阅读说明：本技术 一种基于表面肌电信号的下肢多关节角度估计方法 (Lower limb multi-joint angle estimation method based on surface electromyogram signal ) 是由 孙中波 张鑫 刘克平 王刚 刘永柏 段晓琴 易江 李婉婷 于 2021-08-25 设计创作，主要内容包括：本发明公开了一种基于表面肌电信号的下肢多关节角度估计方法,利用肌肉活跃度和关节实际测量角度,基于最小二乘支持向量机,设计归零神经网络回归模型,实现下肢多关节连续运动意图识别。考虑测量噪声对于信号采集过程中的干扰,设计一种具有抑制噪声能力的抗噪型归零神经网络模型,有效解决识别结果较大偏离真实值的情况,能够克服噪声对于求解回归模型参数的不良影响,实现了在噪声环境下对下肢关节角度的精准识别,本发明对避免患肢康复训练可能产生的二次损伤提供技术参考。(The invention discloses a lower limb multi-joint angle estimation method based on surface electromyogram signals. The interference of the measured noise to the signal acquisition process is considered, an anti-noise type return-to-zero neural network model with the noise suppression capacity is designed, the problem that the recognition result is greatly deviated from the true value is effectively solved, the adverse effect of the noise on the regression model parameter solving can be overcome, the accurate recognition of the joint angle of the lower limb under the noise environment is realized, and the invention provides technical reference for avoiding the secondary damage possibly generated by the rehabilitation training of the affected limb.)

1. A lower limb multi-joint angle estimation method based on surface electromyogram signals is characterized by comprising the following steps:

s1: in order to estimate the active movement intention of the tester, the electromyographic signals of the lateral femoral muscle and the rectus femoris of the lower limb of the tester and the movement angle signals of the knee joint and the hip joint are synchronously collected;

s2: preprocessing the electromyographic signals;

s3: constructing a nonlinear exponential function, and mapping the preprocessed surface electromyographic signals into muscle activity;

s4: constructing a regression model of a return-to-zero neural network based on a least square support vector machine, and identifying the active movement intention of the tester;

s5: and designing an anti-noise return-to-zero neural network model to inhibit the interference of noise on the intention recognition result.

2. The method for estimating the multi-joint angle of the lower limb based on the surface electromyogram signal according to claim 1, wherein the step S4 comprises the following steps:

based on the obtained surface electromyographic signals and the actual joint angles, a least square support vector machine regression model is established to estimate the human lower limb multi-joint continuous motion angles, the human active motion intention can be accurately identified, the muscle activity is input, the joint angles are output, and the specific mathematical expression is as follows:

wherein the content of the first and second substances,the method is a nonlinear function, and maps a nonlinear sample of an original low-dimensional input space into a high-dimensional feature space, so that a nonlinear regression problem is converted into a linear regression problem. In the invention, the intention recognition technology can be equivalent to solving an equality constraint optimization problem described by a least square support vector machine, and the specific form can be expressed as follows:

where i is 1 … N, α is a weight vector of the training sample, b is an offset, e is an error variable, and ζ is a penalty factor. The Lagrange multiplier method is used for converting the original quadratic programming problem into a problem solved by a linear equation set, and the specific form is as follows:

in order to obtain model parameters, human motion intentions are identified, a return-to-zero neural network is established to solve the equation set, and the specific design formula is as follows:

where the parameter gamma represents the convergence rate of the model. According to the error function and the design formula, a return-to-zero neural network model for solving the linear equation set can be obtained, and then a regression model for intention recognition is obtained, wherein the specific expression is as follows:

in the process of intention recognition, an unknown parameter in a least square support vector machine is calculated by using a return-to-zero neural network model, so that a regression model about the muscle activity and the joint angle is obtained, and a parameter training stage is completed. And then substituting the test data into a regression model to estimate the continuous motion angle of the human body lower limb joint, thereby realizing the human body active motion intention identification technology based on the surface electromyogram signal.

3. The method for estimating the multi-joint angle of the lower limb based on the surface electromyogram signal according to claim 1, wherein the step S5 comprises the following steps:

when measuring noise is introduced in the signal acquisition process, great interference is generated on an intention identification result, for example, disturbance generated by other electronic equipment in the surrounding environment or translation of a corresponding sensor occurs during signal acquisition, so that an anti-noise return-to-zero neural network is established to ensure estimation accuracy, and a specific design formula is as follows:

gamma and lambda are adjustable parameters, the integral term is mainly used for resisting noise interference, the anti-noise return-to-zero neural network model used for solving the linear equation set is obtained by combining the error function, the interference of noise on the solving of regression model parameters can be effectively inhibited, the problem that the identification result is greatly deviated from the true value due to noise is solved, the accuracy of human motion intention identification is further ensured, and the accurate identification of the human lower limb joint motion angle under the noise environment is realized.

Technical Field

The invention relates to the field of human body active movement intention identification, in particular to a lower limb multi-joint angle estimation method based on surface electromyogram signals.

Background

In recent years, the number of people with motor dysfunction and even hemiplegia caused by motor central nerve injury or cerebral apoplexy has increased. The traditional treatment means is to restore nerve tissues by means of rehabilitation medicine, and with the improvement of medical technology and the development of robot technology, an exoskeleton robot and a bionic artificial limb become a current rehabilitation training research hotspot. According to the rehabilitation medical theory, the rehabilitation training mode of recognizing the active movement intention of the human body is combined, so that the rehabilitation training device has a positive effect on the rehabilitation of patients. Therefore, how to accurately and reliably recognize the human motion intention is a key problem for the research of the robot system. Compared with the traditional human-computer interaction mode based on program control, the modern robot can actively understand the human motion mode, has independent adaptability, and realizes the robot to assist the limb disease patient to complete corresponding training actions. Biological signals are often used as a way to measure motion information, including electroencephalogram signals, electromyogram signals, and other peripheral nerve electrical signals. The surface electromyogram signal has the advantages of abundant information, noninvasive acquisition and the like, so the surface electromyogram signal is favored by a plurality of researchers.

Currently, the main research content on intent recognition is divided into two parts: one is the classification of discrete motions and the recognition of motion patterns, and the other is the direct decoding of neural motor intention information, such as joint angles, angular velocities, moments, and the like. For the classification problem, the research method is mature, but only a few discrete limb actions can be predicted, when the rehabilitation robot is controlled by applying the prediction result, the rehabilitation robot cannot freely move like human joints, and the premise of ensuring the continuous matching of human-computer motion is the safety control of the rehabilitation robot, so that the estimation of the continuous motion of the human joints through the surface electromyographic signals has a positive effect on the rehabilitation training of patients. According to the invention, the surface electromyographic signals are utilized to identify the multi-joint angles of the lower limbs of the human body, the estimation result can be used as reference input to control the robot, the self-adaptive capacity of the robot is improved, a safe and comfortable training environment can be provided for a patient, and the development of human-computer interaction is promoted.

Disclosure of Invention

The invention discloses a lower limb multi-joint angle estimation method based on a surface electromyogram signal, which provides a lower limb active movement intention identification method combining a least square support vector machine and a return-to-zero neural network by utilizing an original surface electromyogram signal of a tester and an actual movement angle of a knee joint and a hip joint of a lower limb, and realizes accurate identification of a lower limb multi-joint continuous movement angle. In the signal acquisition process, the disturbance generated by other electronic equipment in the surrounding environment is considered, for example, bluetooth equipment, or the corresponding sensor is translated when acquiring signals, and the situation can be regarded as measurement noise and can interfere with signal acquisition, so that the movement intention identification result is influenced. Therefore, the anti-noise return-to-zero neural network model with the noise suppression capability is established, the problem that the recognition result is greatly deviated from the true value due to noise is effectively solved, the recognition precision is guaranteed, the influence of the noise on solving the model parameters is overcome, and technical reference is provided for avoiding secondary damage possibly generated by rehabilitation training of the affected limb. The technical scheme of the invention is as follows by combining the attached drawings of the specification:

a lower limb multi-joint continuous motion estimation method based on surface electromyogram signals comprises the following specific steps:

s1: in order to estimate the active movement intention of the human body, the electromyographic signals of lower limb rectus femoris and vastus lateralis muscles of a tester and the movement angle signals of knee joints and hip joints are synchronously collected;

s2: filtering the acquired original electromyographic signals of the rectus femoris and the vastus lateralis, removing noise and reserving available signals;

s3: mapping the preprocessed surface electromyographic signals into muscle activity by adopting a nonlinear exponential function, and taking the muscle activity of the rectus femoris and the vastus lateralis muscle obtained by calculation as input signals of a regression model;

s4: and constructing a regression model of a return-to-zero neural network based on a least square support vector machine to complete the angle estimation of the knee joint and the hip joint of the lower limb of the human body. The input of the model is the acquired muscle activity, the output of the model is the measured actual joint angle, and the active movement intention of the tester is identified;

s5: a return-to-zero neural network with noise suppression capability is designed, and when the interference of measurement noise in the signal acquisition process is considered, for example, the interference generated by other electronic equipment in the surrounding environment or the translation of a corresponding sensor in the signal acquisition process is considered, the influence of noise on the calculation of model parameters can be effectively suppressed, and therefore the identification precision is guaranteed.

The specific process of step S1 is:

in the invention, a Biopac system is used for collecting the electromyographic signals of the lower limbs of the human body. Surface electromyogram signals of two muscles of a rectus femoris muscle and a lateral femoris muscle of a lower limb of a human body are collected through equipment, and actual joint angle signals of the knee joint and the hip joint moving in a sagittal plane are obtained through an inertia measurement unit. The specific process of step S1 is:

s101: wiping and cleaning the skin surface areas corresponding to the rectus femoris muscles and the vastus lateralis muscles to be collected by using alcohol;

s102: pasting electrode plates on the skin surfaces corresponding to the rectus femoris muscle and the vastus lateralis muscle to be acquired, and connecting the signal acquisition equipment with the electrode plates to share two signal acquisition channels; binding the angle sensors to the thigh and the small leg of the lower limb respectively; the electromyographic signal acquisition equipment is connected with a computer through a network cable port, and signal recording is carried out at the computer end by using software matched with the equipment.

S103: the lower limbs of a tester do periodic treadmill movement in a sagittal plane, and surface electromyographic signals of the rectus femoris and the lateral femoris and angle signals of the knee joints and the hip joints are collected in real time by an electromyographic signal collecting device and an angle sensor.

The specific process of step S2 is:

through step S1, primitive surface electromyogram signals and joint angular movement angle signals of the lower limb rectus femoris and vastus lateralis muscles of the subject are acquired. Since the electromyographic signal data is very weak and unstable, and is also interfered by a large amount of noise, including the electromyographic signal acquisition module, skin surface sweat, temperature, and the like, the electromyographic signal acquired in step S1 needs to be subjected to filtering and denoising processing. In addition, the sampling frequency of the electromyographic signal acquisition system is different from the sampling frequency of the angle sensor, so the electromyographic signal needs to be further sub-sampled, so that the sampling frequency of the electromyographic signal is consistent with the sampling frequency of the angle sensor.

S201: designing a 500HZ high-pass filter to remove the interference of high-frequency signals;

s202: designing a 20HZ low-pass filter to remove the interference of low-frequency signals;

s203: designing a 50HZ notch filter to remove interference of power frequency signals;

s204: the collected electromyographic signals show strong randomness on the amplitude, the absolute value operation can convert the signal amplitude into a positive value, and the contraction strength of muscles can be intuitively reflected. Performing sub-sampling processing on the electromyographic signals subjected to filtering processing in steps S201, S202 and S203 to make the sampling frequency of the electromyographic signals consistent with that of the angle sensor, wherein the specific mathematical expression is as follows:

wherein, N represents the length of the time window, and the value of N is 20 in the invention because the sampling frequency of the electromyographic signal acquisition equipment is 2000Hz, the sampling frequency of the angle sensor is 100Hz, and the difference between the two is 20 times. sEMG_sAnd (n) is represented as electromyographic signals which are subjected to sub-sampling processing, keep the same sampling frequency with the angle sensor, and finally are subjected to normalization processing.

The specific process of step S3 is:

the muscle activity is the size of the muscle reflected by the electrical nerve stimulation, reflects the autonomous contraction strength of the muscle, is more stable than the original surface electromyographic signal, and therefore the muscle activity is used as an input signal of a regression model.

S301: in the invention, a common nonlinear exponential function expression is used for calculating the muscle activity, and the specific mathematical expression is as follows:

wherein m is_j(n) muscle activity of the jth channel, C_jIs a constant term coefficient which determines the degree of non-linearity between the electromyographic signals and the muscle activity. C_jIs usually set in the range of-3 to 0 (linear), and the muscle activity and the joint angle show good correlation with the change of the coefficient, the invention will C_jThe value of (d) is set to 0.8. b_j(n) is the electromyographic signal of the j channel processed by the filter, and the specific expression is as follows:

therein, max (semG)_j(n)) and min (sEMG)_j(n)) are respectively expressed as the maximum value and the minimum value of the surface electromyogram signal sequence of the jth channel.

The specific process of step S4 is:

the muscle activity of the two channels can be calculated through the step S3, and the main purpose of the step is to establish a least square support vector machine regression model for the calculated muscle activity and the actual angle of the hip joint and the knee joint of the lower limb obtained in the step S1, so that the active movement intention of the subject is identified, and the angle of the continuous movement joint is predicted. The input of the regression model is muscle activity, the output is joint angle, and the nonlinear mapping relation of the muscle activity and the joint angle can be specifically expressed as follows:

wherein the content of the first and second substances,is a non-lineAnd the linear function is used for mapping the nonlinear sample of the original low-dimensional input space into a high-dimensional characteristic space, so that the model constructs an optimal hyperplane in the high-dimensional space, and the nonlinear regression problem is converted into the linear regression problem.

S401: the least square support vector machine is evolved from a support vector machine, is a machine learning method, and has the core of solving a convex optimization problem. The least squares support vector machine can be described as the following quadratic programming problem with equality constraints, and the objective function introduces a quadratic term of an error factor, which can be expressed in a specific form:

where i is 1 … N, α is the weight vector of the training samples, b is the offset, and e is the error variable. ζ is a penalty factor representing a coefficient for balancing the maximum interval and the minimum deviation, the higher the value of ζ, the smaller the error that can be tolerated, but easily leads to overfitting.

S402: the optimization problem described in step S401 can be solved by using a lagrangian multiplier method, and the equality constraint optimization problem is converted into an unconstrained optimization problem, where the specific expression of the lagrangian function is as follows:

where ρ is_iExpressed as a Lagrange multiplier, and the derivative of the Lagrange function to the unknown variables alpha, b, epsilon and rho is equal to 0 according to the existence condition of the extremum of the multivariate function, so that the extremum of the unconstrained optimization problem is obtained, and the optimal solution of the problem is obtained. This process can be described as the following mathematical expression:

s403: and establishing a radial basis kernel function. As can be seen from step S402, when mapping to the high-dimensional feature space by non-linearity, the dot product needs to be calculated in the high-dimensional feature space, and therefore, a kernel function method is adopted instead of the dot product operation. The introduction of the kernel function replaces the inner product of a high-dimensional space, thereby greatly reducing the calculation amount and complexity. In the invention, a Gaussian kernel function containing a parameter sigma is used for calculation, and the following mathematical expression is specifically defined:

according to the above analysis, orderThe quadratic programming problem described by the least squares support vector machine can then be transformed into a problem that solves a system of linear equations in the following specific form:

s404: and establishing a return-to-zero neural network. Through steps S401, S402, and S403, the original optimization problem with equality constraints has been transformed into a problem of solving a set of linear equations, and the zeroing neural network is a novel recurrent neural network, and is mainly applied to solving the problem of time-varying quadratic programming. The least square support vector machine describes a special case condition in time-varying quadratic programming, so that the return-to-zero neural network is designed to solve the equation set, and then the parameters of the regression model are obtained, and the specific design idea is as follows:

firstly, the linear equation set obtained in step S403 is recorded as the following equation set form with time-varying parameters, and the specific expression is:

A(t)x(t)＝B(t)

the return-to-zero neural network is an error-based dynamic solving method, and the core of the method is to make each element in an error function converge to 0, and define a matrix type error function as follows:

e(t)＝A(t)x(t)-B(t)

in order to make the error function e (t) tend to 0, a design formula of the dynamic return-to-zero neural network is obtained, and the specific mathematical expression is as follows:

where the parameter γ is a positive value, representing the convergence rate of the nulling neural network model. Combining an error function and a design formula, a differential power system model applied to solving the time-varying quadratic programming problem can be obtained, and the specific mathematical expression is as follows:

the least square support vector machine is constructed as a static quadratic programming problem, and the return-to-zero neural network aims at solving a time-varying system, so that two ends of the equation set obtained in step S403 need to be regarded as a constant matrix and a vector which vary with time. Finally, a model for solving the equation set in step S403 is obtained, and the specific mathematical expression is:

s405: according to the step S404, an equation set for solving the step S403 is designed, so as to obtain a lagrangian multiplier ρ and an offset b, and according to a lagrangian multiplier method, a weight vector α in the regression model can be calculated, so as to obtain a final regression model, where a specific mathematical expression is:

parameters in the regression model are trained through the training samples, and then the test data are substituted into the model, so that the motion angle of the lower limb joint is estimated.

The specific process of step S5 is:

step S4 describes that the lower limb joint angle is identified in the absence of measurement noise interference during the solving process. In practice, noise introduced due to implementation errors or external interference in hardware implementation is difficult to avoid, so that the anti-noise return-to-zero neural network is designed, the situation that the angle estimation value and the actual value have large deviation due to noise is overcome, and accurate identification of the joint angle of the lower limb of the human body in a noise environment is realized.

S501: and designing an anti-noise return-to-zero neural network model based on the improved return-to-zero neural network model designing step. According to step S404, the model with noise suppression capability is evolved from the original return-to-zero neurodynamic system, and the specific design formula is as follows:

wherein γ and λ are adjustable parameters, which respectively control the convergence rate and the noise suppression degree of the model, and can ensure that the index of the error function e (t) converges to 0. The integral term is introduced mainly to suppress noise interference. And (3) expanding the anti-noise type return-to-zero neural network dynamic model by combining the expression of the error function to obtain a concealed neural network model expression as follows:

based on the same-way analysis in step S404, a return-to-zero neural network model with noise suppression capability can be obtained, and is used for solving a linear equation system converted by a least squares support vector machine, so as to obtain regression model parameters, where a specific mathematical expression is:

the invention utilizes the surface electromyographic signals of the muscles of the lower limbs to identify the active movement intention of the human body, and provides an active movement intention identification method based on a least square support vector machine and a return-to-zero neural network, thereby realizing the estimation of the multi-joint angles of the lower limbs and having good prediction effect. In the signal acquisition process, the disturbance generated by other electronic equipment in the surrounding environment is considered, for example, Bluetooth equipment or corresponding sensors translate during signal acquisition, the situation can be regarded as measurement noise and can interfere signal acquisition, and further the movement intention recognition result is influenced, so that the anti-noise return-to-zero neural network is introduced, the influence of the noise on the parameter process of the training model is overcome, the accuracy of final angle estimation is ensured, and technical reference is provided for avoiding secondary damage possibly generated by rehabilitation training of the affected limb.

Drawings

FIG. 1 is a flow chart of a method and a system for estimating a multi-joint angle of a lower limb based on a surface electromyogram signal according to the present invention;

FIG. 2 is a diagram of the original surface electromyography of the lateral thigh muscle and the rectus femoris of the lower limb obtained by the device system during the signal acquisition process of the present invention.

Fig. 3 is a diagram of the actual angle change of the knee joint and hip joint of the lower limb obtained by the angle sensor in the signal acquisition process.

FIG. 4 is an electromyogram of the lateral thigh muscle and the rectus femoris of the lower limb after the signals are preprocessed by filtering, denoising, sub-sampling and the like.

FIG. 5 shows that the muscle activity of the lateral femoral muscle and the rectus femoris of the lower limb is calculated according to the collected surface electromyographic signals.

FIG. 6 is a tracking diagram of the knee joint angle of the lower limb estimated based on the least square support vector machine and the return-to-zero neural network method.

FIG. 7 is a lower limb hip joint angle tracking diagram estimated based on the least square support vector machine and the return-to-zero neural network method.

FIG. 8 is a lower limb knee joint angle tracking error map based on a least squares support vector machine and a return-to-zero neural network method according to the present invention.

FIG. 9 is a lower limb hip joint angle tracking error diagram based on the least squares support vector machine and the return-to-zero neural network method of the present invention.

FIG. 10 is a comparison graph of the tracking of the knee joint angle of the lower limb predicted based on the return-to-zero neural network and the anti-noise return-to-zero neural network model under the condition of noise consideration.

FIG. 11 is a comparison graph of the predicted lower limb hip joint angle tracking based on the return-to-zero neural network and the anti-noise return-to-zero neural network model under the condition of noise consideration.

FIG. 12 is a diagram of the root mean square error of the knee joint angle of the lower limb obtained by adjusting the parameters of the anti-noise type return-to-zero neural network model under the condition of noise consideration.

FIG. 13 is a diagram of the root mean square error of the hip joint angle of the lower limb obtained by adjusting the parameters of the anti-noise type zeroing neural network model under the condition of noise consideration.

Detailed Description

In order to more clearly and completely describe the estimation method and technique and the specific processing and design process of data of the present invention, the present invention will be further described with reference to the accompanying drawings, and those skilled in the art can implement the present invention according to the content of the description:

the invention discloses a surface electromyogram signal-based lower limb multi-joint angle estimation method, a system flow chart is shown in figure 1, and the method comprises the following specific steps:

s1: in order to estimate the active movement intention of the human body, the electromyographic signals of lower limb rectus femoris and vastus lateralis muscles of a tester and the movement angle signals of knee joints and hip joints are synchronously collected;

in step S1, the human body signal collecting device is composed of a Biopac system and an inertial measurement unit. Acquiring surface electromyographic signals of two muscles of the lower limb of a human body by using electromyographic signal acquisition equipment, wherein the surface electromyographic signals comprise thigh rectus muscle and thigh lateral muscle, angle signals comprise actual joint angle signals of the knee joint and the hip joint moving in a sagittal plane, and the specific process of the step S1 is as follows:

s101: wiping and cleaning the skin surface areas corresponding to the rectus femoris muscles and the vastus lateralis muscles which need to be collected by alcohol;

s102: pasting electrode plates on the skin surfaces corresponding to the rectus femoris muscle and the vastus lateralis muscle to be acquired, and connecting the signal acquisition equipment with the electrode plates to share two signal acquisition channels; binding the angle sensors to the thigh and the small leg of the lower limb respectively; the electromyographic signal acquisition equipment is connected with a computer through a network cable port, and signal recording is carried out at the computer end by using software matched with the equipment.

S103: the lower limbs of a tester do periodic treadmill movement in a sagittal plane, and surface electromyographic signals of the rectus femoris and the lateral femoris and angle signals of the knee joints and the hip joints are collected in real time by an electromyographic signal collecting device and an angle sensor.

After the signal collection process of step S1, as shown in fig. 2, the original surface electromyograms of the lateral femoral muscle and the rectus femoris of the lower limb are collected in the signal collection process of the present invention. Fig. 3 shows the real angle change of the knee joint and hip joint of the lower limb.

S2: preprocessing an electromyographic signal;

the surface electromyogram signal data acquired in step S1 is weak and unstable, and therefore cannot be used as it is. In step S2, a high-pass filter, a low-pass filter, and a notch filter are used to perform filtering and denoising processing on the collected electromyographic signals, and meanwhile, the preprocessed electromyographic signals need to be sub-sampled, so that the sampling frequency of the electromyographic signal collecting device is consistent with the sampling frequency of the angle sensor, which includes the following specific processes:

s201: designing a 500HZ high-pass filter to remove the interference of high-frequency signals;

s202: designing a 20HZ low-pass filter to remove the interference of low-frequency signals;

s203: designing a 50HZ notch filter to remove interference of power frequency signals;

s204: the acquired electromyographic signals show strong randomness on amplitude values, the signal amplitude values can be converted into positive values through absolute value operation, and the contraction strength of muscles can be visually reflected. Performing sub-sampling processing on the electromyographic signals subjected to filtering processing in steps S201, S202 and S203 to make the sampling frequency of the electromyographic signals consistent with that of the angle sensor, wherein the specific mathematical expression is as follows:

wherein, N represents the length of the time window, and the value of N is 20 in the invention because the sampling frequency of the electromyographic signal acquisition equipment is 2000Hz, the sampling frequency of the angle sensor is 100Hz, and the difference between the two is 20 times. sEMG_sAnd (n) is represented as electromyographic signals which are subjected to sub-sampling processing, keep the same sampling frequency with the angle sensor, and finally are subjected to normalization processing. Myoelectric signals of the vastus lateralis and rectus femoris after pretreatment are shown in fig. 4.

S3: constructing a nonlinear exponential function, and calculating the muscle activity of the vastus lateralis and the vastus rectus based on the preprocessed surface electromyogram signals;

the muscle activity is the size of the muscle reflected by the electrical nerve stimulation, reflects the autonomous contraction strength of the muscle, is more stable than the original surface electromyographic signal, is associated with the angle change of the limb joint, and is more suitable for being used as the input of a regression model. The specific process is as follows:

s301: establishing a common nonlinear exponential function to calculate the muscle activity based on the collected surface electromyographic signals, wherein the specific mathematical expression is as follows:

wherein m is_j(n) muscle activity of the jth channel, C_jDetermining the degree of non-linearity between the electromyographic signal and the muscle activity as a constant coefficient, C_jThe value of (c) is usually set in the range of-3 to 0 (linear). With the change of the coefficient, the muscle activity and the joint angle show different correlations, and the invention combines the results of the calculation of the coefficient C_jThe value of (d) is set to 0.8. b_j(n) is the electromyographic signal of the j channel processed by the filter, and the specific expression is as follows:

therein, max (semG)_j(n)) and min (sEMG)_j(n)) are respectively expressed as the maximum value and the minimum value of the surface electromyogram signal sequence of the jth channel. Fig. 5 shows a muscle activity diagram of the lateral and rectus femoris muscles calculated based on the surface electromyographic signals according to the present invention.

S4: constructing a regression model of a return-to-zero neural network based on a least square support vector machine, and identifying the active movement intention of the tester;

in step S4, a least squares support vector machine regression model is established based on the muscle activity of the vastus lateralis and rectus femoris calculated in step S3 and the actual joint angles of the knee joint and hip joint calculated in step S1, so as to identify the intention of the subject in active movement and estimate the continuously moving joint angles. The input signal of the regression model is muscle activity, the output signal is joint angle, and the nonlinear mapping relation of the two can be specifically expressed as:

wherein the content of the first and second substances,the method is a nonlinear function, and a nonlinear sample of an original low-dimensional input space is mapped into a high-dimensional feature space, so that a nonlinear regression problem is converted into a linear regression problem.

S401: the least square support vector machine is improved from a support vector machine, is a machine learning method, and has the core of solving a convex optimization problem. The least squares support vector machine can be described as the following quadratic programming problem with equality constraints, and the objective function introduces a quadratic term of an error factor, which can be expressed in a specific form:

where i is 1 … N, α is the weight vector of the training samples, b is the offset, and e is the error variable. ζ is a penalty factor representing a coefficient for balancing the maximum interval and the minimum deviation, the higher the value of ζ, the smaller the error that can be tolerated, but easily leads to overfitting.

S402: the optimization problem described in step S401 can be solved by using a lagrangian multiplier method, and the equality constraint optimization problem is converted into an unconstrained optimization problem, where the specific expression of the lagrangian function is as follows:

where ρ is_iExpressed as a Lagrange multiplier, and the derivative of the Lagrange function to the unknown variables alpha, b, epsilon and rho is equal to 0 according to the existence condition of the extremum of the multivariate function, so that the extremum of the unconstrained optimization problem is obtained, and the optimal solution of the problem is obtained. This process can be described as the following mathematical expression:

s403: and establishing a radial basis kernel function. As can be seen from step S402, when mapping to the high-dimensional feature space by non-linearity, the dot product needs to be calculated in the high-dimensional feature space, and therefore, a kernel function method is adopted instead of the dot product operation. The introduction of the kernel function replaces the inner product of a high-dimensional space, thereby greatly reducing the calculation amount and complexity. In the invention, a Gaussian kernel function containing a parameter sigma is used for calculation, and the following mathematical expression is specifically defined:

according to the above analysis, orderThe quadratic programming problem described by the least squares support vector machine can then be transformed into a problem that solves a system of linear equations in the following specific form:

s404: and establishing a return-to-zero neural network. According to the steps S401, S402 and S403, the original optimization problem with equality constraint is converted into the problem of solving a group of linear equations, and the return-to-zero neural network is a novel recurrent neural network and is mainly applied to solving the problem of time-varying quadratic programming. The least square support vector machine describes a special case condition in time-varying quadratic programming, so that the return-to-zero neural network is designed to solve the equation set, and then the parameters of the regression model are obtained, and the specific design idea is as follows:

firstly, the linear equation set in step S403 is expressed as the following equation set form with time-varying parameters, and the specific expression is:

A(t)x(t)＝B(t)

the return-to-zero neural network is an error-based dynamic solving method, and the core of the method is to make each element in an error function converge to 0, and define a matrix type error function as follows:

e(t)＝A(t)x(t)-B(t)

in order to make the error function e (t) tend to 0, a design formula of the dynamic return-to-zero neural network is obtained, and the specific mathematical expression is as follows:

where the parameter γ is a positive value, representing the convergence rate of the nulling neural network model. Combining an error function and a design formula, a differential power system model applied to solving the time-varying quadratic programming problem can be obtained, and the specific mathematical expression is as follows:

the least squares support vector machine is a static quadratic programming problem, and the return-to-zero neural network aims at solving a time-varying system, so that two ends of the equation set in step S403 need to be regarded as a constant matrix and a vector which vary with time. Finally, a model for solving the equation set in step S403 is obtained, and the specific mathematical expression is:

s405: according to the step S404, an equation set for solving the step S403 is designed, so as to obtain a lagrangian multiplier ρ and an offset b, and according to a lagrangian multiplier method, a weight vector α in the regression model can be calculated, so as to obtain a final regression model, where a specific mathematical expression is:

it should be noted that, in the joint angle estimation process, the lateral femoral muscle and the rectus femoris are selected to estimate the knee joint and the hip joint respectively, and half of the whole data set is used as a training set and the other half is used as a test set respectively. And solving a linear equation set converted by the least square support vector machine by adopting the designed return-to-zero neural network model so as to obtain unknown parameters of the regression model, and substituting the test set into the model to estimate the motion angle of the lower limb multiple joints. Fig. 6 and 7 show joint angle tracking diagrams of knee joints and hip joints of lower limbs predicted based on a least square support vector machine and a return-to-zero neural network method, and show good performance. Fig. 8 and 9 show joint angle tracking error maps of the lower limb knee joint and hip joint according to the prediction method of the present invention.

S5: designing an anti-noise type return-to-zero neural network model with noise suppression capability;

step S4 describes that the lower limb joint angle is identified in the absence of measurement noise interference during the solving process. In practice, noise introduced due to implementation errors or external interference in hardware implementation is difficult to avoid, so that the anti-noise return-to-zero neural network is designed, the situation that the angle estimation value and the actual value have large deviation due to noise is overcome, and accurate identification of the joint angle of the lower limb of the human body in a noise environment is realized.

S501: and designing an anti-noise return-to-zero neural network model based on the improved return-to-zero neural network model designing step. According to step S404, the model with noise suppression capability is evolved from the original return-to-zero neurodynamic system, and the specific design formula is as follows:

wherein γ and λ are adjustable parameters, which respectively control the convergence rate and the noise suppression degree of the model, and can ensure that the index of the error function e (t) converges to 0. The integral term is introduced mainly to suppress noise interference. And (3) expanding the anti-noise type return-to-zero neural network dynamic model by combining the expression of the error function to obtain a concealed neural network model expression as follows:

based on the same-theory analysis in step S404, an anti-noise type return-to-zero neural network model with noise suppression capability can be obtained, and is used for solving a linear equation set converted by a least squares support vector machine, so as to obtain regression model parameters, wherein a specific mathematical expression is as follows:

fig. 10 and 11 show angle tracking diagrams of knee joints and hip joints of lower limbs by different models under constant measurement noise interference, respectively. Because the return-to-zero neural network does not have the noise suppression capability, noise has a large influence on training data, and further influences the parameter for solving the regression model, so that the final angle estimation result has a large deviation from the true value. The anti-noise return-to-zero neural network designed by the invention has the capability of noise suppression, can effectively suppress the influence of noise on the parameters of the training model, further ensures that the anti-noise return-to-zero neural network can have good recognition accuracy even in a noise environment, and provides technical reference for avoiding secondary damage possibly generated by rehabilitation training of the affected limb. Fig. 12 and 13 show root mean square error plots of the knee joint and hip joint under noise. By adjusting parameters in the anti-noise type return-to-zero neural network, the prediction performance of the model is better.