CN109106339A - A kind of On-line Estimation method of elbow joint torque under functional electrostimulation - Google Patents
A kind of On-line Estimation method of elbow joint torque under functional electrostimulation Download PDFInfo
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- CN109106339A CN109106339A CN201811006795.1A CN201811006795A CN109106339A CN 109106339 A CN109106339 A CN 109106339A CN 201811006795 A CN201811006795 A CN 201811006795A CN 109106339 A CN109106339 A CN 109106339A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4528—Joints
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
- A61B5/458—Evaluating the elbow
Abstract
The present invention relates to a kind of On-line Estimation methods of elbow joint torque under functional electrostimulation, by acquiring electro photoluminescence hypozygal torque, angle, angular velocity data offline, muscle skeleton model is modeled in advance, then electro photoluminescence hypozygal angle and angular velocity signal are acquired in real time, real-time adaptive update is carried out to the muscle skeleton model system parameter modeled in advance, realizes the On-line Estimation to torque.The present invention by acquiring joint angles and angular velocity signal realization to the On-line Estimation of torque in real time.
Description
Technical field
The present invention relates to functional electrostimulation technical field, under especially a kind of functional electrostimulation elbow joint torque
Line estimation method.
Background technique
In recent years, the cranial vascular diseases such as spinal cord injury and apoplexy cause the disease incidence of paralysis to be in significant ascendant trend, this
Class illness results in tissue damage, and the different degrees of and different location of tissue damaged can cause different degrees of injury gained in sports,
This not only gives personal and family all to bring biggish burden, also becomes increasingly heavy social concern.Human upper limb locomotion process
In, elbow joint and its attached muscle play the role of vital, the phases such as elbow joint is achievable to be bent and stretched, forearm inward turning and outward turning
Pass movement.Healing and training elbow joint plays a significant role the activity of daily living for improving hemiplegic patient.Suffer from present in hemiplegia
In person's rehabilitation training, functional electrostimulation is generally considered a kind of relatively effective clinical tool.Functional electrostimulation passes through
Electro photoluminescence controls the target muscles of joint motions, induces non-autonomous muscular movement or induces the normal autokinetic movement of Muscle Simulation,
Carrying out the rehabilitation training of patient, improving and then restoring the performance for being stimulated muscle.In order to obtain desired joint motions, and to the greatest extent
The influence that fatigue generates in rehabilitation training may be reduced, need to design and Implement functional electrostimulation adaptive control system, with
Generate appropriate, stable muscular force and corresponding joint torque.In functional electrostimulation adaptive control system, real-time and accurately
Measurement joint moment signal is crucial.
Due to the complexity of human synovial structure, joint moment measurement is technology most challenging in biomechanics Research
One of.The measurement of torque can be applied not only to rehabilitation training, and can be applied to the Training valuation of sportsman, artificial limb and orthoses
The fields such as design, this facilitate the development of torgue measurement method.In the document and disclosed technology delivered at present, joint power
Square measurement has directly or indirectly measurement method.Directly measurement directly measures pass when moving by bulky torgue measurement system
Save torque.Indirect measurement method has dynamometry, reversed biomechanical analysis method and positive biomechanical analysis method.Dynamometry exists
The problems such as testing result as caused by gravity is incorrect and application scenarios are limited.Reversed biomechanical analysis method testing result by
It is limited to muscle skeleton model, and detection site requirements is bigger, equipment is expensive.The positive same testing result of biomechanical approach by
It is limited to muscle skeleton model, and Optimal Parameters are excessive, time complexity height etc..It is existing no matter directly or indirect measurement method,
It cannot achieve the on-line measurement in functional electrostimulation control system to torque signals.
Summary of the invention
In view of this, the purpose of the present invention is to propose to a kind of On-line Estimation sides of elbow joint torque under functional electrostimulation
Method, by acquiring joint angles and angular velocity signal realization in real time to the On-line Estimation of torque.
The present invention is realized using following scheme: a kind of On-line Estimation method of elbow joint torque under functional electrostimulation, packet
Include following steps:
Step S1: the torque, angle and angular velocity signal of offline acquisition elbow joint under functional electrostimulation, and to its into
Row pretreatment；It regard treated torque, angle, angular velocity signal as correcting value, the variation of binding function intensity of electric stimulus is sharp
Parameter identification is carried out to elbow joint muscle skeleton model system with extended pattern Kalman filter, to elbow joint muscle skeleton model
Carry out offline pre- modeling；
Step S2: by angle and angular velocity signal of the online acquisition elbow joint under functional electrostimulation, and it is right simultaneously
It is pre-processed, and then regard treated angle, angular velocity signal as correcting value, the change of binding function intensity of electric stimulus
Change, step S1 is modeled into the parameter of identification as initial value, using extended pattern Kalman filter to the articularis cubiti modeled in advance in advance
Meat bone bone model system parameter carries out real-time adaptive update, realizes the On-line Estimation to elbow joint torque signals.
Further, the state variable x of the state space equation of the elbow joint muscle skeleton model, input u are respectively as follows:
X=[x1 x2 x3 x4 x5 x6]′；
In formula, x1Indicate Angle of Elbow Joint, x2Indicate Elbow Joint Angle Neural speed, x3Indicate the muscle activation of the bicipital muscle of arm, x4Table
Show the process variable of bicipital muscle of arm muscle activation, x5Indicate the muscle activation of the triceps muscle of arm, x6Indicate the mistake of triceps muscle of arm muscle activation
Cheng Liang, u1、u2Indicate system input signal, pw1Indicate the intensity of electric stimulus of the stimulation bicipital muscle of arm, pw2Indicate the stimulation triceps muscle of arm
Intensity of electric stimulus, t indicate the time, TdIndicate muscle physiological operating lag；
The state space equation of the elbow joint muscle skeleton model are as follows:
Y=g (x)
In formula, J indicates that rotary inertia, M indicate elbow joint torque, w0With w1Indicate undamped natural frequency of a mechanical system, D is expressed as
Calcium dynamics damping factor, st (u1) indicate u1Irritation level, st (u2) indicate u2Irritation level, g (x) indicate system
System output equation, f (x, u) indicate system state equation；
With first order difference equation approximate differential, it is separate manufacturing firms structure by elbow joint muscle skeleton model conversion: sets
Input quantity and quantity of state are constant, kT between two periodss≤t<(k+1)Ts；That is xk=xk-1+Ts·f(x,
U), wherein TsFor the sampling period, then model separate manufacturing firms expression formula are as follows:
yk=g (xk)；
In formula, k is iterative cycles number, xkIt is the discrete state variable at k moment, xk-1It is the discrete state change at k-1 moment
Amount, x1,k-1,...,x6,k-1It is the x at k-1 moment respectively1,...,x6, ykIt is the discrete output variable at k moment, f (xk-1,uk-1) table
Show system discrete state equations.
Wherein, the movement of elbow joint is mainly shunk by the coordination of the bicipital muscle of arm and the triceps muscle of arm and is completed, and the triceps muscle of arm is main
That is responsible for elbow joint stretches elbow movement, and the bicipital muscle of arm is mainly responsible for the elbow in the wrong movement of elbow joint.Online acquisition angle and angular speed letter
Number when, the muscle of electro photoluminescence controlled device passes through joint angles sensor and angular-rate sensor acquisition angles, angular speed letter
Number.
Further, the elbow joint torque M is calculated using following formula:
M=Me+Mv+Mg+Ma1-Ma2；
Wherein, MeFor passive moment of elasticity, MvFor passive sticky torque, MgFor gravitational moment, Ma1It is the bicipital muscle of arm under electro photoluminescence
The active torque of generation, Ma2It is the active torque that the triceps muscle of arm generates under electro photoluminescence, and has:
Mv=b1tanh(-b2x2)-b3x2；
Mg=-g1sin(x1)；
In formula, k1、k2、k3、b1、b2、b3、g1It is normal number parameter to be identified.
Further, the active torque M that the bicipital muscle of arm and the triceps muscle of arm generatea1、Ma2By activation dynamics and non-thread
Property static contraction function composition, the activation dynamics includes static recruitment curve and calcium dynamics transmission function.
Further, the measured value raised curve and provide normalization activation unit according to pulse width pw, is labeled as
Irritation level st:
In formula, pwthrIt is the threshold value that muscle unit is raised under electro photoluminescence, pwsatIt is the full of muscle unit recruitment under electro photoluminescence
And value；Elbow joint is started into mobile pulse width and is taken as pwthr, the pulse width approximation that elbow joint reaches full extension is selected
It is selected as pwsat；
Bicipital muscle of arm calcium dynamics are described using the typical second-order system with the input delay time:
In formula, w0It is undamped natural frequency of a mechanical system, D=1 is calcium dynamics damping factor；
Ma1Nonlinear Static contracting function beWherein s0,s1,
s2,s3,s4,s5For constant parameter to be identified, TdIt is muscle physiological operating lag, Td=0.025s；Ma1=Fa1x3；
Triceps muscle of arm calcium dynamics are described using the typical second-order system with the input delay time:
In formula, w1It is undamped natural frequency of a mechanical system, D=1 is calcium dynamics damping factor；
Ma2Nonlinear Static contracting function beWherein s6,s7,
s8,s9,s10,s11For constant parameter to be identified, TdIt is muscle physiological operating lag, Td=0.025s；Ma2=Fa2x5。
Further, described to utilize extended pattern Kalman filter to elbow joint muscle skeleton model system in step S1
Carry out parameter identification specifically:
Parameter identification uses extended pattern Kalman filter EKF, and the parameter that model needs to recognize is 22, and setting parameter battle array is
θ=[k1,k2,k3,b1,b2,b3,g1,s0,…,s11,w0,w1,J]T, while carrying out state and parameter estimation, i.e., it is by parameter amplification
State variable, if the state variable X after amplificationk=[xk；θk], the state-space expression after extension are as follows:
Wherein, EKF includes two processes, and measurement updaue and time update；The measurement updaue process are as follows:
The time renewal process are as follows:
Wherein, I is unit matrix, and Q is process noise covariance, and R is measurement noise covariance, DkIt is one and includes needs
The Jacobin matrix of the parameter of identification, It is identification state matrix,It is pre-
Survey output, PkIt is evaluated error covariance matrix, KkIt is kalman gain,It is prior uncertainty covariance matrix, HkIt is Kalman
Filter factor matrix.
Further, in step S1, offline pre- modeling is carried out to elbow joint muscle skeleton model, exports y are as follows:
In formula, y1Indicate Angle of Elbow Joint, y2It is angular speed, y3It is torque；
After discretization, obtain:
In formula, y1,kIt is k moment Angle of Elbow Joint, y2,kIt is k moment angular speed, y3,kIt is k moment elbow joint torque.
Further, in step S2, the On-line Estimation to elbow joint torque signals specifically: distinguish pre- modeling parameters
Know the initial value that result is set as parameter to be adjusted, export y are as follows:
Y=g (x)=Cx；
Wherein,
After discretization, obtain:
yk=g (xk)=Cxk；
Real-time adaptive update is carried out to the muscle skeleton model system parameter modeled in advance using above structure, is realized to elbow
The estimation of joint moment signal, wherein the On-line Estimation of elbow joint torque under energy property electro photoluminescence are as follows:
Mk=Me,k+Mv,k+Mg,k+Ma1,k-Ma2,k。
Compared with prior art, the invention has the following beneficial effects: the present invention utilizes the human elbow under electro photoluminescence
Angle and angular speed, to muscle skeleton model parameter carry out real-time adaptive update, realize electro photoluminescence under elbow joint torque
Line estimation, and angle and angular speed are easier to acquire in actual operation.The present invention be mainly used for for cerebral apoplexy and
The rehabilitation training of upper limbs of Patients of Spinal.The model for meeting the neuromuscular bone dynamic characteristic under electro photoluminescence is established, is function
Property electric stimulation safe and stable and offer basis can be accurately controlled.
Detailed description of the invention
Fig. 1 forms for the active moment function of the bicipital muscle of arm and the triceps muscle of arm under the electro photoluminescence of the embodiment of the present invention.
Top view when Fig. 2 is the human elbow horizontal movement of the embodiment of the present invention.
Fig. 3 is the pre- modeling flow diagram of the embodiment of the present invention.
Fig. 4 is the On-line Estimation flow diagram of the embodiment of the present invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
Present embodiments provide a kind of On-line Estimation method of elbow joint torque under functional electrostimulation, including following step
It is rapid:
Step S1: the torque, angle and angular velocity signal of offline acquisition elbow joint under functional electrostimulation, and to its into
Row pretreatment；It regard treated torque, angle, angular velocity signal as correcting value, the variation of binding function intensity of electric stimulus is sharp
Parameter identification is carried out to elbow joint muscle skeleton model system with extended pattern Kalman filter, to elbow joint muscle skeleton model
Carry out offline pre- modeling；
Step S2: by angle and angular velocity signal of the online acquisition elbow joint under functional electrostimulation, and it is right simultaneously
It is pre-processed, and then regard treated angle, angular velocity signal as correcting value, the change of binding function intensity of electric stimulus
Change, step S1 is modeled into the parameter of identification as initial value, using extended pattern Kalman filter to the articularis cubiti modeled in advance in advance
Meat bone bone model system parameter carries out real-time adaptive update, realizes the On-line Estimation to elbow joint torque signals.
In the present embodiment, the state variable x of the state space equation of the elbow joint muscle skeleton model, u points of input
Not are as follows:
X=[x1 x2 x3 x4 x5 x6]′；
In formula, x1Indicate Angle of Elbow Joint, x2Indicate Elbow Joint Angle Neural speed, x3Indicate the muscle activation of the bicipital muscle of arm, x4Table
Show the process variable of bicipital muscle of arm muscle activation, x5Indicate the muscle activation of the triceps muscle of arm, x6Indicate the mistake of triceps muscle of arm muscle activation
Cheng Liang, u1、u2Indicate system input signal, pw1Indicate the intensity of electric stimulus of the stimulation bicipital muscle of arm, pw2Indicate the stimulation triceps muscle of arm
Intensity of electric stimulus, t indicate the time, TdIndicate muscle physiological operating lag；
The state space equation of the elbow joint muscle skeleton model are as follows:
Y=g (x)
In formula, J indicates that rotary inertia, M indicate elbow joint torque, w0With w1Indicate undamped natural frequency of a mechanical system, D is expressed as
Calcium dynamics damping factor, st (u1) indicate u1Irritation level, st (u2) indicate u2Irritation level, g (x) indicate system
System output equation, f (x, u) indicate system state equation；
With first order difference equation approximate differential, it is separate manufacturing firms structure by elbow joint muscle skeleton model conversion: sets
Input quantity and quantity of state are constant, kT between two periodss≤t<(k+1)Ts；That is xk=xk-1+Ts·f(x,
U), wherein TsFor the sampling period, then model separate manufacturing firms expression formula are as follows:
yk=g (xk)；
In formula, k is iterative cycles number, xkIt is the discrete state variable at k moment, xk-1It is the discrete state change at k-1 moment
Amount, x1,k-1,...,x6,k-1It is the x at k-1 moment respectively1,...,x6, ykIt is the discrete output variable at k moment, f (xk-1,uk-1) table
Show system discrete state equations.
Wherein, the movement of elbow joint is mainly shunk by the coordination of the bicipital muscle of arm and the triceps muscle of arm and is completed, and the triceps muscle of arm is main
That is responsible for elbow joint stretches elbow movement, and the bicipital muscle of arm is mainly responsible for the elbow in the wrong movement of elbow joint.Online acquisition angle and angular speed letter
Number when, the muscle of electro photoluminescence controlled device passes through joint angles sensor and angular-rate sensor acquisition angles, angular speed letter
Number.
In the present embodiment, the elbow joint torque M is calculated using following formula:
M=Me+Mv+Mg+Ma1-Ma2；
Wherein, MeFor passive moment of elasticity, MvFor passive sticky torque, MgFor gravitational moment, Ma1It is the bicipital muscle of arm under electro photoluminescence
The active torque of generation, Ma2It is the active torque that the triceps muscle of arm generates under electro photoluminescence, and has:
Mv=b1tanh(-b2x2)-b3x2；
Mg=-g1sin(x1)；
In formula, k1、k2、k3、b1、b2、b3、g1It is normal number parameter to be identified.
In the present embodiment, as shown in Figure 1, the active torque M that the bicipital muscle of arm and the triceps muscle of arm generatea1、Ma2By swashing
Movable mechanics and nonlinear Static contracting function composition, the activation dynamics include static recruitment curve and calcium dynamics
Transmission function.
In the present embodiment, the measured value raised curve and provide normalization activation unit according to pulse width pw, mark
It is denoted as irritation level st:
In formula, pwthrIt is the threshold value that muscle unit is raised under electro photoluminescence, pwsatIt is the full of muscle unit recruitment under electro photoluminescence
And value；Elbow joint is started into mobile pulse width and is taken as pwthr, the pulse width approximation that elbow joint reaches full extension is selected
It is selected as pwsat；
Bicipital muscle of arm calcium dynamics are described using the typical second-order system with the input delay time:
In formula, w0It is undamped natural frequency of a mechanical system, D=1 is calcium dynamics damping factor；
Ma1Nonlinear Static contracting function beWherein s0,s1,
s2,s3,s4,s5For constant parameter to be identified, TdIt is muscle physiological operating lag, Td=0.025s；Ma1=Fa1x3；
Triceps muscle of arm calcium dynamics are described using the typical second-order system with the input delay time:
In formula, w1It is undamped natural frequency of a mechanical system, D=1 is calcium dynamics damping factor；
Ma2Nonlinear Static contracting function beWherein s6,s7,
s8,s9,s10,s11For constant parameter to be identified, TdIt is muscle physiological operating lag, Td=0.025s；Ma2=Fa2x5。
In the present embodiment, described to utilize extended pattern Kalman filter to elbow joint muscle skeleton model in step S1
System carries out parameter identification specifically:
Parameter identification uses extended pattern Kalman filter EKF, and the parameter that model needs to recognize is 22, and setting parameter battle array is
θ=[k1,k2,k3,b1,b2,b3,g1,s0,…,s11,w0,w1,J]T, while carrying out state and parameter estimation, i.e., parameter is expanded
For state variable, if the state variable X after amplificationk=[xk；θk], the state-space expression after extension are as follows:
Wherein, EKF includes two processes, and measurement updaue and time update；The measurement updaue process are as follows:
The time renewal process are as follows:
Wherein, I is unit matrix, and Q is process noise covariance, and R is measurement noise covariance, DkIt is one and includes needs
The Jacobin matrix of the parameter of identification, It is identification state matrix,It is pre-
Survey output, PkIt is evaluated error covariance matrix, KkIt is kalman gain, Pk -It is prior uncertainty covariance matrix, HkIt is Kalman
Filter factor matrix.
In the present embodiment, in step S1, offline pre- modeling is carried out to elbow joint muscle skeleton model, exports y are as follows:
In formula, y1Indicate Angle of Elbow Joint, y2It is angular speed, y3It is torque；
After discretization, obtain:
In formula, y1,kIt is k moment Angle of Elbow Joint, y2,kIt is k moment angular speed, y3,kIt is k moment elbow joint torque.
In the present embodiment, in step S2, the On-line Estimation to elbow joint torque signals specifically: ginseng will be modeled in advance
Number identification result is set as the initial value of parameter to be adjusted, exports y are as follows:
Y=g (x)=Cx；
Wherein,
After discretization, obtain:
yk=g (xk)=Cxk；
Real-time adaptive update is carried out to the muscle skeleton model system parameter modeled in advance using above structure, is realized to elbow
The estimation of joint moment signal, wherein the On-line Estimation of elbow joint torque under energy property electro photoluminescence are as follows:
Mk=Me,k+Mv,k+Mg,k+Ma1,k-Ma2,k。
Particularly, under functional electrostimulation designed by the present embodiment the forecasting system use of elbow joint torque specifically include with
Lower three steps:
The first step is rationally placed with electrode slice.Top view when such as Fig. 2 human elbow horizontal movement, two pairs of stimulating electrodes
To the bicipital muscle of arm is attached to, the position S3, S4, angular-rate sensor are attached to the bicipital muscle of arm end to end for the position S1, S2 and the triceps muscle of arm end to end
The position belly of muscle S5, joint angles sensor are pasted at elbow joint E1, E2.
Second step carries out offline pre- modeling to elbow joint skeletal muscle model.A length of T when settingmaxElectric stimulation pulse it is defeated
Enter, the bicipital muscle of arm is stimulated by electric stimulating instrument, while acquiring Angle of Elbow Joint and angular velocity data under functional electrostimulation, utilized
Torque sensor acquires elbow joint torque data.Stimulus signal should have enough dynamic changes, and be distributed at many levels entire
In amplitude range, kinetic characteristic of the elbow joint under different intensity of electric stimulus can be preferably inspired in this way, embody torque
Variation, such as stepped signal, pseudorandom multilevel signal (PRMS), multiple sinusoidal signal and filtering random noise (FRN).
The modeling that contraction of muscle system is carried out with the structure carries out parameter identification in conjunction with extended pattern Kalman filter, completes
Elbow joint skeletal muscle model models in advance, firstly, torque, angle and angular velocity signal under acquisition electro photoluminescence, to the parameter of model
It is initialized, enables k=1；Secondly, measuring more new stage and the time more new stage to model in conjunction with EKF, it is pre- to carry out system
Modeling parameters identification；If lastThen k=k+1 repeats back, otherwise end loop, and pre- modeling terminates, such as Fig. 3
It is shown.
Third step carries out the torque On-line Estimation of elbow joint under functional electrostimulation.By intensity of electric stimulus variation as defeated
Enter, intensity of electric stimulus can be changed by changing current amplitude, frequency, pulsewidth, in conjunction with Angle of Elbow Joint under electro photoluminescence and angle
Speed signal carries out parameter identification, and then realizes the torque On-line Estimation of elbow joint under functional electrostimulation.Firstly, utilizing built in advance
The parameter identification result of mould initializes the parameter of model, enables k=1；Secondly, acquisition real-time angular and angular velocity signal,
In conjunction with EKF is measured to model more new stage and the time more new stage, online adaptive correction, output are carried out to model parameter
Real-time moment values Mk；If last electro photoluminescence is not finished, k=k+1 repeats back, otherwise end loop, as shown in Figure 4.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, is all covered by the present invention.
Claims (8)
1. a kind of On-line Estimation method of elbow joint torque under functional electrostimulation, it is characterised in that: the following steps are included:
Step S1: the torque, angle and angular velocity signal of offline acquisition elbow joint under functional electrostimulation, and it is carried out pre-
Processing；It regard treated torque, angle, angular velocity signal as correcting value, binding function intensity of electric stimulus changes, and utilizes expansion
Exhibition type Kalman filter carries out parameter identification to elbow joint muscle skeleton model system, carries out to elbow joint muscle skeleton model
Offline pre- modeling；
Step S2: by angle and angular velocity signal of the online acquisition elbow joint under functional electrostimulation, and simultaneously to its into
Then row pretreatment regard treated angle, angular velocity signal as correcting value, the variation of binding function intensity of electric stimulus, by
Step S1 models the parameter of identification as initial value, using extended pattern Kalman filter to the elbow joint muscle skeleton modeled in advance in advance
Model system parameter carries out real-time adaptive update, realizes the On-line Estimation to elbow joint torque signals.
2. the On-line Estimation method of elbow joint torque, feature exist under a kind of functional electrostimulation according to claim 1
In: the state variable x of the state space equation of the elbow joint muscle skeleton model, input u are respectively as follows:
X=[x1 x2 x3 x4 x5 x6]′；
In formula, x1Indicate Angle of Elbow Joint, x2Indicate Elbow Joint Angle Neural speed, x3Indicate the muscle activation of the bicipital muscle of arm, x4Indicate the upper arm
The process variable of biceps muscle activation, x5Indicate the muscle activation of the triceps muscle of arm, x6Indicate the process of triceps muscle of arm muscle activation
Amount, u1、u2Indicate system input signal, pw1Indicate the intensity of electric stimulus of the stimulation bicipital muscle of arm, pw2Indicate the stimulation triceps muscle of arm
Intensity of electric stimulus, t indicate the time, TdIndicate muscle physiological operating lag；
The state space equation of the elbow joint muscle skeleton model are as follows:
Y=g (x)
In formula, J indicates that rotary inertia, M indicate elbow joint torque, w0With w1Indicate undamped natural frequency of a mechanical system, D be expressed as calcium from
Subdynamics damping factor, st (u1) indicate u1Irritation level, st (u2) indicate u2Irritation level, g (x) indicate system it is defeated
Equation out, f (x, u) indicate system state equation；
With first order difference equation approximate differential, it is separate manufacturing firms structure by elbow joint muscle skeleton model conversion: sets input
Amount and quantity of state are constant, kT between two periodss≤t<(k+1)Ts；That is xk=xk-1+TsF (x, u),
Wherein, TsFor the sampling period, then model separate manufacturing firms expression formula are as follows:
yk=g (xk)；
In formula, k is iterative cycles number, xkIt is the discrete state variable at k moment, xk-1It is the discrete state variable at k-1 moment,
x1,k-1,...,x6,k-1It is the x at k-1 moment respectively1,...,x6, ykIt is the discrete output variable at k moment, f (xk-1,uk-1) indicate
System discrete state equations.
3. the On-line Estimation method of elbow joint torque, feature exist under a kind of functional electrostimulation according to claim 2
In: the elbow joint torque M is calculated using following formula:
M=Me+Mv+Mg+Ma1-Ma2；
Wherein, MeFor passive moment of elasticity, MvFor passive sticky torque, MgFor gravitational moment, Ma1It is that the bicipital muscle of arm generates under electro photoluminescence
Active torque, Ma2It is the active torque that the triceps muscle of arm generates under electro photoluminescence, and has:
Mv=b1tanh(-b2x2)-b3x2；
Mg=-g1sin(x1)；
In formula, k1、k2、k3、b1、b2、b3、g1It is normal number parameter to be identified.
4. the On-line Estimation method of elbow joint torque, feature exist under a kind of functional electrostimulation according to claim 3
In: the active torque M that the bicipital muscle of arm and the triceps muscle of arm generatea1、Ma2By activation dynamics and nonlinear Static contracting function
Composition, the activation dynamics include static recruitment curve and calcium dynamics transmission function.
5. the On-line Estimation method of elbow joint torque, feature exist under a kind of functional electrostimulation according to claim 4
In: the recruitment curve provides the measured value that normalization activates unit according to pulse width pw, is labeled as irritation level st:
In formula, pwthrIt is the threshold value that muscle unit is raised under electro photoluminescence, pwsatIt is the saturation value that muscle unit is raised under electro photoluminescence；
Elbow joint is started into mobile pulse width and is taken as pwthr, it is by the pulse width proximate selection that elbow joint reaches full extension
pwsat；
Bicipital muscle of arm calcium dynamics are described using the typical second-order system with the input delay time:
In formula, w0It is undamped natural frequency of a mechanical system, D=1 is calcium dynamics damping factor；
Ma1Nonlinear Static contracting function beWherein s0,s1,s2,
s3,s4,s5For constant parameter to be identified, TdIt is muscle physiological operating lag；Ma1=Fa1x3；
Triceps muscle of arm calcium dynamics are described using the typical second-order system with the input delay time:
In formula, w1It is undamped natural frequency of a mechanical system, D=1 is calcium dynamics damping factor；
Ma2Nonlinear Static contracting function beWherein s6,s7,s8,
s9,s10,s11For constant parameter to be identified, TdIt is muscle physiological operating lag；Ma2=Fa2x5。
6. the On-line Estimation method of elbow joint torque, feature exist under a kind of functional electrostimulation according to claim 5
In: it is described that parameter identification tool is carried out to elbow joint muscle skeleton model system using extended pattern Kalman filter in step S1
Body are as follows:
Parameter identification uses extended pattern Kalman filter EKF, and it is 22 that model, which needs the parameter that recognizes, setting parameter battle array be θ=
[k1,k2,k3,b1,b2,b3,g1,s0,…,s11,w0,w1,J]T, while carrying out state and parameter estimation, i.e., parameter is expanded as shape
State variable, if the state variable X after amplificationk=[xk；θk], the state-space expression after extension are as follows:
Wherein, EKF includes two processes, and measurement updaue and time update；The measurement updaue
Process are as follows:
Pk=(I-KkHk)Pk -；
The time renewal process are as follows:
Wherein, I is unit matrix, and Q is process noise covariance, and R is measurement noise covariance, DkIt is one including needing to recognize
Parameter Jacobin matrix, It is identification state matrix,It is that prediction is defeated
Out, PkIt is evaluated error covariance matrix, KkIt is kalman gain, Pk -It is prior uncertainty covariance matrix, HkIt is Kalman filtering
Coefficient matrix.
7. the On-line Estimation method of elbow joint torque, feature exist under a kind of functional electrostimulation according to claim 5
In: in step S1, offline pre- modeling is carried out to elbow joint muscle skeleton model, exports y are as follows:
In formula, y1Indicate Angle of Elbow Joint, y2It is angular speed, y3It is torque；
After discretization, obtain:
In formula, y1,kIt is k moment Angle of Elbow Joint, y2,kIt is k moment angular speed, y3,kIt is k moment elbow joint torque.
8. the On-line Estimation method of elbow joint torque, feature exist under a kind of functional electrostimulation according to claim 5
In: in step S2, the On-line Estimation to elbow joint torque signals specifically: by pre- modeling parameters identification result be set as to
The initial value of adjusting parameter exports y are as follows:
Y=g (x)=Cx；
Wherein,
After discretization, obtain:
yk=g (xk)=Cxk；
Real-time adaptive update is carried out to the muscle skeleton model system parameter modeled in advance using above structure, is realized to elbow joint
The estimation of torque signals, wherein the On-line Estimation of elbow joint torque under energy property electro photoluminescence are as follows:
Mk=Me,k+Mv,k+Mg,k+Ma1,k-Ma2,k。
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| CN113181550A (en) * | 2021-03-18 | 2021-07-30 | 中山大学 | Functional electrical stimulation system and method for regulating and controlling joint moment and rigidity |
| CN114918914A (en) * | 2022-04-26 | 2022-08-19 | 中国科学院自动化研究所 | Human body musculoskeletal simulation control system and simulation device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110653817A (en) * | 2019-08-20 | 2020-01-07 | 南京航空航天大学 | Exoskeleton robot power-assisted control system and method based on neural network |
| CN110653817B (en) * | 2019-08-20 | 2022-04-15 | 南京航空航天大学 | Exoskeleton robot power-assisted control system and method based on neural network |
| CN111202908A (en) * | 2020-01-16 | 2020-05-29 | 电子科技大学 | Palm electrical stimulation control system based on elbow motion driving |
| CN111202908B (en) * | 2020-01-16 | 2023-03-14 | 电子科技大学 | Palm electrical stimulation control system based on elbow motion driving |
| CN111408042A (en) * | 2020-03-27 | 2020-07-14 | 浙江迈联医疗科技有限公司 | Functional electrical stimulation and lower limb exoskeleton intelligent distribution method, device, storage medium and system |
| CN111408042B (en) * | 2020-03-27 | 2023-03-21 | 浙江迈联医疗科技有限公司 | Functional electrical stimulation and lower limb exoskeleton intelligent distribution method, device, storage medium and system |
| CN113181550A (en) * | 2021-03-18 | 2021-07-30 | 中山大学 | Functional electrical stimulation system and method for regulating and controlling joint moment and rigidity |
| CN114918914A (en) * | 2022-04-26 | 2022-08-19 | 中国科学院自动化研究所 | Human body musculoskeletal simulation control system and simulation device |
| CN114918914B (en) * | 2022-04-26 | 2024-03-22 | 中国科学院自动化研究所 | Simulation control system and simulation device for human musculature |
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