CN107678543A - A kind of human hand skin electrode bio-impedance model parameter estimation method based on electric touch equipment - Google Patents
A kind of human hand skin electrode bio-impedance model parameter estimation method based on electric touch equipment Download PDFInfo
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Abstract
A kind of human hand skin electrode bio-impedance model parameter estimation method based on electric touch equipment.Using the recursive algorithm containing forgetting factor, data are weighted with default forgetting factor, new data are made to account for greater weight in parameter Estimation, the new inputoutput data constantly provided using electric touch equipment improves estimated accuracy, and change estimate when parameter changes, realize that parameter is estimated in real time online, use augmentation model simultaneously, the error of the electro photoluminescence amount and actual finger electro photoluminescence amount of complete rational hypothesized model output is coloured noise, rather than white noise ideally, more tally with the actual situation, adapt to the parameter Estimation under different noise situations.Present invention, avoiding the decline and the single interference to parameter Estimation of noise model of recursion step number parameter correction ability after long, the real-time and accuracy to finger Skin Resistance parameter Estimation are improved.The present invention is well-thought, designs advantages of simple, it is easy to accomplish, strong adaptability.
Description
Technical field
The present invention relates to for the impedance ginseng in human hand epidermal-dermal-hypodermis impedance model in electric touch equipment
Number estimation method, more particularly to the epidermal-dermal based on the recurrence extended least squares containing forgetting factor-hypodermis resistance
Impedance parameter method of estimation in anti-model.
Background technology
Tactile sense reproduction refers to distal environment or the tactile data of virtual environment stimulating people's by local haptic apparatus
It is corresponding to feel position so that people can feel in distal environment or virtual environment various power tactile datas (pressure, vibration,
Vibration, skin deformation, spatial resolution, slip sensation, texture, material properties, space sense, object stretching).The U.S., Japan
The importance of haptic interaction feedback device is early had appreciated that with developed countries and regions such as European Union, had been put into a large amount of manpowers,
Material resources and financial resources are studied.In the research starting evening of China in this respect, the current country there is no any man-machine interaction touch feedback
The production firm of equipment.Domestic all business man-machine interaction haptic apparatus are completely dependent on import, and equipment is in hardware and software
Key technology all by foreign countries monopolize, only to user open be that some simply use interface.Therefore, research and development are a kind of has certainly
The electric touch feedback device of main intellectual property for break foreign technology monopolization barrier, promote and promote haptic device technology with
And the autonomous innovation and development in science and technology in correlation machine people field have very important significance.
In order to realize the true reappearance of distal environment or virtual environment, the research and development of haptic feedback devices have become current
Study hotspot and development trend.Compared with other types haptic apparatus, there is light and handy convenient, simple and easy, thorn in electric touch equipment
Swash that high resolution, energy conversion efficiency are high, easy and various types of force feedback equipment integrates, is adapted to all kinds of microprocessors is carried out
The advantages that control.However, the accuracy of existing electric touch equipment human hand skin-electrode bio-impedance model, algorithm for estimating are also
Need to be further improved.For example, Shanghai Communications University Zhang Zhumao and professor Chai Xinyu have studied electric touch equipment, read for blind person
Reading (refers to：Zhang Zhumao, Liu Jie, Zhao Ying, Ren Qiushi, tactiles of the new Yu of bavin based on finger substitute the design and realization of vision system
[J] Chinese medicine physics magazines .2009,4:1293~1298.).Xu Fei the and Zhang Dingguo professors design of Shanghai Communications University
A kind of electric touch equipment is used to tactile and substitutes vision system (referring to：Tactiles of the Zhang Zhumao based on finger substitutes vision system
Development [D] Shanghai Communications Universitys master thesis, 2009).Jiang Qin the and Zhou Qi professors of Chongqing University of Technology devise electricity and touched
Feel, and carry out the research that tactile-vision substitutes and (refer to：Tactile-vision replacement systems of the Jiang Qin based on electrocutaneous stimulation is ground
Study carefully [D] Chongqing University of Technology master thesis, 2013.).However, the studies above does not all take into full account human hand-skin resistance
Anti- model is a time-varying system, because the impedance parameter of finger skin can change with the amplitude and frequency of electric current,
Also can be influenceed by electrode diameter, finger contact area.In addition, Yantao Shen etc. are by electric touch equipment people skin of hand-electricity
Pole bio-impedance model simplification is first order modeling, and finger skin-electrode impedance model is estimated (to refer to：Yantao
Shen,John Gregory,Ning Xi.Stimulation Current Control for Load-aware
Electrotactile haptic rendering:Modeling and Simulation[J].Robotics and
Autonomous Systems,2014,62:81~89.) take into full account, but not that finger epidermis is joined with interelectrode model
Number.
The content of the invention
The purpose of the present invention is to propose to a kind of human hand skin-electrode bio-impedance model parameter based on electric touch equipment to estimate
Meter method, for the human hand skin-electrode bio-impedance model and its time-varying characteristics in electric touch equipment, using containing forgetting factor
Recurrence extended least squares estimation in real time estimation bio-impedance model parameter.The present invention has taken into full account electric touch equipment
Requirement in the finger skin of middle people and the biology relation of electrode impedance and electric touch equipment to impedance estimation real-time, establish
The finger skin of haptic apparatus based on electro photoluminescence-Electrode-biofilm impedance model, it is real-time using recurrence extended least squares
Obtain each impedance parameter value in model.
The present invention is achieved by the following technical solutions.
A kind of human hand skin-electrode bio-impedance model parameter estimation side based on electric touch equipment of the present invention
Method, comprise the following steps：
(1) modeling of human hand skin-electrode bio-impedance
Laplace transform can be obtained by epidermal-dermal-hypodermis impedance model：
X (S)=Y (S) V (R1, R2, R3, C1, C2, S)
Wherein, X (S), Y (S) represent input voltage and the laplace transform of output current, V (R respectively1, R2, R3,
C1, C2, S) represent epidermal-dermal-hypodermis impedance model laplace transform, be abbreviated as V (S).Therefore, X
(S)=Y (S) V (S), is obtained by the relation of Laplace transform and transform：
X (Z)=Y (Z) V (R1, R2, R3, C1, C2, Z)
Wherein X (Z), Y (Z) are respectively X (S), Y (S) transform formula, V (R1, R2, R3, C1, C2, Z) and it is epidermal-dermal-skin
The transform formula of undertissue's impedance model, it is abbreviated as V (Z).Therefore, X (Z)=Y (Z) V (Z), is carried out Z anti transform
Obtain the impedance model of epidermal-dermal-hypodermis：
Y (n)=- f1y(n-1)-f2y(n-2)+f3x(n)+f4x(n-1)+f5(n-2)
Wherein, f1, f2, f3, f4, f5It is to contain variable R1, R2, R3, C1, C2Different functions, x (n), y (n) are represented respectively
N-th samples obtained input voltage and output current value.
Thus the order of expression formula can obtain least-squares parameter estimation model and be:
Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+b2(k-2) wherein, e (k) is system noise to+e (k)
Sound.This model is set to H (k), this is the model built with input/output relation, and the finger skin of reality-Electrode-biofilm is hindered
Anti- model is set to W (k), preset model H (k) can be made to approach realistic model W (k) by parameter estimation algorithm.Impedance parameter R1, R2,
R3, C1, C2Can be by estimating the coefficient a in obtained W (k) through algorithm1, a2, b0, b1, b2Represent.
(2) augmentation parameter Estimation：
(a) white noise is not taken as to noise e (k) and is taken as coloured noise, the parameter being suitable under different noise situations
Estimation and system modelling.It is the white noise that variance is constant to take e (k)=ε (k)+c1 ε (k-1)+c2 ε (k-2) wherein ε (k), can
It is taken as 0.1.
(b) system model is written as：
Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+b2(k-2)+ε(k)+c1ε(k-1)+c2ε(k-2)
Document border parameter matrix is θ=[a1, a2, b0, b1, b2, c1, c2]T。
Remember that parameter estimation matrix isIf matrix initial value is 0.
(3) the recursive least-squares on-line parameter estimation based on the u containing forgetting factor：
(a) recursive algorithm is used, it is real by being continuously added new inputoutput data x (k) and y (k) more new estimation parameters
Present line parameter identification.If obtaining L group data, if passing value matrixT is represented
By matrix transposition, wherein：
WillIt is denoted asInitial valueIf matrixTake initial value P (0)=
CI, wherein C are fully big constants, such as 106, I is 7 × 7 unit matrix, is made Obtain parameter estimation matrix((b) of visible step (3)) is
(b) forgetting factor u is introduced, is madeIt is changed intoImproved by applying time-variant weights coefficient to data
The parameters revision ability of identification process, u values are the constant close to 1, can use 0.95<u<1.K (k) is changed into P (k) is changed into Still it is parameter Estimation matrix stepping type.
(4) after the convergence of parameter matrix the data obtained, parameter in obtained parameter matrix is substituted into preset model
Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+b2x(k-2)+ε(k)+c1ε(k-1)+c2ε(k-
2)
With the impedance model mathematic(al) representation of epidermal-dermal-hypodermis
Y (n)=- f1y(n-1)-f2y(n-2)+f3x(n)+f4x(n-1)+f5(n-2)
Coefficient of comparisons obtains equation group, solves required parameter R1, R2, R3, C1, C2。
Advantages of the present invention：The requirement of real-time that electric touch equipment is estimated impedance parameter has been taken into full account, has employed and contains
The recursive algorithm of forgetting factor, the new inputoutput data improvement estimated accuracy constantly provided using electric touch equipment, and
Estimate is changed when parameter changes, realizes that parameter is estimated in real time online, avoids the long rear parameter correction energy of recursion step number
The decline of power；Augmentation model is used simultaneously, is coloured noise by noise reasonable assumption, rather than white noise ideally.
This more tallies with the actual situation, and adapts to the parameter Estimation under different noise situations, and it is single to parameter Estimation to avoid noise model
Interference.Invention design is total reasonable, well-thought, and design is simple, it is easy to accomplish, strong adaptability.
Brief description of the drawings
Accompanying drawing 1 is epidermal-dermal-hypodermis impedance model schematic diagram.
Accompanying drawing 2 is least-squares parameter estimation model.
Embodiment
The present invention will be described further by following examples.
Step 1：The modeling of human hand skin-electrode bio-impedance.
(a) laplace transform can be obtained by epidermal-dermal-hypodermis impedance model：
Wherein,For the Laplace transform form of impedance model
(b) by Laplace transform and the relation (Z=e of transformSτ, τ is the sampling period)：
Wherein,For the transform form of impedance model
(c) transplant：
(d) Z anti transform is done to obtain：
It is rewritten as：
(e) by the predeterminable finger skin of order-Electrode-biofilm impedance model (the i.e. least-squares parameter estimation mould of (4) formula
Type) order, write default human hand skin-electrode bio-impedance model as least squares formalism：
In formula,To be defeated
Enter output data vector, T is represented matrix transposition, θ=[a1, a2, b0, b1, b2]TFor actual parameter vector, e (k) is system noise
Sound.
Step 2：Recursive least-squares on-line parameter estimation based on the u containing forgetting factor
If the parameter vector of estimation isThe kth time for then corresponding to L group data estimates that output isFormula
In,Object reality output and the difference of estimation output, i.e. residual epsilon (k) areFor L observation, performance indications are taken:
Forgetting factor u (0 is introduced in formula<u<1), i.e., data are applied with time-variant weights coefficient, newest data are added with 1
Power, and k-th of data u beforeL-kWeighting.In slow time-varying parameter system as finger-skin impedance model, commonly
Least square method of recursion with the growth of data, will appear from the phenomenon of data saturation, i.e., with k increase, P (k) and K (k)
Become less and less, cause pairCapability for correcting die down, make newly-increased data little to the regeneration function of estimates of parameters, and draw
After having entered forgetting factor, the renewal of parameter depends primarily on newest data, greatly strengthen parameter Estimation real-time and
Accuracy.
It is required that the least-squares estimation of parameter, exactly seeks the parameter for making object function (6) minimalizationJ is asked for this
First derivative, and make derivative value as 0：
Solve：
In formula
Y=[y (1), y (2) ..., y (L)]T,AndTherefore meet (7) formulaJ minimalizations can be made.
WillIt is denoted asWhereinFor the history inputoutput data square by forgetting factor weighting
Battle array,For current inputoutput data, Y is denoted asOrder
Then
Obtained by (7)：
By (9), (10) obtain：
The least-squares estimation at k moment is represented by：
In formula,
Topology：If A, (A+BC) and (I+CA-1B it is) nonsingular square matrix, then
(A+BC)-1=A-1-A-1B(I+CA-1B)CA-1
Lemma is substituted into (8), even
：
(14) are substituted into (13) to obtain：
By (14), (15) obtain：
By (12), (15), (16) obtain：
Least-squares parameter estimation recurrence formula containing forgetting factor is：
Step 3：Augmentation parameter Estimation
Coloured noise is introduced, that is, takes noise e (k)=ε (k)+c1ε(k-1)+c2(wherein ε (k) is that variance is constant to ε (k-2)
White noise, can be taken as 0.1) substituting into (5)：
Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+b2x(k-2)+ε(k)+c1ε(k-1)+c2ε(k-
2) (18)
Feeding back to L groups by electric touch feedback device has sequential relationship, and one-to-one input voltage x (k) and output electricity
Y (k) data are flowed, its value is included in biography value matrixWherein,
Due toIn ε (k) can not survey, so with its estimateTo replace, i.e.,
In formula：
For new parameter estimation matrix.
WithInstead ofThe recursion extended least square parametes estimation formula of forgetting factor must be contained by substituting into (17)
For
Initial value is set(Null matrix for 1 × 7), P (0)=10^6 × I (I is 7 × 7 unit matrix), structure
Make biography value matrix：
If k<When 0, y (k)=0, x (k)=0, ε (k)=0, the input data obtained every time is assigned to x (k), output data
Y (k) is assigned to, ε (k) can use the white noise that variance is 0.1, and forgetting factor u can be taken as 0.95, and every group of data can be by (20) formula
Obtain parameter estimationBecause valuation initial value is set to 0, at the beginningValue fluctuation it is larger, when taken data volume increases,Value converge on definite value, that is, obtain estimated parameter valueThe a in (18) formula is corresponded to respectively1, a2, b0, b1, b2, c1, c2。
Step 4：Modulus type impedance parameter value
Estimation parameter value is substituted into (18) formula：Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+b2x(k-
2)+ε(k)+c1ε(k-1)+c2ε (k-2) with
Formula coefficient of comparisons obtains：
τ=1 is taken, is solved：
Human hand epidermal-dermal-hypodermis impedance model impedance parameter corresponding to obtaining.
In actual applications, after the finger of people contacts with the electrod-array of electric touch equipment, first one is inputted by constant pressure source
Individual less electro photoluminescence obtains the output current of electrode output by electrode stimulating finger, measurement, then in a small range not
The disconnected amplitude for changing input electro photoluminescence, obtains one group of input voltage x (k) and output current y (k) data, substitutes into default mould
In type, algorithm calculates the least-squares parameter a of human hand skin-electrode bio-impedance model based on this group of data1, a2, b0, b1, b2,
And then try to achieve the impedance parameter R in model with physical significance1, R2, R3, C1, C2.Due to finger skin-Electrode-biofilm modulus of impedance
One slow time-varying system of type, resistance and capacitance parameter change are slow, therefore impedance parameter can be based on after impedance parameter is measured
Input voltage is adjusted, obtains the output current with characteristics such as expected amplitude, frequency, pulsewidths, reaches the realization of electric touch equipment and touches
Feel the purpose of simulation.Further, since using based on the recurrence extended least squares containing forgetting factor, have to model error preferably
Correction capability, the slow change to parameter has stronger ability of tracking, touching simulation can be allowed to have more preferable real-time.When
When impedance parameter changes, corresponding inputoutput data and input/output relation also change, and algorithm is according to newly-increased
Data, dynamically adjust to impedance parameter R1, R2, R3, C1, C2Estimate.If it is desired to keep sense of touch when impedance parameter changes
Constant, system can adjust input stimulus in real time based on the impedance parameter estimate of real-time update, to keep output current
Characteristic is constant to make sense of touch held stationary.
Claims (1)
1. a kind of human hand skin-electrode bio-impedance model parameter estimation method based on electric touch equipment, it is characterized in that including
Following steps：
(1) modeling of human hand skin-electrode bio-impedance
Laplace transform can be obtained by epidermal-dermal-hypodermis impedance model：
X (S)=Y (S) V (R1, R2, R3, C1, C2, S)
Wherein, X (S), Y (S) represent input voltage and the laplace transform of output current, V (R respectively1, R2, R3, C1, C2,
S the laplace transform of epidermal-dermal-hypodermis impedance model) is represented, is abbreviated as V (S)；X (S)=Y (S) V
(S), obtained by the relation of Laplace transform and transform：
X (Z)=Y (Z) V (R1, R2, R3, C1, C2, Z)
Wherein X (Z), Y (Z) are respectively X (S), Y (S) transform formula, V (R1, R2, R3, C1, C2, Z) and it is epidermal-dermal-subcutaneous group
The transform formula of impedance model is knitted, is abbreviated as V (Z)；X (Z)=Y (Z) V (Z), carried out Z anti transform and obtain epidermis-true
The impedance model of skin-hypodermis：
Y (n)=- f1y(n-1)-f2y(n-2)+f3x(n)+f4x(n-1)+f5(n-2)
Wherein, f1, f2, f3, f4, f5It is to contain variable R1, R2, R3, C1, C2Different functions, x (n), y (n) represent n-th respectively
Sample obtained input voltage and output current value；
Thus the order of expression formula obtains least-squares parameter estimation model and is:
Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+b2(k-2)+e(k)
Wherein, e (k) is system noise；This model is set to H (k), finger skin-Electrode-biofilm impedance model of reality is set
For W (k), preset model H (k) can be made to approach realistic model W (k) by parameter estimation algorithm；Impedance parameter R1, R2, R3, C1, C2
By estimating the coefficient a in obtained W (k) through algorithm1, a2, b0, b1, b2Represent；
(2) augmentation parameter Estimation：
(a) noise e (k) is taken as coloured noise, and it is that variance is normal to take e (k)=ε (k)+c1 ε (k-1)+c2 ε (k-2), wherein ε (k)
Several white noises, can be taken as 0.1；
(b) system model is written as：
Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+
b2(k-2)+ε(k)+c1ε(k-1)+c2ε(k-2)
Document border parameter matrix is θ=[a1, a2, b0, b1, b2, c1, c2]T；
Remember that parameter estimation matrix isIf matrix initial value is 0；
(3) the recursive least-squares on-line parameter estimation based on the u containing forgetting factor：
(a) recursive algorithm is used, new inputoutput data x (k) is continuously added and y (k) more new estimation parameters realizes online ginseng
Number identification；L group data are obtained, if passing value matrixT is represented square
Battle array transposition, wherein：
WillIt is denoted asInitial valueIf matrixInitial value P (0)=CI is taken, its
Middle C is fully big constant, and I is 7 × 7 unit matrix, is made Obtain parameter estimation matrixFor
(b) forgetting factor u is introduced, is madeIt is changed intoIdentification is improved by applying time-variant weights coefficient to data
The parameters revision ability of process, u values are the constant close to 1, take 0.95<u<1；K (k) is changed into P (k) is changed into Still it is parameter Estimation matrix stepping type；
(4) after the convergence of parameter matrix the data obtained, parameter in obtained parameter matrix is substituted into preset model
Y (k)=- a1y(k-1)-a2y(k-2)+b0x(k)+b1x(k-1)+b2x(k-2)+ε(k)
+c1ε(k-1)+c2ε(k-2)
With the impedance model mathematic(al) representation of epidermal-dermal-hypodermis
Y (n)=- f1y(n-1)-f2y(n-2)+f3x(n)+f4x(n-1)+f5(n-2)
Coefficient of comparisons obtains equation group, solves required parameter R1, R2, R3, C1, C2。
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| CN111797516A (en) * | 2020-06-17 | 2020-10-20 | 南昌大学 | Electrode-skin impedance model parameter identification method based on stimulation frequency response |
| CN111797516B (en) * | 2020-06-17 | 2022-10-11 | 南昌大学 | Electrode-skin impedance model parameter identification method based on stimulation frequency response |
| CN114288555A (en) * | 2022-01-26 | 2022-04-08 | 云南贝泰妮生物科技集团股份有限公司 | Radio frequency beauty instrument self-adaptive frequency modulation system based on skin impedance |
| CN114288555B (en) * | 2022-01-26 | 2023-02-28 | 云南贝泰妮生物科技集团股份有限公司 | Radio frequency beauty instrument self-adaptive frequency modulation system based on skin impedance |
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