Joint torque estimation and application method and device, computer equipment, readable storage medium and program product

Abstract

The application relates to a joint torque estimation and application method, device, equipment, storage medium and program product, and relates to the technical field of exoskeleton control. The method can improve the control accuracy of an exoskeleton. The method comprises the following steps: extracting a measurement matrix from an electromyogram signal, and performing compressed sensing processing on the electromyogram signal according to the measurement matrix; performing signal reconstruction on the obtained target electromyogram signal to obtain a sparse representation result, performing full-wave rectification processing on the sparse representation result to obtain a signal envelope; estimating an active component and a passive component by a biomechanical model according to the sparse representation result and the signal envelope, obtaining active force and passive force, estimating joint torque according to the active force, the passive force and a moving arm, and obtaining a joint torque value; obtaining an expected angular acceleration and an angular acceleration control parameter according to the joint torque value and a moment of inertia; and generating a control signal according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter. The control signal is used for exoskeleton control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of exoskeleton control, and in particular to a joint torque estimation and application method and device, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] Exoskeleton control technology is a technology that directly interacts with the human body through mechanical devices to achieve assisted force, enhanced force or replacement function of the human body. At present, exoskeleton control technology has a wide range of applications in medical rehabilitation, function enhancement, robot technology and other fields. For this technology, the cooperation of exoskeleton and muscle strength of the human body is crucial to the accuracy and comfort of motion execution.

[0003] In the traditional technology, the exoskeleton system mainly controls the movement of the exoskeleton by acquiring the movement intention of the user through the electromyographic signal. However, the electromyographic signal is easily affected by environmental electrical noise and muscle fatigue, which reduces the stability and reliability of the electromyographic signal, thereby causing the above method to have the problem of low control accuracy. SUMMARY

[0004] Therefore, it is necessary to provide a joint torque estimation and application method, device, computer equipment, computer readable storage medium and computer program product in view of the above technical problems.

[0005] In a first aspect, the present application provides a joint torque estimation and application method, comprising:

[0006] obtaining an electromyographic signal collected, extracting a measurement matrix from the electromyographic signal, and performing compressed sensing processing on the electromyographic signal according to the measurement matrix to obtain a target electromyographic signal;

[0007] performing signal reconstruction on the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, and performing full-wave rectification processing on the sparse representation result to obtain a signal envelope of the target electromyographic signal;

[0008] estimating an active component and a passive component according to the sparse representation result and the signal envelope through a biomechanical model to obtain an active force and a passive force, and estimating a joint torque according to the active force, the passive force and a movement arm to obtain a joint torque value;

[0009] obtaining an expected angular acceleration of the joint and an angular acceleration control parameter corresponding to the expected angular acceleration according to the joint torque value and the moment of inertia, and generating a control signal according to the angular control parameter, the angular velocity control parameter and the angular acceleration control parameter; the control signal is used for exoskeleton control.

[0010] In one of the embodiments, the full-wave rectification processing of the sparse representation result obtains a signal envelope of the target electromyography signal, including:

[0011] The alternating current signal in the sparse representation result is converted into a direct current signal of the same direction to obtain a full-wave rectified signal; the full-wave rectified signal is filtered by a low-pass filter and amplitude calculation to obtain an amplitude of the target electromyography signal; and the amplitude changes over time to form a smooth curve as the signal envelope.

[0012] In one of the embodiments, before the compressive sensing processing of the electromyography signal according to the measurement matrix, further including:

[0013] Obtaining a sparse basis, and performing sparse representation processing of the electromyography signal according to the sparse basis to obtain an electromyography signal in sparse representation;

[0014] The compressive sensing processing of the electromyography signal according to the measurement matrix includes:

[0015] Using the measurement matrix, performing compressive measurement on the electromyography signal in sparse representation to obtain a low-dimensional measurement signal, and performing compressive sensing processing of the electromyography signal according to the low-dimensional measurement signal.

[0016] In one of the embodiments, before the control signal is generated according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter, further including:

[0017] Generating the angle control parameter according to the current angle and the desired angle of the joint, generating the angular velocity control parameter according to the current angular velocity and the desired angular velocity of the joint, and generating the angular acceleration control parameter according to the current angular acceleration and the desired angular acceleration of the joint;

[0018] The control signal is generated according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter, including:

[0019] The angle control parameter, the angular velocity control parameter and the angular acceleration control parameter are input into a controller for fusion calculation according to a control strategy to obtain the control signal.

[0020] In one of the embodiments, before the joint torque value is obtained by estimating the joint torque according to the active force, the passive force and the moving arm, further including:

[0021] The moving arm is obtained according to the current angle of the joint and the moving arm coefficient.

[0022] The joint torque value is obtained by estimating the joint torque according to the active force, the passive force and the motion arm.

[0023] The muscle force is obtained by summing the active force and the passive force, and the joint torque value is obtained by inputting the muscle force and the motion arm into a joint torque estimation model.

[0024] In one of the embodiments, the method further comprises:

[0025] The motion data of the user when wearing the exoskeleton is obtained, and an adjustment amount of the joint torque value is determined according to the motion data; the joint torque value is adjusted according to the adjustment amount to obtain an updated current joint torque value; and the current joint torque value is used to replace the original joint torque value.

[0026] In a second aspect, the application further provides a joint torque estimation and application device, comprising:

[0027] The compression sensing module is configured to obtain the collected electromyographic signal, extract a measurement matrix from the electromyographic signal, perform compression sensing processing on the electromyographic signal according to the measurement matrix, and obtain a target electromyographic signal.

[0028] The full-wave rectification module is configured to perform signal reconstruction on the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, and perform full-wave rectification processing on the sparse representation result to obtain a signal envelope of the target electromyographic signal.

[0029] The torque estimation module is configured to estimate an active component and a passive component by a biomechanical model according to the sparse representation result and the signal envelope to obtain an active force and a passive force, and estimate a joint torque according to the active force, the passive force and a motion arm to obtain a joint torque value.

[0030] The torque application module is configured to obtain a desired angular acceleration of a joint and an angular acceleration control parameter corresponding to the desired angular acceleration according to the joint torque value and a moment of inertia, and generate a control signal according to an angle control parameter, an angular velocity control parameter and the angular acceleration control parameter; and the control signal is used for exoskeleton control.

[0031] In a third aspect, the application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] The acquired electromyographic signal is obtained, a measurement matrix is extracted from the electromyographic signal, compressive sensing processing is performed on the electromyographic signal according to the measurement matrix, and a target electromyographic signal is obtained; signal reconstruction is performed on the target electromyographic signal, a sparse representation result of the target electromyographic signal is obtained, full-wave rectification processing is performed on the sparse representation result, and a signal envelope of the target electromyographic signal is obtained; active components and passive components are estimated by a biomechanical model according to the sparse representation result and the signal envelope, active force and passive force are obtained, joint torque is estimated according to the active force, the passive force and a moving arm, and a joint torque value is obtained; expected angular acceleration of a joint and an angular acceleration control parameter corresponding to the expected angular acceleration are obtained according to the joint torque value and a moment of inertia, and a control signal is generated according to an angle control parameter, an angular velocity control parameter and the angular acceleration control parameter; the control signal is used for exoskeleton control.

[0033] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0034] The acquired electromyographic signal is obtained, a measurement matrix is extracted from the electromyographic signal, compressive sensing processing is performed on the electromyographic signal according to the measurement matrix, and a target electromyographic signal is obtained; signal reconstruction is performed on the target electromyographic signal, a sparse representation result of the target electromyographic signal is obtained, full-wave rectification processing is performed on the sparse representation result, and a signal envelope of the target electromyographic signal is obtained; active components and passive components are estimated by a biomechanical model according to the sparse representation result and the signal envelope, active force and passive force are obtained, joint torque is estimated according to the active force, the passive force and a moving arm, and a joint torque value is obtained; expected angular acceleration of a joint and an angular acceleration control parameter corresponding to the expected angular acceleration are obtained according to the joint torque value and a moment of inertia, and a control signal is generated according to an angle control parameter, an angular velocity control parameter and the angular acceleration control parameter; the control signal is used for exoskeleton control.

[0035] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, the computer program being executed by a processor to implement the following steps:

[0036] The acquired electromyographic signal is obtained, a measurement matrix is extracted from the electromyographic signal, a compressive sensing process is performed on the electromyographic signal according to the measurement matrix, and a target electromyographic signal is obtained; a signal reconstruction is performed on the target electromyographic signal, a sparse representation result of the target electromyographic signal is obtained, a full-wave rectification process is performed on the sparse representation result, and a signal envelope of the target electromyographic signal is obtained; an active component and a passive component are estimated by a biomechanical model according to the sparse representation result and the signal envelope, an active force and a passive force are obtained, a joint torque is estimated according to the active force, the passive force and a motion arm, and a joint torque value is obtained; according to the joint torque value and a moment of inertia, an expected angular acceleration of a joint and an angular acceleration control parameter corresponding to the expected angular acceleration are obtained, and a control signal is generated according to an angle control parameter, an angular velocity control parameter and the angular acceleration control parameter; the control signal is used for exoskeleton control.

[0037] The joint torque estimation and application method, device, computer equipment, computer readable storage medium and computer program product can accurately acquire and use electromyographic signals, effectively avoid the influence of environmental electrical noise, muscle fatigue and other factors on electromyographic signals, thereby improving the stability and reliability of electromyographic signals, improving the control accuracy of the exoskeleton to a certain extent, enhancing the use feeling of the user, and having important significance for promoting the research and application of the exoskeleton system; in addition, the scheme can reduce the complexity of the exoskeleton system while realizing the above technical effects, and relatively improve the control efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other related drawings according to these drawings without creative labor.

[0039] Figure 1 It is an application environment diagram of the joint torque estimation and application method in an embodiment;

[0040] Figure 2 It is a flowchart of the joint torque estimation and application method in an embodiment;

[0041] Figure 3 a flowchart of a signal envelope extraction step in an embodiment;

[0042] Figure 4 a flowchart of a joint torque estimation and application method in an embodiment;

[0043] Figure 5 a structural block diagram of a joint torque estimation and application device in an embodiment;

[0044] Figure 6 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0045] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0046] The joint torque estimation and application method provided by the embodiments of the present application can be applied in an application environment as shown in Figure 1 The terminal communicates with the server through the network. The data storage system can store data required to be processed by the server. The data storage system can be integrated on the server, or placed on the cloud or other network servers.

[0047] Specifically, the joint torque estimation and application method provided by the embodiments of the present application can be executed by the terminal.

[0048] For example, the terminal acquires the collected electromyographic signals, extracts a measurement matrix from the electromyographic signals, performs compressive sensing processing on the electromyographic signals according to the measurement matrix to obtain target electromyographic signals, performs signal reconstruction on the target electromyographic signals to obtain a sparse representation result of the target electromyographic signals, performs full-wave rectification processing on the sparse representation result to obtain a signal envelope of the target electromyographic signals, estimates active components and passive components according to the sparse representation result and the signal envelope through a biomechanical model to obtain active force and passive force, estimates joint torque according to the active force, the passive force and a moving arm to obtain a joint torque value, and finally obtains expected angular acceleration of the joint and angular acceleration control parameters corresponding to the expected angular acceleration according to the joint torque value and the moment of inertia, generates a control signal according to the angular control parameters, the angular velocity control parameters and the angular acceleration control parameters, and uses the control signal for exoskeleton control.

[0049] In the application environment as shown in Figure 1The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones and tablet computers. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0050] In one embodiment, as shown, a joint torque estimation and application method is provided, which is applied to a server in Figure 2 for example, and includes the following steps: Figure 1

[0051] Step S201, acquiring the collected electromyography signal, extracting a measurement matrix from the electromyography signal, and performing compressed sensing processing on the electromyography signal according to the measurement matrix to obtain a target electromyography signal.

[0052] The electromyography signal refers to an electrical activity signal generated by muscle contraction or relaxation, which can be collected by fixing an electromyography signal sensor on the skin surface of the user's body.

[0053] In compressed sensing (CS), the measurement matrix can be a key tool for converting a sparse signal (usually a high-dimensional vector) into a low-dimensional observation through linear measurement.

[0054] Specifically, the terminal acquires the collected electromyography signal in response to the joint torque estimation and application instruction, extracts a measurement matrix from the electromyography signal, and performs compressed sensing processing on the electromyography signal according to the measurement matrix to obtain a target electromyography signal. The basic idea of compressed sensing is that a small amount of non-adaptive linear projection measurement (i.e., measurement matrix) can completely represent the signal, and these measurement matrices can be directly obtained from the electromyography signal.

[0055] Step S202, signal reconstruction is performed on the target electromyography signal to obtain a sparse representation result of the target electromyography signal, and full-wave rectification processing is performed on the sparse representation result to obtain a signal envelope of the target electromyography signal.

[0056] The signal envelope can be a smooth curve of the amplitude of the signal changing with time, which usually represents the envelope line or contour of the signal.

[0057] Full-wave rectification can be to reverse all negative value signals to positive values, thereby obtaining the absolute value of the signal.

[0058] Sparse representation of the signal: first, we sparsely represent the electromyography signal by selecting a proper sparse basis (such as Fourier basis, wavelet basis or DCT basis, etc.). Let the original electromyography signal be x, and its sparse representation in the sparse basis Ψ be s, then:

[0059] ​

[0060] where s is a sparse coefficient vector, most of whose elements are zero or close to zero; a sparse signal refers to a signal in which most of the components are zero or close to zero in a high-dimensional space.

[0061] Measurement of the signal: Then, a random measurement matrix The measurement of the electromyographic signal x is y, that is:

[0062]

[0063]

[0064] where y is the measured signal actually stored and transmitted; is a measurement matrix of mxn, the elements of which follow a certain random distribution (such as Gaussian distribution); m represents the number of measurements; and Ψ is a sparse basis matrix of nxn.

[0065] In step S203, the active component and the passive component are estimated according to the sparse representation result and the signal envelope through a biomechanical model, the active force and the passive force are obtained, the joint torque is estimated according to the active force, the passive force and the moving arm, and the joint torque value is obtained.

[0066] where the biomechanical model can be a Hill-type muscle model, which can be used to describe the force-velocity characteristics and force-length characteristics of muscles under different conditions.

[0067] Specifically, the terminal estimates the active component and the passive component according to the sparse representation result and the signal envelope through the Hill-type muscle model, obtains the active force and the passive force, and then estimates the joint torque according to the active force, the passive force and the moving arm, and obtains the joint torque value.

[0068] In step S204, the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected angular acceleration are obtained according to the joint torque value and the moment of inertia, and the control signal is generated according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter; the control signal is used for exoskeleton control.

[0069] Specifically, the terminal obtains the expected angular acceleration of the joint according to the joint torque value and the moment of inertia, determines the angular acceleration control parameter according to the expected angular acceleration, and then calculates the control signal required for the control of the exoskeleton actuator according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter by using the PID control strategy; the control signal is used for exoskeleton control.

[0070] The joint torque estimation and application method can accurately acquire and use the electromyographic signals, effectively avoid the influence of environmental electric noise, muscle fatigue and other factors on the electromyographic signals, thereby improving the stability and reliability of the electromyographic signals, and improving the control accuracy of the exoskeleton to a certain extent, and enhancing the use feeling of the user, which has important significance for promoting the research and application of the exoskeleton system. In addition, the scheme can realize the above technical effects, and also reduces the complexity of the exoskeleton system, and relatively realizes the improvement of the control efficiency.

[0071] In one embodiment, as shown in Figure 3 The step S202 of full-wave rectification processing the sparse representation result to obtain the signal envelope of the target electromyographic signal includes the following steps.

[0072] Step S301 converts the alternating current signal in the sparse representation result into a direct current signal of the same direction to obtain the full-wave rectified signal.

[0073] Step S302 filters the full-wave rectified signal through a low-pass filter and calculates the amplitude to obtain the amplitude of the target electromyographic signal.

[0074] Step S303 forms a smooth curve of the amplitude changing with time as the signal envelope.

[0075] Specifically, the data rectification can be realized by using the following calculation formula:

[0076] Y(t) = abs(X(t))

[0077] In the above formula, X(t) is the input signal, and Y(t) is the full-wave rectified signal.

[0078] Then, the rectified signal is processed using a low-pass filter to delete the high-frequency part of the signal and retain the low-frequency part to obtain a smooth estimation of the signal amplitude. Then, the amplitude of the electromyographic signal is calculated as a new electromyographic signal amplitude E, and the process can be performed by using the following calculation formula:

[0079]

[0080] Finally, the terminal forms a smooth curve of the amplitude changing with time as the signal envelope.

[0081] In one embodiment, before the electromyographic signal is compressed sensing processed according to the measurement matrix, the method further comprises the following steps:

[0082] obtaining a sparse basis, and performing sparse representation processing on the electromyographic signal according to the sparse basis to obtain the electromyographic signal in sparse representation;

[0083] performing compressed sensing processing on the electromyographic signal according to the measurement matrix, specifically comprising the following steps:

[0084] performing compressed measurement on the electromyographic signal in sparse representation by using the measurement matrix to obtain a low-dimensional measurement signal, and performing compressed sensing processing on the electromyographic signal according to the low-dimensional measurement signal.

[0085] Sparse representation of signal: First, we sparsely represent the electromyographic signal by selecting a proper sparse basis (such as Fourier basis, wavelet basis or DCT basis, etc.). Let the original electromyographic signal be x, and its sparse representation in the sparse basis Ψ be s, then we have:

[0086]

[0087] where s is the sparse coefficient vector, most of whose elements are 0 or close to 0.

[0088] Measurement of signal: Then, the electromyographic signal x is measured by a random measurement matrix Φ to obtain y, i.e.

[0089]

[0090]

[0091] where y is the measurement signal actually stored and transmitted; Φ is an m×n measurement matrix, whose elements follow a certain random distribution (such as Gaussian distribution); m represents the number of measurements; Ψ is an n×n sparse basis matrix.

[0092] Since the representation s of the electromyographic signal x in the basis Ψ is sparse, we can obtain the sparse representation s by solving the following optimization problem:

[0093]

[0094] In one embodiment, before the control signal is generated according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter, the method further comprises the following steps:

[0095] generating the angle control parameter according to the current angle and the desired angle of the joint, generating the angular velocity control parameter according to the current angular velocity and the desired angular velocity of the joint, and generating the angular acceleration control parameter according to the current angular acceleration and the desired angular acceleration of the joint;

[0096] According to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter, a control signal is generated, specifically including the following steps:

[0097] The angle control parameter, the angular velocity control parameter and the angular acceleration control parameter are input into the controller for fusion calculation according to the control strategy to obtain the control signal.

[0098] Specifically, based on the electromyographic signal driven exoskeleton motion control model, the calculated torque value is directly input into the control logic of the exoskeleton as a control parameter. In this process, the estimated torque value is used to calculate the desired angular velocity of the joint.

[0099] According to the basic principle of joint dynamics, the relationship between the torque value T and the angular acceleration α of the joint can be represented by the following calculation formula:

[0100]

[0101] In the above formula, I is the moment of inertia of the joint, ω is the angular velocity of the joint, d is the damping coefficient, and τ is the static friction torque of the joint.

[0102] The joint damping d and the joint static friction torque τ of the exoskeleton have negligible effect on the joint, so the above formula can be simplified as:

[0103]

[0104] Therefore, the angular acceleration α of the joint can be calculated according to the estimated torque value T and the known moment of inertia I.

[0105] Then, according to the calculated desired joint angular acceleration α and the current angular velocity ω and angle θ, the control signal u required to control the exoskeleton actuator is calculated using the PID control strategy:

[0106]

[0107] Where θd, ωd and are the desired joint angle, angular velocity and angular acceleration respectively, and Kp, Ki and Kd are the proportional, integral and derivative coefficients of the PID controller respectively.

[0108] Then, this control signal is sent to the driver of the exoskeleton to generate a corresponding torque to drive the motion of the exoskeleton. Finally, the user's motion data such as joint angle, angular velocity, electromyographic signal, etc. are collected and analyzed in real time, and the estimated torque value is adjusted to make the motion of the exoskeleton more in line with the user's wishes.

[0109] The mathematical variables involved in the above steps are as follows:

[0110] T: torque, I: moment of inertia of the joint, a: angular acceleration, w: angular velocity, d: damping coefficient, T: static friction torque, Q: angle, Qd: desired angle, w: desired angular velocity, u: control signal, Kp, Ki, Kd: proportional, integral and derivative coefficients of the PID controller.

[0111] In one embodiment, the joint torque is estimated according to the active force, the passive force and the moving arm, and before obtaining the joint torque value, the method further comprises the following steps:

[0112] According to the current angle of the joint and the moving arm coefficient, the moving arm is obtained.

[0113] The joint torque is estimated according to the active force, the passive force and the moving arm, and the joint torque value is obtained, which comprises the following steps:

[0114] The muscle force is obtained by summing the active force and the passive force; the muscle force and the moving arm are input into the joint torque estimation model to obtain the joint torque value output by the joint torque estimation model.

[0115] Specifically, since the relationship between the muscle force and the envelope of the surface electromyogram signal is relatively nonlinear, the surface electromyogram signal is first mapped to the range of 0-100 through linear normalization, and then a calculation formula is introduced according to the nonlinear relationship between the muscle force and the surface electromyogram signal:

[0116]

[0117] In the above formula, and are the surface electromyogram signal values after linear normalization and nonlinear normalization, respectively, is a curvature exponent constant of a predefined nonlinear curve.

[0118] First, the reconstructed electromyogram signal and the extracted signal envelope are decoded into the electromyogram signal amplitude E. Then, the active component is estimated:

[0119] Active force:

[0120] Where M represents the active force, a is a calculation constant, and E is the extracted electromyogram signal amplitude.

[0121] At the same time, the muscle deformation L is obtained according to the measured muscle length change, and the passive component is estimated:

[0122] Passive force:

[0123] Where P represents passive force, b is a coefficient, and L is muscle deformation.

[0124] Muscle force: F = M + P,

[0125] Where F represents muscle force.

[0126] Then, the motion arm associated with the joint angle is measured: R = c * Θ, where R is the motion arm, c is a coefficient, and Θ is the angle of the joint.

[0127] Next, the torque is calculated using the muscle force and the motion arm: T = F * R, where T is the torque.

[0128] Then, the calculated torque value is used for torque feedback adjustment, and the estimated torque value is introduced into the control loop of the exoskeleton to adjust the movement of the exoskeleton in real time, making it more consistent with the user's movement intention.

[0129] The mathematical variables involved in the above steps are as follows:

[0130] F: muscle force, M: active force, a: constant, E: muscle signal amplitude, P: passive force, b: coefficient, L: muscle deformation, T: torque, R: motion arm, c: coefficient, Θ: joint angle.

[0131] In one embodiment, the method of the present application further comprises the following steps:

[0132] Obtaining movement data of the user when wearing the exoskeleton, determining the adjustment amount of the joint torque value according to the movement data, and feeding back adjusting the joint torque value according to the adjustment amount to obtain an updated current joint torque value; the current joint torque value is used to replace the original joint torque value.

[0133] Specifically, a green converter station under construction is taken as an example. First, the electromyographic signal sensors are fixed on the upper limb muscles of the experimenter, such as the biceps brachii, triceps brachii, and forearm muscles. These electromyographic signal sensors are selected according to the size of the muscle and the direction of the muscle fiber. After the electromyographic signal acquisition device is started, it begins to collect electromyographic signals, which are then amplified by an amplifier. The obtained signals include electromyographic signals with a size of about 5 μV and other noise signals.

[0134] In the signal preliminary processing stage, the compressed sensing technology is used to process the amplified electromyography signal. The DCT is used as the sparse base to perform sparse representation on the electromyography signal. A Gaussian random measurement matrix is selected to measure the electromyography signal, and 100 data are collected with the noise level controlled at 0.1. In the signal reconstruction stage, the L1 norm minimization algorithm is used to reconstruct the measurement signal, and the calculated electromyography signal amplitude is about 4.8 μV. In the signal envelope extraction stage, the full-wave rectification and the first-order Butterworth low-pass filter are used to process the reconstructed electromyography signal to obtain the envelope of the surface electromyography signal reflecting the amplitude change of the muscle strength. In the signal normalization stage, the surface electromyography signal is mapped to the range of 0-100, and then nonlinear normalization is performed according to the nonlinear relationship between the muscle strength and the surface electromyography signal. The curvature index constant in the formula is selected as 0.6. In the torque estimation stage, the Hill muscle model is used to estimate the torque according to the reconstructed electromyography signal and the electromyography signal amplitude obtained from the envelope. Assuming that the calculation constant a is 0.005, the coefficient b is 0.1, the coefficient c is 0.002, and the joint angle is 45 degrees, the calculated torque value is about 0.8 Nm. In the torque feedback adjustment stage, the estimated torque value is introduced into the control link of the exoskeleton to calculate the expected joint angular velocity. The proportional, integral, and differential coefficients of the PID controller are designed based on this, and then sent to the driver of the exoskeleton for real-time feedback adjustment of the estimated torque value, so that the motion of the exoskeleton is more in line with the user's intention.

[0135] According to this embodiment, it can be seen that the scheme in the application can estimate the joint torque in real time and accurately, which provides strong support for the operation of the green converter station. Through the above series of processing, accurate analysis of the user can be realized, the dynamic information of the user's body is transmitted to the exoskeleton without loss, and fine control is performed, so that the exoskeleton can respond to the operation instruction of the user sensitively, and better experience is provided for the user.

[0136] In one embodiment, as shown in Figure 4 a method for estimating and applying joint torque in a specific embodiment is provided, which specifically includes the following steps:

[0137] In step S401, the collected electromyography signal and sparse base are obtained, the electromyography signal is processed for sparse representation according to the sparse base, and the electromyography signal under sparse representation is obtained. The measurement matrix is used to compress and measure the electromyography signal under sparse representation to obtain a low-dimensional measurement signal, and the electromyography signal is processed by compressed sensing according to the low-dimensional measurement signal to obtain a target electromyography signal.

[0138] In step S402, the target electromyographic signal is subjected to signal reconstruction to obtain a sparse representation result of the target electromyographic signal, the alternating current signal in the sparse representation result is converted into a direct current signal in the same direction to obtain a full-wave rectified signal, the full-wave rectified signal is subjected to filtering processing by a low-pass filter and amplitude calculation to obtain an amplitude of the target electromyographic signal, and the amplitude is changed with time to form a smooth curve as a signal envelope.

[0139] In step S403, the active component and the passive component are estimated according to the sparse representation result and the signal envelope by a biomechanical model to obtain active force and passive force.

[0140] In step S404, a motion arm is obtained according to a current angle of the joint and a motion arm coefficient, the active force and the passive force are summed to obtain muscle force, and the muscle force and the motion arm are input into a joint torque estimation model to obtain a joint torque value output by the joint torque estimation model.

[0141] In step S405, a desired angular acceleration of the joint and an angular acceleration control parameter corresponding to the desired angular acceleration are obtained according to the joint torque value and a moment of inertia.

[0142] In step S406, an angle control parameter is generated according to a current angle and a desired angle of the joint, an angular velocity control parameter is generated according to a current angular velocity and a desired angular velocity of the joint, and an angular acceleration control parameter is generated according to a current angular acceleration and a desired angular acceleration of the joint, the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter are input into a controller for fusion calculation according to a control strategy to obtain a control signal, and the control signal is used for exoskeleton control.

[0143] In step S407, motion data of a user when wearing an exoskeleton is acquired, an adjustment amount of the joint torque value is determined according to the motion data, the joint torque value is adjusted according to the adjustment amount to obtain an updated current joint torque value, and the current joint torque value is used to replace the original joint torque value.

[0144] The beneficial effects brought by the above embodiments are as follows:

[0145] The scheme in the application can accurately acquire and use electromyographic signals, effectively avoid the influence of environmental electric noise, muscle fatigue and other factors on the electromyographic signals, thereby improving the stability and reliability of the electromyographic signals, improving the control accuracy of the exoskeleton to some extent, enhancing the use feeling of the user, and having important significance for promoting the research and application of the exoskeleton system; in addition, the scheme also reduces the complexity of the exoskeleton system while realizing the above technical effects, and relatively improves the control efficiency.

[0146] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0147] Based on the same inventive concept, the embodiments of the present application also provide a joint torque estimation and application device for implementing the joint torque estimation and application method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more joint torque estimation and application device embodiments provided below can refer to the limitations of the joint torque estimation and application method described above, which will not be repeated here.

[0148] In an exemplary embodiment, as shown in Figure 5 A joint torque estimation and application device is provided, comprising:

[0149] The compression sensing module is configured to acquire the collected electromyographic signal, extract a measurement matrix from the electromyographic signal, perform compression sensing processing on the electromyographic signal according to the measurement matrix, and obtain a target electromyographic signal.

[0150] The full-wave rectification module is configured to perform signal reconstruction on the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, and perform full-wave rectification processing on the sparse representation result to obtain a signal envelope of the target electromyographic signal.

[0151] The torque estimation module is configured to estimate an active component and a passive component according to the sparse representation result and the signal envelope through a biomechanical model, obtain an active force and a passive force, estimate a joint torque according to the active force, the passive force and a moving arm, and obtain a joint torque value.

[0152] The torque application module is configured to obtain a desired angular acceleration of the joint and an angular acceleration control parameter corresponding to the desired angular acceleration according to the joint torque value and the moment of inertia, and generate a control signal according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter. The control signal is used for exoskeleton control.

[0153] In one embodiment, the full-wave rectification module is further configured to convert the alternating current signal in the sparse representation result into a direct current signal of the same direction to obtain a full-wave rectified signal; filter the full-wave rectified signal through a low-pass filter and calculate the amplitude to obtain the amplitude of the target electromyography signal; and form a smooth curve according to the change of the amplitude over time as a signal envelope.

[0154] In one embodiment, the joint torque estimation and application device further comprises a sparse representation module configured to obtain a sparse basis, perform sparse representation processing on the electromyography signal according to the sparse basis to obtain the electromyography signal in sparse representation; and a compressed sensing module configured to perform compressed measurement on the electromyography signal in sparse representation by using a measurement matrix to obtain a low-dimensional measurement signal, and perform compressed sensing processing on the electromyography signal according to the low-dimensional measurement signal.

[0155] In one embodiment, the joint torque estimation and application device further comprises a parameter acquisition module configured to generate an angle control parameter according to a current angle and a desired angle of the joint, generate an angular velocity control parameter according to a current angular velocity and a desired angular velocity of the joint, and generate an angular acceleration control parameter according to a current angular acceleration and a desired angular acceleration of the joint; and a torque application module configured to input the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter into a controller for fusion calculation according to a control strategy to obtain a control signal.

[0156] In one embodiment, the joint torque estimation and application device further comprises a motion arm acquisition module configured to obtain a motion arm according to a current angle of the joint and a motion arm coefficient; and a torque estimation module configured to sum the active force and the passive force to obtain muscle force, and input the muscle force and the motion arm into a joint torque estimation model to obtain a joint torque value output by the joint torque estimation model.

[0157] In one embodiment, the joint torque estimation and application device further comprises a torque adjustment module configured to obtain motion data of a user when wearing the exoskeleton, determine an adjustment amount of the joint torque value according to the motion data, feed back the adjustment of the joint torque value according to the adjustment amount to obtain an updated current joint torque value, and replace the original joint torque value with the current joint torque value.

[0158] The above-mentioned modules of the joint torque estimation and application device can be realized by software, hardware and combinations thereof, in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0159] In one exemplary embodiment, a computer device is provided, which can be a terminal, and the internal structure diagram thereof can be as shown in Figure 6The computer device shown in the figure includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, near field communication (NFC) or other technologies. The computer program is executed by the processor to realize a joint torque estimation and application method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0160] Those skilled in the art can understand that, Figure 6 The skilled in the art can understand that,

[0161] In one embodiment, a computer device is also provided, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps in the above method embodiments.

[0162] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to realize the steps in the above method embodiments.

[0163] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to realize the steps in the above method embodiments.

[0164] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0165] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. In the embodiments provided in the present application, any reference to memory, database or other medium can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0166] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, any combination of these technical features is deemed to be within the scope of the present application.

[0167] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for estimating and applying joint torque, characterized in that, The method includes: Acquire the collected electromyographic (EMG) signals, extract the measurement matrix from the EMG signals, obtain the sparse basis, perform sparse representation processing on the EMG signals according to the sparse basis to obtain the EMG signals under sparse representation; use the measurement matrix to perform compressed measurement on the EMG signals under sparse representation to obtain low-dimensional measurement signals, and perform compressed sensing processing on the EMG signals according to the low-dimensional measurement signals to obtain the target EMG signal; The target electromyography (EMG) signal is reconstructed to obtain a sparse representation of the target EMG signal. The alternating current signal in the sparse representation is converted into a direct current signal in the same direction to obtain a full-wave rectified signal. The full-wave rectified signal is then filtered by a low-pass filter and its amplitude is calculated to obtain the amplitude of the target EMG signal. The amplitude variation over time is used to form a smooth curve as the signal envelope of the target EMG signal. Based on the sparse representation results and the signal envelope, the active and passive components are estimated using a biomechanical model to obtain the active force and passive force. Based on the active force, the passive force, and the moving arm, the joint torque is estimated to obtain the joint torque value. Based on the joint torque value and moment of inertia, the desired angular acceleration of the joint and the corresponding angular acceleration control parameters are obtained. Based on the angle control parameters, angular velocity control parameters and the angular acceleration control parameters, a control signal is generated; the control signal is used for exoskeleton control.

2. The method according to claim 1, characterized in that, Before generating the control signal based on the angle control parameters, angular velocity control parameters, and angular acceleration control parameters, the method further includes: The angle control parameters are generated based on the current angle and the desired angle of the joint; the angular velocity control parameters are generated based on the current angular velocity and the desired angular velocity of the joint; and the angular acceleration control parameters are generated based on the current angular acceleration and the desired angular acceleration of the joint. The step of generating a control signal based on the angle control parameters, angular velocity control parameters, and angular acceleration control parameters includes: The angle control parameters, angular velocity control parameters, and angular acceleration control parameters are input into the controller and fused together according to the control strategy to obtain the control signal.

3. The method according to claim 1, characterized in that, Before estimating the joint torque value based on the active force, the passive force, and the moving arm, the method further includes: The motion arm is obtained based on the current angle of the joint and the motion arm coefficient; The step of estimating the joint torque based on the active force, the passive force, and the moving arm to obtain the joint torque value includes: The muscle force is obtained by summing the active force and the passive force. The muscle strength and the moving arm are input into the joint torque estimation model to obtain the joint torque value output by the joint torque estimation model.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Acquire motion data of the user when equipped with the exoskeleton, and determine the adjustment amount of the joint torque value based on the motion data; Based on the adjustment amount, the joint torque value is adjusted accordingly to obtain the updated current joint torque value; the current joint torque value is used to replace the original joint torque value.

5. A device for estimating and applying joint torque, characterized in that, The device includes: The compressed sensing module is used to acquire the collected electromyographic (EMG) signals, extract a measurement matrix from the EMG signals, obtain a sparse basis, perform sparse representation processing on the EMG signals according to the sparse basis to obtain the EMG signals under sparse representation; use the measurement matrix to perform compressed measurement on the EMG signals under sparse representation to obtain a low-dimensional measurement signal, and perform compressed sensing processing on the EMG signals according to the low-dimensional measurement signal to obtain the target EMG signal; A full-wave rectification module is used to reconstruct the target electromyography (EMG) signal to obtain a sparse representation of the target EMG signal. The alternating current (AC) signal in the sparse representation is converted into a direct current (DC) signal in the same direction to obtain a fully rectified signal. The fully rectified signal is then filtered by a low-pass filter and its amplitude is calculated to obtain the amplitude of the target EMG signal. The amplitude variation over time is used to form a smooth curve, which serves as the signal envelope of the target EMG signal. The torque estimation module is used to estimate the active and passive components based on the sparse representation results and the signal envelope through a biomechanical model to obtain the active force and passive force, and to estimate the joint torque based on the active force, the passive force and the moving arm to obtain the joint torque value. The torque application module is used to obtain the desired angular acceleration of the joint and the corresponding angular acceleration control parameters based on the joint torque value and moment of inertia, and to generate a control signal based on the angle control parameters, angular velocity control parameters and the angular acceleration control parameters; the control signal is used for exoskeleton control.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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