CN111544846A - Training and mode switching method for pure idea control rehabilitation robot - Google Patents
Training and mode switching method for pure idea control rehabilitation robot Download PDFInfo
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Abstract
A training and mode switching method for a pure idea control rehabilitation robot comprises the following steps: a user generates motor imagery electroencephalogram signals through a motor vision stimulation module; the electroencephalogram acquisition module acquires synchronous electroencephalogram signals of a user, performs amplification filtering pretreatment, and transmits the signals to an upper computer in a wireless mode; after receiving the electroencephalogram signals, the upper computer performs feature extraction through a filter bank CSP method, optimizes a tree-type SVM classifier through a drosophila algorithm to perform feature classification, and transmits corresponding rehabilitation training control signals to a rehabilitation exercise device; and after receiving the training control signal, the rehabilitation exercise device carries out rehabilitation exercise training on the limbs according to the corresponding control signal. According to the invention, the brain of the user directly participates in rehabilitation training through pure idea control, so that the reconstruction of the neural cortex of the brain of the user is facilitated, the motor neural network is repaired, and the initiative and the enthusiasm of the patient are simultaneously excited, thereby not only improving the convenience and the comfort of rehabilitation therapy, but also improving the effect of rehabilitation therapy.
Description
Technical Field
The invention relates to a training and mode switching method of a rehabilitation robot. In particular to a training and mode switching method for a pure idea control rehabilitation robot.
Background
The number of stroke patients in China is continuously increased along with the entering of the nation into an aging society, one of the most main disability manifestations after stroke is motor dysfunction, wherein about 80% of stroke patients have motor dysfunction, which brings great inconvenience to daily life, and therefore rehabilitation training of motor function after stroke is the first task of recovering motor function and improving life quality of patients. The traditional rehabilitation training method can recover partial self-care ability, but the rehabilitation training method has high cost and poor training effect; meanwhile, the traditional rehabilitation training means mainly adopts passive training, so that the comfort level and convenience of a user during training are reduced.
The motor imagery can realize that limbs do not execute any action when the brain has action intention, and the motor network of the stroke patient is activated and repaired through the motor imagery, so that the motor function of the stroke patient is improved. Because the motor imagery does not depend on residual functions of the patient and is closely related to the active movement of the patient, the rehabilitation effect can be effectively improved and the problems existing in the traditional rehabilitation treatment means can be solved by controlling the rehabilitation robot based on the pure idea of the motor imagery, and the convenience of rehabilitation training is improved; meanwhile, the pure idea control based on the motor imagery can enable the patient to independently select the training mode, active and passive switching is achieved, and the rehabilitation training effect of the patient is improved when the patient has higher training comfort level.
Disclosure of Invention
The invention aims to solve the technical problem of providing a training and mode switching method for a purely idea control rehabilitation robot, which can improve the convenience and comfort of rehabilitation training and improve the effect of rehabilitation training.
The technical scheme adopted by the invention is as follows: a training and mode switching method for a pure idea control rehabilitation robot comprises the following steps:
1) the user selects a training mode of the rehabilitation equipment, if the active training mode is selected, the step 2) is carried out, and if the passive training mode is selected, the upper computer directly sends out a corresponding control command to control the rehabilitation equipment to assist limb movement;
2) the upper computer generates a corresponding motion stimulation video according to a rehabilitation training item selected by a user, the corresponding motion stimulation video is displayed to the user through the display, the motion stimulation video is an image of left upper limb rehabilitation training or left lower limb rehabilitation training or right upper limb rehabilitation training or right lower limb rehabilitation training selected by the user, the user respectively imagines the motion of the left hand, the left leg, the right hand and the tongue corresponding to the displayed image, and an electroencephalogram acquisition device acquires a user synchronous motion imagery electroencephalogram signal and transmits the user synchronous motion imagery electroencephalogram signal to the upper computer in a wireless mode;
3) the upper computer receives a motor imagery electroencephalogram signal of a user, pre-processes the motor imagery electroencephalogram signal, extracts and classifies the characteristics of the motor imagery electroencephalogram signal, extracts the characteristics of the motor imagery electroencephalogram signal by adopting a passband common space mode method, classifies the motor imagery electroencephalogram signal by adopting a fruit fly algorithm-based optimized tree-type support vector machine classifier, converts a classification result into a corresponding rehabilitation training control signal, and transmits the rehabilitation training control signal to a rehabilitation movement device;
4) the rehabilitation equipment drives the user to perform corresponding rehabilitation training actions, and signal data of the training actions of the user are fed back to the upper computer;
5) during the rehabilitation training process, a user can autonomously switch an active training mode and a passive training mode through pure idea according to the fatigue conditions of the brain and limbs of the user;
6) and a database in the upper computer stores the signal data of the training action of the user, and the signal data of the training action of the user is evaluated by adopting a Fugl-Meyer evaluation method.
The invention discloses a training and mode switching method of a pure idea control rehabilitation robot, which applies brain-computer interface technology to a medical rehabilitation system, provides a training and mode switching method of a pure idea control novel rehabilitation robot, can enable the brain of a user to directly participate in rehabilitation training, is beneficial to reconstruction of the nerve cortex of the brain of the user, repairs a motor nerve network, simultaneously excites the initiative and the enthusiasm of the user, and improves the effect of rehabilitation training while improving the convenience and the comfort of rehabilitation training.
Drawings
FIG. 1 is a block diagram of a novel rehabilitation robot system controlled purely mentally in the invention;
FIG. 2 is a block diagram of the electroencephalogram acquisition device in the invention;
FIG. 3 is a block diagram of a training method for controlling a novel rehabilitation robot system purely mentally according to the invention;
FIG. 4 is a schematic diagram of a tree-based SVM of the present invention.
Detailed Description
The following describes a training and mode switching method of a pure idea control rehabilitation robot according to the present invention in detail with reference to the following embodiments and accompanying drawings.
As shown in fig. 3, the training and mode switching method for a pure idea control rehabilitation robot of the present invention includes the following steps:
1) the user selects a training mode of the rehabilitation equipment, if the active training mode is selected, the step 2) is carried out, and if the passive training mode is selected, the upper computer directly sends out a corresponding control command to control the rehabilitation equipment to assist limb movement;
the active training mode is that a user controls the rehabilitation equipment to help limbs to perform rehabilitation training through motor imagery to complete a rehabilitation training task; the passive training mode is that the user directly drives limbs to carry out rehabilitation training by the rehabilitation equipment without motor imagery.
2) The upper computer generates corresponding motion stimulation videos according to the rehabilitation training items selected by the user and displays the corresponding motion stimulation videos for the user through the display, the motion stimulation videos are images of left upper limb rehabilitation training or left lower limb rehabilitation training or right upper limb rehabilitation training or right lower limb rehabilitation training according to the selection of the user, the frequency displayed by the four stimulation videos is the same, and the displayed time is the same. The user respectively imagines the movements of the left hand, the left leg, the right hand and the tongue corresponding to the displayed images, and the electroencephalogram signals of the motor imagery synchronized by the user are collected by the electroencephalogram collecting device and transmitted to the upper computer in a wireless mode;
the electroencephalogram acquisition device adopts a structure disclosed by a patent application with the application number of 201810168228.X and the invention name of portable electroencephalogram acquisition equipment and application thereof in SSVEP and motor imagery, and comprises a system power supply circuit 11, a brain electrode cap transfer wire 12, a PGA amplification circuit 13, an AD converter 14, an STM32 processor 15 and a WIFI module 16, wherein the input end of the brain electrode cap transfer wire 12 is connected with a brain electrode cap for acquiring electroencephalogram signals, the output end of the brain electrode cap transfer wire is sequentially connected with the PGA amplification circuit 13, the AD converter 4 and an STM32 processor 15, the STM32 processor 15 is respectively connected with the PGA amplification circuit 13 and the AD converter 14 for controlling the working states of the PGA amplification circuit 13 and the AD converter 14, the WIFI module 16 is connected with the STM32 processor 15 for communicating the STM32 processor 15 with an upper computer through a wireless local area network, the system power supply circuit 11 is respectively connected with the PGA amplifying circuit 13, the AD converter 14, the STM32 processor 15 and the WIFI module 16 for supplying power.
The electroencephalogram acquisition device is characterized in that according to an international 10-20 system, a user motor imagery electroencephalogram signal is acquired through Fz, FCz, FC1, FC2, FC3, FC4, Cz, C1, C2, C3, C4, CPz, CP1, CP2, P1 and POz electrodes of a brain electrode cap, a reference electrode is placed at the left ear papilla, and a ground electrode is replaced by two independent electrodes CMS and DRL.
The rehabilitation equipment of the invention can adopt a powerful madder medical rehabilitation instrument: the upper limb intelligent feedback training system A300, the upper limb intelligent feedback training system C300, the lower limb rehabilitation training device AL200, the upper and lower limb active and passive training systems L220L, or the four limb linkage intelligent feedback training system AL 450.
3) The upper computer receives a motor imagery electroencephalogram signal of a user, pre-processes the motor imagery electroencephalogram signal, extracts and classifies the characteristics of the motor imagery electroencephalogram signal, extracts the characteristics of the motor imagery electroencephalogram signal by adopting a passband common space mode method, classifies the motor imagery electroencephalogram signal by adopting a fruit fly algorithm-based optimized tree-type support vector machine classifier, converts a classification result into a corresponding rehabilitation training control signal, and transmits the rehabilitation training control signal to a rehabilitation movement device; wherein the content of the first and second substances,
the preprocessing comprises the steps of carrying out spatial filtering on the motor imagery electroencephalogram signals by adopting a common average reference method, and removing eye movement and blink artifacts by adopting an independent component analysis method.
As shown in FIG. 4, the tree-type SVM classifier adopts a tree-type SVM composed of three SVM's, i.e., starting from a root node, dividing the classes contained in the node into two subclasses, further dividing the two subclasses until only one class is contained in the subclasses, training the tree-type SVM classifier on each decision node of the binary tree obtained in the process to realize four classifications, and obtaining an optimal classification function f (x) ∑ a of each SVMiyiK(x,xi) + b wherein aiIs the Lagrangian coefficient, yi∈ { -1,1}, b is a classification threshold, K is a kernel function, a Gaussian radial basis kernel function is adopted as the kernel function of the tree-type support vector machine classifier, and the kernel function is expressed as K (x, x)i)＝exp(-γ‖x-xi‖2),γ>0; and the optimal classification function f (x) is used for predicting the category of the motor imagery electroencephalogram signal, if the discrimination score of the optimal classification function f (x) is greater than 0, the classification result is the category 1, and if not, the classification result is the category 2. The tree support vector machine classifier performs specific classificationThe process is as follows:
(1) dividing all motor imagery electroencephalogram signals into two types of data A, B by using a first support vector machine;
(2) classifying the A-type data into a type 1 and a type 2 by using a second support vector machine; meanwhile, classifying the B-type data into a type 3 and a type 4 by using a third support vector machine; wherein type 1, type 2, type 3, and type 4 represent left upper limb rehabilitation training, left lower limb rehabilitation training, right upper limb rehabilitation training, and right lower limb rehabilitation training, respectively.
The characteristic of the motor imagery electroencephalogram signal is extracted by adopting a passband cospace mode method, the preprocessed motor imagery electroencephalogram signal is subjected to spatial filtering again, then the variance of the previous N rows and the next N rows of data of the motor imagery electroencephalogram signal after the spatial filtering is respectively calculated and used as the characteristic of the motor imagery signal, and N is 2.
The method for classifying the motor imagery electroencephalogram signals by adopting the fruit fly algorithm-based optimized tree-type support vector machine classifier comprises the following steps:
(1) dividing the motor imagery electroencephalogram signals processed by the passband common space mode method into a training set and a testing set, performing initialization training on a tree support vector machine classifier with parameters to be determined by adopting the training set to obtain related hyper-parameters of the testing set, and recording the hyper-parameters as I (C, gamma), wherein C represents a punishment factor of the tree support vector machine classifier, and gamma represents a kernel parameter of the tree support vector machine classifier;
(2) updating the training set, and performing iterative training on the tree-type support vector machine classifier after the initial training in the step (1) by using the updated training set to obtain the relevant hyper-parameters I (C, gamma) of the test set after each iteration;
(3) constructing an objective function by using the tree-type support vector machine classifier with the relevant hyperparameters I (C, gamma) obtained in the step (2), and obtaining the optimized parameters of the tree-type support vector machine classifier by adopting a drosophila optimization algorithm;
(4) and (4) substituting the optimized parameters obtained in the step (3) into a tree-type support vector machine classifier, and obtaining a classification result.
4) The rehabilitation equipment drives the user to perform corresponding rehabilitation training actions, and signal data of the training actions of the user are fed back to the upper computer;
5) during the rehabilitation training process, a user can autonomously switch an active training mode and a passive training mode through pure idea according to the fatigue conditions of the brain and limbs of the user; the method comprises the following steps:
(1) in the training process, entering a state of switching a training mode through a tooth biting action, displaying letter prompts of B or Z on a display screen, wherein B represents a passive training mode, Z represents an active training mode, a user respectively imagines left-hand movement or right-hand movement according to the letter prompts, acquires a motor imagery electroencephalogram signal synchronized with the user through an electroencephalogram acquisition device, and transmits the motor imagery electroencephalogram signal to an upper computer in a wireless mode;
(2) the upper computer receives a motor imagery electroencephalogram signal of a user, after preprocessing, extracts the characteristics of the motor imagery electroencephalogram signal through a common space mode algorithm, classifies the motor imagery electroencephalogram signal through a support vector machine classifier, and transmits the classification result to a rehabilitation motion device; the preprocessing method adopts a common average reference method) to carry out spatial filtering on the motor imagery electroencephalogram signals, and adopts an independent component analysis method to remove eye movement and blink artifacts;
(3) the rehabilitation exercise device is converted into a corresponding rehabilitation training mode according to the classification result, the classification result is converted into a passive training mode if the classification result is the class 1, and the classification result is converted into an active training mode if the classification result is the class 2.
6) And a database in the upper computer stores the signal data of the training action of the user, and the signal data of the training action of the user is evaluated by adopting a Fugl-Meyer evaluation method.
Claims (10)
1. A training and mode switching method for a pure idea control rehabilitation robot is characterized by comprising the following steps:
1) the user selects a training mode of the rehabilitation equipment, if the active training mode is selected, the step 2) is carried out, and if the passive training mode is selected, the upper computer directly sends out a corresponding control command to control the rehabilitation equipment to assist limb movement;
2) the upper computer generates a corresponding motion stimulation video according to a rehabilitation training item selected by a user, the corresponding motion stimulation video is displayed to the user through the display, the motion stimulation video is an image of left upper limb rehabilitation training or left lower limb rehabilitation training or right upper limb rehabilitation training or right lower limb rehabilitation training selected by the user, the user respectively imagines the motion of the left hand, the left leg, the right hand and the tongue corresponding to the displayed image, and an electroencephalogram acquisition device acquires a user synchronous motion imagery electroencephalogram signal and transmits the user synchronous motion imagery electroencephalogram signal to the upper computer in a wireless mode;
3) the upper computer receives a motor imagery electroencephalogram signal of a user, pre-processes the motor imagery electroencephalogram signal, extracts and classifies the characteristics of the motor imagery electroencephalogram signal, extracts the characteristics of the motor imagery electroencephalogram signal by adopting a passband common space mode method, classifies the motor imagery electroencephalogram signal by adopting a fruit fly algorithm-based optimized tree-type support vector machine classifier, converts a classification result into a corresponding rehabilitation training control signal, and transmits the rehabilitation training control signal to a rehabilitation movement device;
4) the rehabilitation equipment drives the user to perform corresponding rehabilitation training actions, and signal data of the training actions of the user are fed back to the upper computer;
5) during the rehabilitation training process, a user can autonomously switch an active training mode and a passive training mode through pure idea according to the fatigue conditions of the brain and limbs of the user;
6) and a database in the upper computer stores the signal data of the training action of the user, and the signal data of the training action of the user is evaluated by adopting a Fugl-Meyer evaluation method.
2. The training and mode switching method of a pure idea control rehabilitation robot according to claim 1, characterized in that the active training mode of step 1) is that the user controls the rehabilitation device to help the limb to perform rehabilitation training by motor imagery to complete the rehabilitation training task; the passive training mode is that the user directly drives limbs to carry out rehabilitation training by the rehabilitation equipment without motor imagery.
3. The method as claimed in claim 1, wherein the four stimulation videos of step 2) are displayed at the same frequency and the same time.
4. The training and mode switching method for a pure idea control rehabilitation robot as claimed in claim 1, wherein the brain electrical acquisition device of step 2) acquires the brain electrical signals of motor imagery of the user through Fz, FCz, FC1, FC2, FC3, FC4, Cz, C1, C2, C3, C4, CPz, CP1, CP2, P1 and POz electrodes of brain electrode cap, the reference electrode is placed at left ear papilla, and the grounding electrode is replaced by CMS and DRL two independent electrodes.
5. The method for training and switching modes of a pure idea control rehabilitation robot according to claim 1, characterized in that the preprocessing of step 3) is to perform spatial filtering on the motor imagery electroencephalogram signal by using a common average reference method, and to remove eye movement and blink artifacts by using an independent component analysis method.
6. The method as claimed in claim 1, wherein the tree support vector machine classifier of step 3) is a tree support vector machine consisting of three support vector machines, that is, the classification of the node is divided into two subclasses from a root node, the two subclasses are further divided until only one class is included in the subclasses, the tree support vector machine classifier is trained for each decision node of the binary tree obtained in the process to realize four classifications, and the optimal classification function f (x) of each support vector machine is ∑ aiyiK(x,xi) + b wherein aiIs the Lagrangian coefficient, yi∈ { -1,1}, b is a classification threshold, K is a kernel function, a Gaussian radial basis kernel function is adopted as the kernel function of the tree-type support vector machine classifier, and the kernel function is expressed as K (x, x)i)＝exp(-γ‖x-xi‖2),γ>0; and the optimal classification function f (x) is used for predicting the category of the motor imagery electroencephalogram signal, if the discrimination score of the optimal classification function f (x) is greater than 0, the classification result is the category 1, and if not, the classification result is the category 2.
7. The method for training and switching modes of the pure idea control rehabilitation robot according to claim 6, wherein the specific process of classifying by the tree-type support vector machine classifier is as follows:
(1) dividing all motor imagery electroencephalogram signals into two types of data A, B by using a first support vector machine;
(2) classifying the A-type data into a type 1 and a type 2 by using a second support vector machine; meanwhile, classifying the B-type data into a type 3 and a type 4 by using a third support vector machine; wherein type 1, type 2, type 3, and type 4 represent left upper limb rehabilitation training, left lower limb rehabilitation training, right upper limb rehabilitation training, and right lower limb rehabilitation training, respectively.
8. The method for training and switching modes of a pure idea control rehabilitation robot according to claim 1, characterized in that the step 3) of extracting the characteristics of the motor imagery electroencephalogram signal by adopting the passband cospace mode method is to perform spatial filtering on the preprocessed motor imagery electroencephalogram signal again, then calculate the variance of the first N rows and the last N rows of data of the motor imagery electroencephalogram signal after spatial filtering respectively, and take N as 2 as the characteristics of the motor imagery signal.
9. The training and mode switching method for the pure idea control rehabilitation robot according to claim 1, characterized in that the step 3) of classifying the motor imagery electroencephalogram signals by optimizing the tree-type support vector machine classifier based on the drosophila algorithm comprises:
(1) dividing the motor imagery electroencephalogram signals processed by the passband common space mode method into a training set and a testing set, performing initialization training on a tree support vector machine classifier with parameters to be determined by adopting the training set to obtain related hyper-parameters of the testing set, and recording the hyper-parameters as I (C, gamma), wherein C represents a punishment factor of the tree support vector machine classifier, and gamma represents a kernel parameter of the tree support vector machine classifier;
(2) updating the training set, and performing iterative training on the tree-type support vector machine classifier after the initial training in the step (1) by using the updated training set to obtain the relevant hyper-parameters I (C, gamma) of the test set after each iteration;
(3) constructing an objective function by using the tree-type support vector machine classifier with the relevant hyperparameters I (C, gamma) obtained in the step (2), and obtaining the optimized parameters of the tree-type support vector machine classifier by adopting a drosophila optimization algorithm;
(4) and (4) substituting the optimized parameters obtained in the step (3) into a tree-type support vector machine classifier, and obtaining a classification result.
10. The method for pure idea control rehabilitation robot training and mode switching according to claim 1, wherein step 5) of switching between the active training mode and the passive training mode by pure idea autonomously comprises:
(1) in the training process, entering a state of switching a training mode through a tooth biting action, displaying letter prompts of B or Z on a display screen, wherein B represents a passive training mode, Z represents an active training mode, a user respectively imagines left-hand movement or right-hand movement according to the letter prompts, acquires a motor imagery electroencephalogram signal synchronized with the user through an electroencephalogram acquisition device, and transmits the motor imagery electroencephalogram signal to an upper computer in a wireless mode;
(2) the upper computer receives a motor imagery electroencephalogram signal of a user, after preprocessing, extracts the characteristics of the motor imagery electroencephalogram signal through a common space mode algorithm, classifies the motor imagery electroencephalogram signal through a support vector machine classifier, and transmits the classification result to a rehabilitation motion device; the preprocessing method comprises the steps of carrying out spatial filtering on the motor imagery electroencephalogram signals by adopting a common average reference method, and removing eye movement and blink artifacts by adopting an independent component analysis method;
(3) the rehabilitation exercise device is converted into a corresponding rehabilitation training mode according to the classification result, the classification result is converted into a passive training mode if the classification result is the class 1, and the classification result is converted into an active training mode if the classification result is the class 2.
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