Training method based on hand function training platform, electronic device and storage medium

Abstract

This invention relates to the field of hand function rehabilitation training technology. More specifically, this invention relates to a training method, electronic device, and storage medium based on a hand function training platform. The method includes: in response to the completion of setting the training difficulty level, real-time acquisition of displacement data of the handle pulling training component; initializing the total number of training repetitions and the effective number of training repetitions to zero; calculating the speed of the handle pulling training component in real-time based on the displacement data; incrementing the total number of training repetitions by one in response to the speed being less than zero; incrementing the effective number of training repetitions by one in response to the peak value of the displacement data being greater than a preset pulling distance threshold for that difficulty level; outputting a training record report and reminding the patient that training at that difficulty level has ended, and setting the difficulty level to a higher level. Using the method of this invention allows for gradual training of the user's hand function, which is more conducive to the recovery of the user's hand function.

Description

Technical Field

[0001] This invention generally relates to the field of hand function rehabilitation training technology. More specifically, this invention relates to a training method, electronic device, and storage medium based on a hand function training platform. Background Technology

[0002] According to relevant literature, there are currently as many as 70 million stroke patients in my country, with more than 2 million new cases each year. More than 50% of stroke patients experience varying degrees of upper limb dysfunction, with hand dysfunction severely limiting hand movement and significantly impacting their quality of life. Clinical rehabilitation of hand dysfunction is primarily achieved through hand function rehabilitation training devices or systems. A hand function rehabilitation training device includes training components, a load application device connected to the training components, and a main unit. Users perform rehabilitation training by pulling or grasping the training components. Existing hand function training devices apply load using weights. Current hand function training methods adjust the load by changing the number of weights, thus controlling the training difficulty level. However, since the mass of the weights cannot be infinitely small, this method cannot precisely adjust the load and requires manual adjustment of the weight quantity, resulting in a poor user experience. Furthermore, existing hand function training methods rely on users selecting a suitable difficulty level based on their own feeling, which may lead to a mismatch between the difficulty level and the user's physical strength, and training at only one difficulty level yields poor results. Summary of the Invention

[0003] To address one or more of the aforementioned technical problems, this invention proposes to automatically select an appropriate training difficulty level by measuring the user's grip strength, and to automatically select a higher difficulty level after completing training at the current level, thereby enabling progressive training. To this end, this invention provides solutions in the following aspects.

[0004] In a first aspect, the present invention provides a training method based on a hand function training platform, the hand function training platform including a handle pulling training component, a load application device for applying load to the handle pulling training component, a pressure sensor for detecting the pressure between the palm and the training component, and multiple photoelectric switches for detecting displacement data of the handle pulling training component; the hand function training platform includes multiple difficulty levels, with higher difficulty levels corresponding to larger load and pulling distance thresholds; the method includes an intelligent training mode, the intelligent training mode including: in response to the completion of the training difficulty level setting, real-time acquisition of displacement data of the handle pulling training component; initializing the total number of training times and the effective number of training times to... Zero; the speed of the handle pulling training component is calculated in real time based on the displacement data; in response to the speed being less than zero, the total number of training sessions is incremented by one; the speed is less than zero when the direction of the speed is opposite to the direction of the user's hand pulling force; in response to the peak value of the displacement data being greater than the preset pulling distance threshold for that difficulty level, the effective number of training sessions is incremented by one; in response to the total number of training sessions reaching a preset value and the effective number of training sessions being greater than or equal to the preset effective number of training sessions threshold, a training record report is output and the patient is reminded that the training for that difficulty level has ended, and the difficulty level is set to a higher difficulty level and the above steps are repeated; the training record report includes the training difficulty level, the maximum value of the displacement data, the effective number of training sessions, and the total number of training sessions.

[0005] In one embodiment, the intelligent training mode further includes: detecting pressure data between the palm and the training component, and initially setting the training difficulty level based on the pressure data.

[0006] In one embodiment, the intelligent training mode further includes: in response to the completion of the training difficulty level setting, initializing the training duration to zero and starting to time the training duration;

[0007] In response to the training duration reaching the preset value, the system reminds the patient that the hand function training has ended and controls the hand function training platform to stop working.

[0008] In one embodiment, the pressure data is positively correlated with the corresponding difficulty level.

[0009] In one embodiment, the load application device is a servo motor, and the method for setting the hand function training platform to the corresponding difficulty level is to adjust the torque output of the servo motor.

[0010] In one embodiment, the hand function training platform further includes a stop button, and the training method based on the hand function training platform further includes, in response to the stop button being triggered, reminding the patient that the hand function training has ended and controlling the hand function training platform to stop working.

[0011] In one embodiment, the method further includes a normal training mode, the normal training mode comprising:

[0012] In response to the user manually selecting the difficulty level, the hand function training platform is set to the corresponding difficulty level;

[0013] In response to the completion of the training difficulty level setting, the displacement data of the handle pulling training component is collected in real time and the training is timed.

[0014] Initialize the total number of training iterations and the effective number of training iterations to zero;

[0015] The speed of the handle pulling training component is calculated in real time based on the displacement data. The total number of training sessions is incremented by one in response to the speed being greater than or less than zero. The speed is less than zero when the direction of the speed is opposite to the direction of the user's hand pulling force.

[0016] If the peak value of the displacement data exceeds the preset pulling distance threshold for that difficulty level, the effective training count is incremented by one.

[0017] In response to the total training time reaching the preset value, a training record report is output to remind the patient that the hand function training has ended and to control the hand function training platform to stop working. The training record report includes the training difficulty level, the maximum value of displacement data, the number of effective training sessions, and the total number of training sessions.

[0018] In a second aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the training method of the present invention based on a hand function training platform.

[0019] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the training method of the hand function training platform of the present invention.

[0020] The technical advantages of this invention are as follows: Different user groups have different physical strengths. For example, there is a large difference between the physical strength of children and adults. If there is only one difficulty level, it cannot be applied to different user groups at the same time. The hand function training method based on the hand function training platform of this invention can automatically adjust the load, so that the same hand function training platform can be applied to more types of user groups. In addition, after completing the training at a certain level, it automatically advances to the next difficulty level, achieving the effect of gradual training and making it more conducive to the recovery of the user's hand function.

[0021] Furthermore, in intelligent training mode, if the user manually sets the initial difficulty level, it may result in the initial difficulty level being too high, causing the user to be unable to pull the training component. By detecting the pressure data between the palm and the training component, the system automatically selects a suitable initial difficulty level for the user before training, thereby improving the user's hand function training experience.

[0022] Furthermore, by using a servo motor as the load application device, the load can be adjusted more precisely and conveniently. Attached Figure Description

[0023] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0024] Figure 1 This is a schematic diagram illustrating the connection relationship between the servo motor and the control unit in an embodiment of the present invention;

[0025] Figure 2 This is a flowchart illustrating a training method based on a hand function training platform according to an embodiment of the present invention;

[0026] Figure 3 This is a schematic flowchart illustrating an embodiment of the intelligent training mode of the present invention;

[0027] Figure 4 This is a schematic flowchart illustrating a typical training mode of an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram illustrating the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0031] Example of a training method based on a hand function training platform:

[0032] Before introducing the training method based on the hand function training platform of the present invention, the hand function training platform will be introduced first.

[0033] The hand function training platform based on the present invention comprises a handle-pull training component, a load application device, a control unit for controlling the hand function training platform, several pressure sensors disposed on the surface of the handle-pull training component, and several photoelectric switches evenly distributed on the moving wheel diameter of the handle-pull training component. The pressure sensors and photoelectric switches are all communicatively connected to the control unit. The handle-pull training component is connected to the load application device to counteract the pulling force of the patient's palm. When the pulling force of the patient's palm exceeds the load, the handle-pull training component can be pulled. The pressure sensors are used to detect the pressure between the palm and the training component when the hand grips the handle-pull training component. The photoelectric switch is used to detect the pulling distance of the handle pulling training component. For example, the movement path length of the handle pulling training component is 20cm. Five photoelectric switches are evenly arranged from the beginning to the end of the movement path. The five photoelectric switches from the beginning to the end are the first photoelectric switch, the second photoelectric switch, the third photoelectric switch, the fourth photoelectric switch, and the fifth photoelectric switch. When the first photoelectric switch is triggered, it means that the handle pulling training component has moved 4cm. When the second photoelectric switch is triggered, it means that the handle pulling training component has moved 8cm. And so on. When the fifth photoelectric switch is triggered, it means that the handle pulling training component has moved 20cm.

[0034] The load is adjustable. The hand function training platform has multiple preset training difficulty levels. At a given difficulty level, only training movements where the hand pulling force is greater than the load and the pulling distance is greater than a threshold are considered effective. Higher difficulty levels correspond to greater loads, requiring more force to pull the handle and the training component. For example, four difficulty levels can be set: the first level corresponds to a load of 5N, the second to 10N, the third to 15N, and the fourth to 20N. In other embodiments, the loads for each difficulty level can be set to other suitable values.

[0035] The pulling distance thresholds for different levels can be the same or different. When the pulling distance thresholds for different levels are equal, the pulling distance threshold can be set to 10cm or other suitable lengths. Since, generally, the greater the load, the more effort the user needs to exert to pull the training component, and the smaller the pulling distance will be, when the pulling distance thresholds for different levels are not equal, the pulling distance thresholds can be set by gradually decreasing the threshold from low to high levels. For example, the pulling distance threshold for the first difficulty level can be set to 15cm, the second difficulty level to 10cm, the third difficulty level to 8cm, and the fourth difficulty level to 5cm. In other embodiments, the pulling distance thresholds for each difficulty level can also be set to other suitable values.

[0036] The load application device can be a weight or a servo motor. If a servo motor is used, the torque output by the servo motor can provide the load. Preferably, in this embodiment, a servo motor is used to provide the load, and the load size is proportional to the torque output by the servo motor. The load size can be adjusted by changing the torque output by the servo motor. Figure 1 As shown in this embodiment, the control unit includes a servo controller, a lower-level machine, and a higher-level machine. The servo controller is connected to the servo motor and communicates with the lower-level machine to receive control commands from the lower-level machine. The lower-level machine communicates with the higher-level machine to receive control commands from the lower-level machine and send them to the servo controller. The higher-level machine is also used to manually select the difficulty level, automatically switch the difficulty level, set the effective training count threshold, and the total number of training sessions. The lower-level machine is also used to set the load and pulling distance thresholds corresponding to each level, and to calculate the number of training sessions and the effective training count.

[0037] The servo motor can be connected to the handle pull-up training assembly via a coupling, a wire component, and other related components. When the pulling force applied to the handle pull-up training assembly is greater than the load, the wire component pulls the servo motor shaft to rotate forward. When the pulling force applied to the handle pull-up training assembly is less than the load, the servo motor shaft rotates in the opposite direction to the corresponding limit position. The specific connection relationship between the servo motor and the handle pull-up training assembly is existing technology and will not be described in detail. Furthermore, if the torque output by the servo motor is too large, the wire component may become entangled in an object during its periodic rotation, causing excessive tension and potentially breaking the wire component. Therefore, a limit device is also provided to limit the rotation of the servo motor shaft to prevent periodic rotation. The wire component can be a rope, wire, or other wire-like material.

[0038] like Figure 2As shown, the training method based on the hand function training platform of the present invention includes an intelligent training mode, which includes:

[0039] S201. In response to the completion of the training difficulty level setting, the displacement data of the handle pulling training component is collected in real time.

[0040] Before starting training for the first time, the initial training difficulty level can be automatically set based on the user's hand strength, or the user can manually set it through the host computer of the hand function training platform. The user's hand strength can be assessed based on the amount of pressure exerted by the user gripping the training component, or by measuring the amount of force the user exerts when pulling the training component, and the user's hand strength can be assessed based on the amount of force exerted.

[0041] Setting the difficulty level of the hand function training platform includes: setting the load to the load corresponding to that difficulty level, and setting the pulling distance threshold to the pulling distance threshold corresponding to that difficulty level. The method for setting the load to the load corresponding to that difficulty level is to adjust the torque output of the servo motor through the embedded program and the servo driver of the hand function training platform, so that the torque output of the servo motor is equal to the torque corresponding to the load at that difficulty level.

[0042] S202. Initialize the values ​​of total training iterations and effective training iterations to zero;

[0043] S203. Calculate the speed of the handle pulling training component in real time based on the displacement data. In response to the speed being less than zero, increment the total number of training sessions by one. The speed being less than zero means that the direction of the speed is opposite to the direction of the user's hand pulling force.

[0044] When users pull the handle-based training component, they typically pull and release it periodically. A velocity less than zero indicates that the user has pulled the handle to a certain position and then released it, allowing it to return to the initial position. The velocity is positive when the handle moves away from the initial position and negative when it moves closer to it. Therefore, the velocity of the handle can accurately determine whether the user has pulled the component, thus allowing for accurate counting of the total number of training repetitions.

[0045] S204. In response to the peak value of the displacement data being greater than the preset pulling distance threshold for the difficulty level, the value of the effective training count is incremented by one.

[0046] Users can only achieve the desired training effect by pulling the handle horizontally to a certain distance, thus playing a positive role in the recovery of hand function. When users periodically pull the handle horizontally to exercise, the displacement of the handle horizontally to exercise will change periodically. In one cycle, the displacement data will first rise from zero to the maximum value, and then fall from the maximum value back to zero. Therefore, by comparing the peak value of the displacement data with the preset pulling distance threshold, it is possible to accurately determine whether the training is effective.

[0047] S205. In response to the total number of training sessions reaching a preset value and the number of effective training sessions being greater than or equal to a preset threshold for the number of effective training sessions, a training record report is output and the patient is reminded that the training at this difficulty level has ended. The difficulty level is then set to a higher level and the above steps are repeated. The training record report includes the training difficulty level, the maximum value of the displacement data, the number of effective training sessions, and the total number of training sessions.

[0048] The preset total number of training sessions can be set to 100, 200, or other suitable numbers. The effective training session threshold is determined based on the preset total number of training sessions, and the ratio of the effective training session threshold to the preset total number of training sessions can be 80%, 90%, or other suitable ratios. For example, if the ratio of the effective training session threshold to the preset total number of training sessions is 80%, and the preset total number of training sessions is 100, then the effective training session threshold equals 80. Only when the user's effective training session threshold is greater than or equal to 80, and the total number of training sessions reaches 100, is the user considered to have completed training at that difficulty level.

[0049] After a user completes training at a certain difficulty level, the training difficulty level will be automatically adjusted to a higher level, allowing the user to train their hand functions gradually and achieve better training results.

[0050] Different user groups have different physical strengths. For example, there is a significant difference between the physical strength of children and adults. If there is only one difficulty level, it cannot be applied to different user groups at the same time. The hand function training method based on the hand function training platform of this invention can automatically adjust the load, so that the same hand function training platform can be applied to more types of user groups. In addition, it automatically selects an appropriate difficulty level for the user before training and automatically advances to the next difficulty level after completing training at a certain level, so as to achieve the effect of gradual training and be more conducive to the recovery of the user's hand function.

[0051] As mentioned in the above embodiments, the initial training difficulty level can be automatically set according to the user's hand strength before the first training session. In one embodiment, the intelligent training mode further includes: detecting the pressure data between the palm and the training component before the first training session, and initially setting the training difficulty level based on the pressure data.

[0052] When using a hand function training platform, the user's hands grip the pull-out training components, pulling them using grip strength and wrist strength. Grip strength refers to the muscle strength of the fingers and palm, while wrist strength refers to the strength of the wrist. Generally, grip strength and wrist strength are positively correlated. When the hand grips the pull-out training components, pressure sensors on the components collect a pressure value. This pressure value positively reflects the grip strength; a higher pressure value indicates greater grip strength, which in turn indicates greater wrist strength, corresponding to a higher difficulty level. In other words, pressure data is positively correlated with the corresponding difficulty level. Therefore, the user can select the most suitable training difficulty level based on the pressure value collected by the pressure sensors.

[0053] like Figure 3 As shown, in another embodiment, the intelligent training mode further includes:

[0054] S301. In response to the completion of the training difficulty level setting, initialize the training duration to zero and start timing the training duration.

[0055] S302. In response to the training duration reaching the preset value, remind the patient that the hand function training has ended and control the hand function training platform to stop working.

[0056] The total training time can be preset to 10 minutes, 20 minutes, or other suitable durations depending on the user's hand or wrist endurance.

[0057] In this embodiment, training ends when the training duration reaches the preset value. In another embodiment, the hand function training platform further includes a stop button. The intelligent training mode further includes: in response to the stop button being triggered, reminding the patient that the hand function training has ended and controlling the hand function training platform to stop working.

[0058] By setting a stop button, training can be stopped in advance when the user reaches exhaustion. The stop button can be a push-button switch located on the power supply terminal of the servo motor. When the button switch is pressed, the servo motor is powered off and stops working.

[0059] The intelligent training mode allows for progressive training. Some users may not like this mode and prefer the normal training mode, where they manually select a difficulty level and train until completion. Figure 4 As shown, in one embodiment, the training method based on the hand function training platform further includes a normal training mode, which includes the following steps:

[0060] S401. In response to the user manually selecting the difficulty level, the hand function training platform is set to the corresponding difficulty level.

[0061] S402. In response to the completion of the training difficulty level setting, the displacement data of the handle pulling training component is collected in real time and the training is started to be timed.

[0062] S403. Initialize the values ​​of total training iterations and effective training iterations to zero;

[0063] S404. Calculate the speed of the handle pulling training component in real time based on the displacement data. In response to the speed being less than zero, increment the total number of training sessions by one. The speed is less than zero when the direction of the speed is opposite to the direction of the user's hand pulling force.

[0064] S405. In response to the peak value of the displacement data being greater than the preset pulling distance threshold for the difficulty level, the value of the effective training count is incremented by one.

[0065] S406. In response to the total training time reaching the preset value, output a training record report, remind the patient that the hand function training has ended and control the hand function training platform to stop working. The training record report includes the training difficulty level, the maximum value of displacement data, the number of effective training sessions, and the total number of training sessions.

[0066] Electronic device example:

[0067] The present invention also provides an electronic device. For example... Figure 5 As shown, the electronic device includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the training method based on the hand function training platform according to the first aspect of the present invention.

[0068] The electronic device also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and therefore will not be described in detail here.

[0069] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0070] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0071] Examples of computer-readable storage media:

[0072] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the training method based on the hand function training platform described in the first aspect of the present invention.

[0073] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (not exhaustive) of computer-readable storage media may include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0074] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0075] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A training method based on a hand function training platform, characterized in that, The hand function training platform includes a handle pulling training component, a load application device for applying load to the handle pulling training component, a pressure sensor for detecting the pressure between the palm and the training component, and multiple photoelectric switches for detecting the displacement data of the handle pulling training component; the hand function training platform includes multiple difficulty levels, with higher difficulty levels corresponding to larger load and pulling distance thresholds; the method includes an intelligent training mode, which includes: In response to the completion of the training difficulty level setting, the displacement data of the handle pulling training component is collected in real time; Initialize the total number of training iterations and the effective number of training iterations to zero; The speed of the handle pulling training component is calculated in real time based on the displacement data. In response to the speed being less than zero, the total number of training sessions is incremented by one. The speed being less than zero means that the direction of the speed is opposite to the direction of the user's hand pulling force. If the peak value of the displacement data is greater than the preset pulling distance threshold for that difficulty level, the effective training count is incremented by one. In response to the total number of training sessions reaching a preset value and the number of effective training sessions being greater than or equal to a preset threshold for the number of effective training sessions, a training record report is output and the patient is reminded that training at this difficulty level has ended. The difficulty level is then set to a higher level, and the above steps are repeated. The training record report includes the training difficulty level, the maximum displacement data, the number of effective training sessions, and the total number of training sessions. The intelligent training mode also includes: before starting training for the first time, detecting the pressure data between the palm and the training component, and initially setting the training difficulty level based on the pressure data. The pressure data is positively correlated with the corresponding difficulty level. Setting the difficulty level of the hand function training platform includes: setting the load to the load corresponding to the difficulty level, and setting the pulling distance threshold to the pulling distance threshold corresponding to the difficulty level. The method of setting the load to the load corresponding to the difficulty level is to adjust the torque output of the servo motor through the embedded program and the servo driver of the hand function training platform so that the torque output of the servo motor is equal to the torque corresponding to the load at the difficulty level.

2. The training method based on the hand function training platform as described in claim 1, characterized in that, The intelligent training mode also includes: in response to the completion of the training difficulty level setting, initializing the training duration to zero and starting to time the training duration; In response to the training duration reaching the preset value, the system reminds the patient that the hand function training has ended and controls the hand function training platform to stop working.

3. The training method based on the hand function training platform as described in claim 1, characterized in that, The load application device is a servo motor, and the method to set the hand function training platform to the corresponding difficulty level is to adjust the torque output of the servo motor.

4. The training method based on the hand function training platform as described in claim 1, characterized in that, The hand function training platform also includes a stop button, and the intelligent training mode further includes: in response to the stop button being triggered, reminding the patient that the hand function training has ended and controlling the hand function training platform to stop working.

5. The training method based on the hand function training platform as described in any one of claims 1 to 4, characterized in that, The method further includes a normal training mode, which includes: In response to the user manually selecting the difficulty level, the hand function training platform is set to the corresponding difficulty level; In response to the completion of the training difficulty level setting, the displacement data of the handle pulling training component is collected in real time and the training is timed. Initialize the total number of training iterations and the effective number of training iterations to zero; The speed of the handle pulling training component is calculated in real time based on the displacement data. The total number of training sessions is incremented by one in response to the speed being greater than or less than zero. The speed is less than zero when the direction of the speed is opposite to the direction of the user's hand pulling force. If the peak value of the displacement data is greater than the preset pulling distance threshold for that difficulty level, the effective training count is incremented by one. In response to the total training time reaching the preset value, a training record report is output to remind the patient that the hand function training has ended and to control the hand function training platform to stop working. The training record report includes the training difficulty level, the maximum value of displacement data, the number of effective training sessions, and the total number of training sessions.

6. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the training method based on the hand function training platform as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, The computer program is used to implement the training method based on the hand function training platform as described in any one of claims 1 to 5 when executed by a processor.

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