Hand function evaluation method, device and equipment and computer readable storage medium

Abstract

The invention discloses a hand function evaluation method, device and equipment and a computer readable storage medium. The method comprises the steps that at least one frame of hand motion image of an evaluated person is acquired through a first image acquisition module; processing the hand motion image through a posture estimation algorithm model carried by the motion analysis model to obtain a hand key point coordinate, and recognizing a gesture in the hand motion image based on the hand key point coordinate through a classification model to obtain a first evaluation result; acquiring a hand grip image of the evaluated person under the counter-acting force of the gesture interaction equipment through a second image acquisition module; detecting the hand grip strength image of the evaluated person through a target detection algorithm model carried by the motion analysis model so as to judge whether the evaluated person can resist the counter-acting force of the interaction equipment or not, and obtaining a second evaluation result; the hand function of the evaluated person is evaluated based on the first evaluation result and the second evaluation result, and the motion function of the evaluated person can be scientifically evaluated.

Description

Technical Field

[0001] The present application relates to the field of smart medical technology, and specifically to a hand function assessment method, device, equipment and computer-readable storage medium. Background Art

[0002] Hand function assessment is one of the important contents of clinical and rehabilitation work. It is an objective basis for evaluating the effect of surgical treatment and guiding rehabilitation exercises. It includes the assessment of motor functions such as hand joint range of motion, muscle strength, muscle tension, sensory functions such as pain and temperature perception, fine touch, as well as more complex sociological functions such as hand appearance and work ability.

[0003] The traditional hand function assessment method requires manual measurement using standardized instruments such as protractors and handgrips, and calculations based on relevant function assessment standards to ultimately obtain the function assessment results. From the perspective of the function assessment process, the traditional function assessment method relies on the evaluator's own professional level and work experience, and there are inevitable subjective factors, so the accuracy of the assessment results is not high. How to improve the accuracy of hand function assessment has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application discloses a hand function assessment method, device, equipment and computer-readable storage medium, which can scientifically assess the hand function of an assessed person with high accuracy.

[0005] In a first aspect, the present application discloses a hand function assessment method, which is applied to a hand function assessment system, wherein the hand function assessment system comprises a first image acquisition module, a second image acquisition module and a motion analysis module, wherein the first image acquisition module and the second image acquisition module are both connected to the motion analysis module, the motion analysis module is equipped with a motion analysis model, and the motion analysis model comprises a gesture recognition algorithm model and a target detection algorithm model, wherein the gesture recognition algorithm model comprises a posture estimation algorithm model and a classification model, and the output of the posture estimation algorithm model is the input of the classification model, and the method comprises:

[0006] Acquire at least one frame of hand movement image of the person being evaluated through the first image acquisition module, wherein the hand movement image is an image of the person being evaluated when training according to a preset training item, and each hand movement image indicates the gesture and movement process information of the person being evaluated;

[0007] The hand motion image is processed by the posture estimation algorithm model carried by the motion analysis model to obtain the coordinates of the key points of the hand, and the gesture in the hand motion image is identified based on the coordinates of the key points of the hand by the classification model to obtain a first evaluation result;

[0008] The hand grip force image of the person being evaluated under the reaction force of the gesture interaction device is obtained by the second image acquisition module; wherein the reaction force of the gesture interaction device is the reverse pulling force corresponding to the force when the person being evaluated tightly grips the gesture interaction device;

[0009] The hand grip strength image of the person being evaluated is detected by using the target detection algorithm model carried by the motion analysis model to determine whether the person being evaluated can resist the reaction force of the interactive device, thereby obtaining a second evaluation result;

[0010] The hand function of the person being evaluated is evaluated based on the first evaluation result and the second evaluation result.

[0011] In this way, at least one frame of hand motion image of the person being evaluated is acquired through the first image acquisition module, the hand motion image is processed through the posture estimation algorithm model carried by the motion analysis module to obtain the coordinates of the key points of the hand, the gesture in the hand motion image is identified based on the coordinates of the key points of the hand through the classification model, and the first evaluation result is obtained, the hand grip image of the person being evaluated under the reaction force of the gesture interaction device is acquired through the second image acquisition module, and the hand grip image of the person being evaluated is detected through the target detection algorithm model carried by the motion analysis module to determine whether the person being evaluated can resist the reaction force of the interaction device, and the second evaluation result is obtained, so that the hand function of the person being evaluated can be evaluated based on the first evaluation result and the second evaluation result. This evaluation process can avoid human subjective judgment, making the evaluation process more objective, more precise, and with higher accuracy.

[0012] In a possible implementation, the hand function assessment system further includes a display module, and the display module is connected to the motion analysis module. The method further includes:

[0013] The hand function evaluation result of the person being evaluated is displayed through the display module.

[0014] In this way, the hand function assessment results of the person being assessed are displayed through the display module so that the doctor can timely understand the hand function assessment results of the person being assessed, and the hand function assessment results can be used for rehabilitation guidance, rehabilitation big data collection and analysis, health monitoring, etc. of the person being assessed.

[0015] In a possible implementation, the hand function assessment system further includes a playback module, and the playback module is connected to the image acquisition module. The method further includes:

[0016] The video of the preset training item is played through the playing module.

[0017] In this way, it is convenient for the person being assessed to learn the preset training items.

[0018] In a possible implementation, the classification model includes one of a random forest model and a support vector machine model.

[0019] In one possible implementation, the gesture interaction device includes a base and a second image acquisition module, the base is provided with a movable mechanism for interactive objects, the second image acquisition module includes a bracket fixed on the base and a camera fixed on the bracket away from the base, and the camera shoots an area on the base.

[0020] In a second aspect, the present application provides a hand function evaluation device, which is applied to a hand function evaluation system, wherein the hand function evaluation system comprises a first image acquisition module, a second image acquisition module and a motion analysis module, wherein the first image acquisition module and the second image acquisition module are both connected to the motion analysis module, the motion analysis module is equipped with a motion analysis model, and the motion analysis model comprises a gesture recognition algorithm model and a target detection algorithm model, wherein the gesture recognition algorithm model comprises a posture estimation algorithm model and a classification model, and the output of the posture estimation algorithm model is the input of the classification model, wherein,

[0021] The first image acquisition module is used to obtain at least one frame of hand movement image of the person being evaluated, wherein the hand movement image is an image of the person being evaluated when training according to a preset training item, and each hand movement image indicates the gesture and movement process information of the person being evaluated;

[0022] The gesture estimation algorithm model carried by the motion analysis module is used to process the hand motion image to obtain the coordinates of the key points of the hand, and to identify the gesture in the hand motion image based on the coordinates of the key points of the hand through the classification model to obtain a first evaluation result;

[0023] The second image acquisition module is used to obtain the hand grip force image of the person being evaluated under the reaction force of the gesture interaction device; wherein the reaction force of the gesture interaction device is the reverse pulling force corresponding to the force when the person being evaluated tightly grips the gesture interaction device;

[0024] The target detection algorithm model carried by the motion analysis module is used to detect the hand grip strength image of the person being evaluated to determine whether the person being evaluated can resist the reaction force of the interactive device, and obtain a second evaluation result; and evaluate the hand function of the person being evaluated based on the first evaluation result and the second evaluation result.

[0025] In a third aspect, an embodiment of the present application provides a terminal device, comprising a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program that supports the device to execute the above method, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method of the above first aspect.

[0026] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method of the first aspect above.

[0027] In a fifth aspect, an embodiment of the present application further provides a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0029] Figure 1 It is a flowchart of a hand function assessment method provided in an embodiment of the present application;

[0030] Figure 2 is a structural schematic diagram of a hand function assessment system provided in an embodiment of the present application;

[0031] Figure 3 It is an application scenario diagram provided by an embodiment of the present application;

[0032] Figure 4 is a structural schematic diagram of a gesture interaction device provided in an embodiment of the present application;

[0033] Figure 5 It is a structural diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0035] First, the main inventive concept of the present application is introduced.

[0036] The traditional hand function assessment method requires the use of standardized instruments and equipment such as protractors and handgrips for manual measurement, and calculations are performed according to relevant function assessment standards to finally obtain the function assessment results. From the perspective of the function assessment process, the traditional function assessment method relies on the professional level and work experience of the assessor himself, and there are inevitable subjective factors, so the accuracy of the assessment result is not high. Based on this, the present application proposes a hand function assessment method, which obtains at least one frame of hand motion image of the person being assessed through a first image acquisition module, processes the hand motion image through a posture estimation algorithm model carried by a motion analysis module, obtains the coordinates of the key points of the hand, and recognizes the gesture in the hand motion image based on the coordinates of the key points of the hand through a classification model to obtain a first assessment result, obtains the hand grip image of the person being assessed under the reaction force of the gesture interaction device through a second image acquisition module, and detects the hand grip image of the person being assessed through a target detection algorithm model carried by the motion analysis module to determine whether the person being assessed can resist the reaction force of the interaction device, and obtains a second assessment result, so that the hand function of the person being assessed can be evaluated based on the first assessment result and the second assessment result. This evaluation process can avoid human subjective judgment, making the evaluation process more objective, more precise and with higher accuracy.

[0037] Figure 1 This is a hand function assessment method provided in an embodiment of the present application, which can be applied to Figure 2 The hand function evaluation system shown in the figure includes a playback module 20, an image acquisition module 21, a motion analysis module 23 and a display module 24 ( Figure 3 ), wherein the image acquisition module 21 includes a first image acquisition module 211 and a second image acquisition module 212. The method may include but is not limited to the following steps:

[0038] Step S101, acquiring at least one frame of hand movement image of the person being evaluated through a first image acquisition module, wherein the hand movement image is an image of the person being evaluated when training according to a preset training item, and each hand movement image indicates the gesture and movement process information of the person being evaluated;

[0039] Before the assessment, the person being assessed can open an account and password, and enter his / her user information into the hand function assessment system, such as name, age, assessment time, assessment location, assessment registration, etc. The hand function assessment system can be an application installed on a computer, mobile phone, or PAD. The person being assessed can enter the above information into the hand function assessment system by himself / herself, or the medical center can arrange for the entry of the above information. After entering the above information, the hand function assessment system can automatically generate a code or number that uniquely identifies each person being assessed.

[0040] In practical applications, the hand movement images of the person being evaluated can be acquired through automated equipment (eg, a camera, etc.).

[0041] In one embodiment, before the assessment, the video of the preset training program can be played by the playback module 20 so that the person being assessed can learn and better master the essentials of the exercise.

[0042] In one embodiment, before the assessment, the video and precautions of the preset training items can be played by the playback module 20 .

[0043] Step S102: Process the hand motion image by using the posture estimation algorithm model carried by the motion analysis module to obtain the coordinates of the key points of the hand, and identify the gestures in the hand motion image based on the coordinates of the key points of the hand by using the classification model to obtain a first evaluation result;

[0044] In a specific implementation, the classification model includes one of a random forest model and a support vector machine model.

[0045] Step S103, obtaining a hand grip force image of the person being evaluated under the reaction force of the gesture interaction device through a second image acquisition module; wherein the reaction force of the gesture interaction device is the reverse pulling force corresponding to the force when the person being evaluated tightly grasps the gesture interaction device;

[0046] In a specific implementation, the gesture interaction device includes a base and a second image acquisition module. The base is provided with a movable mechanism for interactive objects. The second image acquisition module includes a bracket fixed on the base and a camera fixed on the bracket away from the base. The camera shoots an area on the base. Figure 4 As shown, a gesture interaction device provided in an embodiment of the present application includes a fixed part and a rotatable part. The fixed part includes a support structure, a rotating motor, an electromagnet, a control and power supply system, etc. ( Figure 4 Only the electromagnet is shown in the figure), in addition, the fixed part is connected to the camera through the camera bracket in the rotatable part. The rotatable part is composed of a ring bracket, a lever, and an interactive object. The lever and the interactive object are rigidly connected and connected to the ring bracket through a hinge as a fulcrum. The ring bracket can be driven by a rotating motor to rotate on the fixed structure with the camera bracket as the axis. When the system runs to step S103, the control system drives the electromagnet to hit the lever to provide a pulling force for the interactive object. The magnitude of the pulling force can be controlled by controlling the voltage change to affect the thrust of the electromagnet. In actual applications, the control system drives the rotating motor to rotate the ring bracket to rotate the lever where the next interactive object is located to the front of the patient and the electromagnet.

[0047] Step S104: Detect the hand grip strength image of the person being evaluated through the target detection algorithm model carried by the motion analysis module to determine whether the person being evaluated can resist the reaction force of the interactive device, and obtain a second evaluation result; evaluate the hand function of the person being evaluated based on the first evaluation result and the second evaluation result.

[0048] In a specific implementation, the hand function assessment results may include but are not limited to scoring, grading, etc.

[0049] In one embodiment, evaluating the hand function of the person being evaluated based on the first evaluation result and the second evaluation result includes: evaluating the hand function of the person being evaluated using the first evaluation result to obtain an evaluation result.

[0050] In one embodiment, evaluating the hand function of the person to be evaluated based on the first evaluation result and the second evaluation result includes: evaluating the hand function of the person to be evaluated using the second evaluation result to obtain an evaluation result.

[0051] In one embodiment, evaluating the hand function of the person being evaluated based on the first evaluation result and the second evaluation result includes: evaluating the hand function of the person being evaluated using the first evaluation result and the second evaluation result to obtain an evaluation result. Further, the first evaluation result is associated with a corresponding weight w1, and the second evaluation result is associated with a corresponding weight w2, and the evaluation result can be obtained by the following formula:

[0052] Q=A1*w1+A2*A1*w2

[0053] Among them, Q represents the evaluation result, A1 represents the first evaluation result, w1 represents the weight corresponding to the first evaluation result, A2 represents the second evaluation result, and w2 represents the weight corresponding to the second evaluation result.

[0054] In this way, at least one frame of hand motion image of the person being evaluated is acquired through the first image acquisition module, the hand motion image is processed through the posture estimation algorithm model carried by the motion analysis module to obtain the coordinates of the key points of the hand, the gesture in the hand motion image is identified based on the coordinates of the key points of the hand through the classification model, and the first evaluation result is obtained, the hand grip image of the person being evaluated under the reaction force of the gesture interaction device is acquired through the second image acquisition module, and the hand grip image of the person being evaluated is detected through the target detection algorithm model carried by the motion analysis module to determine whether the person being evaluated can resist the reaction force of the interaction device, and the second evaluation result is obtained, so that the hand function of the person being evaluated can be evaluated based on the first evaluation result and the second evaluation result. This evaluation process can avoid human subjective judgment, making the evaluation process more objective, more refined, and with higher accuracy.

[0055] It can be understood that after executing the above step S104, step S105 may also be executed, which is specifically described below:

[0056] Step S105: displaying the hand function evaluation result of the person being evaluated via a display module.

[0057] In this way, the hand function assessment results of the person being assessed are displayed through the display module so that the doctor can timely understand the hand function assessment results of the person being assessed, and the hand function assessment results can be used for rehabilitation guidance, rehabilitation big data collection and analysis, health monitoring, etc. of the person being assessed.

[0058] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0059] It should be further explained that Figure 1 For example, although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0060] One or more exemplary embodiments of the present specification also provide a terminal device, the terminal device 50 may include a processor 501, a memory 504 and a communication module 505, and the processor 501, the memory 504 and the communication module 505 may be interconnected via a bus 506. The memory 504 may be a high-speed random access memory (RAM) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 504 may optionally be at least one storage system located away from the aforementioned processor 501. The memory 504 is used to store application code, and may include an operating system, a network communication module, a user interface module and a data processing program. The communication module 505 is used to interact with external devices; the processor 501 is configured to call the program code, and when executed, the hand function evaluation method according to various embodiments of the present specification described in the above "Exemplary Method" of the present specification is implemented.

[0061] Exemplary computer program products and storage media

[0062] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the overlap detection method for blind and buried holes according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0063] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0064] In addition, an embodiment of the present application may also be a storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the blind buried hole overlap detection method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0065] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0066] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A hand function assessment method, characterized in that: The method is applied to a hand function evaluation system, which includes a first image acquisition module, a second image acquisition module and a motion analysis module, wherein the first image acquisition module and the second image acquisition module are both connected to the motion analysis module, the motion analysis module is equipped with a motion analysis model, and the motion analysis model includes a gesture recognition algorithm model and a target detection algorithm model, wherein the gesture recognition algorithm model includes a posture estimation algorithm model and a classification model, and the output of the posture estimation algorithm model is the input of the classification model. The method includes: Acquire at least one frame of hand movement image of the person being evaluated through the first image acquisition module, wherein the hand movement image is an image of the person being evaluated when training according to a preset training item, and each hand movement image indicates the gesture and movement process information of the person being evaluated; Processing the hand motion image by means of a posture estimation algorithm model carried by the motion analysis module to obtain coordinates of key hand points, and identifying gestures in the hand motion image based on the key hand point coordinates by means of the classification model to obtain a first evaluation result; The hand grip force image of the person being evaluated under the reaction force of the gesture interaction device is obtained by the second image acquisition module; wherein the reaction force of the gesture interaction device is the reverse pulling force corresponding to the force when the person being evaluated tightly grips the gesture interaction device; The hand grip strength image of the person being evaluated is detected by the target detection algorithm model carried by the motion analysis module to determine whether the person being evaluated can resist the reaction force of the interactive device, and obtain a second evaluation result; The hand function of the person being evaluated is evaluated based on the first evaluation result and the second evaluation result.

2. The method according to claim 1, characterized in that: The hand function evaluation system further includes a display module, which is connected to the motion analysis module. The method further includes: The hand function evaluation result of the person being evaluated is displayed through the display module.

3. The method according to claim 1, characterized in that The hand function assessment system further includes a playback module, and the playback module is connected to the image acquisition module. The method further includes: The video of the preset training item is played through the playing module.

4. The method according to any one of claims 1 to 3, characterized in that: The classification model includes one of a random forest model and a support vector machine model.

5. The method according to any one of claims 1 to 3, characterized in that: The gesture interaction device includes a base and a second image acquisition module, the base is provided with a movable mechanism for interactive objects, the second image acquisition module includes a bracket fixed on the base and a camera fixed on the bracket away from the base, and the camera shoots an area on the base.

6. A hand function assessment device, characterized in that: The device is applied to a hand function evaluation system, which includes a first image acquisition module, a second image acquisition module and a motion analysis module, wherein the first image acquisition module and the second image acquisition module are both connected to the motion analysis module, the motion analysis module is equipped with a motion analysis model, and the motion analysis model includes a gesture recognition algorithm model and a target detection algorithm model, wherein the gesture recognition algorithm model includes a posture estimation algorithm model and a classification model, and the output of the posture estimation algorithm model is the input of the classification model, wherein, The first image acquisition module is used to obtain at least one frame of hand movement image of the person being evaluated, wherein the hand movement image is an image of the person being evaluated when training according to a preset training item, and each hand movement image indicates the gesture and movement process information of the person being evaluated; The gesture estimation algorithm model carried by the motion analysis module is used to process the hand motion image to obtain the coordinates of the key points of the hand, and to identify the gesture in the hand motion image based on the coordinates of the key points of the hand through the classification model to obtain a first evaluation result; The second image acquisition module is used to obtain the hand grip force image of the person being evaluated under the reaction force of the gesture interaction device; wherein the reaction force of the gesture interaction device is the reverse pulling force corresponding to the force when the person being evaluated tightly grips the gesture interaction device; The target detection algorithm model carried by the motion analysis module is used to detect the hand grip strength image of the person being evaluated to determine whether the person being evaluated can resist the reaction force of the interactive device, and obtain a second evaluation result; and evaluate the hand function of the person being evaluated based on the first evaluation result and the second evaluation result.

7. The method according to claim 1, characterized in that: The hand function evaluation system further comprises a display module, which is connected to the motion analysis module, wherein: The display module is used to display the hand function assessment result of the person being assessed.

8. A terminal device, characterized in that: The method comprises a processor and a memory, wherein the processor and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

10. A computer program, characterized in that The computer program comprises program instructions which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 5.

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