CN113724833B - Method and system for strengthening virtual induction of walking intention of lower limb dyskinesia patient - Google Patents
Method and system for strengthening virtual induction of walking intention of lower limb dyskinesia patient Download PDFInfo
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Abstract
The invention discloses a method and a system for strengthening virtual induction of walking intention of a patient with lower limb dyskinesia, which are combined with a virtual reality technology and a vital energy induction technology, wherein in the process of patient exercise training, a virtual simulation environment of rehabilitation training is preset, under the induction of a virtual induction source, the patient generates walking intention to execute a preset walking task, in the walking process, the displacement, the walking speed and electromyographic signals on the surface of the lower limb of the patient are respectively acquired, fuzzy reasoning is carried out based on membership functions, the movement state of the patient is judged, meanwhile, the virtual-real displacement difference between the virtual induction source and the actual movement of the patient is calculated, and a self-adaptive human-computer interaction control mode is carried out based on the real-time human body data of the patient, so that the active participation degree of the movement interest and training of the patient is improved, the human-computer interaction efficiency is enhanced, and bad results caused by various negative factors are greatly helped by the patient.
Description
Technical Field
The invention belongs to the technical field of rehabilitation of patients with lower limb dyskinesia, and relates to a method and a system for strengthening virtual induction of walking intention of patients with lower limb dyskinesia.
Background
Along with the continuous aggravation of the aging society, simultaneously, accidental injury or diseases of the nerve skeletal muscle system can cause the injury of the lower limb movement function, and a large number of patients with the lower limb movement dysfunction are generated. Patients need to remove necessary medical treatment, walking training during postoperative rehabilitation is indispensable, in the current rehabilitation treatment, more and more rehabilitation physiotherapists advocate a rehabilitation mode with 'active movement as main and passive movement as auxiliary', and more intelligent Kang Fufu devices are used in sports training. In the rehabilitation exercise training process, it is important that the patient generates an active exercise intention, and the generation of the active exercise intention is not only a sign for promoting the recovery of body functions, but also an important factor for the intelligent control of rehabilitation assistance. However, patients can be accompanied by great pain during exercise rehabilitation training and are extremely fatigued, making them unable to produce a durable strong exercise intent.
In order to promote the rehabilitation exercise interest of the patient and further generate strong exercise intention, a method which is commonly adopted at present is to introduce a virtual reality system, an immersive three-dimensional virtual simulation environment can enable the patient to generate an immersive sensation, however, the conventional virtual reality system is only information output, the interactivity between a virtual scene and the patient is poor, the patient cannot be helped to overcome various negative factors, even the tiredness of the patient is accelerated, and the induction effect is poor.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a method and a system for strengthening virtual induction of walking intention of a person with lower limb dyskinesia.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
a walking intention strengthening virtual induction method for lower limb dyskinesia patients comprises the following steps:
presetting a virtual simulation scene, and setting a virtual induction source, wherein the virtual induction source induces a patient to execute a preset walking task;
acquiring displacement, walking speed and lower limb surface electromyographic signals of a patient in the walking process;
blurring speed characteristics, muscle force characteristics and muscle fatigue characteristics of walking in the walking process, carrying out fuzzy reasoning, and judging the movement state of a patient;
calculating a virtual-real displacement difference between the virtual induction source and the actual motion of the patient;
based on the motion state of the patient and the virtual-real displacement difference between the virtual induction source and the actual motion of the patient, each parameter in the virtual simulation scene is adjusted in real time until the task is completed.
The invention further improves that:
the fuzzy reasoning is calculated based on membership functions, and the specific method comprises the following steps:
according to the characteristic vector of the walking speed, the characteristic vector group of the muscle force and the characteristic vector group of the muscle fatigue, a characteristic vector u is established and is used as the input of a fuzzy reasoning model:
u＝[v iEMG RMS MPFMF] T (1)
wherein v represents a velocity feature vector; the iEMG represents an integral myoelectricity value, the RMS represents a root mean square value, the iEMG and the RMS form a muscle force characteristic vector group, the average power frequency of the MPF, the median frequency of the MF table and the MPF and the MF form a muscle fatigue characteristic vector group;
setting the number of input parameters, adding membership functions and adding input vectors in a Fuzzy tool box, generating a FIS object, setting the evaluation standard of the feature vectors as n Fuzzy sets and representing the n Fuzzy sets as:
I＝(A 1 ,A 2 ,…,A n ) (2)
wherein A represents membership function matrix, element A 1 To A n Indicating that the level of the signal is from weak to strong;
setting a walking task according to the medical physiological evaluation grade of a patient, taking the acquired signals as input values, training in a Fuzzy tool box, and obtaining a membership function of each Fuzzy set when a training object converges;
performing fuzzy synthesis on the feature vector u and the membership function matrix A to form a fuzzy evaluation matrix;
the signal strength of each level in the ambiguity set I is described as:
m＝{0,1,2,…,n} T (3)
wherein n is the number of elements contained in the fuzzy set I;
multiplying the result points of the fuzzy evaluation matrix by the signal intensity matrix of each level in the fuzzy set I to obtain the mathematical expected value of the signal intensity of each signal in the characteristic vector u;
and carrying out fuzzy judgment on the average value of the mathematical expected values of each feature vector, and evaluating the motion state of the patient based on the fuzzy judgment result to obtain an evaluation result.
The speed characteristic vector calculating method comprises the following steps:
in the formula, v 0 Represents an initial speed, t represents time, a t Indicating the acceleration at the current time;
the characteristic vector group of the muscle force is characterized by respectively taking an integral myoelectricity value iEMG and a root mean square value RMS, and the calculation method is as follows:
wherein N represents the total number of samples collected by the surface myoelectric sensor; x is x k Representing kth sample data in the N samples;
the characteristic vector group of the muscle fatigue is characterized by an average power frequency MPF and a median frequency MF respectively, and the calculation method comprises the following steps:
wherein f represents frequency, P f Representing the power of the current frequency.
The specific method for the fuzzy judgment comprises the following steps:
let the feature vector v be the velocity feature and be denoted as E v When (when)When the patient movement speed is determined to be low, whenWhen the patient is judged to have high movement speed;
the mathematical expected average E of the eigenvector iEMG and the eigenvector RMS i As a muscle force characteristic, whenWhen the muscle movement is weak, the force of the muscle is judged to be weak>When the muscle movement is judged to be strong;
the mathematical expected average E of the eigenvector MPF and the eigenvector MF m As a characteristic description of muscle fatigue, whenIn the case of muscular fatigue, when +.>When the muscle is not tired, judging;
pair E v 、E i And E is m Respectively making fuzzy determination based on the fuzzyAnd evaluating the movement state of the patient according to the judgment result to obtain an evaluation result.
The evaluation result of the motion state of the patient is divided into five grades, and the virtual induced walking task speed corresponding to each grade is set to be 3.5km/h,3.0km/h,2.0km/h,1.0km/h and 0.5km/h.
The specific calculation method of the virtual-real displacement difference between the virtual induction source and the actual motion of the patient comprises the following steps:
displacement x of read-induced source 0 Calculating the displacement x of the patient from the inertial sensor:
the displacement difference s of the induction source from the patient was calculated every 15 seconds:
s＝x 0 -x (10)
s 0 for the displacement difference between the preset patient and the induction source, when s is less than or equal to s 0 When the induction source speed is regulated, the current speed of the induction source is increased by 0.1km/h; when s is>s 0 And when the induction source speed is regulated, the current speed of the induction source is reduced by 0.1km/h, so that the preset distance between the induction source and a patient is ensured to be kept all the time.
A walking intention strengthening virtual induction system for lower limb dyskinesia patients comprises a virtual induction module and a movement state detection and evaluation module;
the virtual induction module is used for presetting a virtual simulation scene, setting a virtual induction source, inducing a patient to execute a preset walking task, and simultaneously adjusting each parameter in the virtual simulation scene in real time based on the motion state of the patient and the virtual-real displacement difference between the patient and the virtual induction source until the task is completed;
the motion state monitoring and evaluating module is used for collecting displacement, walking speed and lower limb surface electromyographic signals in the walking process, blurring walking speed characteristics, muscle force characteristics and muscle fatigue characteristics in the walking process, carrying out fuzzy reasoning and judging the motion state of a patient; a virtual-to-real displacement difference between the virtual-induced source and the actual motion of the patient is calculated.
Further, the virtual induction module comprises a preset database and a dynamic database;
the preset database is used for presetting a virtual simulation scene of rehabilitation training;
the dynamic database adjusts various parameters of the virtual simulation scene in real time based on the motion state of the patient and the virtual-real displacement difference between the virtual induction source and the actual motion of the patient until all tasks are completed.
Further, the virtual simulation scene is displayed through the virtual reality glasses module, and the display content comprises a virtual simulation environment, a parameter display interface and a virtual induction source.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a virtual induction method and a virtual induction system for strengthening walking intention of a patient with lower limb dyskinesia, which are combined with a virtual reality technology and a living organism induction technology, wherein in the process of patient exercise training, a virtual simulation environment of rehabilitation training is preset, under the induction of a virtual induction source, the patient generates walking intention to further execute a preset walking task, in the process of walking, the displacement distance, the walking speed and electromyographic signals of the surface of the patient in the process of walking are respectively acquired, fuzzy reasoning is carried out based on membership functions, the movement state of the patient is judged, meanwhile, virtual-real displacement difference between the virtual induction source and the actual movement of the patient is calculated, and a self-adaptive man-machine interaction control mode is carried out based on real-time human body data of the patient, so that the active participation degree of patient movement interest and training is improved, the man-machine interaction efficiency is enhanced, bad results brought by various negative factors are greatly helped to the patient, the defect of the traditional virtual induction method is effectively compensated, the interactivity of the virtual induction system is enhanced, the continuous and strong movement intention is induced according to the state of the patient is improved, and a foundation is laid for the patient active rehabilitation training.
Drawings
For a clearer description of the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system workflow diagram of the present invention;
FIG. 2 is a diagram showing a method for determining the movement state of a patient suffering from lower limb movement dysfunction;
FIG. 3 is a flow chart of a method for adaptively adjusting the speed of the induced source motion to a predetermined speed.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "upper," "lower," "horizontal," "inner," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the term "horizontal" if present does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention is described in further detail below with reference to the attached drawing figures:
referring to fig. 1, an embodiment of the invention discloses a method for strengthening virtual induction of walking intention of a patient with lower limb dyskinesia, a computer sends a task instruction, a patient with lower limb dyskinesia is virtually induced to complete walking training according to information in a preset database, the patient wears virtual reality glasses, the virtual reality glasses are provided with virtual reality display interfaces, the display interfaces comprise virtual simulation environments, parameter display interfaces and induction sources, corresponding walking tasks are completed according to the content of the virtual reality display interfaces, meanwhile, real-time movement state characteristics formed by fuzzy reasoning of muscle force characteristics, muscle fatigue characteristics and walking speed characteristics of the patient and virtual-real displacement differences between the patient and the virtual induction sources are stored in a dynamic database, and the virtual induction sources are adjusted according to data in the dynamic database and displayed in the parameter display interfaces, so that self-adaptive continuous induction is realized.
Referring to fig. 2, myoelectric signals on the surface of the lower limb of a patient with lower limb movement dysfunction are collected through a myoelectric sensor, movement information of the patient with lower limb movement dysfunction is collected through an inductive sensor, muscle force characteristics, muscle fatigue characteristics and walking speed characteristics are formed, the three characteristics are blurred based on membership functions, fuzzy reasoning is completed, and the movement state of the patient is judged and output.
Referring to FIG. 3, the velocity v of the read inducing source, the displacement x of the read inducing source 0 Calculating the walking displacement x of the patient based on the sensor, starting a timer, calculating the displacement difference s between the induction source and the patient with lower limb movement dysfunction every 15 seconds, and when s is less than or equal to s 0 When the current speed of the induction source is increased by 0.1km/h, when s>s 0 And (3) slowing down the current speed of the induction source by 0.1km/h, and finally, outputting the adjusted speed to the system, and circularly reciprocating until all tasks are completed.
The method for strengthening virtual induction of walking intention of the lower limb dyskinesia patient specifically comprises the following steps:
step 1: wearing virtual reality glasses by a patient, presetting a virtual simulation environment of rehabilitation training according to the rehabilitation condition of the patient, and executing a preset walking task under the induction of a virtual induction source; and the inertial sensor and the myoelectric sensor are worn to respectively acquire the displacement distance, the walking speed and the myoelectric signals of the lower limb surface of the patient in the walking process.
Step 2: calculating a walking speed v as a feature vector representing the walking speed:
in the formula, v 0 Represents an initial speed, t represents time, a t The acceleration at the current time is acquired by an inertial sensor.
Step 3: the integrated myoelectric value iEMG and the root mean square value RMS are calculated as a set of eigenvectors characterizing muscle force:
wherein N represents the total number of samples collected by the surface myoelectric sensor; x is x k Represents the kth sample data in the N samples.
Step 4: the average power frequency MPF and the median frequency MF are calculated as a set of eigenvectors characterizing muscle fatigue:
where f represents frequency and P (f) represents power of the current frequency.
Step 5: according to the feature vector, a feature vector u is established and is used as an input of a fuzzy inference model:
u＝[v iEMG RMS MPFMF] T (1)
step 6: using a Fuzzy tool box in Matlab, setting the number of input parameters, adding membership functions, adding input vectors, generating a FIS object, setting the evaluation standard of the feature vectors as n Fuzzy sets, and representing the n Fuzzy sets as:
wherein A represents membership function matrix, element A 1 To A n Indicating that the intensity level of the signal is from weak to strong.
I＝(A 1 ,A 2 ,…,A n ) (2)
Step 7: according to the medical physiological evaluation grade of the patient with lower limb movement dysfunction, executing the step 1 and starting a walking task of a corresponding grade, taking the acquired signal as an input value of the step 6, training in a Fuzzy tool box, and obtaining a membership function corresponding to each Fuzzy set when a training object converges;
the physiological evaluation grades are shown in table 1:
table 1 physiological evaluation rating
Step 8: performing fuzzy synthesis on the feature vector u and the membership function matrix A to form a fuzzy evaluation matrix;
step 9: the signal strength of each level in the ambiguity set I is described as:
m＝{0,1,2,…,n} T (3)
wherein n is the number of elements contained in the fuzzy set I.
Step 10: multiplying the result points in the step 8 by the matrix of the step 9 to obtain the mathematical expected value of the signal intensity of each signal in the feature vector u;
step 11: the feature vector v is described as a speed feature, denoted as E v The two types are fast and slow, namelyFor slow (or->Is fast;
step 12: the mathematical expected average E of the eigenvector iEMG and the eigenvector RMS i As muscle force characteristics, it is classified into strong and weak categories, namelyWeak (or->Is strong;
step 13: the mathematical expected average E of the eigenvector MPF and the eigenvector MF m As a description of muscle fatigue characteristics, there are two categories of fatigue-free, i.eIs fatigueLao, I/O (fatigue)>Is not tired;
step 14: pair E v 、E i 、E m Performing fuzzy judgment, and evaluating the lower limb dyskinesia patient according to a movement state judgment table, wherein the final judgment result is divided into five grades: A. b, C, D, E the speeds of the corresponding virtual induction walking tasks of each grade are set to be 3.5km/h,3.0km/h,2.0km/h,1.0km/h and 0.5km/h.
The discrimination table is shown in table 2,
table 2 motion state discrimination table
The method for adaptively adjusting the preset speed of the motion of the induction source is based on the sense of the patient with lower limb dyskinesia, and marks the displacement difference between the patient and the induction source as the effective distance s when the patient can be stimulated to generate the intention of' the patient can catch up with the front induction source to a large extent within the distance 0 ；
Displacement x of read-induced source 0 Calculating the displacement x of the patient with lower limb movement dysfunction according to the inertial sensor:
the displacement difference s between the induction source and the patient with lower limb movement dysfunction is calculated every 15 seconds:
s＝x 0 -x (10)
when s is less than or equal to s 0 When the system intervenes the speed of the induction source, the current speed of the induction source is increased by 0.1km/h, when s>s 0 During the process, the system intervenes in the speed of the induction source, so that the current speed of the induction source is reduced by 0.1km/h, and a certain effective distance is kept between the induction source and a patient all the time;
the embodiment of the invention also discloses a walking intention strengthening virtual induction system for the lower limb dyskinesia, which comprises a virtual induction module, a virtual reality eye module and a movement state detection and evaluation module;
the virtual induction module is used for processing and storing system data, comprises a preset database and a dynamic database, wherein the preset database is used for presetting a virtual scene, the dynamic database is used for storing the virtual-real displacement difference between a virtual induction source and a patient and the data of the motion state characteristics of the patient in real time, and the virtual environment parameters are adjusted in real time according to the fed-back data;
the virtual reality glasses module is used for receiving the instruction sent by the virtual induction module and displaying a virtual induction scene;
the motion state monitoring and evaluating module comprises an myoelectricity acquisition unit and a motion data acquisition unit, evaluates the current motion state of the patient according to the myoelectricity signals on the surface of the lower limb of the patient and walking speed information, and outputs data to the virtual induction module.
The virtual reality glasses module comprises a virtual simulation environment, a parameter display interface and a virtual induction source, wherein the induction source is positioned in the virtual simulation environment, and the virtual induction module guides a patient with lower limb movement dysfunction to complete a walking task through action guidance of the induction source, text display and voice prompt in the virtual simulation environment. The induction source is a simulated virtual character or a simulated virtual pet or a virtual gold coin, can be selected according to the interest of a patient, and the motion speed of the induction source is driven by real-time data in a database;
the above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The method for strengthening virtual induction of walking intention of the patient with lower limb dyskinesia is characterized by comprising the following steps of:
presetting a virtual simulation scene, and setting a virtual induction source, wherein the virtual induction source induces a patient to execute a preset walking task;
acquiring displacement, walking speed and lower limb surface electromyographic signals of a patient in the walking process;
blurring speed characteristics, muscle force characteristics and muscle fatigue characteristics of walking in the walking process, carrying out fuzzy reasoning, and judging the movement state of a patient;
the fuzzy reasoning is calculated based on membership functions, and the specific method comprises the following steps:
according to the characteristic vector of the walking speed, the characteristic vector group of the muscle force and the characteristic vector group of the muscle fatigue, establishing the characteristic vectoruAs input to the fuzzy inference model:
in the method, in the process of the invention,vrepresenting a velocity feature vector;iEMGthe value of the integrated myoelectricity is represented,RMSthe root mean square value is represented by the root mean square value,iEMGandRMSa set of muscle force feature vectors is formed,MPFthe frequency of the average power is determined,MFthe median frequency is shown in the table,MPFandMFforming a muscle fatigue characteristic vector group;
setting the number of input parameters, adding membership functions and adding input vectors in a Fuzzy tool box, generating a FIS object, and setting the evaluation standard of the feature vectors asnThe fuzzy sets are expressed as:
in the method, in the process of the invention,Arepresenting membership function matrix, elementsA 1 To the point ofA n Indicating that the level of the signal is from weak to strong;
setting a walking task according to the medical physiological evaluation grade of a patient, taking the acquired signals as input values, training in a Fuzzy tool box, and obtaining a membership function of each Fuzzy set when a training object converges;
feature vectoruAnd membership function matrixAProceeding withFuzzy synthesis is carried out to form a fuzzy evaluation matrix;
will blur the setIThe signal strength of each level of (a) is described as:
in the method, in the process of the invention,n'as fuzzy setsIThe number of the elements contained in the steel;
multiplying the result points of the fuzzy evaluation matrix by the fuzzy setIEach level signal intensity matrix in the plurality of levels to obtain a feature vectoruA mathematical expectation of signal strength for each signal;
carrying out fuzzy judgment on the average value of the mathematical expected values of each feature vector, and evaluating the motion state of the patient based on the fuzzy judgment result to obtain an evaluation result;
calculating a virtual-real displacement difference between the virtual induction source and the actual motion of the patient;
based on the motion state of the patient and the virtual-real displacement difference between the virtual induction source and the actual motion of the patient, each parameter in the virtual simulation scene is adjusted in real time until the task is completed.
2. The method for strengthening virtual induction of walking intention of lower limb dyskinesia according to claim 1, wherein the method for calculating the velocity feature vector is as follows:
in the method, in the process of the invention,v 0 indicating the initial velocity of the vehicle,tthe time is represented by the time period of the day,a t indicating the acceleration at the current time;
the characteristic vector groups of the muscle force are respectively used for integrating myoelectricity valuesiEMGAnd root mean square valueRMSAs a characterization, the calculation method is:
in the method, in the process of the invention,Nrepresenting the total number of samples collected by the surface myoelectric sensor;x k representation ofNThe first sample ofkSample data;
the characteristic vector groups of the muscle fatigue are respectively in average power frequencyMPFAnd median frequencyMFAs a characterization, the calculation method is:
in the method, in the process of the invention,fthe frequency is represented by a frequency value,P f representing the power of the current frequency.
3. The method for strengthening virtual induction of walking intention of lower limb dyskinesia according to claim 2, wherein the specific method for fuzzy determination is as follows:
feature vectorvAs a speed feature, it is noted thatE v When (when)When the patient is judged to have low movement speed, when +.>When the patient is judged to have high movement speed;
feature vectoriEMGAnd feature vectorRMSIs the mathematically expected mean value of (2)E i As a muscle force characteristic, whenWhen the muscle movement is weak, the force of the muscle is judged to be weak>When the muscle movement is judged to be strong;
feature vectorMPFAnd feature vectorMFIs the mathematically expected mean value of (2)E m As a characteristic description of muscle fatigue, whenIn the case of muscular fatigue, when +.>When the muscle is not tired, judging;
for a pair ofE v 、E i AndE m and respectively carrying out fuzzy judgment, and evaluating the motion state of the patient based on the fuzzy judgment result to obtain an evaluation result.
4. The method for strengthening virtual induction of walking intention of lower limb dyskinesia patient according to claim 3, wherein the evaluation result of the patient's movement state is divided into five grades, and the virtual induction walking task speed corresponding to each grade is set to 3.5km/h,3.0km/h,2.0km/h,1.0km/h,0.5km/h.
5. The method for strengthening virtual induction of walking intention of lower limb dyskinesia patient according to claim 1, wherein the specific calculation method of virtual-real displacement difference between virtual induction source and actual movement of patient is as follows:
displacement of read-induced sourcex 0 Calculating displacement of patient based on inertial sensorx：
Calculating displacement difference between induction source and patient every 15 secondss：
s 0 For the displacement difference between the preset patient and the induction source, whens≤s 0 When the induction source speed is regulated, the current speed of the induction source is increased by 0.1km/h; when (when)s＞s 0 When adjustingThe speed of the induction source is reduced by 0.1km/h, so as to ensure that the preset distance is kept between the induction source and the patient all the time.
6. A virtual induction system for strengthening walking intention of a lower limb dyskinesia patient for realizing the method of claim 1, which is characterized by comprising a virtual induction module and a movement state detection and evaluation module;
the virtual induction module is used for presetting a virtual simulation scene, setting a virtual induction source, inducing a patient to execute a preset walking task, and simultaneously adjusting each parameter in the virtual simulation scene in real time based on the motion state of the patient and the virtual-real displacement difference between the patient and the virtual induction source until the task is completed;
the motion state monitoring and evaluating module is used for collecting displacement, walking speed and lower limb surface electromyographic signals in the walking process, blurring walking speed characteristics, muscle force characteristics and muscle fatigue characteristics in the walking process, carrying out fuzzy reasoning and judging the motion state of a patient; a virtual-to-real displacement difference between the virtual-induced source and the actual motion of the patient is calculated.
7. The virtual induction system for strengthening walking intention of lower limb dyskinesia patients according to claim 6, wherein the virtual induction module comprises a preset database and a dynamic database;
the preset database is used for presetting a virtual simulation scene of rehabilitation training;
the dynamic database adjusts various parameters of the virtual simulation scene in real time based on the motion state of the patient and the virtual-real displacement difference between the virtual induction source and the actual motion of the patient until all tasks are completed.
8. The system for strengthening virtual induction of walking intention of lower limb dyskinesia according to claim 7, wherein the virtual simulation scene is displayed by a virtual reality glasses module, and the display content comprises a virtual simulation environment, a parameter display interface and a virtual induction source.
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