CN109259739B - Myoelectricity estimation method of wrist joint movement moment - Google Patents

Myoelectricity estimation method of wrist joint movement moment Download PDF

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CN109259739B
CN109259739B CN201811369151.9A CN201811369151A CN109259739B CN 109259739 B CN109259739 B CN 109259739B CN 201811369151 A CN201811369151 A CN 201811369151A CN 109259739 B CN109259739 B CN 109259739B
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wrist joint
moment
muscle
wrist
joint
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CN109259739A (en
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张小栋
孙晓峰
陆竹风
李瀚哲
张毅
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Xian Jiaotong University
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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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/22—Ergometry; Measuring muscular strength or the force of a muscular blow
    • A61B5/224—Measuring muscular strength
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/54—Artificial arms or hands or parts thereof
    • A61F2/58—Elbows; Wrists ; Other joints; Hands
    • A61F2/583—Hands; Wrist joints
    • A61F2/585—Wrist joints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2/70—Operating or control means electrical
    • A61F2/72—Bioelectric control, e.g. myoelectric

Abstract

The invention discloses a myoelectricity estimation method of wrist joint movement moment. The method collects surface electromyogram signals of 6 muscles of the forearm in the process of bending and stretching the wrist joint of a user, and calculates the muscle contraction time by utilizing a TKE operator. The isometric contraction maximum tension of each muscle is synchronously measured by using the isokinetic muscle force testing system and the electromyograph. Establishing a positive muscle-bone model of the wrist joint, inputting the maximum contraction tension of the muscle contraction time and the equal length of each muscle, and outputting the estimated moment at the bending and stretching limit position of the wrist joint. And (3) acquiring kinematic data in the wrist joint bending and stretching process by using a three-dimensional motion capture system, and solving out the reference moment at the limit position. And taking the square of the error between the model estimated moment and the reference moment as a target function, and completing the calibration of the wrist joint positive musculoskeletal model by using a conjugate gradient method to realize the estimation of the surface electromyographic signals on the wrist joint moment. The invention can be applied to the fields of myoelectric artificial hands, rehabilitation therapy, raw electromechanical integration and the like.

Description

Myoelectricity estimation method of wrist joint movement moment
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of intelligent artificial hand and bioelectricity integration, and relates to a myoelectricity estimation method of wrist joint movement moment.
[ background of the invention ]
At present, the number of hand loss patients caused by accidents, diseases and the like is tens of millions. The existing medical level can not realize hand regeneration, so that the artificial limb is the only way for recovering hand functions of a hand-missing patient. The myoelectric artificial hand senses human motion intention based on myoelectric signal characteristics on the surface of a hand arm and controls the artificial hand to realize corresponding operation, thereby meeting the daily life requirements of hand disabled patients. Compared with the traditional artificial hand, the function of the artificial hand is more perfect, and the phantom limb feeling of the patient can be better improved.
Excitation signals generated by the brain are transmitted to muscle fibers via the nervous system, thereby generating action potentials. On one hand, action potentials are mutually superposed on the surface of a human body to form a surface electromyographic signal; on the other hand, action potential is transmitted along muscle fibers in multiple directions to cause the muscle fibers to contract, so that muscle force is generated and the skeleton is driven to move around the joint. Since the generation of the surface electromyogram signal usually occurs prior to the actual motion, the surface electromyogram signal can predict the human body movement intention to some extent. The myoelectric artificial hand just utilizes the relationship between the surface myoelectric signal and the human motion intention to help the hand-missing patient to meet the requirements of daily life and work. The existing myoelectric artificial hand mainly focuses on classification of gripping actions of the artificial hand in the aspect of human motion intention perception, and research of moment in the gripping process is omitted. In order to ensure the stability of the artificial hand grip, in addition to providing a proper hand grip force, it is also important to estimate the grip force of the wrist joint. Based on the above, the research on the estimation of the forearm surface electromyographic signal on the wrist joint movement moment of the user is a key point in the current electromyographic artificial hand research field, and the estimation of the maximum moment at the limit position is particularly important.
[ summary of the invention ]
The invention provides a myoelectric estimation method of wrist joint movement moment for increasing the perception of surface myoelectric signals to human movement intention.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a myoelectric estimation method of wrist joint movement moment comprises the following steps:
step 1: establishing a positive musculoskeletal model of a wrist joint of a user;
step 2: synchronously measuring the isometric contraction maximum tension of each muscle of the forearm by using a multi-joint isokinetic muscle force testing system and a surface electromyography;
and step 3: the method comprises the steps that when a three-dimensional motion capture system is used for collecting kinematic data of wrist joints of a user at 3 motion speeds, a surface electromyograph is used for synchronously collecting surface electromyographic signals of 6 muscles of a forearm of a hand, preprocessing is carried out, and the muscle contraction time is calculated by a TKE operator;
and 4, step 4: acquiring kinematic data of a wrist joint of a user by using a three-dimensional motion capture system, establishing a simplified model of the wrist joint of the user, and performing inverse dynamics solution to obtain a moment at a bending and stretching limit position of the wrist joint of the user;
and 5: and (5) completing calibration of the wrist joint positive musculoskeletal model by using a conjugate gradient method.
Compared with the prior art, the invention has the following beneficial effects:
the invention is easy to operate and guarantees high accuracy. The multi-joint isokinetic muscle strength testing system can provide an isometric contraction tension measuring mode for measuring peak moments of joints at a plurality of fixed angles, and the muscle isometric contraction maximum tension obtained by the method is high in precision. The three-dimensional motion capture system has high resolution and high sampling rate, so that the reliability of the captured wrist joint kinematic data is high. The surface electromyograph has high sampling rate, and can detect the muscle contraction condition of the surface electromyograph signal reaction in real time and map the muscle strength. All three instruments have synchronous acquisition interfaces, and synchronous acquisition can be realized between every two instruments.
The established positive musculoskeletal model for estimating the wrist joint movement moment of the user through the surface electromyographic signals has better practical applicability. According to the invention, a Hill muscle force model is improved, a surface electromyogram signal is introduced, and muscle contraction time is calculated according to the starting and stopping time of the surface electromyogram signal, so that the relation between the surface electromyogram signal and joint torque is established. And ideal parameters of the model are sought by utilizing the thought of numerical optimization, so that the phenomenon of poor practical applicability caused by the fact that biomechanics parameters cannot be directly measured in the traditional muscle force model is avoided. The establishment of the model provides a new idea in the fields of medical rehabilitation, human-machine engineering and the like.
[ description of the drawings ]
FIG. 1 is a block diagram of an overall method for estimating the moment of motion of a wrist joint of a user by using electromyographic signals of the surface of the forearm of a human hand;
FIG. 2 is a schematic representation of a kinetic model of a user's wrist joint;
FIG. 3 is the calculation of the muscle contraction time of 6 muscles throughout flexion and extension;
FIG. 4 is a diagram of a wrist joint positive musculoskeletal model calibration method;
FIG. 5 is a flow chart of optimization model parameters by conjugate gradient method.
[ detailed description ] embodiments
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments, and are not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
In the context of the present disclosure, when a layer/element is referred to as being "on" another layer/element, it can be directly on the other layer/element or intervening layers/elements may be present. In addition, if a layer/element is "on" another layer/element in one orientation, then that layer/element may be "under" the other layer/element when the orientation is reversed.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, the wrist joint positive musculoskeletal model is built, and joint torque estimated by inputting surface electromyographic signals is output. On one hand, surface electromyographic signals of forearm muscles in the process of bending and stretching movement of wrist joints of a user are collected, muscle contraction time is obtained and used as model input, and estimated moment when the wrist joints are bent and stretched to the limit positions is obtained on the basis of initial values of parameters given by the model. On the other hand, inverse dynamics calculation is carried out by utilizing the wrist joint kinematics data collected by the three-dimensional motion capture system, so as to obtain the wrist joint motion calculation moment. And taking the calculated moment as a model reference value, taking the square of the error between the calculated moment and the estimated moment as a target function, completing model off-line parameter learning by using a conjugate gradient method, finally obtaining a stable wrist joint positive musculoskeletal model, realizing the purpose of inputting the surface electromyographic signals again and outputting the corresponding wrist joint movement moment. The specific embodiment is as follows:
the first step is as follows: establishing a positive musculoskeletal model of a wrist joint of a user, wherein the specific method comprises the following steps:
(1) single muscle force calculation based on surface electromyography signals:
at present, a muscle force model widely applied is a Hill three-element model, and the model is formed by series-parallel connection of a contraction unit, a parallel connection elastic unit and a series connection elastic unit. The contractile units represent actin microfilaments and myosin microfilaments in the sarcomere. Which can produce active tension when excited. The series elastic units represent muscle microfilaments, transverse bridge leap disks and tendon structures at two ends. When the contraction unit is excited, the series connection of the elastic units enables the muscle to have elasticity. The parallel elastic units represent connective tissues such as fascial membrane and sarcolemma. It is a passive tension that produces a spring force when it is pulled. The whole muscle can be considered to be formed by a plurality of such models which are connected in series and in series.
The Hill equation can be described as:
(a+F)(V+b)＝b(F0+a)
the method is simplified as follows:
Figure BDA0001869369440000051
wherein a and b are experimental parameters, F0Is the isometric maximum tension, V is the muscle contraction velocity, F0V is the initial length of the muscle fiber, the temperature, the chemical composition of the surrounding environment and
Figure BDA0001869369440000053
concentration, etc.
The muscle contraction velocity V can be expressed as:
Figure BDA0001869369440000052
wherein t is a muscle contraction time and Δ l is a muscle contraction length. The time required for the muscle to reach the maximum force value is 300-400 ms, and the force action time in many exercises is much shorter than the time, so that the direct measurement is difficult. To solve this problem, the surface electromyogram signal duration can be used as the muscle contraction time, and the surface electromyogram signal duration can be determined using the TKE operator. The muscle contraction length was optimized as an experimental parameter.
The final simplified expression for the individual muscle forces is:
Figure BDA0001869369440000061
(2) solving the wrist joint moment under the combined action of a plurality of muscles:
according to the bone lever principle, the rotation moment generated on the joint by the single muscle contraction force is as follows:
Ti＝ri×Fi·cos(φi)i＝1,2,…,N
in the formula, riIs a displacement vector from the joint rotation center to a force action point; fiIs a muscle force vector; phi is aiIs a muscle pinnate angle; n is the number of muscles.
The muscles of the upper limbs of the human body are mostly fusiform muscles tightly attached to the ulna and radius of the forearm, so the pinnate angle phiiAnd riLess, and take place small change in the motion process, so direct measurement is inconvenient, in order to make wrist joint positive musculoskeletal model simple easy to use, converts it into:
Ti＝ki·Fii＝1,2,…,N
in the formula, ki＝ri·cos(φi) Is a scaling factor.
Determining the moment contribution of each muscle to the joint and adding the moment contributions to the joint to obtain the wrist joint moment, namely:
Figure BDA0001869369440000062
thus, a wrist joint positive musculoskeletal model is obtained, surface electromyographic signals are achieved, and wrist joint movement moment is estimated, namely:
Figure BDA0001869369440000071
the second step is that: the isometric contraction maximum tension of each muscle of the forearm is synchronously measured by using a multi-joint isokinetic muscle force testing system and a surface electromyography, and the specific method comprises the following steps:
selecting the torque of the right wrist joint of the testee as a test object, properly warming up the testee before the experiment, and wiping the right forearm with alcohol. In order to ensure the standardization of experimental operation and the accuracy of test data, a subject fixes the posture and aligns the joints according to an operation manual of a multi-joint isokinetic muscle strength test system, and selects forearm extensor carpi radialis longus, flexor carpi radialis, extensor carpi ulnaris longus, flexor carpi ulnaris, extensor digitorum communis and flexor digitorum superficialis as muscle measuring points, and synchronously acquires surface electromyographic signals of 6 muscles of the forearm by using a surface electromyograph.
The palm and the forearm are kept horizontal to be 0 degree, and the bending and extending range of the wrist joint of the user is between 60 degrees of wrist extension and 75 degrees of wrist flexion. In the experimental process, an isometric tension measurement mode is selected to measure the peak moments of flexors and extensors of a subject under the conditions of 30 degrees and 60 degrees of wrist extension and 30 degrees and 60 degrees of wrist flexion respectively. During the test, each angle is repeated for 3 times, the maximum force of the testee is required to be reached within 1-2s and kept for 5s, and the peak moments of the flexors and the extensors at all angles are obtained. The maximum peak moment obtained by the flexors and the extensors at each angle is respectively selected as the peak moment at the angle, the peak moments of the flexors and the extensors at four angles are compared, and the maximum value is selected as the maximum peak moment, namely the isometric contraction maximum tension of the flexors and the extensors. And synchronously acquiring myoelectric signals of 6 muscle surfaces of the forearm of the human hand, preprocessing the myoelectric signals, extracting characteristic values of the active intensity of the myoelectric signals of the surfaces, and calculating the maximum amplitude of the active intensity of the myoelectric signals of the surfaces of all channels. Calculating the ratio of the maximum amplitude of the surface electromyographic signal activity of the extensor carpi radialis longus, the extensor carpi ulnaris longus and the extensor digitorum communis, and calculating the maximum tension of isometric contraction of each extensor by multiplying the maximum peak moment of the extensor digitorum longus by the ratio of the maximum amplitude of the electromyographic signal activity of each muscle. The maximum tension of isometric contraction of the flexor carpi radialis, flexor carpi ulnaris and flexor digitalis is obtained in the same way.
The third step: the surface electromyography is used for acquiring surface electromyography signals of 6 muscles of the forearm of a human hand under 3 motion speeds of the wrist joint of the user, preprocessing is carried out, and the muscle contraction time is calculated by using a TKE operator. The specific method comprises the following steps:
(1) collecting and preprocessing surface electromyographic signals:
selecting 6 muscles of the forearm related to the bending and stretching movement of the wrist joint of the user, wherein the muscles are respectively flexor carpi radialis, extensor carpi radialis longus, flexor carpi ulnaris, extensor carpi ulnaris longus, extensor digitorum communis and flexor digitorum superficialis. And acquiring surface electromyography signals by using a surface electromyograph, and performing band-pass filtering and notch filtering on the surface electromyograph to remove noise interference.
(2) Calculating muscle contraction time:
and detecting the starting and stopping time of the surface electromyographic signal by using the TKE operator, and calculating the duration time of the surface electromyographic signal as the muscle contraction time. The surface electromyogram signal can be characterized as a string of discrete digital signals, and for a given discrete signal, the TKE operator ψ (n) can be described as:
Figure BDA0001869369440000081
wherein the content of the first and second substances,
Figure BDA0001869369440000082
the method comprises the following steps of performing mean value removing processing on a surface electromyographic signal, wherein N is the total length of the surface electromyographic signal sequence, and M is the length of a background noise signal.
Figure BDA0001869369440000083
In the process of detecting the starting time of the surface electromyographic signal, a proper threshold value needs to be set according to the mean value and the mean square error of the TKE operator. The calculation formulas of the TKE operator mean value and the mean square error are respectively as follows:
Figure BDA0001869369440000084
Figure BDA0001869369440000085
the threshold value of the obtained surface electromyogram signal is as follows:
Th＝μ0+j0
wherein j is a threshold multiplier, and a proper threshold is selected by adjusting the value of j to judge whether the muscle moves or not.
j∈[5,7],j＝5
Comparing the TKE operator psi (n) of the surface myoelectric signal with the threshold Th to obtain a binarization state function s (n).
Figure BDA0001869369440000091
s (n) is a string of 0 and 1 alternating sequences representing the state of muscle contraction. Because the surface electromyogram signal is seriously interfered by noise, the peak noise signal is misjudged as the occurrence of muscle action when the action does not occur; however, during the continuous contraction of the muscle, the resting state may be determined as the muscle action is not occurring due to the instability of the surface electromyographic signal. In order to remove the errors caused by the above two cases, further processing on s (n) is required:
firstly, the interval between s and n is less than T1All 0 between two 1's are set to 1, which is used for removing the error caused by the instability of the surface electromyogram signal caused by the too fast muscle contraction frequency.
Then, the interval of s (n) is less than T2All 1's between two 0's are set to 0's for removing the interference caused by the spike.
T1When the action occurs, the normal inactive mark interval appears in the action continuous area; t is2Indicating that no action is occurring, a normal pseudo-active identification interval occurs in the inactive region. The detection of the start and stop moments of the surface myoelectric signal by the TKE operator is shown with reference to FIG. 3.
The muscle contraction time is then:
t＝max(n|s(n)＝1)-min(n|s(n)＝1),n＝1,2,…,M,…,N
the fourth step: in the process of the third step, the three-dimensional motion capture system is used for synchronously acquiring the kinematic data of the wrist joint of the user at 3 motion speeds, a simplified model of the wrist joint of the user is established, inverse dynamics solution is carried out, and the moment at the bending and stretching limit position of the wrist joint of the user is obtained. The specific method comprises the following steps:
(1) and designing a user wrist joint kinematics information acquisition experiment. The subject adopts a sitting posture state, the upper arm and the lower arm are kept horizontal, and the wrist does bending and stretching movement at three different speeds along with the indication video. The human hand and the arm can be regarded as rigid bodies, three non-collinear mark points are respectively stuck on the rigid bodies, and the kinematic data of the position change of the wrist joint of the user in the space can be obtained by utilizing a three-dimensional motion capture system.
(2) And establishing a wrist joint simplified model, as shown in a reference figure 2, carrying out inverse dynamics solution on the wrist joint pose information, calculating the wrist joint motion moment, and selecting the maximum value in the bending and stretching process as the joint moment value at the bending and stretching limit position of the wrist joint. The upper limbs of the human body are simplified into a connecting rod form, and the human hand and the wrist joint are regarded as a single connecting rod which is connected with the vertical surface through a hinge. The degree of freedom is relatively small, and the number of rigid bodies is only one, so that the Lagrange method is selected to perform inverse dynamics solution on the wrist joint of the user.
Lagrangian dynamics describe concepts based on system energy. For any mechanical system, the Lagrangian function L is defined as the total kinetic energy E of the systemkAnd total potential energy EpThe difference, namely:
Figure BDA0001869369440000101
wherein q is [ q ]1,q2,…,qn]Are generalized coordinates representing kinetic and potential energy,
Figure BDA0001869369440000102
is the corresponding generalized velocity.
With the lagrange function L, the kinetic equation of the system is:
Figure BDA0001869369440000103
where τ is the joint driving moment vector of n × 1 since potential energy Ep does not contain
Figure BDA0001869369440000104
The kinetic equation thus becomes:
Figure BDA0001869369440000105
as for the simplified model of the human upper limb wrist joint, as shown in reference to FIG. 2, the generalized coordinates of the wrist are set as
Figure BDA0001869369440000106
Flexion is positive and extension is negative.
The moment of inertia of the hand connecting rod is as follows:
Figure BDA0001869369440000111
the kinetic energy and the potential energy of the hand connecting rod are as follows in sequence:
Figure BDA0001869369440000112
Figure BDA0001869369440000113
in the formula I0The distance between the palm centroid and the wrist joint. Then:
Figure BDA0001869369440000114
Figure BDA0001869369440000115
Figure BDA0001869369440000116
Figure BDA0001869369440000117
the wrist flexion/extension moment M is therefore:
Figure BDA0001869369440000118
the fifth step: the calibration of the wrist joint positive musculoskeletal model is completed by using a conjugate gradient method, which comprises the following specific steps:
(1) determining an objective function:
the maximum muscle isometric contraction tension in the final expression of the wrist joint positive musculoskeletal model is determined in the second step, and other parameters need to be further optimized. Giving a, b, kiAnd (5) calculating the muscle contraction time according to the method of the fourth step, inputting the muscle contraction time into the positive muscle bone model, and outputting the corresponding wrist joint estimated moment. Taking the calculated moment solved by inverse dynamics in the fourth step as a reference value of the model output, and solving the square of the error between the estimated moment and the reference moment as an objective function, the objective function is as follows:
Figure BDA0001869369440000119
in the formula, n is the number of sample points in the whole sample; t isest(j) -estimating the moment, T, of the wrist joint at the jth extreme position estimated from the forward musculoskeletal modelcal(j) The reference moment of the wrist joint at the j limit position is solved by the kinematic data in an inverse dynamic way.
The objective function is further simplified to the following expression:
Y＝f(ki,Δlm,a,b)
(2) and (3) finishing the optimization of the parameters of the objective function based on a conjugate gradient method:
the target function and each parameter have nonlinear relation, so the target function is optimized by adopting a conjugate gradient method, and proper parameters are determined to make the target function reach the minimum value. The calibration process of the wrist positive musculoskeletal model is shown with reference to fig. 4.
Setting iteration precision to 0.1 and each parameter initial value x0＝[ki 0,Δl0,a0,b0]TAnd calculating the gradient at the initial point, and searching along the negative gradient direction of the initial point for the first time.
Figure BDA0001869369440000121
If | | | f (x)0) If | | is less than or equal to the threshold, stopping iteration and outputting solution x of the equation*＝x0。
Otherwise, a new search direction d is determinedn+1And step size βnAnd (4) continuing iteration:
Figure BDA0001869369440000122
Figure BDA0001869369440000123
thus, a new search point is obtained:
xn+1＝xn+βndn
calculating the gradient of the new search point checks whether the iteration precision requirement is met, namely:
Figure BDA0001869369440000131
the process flow of optimizing model parameters by conjugate gradient method is shown in reference to fig. 5.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (5)

1. A myoelectric estimation method of wrist joint movement moment is characterized by comprising the following steps:
step 1: establishing a positive musculoskeletal model of a wrist joint of a user, wherein the specific method comprises the following steps:
step 1-1: single muscle force calculation based on surface electromyography signals:
using the Hill ternary model, the Hill equation is described as:
(a+F)(V+b)＝b(F0+a)
the method is simplified as follows:
Figure FDA0002502794910000011
wherein a and b are experimental parameters, F0Is the isometric maximum tension, V is the muscle contraction velocity, F0V is the initial length of the muscle fiber, the temperature, the chemical composition of the surrounding environment and
Figure FDA0002502794910000014
a function of concentration;
the muscle contraction velocity V is expressed as:
Figure FDA0002502794910000012
wherein t is the muscle contraction time, and delta l is the muscle contraction length; the duration time of the surface electromyographic signal is used as the muscle contraction time, and the duration time of the surface electromyographic signal can be calculated by using a Teager-Kaiser Energy operator; optimizing the muscle contraction length as an experimental parameter;
the final simplified expression for the individual muscle forces is:
Figure FDA0002502794910000013
step 2-2: solving the wrist joint moment under the combined action of a plurality of muscles:
according to the bone lever principle, the rotation moment generated on the joint by the single muscle contraction force is as follows:
Ti＝ri×Fi·cos(φi),i＝1,2,…,Nmuscle
in the formula, riIs a displacement vector from the joint rotation center to a force action point; fiIs a muscle force vector; phi is aiIs a muscle pinnate angle; n is a radical ofmuscleIs the number of muscles;
it is converted into:
Ti＝ki·Fi,i＝1,2,…,N
in the formula, ki＝ri·cos(φi) Is a proportionality coefficient;
determining the moment contribution of each muscle to the joint and adding the moment contributions to the joint to obtain the moment of the wrist joint:
Figure FDA0002502794910000021
thus, a wrist joint positive musculoskeletal model is obtained, the estimation of the wrist joint movement moment by the surface electromyographic signals is realized, and the method comprises the following steps:
Figure FDA0002502794910000022
step 2: synchronously measuring the isometric contraction maximum tension of each muscle of the forearm by using a multi-joint isokinetic muscle force testing system and a surface electromyography;
and step 3: collecting wrist joint kinematic data of a user wrist joint at 3 motion speeds by using a three-dimensional motion capture system, synchronously collecting surface electromyography signals of 6 muscles on a forearm of a hand by using a surface electromyograph, preprocessing the signals, and calculating muscle contraction time by using a Teager-Kaiser Energy operator;
and 4, step 4: acquiring kinematic data of a wrist joint of a user by using a three-dimensional motion capture system, establishing a simplified model of the wrist joint of the user, and performing inverse dynamics solution to obtain a moment at a bending and stretching limit position of the wrist joint of the user;
and 5: and (5) completing calibration of the wrist joint positive musculoskeletal model by using a conjugate gradient method.
2. The myoelectric estimation method of wrist joint movement moment according to claim 1, characterized in that the specific method of step 2 is as follows:
selecting forearm extensor carpi radialis longus, flexor carpi radialis longus, extensor carpi ulnaris longus, flexor carpi ulnaris, extensor digitorum communis and flexor digitorum superficialis as muscle measuring points, and synchronously acquiring surface electromyography signals of 6 muscles of the forearm by using a surface electromyograph;
the palm and the forearm are kept horizontal to be 0 degree, and the bending and extending range of the wrist joint of the user is between 60 degrees of wrist extension and 75 degrees of wrist flexion; selecting an isometric tension measurement mode, and respectively measuring peak moments of flexors and extensors of a subject under the conditions of 30 degrees and 60 degrees of wrist extension and 30 degrees and 60 degrees of wrist flexion; during testing, repeating each angle for 3 times, reaching the maximum strength within 1-2s and keeping for 5s to obtain the peak torque of the flexors and the extensors at each angle; respectively selecting the maximum peak moment obtained by the flexors and the extensors at each angle as the peak moment at the angle, then comparing the peak moments of the flexors and the extensors at four angles, and selecting the maximum value as the maximum peak moment, namely the isometric contraction maximum tension of the flexors and the extensors; synchronously acquiring myoelectric signals of 6 muscle surfaces of the forearm of a human hand, preprocessing the myoelectric signals, extracting characteristic values of the active intensity of the myoelectric signals of the surfaces, and calculating the maximum amplitude of the active intensity of the myoelectric signals of the surfaces of all channels; calculating the ratio of the maximum amplitude of the surface electromyographic signal activity intensity of the extensor carpi radialis longus, the extensor carpi ulnaris longus and the extensor digitorum communis, and calculating the isometric contraction maximum tension of each extensor by multiplying the maximum peak moment of the extensor digitorum longus by the ratio of the maximum amplitude of the surface electromyographic signal activity intensity; the maximum tension of isometric contraction of the flexor carpi radialis, flexor carpi ulnaris and flexor digitalis is obtained in the same way.
3. The myoelectric estimation method of wrist joint movement moment according to claim 1, characterized in that the specific method of step 3 is as follows:
step 3-1: collecting and preprocessing surface electromyographic signals:
selecting 6 muscles of the forearm related to the bending and stretching movement of the wrist joint of the user, wherein the muscles are respectively flexor carpi radialis, extensor carpi radialis longus, flexor carpi ulnaris, extensor carpi ulnaris longus, extensor digitorum communis and flexor digitorum superficialis; acquiring a surface electromyogram signal by using a surface electromyogram, and performing band-pass filtering and notch filtering on the surface electromyogram signal to remove noise interference;
step 3-2: calculating muscle contraction time:
detecting the starting and stopping time of the surface electromyographic signals by using a Teager-Kaiser Energy operator, and calculating the duration time of the surface electromyographic signals as muscle contraction time; the surface electromyography signal can be characterized as a string of discrete digital signals, and for a given discrete signal, the Teager-Kaiser Energy operator ψ (n) can be described as:
Figure FDA0002502794910000046
wherein the content of the first and second substances,
Figure FDA0002502794910000041
the method comprises the following steps of (1) performing mean value removing processing on a surface electromyographic signal, wherein N is the total length of a surface electromyographic signal sequence, and M is the length of a background noise signal;
Figure FDA0002502794910000042
in the process of detecting the surface electromyogram signal starting moment, a proper threshold value needs to be set according to the mean value and the mean square error of a Teager-Kaiser Energy operator; the mean and mean square error calculation formulas of the Teager-Kaiser Energy operator are respectively as follows:
Figure FDA0002502794910000043
Figure FDA0002502794910000044
the threshold value of the obtained surface electromyogram signal is as follows:
Th＝μ0+j0
wherein j is a threshold multiplier, and a proper threshold is selected by adjusting the value of j to judge whether the muscle moves or not;
j∈[5,7]
comparing a Teager-Kaiser Energy operator psi (n) of the surface myoelectric signal with a threshold Th to obtain a binarization state function s (n);
Figure FDA0002502794910000045
s (n) is a string of 0, 1 alternate sequences representing muscle contraction states; in order to remove the error, s (n) needs to be further processed:
firstly, the interval between s and n is less than T1All 0 between two 1 are set to be 1, and the method is used for removing errors caused by instability of surface electromyographic signals due to too fast muscle contraction frequency;
then, the interval of s (n) is less than T2All 1 between two 0 are set to 0 for removing the interference caused by the peak signal;
T1when the action occurs, the normal inactive mark interval appears in the action continuous area; t is2Indicating that a normal pseudo-activity flag interval occurs in the inactive region when no action occurs; detecting the starting and stopping time condition of the surface myoelectric signal by using a Teager-Kaiser Energy operator;
the muscle contraction time is then:
t＝max(n|s(n)＝1)-min(n|s(n)＝1),n＝1,2,…,M,…,N。
4. the myoelectric estimation method of wrist joint movement moment according to claim 1, characterized in that the specific method of step 4 is as follows:
step 4-1: designing a user wrist joint kinematics information acquisition experiment; the testee adopts a sitting posture state, the upper arm and the lower arm are kept horizontal, and the wrist does bending and stretching movement at three different speeds; the hand and the arm are regarded as rigid bodies, three non-collinear mark points are respectively pasted on the rigid bodies, and the kinematic data of the position change of the wrist joint of the user in the space is obtained by utilizing a three-dimensional motion capture system;
step 4-2: establishing a wrist joint simplified model, carrying out inverse dynamics solution on the pose information of the wrist joint, calculating the motion moment of the wrist joint, and selecting the maximum value in the bending and stretching process as the joint moment value at the bending and stretching limit position of the wrist joint; the upper limbs of the human body are simplified into a connecting rod form, and the hand and the wrist joint of the human body are regarded as a single connecting rod which is connected with the vertical surface through a hinge; performing inverse dynamics solution on the wrist joint of the user by adopting a Lagrange method;
the Lagrange function L is defined as the total kinetic energy E of the systemkAnd total potential energy EpThe difference, namely:
Figure FDA0002502794910000061
wherein q is [ q ]1,q2,…,qn]Are generalized coordinates representing kinetic and potential energy,
Figure FDA0002502794910000062
is the corresponding generalized velocity;
with the lagrange function L, the kinetic equation of the system is:
Figure FDA0002502794910000063
in the formula, due to potential energy EpDoes not contain
Figure FDA0002502794910000064
The kinetic equation thus becomes:
Figure FDA0002502794910000065
for a simplified model of the wrist joint, the generalized coordinates of the wrist are set as
Figure FDA0002502794910000066
The flexion is positive and the extension is negative;
the moment of inertia of the hand connecting rod is as follows:
Figure FDA0002502794910000067
the kinetic energy and the potential energy of the hand connecting rod are as follows in sequence:
Figure FDA0002502794910000068
Figure FDA0002502794910000069
in the formula I0The distance between the palm centroid and the wrist joint; then:
Figure FDA00025027949100000610
Figure FDA00025027949100000611
Figure FDA00025027949100000612
Figure FDA0002502794910000071
the wrist joint bending/stretching moment M is therefore:
Figure FDA0002502794910000072
5. the myoelectric estimation method of wrist joint movement moment according to claim 4, characterized in that the specific method of step 5 is as follows:
step 5-1: determining an objective function:
giving a, b, kiCalculating muscle contraction time according to the method in the step 3, inputting the muscle contraction time into the positive muscle bone model, and outputting corresponding wrist joint estimated moment; taking the calculated moment solved by inverse dynamics in the step 4 as a reference value of the model output, and solving the square of the error between the estimated moment and the reference moment as an objective function, the objective function is as follows:
Figure FDA0002502794910000073
in the formula, n represents the number of sample points in the whole sample; t isest(j) Representing the estimated moment of the wrist joint at the jth extreme position, T, estimated from the forward musculoskeletal modelcal(j) Representing the wrist joint reference moment at the j extreme position solved by inverse dynamics of the kinematic data;
the objective function is further simplified to the following expression:
Y＝f(ki,Δlm,a,b)
step 5-2: and (3) finishing the optimization of the parameters of the objective function based on a conjugate gradient method:
the target function and each parameter have nonlinear relation, so the target function is optimized by adopting a conjugate gradient method, and proper parameters are determined to enable the target function to reach the minimum value;
setting iteration precision to 0.1 and each parameter initial value
Figure FDA0002502794910000074
Calculating the gradient at the initial point, and searching along the negative gradient direction of the initial point for the first time;
Figure FDA0002502794910000081
if | | | f (x)0) If | | is less than or equal to the threshold, stopping iteration and outputting solution x of the equation*＝x0；
Otherwise, a new search direction d is determinedn+1And step size βnAnd (4) continuing iteration:
Figure FDA0002502794910000082
Figure FDA0002502794910000083
thus, a new search point is obtained:
xn+1＝xn+βndn
calculating the gradient of the new search point checks whether the iteration precision requirement is met, namely:
Figure FDA0002502794910000084
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