CN114227673A - Human body electromyographic signal direct-drive joint torque mapping method - Google Patents

Human body electromyographic signal direct-drive joint torque mapping method Download PDF

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CN114227673A
CN114227673A CN202111437003.8A CN202111437003A CN114227673A CN 114227673 A CN114227673 A CN 114227673A CN 202111437003 A CN202111437003 A CN 202111437003A CN 114227673 A CN114227673 A CN 114227673A
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muscle
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tendon
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CN114227673B (en
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赵永恩
衣淳植
姜峰
杨炽夫
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Harbin Institute Of Technology Robot Innovation Center Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J13/00—Controls for manipulators
    • B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
    • B25J13/087—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices for sensing other physical parameters, e.g. electrical or chemical properties
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
    • B25J19/02—Sensing devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/163—Programme controls characterised by the control loop learning, adaptive, model based, rule based expert control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion

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Abstract

The invention relates to a human body electromyographic signal direct-drive joint torque mapping method. The invention relates to the technical field of exoskeleton robot auxiliary control, which performs nerve activation and muscle activation extraction to acquire nerve activation information; establishing a muscle-tendon model relation, and determining a muscle force-length relation; determining the pinnate angle according to the relationship of muscle force and length; determining the length and force of the muscle-tendon unit according to the pinnate angle; and determining joint torque according to the length of the muscle-tendon unit and the force exerted by the muscle-tendon unit, and identifying parameters. The invention simplifies the original model, carries out continuous processing to enable the model to be miniaturized, embeds the model into a neural network, adopts a few-sample learning strategy, and estimates different individual physiological parameters according to the similarity based on multi-person physiological characteristic comparison.

Description

Human body electromyographic signal direct-drive joint torque mapping method
Technical Field
The invention relates to the technical field of exoskeleton robot auxiliary control, in particular to a human body electromyographic signal direct-drive joint torque mapping method.
Background
The exoskeleton robot can assist the human body to move, can work instead of human body joints, and has unique significance and advantages in the aspects of recovery of patients with stroke and paraplegia, reduction of labor injury of physical workers, assistance of long-distance walking and the like. The human body joint force application is key information in the assisting process of the exoskeleton robot, accurate real-time estimation of the human body joint force application ensures man-machine cooperation, and meanwhile, the assisting efficiency of the exoskeleton robot is improved.
The invention aims to provide a novel method for mapping an electromyographic signal to joint moment by utilizing a nerve-muscle-skeleton model. The nerve-muscle-skeleton model is formed by respectively modeling and combining human physiological parameters based on anatomical measurement and human motor system nerves, muscles and bones, and can effectively provide a calculation model for human inverse dynamics analysis and musculoskeletal layer pathological analysis. The model is widely applied to the aspects of accurate rehabilitation, robot control, human body simulation modeling and the like. In order to reduce the influence of the difference of physiological parameters among different individuals, the nerve-muscle-skeleton model needs to perform parameter identification so as to achieve the purpose of individual adaptation.
(1) The traditional nerve-muscle-skeleton model is complex, the method is tedious, and the solution consumes long time: the mathematical expression of the traditional nerve-muscle-skeleton model is a variable constant first-order differential equation, the model is complex, the time consumption of the solving process is long, and the application value of the model on the exoskeletal robot is reduced.
(2) The differential equation solution needs to be subjected to a numerical solution method, and has the discreteness: the traditional method generally adopts a grid method, and the method needs numerical solution and is discrete.
Disclosure of Invention
The invention provides a human body electromyographic signal direct-drive joint moment mapping method for achieving the purpose of individual adaptation by performing parameter identification on a nerve-muscle-skeleton model, and provides the following technical scheme:
a human body electromyographic signal direct-drive joint torque mapping method comprises the following steps:
step 1: carrying out nerve activation and muscle activation extraction to obtain nerve activation information;
step 2: establishing a muscle-tendon model relation, and determining a muscle force-length relation;
and step 3: determining the pinnate angle according to the relationship of muscle force and length;
and 4, step 4: determining the length and force of the muscle-tendon unit according to the pinnate angle;
and 5: and determining joint torque according to the length of the muscle-tendon unit and the force exerted by the muscle-tendon unit, and identifying parameters.
Preferably, the step 1 specifically comprises:
the collected sEMG signals are used for extracting muscle activation from the sEMG signals;
after 50-500Hz band-pass filtering, rectification and normalization, according to the determined nerve activation information u (t):
Figure BDA0003381820760000021
wherein u (t) is the neural activation at the tth sample point; alpha, beta1,β2Is a neural activation coefficient; d is a time delay; t isEIs a sampling time interval;
muscle activation was then determined by neural activation a (t):
Figure BDA0003381820760000022
preferably, the step 2 specifically comprises:
establishing a muscle force-length relation, wherein a muscle-tendon model consists of an active contraction unit, a passive contraction unit and elastic tendons; according to the musculoskeletal model, the relationship between the active contractile force and the length of muscle fiber fA(l) And the relationship of passive contractility to muscle fiber length fP(l)；
For these two muscle forces to muscle fiber length relationships are non-linear, the active contractile force to muscle fiber length relationship is represented by the following equation:
fA(l)＝sin(-1.317·l2-0.403·l+2.454)
Figure BDA0003381820760000031
l is the length of the muscle fiber after normalization, represented by the following formula:
Figure BDA0003381820760000032
wherein lmIt is meant the length of the muscle fiber,
Figure BDA0003381820760000034
refers to the optimal muscle fiber length.
Preferably, the step 3 specifically comprises:
tendon and muscle fiber in series, tendon force FtAnd muscle force FmThe relationship is determined by:
Ft＝Fmcos(φ)
where φ is the pinnate angle, i.e., the angle between the tendon and muscle fiber;
the swing angle of the contracting muscle is predicted using a simplified model, and the pinnate angle at which the sampling point is denoted as t is represented by the following equation:
Figure BDA0003381820760000033
wherein phi isoThe pinnate angle at the optimum fiber length for the muscle;
calculated feather angles phi (t) and fA,fPThe force condition of the whole muscle-tendon unit, namely the force of a certain physiological muscle is determined.
Preferably, the step 4 specifically includes:
for the solution of muscle force, the solution of the magnitude of the tendon length is determined by:
lmt＝lt+lmcos(φ)
Figure BDA0003381820760000041
Figure BDA0003381820760000042
wherein lmtIs the length of the muscle tendon,/tTendon length;
muscle tendon length was determined by the following formula:
lmt(Q1,Q2,Q3,Q4)＝a1+a2f1(Q1,Q2,Q3,Q4)+a3f2(Q1,Q2,Q3,Q4)+···+anfn-1(Q1,Q2,Q3,Q4)
wherein, aiExpressed as undetermined coefficient, fiThe method is characterized in that a general nonlinear coefficient is selected according to the shape of a fitted surface; and Q1,Q2,Q3,Q4Is the number of joint angles assumed by the Delp model; determining the length of the corresponding muscle tendon by using the measured joint angle information and performing a fitting solution;
muscle-tendon unit exertion is determined by the following formula:
Figure BDA0003381820760000043
preferably, the step 5 specifically comprises:
the joint moment is determined by:
Figure BDA0003381820760000044
wherein M isjRepresents the joint moment r of the joint j when the joint angle is theta and the sampling time is ti(theta) is the moment arm of the joint when the ith skeletal muscle is at the joint angle theta, Fi mt(θ, t) is the muscle force of the ith skeletal muscle to the joint j at the joint angle θ;
the moment arm is determined by:
Figure BDA0003381820760000045
preferably, the parameter identification in step 5 is specifically:
the coefficient a of each muscle is ═ a through Adam random gradient descent optimization method1,a2,a3]Maximum isometric force of muscle
Figure BDA0003381820760000051
Tendon relaxation length
Figure BDA0003381820760000052
Optimum length of muscle fiber
Figure BDA0003381820760000053
Muscle activation non-linear factors a are identified in turn by the sensitivity relative to the model.
The invention has the following beneficial effects:
in the existing nerve-muscle-skeleton model, the solution of the whole muscle force is essentially to solve a variable constant first-order differential equation
Figure BDA0003381820760000056
And wherein the physiological parameters of individual differences
Figure BDA0003381820760000055
φoThe human joint moment reverse identification is needed according to the measurement of the optical dynamic capturing and the sole pressure measuring plate. Obviously, the parameters of the differential equation are identifiedThe calculation amount is large, and the time consumption is long. The invention can reasonably simplify the model according to physiological characteristics, thereby realizing microminiaturization of the nerve-muscle-skeleton model. Rigid tendon: the maximum deformation of the tendon in muscle activation is about 11%, and the deformation can be ignored, so that the tendon is simplified into a rigid part; simplifying the relationship of muscle force-muscle contraction speed: the introduction of this relationship results in a model containing the length of the muscle fiber lmSo that the model mathematical form becomes a differential equation. In order to simplify the relationship, the contraction speed of the muscle along with the change of the stress is set to be 1, namely the relationship between the muscle force and the muscle contraction speed is considered to be small and is ignored. The original model is simplified, continuous processing is carried out to enable the model to be miniaturized, the model is embedded into a neural network, a few-sample learning strategy is adopted, comparison is carried out on the basis of physiological characteristics of multiple persons, and different individual physiological parameters are estimated according to similarity.
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FIG. 1 is a diagram of a muscle tendon model;
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples.
The first embodiment is as follows:
as shown in fig. 1 to 2, the specific optimized technical solution adopted to solve the above technical problems of the present invention is: the invention relates to a human body electromyographic signal direct-drive joint torque mapping method.
A human body electromyographic signal direct-drive joint torque mapping method comprises the following steps:
step 1: carrying out nerve activation and muscle activation extraction to obtain nerve activation information;
preferably, the step 1 specifically comprises:
the collected sEMG signals are used for extracting muscle activation from the sEMG signals;
after 50-500Hz band-pass filtering, rectification and normalization, according to the determined nerve activation information u (t):
Figure BDA0003381820760000061
wherein u (t) is the neural activation at the tth sample point; alpha, beta1,β2Is a neural activation coefficient; d is a time delay; t isEIs a sampling time interval;
muscle activation was then determined by neural activation a (t):
Figure BDA0003381820760000062
step 2: establishing a muscle-tendon model relation, and determining a muscle force-length relation;
the step 2 specifically comprises the following steps:
establishing a muscle force-length relation, wherein a muscle-tendon model consists of an active contraction unit, a passive contraction unit and elastic tendons; according to the musculoskeletal model, the relationship between the active contractile force and the length of muscle fiber fA(l) And the relationship of passive contractility to muscle fiber length fP(l)；
For these two muscle forces to muscle fiber length relationships are non-linear, the active contractile force to muscle fiber length relationship is represented by the following equation:
fA(l)＝sin(-1.317·l2-0.403·l+2.454)
Figure BDA0003381820760000063
l is the length of the muscle fiber after normalization, represented by the following formula:
Figure BDA0003381820760000064
wherein lmIt is meant the length of the muscle fiber,
Figure BDA0003381820760000065
refers to the optimal muscle fiber length.
And step 3: determining the pinnate angle according to the relationship of muscle force and length;
the step 3 specifically comprises the following steps:
tendon and muscle fiber in series, tendon force FtAnd muscle force FmThe relationship is determined by:
Ft＝Fm cos(φ)
where φ is the pinnate angle, i.e., the angle between the tendon and muscle fiber;
the swing angle of the contracting muscle is predicted using a simplified model, and the pinnate angle at which the sampling point is denoted as t is represented by the following equation:
Figure BDA0003381820760000071
wherein phi isoThe pinnate angle at the optimum fiber length for the muscle;
calculated feather angles phi (t) and fA,fPThe force condition of the whole muscle-tendon unit, namely the force of a certain physiological muscle is determined.
And 4, step 4: determining the length and force of the muscle-tendon unit according to the pinnate angle;
the step 4 specifically comprises the following steps:
for the solution of muscle force, the solution of the magnitude of the tendon length is determined by:
lmt＝lt+lm cos(φ)
Figure BDA0003381820760000072
Figure BDA0003381820760000073
wherein lmtIs the length of the muscle tendon,/tTendon length;
muscle tendon length was determined by the following formula:
lmt(Q1,Q2,Q3,Q4)＝a1+a2f1(Q1,Q2,Q3,Q4)+a3f2(Q1,Q2,Q3,Q4)+···+anfn-1(Q1,Q2,Q3,Q4)
wherein, aiExpressed as undetermined coefficient, fiThe method is characterized in that a general nonlinear coefficient is selected according to the shape of a fitted surface; and Q1,Q2,Q3,Q4Is the number of joint angles assumed by the Delp model; determining the length of the corresponding muscle tendon by using the measured joint angle information and performing a fitting solution;
muscle-tendon unit exertion is determined by the following formula:
Figure BDA0003381820760000081
and 5: and determining joint torque according to the length of the muscle-tendon unit and the force exerted by the muscle-tendon unit, and identifying parameters.
The step 5 specifically comprises the following steps:
the joint moment is determined by:
Figure BDA0003381820760000082
wherein M isjRepresents the joint moment r of the joint j when the joint angle is theta and the sampling time is ti(theta) is the moment arm of the joint when the ith skeletal muscle is at the joint angle theta, Fi mt(θ, t) is the muscle force of the ith skeletal muscle to the joint j at the joint angle θ;
the moment arm is determined by:
Figure BDA0003381820760000083
the parameter identification in the step 5 specifically comprises:
the coefficient a of each muscle is ═ a through Adam random gradient descent optimization method1,a2,a3]Maximum isometric force of muscle
Figure BDA0003381820760000084
Tendon relaxation length
Figure BDA0003381820760000085
Optimum length of muscle fiber
Figure BDA0003381820760000086
Muscle activation non-linear factors a are identified in turn by the sensitivity relative to the model.
The second embodiment is as follows:
step one, collecting sEMG signals of lower limbs of a human body and extracting muscle activation information. The specific process is as follows:
the signal acquisition equipment adopts a Delsys Trigno electromyography acquisition system and comprises a wireless communication base station and 16 wireless electromyography electrodes, wherein an EMG signal collector is arranged in each electrode. The Delsys Trigno electromyography acquisition system supports an off-line acquisition mode and an on-line acquisition mode: and (3) packaging and sending the EMG signals after the experiment is finished in an offline acquisition mode, setting the single-time transmission data length in the online acquisition mode, and transmitting the EMG signals once when the EMG signal recording length reaches a set value. Seven muscle surface skins of the lateral femoral muscle, the medial femoral muscle, the biceps femoris muscle, the semimembranosus muscle, the rectus femoris muscle, the lateral gastrocnemius muscle and the medial gastrocnemius muscle of the test subject were subjected to depilation and exfoliating treatment, and then 7 Delsys electrodes were attached to the skin surfaces of these muscles to extract sEMG signals. The Delsys data acquisition mode selects the offline mode. And performing band-pass filtering, rectification and normalization on the acquired signals, and calculating nerve activation information and muscle activation information.
Secondly, the joint angles measured by the VICON infrared motion capture system and the collected sEMG signals are combined with the joint force measured by the AMTI force measuring platformMoment TmeaOn the basis of the reference, the respective muscle of each muscle is identified according to the muscle
Figure BDA0003381820760000091
Figure BDA0003381820760000092
Step three, dividing the muscles into three units UN (including vastus lateralis, vastus medialis and biceps femoris) and BA according to different driving joints1(including semimembraneous, rectus femoris), BA2(including the measurement in the gastrocnemius and the lateral side of the gastrocnemius), identifying the scaling factor a of each of the three muscle units according to the muscle unitu:
Figure BDA0003381820760000093
Step four, in the process of measuring the joint moment, the knee joint angles are 0, 45 and 90 degrees, and the joint moments at corresponding moments are respectively substituted into the following formula for calculation:
Figure BDA0003381820760000094
Figure BDA0003381820760000095
Figure BDA0003381820760000096
Figure BDA0003381820760000097
wherein the content of the first and second substances,
Figure BDA0003381820760000101
after being regularizedThe muscle-tendon unit force of the ith muscle in (e) is tendon strain. Corresponding the knee joint angles of 0, 45 and 90 degrees
Figure BDA0003381820760000102
Adding and averaging to obtain the average value of each muscle
Figure BDA0003381820760000103
And (5) initial value.
Step five, the joint torque T measured by combining the infrared dynamic capturing system with the force measuring platformmeaOn the basis of the reference, the respective muscle of each muscle is identified according to the muscle
Figure BDA0003381820760000104
Figure BDA0003381820760000105
Step six, joint torque T measured by combining an infrared dynamic capturing system with a force measuring platformmeaCalculated by step four as reference
Figure BDA0003381820760000106
For the initial value, identifying each muscle according to the muscle
Figure BDA0003381820760000107
Figure BDA0003381820760000108
Step seven, the joint torque T measured by combining the infrared dynamic capturing system with the force measuring platformmeaFor reference, the respective muscle activation nonlinear factor A of each muscle is identified according to the musclei：
Figure BDA0003381820760000109
The above description is only a preferred embodiment of the human body electromyogram signal direct-drive joint torque mapping method, and the protection range of the human body electromyogram signal direct-drive joint torque mapping method is not limited to the above embodiments, and all technical solutions belonging to the idea belong to the protection range of the present invention. It should be noted that modifications and variations which do not depart from the gist of the invention will be those skilled in the art to which the invention pertains and which are intended to be within the scope of the invention.

Claims (7)

1. A human body electromyographic signal direct-drive joint torque mapping method is characterized by comprising the following steps: the method comprises the following steps:
step 1: carrying out nerve activation and muscle activation extraction to obtain nerve activation information;
step 2: establishing a muscle-tendon model relation, and determining a muscle force-length relation;
and step 3: determining the pinnate angle according to the relationship of muscle force and length;
and 4, step 4: determining the length of the muscle-tendon unit and the force of the muscle-tendon unit according to the pinnate angle;
and 5: and determining joint torque according to the length of the muscle-tendon unit and the force exerted by the muscle-tendon unit, and identifying parameters.
2. The human body electromyographic signal direct-drive joint torque mapping method according to claim 1, wherein: the step 1 specifically comprises the following steps:
the collected sEMG signals are used for extracting muscle activation from the sEMG signals;
after 50-500Hz band-pass filtering, rectification and normalization, according to the determined nerve activation information u (t):
Figure FDA0003381820750000011
wherein u (t) is the neural activation at the tth sample point; alpha, beta1，β2Is a neural activation coefficient; d is a time delay; t isEAs a sampling timeSpacing;
muscle activation was then determined by neural activation a (t):
Figure FDA0003381820750000012
3. the human body electromyographic signal direct-drive joint torque mapping method according to claim 2, wherein: the step 2 specifically comprises the following steps:
establishing a muscle force-length relation, wherein a muscle-tendon model consists of an active contraction unit, a passive contraction unit and elastic tendons; according to the musculoskeletal model, the relationship between the active contractile force and the length of muscle fiber fA(l) And the relationship of passive contractility to muscle fiber length fP(l)；
For these two muscle forces to muscle fiber length relationships are non-linear, the active contractile force to muscle fiber length relationship is represented by the following equation:
fA(l)＝sin(-1.317·l2-0.403·l+2.454)
Figure FDA0003381820750000021
l is the length of the muscle fiber after normalization, represented by the following formula:
Figure FDA0003381820750000022
wherein lmIt is meant the length of the muscle fiber,
Figure FDA0003381820750000023
refers to the optimal muscle fiber length.
4. The human body electromyographic signal direct-drive joint torque mapping method according to claim 3, wherein: the step 3 specifically comprises the following steps:
tendon and muscle fiber in series, tendon force FtAnd muscle force FmThe relationship is determined by:
Ft＝Fmcos(φ)
where φ is the pinnate angle, i.e., the angle between the tendon and muscle fiber;
the swing angle of the contracting muscle is predicted using a simplified model, and the pinnate angle at which the sampling point is denoted as t is represented by the following equation:
Figure FDA0003381820750000024
wherein phi isoThe pinnate angle at the optimum fiber length for the muscle;
calculated feather angles phi (t) and fA，fPThe force condition of the whole muscle-tendon unit, namely the force of a certain physiological muscle is determined.
5. The human body electromyographic signal direct-drive joint torque mapping method according to claim 4, wherein: the step 4 specifically comprises the following steps:
for the solution of muscle force, the solution of the magnitude of the tendon length is determined by:
lmt＝lt+lmcos(φ)
Figure FDA0003381820750000031
Figure FDA0003381820750000032
wherein lmtIs the length of the muscle tendon,/tTendon length;
muscle tendon length was determined by the following formula:
lmt(Q1，Q2，Q3，Q4)＝a1+a2f1(Q1，Q2，Q3，Q4)+a3f2(Q1，Q2，Q3，Q4)+…+anfn-1(Q1，Q2，Q3，Q4)
wherein, aiExpressed as undetermined coefficient, fiThe method is characterized in that a general nonlinear coefficient is selected according to the shape of a fitted surface; and Q1，Q2，Q3，Q4Is the number of joint angles assumed by the Delp model; determining the length of the corresponding muscle tendon by using the measured joint angle information and performing a fitting solution;
muscle-tendon unit exertion is determined by the following formula:
Figure FDA0003381820750000033
6. the human body electromyographic signal direct-drive joint torque mapping method according to claim 5, wherein: the step 5 specifically comprises the following steps:
the joint moment is determined by:
Figure FDA0003381820750000034
wherein M isjRepresents the joint moment r of the joint j when the joint angle is theta and the sampling time is ti(theta) is the moment arm of the joint when the ith skeletal muscle is at the joint angle theta, Fi mt(θ, t) is the muscle force of the ith skeletal muscle to the joint j at the joint angle θ;
the moment arm is determined by:
Figure FDA0003381820750000035
7. the human body electromyographic signal direct-drive joint torque mapping method according to claim 6, wherein: the parameter identification in the step 5 specifically comprises:
the coefficient a of each muscle is ═ a through Adam random gradient descent optimization method1,a2,a3]Maximum isometric force of muscle
Figure FDA0003381820750000041
Tendon relaxation length
Figure FDA0003381820750000042
Optimum length of muscle fiber
Figure FDA0003381820750000043
Muscle activation non-linear factors a are identified in turn by the sensitivity relative to the model.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20100122724A (en) * 2009-05-13 2010-11-23 연세대학교 산학협력단 System and method for estimating muscle force using joint angles in real-time
CN108324503A (en) * 2018-03-16 2018-07-27 燕山大学 Healing robot self-adaptation control method based on flesh bone model and impedance control
CN108994833A (en) * 2018-07-26 2018-12-14 北京机械设备研究所 A kind of joint assistance control method based on myoelectricity liveness feedback
CN109145837A (en) * 2018-08-28 2019-01-04 厦门理工学院 Face emotion identification method, device, terminal device and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20100122724A (en) * 2009-05-13 2010-11-23 연세대학교 산학협력단 System and method for estimating muscle force using joint angles in real-time
CN108324503A (en) * 2018-03-16 2018-07-27 燕山大学 Healing robot self-adaptation control method based on flesh bone model and impedance control
CN108994833A (en) * 2018-07-26 2018-12-14 北京机械设备研究所 A kind of joint assistance control method based on myoelectricity liveness feedback
CN109145837A (en) * 2018-08-28 2019-01-04 厦门理工学院 Face emotion identification method, device, terminal device and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
姜峰 等: "利用肌电信号求解关节力矩的研究及应用综述", 《智能系统学报》, vol. 15, no. 2, pages 193 - 203 *

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