Control method and device of upper limb rehabilitation device, storage medium and electronic equipment

Abstract

The invention provides an upper limb rehabilitation device control method and device, a storage medium and electronic equipment, and the control method comprises the steps: carrying out analog simulation based on an upper limb model to obtain first motion information of an upper limb, and obtaining second motion information of the upper limb based on the first motion information and a surface electromyogram signal, the first motion information at least comprises a joint angle, a first muscle force, a first joint torque and a first muscle activation function; determining motion control information based on the first motion information and the second motion information; the rehabilitation device is controlled to act based on the motion control information, third motion information is obtained based on the surface electromyogram signals, and the third motion information at least comprises a third muscle activation function and third muscle force; an output of the muscle force is adjusted based on a comparison of the third muscle force and the first muscle force. By making the personalized musculoskeletal model, the personalized health plan is made for the patient, and the purpose of helping the patient recover the upper limb elbow joint flexion function is achieved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical devices, and in particular, to a control method, device, storage medium and electronic equipment for an upper limb rehabilitation device. Background Art

[0002] Upper limb rehabilitation robots can make up for some of the shortcomings of current stroke rehabilitation training. The robot pulls the affected limb and repeatedly moves along a preset trajectory. This can stimulate the motor nerves of patients who need a longer rehabilitation time to promote the recovery of the affected limb while avoiding muscle atrophy. Human movement is powered by skeletal muscles. Under the control of the nervous system, it receives electrical signal instructions transmitted by neurons, and then generates muscle force through muscle fiber contraction and drives the bones to complete basic limb movements, as well as more complex movements.

[0003] However, existing rehabilitation devices cannot be customized according to the actual conditions of patients. Summary of the invention

[0004] The purpose of the embodiments of the present disclosure is to provide a control method, device, storage medium and electronic device for an upper limb rehabilitation device to solve the above-mentioned problems existing in the prior art.

[0005] In order to solve the above technical problems, an embodiment of the present disclosure provides a control method for an upper limb rehabilitation device, comprising:

[0006] Acquire first motion information of the upper limb by simulation based on the upper limb model, and acquire second motion information of the upper limb based on the first motion information and the surface electromyography signal, wherein the first motion information at least includes a joint angle, a first muscle force, a first joint torque, and a first muscle activation function;

[0007] determining motion control information based on the first motion information and the second motion information;

[0008] Controlling the action of the rehabilitation device based on the motion control information, and acquiring third motion information based on the surface electromyography signal, wherein the third motion information at least includes a third muscle activation function and a third muscle force;

[0009] The output of the muscle force is adjusted based on the comparison result between the third muscle force and the first muscle force.

[0010] In some embodiments, the step of obtaining first motion information of the upper limb by simulating the upper limb model, and obtaining second motion information of the upper limb based on the first motion information and the surface electromyography signal includes:

[0011] Collecting trajectory information of predetermined marker points to form an upper limb model;

[0012] Acquire the first motion information of the upper limb based on the upper limb model;

[0013] The first motion information is input into a musculoskeletal mechanism model to obtain second motion information of the upper limb, where the second motion information at least includes a second muscle force, a second joint torque, and a second muscle activation function.

[0014] In some embodiments, after collecting the trajectory information of the predetermined marking points to form the upper limb model, the method further includes:

[0015] The adjustment of the upper limb model is achieved by calculating the scaling factor through the distance between the model marking point on the upper limb model and the predetermined marking point corresponding to the motion capture experiment.

[0016] In some embodiments, inputting the first motion information into a musculoskeletal mechanism model to obtain second motion information of an upper limb includes:

[0017] Constructing a musculoskeletal mechanism model, wherein the musculoskeletal mechanism model at least includes a muscle fiber active force contraction mathematical model, a muscle fiber passive force contraction model, a joint torque calibration model, and a muscle activation degree model;

[0018] Inputting the muscle fiber length, the muscle fiber contraction speed, the peak value of the first muscle force, and the first muscle activation function into the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model to determine the second muscle force, and determining the second joint torque based on the second muscle force through the calibration model of the joint torque;

[0019] A second muscle activation function is determined based on the surface electromyographic signal and the muscle activation model.

[0020] In some embodiments, the musculoskeletal mechanism model is constructed by:

[0021] The muscle is abstracted as a muscle structure model which is constructed by a series elastic unit generating an active contraction unit and a parallel elastic unit. The active contraction unit and the parallel elastic unit are connected to the series elastic unit through a feather angle.

[0022] In some embodiments, determining the second muscle activation function based on the surface electromyography signal and the muscle activation model includes:

[0023] The preprocessed surface electromyography signal is input into the neural activation model to output a neural activation function;

[0024] The neural activation function is input into the muscle activation model to output a second muscle activation function.

[0025] In some embodiments, adjusting the output of the muscle force based on the comparison result between the third muscle force and the first muscle force includes:

[0026] The third muscle force is compared with the first muscle force. When the third muscle force is less than the first muscle force, the optimal activation time t in the muscle activation degree change curve represented by the third muscle activation function is adjusted to stimulate the muscle in advance and increase the muscle force.

[0027] Another aspect of the present disclosure provides a control device for an upper limb rehabilitation device, comprising:

[0028] A first acquisition module is used to obtain first motion information of the upper limb by simulation based on the upper limb model, and obtain second motion information of the upper limb based on the first motion information and the surface electromyography signal, wherein the first motion information at least includes a joint angle, a first muscle force, a first joint torque, and a first muscle activation function;

[0029] a determination module, configured to determine motion control information based on the first motion information and the second motion information;

[0030] A second acquisition module, configured to control the movement of the rehabilitation device based on the movement control information, and acquire third movement information based on the surface electromyography signal, wherein the third movement information at least includes a third muscle activation function and a third muscle force;

[0031] The regulating module is used to regulate the output of the muscle force based on the comparison result between the third muscle force and the first muscle force.

[0032] The present disclosure also provides a storage medium storing a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0033] The present disclosure also provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above method when executing the computer program in the memory.

[0034] The disclosed embodiment constructs a model and outputs the coordinated and antagonistic control between muscles that control joint flexion movements, thereby achieving real-time upper limb coordinated motion trajectory planning of motion rehabilitation trajectories; and plans the rehabilitation motion trajectory of the rehabilitation robot according to the motion rehabilitation trajectory.

[0035] The disclosed embodiment develops a personalized musculoskeletal model for the patient, analyzes the patient's movement trajectory in real time, and develops a personalized health plan for the patient based on individual differences, thereby helping the patient recover the elbow flexion function of the upper limb and promoting the rehabilitation process. The robot drives the affected limb to perform elbow flexion rehabilitation exercises, which can stimulate the patient's neural circuits and achieve ideal rehabilitation effects.

[0036] The device, storage medium and electronic device disclosed in the present invention have all the beneficial effects of the above-mentioned method, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 This is one of the step schematic diagrams of the control method of the upper limb rehabilitation device according to an embodiment of the present disclosure;

[0039] Figure 2 This is a second schematic diagram of the steps of the control method of the upper limb rehabilitation device according to an embodiment of the present disclosure;

[0040] Figure 3 This is the third step schematic diagram of the control method of the upper limb rehabilitation device according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] Various aspects and features of the present disclosure are described herein with reference to the drawings.

[0042] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0043] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0044] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0045] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0046] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0047] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to substantially any combination.

[0048] The first embodiment of the present disclosure provides a control method for an upper limb rehabilitation device, wherein the rehabilitation device may be, for example, a rehabilitation robot, in particular, an exoskeleton rehabilitation robot, or other devices capable of achieving rehabilitation, and is used to rehabilitate the upper limb, for example, the elbow joint, and may also be used to rehabilitate other joints. Figure 1 As shown, the control method includes:

[0049] S101, obtaining first motion information of the upper limbs by simulation based on an upper limb model, and obtaining second motion information of the upper limbs based on the first motion information and a surface electromyography signal, wherein the first motion information includes at least a joint angle, a first muscle force, a first joint torque, and a first muscle activation function.

[0050] In this step, the first motion information of the upper limb is obtained by simulation based on the upper limb model, and the second motion information of the upper limb is obtained based on the first motion information and the surface electromyography signal. In this step, it is necessary to obtain the first motion information of the upper limb based on the individual motion process of the object to be measured on the basis of the upper limb model, and further collect the surface electromyography signal of the upper limb on the basis of the first motion information obtained and obtain the second motion information of the upper limb. The first motion information here includes at least the joint angle, the first muscle force, the first joint torque and the first muscle activation function.

[0051] Furthermore, if Figure 2 As shown, the method of obtaining first motion information of the upper limb by simulation based on the upper limb model, and obtaining second motion information of the upper limb based on the first motion information and the surface electromyography signal, includes:

[0052] S201, collecting trajectory information of predetermined marking points to form an upper limb model.

[0053] In this step, the trajectory information of the predetermined marker points is collected to form an upper limb model. The upper limb model in step S101 needs to be pre-built, wherein when the upper limb of the subject to be tested moves, the motion trajectory information of the predetermined marker points is obtained by the acquisition system based on the motion capture technology, wherein the predetermined marker points are pre-set on the upper limb of the subject to be tested, and then the structural model of the upper limb including the elbow joint is further constructed by, for example, an open source OpenSim simulation software platform, and the upper limb model of the subject is further obtained by scaling technology.

[0054] Specifically, the acquisition system here adopts VICON's infrared motion capture system, which mainly includes a motion camera and data analysis software, wherein the motion camera is mainly used to shoot the movement of the predetermined marking points during the bending process of the upper limb, and then transmit the captured image to the data analysis software, and calculate the motion parameters of the flexion of the upper limb, such as the elbow joint. When shooting the action, it is necessary to affix the predetermined marking points to the key parts of the object to be measured in advance, perform three-dimensional digital calculations on the dynamic performance of the predetermined marking points, and obtain its kinematic data through analysis, such as speed, displacement, etc. When the motion camera shoots continuously at a high frame rate, the motion trajectory information of the predetermined marking points is obtained, so that by marking the anatomical marking points of the human body, the three-dimensional coordinates of each predetermined marking point in the continuous space can be obtained, thereby obtaining the movement of the entire upper limb, and at the same time forming a model marking point corresponding to the predetermined marking point on the upper limb model.

[0055] Furthermore, in this step, the scaling factor can be calculated by comparing the distance between the model markers on the simulated upper limb model and the predetermined markers corresponding to the motion capture experiment to achieve the adjustment of the upper limb model, which includes calculating the geometric dimensions of each part of each limb in the upper limb model, adjusting the positions of the starting point, center of mass, joint point and auxiliary point in each joint coordinate system, etc. In addition, the local weight of each part in the upper limb model can be adjusted according to the weight of the subject, so that the sum of the weight of each part on the upper limb model is equal to the subject's own weight; according to the distance or length model components such as ligaments and muscle actuators, the local inertia tensor is dynamically updated in any case to generate the subject's upper limb model.

[0056] Specifically, for example, the following steps are included:

[0057] Step a: Considering that the scaling is based on the height and weight of the subject to be tested, the length and mass of each link are scaled according to the proportional relationship between the distance between the experimental marker and the model marker. The proportional scaling principle formula here is as follows:

[0058]

[0059] Where s k ——proportional factor; e k ——The length of the limb along the X, Y, and Z axes in the generalized coordinate system; l k ——The length of the original upper limb model along the X, Y, and Z axes in the generalized coordinate system.

[0060] Here, the inverse kinematics method can be used to determine the joint angle that best matches the trajectory of the predetermined marker point of the experiment, as shown in the following formula. In this process, the weighted least squares method is used to calculate the difference between the three-dimensional coordinates of the predetermined marker point and the three-dimensional coordinates of the corresponding point in the upper limb model, and the obtained value is controlled to the minimum value.

[0061] On the basis of the static scaling calibration in step a, the upper limb model is enabled to move using the collected human kinematics data, and the model is dynamically calibrated to establish the best personalized upper limb model.

[0062]

[0063] In the above formula, are the coordinates of the predetermined marker points and the model marker points of the upper limb model, is the coordinate of the experimental joint and the model joint; w i 、w j is the corresponding weight.

[0064] S202: Acquire the first motion information of the upper limb based on the upper limb model.

[0065] After collecting the trajectory information of the predetermined marking points to form an upper limb model through the above-mentioned step S201, in this step, the first motion information of the upper limb is obtained based on the upper limb model, and the first motion information at least includes the joint angle, the first muscle force, the first joint torque and the first muscle activation function.

[0066] Specifically, an upper limb dynamics simulation method based on the OpenSim simulation software platform is implemented, which is mainly divided into three steps, namely scale measurement, inverse kinematics (IK) and computational muscle control (CMC). The general model is gradually matched with the measured values ​​and physiological characteristics of the subjects through motion capture data, and then the torque and force of muscle control are calculated in combination with the joint angles solved by inverse kinematics.

[0067] Specifically, in this step, the kinematics and forward dynamics simulation of the joint motion process of the upper limb model based on the object is performed, and firstly, the information of the joint angle θ of the upper limb of the object and the angular velocity of the joint motion can be output. Information and angular acceleration Specifically, the change information of the joint angle of the upper limb in the movement action can be output here, and based on the change information, the timing signal of the joint angle changing with time [Formula (1)] is output through inverse kinematics IK as the information of the joint angle.

[0068] In addition, in this step, the first muscle force of the upper limb can be further determined through the information of the joint angle. The first muscle force here is the muscle force of the subject to be measured that has not been processed by the rehabilitation device.

[0069] Furthermore, the information of the joint angle, the information of the angular velocity and the information of the angular acceleration can be imported into the upper limb model, and the first joint torque of the upper limb, such as the elbow joint, can be obtained through the inverse dynamics simulation of the OpenSim software platform. The first joint torque here is used to verify the accuracy and reliability of the above-mentioned upper limb model.

[0070] Furthermore, in this step, the first muscle activation function a(t), that is, the muscle activation degree, of the upper limb during the movement process can also be obtained through forward dynamics simulation. The first muscle activation function here is the corresponding relationship between the muscle activation degree and the time change.

[0071] Specifically, firstly, based on the movement of the upper limbs, the movement of predetermined marker points is acquired through, for example, an acquisition system based on motion capture technology, so as to obtain data such as joint angle, angular velocity, angular acceleration, and then, for example, the change data such as the joint angle, angular velocity, angular acceleration, etc. are brought into the inverse dynamics module of the OpenSim simulation software platform to solve the joint torque and muscle force.

[0072] Furthermore, based on the kinematic analysis of the muscle arm diagram of the upper limb, it is possible to solve K at a certain movement moment. A , K B And the generalized position coordinates of the joint position O, that is, we can get

[0073] The joint moment arm is obtained by the following formula (3).

[0074]

[0075] Lian Li

[0076]

[0077] M m ——Joint torque of the mth joint

[0078] h i——The joint moment arm of the i-th muscle involved in the movement of the m-th joint;

[0079] Furthermore, the first joint torque and the first muscle force of the skeleton can be obtained by combining the above formula (3) to formula (5):

[0080] In addition, after simulating the upper limb model through the OpenSim software platform, the first muscle activation function, that is, the muscle activation degree, can be obtained. Its value is considered to be approximately proportional to the first muscle force. Based on the first muscle force, the normalized first muscle activation function can be obtained, which can be obtained by the following formula (6).

[0081]

[0082] In addition, according to the constructed upper limb model, parameters such as muscle fiber length and muscle fiber contraction speed can be obtained when the upper limb is in motion. Specifically, the actual human motion data collected by motion capture technology is used as the input of the upper limb model, and the required curves of muscle fiber length, muscle fiber contraction speed, etc. changing with time can be obtained. Specifically, the bending activity of the upper limb, such as the elbow joint, is simulated by the OpenSim software platform to obtain the muscle fiber length (L) of the upper limb. M ) and muscle fiber contraction velocity (v CE ) and the corresponding relationship between the above parameters and time.

[0083] S203: Input the first motion information into a musculoskeletal mechanism model to obtain second motion information of the upper limb, where the second motion information at least includes a second muscle force, a second joint torque, and a second muscle activation function.

[0084] After obtaining the first motion information of the upper limb based on the upper limb model through the above step S202, in this step, the first motion information is input into the musculoskeletal mechanism model to obtain the second motion information of the upper limb, and the second motion information at least includes a second muscle force, a second joint torque and a second muscle activation function.

[0085] Furthermore, if Figure 3 As shown, the step of inputting the first motion information into the musculoskeletal mechanism model to obtain the second motion information of the upper limb includes:

[0086] S301, constructing a musculoskeletal mechanism model, wherein the musculoskeletal mechanism model at least includes a muscle fiber active force contraction mathematical model, a muscle fiber passive force contraction model, a joint torque calibration model, and a muscle activation degree model.

[0087] In this step, a musculoskeletal mechanism model is constructed, and the musculoskeletal mechanism model at least includes a muscle fiber active force contraction mathematical model, a muscle fiber passive force contraction model, and a joint torque calibration model. Specifically, when converting the first motion information into the second motion information, it is necessary to first construct a muscle fiber active force contraction mathematical model and a muscle fiber passive force contraction model to determine the second motion information.

[0088] Specifically, the improved Hill musculoskeletal model is first determined, and the muscle fiber active force contraction model, the muscle fiber passive force contraction model, etc. are represented by the improved Hill muscle model. Among them, the muscle is abstracted as a muscle structure model constructed by the series elastic unit (SE) generating the active contraction unit (CE) and the parallel elastic unit (PE). At the same time, the model can well reflect the changes in muscle contraction. The active contraction unit and the parallel elastic unit are connected to the series elastic unit through the feather angle (α) to form a muscle model, thereby determining the muscle fiber active force contraction mathematical model [see formula (8)], the muscle fiber passive force contraction model [see formula (10)], the joint torque calibration model [see formula (12)] and the muscle activation model [see formula (15)]. Based on the combination of the above models, a musculoskeletal mechanism model is established, wherein the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model can be simplified to a mathematical function y=f(α) [see formula (11)] for determining the second muscle force.

[0089] In this embodiment, through the analysis of the muscle contraction model of the Hill musculoskeletal model, it can be seen that the muscle contraction dynamics model is mainly affected by muscle activation, muscle cross-sectional area, muscle fiber length and muscle contraction rate. Among them, muscle activation can be solved with the help of the muscle activation model. In this embodiment, the influence of muscle activation on muscle contraction force is mainly considered.

[0090] Specifically, through the study of Hill's musculoskeletal model, it is found that muscle contraction force is mainly generated by active contraction and passive contraction of muscles. Among them, active contraction force can be regarded as generated by the contraction movement of muscle fibers, while parallel elastic force originates from the elastic deformation of the surrounding tissues, and its magnitude is only affected by the maximum isometric contraction force and the influencing factor between the length of the passive elastic unit and the muscle force. The series elastic force is generated by the series elastic unit, which relies on the tendons to combine the musculoskeletal system and use muscle force to drive the movement of the bones, thereby realizing the movement of the joints. Therefore, the muscle contraction force of muscle fibers can be obtained by adding the active and parallel elastic forces, so the muscle fiber force F f for:

[0091] F f =F CE +F PE Formula (7)

[0092] Among them, F f is the muscle contraction force of the muscle fiber, F CE is the active contraction force generated by the active contraction unit CE, F PE is the parallel elastic force generated by the parallel elastic unit PE, which affects the muscle fiber F CE The main factors affecting the size of the active force are muscle activation, muscle fiber length and muscle contraction speed.

[0093] The muscle fiber active force contraction mathematical model is expressed by the following formula:

[0094]

[0095] Where g max v is the maximum normalized muscle extension force, with a value of 1.5. Usually, it is around 1.4 for young people, and 1.8 is more suitable for the elderly. M is the normalized muscle fiber contraction velocity; A is the muscle fiber active force-velocity curve factor in the Hill model, which is 0.25, and is generally 0.3 for the elderly; γ is the active muscle force-length relationship shape factor, which is usually 0.45; L M is the muscle fiber length; The peak force can be adjusted according to the subject.

[0096] Furthermore, v M It is expressed by the following formula:

[0097]

[0098] Where v max is the maximum contraction velocity of muscle fibers, which is approximately Unit: m / s, ideal muscle fiber length, the length of the muscle when it is in maximum isometric contraction is (This can be obtained from the graph of muscle fiber length changing over time).

[0099] The upper limb elbow flexion activity was simulated through the OpenSim software platform to obtain the passive contraction model of muscle fibers, that is, the corresponding relationship between muscle fiber force and muscle activation degree, muscle fiber length change and muscle fiber contraction speed.

[0100]

[0101] Medium k PE is the exponential factor of passive force-length, with a value of 5; usually the exponential factor of passive force-length for the elderly is k PE The value is 4, and the value for young people is 5;

[0102] L is the maximum passive muscle tension strain, which is 0.6. The maximum passive muscle tension strain of the elderly is generally 0.5, while that of the young is 0.6; M is the muscle fiber length.

[0103] The magnitude of the second muscle force output through the muscle fiber is:

[0104]

[0105] Where φ is the feathering angle.

[0106] Among them, when the elbow joint is flexed, the biceps are mainly used to exert force, and the triceps work together. According to the muscle physiological parameter table, the feather angle of the biceps is 0, and the feather angle of the triceps is generally less than 20° and is often ignored. Therefore, in general, during upper limb activities, the muscle fiber force F f Approximately equal to the muscle force F.

[0107] The second joint torque here is solved as:

[0108]

[0109] θ is the joint angle; M j (θ, t) represents the joint torque of joint j when the joint angle is θ and the sampling time is t; r i (θ) is the joint force arm of the i-th skeletal muscle on joint j when the joint angle is θ; F i (θ, t) is the muscle force of the i-th skeletal muscle on joint j when the joint angle is θ.

[0110] S302, input muscle fiber length, muscle fiber contraction speed, peak value of the first muscle force, and the first muscle activation function into the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model to determine the second muscle force, and determine the second joint torque based on the second muscle force through the calibration model of the joint torque.

[0111] After constructing the musculoskeletal mechanism model through the above step S301, in this step, the muscle fiber length, muscle fiber contraction speed, peak value of the first muscle force, and the first muscle activation function are input into the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model to determine the second muscle force, and the second joint torque is determined based on the second muscle force through the calibration model of the joint torque.

[0112] Specifically, the muscle fiber length (L M ), muscle fiber contraction speed (v CE ), peak value of the first muscle force The first muscle activation function a(t) and other parameters are input into the constructed muscle fiber active force contraction mathematical model [the above formula (8)] and the muscle fiber passive force contraction model [the above formula (10)], and the muscle fiber active force F is output. CE and muscle fiber passive force F PE , F CE and F PE Substitute the above formula (11) to calculate the second muscle force F.

[0113] In this step, the second joint torque can also be obtained by constructing the calibration model of the joint torque, that is, the second muscle force is brought into the calibration model of the joint torque, that is, input into the calculation formula of the joint torque [the above formula (12)], and the second elbow joint torque is output.

[0114] In addition, the second joint torque obtained in this step can be used to compare with the simulation result of the OpenSim software platform, i.e., the first joint torque, to verify the accuracy of the model. For example, the parameters in the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model can be adjusted according to the comparison result of the first joint torque and the second joint torque.

[0115] This step also includes adjusting the musculoskeletal mechanism model based on the first joint torque and the second joint torque, and in particular, adjusting the model parameters of the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model.

[0116] S303: Determine a second muscle activation function based on the surface electromyography signal and the muscle activation model.

[0117] After the muscle fiber length, muscle fiber contraction speed, peak value of the first muscle force, and the first muscle activation function are input into the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model through the above step S302 to determine the second muscle force, and the second joint torque is determined based on the second muscle force through the calibration model of the joint torque, in this step, the second muscle activation function is determined based on the surface electromyography signal and the muscle activation model.

[0118] In this step, the surface electromyographic signal of the object to be tested is first collected. Before solving the muscle activation function and muscle force, the surface electromyographic signal must be preprocessed and then converted into the neural activation function u(t).

[0119] Specifically, when the subject to be tested performs an unloaded elbow flexion and extension movement, the synchronously collected surface electromyography signals are processed. Here, a surface electromyography acquisition device is used to obtain surface electromyography signals. Since the selected surface electromyography signals are prone to muscle impedance, skin sweat, epidermal hair and external electromagnetic interference, the collected surface electromyography has a series of noises.

[0120] In the specific acquisition process, the electrodes are placed on the biceps, brachialis, and triceps. Interference signals in the environment, such as industrial frequency interference signals of AC power supply, are mainly concentrated in the 50Hz frequency band. During muscle contraction, the mutual movement of the surface electrodes and the skin introduces artifacts and baseline drift, which are mainly concentrated in the low-frequency band below 10Hz. Therefore, in the process of denoising, it is necessary to distinguish the electromyographic signals and noise in the low-frequency band. Specifically, the wavelet threshold denoising method is used to denoise the signal, and the wavelet decomposition layer number is selected as eight layers. The wavelet basis function is "Sym10" to perform wavelet decomposition on the electromyographic signal. It has excellent time-frequency local characteristics, high smoothness, and the algorithm is easy to implement, with high frequency resolution and high signal-to-noise ratio for denoising sEMG signals.

[0121] The preprocessed surface electromyographic signal is input into the neural activation model [Formula (13)] to output the neural activation function u(t), and the neural activation function u(t) is input into the muscle activation model [Formula (15)] to output the second muscle activation function a(t).

[0122] Specifically, the neural activation model is used to solve the neural activation function u(t), where u(t) is the muscle excitation signal obtained after normalizing the electromyographic signal. u(t) is substituted into the muscle activation degree model to solve the second muscle activation function, namely the muscle activation degree a(t). In addition, the muscle force is calculated by substituting a(t) into the neuromusculoskeletal model.

[0123] Specifically, the formula for solving the neural activation function u is as follows:

[0124]

[0125] Where: u(t) is the neural activation at the t-th sampling point; e is the electromyographic signal; α, β1, and β2 are neural activation coefficients; this is a recursive model, and the single neural activation intensity u(t) is related to the neural activation intensities u(t-1) and u(t-2) of the previous two times; d is the time delay; T E is the sampling time interval.

[0126] The above neural activation coefficient satisfies the following formula:

[0127]

[0128] After obtaining the neural activation function from the surface electromyographic signal, the muscle activation function can be calculated and estimated. The second muscle activation function can be calculated through the simplified equation, that is, the muscle activation model:

[0129]

[0130] Where A(-3, 0) is a nonlinear factor. Where u(t) is the neural activation function; a(t) is the muscle activation function.

[0131] S102: Determine motion control information based on the first motion information and the second motion information.

[0132] After obtaining the first motion information of the upper limbs through simulation based on the upper limb model in step S101, and obtaining the second motion information of the upper limbs based on the first motion information and the surface electromyography signal, in this step, the motion control information is determined based on the first motion information and the second motion information, and the motion control information at least includes a third muscle force and a third muscle activation function. Specifically, firstly, the second muscle force, the second joint torque and the second muscle activation function obtained in the above step are respectively compared with the first muscle force, the first joint torque and the first muscle activation function, and the model parameters of each model in the musculoskeletal mechanism model are modified according to the comparison results.

[0133] When the second muscle force, the second joint torque and the second muscle activation function are respectively the same as the first muscle force, the first joint torque and the first muscle activation function, the output motion control information is determined. The motion control information is used to control the action of the rehabilitation device, where the motion control information is the motion information of the upper limb of the subject to be tested and the control information for each driving device in the rehabilitation device, where the rehabilitation device can be, for example, an exoskeleton rehabilitation robot.

[0134] S103, controlling the movement of the rehabilitation device based on the movement control information, and acquiring third movement information based on the surface electromyography signal, wherein the third movement information at least includes a third muscle activation function and a third muscle force.

[0135] After determining the motion control information based on the first motion information and the second motion information through the above step S102, in this step, the action of the rehabilitation device is controlled based on the motion control information, and the third motion information is obtained based on the surface electromyography signal, and the third motion information at least includes a third muscle activation function and a third muscle force.

[0136] Specifically, the third motion information obtained here includes parameters such as joint angle, joint torque, angular velocity, angular acceleration, etc., and the third motion information is input into, for example, an exoskeleton rehabilitation robot controller, and the rehabilitation device is controlled by, for example, driving a motor to rotate. Figure 2 】Drive the affected limb to perform actions such as elbow flexion.

[0137] In this step, forward and inverse kinematics and dynamic mathematical models are built and simulated based on the physiological structure of the upper limbs and the rehabilitation exercise requirements. At the same time, motion control information based on information such as joint angles and joint torques is sent to the lower computer, thereby controlling the rehabilitation device such as the exoskeleton rehabilitation robot to perform rehabilitation exercises.

[0138] The wearable upper limb rehabilitation robot applicable to the present embodiment comprises a support frame and a wearable bionic arm, wherein the wearable bionic arm comprises a shoulder joint component, an upper arm component, an elbow joint component, a forearm component, a wrist joint component and a hand component which are connected in sequence, and further comprises an electromyographic sensor, a power device, etc., which is connected to the patient's upper limb through a bandage, and plans the motion trajectory of the rehabilitation robot according to the motion rehabilitation trajectory, thereby driving the patient's upper limb to perform rehabilitation training, and at the same time, after collecting the surface electromyographic signal through the electromyographic sensor, it is brought into the neuromuscular activation model, and the motion trajectory and motion amplitude are adjusted through the feedback link, and at the same time, by establishing a musculoskeletal model with the characteristics of the rehabilitator, the characteristics of the patient's muscle physiological parameters are analyzed to formulate a specialized rehabilitation plan, and the comparative analysis further optimizes the rehabilitation exercise training control trajectory generation model, which can be expected to achieve the purpose of helping patients to perform autonomous training, which can not only alleviate the problem of physician shortage, but also reduce rehabilitation costs and improve rehabilitation efficiency.

[0139] The motion control system here controls, for example, a rehabilitation device (exoskeleton rehabilitation robot) to move according to a preset trajectory. Among them, the geometric relationship between the robot's execution end and the position of each joint is determined based on kinematic analysis; based on dynamic analysis, the relationship between the position of each joint on the robot and the driving torque is determined. Here, for example, the motion control system of the exoskeleton rehabilitation robot is mainly composed of STM32 control board, DC driver, DC drive motor, encoder and other modules. The core control chip parses the rehabilitation training instructions, obtains the motion parameters of each motor and issues control instructions at the same time, realizes the drive of each joint of the rehabilitation robot, in order to achieve the rehabilitation training task.

[0140] The execution program is compiled through computer programming software (PC host computer) and downloaded to the core control chip (STM32 lower controller) through SWD. The control generates the corresponding PWM duty cycle and completes the control of the DC brush motor through the DC brush driver in order to achieve the control requirements.

[0141] The joints of the exoskeleton rehabilitation robot for the upper limbs need to reciprocate, which requires the motor to be able to achieve forward and reverse rotation, and a reversible PWM system is needed. The CAN bus is used to implement distributed control, which can control several motors at the same time, truly achieving real-time and synchronization. The drive module composed of servo motors and DC drivers is responsible for driving each joint of the robot arm. This design mainly uses USART and TTL communication. USART communication is responsible for the communication between the microcontroller and the host computer, and TTL communication is responsible for the communication between the control board and the encoder.

[0142] The executable program is compiled by computer programming software (PC host) and downloaded to the core control chip (STM32 lower controller) through SWD, and the corresponding PWM duty cycle is controlled to complete the control of the DC brush motor through the DC brush driver in order to achieve the control requirements. The joints of the upper limb exoskeleton rehabilitation robot need to reciprocate, which requires the motor to be able to achieve forward and reverse rotation, and a reversible PWM system is required. The CAN bus is used to implement distributed control, which can control several motors at the same time, truly achieving real-time and synchronization. The drive module composed of servo motors and DC drivers is responsible for the drive of each joint of the robot arm. This design mainly uses USART and TTL communication. USART communication is responsible for the communication between the microcontroller and the host computer, and TTL communication is responsible for the communication between the control board and the encoder.

[0143] Correspondingly, in this step, when the affected limb is moving, the surface electromyographic signal of the subject is collected, and the acquired surface electromyographic signal is rectified, denoised and other signal preprocessed again, and then brought into [Formula (13)] to output the neural activation function u(t); the neural activation function u(t) is substituted into the muscle activation model [Formula (15)], and the third muscle activation function a(t) is obtained through the muscle activation model. Then, a(t) is brought into the muscle fiber active force contraction mathematical model [Formula (8)] and the muscle fiber passive force contraction model [Formula (10)] to re-output the third muscle force F'.

[0144] S104: Adjust the output of the muscle force based on the comparison result between the third muscle force and the first muscle force.

[0145] After controlling the action of the rehabilitation device based on the motion control information through the above-mentioned step S103 and obtaining the third motion information based on the surface electromyography signal, wherein the third motion information at least includes a third muscle activation function and a third muscle force, in this step, the output of the muscle force is adjusted based on the comparison result between the third muscle force and the first muscle force.

[0146] Specifically, in this step, after acquiring the third motion information by collecting the surface electromyography signal, the third muscle force in the third motion information is compared with the first muscle force. When the third muscle force is less than the first muscle force, the optimal activation time t is found in the muscle activation change curve represented by the third muscle activation function, and the muscle is stimulated in advance to achieve the purpose of improving muscle activation, thereby increasing muscle strength.

[0147] The disclosed embodiment constructs a model to output the coordination and antagonistic control between the muscles that control the elbow flexion movement, thereby realizing real-time upper limb coordinated movement trajectory planning of the movement rehabilitation trajectory and planning the rehabilitation movement trajectory of the rehabilitation robot according to the movement rehabilitation trajectory. The disclosed embodiment can use a robot as a medium to develop a personalized musculoskeletal model for the patient, analyze the patient's movement trajectory in real time, and develop a personalized health plan for the patient based on the individual, so as to help the patient recover the upper limb elbow flexion function and promote the rehabilitation process. By driving the affected limb to perform elbow flexion rehabilitation exercises by the robot, the patient's neural circuit can be stimulated to achieve an ideal rehabilitation effect.

[0148] The disclosed embodiment develops a personalized musculoskeletal model for the patient, analyzes the patient's movement trajectory in real time, and develops a personalized health plan for the patient based on individual differences, thereby helping the patient recover the elbow flexion function of the upper limb and promoting the rehabilitation process. The robot drives the affected limb to perform elbow flexion rehabilitation exercises, which can stimulate the patient's neural circuits and achieve ideal rehabilitation effects.

[0149] Based on the same inventive concept, a second embodiment of the present disclosure provides a control device for an upper limb rehabilitation device, comprising:

[0150] A first acquisition module is used to obtain first motion information of the upper limb by simulation based on the upper limb model, and obtain second motion information of the upper limb based on the first motion information and the surface electromyography signal, wherein the first motion information at least includes a joint angle, a first muscle force, a first joint torque, and a first muscle activation function;

[0151] a determination module, configured to determine motion control information based on the first motion information and the second motion information;

[0152] A second acquisition module, configured to control the movement of the rehabilitation device based on the movement control information, and acquire third movement information based on the surface electromyography signal, wherein the third movement information at least includes a third muscle activation function and a third muscle force;

[0153] The regulating module is used to regulate the output of the muscle force based on the comparison result between the third muscle force and the first muscle force.

[0154] Furthermore, the first acquisition module includes:

[0155] A collection unit, used for collecting trajectory information of predetermined marking points to form an upper limb model;

[0156] A first acquisition unit, configured to acquire the first motion information of the upper limb based on the upper limb model;

[0157] The second acquisition unit is used to input the first motion information into the musculoskeletal mechanism model to obtain second motion information of the upper limb, where the second motion information at least includes a second muscle force, a second joint torque and a second muscle activation function.

[0158] Furthermore, the first acquisition module further includes:

[0159] The adjustment unit is used to calculate the scaling factor by the distance between the model marking point on the upper limb model and the predetermined marking point corresponding to the motion capture experiment, so as to adjust the upper limb model.

[0160] Furthermore, the second acquiring unit includes:

[0161] A construction subunit is used to construct a musculoskeletal mechanism model, wherein the musculoskeletal mechanism model at least includes a muscle fiber active force contraction mathematical model, a muscle fiber passive force contraction model, a joint torque calibration model, and a muscle activation degree model;

[0162] A first determination subunit is used to input the muscle fiber length, the muscle fiber contraction speed, the peak value of the first muscle force, and the first muscle activation function into the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model to determine the second muscle force, and determine the second joint torque based on the second muscle force through the calibration model of the joint torque;

[0163] The second determination subunit is used to determine a second muscle activation function based on the surface electromyography signal and the muscle activation degree model.

[0164] Furthermore, the musculoskeletal mechanism model is constructed in the following manner:

[0165] The muscle is abstracted as a muscle structure model which is constructed by a series elastic unit generating an active contraction unit and a parallel elastic unit. The active contraction unit and the parallel elastic unit are connected to the series elastic unit through a feather angle.

[0166] Furthermore, the second determination subunit is also used to input the preprocessed surface electromyography signal into the neural activation model to output a neural activation function; and input the neural activation function into the muscle activation model to output a second muscle activation function.

[0167] Furthermore, the adjustment module is specifically used to: compare the third muscle force with the first muscle force, and when the third muscle force is less than the first muscle force, adjust the optimal activation time in the muscle activation degree change curve represented by the third muscle activation function to stimulate the muscle in advance and increase muscle strength.

[0168] The disclosed embodiment develops a personalized musculoskeletal model for the patient, analyzes the patient's movement trajectory in real time, and develops a personalized health plan for the patient based on individual differences, thereby helping the patient recover the elbow flexion function of the upper limb and promoting the rehabilitation process. The robot drives the affected limb to perform elbow flexion rehabilitation exercises, which can stimulate the patient's neural circuits and achieve ideal rehabilitation effects.

[0169] A third embodiment of the present disclosure provides a storage medium storing a computer program. When the computer program is executed by a processor, the steps of implementing the above method include:

[0170] S11, performing simulation based on the upper limb model to obtain first motion information of the upper limb, and obtaining second motion information of the upper limb based on the first motion information and the surface electromyography signal, wherein the first motion information at least includes a joint angle, a first muscle force, a first joint torque, and a first muscle activation function;

[0171] S12, determining motion control information based on the first motion information and the second motion information;

[0172] S13, controlling the action of the rehabilitation device based on the motion control information, and acquiring third motion information based on the surface electromyography signal, wherein the third motion information at least includes a third muscle activation function and a third muscle force;

[0173] S14, adjusting the output of the muscle force based on the comparison result between the third muscle force and the first muscle force.

[0174] The storage medium of the present disclosure stores a computer program, which can implement the methods in different implementation modes of the above-mentioned first embodiment when executed by a processor.

[0175] The disclosed embodiment develops a personalized musculoskeletal model for the patient, analyzes the patient's movement trajectory in real time, and develops a personalized health plan for the patient based on individual differences, thereby helping the patient recover the elbow flexion function of the upper limb and promoting the rehabilitation process. The robot drives the affected limb to perform elbow flexion rehabilitation exercises, which can stimulate the patient's neural circuits and achieve ideal rehabilitation effects.

[0176] A fourth embodiment of the present disclosure provides an electronic device, the electronic device comprising at least a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the above method when executing the computer program on the memory, specifically comprising:

[0177] S21, performing simulation based on the upper limb model to obtain first motion information of the upper limb, and obtaining second motion information of the upper limb based on the first motion information and the surface electromyography signal, wherein the first motion information at least includes a joint angle, a first muscle force, a first joint torque, and a first muscle activation function;

[0178] S22, determining motion control information based on the first motion information and the second motion information;

[0179] S23, controlling the action of the rehabilitation device based on the motion control information, and acquiring third motion information based on the surface electromyography signal, wherein the third motion information at least includes a third muscle activation function and a third muscle force;

[0180] S23, adjusting the output of the muscle force based on the comparison result between the third muscle force and the first muscle force.

[0181] The memory of the electronic device of the present disclosure stores a computer program, and the processor implements the methods of other implementations in the above-mentioned first embodiment when executing the computer program in the memory.

[0182] The disclosed embodiment develops a personalized musculoskeletal model for the patient, analyzes the patient's movement trajectory in real time, and develops a personalized health plan for the patient based on individual differences, thereby helping the patient recover the elbow flexion function of the upper limb and promoting the rehabilitation process. The robot drives the affected limb to perform elbow flexion rehabilitation exercises, which can stimulate the patient's neural circuits and achieve ideal rehabilitation effects.

[0183] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0184] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0185] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0186] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The units described as separate components may or may not be physically separated, and the components shown as units 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.

[0188] 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0189] If the integrated module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0190] In addition, the features of the embodiments shown in the drawings of the present application or the various embodiments mentioned in this specification are not necessarily understood as independent embodiments. Instead, each feature described in one example of an embodiment can be combined with one or more other desired features from other embodiments to produce other embodiments not described in words or with reference to the drawings.

[0191] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A control method for an upper limb rehabilitation device, characterized in that: include: Acquire first motion information of the upper limb by simulation based on the upper limb model, and acquire second motion information of the upper limb based on the first motion information and the surface electromyography signal, wherein the first motion information at least includes a joint angle, a first muscle force, a first joint torque, and a first muscle activation function; determining motion control information based on the first motion information and the second motion information; Controlling the action of the rehabilitation device based on the motion control information, and acquiring third motion information based on the surface electromyography signal, wherein the third motion information at least includes a third muscle activation function and a third muscle force; The output of the muscle force is adjusted based on the comparison result between the third muscle force and the first muscle force.

2. The control method according to claim 1, characterized in that: The method of obtaining first motion information of the upper limb by simulating the upper limb model, and obtaining second motion information of the upper limb based on the first motion information and the surface electromyography signal, includes: Collecting trajectory information of predetermined marker points to form an upper limb model; Acquire the first motion information of the upper limb based on the upper limb model; The first motion information is input into a musculoskeletal mechanism model to obtain second motion information of the upper limb, where the second motion information at least includes a second muscle force, a second joint torque, and a second muscle activation function.

3. The control method according to claim 2, characterized in that: After collecting the trajectory information of the predetermined marker points to form the upper limb model, it also includes: The adjustment of the upper limb model is achieved by calculating the scaling factor through the distance between the model marking point on the upper limb model and the predetermined marking point corresponding to the motion capture experiment.

4. The control method according to claim 2, characterized in that: The step of inputting the first motion information into a musculoskeletal mechanism model to obtain second motion information of the upper limb comprises: Constructing a musculoskeletal mechanism model, wherein the musculoskeletal mechanism model at least includes a muscle fiber active force contraction mathematical model, a muscle fiber passive force contraction model, a joint torque calibration model, and a muscle activation degree model; Inputting the muscle fiber length, the muscle fiber contraction speed, the peak value of the first muscle force, and the first muscle activation function into the muscle fiber active force contraction mathematical model and the muscle fiber passive force contraction model to determine the second muscle force, and determining the second joint torque based on the second muscle force through the calibration model of the joint torque; A second muscle activation function is determined based on the surface electromyographic signal and the muscle activation model.

5. The control method according to claim 4, characterized in that: The musculoskeletal mechanism model is constructed in the following way: The muscle is abstracted as a muscle structure model which is constructed by a series elastic unit generating an active contraction unit and a parallel elastic unit. The active contraction unit and the parallel elastic unit are connected to the series elastic unit through a feather angle.

6. The control method according to claim 4, characterized in that: The determining of the second muscle activation function based on the surface electromyographic signal and the muscle activation degree model comprises: The preprocessed surface electromyography signal is input into the neural activation model to output a neural activation function; The neural activation function is input into the muscle activation model to output a second muscle activation function.

7. The control method according to claim 1, characterized in that: The adjusting the output of the muscle force based on the comparison result between the third muscle force and the first muscle force comprises: The third muscle force is compared with the first muscle force. When the third muscle force is less than the first muscle force, the optimal activation time in the muscle activation degree change curve represented by the third muscle activation function is adjusted to stimulate the muscle in advance and increase the muscle force.

8. A control device for an upper limb rehabilitation device, characterized in that: include: A first acquisition module is used to obtain first motion information of the upper limb by simulation based on the upper limb model, and obtain second motion information of the upper limb based on the first motion information and the surface electromyography signal, wherein the first motion information at least includes a joint angle, a first muscle force, a first joint torque, and a first muscle activation function; a determination module, configured to determine motion control information based on the first motion information and the second motion information; A second acquisition module, configured to control the movement of the rehabilitation device based on the movement control information, and acquire third movement information based on the surface electromyography signal, wherein the third movement information at least includes a third muscle activation function and a third muscle force; The regulating module is used to regulate the output of the muscle force based on the comparison result between the third muscle force and the first muscle force.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program on the memory.

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