Upper limb training level assessment method, device, electronic equipment and medium

Abstract

The present application provides an upper limb training level assessment method, device, electronic device and medium, which effectively solves the problem that the existing rehabilitation training method cannot obtain the information of the patient's motor function being improved in time and thus cannot match the corresponding rehabilitation training method. The assessment method includes: when the preset assessment conditions are met in response, the training data of different training actions of the patient at the current training level is obtained; based on the muscle strength data and timestamps of different training actions, multiple muscle strength change rates of the patient in the process of completing the training action are calculated, and the mechanical arm is called to assist the patient based on the multiple muscle strength change rates; the fit of each training action completed by the patient with the assistance of the mechanical arm is calculated with the corresponding preset standard training action, and the fit of different training actions is obtained; the fit under each training action is integrated to determine the assessment result of the current training level to ensure the training effect of the patient.

Description

Technical Field

[0001] The present application relates to the field of sports rehabilitation training, and more specifically, to an upper limb training level assessment method, device, electronic equipment and medium. Background Art

[0002] In the technical schemes for active and passive training of existing upper limb rehabilitation training equipment, including upper limb rehabilitation training robots, the power-assistance function is more reflected in the evaluation of the patient's upper limb motor function level or motor function ability level before the rehabilitation training begins. Before the rehabilitation training begins, a patient is selected in the system, and the corresponding rehabilitation training difficulty, power-assistance level and other attributes are set according to the patient's existing evaluation results, and the upper limb rehabilitation training is applied in the training scene. During the initial training period or even the entire training process, fixed evaluation results are always used as indicators such as patient training power, difficulty, and duration as a reference for rehabilitation training. The rehabilitation training conducted by the patient during the training process has no conditions for timely reflection of the results of improved motor function, and the training power, difficulty level, duration and other attributes of the training rehabilitation process cannot be adjusted in real time. This may cause the level of rehabilitation training to be mismatched with the patient's own motor ability, which may cause the accuracy and effectiveness of the rehabilitation training of the patient to decrease over a period of time, and even delay the progress of the patient's motor rehabilitation. If you want to obtain a more accurate motor function level of the patient, you can only re-evaluate and then use the results of the re-evaluation to update and influence the next stage of rehabilitation exercise training. This evaluation method has certain disadvantages in effectiveness, and the evaluation result data does not match the actual rehabilitation progress. Summary of the invention

[0003] In view of this, the purpose of the present application is to provide an upper limb training level assessment method, device, electronic device and medium, which effectively solves the problem that the existing rehabilitation training method cannot obtain information on the improvement of the patient's motor function in a timely manner and thus cannot match the corresponding rehabilitation training method.

[0004] In a first aspect, an embodiment of the present application provides a method for evaluating an upper limb training level, the method comprising:

[0005] When the response meets the preset evaluation condition, the training data of different training actions of the patient at the current training level are obtained, and the training data are collected by the force sensor in the mechanical arm of the upper limb training device; each type of training data includes muscle force data of the patient acting on the mechanical arm and a timestamp; the different training levels have corresponding training actions;

[0006] Based on the muscle strength data and timestamps of different training movements, multiple muscle strength change rates of the patient in the process of completing the training movement are calculated, so as to call the robotic arm to assist the patient based on the multiple muscle strength change rates; the muscle strength change rate represents the patient's continuous movement ability in the direction of muscle strength;

[0007] Calculating the degree of fit between each training action completed by the patient with the assistance of the robotic arm and the corresponding preset standard training action, and obtaining the fit of different training actions; the fit represents the patient's ability to perform the movement of the preset standard training action;

[0008] The fit degree under each training action is integrated to determine the evaluation result of the current training level; the evaluation result is used to determine the patient's athletic ability at the current training level.

[0009] In combination with the first aspect, the embodiment of the present application provides a first possible implementation of the first aspect, wherein determining the evaluation result of the current training level includes:

[0010] Determine whether the evaluation result of the current training level determined based on the fit degree meets a preset upgrade condition; the preset upgrade condition is that the value of the evaluation result reaches a preset upgrade threshold;

[0011] If the preset upgrade conditions are met, the patient's training level is adjusted to the next training level of the current training level, so that the patient can train based on the training movements and training difficulty corresponding to the next training level.

[0012] In combination with the first aspect, the embodiment of the present application provides a second possible implementation of the first aspect. If it is not met, the training time of the upper limb training equipment at the current training level is increased to improve the patient's fit in completing the training movements at the current training level.

[0013] In combination with the first aspect, the embodiment of the present application provides a third possible implementation of the first aspect, wherein after calculating multiple muscle force change rates during the patient's completion of the training action, the method comprises:

[0014] Calling multiple muscle force change rates of the patient in the process of completing the training action;

[0015] Determine whether a plurality of muscle strength change rates corresponding to the training action meet a filtering condition, wherein the filtering condition is that at least some of the muscle strength change rates meet a preset muscle strength change rate floating condition or at least one muscle strength change rate increases to a preset instantaneous threshold;

[0016] If it meets the requirements, the muscle force change rate is filtered.

[0017] In combination with the first aspect, the embodiment of the present application provides a fourth possible implementation of the first aspect, wherein the calling of a mechanical arm to assist the patient based on multiple muscle force change rates includes:

[0018] Determine whether the multiple muscle force change rates meet the power-assistance condition, where the power-assistance condition is that the value of the muscle force change rate is greater than a preset power-assistance threshold;

[0019] If it meets the requirements, the autonomous movement direction of the robotic arm will be adjusted to the patient's muscle force direction, and the movement speed of the robotic arm will be controlled to assist the patient.

[0020] In combination with the first aspect, the embodiment of the present application provides a fifth possible implementation of the first aspect, wherein the integration of the fit degree under each training action to determine the evaluation result of the current training level includes:

[0021] Obtain the calculated fit degrees corresponding to different training actions respectively;

[0022] The evaluation result is obtained by summing up the fit degrees corresponding to different training movements.

[0023] In combination with the first aspect, the embodiment of the present application provides a sixth possible implementation of the first aspect, wherein after integrating the fit degree under each training action and determining the evaluation result of the current training level, the method includes:

[0024] While adjusting the upper limb training equipment to the next training level, an assessment report and training suggestions are generated according to the assessment results, wherein the assessment report includes the assessment results, training data, and the calculated muscle strength change rate.

[0025] In a second aspect, an upper limb training level assessment device provided by an embodiment of the present application comprises:

[0026] A response module, used for obtaining training data of different training actions of the patient at the current training level in response to meeting a preset evaluation condition, wherein the training data is collected by a force sensor in the mechanical arm of the upper limb training device; each type of training data includes muscle force data of the patient acting on the mechanical arm and a timestamp; and the different training levels have corresponding training actions;

[0027] A power assist module is used to calculate multiple muscle force change rates of the patient during the training action based on the muscle force data and timestamps of different training actions, so as to call the robotic arm to assist the patient based on the multiple muscle force change rates; the muscle force change rate represents the patient's continuous movement ability in the direction of muscle force;

[0028] A calculation module, used to calculate the fit between each training action completed by the patient with the assistance of the robotic arm and the corresponding preset standard training action, and obtain the fit of different training actions; the fit represents the patient's ability to perform the movement of the preset standard training action;

[0029] The fusion module is used to fuse the fit degree under each training action to determine the evaluation result of the current training level; the evaluation result is used to determine the patient's athletic ability at the current training level.

[0030] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned upper limb training level assessment method are performed.

[0031] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a controller, the steps of the above-mentioned upper limb training level assessment method are executed.

[0032] The upper limb training level evaluation method provided by the present invention firstly obtains training data of different training actions of the patient at the current training level in response to meeting the preset evaluation conditions, and the training data is collected by the force sensor in the mechanical arm of the upper limb training device; each type of training data includes muscle strength data and timestamp of the patient acting on the mechanical arm; the different training levels have corresponding training actions; secondly, based on the muscle strength data and timestamps of different training actions, multiple muscle strength change rates of the patient in the process of completing the training action are calculated, so as to call the mechanical arm to assist the patient based on the multiple muscle strength change rates; the muscle strength change rate represents the patient's continuous movement ability in the muscle force direction; then, the fit between each training action completed by the patient with the help of the mechanical arm and the corresponding preset standard training action is calculated to obtain the fit of different training actions; the fit represents the patient's movement execution ability for the preset standard training action; finally, the fit under each training action is integrated to determine the evaluation result of the current training level; the evaluation result is used to determine the patient's movement ability at the current training level, thereby achieving the result of improving the patient's movement ability in real time, and adjusting the patient's training level and training action in real time to ensure the patient's training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A flow chart of a first upper limb training level assessment method provided in an embodiment of the present application is shown;

[0035] Figure 2 A flowchart for determining whether a robotic arm provides assistance is shown in an embodiment of the present application;

[0036] Figure 3 A flow chart of obtaining evaluation results provided by an embodiment of the present application is shown;

[0037] Figure 4 A schematic structural diagram of a first vehicle remote control device provided in an embodiment of the present application is shown;

[0038] Figure 5 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0039] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of order, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0040] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0041] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0042] Existing upper limb training equipment, including upper limb training robots, always use fixed evaluation results as a reference for patient training assistance, difficulty, duration and other indicators during the initial training period or even the entire training process for rehabilitation training. The results of the rehabilitation training performed by the patient during the training process cannot be reflected in a timely manner for the improvement of motor function, nor can the training assistance, difficulty level, duration and other attributes of the training rehabilitation process be adjusted in real time. This may cause the level of rehabilitation training to be mismatched with the patient's own motor ability, which may cause the accuracy and effectiveness of the patient's rehabilitation training to be reduced over a period of time, and even delay the progress of the patient's motor rehabilitation.

[0043] Based on this, the embodiments of the present application provide an upper limb training level assessment method, device, electronic device and medium, which are described below through embodiments.

[0044] Example 1

[0045] To facilitate understanding of this embodiment, firstly, a method for evaluating the upper limb training level disclosed in the embodiment of this application is described in detail. Figure 1 The flowchart of an upper limb training level assessment method shown in the figure is a flowchart of an upper limb training level assessment method provided in the present application, which is performed based on a processor, a controller, a server, a computer or other equipment with data analysis and calculation in an upper limb training device, and the method includes:

[0046] S101, when a response satisfies a preset evaluation condition, obtaining training data of different training actions of the patient at a current training level, wherein the training data is collected by a force sensor in a mechanical arm of an upper limb training device; each type of training data includes muscle force data of the patient acting on the mechanical arm and a timestamp; and the different training levels have corresponding training actions;

[0047] S102, based on the muscle strength data and timestamps of different training movements, calculating multiple muscle strength change rates of the patient in the process of completing the training movement, so as to call the robotic arm to assist the patient based on the multiple muscle strength change rates; the muscle strength change rate represents the patient's continuous movement ability in the direction of muscle strength;

[0048] S103, calculating the degree of fit between each training movement completed by the patient with the assistance of the robotic arm and the corresponding preset standard training movement, and obtaining the degree of fit of different training movements; the degree of fit represents the patient's ability to perform the movement of the preset standard training movement;

[0049] S104, integrating the degree of fit under each training action to determine an evaluation result of the current training level; the evaluation result is used to determine the patient's athletic ability at the current training level.

[0050] In step S101, the current training level of the patient when using the upper limb training device is obtained by the doctor's diagnosis of the patient, and the upper limb training device or the computer or the server are preset with training scenes and training actions corresponding to different training levels, and the training scenes and training actions are set for the injured upper limb part of the patient. For example, if the patient's shoulder is injured, the set training action may be to raise the arm to a certain extent. When the upper limb training device including the mechanical arm evaluates the patient's motor ability level in response to a preset evaluation condition, the preset evaluation condition may be that the patient's training time based on the upper limb training device reaches a preset time, or may be that the patient receives an evaluation instruction issued by a doctor or other medical staff, or may be that the number of training times based on the upper limb training device of the patient reaches a preset number threshold, or may be other special preset evaluation conditions. If the patient completes all the training movements in the current training level, and the time to complete all the training movements becomes shorter and shorter, that is, the patient's proficiency in the training movements of the current training level is improved to the preset proficiency threshold, and when the preset evaluation conditions are met, the training data of the patient's different training movements at the current training level are obtained, and the training data is collected by the force sensor in the mechanical arm of the upper limb training device; the force sensor is generally installed at the end of the mechanical arm, and each type of training data includes the muscle strength data and a timestamp of the patient acting on the mechanical arm; and when the muscle strength data is collected, it is collected based on the spatial coordinate system, so the muscle strength data is a vector, and the muscle strength data is collected when the patient uses the mechanical arm in the upper limb training device, and can only be collected when the patient exerts force, so the muscle strength data can characterize the patient's movement condition, and the patient's motor ability can also be judged from the muscle strength data.

[0051] In step S102, multiple muscle strength change rates of the patient during the completion of a training action are obtained based on the muscle strength data included in the training data and the timestamp of the collected muscle strength data. The muscle strength change rate formula is shown in formula (1):

[0052]

[0053] Among them, F t+1 is the muscle strength data at n+1 seconds, F tis the muscle strength data at n seconds, Δf is the muscle strength change rate, and is also the muscle strength change data between the previous and next timestamps, and the acquisition period is set to 1s, that is, if a training action is completed in 6s, then during the completion of this training action, 5 muscle strength change rates can be obtained. If the 5 muscle strength change rates continue to increase, it indicates that the patient continues to exert force in this training action, that is, the patient has the continuous movement ability in the muscle strength direction. Therefore, the muscle strength change rate represents the patient's continuous movement ability in the muscle strength direction, and based on the different training actions in the current training level of the patient, multiple muscle strength change rates are obtained, so that the robotic arm can be called to assist the patient based on multiple muscle strength change rates.

[0054] In the specific implementation process of step S102, one implementation method is: the robot arm is called based on multiple muscle force change rates to assist the patient, such as Figure 2 As shown, including:

[0055] S10211, determining whether a plurality of muscle force change rates meet a power-assistance condition, wherein the power-assistance condition is that the value of the muscle force change rate is greater than a preset power-assistance threshold;

[0056] S10212. If it meets the requirements, the autonomous movement direction of the robotic arm is adjusted to the patient's muscle force direction, and the movement speed of the robotic arm is controlled to assist the patient.

[0057] In steps S10211-S10212, it is determined whether the multiple muscle force change rates obtained by the patient in the process of completing the training action meet the assistance condition for the robotic arm to assist the patient, the assistance condition is that the value of the muscle force change rate is greater than a preset assistance threshold, where the preset assistance threshold is 0, and can also be other numbers set according to the specific situation. If the assistance condition is met, that is, when the muscle force change rate is greater than 0, it indicates that the patient is exerting force, that is, the patient has the ability to exert force, and the patient's intended direction is obtained based on the muscle force direction in the muscle force data. At this time, the relevant interface of the robotic arm is controlled to adjust the automatic movement direction of the robotic arm to the patient's muscle force direction or intended direction, that is, the robotic arm assists the patient. Exemplarily, the damping coefficient of the robotic arm can be reduced, and the movement speed of the robotic arm can be controlled to assist the patient. The movement speed of the robotic arm can be determined according to the patient's current training level; when the muscle force change rate does not meet the assistance condition, that is, the muscle force change rate is less than 0, it means that the patient's current training level is consistent with the patient's current motor ability state, and the robotic arm does not need assistance, and the patient drives the robotic arm to complete the training action.

[0058] In the specific implementation process of step S102, one implementation method is: after calculating multiple muscle force change rates of the patient during the process of completing the training action, it includes:

[0059] S10221, calling multiple muscle force change rates obtained by the patient during the process of completing the training action;

[0060] S10222, determining whether a plurality of muscle strength change rates corresponding to the training action meet a filtering condition, wherein the filtering condition is that at least some of the muscle strength change rates meet a preset muscle strength change rate floating condition or at least one muscle strength change rate increases to a preset instantaneous threshold;

[0061] S10223. If it meets the requirements, filter the muscle force change rate.

[0062] In steps S10221-S10223, multiple muscle strength change rates obtained by the patient during the training movement are called. If a certain muscle strength change rate meets the filtering conditions, that is, a certain or part of the muscle strength change rate meets the preset muscle strength change rate floating conditions or at least one muscle strength change rate increases to a preset instantaneous threshold, that is, some muscle strength change rates fluctuate up and down, or a certain muscle strength change rate increases to a preset instantaneous threshold, that is, the muscle strength change rate suddenly changes, wherein the instantaneous threshold can be set according to actual conditions, then it indicates that the patient at this time is very likely to have muscle spasm during training. At this time, the robotic arm is controlled to stop moving to protect the patient, and at the same time, the muscle strength change rate is filtered out, and the training data corresponding to the muscle strength change rate is screened out, thereby ensuring the accuracy of assisting the patient and the accuracy of the evaluation results.

[0063] In step S103, the patient completes the training movement with the assistance of the robotic arm, and the degree of fit between each training movement completed by the patient with the assistance of the robotic arm and the corresponding preset standard training movement is calculated, that is, there is a preset standard training movement in the upper limb training device or server or computer. Exemplarily, the upper limb training device or server or computer performs feature extraction or trajectory extraction on the training movement completed by the patient, and compares it with the features or trajectories in the preset standard movement. The compared features may be joint angles, movement speeds, accelerations and other features that reflect the morphology and dynamic characteristics of the movement. The feature extraction or trajectory extraction method may be implemented by an existing mature neural network algorithm, so as to obtain the degree of fit between the training movement completed by the patient and the preset standard movement, and the fit is a specific numerical value; and if there are different training movements in the entire current training level, multiple fits are obtained.

[0064] In step S104, the fit degrees corresponding to the multiple training movements are calculated, the multiple fit degrees are fused, and the evaluation result of the current training level is determined based on the fused result; the evaluation result is used to determine the patient's athletic ability at the current training level.

[0065] In the specific implementation process of step S104, one implementation method is: the fit degree under each training action is integrated to determine the evaluation result of the current training level, such as Figure 3 As shown, including:

[0066] S10411, respectively obtaining the calculated fit degrees corresponding to different training movements;

[0067] S10412. Sum the fit degrees corresponding to different training movements to obtain an evaluation result.

[0068] That is, in steps S10411-S10412, the degrees of fit corresponding to the obtained multiple training actions are summed up, and the sum of the obtained degrees of fit is the evaluation result of the current training level.

[0069] In the specific implementation process of step S104, another implementation method is: the evaluation result of determining the current training level includes:

[0070] S10421, judging whether the evaluation result of the current training level determined based on the fit degree meets a preset upgrade condition; the preset upgrade condition is that the value of the evaluation result reaches a preset upgrade threshold;

[0071] S10422. If the preset upgrade condition is met, the patient's training level is adjusted to the next training level of the current training level, so that the patient can perform training based on the training movements and training difficulty corresponding to the next training level.

[0072] That is, in steps S10421-S10422, when the obtained evaluation results meet the preset upgrade conditions, and the preset upgrade conditions are that the numerical value of the evaluation results reaches the preset upgrade threshold, the patient's training level is automatically adjusted to the next training level of the current training level, so that the patient can train based on the training movements and training difficulty corresponding to the next training level, and during the adjustment process, the patient is reminded through devices such as speakers that he has entered the next level of training, and the doctor is reminded of the patient's training status by sending a message or email, so that the doctor can better understand the patient's condition.

[0073] In the specific implementation process of step S104, there is another implementation method: if it does not meet the requirements, increase the training time of the upper limb training equipment at the current training level to improve the patient's fit in completing the training movements at the current training level.

[0074] That is, when the evaluation result of the patient does not meet the upgrade condition, the training duration of the upper limb training device at the current training level is increased to improve the fitting degree of the patient in completing the training actions at the current training level, so that the evaluation result determined based on the fitting degree meets the preset upgrade condition. It can also be to increase the damping coefficient of the robotic arm, that is, the rate of change of muscle strength within a fixed period is greater than the resistance adjustment coefficient k corresponding to a certain muscle strength level (0 < k < 100%), and the direction of the muscle strength is consistent with the movement direction, then the damping coefficient of the robotic arm is adjusted, and the damping coefficient of the robotic arm is increased by k to provide greater resistance to the patient, or to increase the movement speed of the robotic arm, so that after multiple trainings, the patient's fitting degree at the current training level is improved, so that the evaluation result meets the preset upgrade condition, which is beneficial to the patient to improve the ability to move.

[0075] In the specific implementation process of step S104, there is another implementation method: after fusing the fitting degree under each training action and determining the evaluation result of the current training level, it includes:

[0076] While adjusting the upper limb training device to the next training level, an evaluation report and training suggestions are generated according to the evaluation result. The evaluation report includes the evaluation result, training data, and the calculated rate of change of muscle strength.

[0077] While the upper limb training device is adjusted to the next training level, devices such as the upper limb training device or a computer generate an evaluation report and training suggestions according to the evaluation result. The evaluation report includes the evaluation result, training data, and the calculated rate of change of muscle strength, and may also include the result of whether to upgrade corresponding to the evaluation result. The training suggestions are obtained based on the patient's training data at the current level and during the training process. For example, it is recommended to lengthen the patient's training time and reduce the number of training times, etc. Doctors can adjust the training tasks of the patient at the current training level or the next level according to this suggestion and in combination with the actual situation.

[0078] Embodiment 2

[0079] The present application also provides an upper limb training level evaluation device, as Figure 4 shown in the block diagram of an upper limb training level evaluation device. The functions implemented by this upper limb training level evaluation device correspond to the steps of performing an upper limb training level evaluation method on a terminal device. This device can be understood as a component of a server including a processor. The device includes:

[0080] A response module 401, configured to obtain training data of different training actions of a patient at the current training level when a preset evaluation condition is met. The training data is collected by a force sensor in the robotic arm of the upper limb training device; each type of training data includes muscle strength data and a timestamp of the patient acting on the robotic arm; different training levels have corresponding training actions;

[0081] The assisting module 402 is used to calculate multiple muscle force change rates of the patient during the training action based on the muscle force data and timestamps of different training actions, so as to call the robotic arm to assist the patient based on the multiple muscle force change rates; the muscle force change rate represents the patient's continuous movement ability in the muscle force direction;

[0082] The calculation module 403 is used to calculate the fit between each training action completed by the patient with the assistance of the robot arm and the corresponding preset standard training action, and obtain the fit of different training actions; the fit represents the patient's ability to perform the movement of the preset standard training action;

[0083] The fusion module 404 is used to fuse the fit degree of each training action to determine the evaluation result of the current training level; the evaluation result is used to determine the patient's athletic ability at the current training level.

[0084] In a feasible implementation, the fusion module 404 includes:

[0085] Determine whether the evaluation result of the current training level determined based on the fit degree meets a preset upgrade condition; the preset upgrade condition is that the value of the evaluation result reaches a preset upgrade threshold;

[0086] If the preset upgrade conditions are met, the patient's training level is adjusted to the next training level of the current training level, so that the patient can train based on the training movements and training difficulty corresponding to the next training level.

[0087] In a feasible implementation manner, the fusion module 404 further includes:

[0088] If not, increase the training time of the upper limb training equipment at the current training level to improve the patient's fit in completing the training movements at the current training level.

[0089] In a feasible implementation, the assistance module 402 includes:

[0090] Calling multiple muscle force change rates of the patient in the process of completing the training action;

[0091] Determine whether a plurality of muscle strength change rates corresponding to the training action meet a filtering condition, wherein the filtering condition is that at least some of the muscle strength change rates meet a preset muscle strength change rate floating condition or at least one muscle strength change rate increases to a preset instantaneous threshold;

[0092] If it meets the requirements, the muscle force change rate is filtered.

[0093] In a feasible implementation manner, the assistance module 402 further includes:

[0094] Determine whether the multiple muscle force change rates meet the power-assistance condition, where the power-assistance condition is that the value of the muscle force change rate is greater than a preset power-assistance threshold;

[0095] If it meets the requirements, the autonomous movement direction of the robotic arm will be adjusted to the patient's muscle force direction, and the movement speed of the robotic arm will be controlled to assist the patient.

[0096] In a feasible implementation manner, the fusion module 404 further includes:

[0097] Obtain the calculated fit degrees corresponding to different training actions respectively;

[0098] The evaluation result is obtained by summing up the fit degrees corresponding to different training movements.

[0099] In a feasible implementation manner, the fusion module 404 further includes:

[0100] While adjusting the upper limb training equipment to the next training level, an assessment report and training suggestions are generated according to the assessment results, wherein the assessment report includes the assessment results, training data, and the calculated muscle strength change rate.

[0101] Example 3

[0102] The present application also provides an electronic device, such as Figure 5 As shown, it includes: a processor 501, a memory 502 and a bus 503, the memory 502 stores machine-readable instructions executable by the processor 501, when the electronic device is running, the processor 501 communicates with the memory 502 through the bus 503, and when the machine-readable instructions are executed by the processor 501, the steps of the upper limb training level assessment method as described are performed.

[0103] Example 4

[0104] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the upper limb training level assessment method as described above are executed. For specific implementation, please refer to the method embodiment, which will not be described in detail here.

[0105] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0106] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0108] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a platform server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0109] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating upper limb training level, characterized in that: The method comprises: When the response meets the preset evaluation condition, the training data of different training actions of the patient at the current training level are obtained, and the training data are collected by the force sensor in the mechanical arm of the upper limb training device; each type of training data includes muscle force data of the patient acting on the mechanical arm and a timestamp; the different training levels have corresponding training actions; Based on the muscle strength data and timestamps of different training movements, multiple muscle strength change rates of the patient in the process of completing the training movement are calculated, so as to call the robotic arm to assist the patient based on the multiple muscle strength change rates; the muscle strength change rate represents the patient's continuous movement ability in the direction of muscle strength; Calculating the degree of fit between each training action completed by the patient with the assistance of the robotic arm and the corresponding preset standard training action, and obtaining the fit of different training actions; the fit represents the patient's ability to perform the movement of the preset standard training action; The fit degree under each training action is integrated to determine the evaluation result of the current training level; the evaluation result is used to determine the patient's athletic ability at the current training level; After calculating the multiple muscle force change rates of the patient during the process of completing the training action, the method includes: Calling multiple muscle force change rates of the patient in the process of completing the training action; Determine whether a plurality of muscle strength change rates corresponding to the training action meet a filtering condition, wherein the filtering condition is that at least some of the muscle strength change rates meet a preset muscle strength change rate floating condition or at least one muscle strength change rate increases to a preset instantaneous threshold; If it meets the criteria, the patient is deemed to have muscle spasm, the robotic arm is controlled to stop moving, and the muscle force change rate is filtered.

2. The method according to claim 1, characterized in that The evaluation result of determining the current training level includes: Determine whether the evaluation result of the current training level determined based on the fit degree meets a preset upgrade condition; the preset upgrade condition is that the value of the evaluation result reaches a preset upgrade threshold; If the preset upgrade conditions are met, the patient's training level is adjusted to the next training level of the current training level, so that the patient can train based on the training movements and training difficulty corresponding to the next training level.

3. The method according to claim 2, characterized in that If not, increase the training time of the upper limb training equipment at the current training level to improve the patient's fit in completing the training movements at the current training level.

4. The method according to claim 1, characterized in that: The method of calling a robotic arm to assist the patient based on multiple muscle force change rates includes: Determine whether the multiple muscle force change rates meet the power-assistance condition, where the power-assistance condition is that the value of the muscle force change rate is greater than a preset power-assistance threshold; If it meets the requirements, the autonomous movement direction of the robotic arm will be adjusted to the patient's muscle force direction, and the movement speed of the robotic arm will be controlled to assist the patient.

5. The method according to claim 1, characterized in that The integration of the fit degree under each training action to determine the evaluation result of the current training level includes: Obtain the calculated fit degrees corresponding to different training actions respectively; The evaluation result is obtained by summing up the fit degrees corresponding to different training movements.

6. The method according to claim 1, characterized in that After integrating the fit degree of each training action and determining the evaluation result of the current training level, the following steps are included: While adjusting the upper limb training equipment to the next training level, an assessment report and training suggestions are generated according to the assessment results, wherein the assessment report includes the assessment results, training data, and the calculated muscle strength change rate.

7. An upper limb training level assessment device, characterized in that: The device comprises: A response module, used for obtaining training data of different training actions of the patient at the current training level in response to meeting a preset evaluation condition, wherein the training data is collected by a force sensor in the mechanical arm of the upper limb training device; each type of training data includes muscle force data of the patient acting on the mechanical arm and a timestamp; and the different training levels have corresponding training actions; A power assist module is used to calculate multiple muscle force change rates of the patient during the training action based on the muscle force data and timestamps of different training actions, so as to call the robotic arm to assist the patient based on the multiple muscle force change rates; the muscle force change rate represents the patient's continuous movement ability in the direction of muscle force; A calculation module, used to calculate the fit between each training action completed by the patient with the assistance of the robotic arm and the corresponding preset standard training action, and obtain the fit of different training actions; the fit represents the patient's ability to perform the movement of the preset standard training action; A fusion module is used to fuse the fit of each training action to determine the evaluation result of the current training level; the evaluation result is used to determine the patient's athletic ability at the current training level; The power-assisting module comprises: Calling multiple muscle force change rates of the patient in the process of completing the training action; Determine whether a plurality of muscle strength change rates corresponding to the training action meet a filtering condition, wherein the filtering condition is that at least some of the muscle strength change rates meet a preset muscle strength change rate floating condition or at least one muscle strength change rate increases to a preset instantaneous threshold; If it meets the criteria, the patient is deemed to have muscle spasm, the robotic arm is controlled to stop moving, and the muscle force change rate is filtered.

8. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the upper limb training level assessment method as described in any one of claims 1 to 6 are performed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a controller, executes the steps of the upper limb training level assessment method as described in any one of claims 1 to 6.

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