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

Abstract

The invention relates to a joint torque estimation and application method, device and equipment, a storage medium and a program product, and relates to the technical field of exoskeleton control. The method can improve the control precision of the exoskeleton. The method comprises the following steps: extracting a measurement matrix from an electromyographic signal, and performing compressed sensing processing on the electromyographic signal according to the measurement matrix; performing signal reconstruction on the obtained target electromyographic signal to obtain a sparse representation result, and performing full-wave rectification processing on the sparse representation result to obtain a signal envelope; according to the sparse representation result and the signal envelope, active components and passive components are estimated through a biomechanical model, active force and passive force are obtained, joint torque is estimated according to the active force, the passive force and the motion arm, and a joint torque value is obtained; obtaining an expected angular acceleration and an angular acceleration control parameter according to the joint torque value and the rotational 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 field of exoskeleton control technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for estimating and applying joint torque. Background Art

[0002] Exoskeleton control technology is a technology that directly interacts with the human body through mechanical devices to achieve auxiliary force, enhancement force or replacement function of the human body. At present, exoskeleton control technology is widely used in medical rehabilitation, functional enhancement, robotics and other fields. For this technology, the coordination of exoskeleton and human muscle strength is crucial to the accuracy and comfort of its action execution.

[0003] Traditional exoskeleton systems mainly use electromyographic signals to obtain the user's movement intention to control the movement of the exoskeleton. However, electromyographic signals are easily affected by factors such as environmental electrical noise and muscle fatigue, which reduces the stability and reliability of electromyographic signals, resulting in the problem of low control accuracy in the above method. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for estimating and applying joint torque in response to the above-mentioned technical problems.

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

[0006] Acquire the collected electromyographic signal, extract a measurement matrix from the electromyographic signal, perform compressed sensing processing on the electromyographic signal according to the measurement matrix, and 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] Estimate active components and passive components through a biomechanical model according to the sparse representation result and the signal envelope to obtain active force and passive force, and estimate joint torque according to the active force, the passive force and the motion arm to obtain a joint torque value;

[0009] According to the joint torque value and the moment of inertia, the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected angular acceleration are obtained, and a 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.

[0010] In one embodiment, the step of performing full-wave rectification on the sparse representation result to obtain the signal envelope of the target electromyographic signal includes:

[0011] 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 filtered and the amplitude is calculated through a low-pass filter to obtain the amplitude of the target electromyographic signal; the amplitude changes over time to form a smooth curve as the signal envelope.

[0012] In one of the embodiments, before performing compressed sensing processing on the electromyographic signal according to the measurement matrix, the method further includes:

[0013] Obtaining a sparse basis, and performing sparse representation processing on the electromyographic signal according to the sparse basis to obtain the electromyographic signal under sparse representation;

[0014] The performing compressed sensing processing on the electromyographic signal according to the measurement matrix includes:

[0015] The measurement matrix is ​​used to compress and measure the electromyographic signal under the sparse representation to obtain a low-dimensional measurement signal, and the electromyographic signal is subjected to compressive sensing processing according to the low-dimensional measurement signal.

[0016] In one embodiment, before generating the control signal according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter, the method further comprises:

[0017] Generate the angle control parameter according to the current angle and the expected angle of the joint, generate the angular velocity control parameter according to the current angular velocity and the expected angular velocity of the joint, and generate the angular acceleration control parameter according to the current angular acceleration and the expected angular acceleration of the joint;

[0018] The step of generating a control signal according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter comprises:

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

[0020] In one embodiment, the estimating the joint torque according to the active force, the passive force and the motion arm, before obtaining the joint torque value, further comprises:

[0021] Obtaining the motion arm according to the current angle of the joint and the motion arm coefficient;

[0022] The estimating the joint torque according to the active force, the passive force and the motion arm to obtain the joint torque value includes:

[0023] The active force and the passive force are summed to obtain muscle force; the muscle force and the motion arm are input into a joint torque estimation model to obtain the joint torque value output by the joint torque estimation model.

[0024] In one embodiment, the method further comprises:

[0025] The motion data of the user when wearing the exoskeleton is obtained, and the 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; the current joint torque value is used to replace the original joint torque value.

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

[0027] A compressed sensing module is used to obtain the collected electromyographic signal, extract a measurement matrix from the electromyographic signal, and perform compressed sensing processing on the electromyographic signal according to the measurement matrix to obtain a target electromyographic signal;

[0028] A full-wave rectification module is used 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] A torque estimation module, used for estimating active components and passive components through a biomechanical model according to the sparse representation result and the signal envelope to obtain active force and passive force, and estimating joint torque according to the active force, the passive force and the motion arm to obtain a joint torque value;

[0030] The torque application module is used to obtain the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected 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.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Acquire the collected electromyographic signal, extract the measurement matrix from the electromyographic signal, perform compressed sensing processing on the electromyographic signal according to the measurement matrix to obtain the target electromyographic signal; perform signal reconstruction on the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, perform full-wave rectification processing on the sparse representation result to obtain the signal envelope of the target electromyographic signal; estimate the active component and the passive component through the biomechanical model according to the sparse representation result and the signal envelope to obtain the active force and the passive force, estimate the joint torque according to the active force, the passive force and the moving arm to obtain the joint torque value; obtain the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected 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.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0034] Acquire the collected electromyographic signal, extract the measurement matrix from the electromyographic signal, perform compressed sensing processing on the electromyographic signal according to the measurement matrix to obtain the target electromyographic signal; perform signal reconstruction on the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, perform full-wave rectification processing on the sparse representation result to obtain the signal envelope of the target electromyographic signal; estimate the active component and the passive component through the biomechanical model according to the sparse representation result and the signal envelope to obtain the active force and the passive force, estimate the joint torque according to the active force, the passive force and the moving arm to obtain the joint torque value; obtain the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected 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.

[0035] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0036] Acquire the collected electromyographic signal, extract the measurement matrix from the electromyographic signal, perform compressed sensing processing on the electromyographic signal according to the measurement matrix to obtain the target electromyographic signal; perform signal reconstruction on the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, perform full-wave rectification processing on the sparse representation result to obtain the signal envelope of the target electromyographic signal; estimate the active component and the passive component through the biomechanical model according to the sparse representation result and the signal envelope to obtain the active force and the passive force, estimate the joint torque according to the active force, the passive force and the moving arm to obtain the joint torque value; obtain the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected 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.

[0037] The above-mentioned estimation and application method, device, computer equipment, computer-readable storage medium and computer program product of joint torque, by collecting electromyographic signals on the surface of the user's skin, compressing and sensing the electromyographic signals and reconstructing the signals, screening out effective signal information, and performing full-wave rectification on the effective signals to extract the signal envelope, and then estimating the joint torque through a biomechanical model and using a high-order statistical method, and finally feeding back the estimated joint torque value into the control link of the exoskeleton, realizing the function of real-time adjustment of the exoskeleton movement, making it more in line with the user's movement intention. The scheme in this application can accurately obtain 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, and to a certain extent improve the control accuracy of the exoskeleton, while enhancing the user's use feeling, which is of great significance for promoting the research and development and application of exoskeleton systems; in addition, while achieving the above-mentioned technical effects, this scheme also reduces the complexity of the exoskeleton system and relatively improves the control efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0039] FIG1 is a diagram of an application environment of a method for estimating and applying joint torque in one embodiment;

[0040] Figure 2 is a flow chart of a method for estimating and applying joint torque in one embodiment;

[0041] Figure 3 Schematic diagram of a flow chart of a signal envelope extraction step in one embodiment;

[0042] Figure 4 is a flow chart of a method for estimating and applying joint torque in a specific embodiment;

[0043] Figure 5 is a structural block diagram of a device for estimating and applying joint torque in one embodiment;

[0044] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] The joint torque estimation and application method provided in the embodiment of the present application can be applied in the application environment as shown in FIG1 . The terminal communicates with the server via a network. The data storage system can store data that the server needs to process. The data storage system can be integrated on the server, or placed on the cloud or other network servers.

[0047] Specifically, the method for estimating and applying joint torque provided in the embodiment of the present application can be executed by a terminal.

[0048] Exemplarily, the terminal acquires the collected electromyographic signal, extracts the measurement matrix from the electromyographic signal, performs compressed sensing processing on the electromyographic signal according to the measurement matrix, and obtains the target electromyographic signal; then, the target electromyographic signal is reconstructed to obtain a sparse representation result of the target electromyographic signal, and the sparse representation result is full-wave rectified to obtain the signal envelope of the target electromyographic signal; then, the active component and the passive component are estimated through the biomechanical model according to the sparse representation result and the signal envelope to obtain the active force and the passive force, and the joint torque is estimated according to the active force, the passive force and the moving arm to obtain the joint torque value; finally, according to the joint torque value and the moment of inertia, the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected angular acceleration are obtained, 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.

[0049] In the application environment shown in FIG1 , the terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers. The server may be implemented as an independent server or a server cluster consisting of multiple servers.

[0050] In one embodiment, Figure 2 As shown, a method for estimating and applying joint torque is provided, and the method is applied to the server in FIG1 as an example for explanation, including the following steps:

[0051] Step S201, acquiring the collected electromyographic signal, 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.

[0052] Among them, electromyographic signals refer to electrical activity signals generated by muscles when they contract or relax, which can be collected by fixing electromyographic signal sensors on the skin surface of the user's body.

[0053] In Compressed Sensing (CS), the measurement matrix can be a key tool to transform sparse signals (usually high-dimensional vectors) into low-dimensional observations through linear measurements.

[0054] Specifically, the terminal responds to the estimation and application instructions of the joint torque, obtains the collected electromyographic signal, extracts the measurement matrix from the electromyographic signal, and performs compressed sensing processing on the electromyographic signal according to the measurement matrix to obtain the target electromyographic signal. The basic idea of ​​compressed sensing is that only a small number of non-adaptive linear projection measurements (i.e., measurement matrices) are needed to fully represent the signal, and these measurement matrices can be directly obtained from the electromyographic signal.

[0055] Step S202, reconstructing 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.

[0056] The signal envelope may refer to a smooth curve showing how the amplitude of a signal changes over time, usually representing the envelope or contour of the signal.

[0057] Among them, full-wave rectification can be to invert all negative signals to positive values, so as to obtain the absolute value of the signal.

[0058] Sparse representation of signals: First, we sparsely represent the EMG signal by selecting an appropriate sparse basis (such as Fourier basis, wavelet basis or DCT basis, etc.). Let the original EMG signal be x, and its sparse representation on the sparse basis Ψ be s, then:

[0059]

[0060] Among them, 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 components are zero or close to zero in a high-dimensional space.

[0061] Signal measurement: Then, through a random measurement matrix The electromyographic signal x is measured to obtain y, that is:

[0062]

[0063]

[0064] Among them, y is the measurement signal actually stored and transmitted; is an m×n measurement matrix whose elements obey a random distribution (such as Gaussian distribution); m represents the number of measurements; Ψ is an n×n sparse basis matrix.

[0065] Step S203, estimate the active component and the passive component through the biomechanical model according to the sparse representation result and the signal envelope to obtain the active force and the passive force, and estimate the joint torque according to the active force, the passive force and the moving arm to obtain the joint torque value.

[0066] The biomechanical model may 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 through the Hill-type muscle model according to the sparse representation result and the signal envelope to obtain 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 to obtain the joint torque value.

[0068] Step S204, obtaining the desired angular acceleration of the joint and the angular acceleration control parameter corresponding to the desired angular acceleration according to the joint torque value and the 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.

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

[0070] The above-mentioned method for estimating and applying joint torque collects electromyographic signals on the surface of the user's skin, performs compressed sensing processing and signal reconstruction on the electromyographic signals, screens out effective signal information, performs full-wave rectification processing on the effective signals to extract the signal envelope, and then estimates the joint torque through a biomechanical model and using a high-order statistical method. Finally, the estimated joint torque value is fed back into the control link of the exoskeleton to achieve the function of real-time adjustment of the exoskeleton movement, making it more in line with the user's movement intention. The scheme in this application 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, and to a certain extent improves the control accuracy of the exoskeleton, while enhancing the user's feeling of use, which is of great significance for promoting the research and development and application of exoskeleton systems; in addition, while achieving the above-mentioned technical effects, this scheme also reduces the complexity of the exoskeleton system and relatively improves the control efficiency.

[0071] In one embodiment, if Figure 3 As shown, in the above step S202, the sparse representation result is subjected to full-wave rectification processing to obtain the signal envelope of the target electromyographic signal, which specifically includes the following steps:

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

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

[0074] Step S303: forming a smooth curve by changing the amplitude over time as the signal envelope.

[0075] Specifically, data rectification can be achieved 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 signal after full-wave rectification.

[0078] Then, a low-pass filter is used to process the rectified signal, deleting the high-frequency part of the signal and retaining the low-frequency part to obtain a smooth estimate of the signal amplitude. After that, the amplitude of the electromyographic signal is calculated as the new electromyographic signal amplitude E. This process can be performed using the following calculation formula:

[0079]

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

[0081] In one embodiment, before performing compressed sensing processing on the electromyographic signal according to the measurement matrix, the following steps are specifically included:

[0082] Obtain a sparse basis, perform sparse representation processing on the electromyographic signal according to the sparse basis, and obtain the electromyographic signal under sparse representation;

[0083] Compressed sensing processing of electromyographic signals is performed according to the measurement matrix, which specifically includes the following steps:

[0084] The measurement matrix is ​​used to compress and measure the electromyographic signal under sparse representation to obtain a low-dimensional measurement signal, and the electromyographic signal is subjected to compressed sensing processing based on the low-dimensional measurement signal.

[0085] Sparse representation of signals: First, we sparsely represent the EMG signal by selecting an appropriate sparse basis (such as Fourier basis, wavelet basis or DCT basis, etc.). Let the original EMG signal be x, and its sparse representation on the sparse basis Ψ be s, then:

[0086]

[0087] Among them, s is a sparse coefficient vector, most of its elements are 0 or close to 0.

[0088] Signal measurement: Then, the electromyographic signal x is measured by a random measurement matrix Φ to obtain y, that is:

[0089]

[0090]

[0091] Among them, y is the measurement signal actually stored and transmitted; Φ is an m×n measurement matrix whose elements obey 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 EMG signal x on the basis Ψ is sparse, we can obtain the sparse representation s by solving the following optimization problem:

[0093]

[0094] In one embodiment, before generating the control signal according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter, the following steps are also included:

[0095] Generate an angle control parameter according to the current angle and the expected angle of the joint, generate an angular velocity control parameter according to the current angular velocity and the expected angular velocity of the joint, and generate an angular acceleration control parameter according to the current angular acceleration and the expected 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, which specifically includes the following steps:

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

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

[0099] According to the basic principles of joint dynamics, the relationship between the torque value T and the angular acceleration α of the joint can be expressed 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 influence of the joint damping d and the joint static friction torque τ reproduced by the external bone marrow on the joint can be ignored, so the above formula can be simplified to:

[0103]

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

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

[0106]

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

[0108] Then, this control signal is sent to the exoskeleton's driver, causing it to generate a corresponding torque to drive the exoskeleton's movement. Finally, the user's motion data, such as joint angle, angular velocity, and electromyographic signals, are collected and analyzed in real time, and the estimated torque value is adjusted based on feedback to make the exoskeleton's movement 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, α: angular acceleration, ω: angular velocity, d: damping coefficient, τ: static friction torque, θ: angle, θd: desired angle, ωd: desired angular velocity, u: control signal, Kp, Ki, Kd: proportional, integral and derivative coefficients of the PID controller.

[0111] In one embodiment, before estimating the joint torque based on the active force, the passive force and the moving arm and obtaining the joint torque value, the following steps are specifically included:

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

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

[0114] The muscle force is obtained by summing the active force and the passive force; the muscle force and the motion 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 muscle force and the envelope of the surface electromyographic signal is relatively nonlinear, the surface electromyographic signal is first mapped to the range of 0-100 by linear normalization, and then the calculation formula is introduced according to the nonlinear relationship between muscle force and the surface electromyographic signal:

[0116]

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

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

[0119] Main driving force:

[0120] Among them, M represents the main force, a is the calculation constant, and E is the amplitude of the extracted electromyographic signal.

[0121] At the same time, the passive component is estimated based on the measured change in muscle length to obtain the muscle deformation L:

[0122] Passive power:

[0123] Among them, P represents passive force, b is the coefficient, and L is the muscle deformation.

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

[0125] Among them, F stands for muscle strength.

[0126] Then, by measuring the motion arm related to the joint angle: R = c * Θ, where R is the motion arm, c is the coefficient, and Θ is the angle of the joint.

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

[0128] This calculated torque value is then used for torque feedback adjustment, and this estimated torque value is introduced into the control link of the exoskeleton to make real-time adjustments to the movement of the exoskeleton to make it more in line with the user's movement intentions.

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

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

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

[0132] The motion data of the user when wearing the exoskeleton is obtained, and the 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 the updated current joint torque value; the current joint torque value is used to replace the original joint torque value.

[0133] Specifically, we take a green converter station under construction as an example. First, we fix the electromyographic signal sensors on the relevant muscles of the experimenter's upper limbs, such as the biceps, triceps and forearm muscles. These electromyographic signal sensors are selected according to the size of the muscle and the direction of the muscle fibers. After the electromyographic signal acquisition device is enabled, it starts to collect electromyographic signals, which are then amplified by the amplifier. The obtained signals include electromyographic signals of about 5μV and other noise signals.

[0134] In the initial signal processing stage, the compressed sensing technology is used to process the amplified EMG signal. The DCT is used as the sparse basis to sparsely represent the EMG signal. We selected a Gaussian random measurement matrix to measure the EMG signal, collected 100 data, and controlled the noise level at 0.1. In the signal reconstruction stage, we used the L1 norm minimization algorithm to reconstruct the measured signal, and the calculated EMG signal amplitude was about 4.8μV. In the signal envelope extraction stage, the reconstructed EMG signal was processed by full-wave rectification and a first-order Butterworth low-pass filter to obtain the envelope of the surface EMG signal that reflects the change in muscle force amplitude. In the signal normalization stage, the surface EMG signal is mapped to the range of 0-100, and then nonlinear normalization is performed according to the nonlinear relationship between muscle force and surface EMG 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 EMG signal and the EMG signal amplitude obtained by 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 torque value is calculated to be about 0.8Nm. 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. Based on this, the proportional, integral and differential coefficients of the PID controller are designed and then sent to the exoskeleton driver to adjust the estimated torque value in real time, so that the movement of the exoskeleton is more in line with the user's wishes.

[0135] According to this embodiment, it can be seen that the solution in this application can estimate the joint torque in real time and accurately, providing strong support for the operation of the green converter station. Through the above series of processing, it is possible to accurately analyze the user, transmit the user's body dynamic information losslessly to the exoskeleton, and perform fine control, so that the exoskeleton can sensitively respond to the user's operating instructions, providing a better experience for the user.

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

[0137] Step S401, obtain the collected electromyographic signal and sparse basis, perform sparse representation processing on the electromyographic signal according to the sparse basis, and obtain the electromyographic signal under sparse representation; use the measurement matrix to perform compression measurement on the electromyographic signal under sparse representation to obtain a low-dimensional measurement signal, and perform compressive sensing processing on the electromyographic signal according to the low-dimensional measurement signal to obtain a target electromyographic signal.

[0138] Step S402, reconstruct the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, convert the AC signal in the sparse representation result into a DC signal in 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 electromyographic signal; form a smooth curve of the amplitude change over time as the signal envelope.

[0139] Step S403, estimating active components and passive components through a biomechanical model according to the sparse representation result and the signal envelope, and obtaining active force and passive force.

[0140] Step S404, obtain the motion arm according to the current angle of the joint and the motion arm coefficient; obtain the muscle force by summing the active force and the passive force; input the muscle force and the motion arm into the joint torque estimation model to obtain the joint torque value output by the joint torque estimation model.

[0141] Step S405, obtaining the desired angular acceleration of the joint and the angular acceleration control parameter corresponding to the desired angular acceleration according to the joint torque value and the moment of inertia.

[0142] Step S406, generating an angle control parameter according to the current angle and the expected angle of the joint, generating an angular velocity control parameter according to the current angular velocity and the expected angular velocity of the joint, and generating an angular acceleration control parameter according to the current angular acceleration and the expected angular acceleration of the joint; inputting the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter into the controller, performing fusion calculation according to the control strategy, and obtaining a control signal; the control signal is used for exoskeleton control.

[0143] Step S407, obtaining the motion data of the user when wearing the exoskeleton, and determining the adjustment amount of the joint torque value according to the motion data; according to the adjustment amount, feeding back the adjusted joint torque value to obtain the updated current joint torque value; 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 solution in the present application can accurately acquire and use electromyographic signals, effectively preventing electromyographic signals from being affected by environmental electrical noise, muscle fatigue and other factors, thereby improving the stability and reliability of electromyographic signals, and to a certain extent improving the control accuracy of the exoskeleton, while enhancing the user's sense of use, which is of great significance for promoting the research and development and application of exoskeleton systems; in addition, while achieving the above-mentioned technical effects, this solution also reduces the complexity of the exoskeleton system and relatively improves the control efficiency.

[0146] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

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

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

[0149] A compressed sensing module is used to obtain the collected electromyographic signals, extract the measurement matrix from the electromyographic signals, perform compressed sensing processing on the electromyographic signals according to the measurement matrix, and obtain the target electromyographic signals;

[0150] A full-wave rectification module is used to reconstruct the target electromyographic signal to obtain a sparse representation result of the target electromyographic signal, and perform full-wave rectification on the sparse representation result to obtain a signal envelope of the target electromyographic signal;

[0151] The torque estimation module is used to estimate the active component and the passive component through the biomechanical model according to the sparse representation result and the signal envelope, obtain the active force and the passive force, and estimate the joint torque according to the active force, the passive force and the motion arm to obtain the joint torque value;

[0152] The torque application module is used to obtain the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected angular acceleration according to the joint torque value and the moment of inertia, and to 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 rectifier module is also used to convert the AC signal in the sparse representation result into a DC signal in the same direction to obtain a full-wave rectified signal; the full-wave rectified signal is filtered and the amplitude is calculated through a low-pass filter to obtain the amplitude of the target electromyographic signal; the amplitude changes over time to form a smooth curve as the signal envelope.

[0154] In one embodiment, the joint torque estimation and application device also includes a sparse representation module, which is used to obtain a sparse basis, perform sparse representation processing on the electromyographic signal according to the sparse basis, and obtain the electromyographic signal under sparse representation; the compressed sensing module is also used to use the measurement matrix to perform compressive measurement on the electromyographic signal under sparse representation to obtain a low-dimensional measurement signal, and perform compressed sensing processing on the electromyographic signal based on the low-dimensional measurement signal.

[0155] In one embodiment, the joint torque estimation and application device also includes a parameter acquisition module, which is used to generate angle control parameters according to the current angle and expected angle of the joint, generate angular velocity control parameters according to the current angular velocity and expected angular velocity of the joint, and generate angular acceleration control parameters according to the current angular acceleration and expected angular acceleration of the joint; the torque application module is also used to input the angle control parameters, angular velocity control parameters and angular acceleration control parameters into the controller, perform fusion calculations according to the control strategy, and obtain a control signal.

[0156] In one embodiment, the joint torque estimation and application device also includes a motion arm acquisition module, which is used to obtain the motion arm based on the current angle of the joint and the motion arm coefficient; the torque estimation module is also used to sum the active force and the passive force to obtain the muscle force; the muscle force and the motion arm are input into the joint torque estimation model to obtain the joint torque value output by the joint torque estimation model.

[0157] In one embodiment, the joint torque estimation and application device also includes a torque adjustment module, which is used to obtain the motion data of the user when equipped with an 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 to obtain an updated current joint torque value; the current joint torque value is used to replace the original joint torque value.

[0158] Each module in the above-mentioned joint torque estimation and application device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0159] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. 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 operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for estimating and applying joint torque is implemented. The display unit of the computer device is used to form a visually visible picture, which may 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, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0160] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0161] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0162] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

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

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

[0165] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application 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 memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic 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 and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0166] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0167] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations 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 shall be subject to the attached claims.

Claims

1. A method for estimating and applying joint torque, characterized in that: The method comprises: Acquire the collected electromyographic signal, extract a measurement matrix from the electromyographic signal, perform compressed sensing processing on the electromyographic signal according to the measurement matrix, and obtain a target electromyographic signal; 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; Estimate active components and passive components through a biomechanical model according to the sparse representation result and the signal envelope to obtain active force and passive force, and estimate joint torque according to the active force, the passive force and the motion arm to obtain a joint torque value; According to the joint torque value and the moment of inertia, the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected angular acceleration are obtained, and a 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.

2. The method according to claim 1, characterized in that The step of performing full-wave rectification on the sparse representation result to obtain the signal envelope of the target electromyographic signal comprises: Converting the alternating current signal in the sparse representation result into a direct current signal in the same direction to obtain a full-wave rectified signal; The full-wave rectified signal is filtered through a low-pass filter and the amplitude is calculated to obtain the amplitude of the target electromyographic signal; The amplitude changes over time to form a smooth curve as the signal envelope.

3. The method according to claim 1, characterized in that Before performing compressed sensing processing on the electromyographic signal according to the measurement matrix, the method further includes: Obtaining a sparse basis, and performing sparse representation processing on the electromyographic signal according to the sparse basis to obtain the electromyographic signal under sparse representation; The performing compressed sensing processing on the electromyographic signal according to the measurement matrix includes: The measurement matrix is ​​used to compress and measure the electromyographic signal under the sparse representation to obtain a low-dimensional measurement signal, and the electromyographic signal is subjected to compressive sensing processing according to the low-dimensional measurement signal.

4. The method according to claim 1, characterized in that Before generating the control signal according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter, the method further includes: Generate the angle control parameter according to the current angle and the expected angle of the joint, generate the angular velocity control parameter according to the current angular velocity and the expected angular velocity of the joint, and generate the angular acceleration control parameter according to the current angular acceleration and the expected angular acceleration of the joint; The step of generating a control signal according to the angle control parameter, the angular velocity control parameter and the angular acceleration control parameter comprises: The angle control parameter, the angular velocity control parameter and the angular acceleration control parameter are input into a controller and fused and calculated according to a control strategy to obtain the control signal.

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

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Acquiring motion data of a user when wearing the exoskeleton, and determining an adjustment amount of the joint torque value according to the motion data; According to the adjustment amount, the joint torque value is adjusted by feedback to obtain an updated current joint torque value; the current joint torque value is used to replace the original joint torque value.

7. A device for estimating and applying joint torque, characterized in that: The device comprises: A compressed sensing module is used to obtain the collected electromyographic signal, extract a measurement matrix from the electromyographic signal, and perform compressed sensing processing on the electromyographic signal according to the measurement matrix to obtain a target electromyographic signal; A full-wave rectification module is used 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; A torque estimation module, used for estimating active components and passive components through a biomechanical model according to the sparse representation result and the signal envelope to obtain active force and passive force, and estimating joint torque according to the active force, the passive force and the motion arm to obtain a joint torque value; The torque application module is used to obtain the expected angular acceleration of the joint and the angular acceleration control parameter corresponding to the expected 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.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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