CN-116584961-B - Human lower limb movement intention recognition and exoskeleton robot angle prediction control method

CN116584961BCN 116584961 BCN116584961 BCN 116584961BCN-116584961-B

Abstract

A human body lower limb movement intention recognition and exoskeleton robot angle prediction control method comprises the following steps of 1) collecting human body surface electromyographic signals, simultaneously converting joint angle values into joint signals for recording, 2) preprocessing the collected signals, 3) denoising and filtering the preprocessed signals, 4) intercepting the electromyographic signals after denoising to obtain action fragments in the electromyographic signals, extracting characteristic values of the action fragments, 5) converting the action fragments into frequency domain signals, carrying out frequency domain analysis on the action fragments to obtain median frequencies of the action fragments, 6) constructing a regression model by using the extracted characteristic values as indexes of measurement accuracy by using a BP neural network, and carrying out lower limb movement intention recognition by using the regression model, and 7) constructing a NARX neural network for joint angle prediction. The invention enables the exoskeleton robot to synchronously realize the estimation and prediction of the motion intention of the lower limbs of the human body and the angles of the joints of the lower limbs.

Inventors

SHI XIN
YE XIANGQING
ZHU TIANHAO
LI XIAOKANG

Assignees

重庆大学

Dates

Publication Date: 20260512
Application Date: 20230523

Claims (6)

1. The human lower limb movement intention recognition and exoskeleton robot angle prediction control method is characterized by comprising the following steps of: 1) Collecting human surface electromyographic signals, and simultaneously converting joint angle values collected by a joint angle meter into joint signals for recording; The joint angles include hip joint angles and knee joint angles; 2) Preprocessing the collected electromyographic signals and joint signals by using a Butterworth band-pass filter to obtain preprocessed electromyographic signals and joint signals; 3) Noise reduction filtering is carried out on the preprocessed electromyographic signals and joint signals by using discrete wavelet transformation, so that the noise-reduced electromyographic signals and joint signals are obtained; 4) Intercepting the electromyographic signals after noise reduction by utilizing a sliding window method to obtain action fragments in the electromyographic signals, and extracting characteristic values of the action fragments; The step of intercepting the myoelectric signal after noise reduction by utilizing a sliding window method to obtain an action segment in the myoelectric signal comprises the following steps of: 4.1 Setting a interception threshold value, a sliding window width and a stepping value; 4.2 Determining a start position of the action segment; 4.3 Determining a terminal position of the action segment; 4.4 Intercepting the action segment in the electromyographic signal through the start end position and the end position of the action segment; 5) Converting the intercepted action fragments into frequency domain signals through fast Fourier transformation, and carrying out frequency domain analysis on the intercepted action fragments to obtain median frequencies of the action fragments; 6) The extracted characteristic value and the median frequency are used as indexes for measuring accuracy, a BP neural network is utilized to construct a regression model, and the lower limb movement intention is identified through the regression model; the lower limb movement intention comprises ascending stairs, ascending slopes, walking on a flat ground, descending stairs and descending slopes; When the exercise is intended to walk on level ground, the corresponding knee joint angle ranges from-1.3 ° to 71.55 °, and the hip joint angle ranges from-3 ° to 13.18 °; When the exercise is intended to go upstairs, the corresponding knee joint angle ranges from-92.52 ° to 1.98 °, and the hip joint angle ranges from-0.36 ° to 19.57 °; when the movement is intended to be uphill, the corresponding knee angle ranges from-60.34 ° to 13.59 °, and the hip angle ranges from-1.08 ° to 14.17 °; When the exercise is intended to go down stairs, the corresponding knee joint angle range is-93.015 degrees to 0.09 degrees, and the hip joint angle range is-1.35 degrees to 18.49 degrees; When the movement is intended to be downhill, the corresponding knee joint angle ranges from-63 ° to 1.125 °, and the hip joint angle ranges from-2.57 ° to 17.01 °; 7) And constructing an NARX neural network, and predicting the angle of the exoskeleton robot joint corresponding to the current lower limb movement intention by using the NARX neural network.
2. The method for recognizing movement intention of lower limbs and controlling angle prediction of exoskeleton robot according to claim 1, wherein the collection site of the human surface electromyographic signals comprises rectus femoris, medial femoral muscle, biceps femoris, tibialis anterior, lateral gastrocnemius and soleus.
3. The method for recognizing motion intention of lower limbs of human body and controlling angle prediction of exoskeleton robot according to claim 1, wherein the characteristic values of the motion segments include average absolute value, root mean square, standard deviation and zero crossing point.
4. The method for identifying the movement intention of the lower limbs of the human body and predicting and controlling the angles of the exoskeleton robot according to claim 1, wherein the step of constructing the regression model using the BP neural network by using the extracted eigenvalue and median frequency as the index of the measurement accuracy comprises: 6.1 Importing a data set for storing the movement intention of the lower limb and the corresponding characteristic value and median frequency; 6.2 Dividing the data set into a training set and a testing set; 6.3 Normalizing the training set and the testing set to obtain a normalized training set and a normalized testing set; 6.4 Initializing related parameters and constructing a BP neural network; 6.5 Training the BP neural network through the training set after normalization treatment, and determining the optimal hidden layer number to obtain a regression model; 6.6 Testing the regression model of the optimal hidden layer through the test set after normalization processing, performing inverse normalization and index error analysis on the test result, returning to the step 6.1) if the index error is greater than a preset error threshold value, ending training if the index error is less than or equal to the preset error threshold value, and outputting the regression model.
5. The method for recognizing motion intention of lower limbs and controlling angle prediction of exoskeleton robot for human body according to claim 4, wherein the step of training the BP neural network by the training set after normalization processing, and determining the optimal hidden layer number comprises: 6.5.1 Calculating the hidden layer number of the a-th iteration, namely: In the formula, The number of hidden layer nodes is m, the number of input layer nodes is m, the number of output layer nodes is n, and the initial value of a is 1;a, which is an integer; 6.5.2 To (3) As the number of hidden layers of the current BP neural network, training the BP neural network by using the training set after normalization processing, and calculating the mean square error of the BP neural network; 6.5.3 Judging whether a is smaller than k and k is the maximum threshold value of the layer number, if yes, enabling a=a+1 and returning to the step 6.5.1), otherwise, entering the step 6.5.4; 6.5.4 Corresponding with least mean square error As the number of hidden layers of the BP neural network.
6. The method for identifying the movement intention of the lower limbs of the human body and predicting and controlling the angles of the exoskeleton robot according to claim 1, wherein the NARX neural network is as follows: In the formula, 、 The input and output of the NARX neural network at the time t are respectively; Is the maximum order of the input delay; Is the maximum order of the output delay; Is a historical input relative to time t; is a historical output relative to time t; and fitting the obtained nonlinear function to the NARX neural network.

Description

Human lower limb movement intention recognition and exoskeleton robot angle prediction control method Technical Field The invention relates to the technical field of human exoskeleton control, in particular to a human lower limb movement intention recognition and exoskeleton robot angle prediction control method. Background The prior researchers put forward a plurality of new methods for researching electromyographic signals and classifying lower limb actions, such as analyzing the electromyographic signals by utilizing a differential surface electromyographic signal (Surface electromyography, sEMG) real-time characteristic extraction algorithm, amplifying and filtering the electromyographic signals by utilizing a double-resistance-capacitance active trap circuit, estimating the moment by utilizing the fusion of the signals so as to reflect the motion state of a human body, and assisting the motion of the human body by utilizing an exoskeleton robot with seven joints. The research of the existing lower limb rehabilitation robot can realize the simple control of the rehabilitation mechanism based on the identification of the movement intention of the lower limb of the human body to a certain extent, but the exoskeleton robot has the working mode of attaching to the human body, so that the body of the exoskeleton robot needs to be highly cooperated with the wearer, and the existing research is mostly applied to the optimization of exoskeleton hardware, so that how to effectively classify the movement of the lower limb is the primary problem of obtaining the accurate movement intention of the lower limb of the human body. The sEMG signals contain a large amount of biological information, and human behaviors can be predicted by analyzing and processing the information, so that the research of the lower limb movement classification based on the surface electromyographic signals has an indispensable significance for the development of rehabilitation robots. The exoskeleton robot needs to be matched with the wearer in a highly cooperative manner, the existing lower limb rehabilitation robot can realize identification based on the movement intention of the lower limb of the human body to a certain extent, and simple control of a rehabilitation mechanism is realized, but the existing research is mostly focused on optimizing exoskeleton hardware, and the research on how to effectively classify the lower limb actions and obtain the accurate movement intention of the lower limb of the human body is still in depth. Disclosure of Invention The invention aims at solving the problem that the existing human lower limb exoskeleton robot can not effectively combine the human lower limb movement intention recognition with the angle prediction to assist the human lower limb movement. Provides a human lower limb movement intention recognition and exoskeleton robot angle prediction control method. The technical scheme adopted for realizing the purpose of the invention is that the method for identifying the movement intention of the lower limb of the human body and predicting and controlling the angle of the exoskeleton robot comprises the following steps: 1) And acquiring human surface electromyographic signals, and simultaneously converting the joint angle values acquired by the joint angle meter into joint signals for recording. 2) And preprocessing the acquired electromyographic signals and joint signals by using a Butterworth band-pass filter to obtain preprocessed electromyographic signals and joint signals. 3) And performing noise reduction filtering on the preprocessed electromyographic signals and joint signals by using discrete wavelet transformation to obtain the noise-reduced electromyographic signals and joint signals. 4) And intercepting the electromyographic signals after noise reduction by utilizing a sliding window method to obtain action fragments in the electromyographic signals, and extracting characteristic values of the action fragments. 5) Converting the intercepted action fragments into frequency domain signals through fast Fourier transformation, and carrying out frequency domain analysis on the intercepted action fragments to obtain the median frequency of the action fragments. 6) And constructing a regression model by using the extracted characteristic value and the median frequency as indexes of measurement accuracy and utilizing the BP neural network, and identifying the lower limb movement intention through the regression model. 7) And constructing an NARX neural network, and predicting the angle of the exoskeleton robot joint corresponding to the current lower limb movement intention by using the NARX neural network. Further, the collecting part of the human surface electromyographic signals comprises rectus femoris, medial femoral muscle, biceps femoris, tibialis anterior, lateral gastrocnemius and soleus. Further, the joint angles include hip joint angles and knee joint angles. Further, the movemen