CN113057850A - Recovery robot control method based on probability motion primitive and hidden semi-Markov - Google Patents

Recovery robot control method based on probability motion primitive and hidden semi-Markov Download PDF

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CN113057850A
CN113057850A CN202110264726.6A CN202110264726A CN113057850A CN 113057850 A CN113057850 A CN 113057850A CN 202110264726 A CN202110264726 A CN 202110264726A CN 113057850 A CN113057850 A CN 113057850A
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markov
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rehabilitation robot
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CN113057850B (en
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徐宝国
汪逸飞
邓乐莹
王欣
宋爱国
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Southeast University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H1/00—Apparatus for passive exercising; Vibrating apparatus; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
    • A61H1/02—Stretching or bending or torsioning apparatus for exercising
    • A61H1/0274—Stretching or bending or torsioning apparatus for exercising for the upper limbs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/16—Physical interface with patient
    • A61H2201/1602—Physical interface with patient kind of interface, e.g. head rest, knee support or lumbar support
    • A61H2201/1635—Hand or arm, e.g. handle
    • A61H2201/1638—Holding means therefor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/50—Control means thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/50—Control means thereof
    • A61H2201/5007—Control means thereof computer controlled
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/50—Control means thereof
    • A61H2201/5058—Sensors or detectors
    • A61H2201/5061—Force sensors

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Abstract

The invention designs a rehabilitation robot control method based on probability motion primitives and hidden semi-Markov, which comprises the following steps: (1) and recording the motion information of the upper limbs on the healthy side for a plurality of times, wherein the motion information comprises the rigidity, the track and the like of the tail ends of the arms. (2) And generalizing the rigidity recorded in the step (1) through the probability motion primitive. (3) Generalizing the data recorded in (1) using a hidden semi-Markov model to generate a trajectory. (4) The generalized tracks are mirrored. (5) And carrying out variable impedance control on the tail end of the rehabilitation robot through the information after the mirror image. The invention uses the probability motion primitive and the hidden semi-Markov to generate the control parameters of the rehabilitation robot for the first time, can effectively utilize the side-healthy limb of the patient to assist the rehabilitation training, controls the rehabilitation robot by simulating the motion of the side-healthy limb, can achieve better rehabilitation training effect, simultaneously improves the rehabilitation efficiency, and greatly reduces the workload of the rehabilitation doctor.

Description

Recovery robot control method based on probability motion primitive and hidden semi-Markov
Technical Field
The invention belongs to the field of machine simulation learning, relates to a rehabilitation robot control method, and particularly relates to a rehabilitation robot variable impedance control method based on probabilistic motion primitives (ProMP) and hidden semi Markov (HSMM), so that the track generation of a rehabilitation robot under mirror image control is optimized.
Background
The rehabilitation robot is the combination of industrial robot and medical robot, mainly is in order to meet medical care personnel and recovered demand, and the auxiliary patient moves the sick limb or the joint that has the trouble to reach the recovered purpose of help patient. At present, the wearable type (exoskeleton type) and the independent type are mainly divided.
In machine learning, how to generate required control parameters by using motion data of machine generalization teaching is a big problem, and a common method can be solved by using probability motion primitives. The probability motion primitive is improved on the basis of the motion primitive, and the parameter vector is represented in a probability distribution mode through the operation of probability theory; the method has the advantages of being adaptive to new targets, easy to adjust control parameters and the like.
In human-machine teaching, hidden markov models are often used to analyze a sequence of states, which assumes that the next state is only related to the current state. The hidden semi-Markov model expresses the probability of a certain state staying by a time probability function, so that time information can be better expressed. Generalization of the taught motion data can be achieved by using a formula of gaussian linear regression.
For an independent upper limb rehabilitation robot, impedance control is one of the common control modes, the main advantages of the independent upper limb rehabilitation robot are high flexibility and good robustness to disturbance and uncertainty, and the independent upper limb rehabilitation robot is a common mode for realizing force control, so the independent upper limb rehabilitation robot is very suitable for being applied to a rehabilitation scene to avoid secondary damage to limbs of a patient. The significance of the variable impedance control is that the stiffness value can be adjusted in time.
Disclosure of Invention
In order to solve the problems, the invention discloses a rehabilitation robot control method based on probabilistic motion primitives and hidden semi-Markov, which is used for generating control parameters of a rehabilitation robot for the first time, simulates the motion of the upper limb on the healthy side to generate a rehabilitation motion track of the affected limb through machine learning, can effectively utilize the healthy side limb of a patient to assist rehabilitation training, controls the rehabilitation robot through simulating the motion of the healthy side limb, can achieve better rehabilitation training effect, simultaneously improves rehabilitation efficiency and greatly reduces the workload of a rehabilitation doctor.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a rehabilitation robot control method based on probabilistic motion primitives and hidden semi-Markov, comprising the steps of:
(1) recording the motion information of the upper limbs on the healthy side for a plurality of times, wherein the motion information comprises the rigidity, the track and other information of the tail ends of the arms;
(2) generalizing the rigidity recorded in the step (1) through a probability motion primitive;
(3) generalizing the data recorded in step (1) using a hidden semi-Markov model to generate a trajectory. Wherein, the parameters of the hidden semi-Markov model are estimated by an EM algorithm (Expectation-Maximization), and then the control parameters are calculated by a Gaussian regression algorithm;
(4) mirroring the generalized tracks;
(5) and performing variable impedance control on the tail end of the rehabilitation robot through the mirrored track and the changed rigidity information.
Further, in the step (1), the stiffness and track (including position and velocity) information recorded during teaching are written into a vector form, including:
xD，t＝(x1，t，...，xd，t)T
Figure BDA0002971887690000025
kD，t＝(k1，t，...，kd，t)T
wherein D represents a degree of freedom and t represents time.
Further, the step (2) includes the following sub-steps:
(a1) determining a muscle stiffness vector y through the motion information extracted in the step (1)tAn expression;
(a2) writing y according to the probabilistic motion primitive formulatA gaussian distribution expression of (a);
(a3) for the stiffness data extracted by the teaching, a maximum likelihood estimation algorithm is applied to obtain muω*，∑ωA first step of; and calculates the control parameters
Figure BDA0002971887690000021
Further, the step (3) includes the following sub-steps:
(b1) respectively establishing a hidden semi-Markov model for each freedom degree of the joint, and writing an expression;
(b2) calculating hidden semi-Markov model parameters in each degree of freedom by using an EM (effective man-machine) algorithm to obtain parameter values;
(b3) writing a corresponding expression of the probability of the ith state at the time t;
(b4) calculating control parameters using a Gaussian regression formula
Figure BDA0002971887690000022
And
Figure BDA0002971887690000023
wherein
Figure BDA0002971887690000024
Is a track.
Further, in the step (5), the adopted method is variable impedance control, and compliance control is realized through a corresponding strategy of the variable impedance control by using the control parameters calculated in the steps (2) and (3).
The invention has the beneficial effects that:
the invention uses the probability motion primitive and the hidden semi-Markov in the machine learning to generate the control parameter of the rehabilitation robot, and uses the variable impedance method to control, compared with the traditional method of directly extracting data to control, the flexibility is high, the limbs of the patient can be better protected from secondary damage, and the better rehabilitation training effect can be achieved.
Drawings
Fig. 1 is a flowchart of a rehabilitation robot control method based on probabilistic motion primitives and hidden semi-markov models according to the present invention.
Fig. 2 is a schematic diagram of impedance control in the rehabilitation robot control method based on probabilistic motion primitives and hidden semi-markov according to the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
As shown in the figure, a rehabilitation robot control method based on probabilistic motion primitives and hidden semi-markov specifically includes the following steps:
(1) and recording the motion information of the upper limbs on the healthy side for a plurality of times, wherein the motion information comprises the rigidity, the track and the like of the tail ends of the arms.
(2) And generalizing the rigidity recorded in the step (1) through the probability motion primitive.
(3) Generalizing the data recorded in (1) using a hidden semi-Markov model to generate a trajectory. The parameters of the hidden semi-Markov model are estimated by EM algorithm (Expectation-Maximization), and then the control parameters are calculated by Gaussian regression algorithm.
(4) The generalized tracks are mirrored.
(5) And performing variable impedance control on the tail end of the rehabilitation robot through the mirrored track and the changed rigidity information.
For the step (1), the information of the rigidity, the position and the speed recorded during the teaching is written into a vector form, and the method comprises the following steps:
xD，t＝(x1，t，...，xd，t)T
Figure BDA0002971887690000031
kD，t＝(k1，t，...，kd，t)T
wherein D represents a degree of freedom and t represents time
For the step (2), the method comprises the following sub-steps:
(a1) determining a vector by the motion information extracted in (1)
yt＝(k1，t，k2，t，...，kd，t)T＝Ψtω+∈y
Figure BDA0002971887690000032
Wherein k istMuscle stiffness data for d degrees of freedom, t 0, 1yFor Gaussian distributed noise with an expected 0, the variance is Σy，φi，tFor time-dependent basis functions, for repetitive regular movements, the basis functions can be:
Figure BDA0002971887690000041
wherein the content of the first and second substances,
Figure BDA0002971887690000042
where z (t) is an arbitrary monotonically increasing function of time, h is the bandwidth, ciIs the center of the ith basis function
(a2) According to the probability motion primitive formula, there are:
Figure BDA0002971887690000043
wherein
Figure BDA0002971887690000044
Representing a Gaussian distribution, θ ═ (. mu.) ═ω，∑ω) As a parameter
(a3) For the stiffness data extracted by the teaching, a maximum likelihood estimation algorithm is applied to obtain muω*，∑ωA first step of; and order the control parameters
Figure BDA0002971887690000045
For the step (3), the following substeps are included:
(b1) the hidden semi-Markov model is respectively established for each degree of freedom of the joint, and can be represented by the following parameters:
Figure BDA0002971887690000046
wherein, piiIs the probability that the ith state is the initial state, ai，jK is the total number of states, which is the probability of transitioning from state j to the next state i;
Figure BDA0002971887690000047
a probability density function for the duration of the ith state, obeying a gaussian distribution, for which there are:
Figure BDA0002971887690000048
wherein t is 1, 2max，
Figure BDA0002971887690000049
Eta is a constant set between 2 and 3, TmaxTo teach the total number of samples of the data vector,
at each time t of the ith state, the observed data obeys a Gaussian distribution, μi，∑iRespectively, the expectation and variance of the distribution, for which there are:
Figure BDA00029718876900000410
wherein the content of the first and second substances,
Figure BDA00029718876900000411
for the position and velocity values observed at time t,
Figure BDA00029718876900000412
(b2) using EM algorithms for each degree of freedom
Figure BDA0002971887690000051
And
Figure BDA0002971887690000052
calculating parameters in the distribution to obtain parameter values
(b3) For the probability at the ith state at time t, there is the formula:
Figure BDA0002971887690000053
Figure BDA0002971887690000054
Figure BDA0002971887690000055
wherein x1Is an initial position
(b4) Calculating control parameters using a Gaussian regression formula
Figure BDA0002971887690000056
And
Figure BDA0002971887690000057
comprises the following steps:
Figure BDA0002971887690000058
wherein the content of the first and second substances,
Figure BDA0002971887690000059
the velocity of the track of the mechanical arm at the time t is obtained by integrating the initial positiontrace of time t
Figure BDA00029718876900000510
In the step (5), the control parameters calculated in the step (2) and the step (3) include:
Figure BDA00029718876900000511
wherein KjIs the main diagonal line of (k)1，t*，k2，t*，...，kd，tDiagonal array of elements, D)jFor the corresponding damping matrix, τcmdMoment of the mechanical arm;
Figure BDA00029718876900000512
the desired position and velocity vector can be derived from (3); x is the number ofmsr，
Figure BDA00029718876900000513
For the current position and velocity vector, τdynThe dynamic force of the system is compensated, such as gravity, Coriolis force and the like.
And outputting corresponding torque according to an impedance control formula by detecting a force signal of the arm of the user.
The invention uses the probability motion primitive and the hidden semi-Markov to generate the control parameter of the rehabilitation robot for the first time, simulates the motion of the upper limb on the healthy side to generate the rehabilitation motion trail of the affected limb through machine learning, can effectively utilize the healthy side limb of the patient to assist rehabilitation training, uses the variable impedance method for control, has high flexibility compared with the traditional method for directly extracting data for control, can better protect the limb of the patient from secondary damage, and can achieve better rehabilitation training effect; meanwhile, the rehabilitation efficiency is improved, and the workload of the rehabilitation doctor is greatly reduced.

Claims (5)

1. A rehabilitation robot control method based on probability motion primitives and hidden semi-Markov is characterized in that: the method comprises the following steps:
(1) recording the motion information of the upper limbs on the healthy side for a plurality of times, wherein the motion information comprises the information of the rigidity, the position and the speed of the tail end of the arm;
(2) generalizing the rigidity recorded in the step (1) through a probability motion primitive;
(3) generalizing the data recorded in the step (1) by using a hidden semi-Markov model to generate a track; wherein, the parameters of the hidden semi-Markov model are estimated by an EM algorithm, and then the control parameters are calculated by a Gaussian regression algorithm;
(4) mirroring the generalized tracks;
(5) and performing variable impedance control on the tail end of the rehabilitation robot through the mirrored track and the changed rigidity information.
2. The rehabilitation robot variable impedance control method based on probabilistic motion primitives and hidden semi-markov according to claim 1, wherein the step (2) comprises the following sub-steps:
(a1) determining a muscle stiffness vector y through the motion information extracted in the step (1)tAn expression;
(a2) writing y according to the probabilistic motion primitive formulatA gaussian distribution expression of (a);
(a3) for the stiffness data extracted by the teaching, a maximum likelihood estimation algorithm is applied to obtain muω *，Σω *(ii) a And calculates the control parameters
Figure FDA0002971887680000011
3. The rehabilitation robot variable impedance control method based on probabilistic motion primitives and hidden semi-markov according to claim 1, wherein the step (3) comprises the following sub-steps:
(b1) respectively establishing a hidden semi-Markov model for each freedom degree of the joint, and writing an expression;
(b2) calculating hidden semi-Markov model parameters in each degree of freedom by using an EM (effective man-machine) algorithm to obtain parameter values;
(b3) writing a corresponding expression of the probability of the ith state at the time t;
(b4) calculating control parameters using a Gaussian regression formula
Figure FDA0002971887680000012
And
Figure FDA0002971887680000013
wherein
Figure FDA0002971887680000014
Is a track.
4. The rehabilitation robot variable impedance control method based on probabilistic motion primitives and hidden semi-markov according to claim 1, wherein in the step (5), the adopted method is variable impedance control, and compliance control is realized by using the control parameters calculated in the steps (2) and (3) and a corresponding strategy of the variable impedance control.
5. The rehabilitation robot control method based on probabilistic motion primitives and hidden semi-markov models according to claim 1, wherein: in the step (5), the adopted method is variable impedance control, and compliance control is realized through a corresponding strategy of the variable impedance control by using the control parameters calculated in the steps (2) and (3).
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