A myo-skeletal coordinated vibration stimulation assisted deformity correction control method and robot

Abstract

The embodiment of the application provides a kind of muscle-bone coordinated vibration stimulation auxiliary deformity correction control method and robot, comprising: the vibration frequency of the vibration frequency that first oscillator model output by pre-construction is collected as the input of first fuzzy PID controller, by the first pressure signal for controlling the inflation and deflation of pneumatic component that controller outputs, make pneumatic component exert vibration stimulation to bone hard tissue;With the vibration frequency of the vibration frequency that second vibrator model output by pre-construction is collected as the input of second fuzzy PID controller, controller outputs the second pressure signal for controlling the inflation and deflation of air bag, to the vibration frequency that second vibrator model output is the input of hysteresis compensation network, by hysteresis compensation network output hysteresis compensation signal, make air bag according to the inflation and deflation of second pressure signal and hysteresis compensation signal and carry out vibration stimulation to muscle soft tissue, can promote the coordinated rehabilitation of bone hard tissue and muscle soft tissue.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of rehabilitation assistive devices, in particular to a muscle-bone coordinated vibration stimulation assisted deformity correction control method and robot. BACKGROUND

[0002] Traditional postoperative rehabilitation of lower limb skeletal deformity correction completely relies on the body's own repair, and the rehabilitation cycle is long, the effect is not significant, and complications are many. Wearing an assisted correction robot can promote the rehabilitation of the affected limb, and the assisted correction robot includes a skeletal hard tissue correction mechanism and a muscle soft tissue correction mechanism. In the rehabilitation process, the correction parameters of the two correction mechanisms need to be adjusted multiple times respectively, and different correction forces are applied to the affected limb. The manual adjustment of the correction is not only tedious to operate, but also inaccurate, and the coordinated rehabilitation of skeletal hard tissue and muscle soft tissue cannot be achieved. SUMMARY

[0003] Therefore, the purpose of the embodiment of the present application is to provide a muscle-bone coordinated vibration stimulation assisted deformity correction control method and robot, which can adaptively control the vibration stimulation applied to the affected limb by the assisted correction robot, and promote the coordinated rehabilitation of skeletal hard tissue and muscle soft tissue.

[0004] To achieve the above purpose, the embodiment of the present application provides a muscle-bone coordinated vibration stimulation assisted deformity correction control method for controlling an assisted correction robot, the assisted correction robot including a variable stiffness external fixator for correcting skeletal hard tissue and a flexible corrector for correcting muscle soft tissue, the variable stiffness external fixator being provided with a pneumatic component and a skeletal vibration sensor, and the flexible corrector being provided with an air bag and a muscle vibration sensor; the method comprising:

[0005] constructing a first oscillator model according to a preset optimal skeletal vibration frequency;

[0006] taking the vibration frequency output by the first oscillator model and the vibration frequency collected by the skeletal vibration sensor as the input of a first fuzzy PID controller, outputting a first pressure signal for controlling the pneumatic component to charge and discharge by the first fuzzy PID controller, so that the pneumatic component applies vibration stimulation to the skeletal hard tissue through charging and discharging;

[0007] constructing a second oscillator model according to a preset optimal muscle vibration frequency;

[0008] The vibration frequency output by the second vibrator model and the vibration frequency collected by the muscle vibration sensor are inputs of a second fuzzy PID controller, a second pressure signal for controlling inflation and deflation of the air bag is output by the second fuzzy PID controller, the vibration frequency output by the second vibrator model is an input of a hysteresis compensation network, a hysteresis compensation signal is output by the hysteresis compensation network, and the air bag is inflated and deflated according to the second pressure signal and the hysteresis compensation signal to apply vibration stimulation to the muscle soft tissue.

[0009] Optionally, the method further comprises:

[0010] Collecting the real-time vibration frequency of the bone hard tissue and the real-time vibration frequency of the muscle soft tissue by using a measuring instrument;

[0011] Adjusting the bone optimal vibration frequency according to the real-time vibration frequency of the bone hard tissue;

[0012] Adjusting the first oscillator model according to the adjusted bone optimal vibration frequency;

[0013] Adjusting the muscle optimal vibration frequency according to the real-time vibration frequency of the muscle soft tissue;

[0014] Adjusting the second oscillator model according to the adjusted muscle optimal vibration frequency.

[0015] Optionally, the first oscillator model is adjusted according to the adjusted bone optimal vibration frequency, and the method is:

[0016]

[0017] Wherein, n is the total number of oscillators, each oscillator contains an extensor neuron and a flexor neuron, T r ,T a respectively represent the rise time constant of excitation and the adaptation time constant of self-inhibition, which determine the vibration frequency output by the oscillator, α is the mutual inhibition coefficient between neurons, β is the adaptation coefficient, w ij represents the connection weight matrix of the jth oscillator to the ith oscillator, is the internal excitation state of the extensor neuron e and the flexor neuron f of the ith oscillator, is the self-inhibition state of the extensor neuron e and the flexor neuron f of the ith oscillator, is the first-order derivative with respect to time, represents the output of the jth oscillator, represents the output of the ith oscillator, The feedback term obtained by the outer feedback loop of the i-th oscillator, c is the external excitation input of the neuron, which is determined according to the initial measured optimal vibration frequency of the bone, f bon_0 is the initial measured optimal vibration frequency of the bone, f bon_new is the adjusted optimal vibration frequency of the bone, f mus_new is the adjusted optimal vibration frequency of the muscle, φ() and ψ() are feedback term mapping functions, ε is a number to avoid zero denominator, μ is an adjustment factor, g() is an input term mapping function, which is a linear function about the initial measured optimal vibration frequency of the bone, k, b are weight values in the linear function, T r ,T a is determined by the external excitation input c and the feedback term through the F() parameter mapping function.

[0018] Optionally, according to the adjusted optimal vibration frequency of the muscle, the second oscillator model is adjusted, and the method is:

[0019]

[0020] wherein x, y are state quantities of the system space of the oscillator, are first-order derivatives of x, y with respect to time, τ is the frequency of the oscillator, μ, k are system parameters, E is an energy function, E0 is an initial energy; f mus_0 is the initial measured optimal vibration frequency of the muscle, f mus_new is the adjusted optimal vibration frequency of the muscle, f bon_new is the adjusted optimal vibration frequency of the bone, η is an influence factor of bone rehabilitation on muscle rehabilitation.

[0021] Optionally, the first fuzzy PID controller comprises a first PID controller and a first fuzzy controller, the first fuzzy controller takes, as inputs, a vibration error between the vibration frequency output by the first oscillator model and the vibration frequency collected by the bone vibration sensor, and a change rate of the vibration error, and outputs a first control parameter; the first PID controller takes the vibration error as an input, and outputs the first pressure signal according to the first control parameter.

[0022] Optionally, the second fuzzy PID controller comprises a second PID controller and a second fuzzy controller, the second fuzzy controller takes, as inputs, a vibration error between the vibration frequency output by the second oscillator model and the vibration frequency collected by the muscle vibration sensor, and a change rate of the vibration error, and outputs a second control parameter; the second PID controller takes the vibration error as an input, and outputs the second pressure signal according to the second control parameter.

[0023] Optionally, the pneumatic component charges and discharges according to a pressure difference between the first pressure signal and a current pressure.

[0024] Optionally, the air bag charges and discharges according to a pressure difference between the accumulated signal and a current pressure; wherein the accumulated signal is the sum of the second pressure signal and the hysteresis compensation signal.

[0025] Optionally, the mathematical model of the hysteresis compensation network is:

[0026]

[0027] Wherein, f d represents the input of the network, that is, the vibration frequency output by the second oscillator model; P f represents the output of the hysteresis compensation network, that is, the pressure value used for feedforward compensation; represents the weight coefficient of the input node of the input layer to the i-th neuron node of the first layer hidden layer, represents the weight coefficient of the i-th neuron node of the first layer hidden layer to the j-th neuron node of the second layer hidden layer, w 2j represents the weight coefficient of the j-th neuron node of the second hidden layer to the output node of the output layer, N is the number of neuron nodes of the first hidden layer, M is the number of neuron nodes of the second hidden layer, α i represents the output of the i-th neuron node of the first layer hidden layer, β j is the output of the j-th neuron node of the second layer hidden layer.

[0028] The application also provides a muscle-bone coordinated vibration stimulation auxiliary deformity correction control robot, which is used for controlling an auxiliary correction robot, the auxiliary correction robot comprising a variable stiffness external fixator for correcting skeletal hard tissues and a flexible corrector for correcting muscle soft tissues, the variable stiffness external fixator being provided with a pneumatic component and a skeletal vibration sensor, and the flexible corrector being provided with an air bag and a muscle vibration sensor; the robot comprising:

[0029] A first construction module is configured to construct a first oscillator model according to a preset optimal vibration frequency of a bone.

[0030] A bone auxiliary rehabilitation module is configured to take the vibration frequency output by the first oscillator model and the vibration frequency collected by the skeletal vibration sensor as inputs of a first fuzzy PID controller, output a first pressure signal used for controlling the pneumatic component to charge and discharge from the first fuzzy PID controller, so that the pneumatic component applies vibration stimulation to the skeletal hard tissues by charging and discharging.

[0031] A second construction module is configured to construct a second oscillator model according to a preset optimal vibration frequency of a muscle.

[0032] The muscle auxiliary rehabilitation module is used for taking the vibration frequency output by the second vibrator model and the vibration frequency collected by the muscle vibration sensor as the input of a second fuzzy PID controller, outputting a second pressure signal for controlling the inflation and deflation of the air bag from the second fuzzy PID controller, taking the vibration frequency output by the second vibrator model as the input of a hysteresis compensation network, outputting a hysteresis compensation signal from the hysteresis compensation network, and making the air bag inflate and deflate according to the second pressure signal and the hysteresis compensation signal to apply vibration stimulation to the muscle soft tissue.

[0033] As can be seen from the above, the muscle-bone coordinated vibration stimulation auxiliary deformity correction control method and robot provided by the embodiments of the present application utilize the first fuzzy PID controller to perform feedback control on the inflation and deflation of the pneumatic component, and utilize the second fuzzy PID controller to perform feedback control on the inflation and deflation of the air bag, so as to apply vibration stimulation to the bone hard tissue and the muscle soft tissue during the inflation and deflation of the pneumatic component and the air bag. Through the adaptive control on the pneumatic component and the air bag, appropriate vibration stimulation can be applied to the bone hard tissue and the muscle soft tissue during the rehabilitation process, the coordinated rehabilitation of the bone hard tissue and the muscle soft tissue is promoted, the rehabilitation cycle is significantly shortened, and the rehabilitation effect is improved. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only the embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0035] Figure 1 The structure schematic diagram of the auxiliary correction robot of the embodiments of the present application;

[0036] Figure 2 The structure schematic diagram of the flexible corrector of the embodiments of the present application;

[0037] Figure 3 The structure schematic diagram of the variable stiffness external fixator of the embodiments of the present application;

[0038] Figure 4 The structure schematic diagram of the pneumatic component of the embodiments of the present application;

[0039] Figure 5 The control method flow schematic diagram of the embodiments of the present application;

[0040] Figure 6 The control flow schematic diagram of the embodiments of the present application;

[0041] Figure 7 a vibration stimulation effect diagram of an embodiment of the present application;

[0042] Figure 8 a control flow diagram of a bone hard tissue deformity correction of an embodiment of the present application;

[0043] Figure 9 a control flow diagram of a muscle soft tissue deformity correction of an embodiment of the present application;

[0044] Figure 10 a robot structure block diagram of an embodiment of the present application;

[0045] Figure 11 an electronic device structure block diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the objects, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below with reference to specific embodiments and drawings.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood as the common meanings understood by those skilled in the art to which the present disclosure belongs. The terms "first", "second" and similar terms used in the embodiments of the present application do not represent any order, number or importance, but are only used to distinguish different components. The terms "include", "contain" and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right" and the like only represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships may also change accordingly.

[0048] Research has proved that exercise and external vibration stimulation can accelerate the secretion of muscle factors such as Irisin, Matrix Metalloproteinase 2 (MMP2), Beta-Amino Isobutyric Acid (BAIBA) and other muscle factors, and regulate and repair the physiological function characteristics of muscles and bones; stimulate the secretion of Osteocalcin (OCN) and Fibroblast Growth Factor (FCF9) by bones, and improve the function and quality of bones and muscles. Through the research results and statistical data of existing exercise, electricity, sound, light, magnet and other multi-physical factor rehabilitation methods, it is found that vibration stimulation has a good promoting effect on the postoperative rehabilitation of patients with lower limb bone deformity correction. Patients can apply vibration stimulation to the affected area by wearing an auxiliary correction robot and using the vibration generated by the pneumatic components of the auxiliary correction robot during inflation and deflation. However, the pneumatic components have strong nonlinear characteristics, and the stability of the working speed and output force is poor, and the vibration stimulation required by the affected area during rehabilitation is dynamically changing, and at the same time, the rehabilitation process should ensure the coordinated promotion of bone hard tissue and muscle soft tissue.

[0049] In view of the above reasons, the muscle-bone coordinated vibration stimulation auxiliary deformity correction control method provided by the embodiments of the present application uses a first fuzzy PID controller to output a control signal for controlling the inflation and deflation of the pneumatic component according to the preset optimal vibration frequency of the bone and the real-time collected vibration frequency, and the pneumatic component applies vibration stimulation to the bone hard tissue during inflation and deflation; at the same time, a second fuzzy PID controller is used to output a control signal for controlling the inflation and deflation of the air bag according to the preset optimal vibration frequency of the muscle and the real-time collected vibration frequency, and the air bag applies vibration stimulation to the muscle soft tissue during inflation and deflation according to the control signal and the hysteresis compensation signal; through adaptive control of the pneumatic component and the air bag, appropriate vibration stimulation can be applied to the bone hard tissue and the muscle soft tissue during the rehabilitation process, and the coordinated rehabilitation of the bone hard tissue and the muscle soft tissue is promoted.

[0050] In the following, the technical solutions of the present application are further described in detail through specific embodiments.

[0051] As shown in Figures 1-4 In order to promote the rehabilitation of the affected limb, an auxiliary correction robot is worn on the affected limb in the present embodiment, the auxiliary correction robot includes a flexible corrector 10 for correcting muscle soft tissue and a variable stiffness external fixator 20 for correcting bone hard tissue, the flexible corrector 10 is provided with an air bag and a muscle vibration sensor, the variable stiffness external fixator 20 is provided with a pneumatic component and a bone vibration sensor, and a gas source 30 is used to provide gas for the pneumatic component and the air bag.

[0052] Specifically, the flexible corrector 10 includes a medial thigh muscle patch 11, a lateral thigh muscle patch 12, a first air bag 13, and a magnetic joint 14. The medial thigh muscle patch 11 and the lateral thigh muscle patch 12 are attached to the medial thigh muscle and the lateral thigh muscle respectively, and muscle vibration sensors are arranged in the medial thigh muscle patch 11 and the lateral thigh muscle patch 12 to collect vibration signals of the muscles in real time. The first air bag 13 is arranged on a side of the muscle patch away from the thigh muscle, one end of the first air bag 13 is connected to the muscle patch, the other end is connected to the magnetic joint 14, and the magnetic joint 14 is adsorbed on the variable stiffness external fixator 20. In some modes, the first air bag 13 is a double-cavity auxiliary traction air bag, which can assist in traction correction of soft tissues such as muscles by elongation or contraction during inflation and deflation.

[0053] The variable stiffness external fixator 20 includes an upper fixing ring 21, a lower fixing ring 24, at least one bone needle 23, and at least one pneumatic component 22. The two ends of the pneumatic component 22 are respectively detachably connected to the upper fixing ring 21 and the lower fixing ring 24, and one end of the bone needle 23 is inserted into the bone and the other end is detachably connected to the upper fixing ring 21 through a bone needle fixing part. The pneumatic component 22 includes an upper support rod 22-2, a lower support rod 22-5, a gas cylinder, and a second air bag 22-4 arranged in the gas cylinder. One end of the upper support rod 22-2 is hinged to the upper fixing ring 21, and the other end extends into the gas cylinder and abuts against the second air bag 22-4. One end of the lower support rod 22-5 is hinged to the lower fixing ring 24, and the other end extends into the gas cylinder and abuts against the second air bag 22-4. The second air bag 22-4 is connected in communication with the gas source 30 through a quick insertion air port 22-3. The second air bag 22-4 is elongated or contracted in the gas cylinder during inflation and deflation, thereby elongating or contracting the upper support rod 22-2 and the lower support rod 22-5 to adjust the length of the variable stiffness external fixator 20, so as to adjust the traction force and the traction angle of the variable stiffness external fixator on the limb bone. The pneumatic component 22 is also provided with a sensor mounting part for mounting a bone vibration sensor and other sensors. The bone vibration sensor is used to collect vibration signals of the bone in real time.

[0054] As shown in Figures 5-7 The muscle-bone coordinated vibration stimulation assisted deformity correction control method provided by the embodiments of the present application is used for adaptive control of an assisted correction robot, and the method comprises the following steps:

[0055] S501: constructing a first oscillator model according to a preset optimal vibration frequency of a bone;

[0056] S502: taking the vibration frequency output by the first oscillator model and the vibration frequency collected by the bone vibration sensor as inputs of a first fuzzy PID controller, outputting a first pressure signal for controlling inflation and deflation of the pneumatic component by the first fuzzy PID controller, so as to make the pneumatic component apply vibration stimulation to the bone hard tissue through inflation and deflation;

[0057] In this embodiment, in order to promote the rehabilitation of the skeletal hard tissue, a first oscillator model is constructed according to the optimal vibration frequency of the skeleton, the vibration frequency output by the first oscillator model and the vibration frequency collected by the skeletal vibration sensor are taken as the inputs of a first fuzzy PID controller, a first pressure signal for controlling the inflation and deflation of the pneumatic component of the variable stiffness external fixator is output by the first fuzzy PID controller, the pneumatic component is inflated and deflated under the control of the first pressure signal, and the skeletal hard tissue is subjected to vibration stimulation of a suitable frequency in the process of inflation and deflation, so as to promote the secretion of skeletal factors by the skeletal hard tissue, promote the recovery of the skeleton and regulate the muscle metabolic function.

[0058] In some embodiments, the inherent frequency and the resonance frequency of the affected limb are measured by using a vibration measuring instrument, and the optimal vibration frequency of the skeleton and the optimal vibration frequency of the muscle for rehabilitation training are determined according to the measured inherent frequency and resonance frequency. The selection principle of the optimal vibration frequency of the skeleton is to avoid the resonance frequency, select the frequency that the human body feels comfortable and can maximize the stimulation of the nerve muscle motor unit discharge rate; the optimal vibration frequency of the muscle is the vibration frequency that can maximize the activation of muscle activity, and can have the greatest impact on the nerve muscle performance in the shortest time. Optionally, the vibration measuring instrument includes an electromyographic biofeedback instrument, a multi-lead physiological instrument and the like.

[0059] In this embodiment, considering that the variable stiffness external fixator has multi-joint coupling motion and the pneumatic component has hysteresis characteristics, and the Kimura oscillator has a stable limit cycle and can generate a stable rhythm signal, the first oscillator model is implemented based on the Kimura oscillator, the vibration frequency required for assisting the rehabilitation of the skeletal hard tissue is generated by using the first oscillator model, and the mathematical model of the Kimura oscillator is as follows:

[0060]

[0061] wherein n is the total number of oscillators, 2 oscillators are used, each oscillator contains an extensor neuron and a flexor neuron. r a respectively represent the rise time constant of excitation and the adaptation time constant of self-inhibition, which together control the frequency of the oscillator, i.e., determine the vibration frequency output by the oscillator to be the optimal vibration frequency of the skeleton, α is a mutual inhibition coefficient between neurons, reflecting the mutual coupling of the extensor neuron and the flexor neuron, β is an adaptation coefficient, reflecting the influence degree of the internal state of the neuron, and w ij represents the connection weight matrix of the jth oscillator to the ith oscillator, is the internal excitation state of the extensor neuron e and the flexor neuron f of the ith oscillator, is the self-inhibition state of the extensor neuron e and the flexor neuron f of the ith oscillator, is​ the first order derivative of time, i.e. denotes the output of the jth oscillator, denotes the output of the ith oscillator, is the feedback term obtained by the external feedback loop of the ith oscillator, c is the direct current excitation input of the neuron, and controls the output amplitude of the oscillator.

[0062] As shown in Figure 6 , 8 After the first oscillator model is constructed based on the optimal vibration frequency of the bone, the first fuzzy PID controller is used to perform feedback control on the pneumatic components of the variable stiffness external fixator. The first fuzzy PID controller includes a first PID controller and a first fuzzy controller. The first fuzzy controller takes the vibration error between the vibration frequency output by the first oscillator model and the vibration frequency collected by the bone vibration sensor, and the change rate of the vibration error as input, and outputs the first control parameters K I , K D of the first PID controller.

[0063] The pneumatic components are inflated and deflated according to the pressure difference between the first pressure signal and the current pressure. That is, the pneumatic components are inflated and deflated under the control of the pressure difference between the first pressure signal output by the first PID controller and the current pressure. During the inflation and deflation process, the traction force and the traction angle of the variable stiffness external fixator on the limb bone are adjusted.

[0064] In some modes, the variable stiffness external fixator 20 is provided with six pneumatic components for correcting the bone hard tissue from different angles. Correspondingly, the first fuzzy PID controller for controlling the six pneumatic components is configured. Each first fuzzy PID controller takes the vibration error between the vibration frequency output by the first oscillator model and the vibration frequency collected by the bone vibration sensor as input, and outputs the first pressure signal for controlling the corresponding pneumatic component. Each pneumatic component is inflated and deflated under the control of the pressure difference between the corresponding first pressure signal and the current pressure. During the inflation and deflation process, the limb bone at the corresponding position is subjected to vibration stimulation.

[0065] S503: Construct a second oscillator model according to a preset optimal muscle vibration frequency;

[0066] S504: The vibration frequency output by the second vibrator model and the vibration frequency collected by the muscle vibration sensor are taken as inputs of the second fuzzy PID controller, a second pressure signal for controlling inflation and deflation of the air bag is output by the second fuzzy PID controller, the vibration frequency output by the second vibrator model is taken as input of the hysteresis compensation network, and a hysteresis compensation signal is output by the hysteresis compensation network, so that the air bag is inflated and deflated according to the second pressure signal and the hysteresis compensation signal, and vibration stimulation is applied to the muscle soft tissue.

[0067] In this embodiment, in order to promote the rehabilitation of muscle soft tissue, a second vibrator model is constructed according to the optimal muscle vibration frequency, the vibration frequency output by the second vibrator model and the vibration frequency collected by the muscle vibration sensor are taken as inputs of the second fuzzy PID controller, a second pressure signal for controlling inflation and deflation of the flexible corrector is output by the second fuzzy PID controller, and the air bag is inflated and deflated under the control of the second pressure signal, so that vibration stimulation of appropriate frequency is applied to the muscle soft tissue during the inflation and deflation process, muscle factors are secreted to promote muscle growth and metabolism function.

[0068] In this embodiment, the muscle patch is used to correct muscle soft tissue, which has large structural flexibility, strong nonlinearity, many influencing factors and poor controllability. Therefore, a second vibrator model is constructed based on the RBF_DMP vibrator with strong nonlinear fitting ability and learning ability. The RBF_DMP vibrator includes a DMP vibrator and an RBF network, wherein the DMP vibrator is used to generate vibration frequency required for auxiliary muscle soft tissue rehabilitation, and the mathematical model thereof is:

[0069]

[0070] wherein x and y are state variables of the system space of the vibrator, are first-order derivatives of x and y with respect to time, τ is the frequency of the vibrator, i.e. the optimal muscle vibration frequency, μ and k are system parameters, and E is an energy function, and E0 is an initial energy.

[0071] The RBF network is used to model the parameters E0, μ and k of the DMP vibrator. The RBF network includes an input layer, a hidden layer and an output layer. The activation function from the input layer to the hidden layer adopts a radial basis kernel function (Radial Basis Function, RBF), and the output layer includes three output nodes, and the network output thereof is:

[0072]

[0073]

[0074] φ(t)=arctan(x,y) (3)

[0075] where φ(t) is the output of the input layer, i.e. the output of the DMP oscillator at time t, c m is the center vector of the mth neuron of the hidden layer, σ m is used to determine the width of the odd function around the center point, N is the number of nodes of the hidden layer, for example, the number of nodes can be 32, j(φ) m represents the output of the mth neuron node, w m is the connection weight between the mth neuron node and the output node, is the transpose of w m , x and y are the state variables of the system space of the oscillator in formula (2), f is the output of the RBF network, which is a 3x1 vector, and the three elements of the vector correspond to the parameters E0, μ, k of the DMP oscillator, i.e. f = [E0, μ, k] T , the superscript T represents the transpose operation.

[0076] As shown in Figure 6 , 9 , after constructing the second oscillator model based on the optimal vibration frequency of the muscle, the second fuzzy PID controller is used for feedback control of the air bag of the flexible corrector. The second fuzzy PID controller includes a second PID controller and a second fuzzy controller. The second fuzzy controller takes the vibration error f d between the vibration frequency f l output by the second oscillator model and the vibration frequency f e collected by the muscle vibration sensor, and the rate of change of the vibration error as input, and outputs the second control parameters K I , K D ; the second PID controller takes the vibration error f e as input, and outputs the second pressure signal P I according to the second control parameters K D , K d .

[0077] Compared with the control of the skeletal part, the control of the muscle part is more complex. The vibration stimulation acting on the muscle part according to the second pressure signal output by the second fuzzy PID controller deviates from the actual vibration stimulation received by the muscle part. In order to reduce the deviation, a hysteresis compensation network is added. The input of the hysteresis compensation network is the vibration frequency f d output by the second oscillator model, and the output of the hysteresis compensation network is a hysteresis compensation signal P f . The cumulative signal of the second pressure signal output by the second fuzzy PID controller and the hysteresis compensation signal is a control signal used to control the inflation and deflation of the air bag.

[0078] In some ways, the hysteresis compensation network is implemented based on a FNN fuzzy neural network, the hysteresis compensation network includes an input layer, two hidden layers and an output layer, both of the two hidden layers have 8 neurons, the input layer includes one neuron, and the output layer includes one output node, the mathematical model of the hysteresis compensation network is:

[0079]

[0080] Wherein, f d represents the input of the network, that is, the vibration frequency output by the second oscillator model; P f represents the output of the network, that is, the pressure value used for feedforward compensation of the output pressure value of the second fuzzy PID controller; w0, w1, w2 respectively represent the weight coefficients of the input layer to the first layer hidden layer, the first layer hidden layer to the second layer hidden layer, and the second layer hidden layer to the output layer, which are obtained by network training. Specifically, represents the weight coefficient of the input node of the input layer to the i-th neuron node of the first layer hidden layer, represents the weight coefficient of the i-th neuron node of the first layer hidden layer to the j-th neuron node of the second layer hidden layer, w 2j represents the weight coefficient of the j-th neuron node of the second hidden layer to the output node of the output layer. N and M respectively represent the number of neuron nodes of the first hidden layer and the number of neuron nodes of the second hidden layer. α i ,β j respectively represent the output of the i-th neuron node of the first layer hidden layer and the output of the j-th neuron node of the second layer hidden layer.

[0081] The sum of the pressure values of the second pressure signal and the hysteresis compensation signal is calculated, and the pressure difference formed between the sum and the current pressure of the air bag is used to control the air bag to inflate and deflate, and the muscle soft tissue is stimulated by vibration during the inflation and deflation process. That is, the air bag inflates and deflates under the control of the pressure difference P d between the cumulative signal of the second pressure signal P f output by the second PID controller and the hysteresis compensation signal P e and the current pressure P, and adjusts the correction force of the air bag on the muscle of the affected limb during the inflation and deflation process.

[0082] Wherein, the flow formula of the air bag during the inflation and deflation process is:

[0083]

[0084] Wherein, p is the internal pressure of the air bag, v is the volume of the air bag cavity, Q m is the mass flow rate of gas, R gis the gas constant, and T0 is the absolute temperature of the gas in the cavity. The affected limb activates type II muscle fibers under the vibration stimulation, thereby improving the muscle contraction efficiency, accelerating the rehabilitation of muscle soft tissues, and secreting muscle factors under the vibration stimulation, further promoting the rehabilitation of the lower limb skeleton.

[0085] In some embodiments, to synergistically promote the synchronous rehabilitation of the skeletal hard tissue and the muscle soft tissue, the control method further comprises:

[0086] Collecting the real-time vibration frequency of the skeletal hard tissue and the real-time vibration frequency of the muscle soft tissue by using a measuring instrument;

[0087] Adjusting the optimal vibration frequency of the bone according to the real-time vibration frequency of the skeletal hard tissue;

[0088] Adjusting the first oscillator model according to the adjusted optimal vibration frequency of the bone;

[0089] Adjusting the optimal vibration frequency of the muscle according to the real-time vibration frequency of the muscle soft tissue;

[0090] Adjusting the second oscillator model according to the adjusted optimal vibration frequency of the muscle.

[0091] In this embodiment, after the vibration stimulation is applied to the affected limb, the optimal vibration frequency of the human body will change slightly. To provide the most appropriate vibration stimulation in the rehabilitation process, the first oscillator model and the second oscillator model need to be updated according to the real-time optimal vibration frequency of the bone and the real-time optimal vibration frequency of the muscle, respectively. At the same time, considering the correlation between the muscle soft tissue and the skeletal hard tissue, to promote the synchronous rehabilitation of the two, the vibration frequency of the skeletal hard tissue and the vibration frequency of the muscle soft tissue should be considered when adjusting the oscillator model.

[0092] In some ways, the natural frequency and the resonance frequency of the affected limb can be determined in real time by using a measuring instrument, and the adjusted optimal vibration frequency of the bone and the adjusted optimal vibration frequency of the muscle are determined according to the real-time measured natural frequency and resonance frequency, on the basis of which the first oscillator model and the second oscillator model are adjusted. Wherein, the method for adjusting the first oscillator model according to the adjusted optimal vibration frequency of the bone is:

[0093]

[0094] Wherein, c is the external input excitation of the oscillator, which is determined according to the initially measured optimal vibration frequency of the bone. f bon_0 is the initially measured optimal vibration frequency of the bone, f bon_new is the adjusted optimal vibration frequency of the bone, f mus_new is the adjusted optimal vibration frequency of the muscle. φ() and ψ() are feedback item mapping functions, and ε in the function is a very small number to avoid zero in the denominator, for example, ε can be taken as 10 -7, μ is an adjustment factor, taking a value of 0.1, g() is an input item mapping function, which is a linear function about the initial measured optimal vibration frequency of the bone, k, b are weight values in the linear function, and the initial values are 0.3 and 10 respectively. T r ,T a The input item c and the feedback item are determined through an F() parameter mapping function, which is a non-deterministic function and is obtained through actual value training of the perceptron. The parameters α, β are determined through a trial-and-error method.

[0095] According to the adjusted optimal vibration frequency of the muscle, the second oscillator model is adjusted, and the method is as follows:

[0096]

[0097] wherein, f mus_0 is the initial measured optimal vibration frequency of the muscle, f mus_new is the adjusted optimal vibration frequency of the muscle, f bon_new is the adjusted optimal vibration frequency of the bone, and η is an influence factor of the bone rehabilitation on the muscle rehabilitation.

[0098] In the embodiment, in the process of wearing the auxiliary correction robot to assist in the rehabilitation correction of the affected limb, the recovery of the bone hard tissue and the muscle soft tissue is measured in real time at different rehabilitation stages, the optimal vibration frequency of the bone and the optimal vibration frequency of the muscle required for the next stage of rehabilitation are determined, the first oscillator model and the second oscillator model are adjusted according to the re-determined optimal vibration frequency of the bone and the optimal vibration frequency of the muscle, and the bone hard tissue and the muscle soft tissue are corrected based on the re-adjusted first oscillator model and the second oscillator model, so as to effectively promote the coordinated rehabilitation process of the bone hard tissue and the muscle soft tissue.

[0099] The embodiment of the application provides a muscle-bone coordinated vibration stimulation auxiliary deformity correction control method, which is used for adaptively controlling an auxiliary correction robot worn on an affected limb. On the one hand, a first fuzzy PID controller is used to perform feedback control on a pneumatic component according to the optimal vibration frequency of the bone and the feedback vibration frequency of the bone, the inflation and deflation frequency of the pneumatic component is controlled, and vibration stimulation applied to the bone hard tissue is adjusted; on the other hand, a second fuzzy PID controller is used to perform feedback control on an air bag according to the optimal vibration frequency of the muscle and the feedback vibration frequency of the muscle, and a hysteresis compensation signal is used to perform hysteresis compensation on the air bag, so as to reduce the control deviation, the inflation and deflation frequency of the air bag is controlled, and vibration stimulation applied to the muscle soft tissue is adjusted; on the other hand, in the rehabilitation process, the optimal vibration frequency of the bone and the optimal vibration frequency of the muscle are updated by using the real-time measured vibration frequency of the affected limb, the first oscillator model and the second oscillator model are updated, and the coordinated rehabilitation of the bone hard tissue and the muscle soft tissue is promoted.

[0100] It should be noted that the method of the embodiments of the present application can be executed by a single device, such as a computer or a server, etc. The method of the embodiments can also be applied to a distributed scenario, and be completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiments of the present application, and the multiple devices can interact with each other to complete the method.

[0101] It should be noted that the above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than those in the embodiments and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous.

[0102] As Figure 10 shown, the embodiments of the present application also provide a muscle-bone coordinated vibration stimulation assisted deformity correction control robot for controlling an assisted correction robot. The robot comprises:

[0103] A first construction module is configured to construct a first oscillator model according to a preset optimal vibration frequency of a bone;

[0104] A bone assisted rehabilitation module is configured to take the vibration frequency output by the first oscillator model and the vibration frequency collected by a bone vibration sensor as inputs of a first fuzzy PID controller, output a first pressure signal for controlling the inflation and deflation of a pneumatic component from the first fuzzy PID controller, and make the pneumatic component apply vibration stimulation to the bone hard tissue through inflation and deflation.

[0105] A second construction module is configured to construct a second oscillator model according to a preset optimal vibration frequency of a muscle;

[0106] A muscle assisted rehabilitation module is configured to take the vibration frequency output by the second vibration model and the vibration frequency collected by a muscle vibration sensor as inputs of a second fuzzy PID controller, output a second pressure signal for controlling the inflation and deflation of an air bag from the second fuzzy PID controller, take the vibration frequency output by the second vibration model as an input of a hysteresis compensation network, output a hysteresis compensation signal from the hysteresis compensation network, and make the air bag inflate and deflate according to the second pressure signal and the hysteresis compensation signal to apply vibration stimulation to the muscle soft tissue.

[0107] For the convenience of description, the above device is described in various modules according to functions. Of course, the functions of the modules can be implemented in the same or multiple software and / or hardware when implementing the embodiments of the present application.

[0108] The device of the above embodiments is used to implement the corresponding method in the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0109] Figure 11 A more specific electronic device hardware structure schematic diagram provided by the present embodiment is shown, which can include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 for internal communication.

[0110] The processor 1010 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the present embodiment.

[0111] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the present embodiment are implemented by software or firmware, the related program codes are saved in the memory 1020 and called and executed by the processor 1010.

[0112] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0113] The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize the communication interaction between the present device and other devices. The communication module can realize communication through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0114] The bus 1050 includes a path for transferring information between the various components (for example, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040) of the device.

[0115] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain the components necessary to implement the embodiments of the present application, and does not have to contain all the components shown in the figure.

[0116] The electronic device of the above embodiment is used to implement the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described here.

[0117] Those skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to limit the scope of the present disclosure (including claims) to these examples; under the idea of the present disclosure, the above embodiments or technical features in different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.

[0118] In addition, in order to simplify the description and discussion, and so as not to make the embodiments of the present application difficult to understand, the known power / ground connections of integrated circuit (IC) chips and other components can or can not be shown in the provided drawings. In addition, the devices can be shown in the form of block diagrams in order to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (i.e. these details should be fully within the understanding of those skilled in the art). Where specific details (e.g. circuits) are set forth in order to describe an exemplary embodiment of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations on these specific details. Therefore, these descriptions should be considered as illustrative rather than limiting.

[0119] Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g. dynamic RAM (DRAM)) can use the embodiments discussed.

[0120] Embodiments of the present application are intended to embrace all such alterations, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any and all such alterations, modifications, equivalents, improvements and the like are intended to be encompassed by the protected scope of this disclosure.

Claims

1. A myo-skeletal coordinated vibration stimulation assisted deformity correction control robot for controlling an assisted correction robot, characterized by, The auxiliary correction robot comprises a variable stiffness external fixator for correcting skeletal hard tissue, and a flexible corrector for correcting muscle soft tissue, the variable stiffness external fixator is provided with a pneumatic component and a skeletal vibration sensor, and the flexible corrector is provided with an air bag and a muscle vibration sensor; the robot comprises: A first construction module is configured to construct a first oscillator model according to a preset optimal vibration frequency of a bone; A skeletal auxiliary rehabilitation module is configured to take the vibration frequency output by the first oscillator model and the vibration frequency collected by the skeletal vibration sensor as inputs of a first fuzzy PID controller, output a first pressure signal for controlling inflation and deflation of the pneumatic component from the first fuzzy PID controller, and make the pneumatic component apply vibration stimulation to the skeletal hard tissue through inflation and deflation; A second construction module is configured to construct a second oscillator model according to a preset optimal vibration frequency of a muscle; A muscle auxiliary rehabilitation module is configured to take the vibration frequency output by the second oscillator model and the vibration frequency collected by the muscle vibration sensor as inputs of a second fuzzy PID controller, output a second pressure signal for controlling inflation and deflation of the air bag from the second fuzzy PID controller, take the vibration frequency output by the second oscillator model as an input of a hysteresis compensation network, output a hysteresis compensation signal from the hysteresis compensation network, and make the air bag inflate and deflate according to the second pressure signal and the hysteresis compensation signal to apply vibration stimulation to the muscle soft tissue; An adjustment module is configured to collect real-time vibration frequencies of the skeletal hard tissue and the muscle soft tissue by using a measuring instrument, adjust the optimal vibration frequency of the bone according to the real-time vibration frequency of the skeletal hard tissue, adjust the first oscillator model according to the adjusted optimal vibration frequency of the bone, adjust the optimal vibration frequency of the muscle according to the real-time vibration frequency of the muscle soft tissue, and adjust the second oscillator model according to the adjusted optimal vibration frequency of the muscle.

2. The robot of claim 1, wherein, The method for adjusting the first oscillator model according to the adjusted optimal vibration frequency of the bone is as follows: （6） wherein, N is the total number of oscillators, each oscillator contains extensor neuron and flexor neuron, τu and τd represent the rise time constant of excitation and the adaptation time constant of inhibition respectively, both determine the oscillation frequency of the oscillator output, Kij is the mutual inhibition coefficient between neurons, α is the adaptation coefficient, Wij represents the connection weight matrix from the i-th oscillator to the j-th oscillator, Wij represents the connection weight matrix from the i-th oscillator to the j-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, i xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, e xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, f xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, i xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, i xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, xi and xi+1 represent the internal excitation state of the extensor neuron and the flexor neuron of the i-th oscillator, 3. The robot according to claim 1 or 2, characterized in that The method for adjusting the second oscillator model according to the adjusted optimal vibration frequency of the muscle is as follows: （7） in, For the system space state variables of the oscillator, They are The first derivative with respect to time, It is the frequency of the oscillator. These are system parameters. Let be the energy function. Initial energy; This is the optimal vibration frequency of the muscle as initially determined. This is the optimal vibration frequency for the adjusted muscles. This is the optimal vibration frequency for the adjusted bones. Factors influencing skeletal rehabilitation on muscle rehabilitation.

4. The robot of claim 1, wherein, The first fuzzy PID controller comprises a first PID controller and a first fuzzy controller, the first fuzzy controller takes a vibration error between the vibration frequency output by the first oscillator model and the vibration frequency collected by the skeletal vibration sensor, and a change rate of the vibration error as inputs, and outputs a first control parameter, and the first PID controller takes the vibration error as an input, and outputs the first pressure signal according to the first control parameter.

5. The robot of claim 1, wherein, The second fuzzy PID controller comprises a second PID controller and a second fuzzy controller, the second fuzzy controller takes a vibration error between the vibration frequency output by the second oscillator model and the vibration frequency collected by the muscle vibration sensor, and a change rate of the vibration error as inputs, and outputs a second control parameter, and the second PID controller takes the vibration error as an input, and outputs the second pressure signal according to the second control parameter.

6. The robot of claim 1, wherein, The pneumatic component charges or discharges according to a pressure difference between the first pressure signal and a current pressure.

7. The robot of claim 1, wherein, The air bag charges or discharges according to a pressure difference between an accumulated signal and a current pressure; wherein the accumulated signal is a sum of the second pressure signal and the hysteresis compensation signal.

8. The robot of claim 1, wherein, The mathematical model of the hysteresis compensation network is: （4） wherein, represents the input of the network, i.e. the vibration frequency of the second oscillator model output; represents the output of the hysteresis compensation network, i.e. the pressure value used for performing the feedforward compensation; represents the weight coefficient of the input node of the input layer to the first neuron node of the first hidden layer, represents the weight coefficient of the first neuron node of the first hidden layer to the first neuron node of the second hidden layer, represents the weight coefficient of the first neuron node of the second hidden layer to the output node of the output layer, N is the number of neuron nodes of the first hidden layer, and M is the number of neuron nodes of the second hidden layer, represents the output of the first neuron node of the first hidden layer, is the output of the first neuron node of the second hidden layer.

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