nerves in the brain. Furthermore, the area of ​​the cerebral cortex that controls the hand is large, making hand dysfunction difficult to recover from. Compared with traditional passive rehabilitation training, using EEG or EMG signals to control hand function rehabilitation equipment can incorporate active consciousness into rehabilitation treatment, increasing the patient's brain motor neuron activity. Repeated training can strengthen the connection between the brain's motor nerves and spinal nerves, achieving comprehensive intervention in the central spinal cord and peripheral nerves.

[0003] Electromyographic signals have high amplitude, are easily detectable, and have distinct features, making them easy to classify. EEG signals also offer advantages such as high temporal resolution and ease of acquisition. However, for patients with movement disorders, there is currently no effective method for predicting movement.

[0004] It is necessary to provide a method to improve the effectiveness of gesture command recognition. Summary of the Invention

[0005] The embodiments of this specification provide a method, device, and electronic device for brain-muscle combined gesture command recognition to improve the effectiveness of gesture command recognition.

[0006] The present invention provides a method for recognizing gesture commands by combining brain and muscle, including:

[0007] The user wears an EEG acquisition device and an EMG acquisition device on the healthy arm, and sends gesture information to a display device. The gesture is played on the display device, and the hand freely follows the gesture, and synchronizes the collected EEG signals, EMG signals, and the image of the healthy hand movement in the free state;

[0008] Determining whether the healthy hand movement meets a preset condition according to the gesture, and storing the healthy hand movement that meets the condition and the synchronously collected EEG signal and EMG signal in a storage medium;

[0009] Playing the gestures on the display device and limiting the state of the healthy hand, synchronously collecting EEG signals and EMG signals in the restricted state, adding timestamps to the signals and storing them in a storage medium;

[0010] Use EEG and EMG signals in restricted and free states to train a gesture prediction model;

[0011] The electroencephalogram (EEG) signals and electromyographic (EMG) signals of stroke patients are collected and the hand movements are predicted using the gesture prediction model.

[0012] The embodiment of this specification provides a brain-muscle combined gesture command recognition device, including:

[0013] A free-state signal acquisition module: the user wears an EEG acquisition device and an EMG acquisition device on the healthy arm, sends gesture information to a display device, plays the gesture on the display device, and the hand freely follows the gesture, and synchronizes the collected EEG signals, EMG signals, and images of the healthy hand movements in the free state;

[0014] a judgment module, which judges whether the healthy hand movement meets a preset condition according to the gesture, and stores the healthy hand movement that meets the condition and the synchronously collected EEG signal and EMG signal in a storage medium;

[0015] a restricted state signal acquisition module, which plays the gesture and restricts the state of the healthy hand through the display device, synchronously acquires the EEG signal and the EMG signal in the restricted state, and stores them in a storage medium after adding a time stamp;

[0016] The training module uses EEG and EMG signals in the restricted and free states to train the gesture prediction model;

[0017] The prediction module collects the electroencephalogram (EEG) signals and electromyographic (EMG) signals of the stroke patient and uses the gesture prediction model to predict hand movements.

[0018] An embodiment of this specification further provides an electronic device, wherein the electronic device includes:

[0019] A processor; and a memory storing a computer executable program, wherein the executable program causes the processor to perform any one of the above methods when executed.

[0020] An embodiment of this specification further provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above methods is implemented.

[0021] The various technical solutions provided in the embodiments of this specification are to synchronously collect EEG signals, EMG signals and images of healthy hand movements in a free state, determine whether the healthy hand movements meet preset conditions based on the gestures, and store them if they meet the conditions, play the gestures through a display device and restrict the state of the healthy hand, synchronously collect EEG signals and EMG signals in a restricted state, store them in a storage medium after adding a timestamp, train a gesture prediction model using EEG signals and EMG signals in a restricted state and a free state, collect EEG signals and EMG signals from stroke patients and use the gesture prediction model to predict hand movements. By combining EEG signals and EMG signals in a restricted state and a free state, taking into account EEG signals and EMG signals in an idle state, it is possible to effectively predict the patient's gesture instructions, thereby improving effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A schematic diagram of the principle of a brain-muscle combined gesture command recognition method provided in an embodiment of this specification;

[0024] Figure 2 A schematic diagram of the principle of a brain-muscle combined gesture command recognition method provided in an embodiment of this specification;

[0025] Figure 3 A schematic diagram of the structure of a brain-muscle combined gesture command recognition device provided in an embodiment of this specification;

[0026] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification;

[0027] Figure 5 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of this specification. DETAILED DESCRIPTION

[0028] Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in various forms, and it should not be understood that the present invention is limited to the embodiments set forth herein. On the contrary, providing these exemplary embodiments enables the present invention to be more comprehensive and complete, making it easier to fully convey the inventive concept to those skilled in the art. In the figures, the same reference numerals represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0029] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0030] In the description of specific embodiments, the features, structures, characteristics, or other details of the present invention are described to enable those skilled in the art to fully understand the embodiments. However, this does not preclude those skilled in the art from practicing the technical solutions of the present invention without one or more of the specific features, structures, characteristics, or other details.

[0031] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0034] Figure 1 A schematic diagram of the principle of a brain-muscle combined gesture command recognition method provided in an embodiment of this specification, the method may include:

[0035] S101: The user wears an EEG acquisition device and an EMG acquisition device on the healthy arm, sends gesture information to a display device, plays gestures through the display device, and the hand freely follows the gestures, and synchronizes the collected EEG signals, EMG signals and images of the healthy hand movements in a free state.

[0036] Time correspondence can be achieved by adding time stamps to EEG signals and EMG signals.

[0037] The unaffected hand refers to the healthy arm.

[0038] S102: Determine whether the healthy hand movement meets preset conditions based on the gesture, and store the healthy hand movement that meets the conditions and the synchronously collected EEG signals and EMG signals in a storage medium.

[0039] The preset condition may refer to whether the speed and amplitude meet the conditions.

[0040] Storing the healthy hand movements that meet the conditions and the synchronously collected EEG signals and EMG signals in the storage medium can avoid analyzing and processing unqualified signals, thereby avoiding their interference with the model and improving the accuracy of the model.

[0041] S103: Play the gesture through the display device and limit the state of the healthy hand, synchronously collect the EEG signal and EMG signal in the restricted state, add a timestamp to them and store them in a storage medium.

[0042] In this way, the restricted state can simulate the patient's state, thereby facilitating the subsequent analysis of the real patient's EEG and EMG signals.

[0043] S104: Using the EEG signals and EMG signals in the restricted state and the free state to train the gesture prediction model.

[0044] In the embodiments of this specification, it also includes:

[0045] The samples are used to train an action feature extraction model, and the feature extraction model is used to extract features of the EEG signals and EMG signals in the restricted state and the free state and then classify them.

[0046] The signal features representing gestures are extracted through the action feature extraction model.

[0047] In the embodiment of this specification, the method of training the gesture prediction model using EEG signals and EMG signals in a restricted state and a free state includes:

[0048] Classifying the EEG signals and EMG signals in the restricted state and the free state according to the actions;

[0049] Extract the difference features of EEG signals and EMG signals in restricted and free states;

[0050] The gesture prediction model is trained using the difference features.

[0051] In the embodiment of this specification, the classification of the EEG signals and EMG signals in the restricted state and the free state according to the action includes:

[0052] Use timestamps to classify EEG and EMG signals according to actions.

[0053] The restricted state may be controlling the hand to be in a stationary state using the device.

[0054] In the embodiment of this specification, the step of training the gesture prediction model using the difference features includes:

[0055] According to the actions of EEG signals and EMG signals in the free state, action labels are set for the difference features of EEG signals and EMG signals in the restricted state;

[0056] According to the supervised learning method, the gesture prediction model is trained using the differential features of EEG signals and EMG signals with action labels.

[0057] S105: Collecting EEG signals and EMG signals of the stroke patient and using the gesture prediction model to predict hand movements.

[0058] This method synchronously collects EEG signals, EMG signals, and images of the healthy hand's movements in a free state. Based on the gestures, it determines whether the healthy hand's movements meet preset conditions. If they do, the gestures are stored. The gestures are played on a display device while the healthy hand's state is restricted. EEG signals and EMG signals in the restricted state are synchronously collected, timestamped, and stored in a storage medium. A gesture prediction model is trained using EEG and EMG signals in both the restricted and free states. EEG and EMG signals from stroke patients are collected and used to predict hand movements using the gesture prediction model. By combining EEG and EMG signals from both the restricted and free states, and taking into account EEG and EMG signals in the idle state, the method effectively predicts the patient's gesture commands, improving effectiveness.

[0059] In the embodiment of this specification, the acquisition of EEG signals and EMG signals of stroke patients includes:

[0060] Collect EEG signals from stroke patients and simultaneously collect EMG signals from the affected arm.

[0061] In an embodiment of the present specification, the method may further include: utilizing a motion control device to assist hand movement according to the predicted gesture.

[0062] In this way, patients can be assisted in rehabilitation training.

[0063] Figure 2 The schematic diagram of the principle of a brain-muscle combined gesture command recognition method provided in the embodiments of this specification may specifically include:

[0064] The patient wears an EEG acquisition device on his head and an EMG acquisition device on his healthy arm;

[0065] A display screen displays multiple hand movement gestures, and the patient's healthy hand imitates the gestures indicated on the screen one by one multiple times. An EEG acquisition device collects the patient's EEG signals, an EMG acquisition device collects the patient's EMG signals, and an image acquisition device records the movement images of the healthy hand in sequence. The completion of each movement is judged, and when the movement is completed well, the EEG signals, EMG signals, and movement images with timestamps are all stored in a storage medium.

[0066] The recorded real hand movement images are played on the display screen according to the above steps in sequence, but the unaffected hand is not actually subjected to obvious movement. The patient's brain imagines the movement multiple times, and the EEG acquisition device collects the patient's EEG signals, and the EMG acquisition device collects the patient's EMG signals. All EEG signals and EMG signals with time stamps are stored in a storage medium;

[0067] The two sets of EEG signals and EMG signals stored in the medium are processed and features are extracted respectively, and classification processing is performed based on feature machine learning;

[0068] Perform differential analysis on the two sets of EEG and EMG signals, combine the features to build a preliminary personalized gesture prediction dynamic model and store it in the medium;

[0069] The affected arm is equipped with an electromyographic acquisition device;

[0070] The storage medium randomly sends motor imagery gestures through the display screen and simultaneously collects motor imagery EEG signals and EMG signals;

[0071] The EEG signals and EMG signals are input into the gesture prediction model to generate gesture prediction instructions.

[0072] Figure 3 This is a schematic diagram of the structure of a brain-muscle combined gesture command recognition device provided in an embodiment of this specification. The device may include:

[0073] In the free-state signal acquisition module 301, the user wears an EEG acquisition device and an EMG acquisition device on the healthy arm, sends gesture information to a display device, plays the gesture on the display device, and the hand freely follows the gesture, and synchronizes the collected EEG signals, EMG signals, and the image of the healthy hand movement in the free state;

[0074] The judgment module 302 judges whether the healthy hand movement meets the preset conditions according to the gesture, and stores the healthy hand movement that meets the conditions and the synchronously collected EEG signal and EMG signal in a storage medium;

[0075] The restricted state signal acquisition module 303 plays the gesture and restricts the state of the healthy hand through the display device, synchronously collects the EEG signal and the EMG signal in the restricted state, adds a time stamp to the signal and stores it in a storage medium;

[0076] A training module 304 trains a gesture prediction model using EEG signals and EMG signals in a restricted state and a free state;

[0077] The prediction module 305 collects the EEG signals and EMG signals of the stroke patient and uses the gesture prediction model to predict hand movements.

[0078] The device synchronously collects EEG signals, EMG signals, and images of the healthy hand's movements in a free state. Based on the gestures, it determines whether the healthy hand's movements meet preset conditions. If they do, it stores the information. The gestures are played back on a display device while the healthy hand's state is restricted. The EEG and EMG signals in the restricted state are synchronously collected, timestamped, and stored in a storage medium. A gesture prediction model is trained using the EEG and EMG signals in both the restricted and free states. The EEG and EMG signals of stroke patients are then collected and used to predict hand movements using the gesture prediction model. By combining EEG and EMG signals in both the restricted and free states, and taking into account both EEG and EMG signals in the idle state, the device can effectively predict the patient's gesture commands, improving effectiveness.

[0079] Based on the same inventive concept, this specification also provides an electronic device

[0080] The following describes an electronic device embodiment of the present invention, which can be considered a specific physical implementation of the method and apparatus embodiments of the present invention described above. Details described in the electronic device embodiment of the present invention should be considered supplementary to the above-mentioned method or apparatus embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above-mentioned method or apparatus embodiments.

[0081] Figure 4 This is a schematic diagram of the structure of an electronic device provided in the embodiment of this specification. Figure 4 The electronic device 400 according to this embodiment of the present invention will be described. Figure 4 The electronic device 400 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0082] like Figure 4 As shown, electronic device 400 is implemented as a general-purpose computing device. Components of electronic device 400 may include, but are not limited to, at least one processing unit 410, at least one storage unit 420, a bus 430 connecting various system components (including storage unit 420 and processing unit 410), a display unit 440, and the like.

[0083] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 performs the steps according to various exemplary embodiments of the present invention described in the above processing method section of this specification. For example, the processing unit 410 can perform the following steps: Figure 1 Steps shown.

[0084] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 4201 and / or a cache memory unit 4202 , and may further include a read-only memory unit (ROM) 4203 .

[0085] The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0086] Bus 430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0087] The electronic device 400 may also communicate with one or more external devices 500 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device 400 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 through the bus 430. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with electronic device 400, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0088] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium is enabled to implement the above method of the present invention, that is: Figure 1 The method shown.

[0089] Figure 5 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of this specification.

[0090] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer-readable media. The computer-readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0091] The computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. The data signal propagated may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or component. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0092] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages.

[0093] The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0094] In summary, the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.

[0095] In practice, general data processing devices such as microprocessors or digital signal processors (DSPs) are used to implement some or all of the functions of some or all of the components in accordance with the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium or can have the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0096] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

[0097] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0098] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A brain-muscle combined gesture command recognition method, characterized in that: include: The user wears an EEG acquisition device and an EMG acquisition device on the healthy arm, and sends gesture information to a display device. The gesture is played on the display device, and the hand freely follows the gesture, and synchronizes the collected EEG signals, EMG signals, and the image of the healthy hand movement in the free state; Determining whether the healthy hand movement meets a preset condition according to the gesture, and storing the healthy hand movement that meets the condition and the synchronously collected EEG signal and EMG signal in a storage medium; Playing the gestures on the display device and limiting the state of the healthy hand, synchronously collecting EEG signals and EMG signals in the restricted state, adding timestamps to the signals and storing them in a storage medium; Use EEG and EMG signals in restricted and free states to train a gesture prediction model; The electroencephalogram (EEG) signals of the stroke patient are collected, and the electromyographic (EMG) signals of the affected arm are collected simultaneously, and the hand movements are predicted using the gesture prediction model.

2. The method according to claim 1, characterized in that The method of training the gesture prediction model using EEG signals and EMG signals in a restricted state and a free state includes: Classifying the EEG signals and EMG signals in the restricted state and the free state according to the actions; Extract the difference features of EEG signals and EMG signals in restricted and free states; The gesture prediction model is trained using the difference features.

3. The method according to claim 2, characterized in that The classifying of the EEG signals and EMG signals in the restricted state and the free state according to the actions includes: Use timestamps to classify EEG and EMG signals according to actions.

4. The method according to claim 2, characterized in that The method of training a gesture prediction model using the difference features includes: According to the actions of EEG signals and EMG signals in the free state, action labels are set for the difference features of EEG signals and EMG signals in the restricted state; According to the supervised learning method, the gesture prediction model is trained using the differential features of EEG signals and EMG signals with action labels.

5. The method according to claim 2, characterized in that Also includes: The samples are used to train an action feature extraction model, and the feature extraction model is used to extract features of the EEG signals and EMG signals in the restricted state and the free state and then classify them.

6. The method according to claim 1, characterized in that Also includes: Use motion control devices to assist hand movements based on predicted gestures.

7. A brain-muscle combined gesture command recognition device, characterized in that: include: A free-state signal acquisition module: the user wears an EEG acquisition device and an EMG acquisition device on the healthy arm, sends gesture information to a display device, plays the gesture on the display device, and the hand freely follows the gesture, and synchronizes the collected EEG signals, EMG signals, and images of the healthy hand movements in the free state; a judgment module, which judges whether the healthy hand movement meets a preset condition according to the gesture, and stores the healthy hand movement that meets the condition and the synchronously collected EEG signal and EMG signal in a storage medium; a restricted state signal acquisition module, which plays the gesture and restricts the state of the healthy hand through the display device, synchronously acquires the EEG signal and the EMG signal in the restricted state, and stores them in a storage medium after adding a time stamp; The training module uses EEG and EMG signals in the restricted and free states to train the gesture prediction model; The prediction module collects the EEG signals of the stroke patient, synchronously collects the EMG signals of the affected arm, and uses the gesture prediction model to predict the hand movements.

8. An electronic device, wherein: The electronic device includes: A processor; and a memory storing a computer executable program, which, when executed, causes the processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 6 is implemented.

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