CN111408042B - Functional electrical stimulation and lower limb exoskeleton intelligent distribution method, device, storage medium and system - Google Patents

Functional electrical stimulation and lower limb exoskeleton intelligent distribution method, device, storage medium and system Download PDF

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CN111408042B
CN111408042B CN202010229670.6A CN202010229670A CN111408042B CN 111408042 B CN111408042 B CN 111408042B CN 202010229670 A CN202010229670 A CN 202010229670A CN 111408042 B CN111408042 B CN 111408042B
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万小姣
傅向向
朱威灵
寿梦婕
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Zhejiang Meilian Medical Technology Co ltd
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    • A—HUMAN NECESSITIES
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Abstract

The invention relates to a functional electrical stimulation and lower limb exoskeleton intelligent distribution method, a device, a storage medium and a system. The technical scheme of the invention is as follows: s01, obtaining bone dynamic characteristic parameters of a user; s02, obtaining the angle, the angular speed and the angular acceleration of each joint of the lower limb of the user; s03, determining that the motion stage of the lower limbs of the user is a swing stage or a support stage; s04, inputting the bone dynamic characteristic parameters of the user and the angle, the angular velocity and the angular acceleration of each joint into the exoskeleton robot inverse dynamics model to obtain the moment L of each joint ext (ii) a S05, respectively determining the moment L of each joint according to the motion stage of the lower limb of the user ext Moment L applied by a motion motor in the lower limb exoskeleton equipment d And a movement moment L applied by the user or caused by the functional electrical stimulation acting on the user hm The relationship between them. The invention is suitable for the field of rehabilitation training.

Description

Functional electrical stimulation and lower limb exoskeleton intelligent distribution method, device, storage medium and system
Technical Field
The invention relates to a functional electrical stimulation and lower limb exoskeleton intelligent distribution method, a device, a storage medium and a system. Is applicable to the field of rehabilitation training.
Background
The population base of the disabled people in China is huge, and the number of the patients with the physical disabilities in China is further increased along with the further aggravation of the aging of the society. In the physically handicapped, only some amputees are present, and the rest are mostly acquired physical motor dysfunction patients, such as hemiplegia caused by stroke, paraplegia caused by spinal cord injury, etc., and these patients are very likely to recover the physical motor function through proper and effective rehabilitation training. Traditional rehabilitation training is mostly assisted by rehabilitation doctors to carry out passive training, wastes time and energy and has an insignificant curative effect.
At present, the rehabilitation medical resources in China are very short, and the number of professional rehabilitation institutions, equipment and rehabilitation doctors is far from meeting the increasing rehabilitation requirements. With the rapid development of artificial intelligence, medical techniques of robot-assisted rehabilitation are gradually popularized.
The exoskeleton equipment can provide larger moment assistance for a paralyzed patient, has good assistance effect on movements which are difficult to realize, such as the flexion of hip joints, and can relieve the pressure of medical personnel to a certain extent, but passively drives the affected limb to train through the robot, the training mode is single, the participation consciousness of the patient is low, and the recovery effect is limited.
Functional electrical stimulation is a nerve rehabilitation technology which utilizes artificial weak current pulse signals to stimulate muscle contraction of a patient and rebuild limb movement functions, and is beneficial to promoting blood circulation, preventing muscle disuse atrophy and improving muscle activity. But the use of functional electrical stimulation can cause muscle fatigue, which limits the duration of patient standing and walking activities; and for some long-term paralyzed patients, the torque generated by functional electric stimulation is insufficient, and the control is difficult.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in view of the above problems, a method, an apparatus, a storage medium and a system for functional electrical stimulation and intelligent allocation of lower extremity exoskeleton are provided.
The technical scheme adopted by the invention is as follows: a functional electrical stimulation and lower limb exoskeleton intelligent distribution method is characterized in that:
s01, obtaining bone dynamic characteristic parameters of a user;
s02, obtaining the angle, the angular speed and the angular acceleration of each joint of the lower limb of the user;
s03, determining that the motion stage of the lower limbs of the user is a swing stage or a support stage;
s04, inputting the bone dynamic characteristic parameters of the user and the angle, the angular velocity and the angular acceleration of each joint into the exoskeleton robot inverse dynamics model to obtain the moment L of each joint ext ；
S05, respectively determining the moment L of each joint according to the motion stage of the lower limb of the user ext (ii) a t and the moment L applied by a motion motor in the lower limb exoskeleton equipment d And a movement moment L applied by the user or caused by the functional electrical stimulation acting on the user hm The relationship between;
s06, acquiring the muscle state of the user, and obtaining the moment L of each joint based on S04 when the muscle fatigue of the user or the moment generated by the user is too small ext And S05 determining the moment L of each joint ext And L d And L hm The relationship (d) increases the output of the lower extremity exoskeleton device; reducing the output of the lower extremity exoskeleton device when the muscles of the user are capable of generating sufficient torque;
the exoskeleton robot inverse dynamics model is established according to skeleton dynamic parameters and the combination of the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the relation between the moment of each joint of the lower limb, and is obtained after walking training data training of a large number of healthy users.
The step of determining that the motion phase of the lower limbs of the user is a swing phase or a support phase comprises the following steps:
obtaining the sole pressure of a user;
when the sole pressure is zero, the lower limbs of the user are in a swing stage; when the sole pressure is not zero, the lower limb of the user is in a support stage.
The relationship between the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the moment of each joint of the lower limb comprises:
Figure BDA0002428912930000031
wherein
Figure BDA0002428912930000032
Respectively representing the angle, the angular velocity and the angular acceleration of each joint;
Figure BDA0002428912930000033
is a Coriolis item;
Figure BDA0002428912930000038
is an inertia matrix;
Figure BDA0002428912930000037
is a moment of gravity; l is p The expression of the passive moment for each target connection is as follows:
Figure BDA0002428912930000034
where θ is the anatomical joint angle, i.e., the angle between the location of interest and the anatomical location of the connected segments, θ 0 And d j (j =1, 2.., 6) is a normal number.
Moment L of each joint ext And L d And L hm Relation of (1), L ext ＝f(L d ，L hm ) The method comprises the following steps:
when in the swing phase:
L ext ＝L d +λL hm
wherein λ is a sensitivity parameter;
when in the support stage, fixed point control is carried out:
Figure BDA0002428912930000035
kp is proportional coefficient, kd is differential coefficient, G is gravity vector, q is proportional coefficient d 、
Figure BDA0002428912930000036
The angular velocity vector, the angular acceleration vector and the angular vector of the joint are adopted when the joint is at the fixed point during the fixed point control.
A functional electrical stimulation and lower limb exoskeleton intelligent distribution device is characterized in that:
the characteristic parameter acquisition module is used for acquiring the skeletal dynamics characteristic parameters of the user;
the data acquisition module is used for acquiring the angle, the angular speed and the angular acceleration of each joint of the lower limb of the user;
the stage determining module is used for determining that the motion stage of the lower limbs of the user is a swing stage or a support stage;
joint moment acquisition device for measuring the moment of a userInputting the bone dynamic characteristic parameters and the angle, the angular velocity and the angular acceleration of each joint into the exoskeleton robot inverse dynamics model to obtain the moment L of each joint ext ；
A mutual relation determining device for respectively determining the moment L of each joint according to the motion stage of the lower limb of the user ext Moment L applied by a motion motor in the lower limb exoskeleton equipment d And a movement moment L applied by the user or caused by the functional electrical stimulation acting on the user hm The relationship between;
an intelligent control module for acquiring muscle state of the user and obtaining torque L of each joint based on the joint torque acquisition device when muscle fatigue of the user or torque generated by the user is too small ext And the moment L of each joint determined by the correlation determination means ext And L d And L hm The relationship (d) increases the output of the lower extremity exoskeleton device; reducing the output of the lower extremity exoskeleton device when the muscles of the user are capable of generating sufficient torque;
the exoskeleton robot inverse dynamics model is established according to skeleton dynamic parameters and by combining the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the relation between the moment of each joint of the lower limb, and is obtained after the training of walking training data of a large number of healthy users.
The phase determination module comprises:
the pressure acquisition module is used for acquiring the sole pressure of the user;
the judgment module is used for enabling the lower limbs of the user to be in a swing stage when the sole pressure is zero; when the sole pressure is not zero, the lower limb of the user is in a support stage.
The relationship between the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the moment of each joint of the lower limb comprises:
Figure BDA0002428912930000041
wherein
Figure BDA0002428912930000042
Respectively representing the angle, the angular velocity and the angular acceleration of each joint;
Figure BDA0002428912930000043
is a Coriolis term;
Figure BDA0002428912930000045
is an inertia matrix;
Figure BDA0002428912930000046
is a moment of gravity; l is p The expression of the passive moment for each target connection is as follows:
Figure BDA0002428912930000044
where θ is the anatomical joint angle, i.e., the angle between the location of interest and the anatomical location of the connected segments, θ 0 And d j (j =1, 2.., 6) is a normal number.
Moment L of each joint ext And L d And L hm Relation of (1), L ext ＝f(L d ，L hm ) The method comprises the following steps:
when in the swing phase:
L ext ＝L d +λL hm
wherein λ is a sensitivity parameter;
when in the support stage, fixed point control is carried out:
Figure BDA0002428912930000051
kp is proportional coefficient, kd is differential coefficient, G is gravity vector, q is proportional coefficient d 、
Figure BDA0002428912930000052
The angle vector, the angular velocity vector and the angular acceleration vector of the joint at the fixed point in the fixed point control are adopted；
A storage medium having a computer program stored thereon for execution by a processor, the computer program comprising: the computer program when executed implements the steps of the method for functional electrical stimulation and intelligent allocation of lower extremity exoskeleton.
An apparatus, characterized by: the device comprises a functional electric stimulation module, a lower limb exoskeleton module, an intelligent control module, a signal acquisition module for acquiring the angle, the angular velocity and the angular acceleration of each joint of the lower limb of a user, and a muscle force sensor attached to the leg of the user for acquiring the muscle state of the user, wherein a pressure sensor is arranged on the sole of the lower limb exoskeleton module;
the intelligent control module has a processor and a memory, the memory having stored thereon a computer program executable by the processor, the computer program when executed implementing the steps of the method for intelligent assignment of functional electrical stimulation and lower extremity exoskeleton.
The invention has the beneficial effects that:
the exoskeleton device and the functional electric stimulation are coordinated and controlled, so that the defect of single use of the exoskeleton device and the functional electric stimulation is overcome, the feeling of a patient on the affected side muscle is enhanced, the active participation degree of the patient is increased, the reconstruction of the damaged nervous system is facilitated, and the rehabilitation training effect is improved.
The invention can timely adjust or suspend electrical stimulation to recover the muscle by monitoring the state of the affected muscle of the patient under functional electrical stimulation in real time. When the muscle fatigue of the patient or the moment generated by the patient is too small, the output of the exoskeleton is increased to drive the patient to move; when the patient's muscle can produce sufficient moment, then reduce the ectoskeleton output, reduce equipment power consumption, increase the duration and the continuity of single training.
According to the invention, by adopting a staged control method, the accuracy of tracking the movement locus of the joint is improved, and meanwhile, a user can drive the exoskeleton equipment without great force, so that the comfort level of the equipment is increased.
According to the invention, the exoskeleton device and the electrical stimulation device are controlled through an intelligent algorithm, parameters of the devices can be adjusted in time according to the motion and physiological conditions of the patient, the personalized training requirement of the user is met, and the patient has better training experience.
Drawings
Fig. 1 is a flowchart illustrating steps of the allocation method according to the present embodiment.
Fig. 2 is a block diagram of the distribution device according to the present embodiment.
Detailed Description
The embodiment is a method for intelligently distributing functional electrical stimulation and lower limb exoskeleton, which comprises the following steps:
and S01, acquiring the skeletal dynamics characteristic parameters of the user input by the doctor and data such as height, weight, autonomous muscle strength and the like of the patient.
The bone dynamic characteristic parameters comprise damping coefficient, stiffness, dullness coefficient, elasticity and the like; the height data is helpful for a doctor to help a patient to select the exoskeleton equipment with a proper size, so that the exoskeleton equipment is convenient to wear; the weight of the limb can be estimated according to the weight of the patient, and the weight vector G is determined; the self-muscle force can help doctors to set relevant parameters of functional electrical stimulation, and can also be compared before and after training to be used as the evaluation of training effect.
S02, acquiring the angle, the angular speed and the angular acceleration of each joint (hip joint, knee joint and the like) of the lower limb of the user.
S03, determining that the motion stage of the lower limbs of the user is a swing stage or a support stage;
s031, obtain user' S plantar pressure;
s032, when the sole pressure is zero, the lower limbs of the user are in a swing stage; when the sole pressure is not zero, the lower limb of the user is in a support stage.
In the embodiment, the toe-off is taken as a starting point, the heel landing is taken as an end point, the time period is a swing stage, and the swing stage is a motion stage that the sole of a foot is not in contact with the ground; the starting point is heel landing, the ending point is toe-off, the time period is a support stage, and the support stage is a movement stage in which the sole of the foot is in contact with the ground. In this example, the switching between the swing phase and the support phase is performed by the plantar pressure signal.
S04, obtaining the bone dynamic characteristic parameters of the userInputting the angle, the angular velocity and the angular acceleration of each joint into the exoskeleton robot inverse dynamics model to obtain the moment L of each joint ext (including hip and knee joints, etc.).
In the embodiment, the external skeletal robot inverse dynamics model is established according to skeletal dynamics parameters and by combining the relationship among the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the moment of each joint of the lower limb, and is obtained after the walking training data of a large number of healthy users are trained.
The lagrange-euler equations in this example can be written as an expression of the exoskeleton dynamics equation as follows:
Figure BDA0002428912930000071
wherein
Figure BDA0002428912930000072
The angle, angular velocity, and angular acceleration of each joint are represented.
Figure BDA0002428912930000073
Is a Coriolis term that is,
Figure BDA0002428912930000075
is a matrix of the inertia, and,
Figure BDA0002428912930000076
is the moment of gravity, L p Is the passive moment of each target connection, and the expression is as follows:
Figure BDA0002428912930000074
where θ is the anatomical joint angle, i.e., the angle between the location of interest and the anatomical location of the connected segments, θ 0 And d j (j =1, 2.., 6) is a normal number.
L in the formula (1) ext The torque applied to the system from the outside can be expressed as follows:
L ext ＝f(L d ，L hm ) (3)
wherein L is d Is the moment applied by a motor in the lower limb exoskeleton, L hm Is the user applied torque (or the movement torque caused by functional electrical stimulation).
In the embodiment, an inverse dynamics model of the exoskeleton robot is established according to the skeleton dynamic characteristic parameters of a normal person and by combining with an exoskeleton system according to the formula (1), and the model is trained by combining a neural network with collected data of walking training of hundreds of healthy users wearing exoskeleton equipment.
After a model is trained by a neural network, when the angle, the angular velocity and the angular acceleration of the joint (including hip joint, knee joint and the like) of a patient are input, the system outputs the moment L of each joint ext 。
S05, respectively determining the moment L of each joint according to the motion stage of the lower limb of the user ext Moment L applied by a motion motor in the lower limb exoskeleton equipment d And a movement moment L applied by the user or caused by the functional electrical stimulation acting on the user hm The relationship between them.
The formula (3) assignment is particularly differentiated according to the support phase and the swing phase, which are switched according to the plantar pressure:
when in the swing phase:
L ext ＝L d +λL hm (4)
where λ is the sensitivity parameter.
When in the support stage, fixed point control is carried out:
Figure BDA0002428912930000081
kp in the formula (5) is a proportionality coefficient, kd is a differential coefficient, G is a gravity vector, q is a gravity vector d 、
Figure BDA0002428912930000082
For joints at fixed points when using fixed-point controlAn angular velocity vector, and an angular acceleration vector.
In the stage, by means of the formula 3, the exoskeleton can be controlled by a small moment only by utilizing the sensitivity coefficient in the swing stage, and the comfort degree of the user in the training process is enhanced.
S06, acquiring muscle states of the user, and obtaining moments L of all joints based on S04 when the muscles of the user are tired or the moments generated by the user are too small ext And S05 determining the moment L of each joint ext And L d And L hm The relationship (d) increases the output of the lower extremity exoskeleton device; the output of the lower extremity exoskeleton device is reduced when the muscles of the user are able to generate sufficient torque.
When a patient is in muscle fatigue under electrical stimulation, the muscle force sensor-based device can prompt in time, so that the electrical stimulation parameters are adjusted or the electrical stimulation equipment is suspended to recover the muscles. And when the muscle fatigue stage or the moment generated by the patient is too small, the output of the exoskeleton is controlled to be increased, and the affected limb of the patient is continuously driven to move. When the patient can generate enough movement torque under the electric stimulation, the system can control and reduce the output of the exoskeleton, reduce the power consumption of equipment, enhance the active participation degree of the patient and prolong the training time.
The embodiment also provides an intelligent distribution device for functional electrical stimulation and lower limb exoskeleton, which comprises a characteristic parameter acquisition module, an information acquisition module, a stage determination module, a joint moment acquisition device, a mutual relation determination device and an intelligent control module.
In the embodiment, the characteristic parameter acquisition module is used for acquiring the bone dynamic characteristic parameters of the user; the information acquisition module is used for acquiring the angle, the angular speed and the angular acceleration of each joint of the lower limb of the user; the stage determining module is used for determining that the motion stage of the lower limbs of the user is a swing stage or a support stage; the joint moment acquisition device is used for inputting the bone dynamic characteristic parameters of the user and the angle, the angular velocity and the angular acceleration of each joint into the exoskeleton robot inverse dynamics model to obtain the moment L of each joint ext (ii) a The mutual relation determining device is used for respectively determining each user lower limb according to the motion stage of the user lower limbMoment L of joint ext Moment L applied by a motion motor in the lower limb exoskeleton equipment d And a movement moment L applied by the user or caused by the functional electrical stimulation acting on the user hm The relationship between; the intelligent control module is used for acquiring the muscle state of the user, and when the muscle fatigue of the user or the moment generated by the user is too small, the moment L of each joint acquired by the joint moment acquisition device ext And the moment L of each joint determined by the correlation determination device ext And L d And L hm The relationship (d) increases the output of the lower extremity exoskeleton device; the output of the lower extremity exoskeleton device is reduced when the muscles of the user are able to generate sufficient torque.
The present embodiment also provides a storage medium having a computer program stored thereon for execution by a processor, wherein the computer program is configured to implement the steps of the method for intelligent allocation of functional electrical stimulation and lower extremity exoskeleton of the present embodiment.
The embodiment also provides a device, which comprises a functional electric stimulation module, a lower limb exoskeleton module, a signal acquisition module, an intelligent control module, a muscle force sensor and a pressure sensor, wherein the muscle force sensor acquires the muscle state of a user; the pressure sensor is arranged on the sole of the lower limb exoskeleton module to acquire the pressure of the sole of the user.
In this example, the signal acquisition module further includes one or more of a camera, an angle sensor, a velocity sensor, an acceleration sensor, a microwave doppler sensor, an ultrasonic motion detector, an infrared motion sensor, a photoelectric encoder, a pressure sensor, a muscle strength sensor, and a moment sensor to acquire user data, where the user data includes motion data such as joint angle, angular acceleration, step width step length, gait time period, gravity center inclination angle mechanics data, action moment, gravity moment, and physiological data such as pressure and muscle strength.
The intelligent control module in this embodiment has a processor and a memory, and the memory stores a computer program executable by the processor, and the computer program when executed implements the steps of the method for intelligently allocating the functional electrical stimulation and the lower extremity exoskeleton in this embodiment.

Claims (10)

1. A functional electrical stimulation and lower limb exoskeleton intelligent distribution method is characterized in that:
s01, obtaining bone dynamic characteristic parameters of a user;
s02, obtaining the angle, the angular speed and the angular acceleration of each joint of the lower limb of the user;
s03, determining that the motion stage of the lower limbs of the user is a swing stage or a support stage;
s04, inputting the bone dynamic characteristic parameters of the user and the angle, the angular velocity and the angular acceleration of each joint into the exoskeleton robot inverse dynamics model to obtain the moment L of each joint ext ；
S05, respectively determining the moment L of each joint according to the motion stage of the lower limb of the user ext Moment L applied by a motion motor in the lower limb exoskeleton equipment d And a movement moment L applied by the user or caused by the functional electrical stimulation acting on the user hm The relationship between;
s06, acquiring the muscle state of the user, and obtaining the moment L of each joint based on S04 when the muscle fatigue of the user or the moment generated by the user is too small ext And S05 determining the moment L of each joint ext And L d And L hm The relationship (d) increases the output of the lower extremity exoskeleton device; reducing the output of the lower extremity exoskeleton device when the muscles of the user are capable of generating sufficient torque;
the exoskeleton robot inverse dynamics model is established according to skeleton dynamic parameters and by combining the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the relation between the moment of each joint of the lower limb, and is obtained after the training of walking training data of a large number of healthy users.
2. The method of claim 1, wherein the method comprises the steps of: the step of determining that the motion phase of the lower limbs of the user is a swing phase or a support phase comprises the following steps:
obtaining the sole pressure of a user;
when the sole pressure is zero, the lower limbs of the user are in a swing stage; when the sole pressure is not zero, the lower limb of the user is in a support stage.
3. The method of claim 1, wherein the method comprises the steps of: the relationship between the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the moment of each joint of the lower limb comprises:
Figure FDA0002428912920000021
wherein
Figure FDA0002428912920000022
Respectively representing the angle, the angular velocity and the angular acceleration of each joint;
Figure FDA0002428912920000023
is a Coriolis item;
Figure FDA0002428912920000024
is an inertia matrix;
Figure FDA0002428912920000025
is a moment of gravity; l is p The expression of the passive moment for each target connection is as follows:
Figure FDA0002428912920000026
where θ is the anatomical joint angle, i.e., the angle between the location of interest and the anatomical location of the connected segments, θ 0 And d j (j =1, 2.., 6) is a normal number.
4. The method of claim 3 for intelligent distribution of functional electrical stimulation and lower extremity exoskeletonThe method is characterized in that: moment L of each joint ext And L d And L hm Relation of (1), L ext ＝f(L d ，L hm ) The method comprises the following steps:
when in the swing phase:
L ext ＝L d +λL hm
wherein λ is a sensitivity parameter;
when in the support stage, fixed point control is carried out:
Figure FDA0002428912920000027
kp is proportional coefficient, kd is differential coefficient, G is gravity vector, q is proportional coefficient d 、
Figure FDA0002428912920000028
The angular velocity vector, the angular acceleration vector and the angular vector of the joint are adopted when the joint is at the fixed point during the fixed point control.
5. A functional electrical stimulation and lower limb exoskeleton intelligent distribution device is characterized in that:
the characteristic parameter acquisition module (1) is used for acquiring the bone dynamic characteristic parameters of a user;
the data acquisition module (2) is used for acquiring the angle, the angular speed and the angular acceleration of each joint of the lower limb of the user;
the stage determining module (3) is used for determining that the motion stage of the lower limbs of the user is a swing stage or a support stage;
a joint moment acquisition device (4) for inputting the bone dynamic characteristic parameters of the user and the angle, the angular velocity and the angular acceleration of each joint into the exoskeleton robot inverse dynamics model to obtain the moment L of each joint ext ；
A mutual relation determination device (5) for determining the moment L of each joint according to the movement phase of the lower limb of the user ext Moment L applied by a motion motor in the lower limb exoskeleton equipment d And the userMovement moment L applied to or caused by functional electrical stimulation on the user hm The relationship between them;
an intelligent control module (6) for acquiring the muscle state of the user and obtaining the moment L of each joint based on the joint moment acquisition device (4) when the muscle of the user is tired or the moment generated by the user is too small ext And the moments L of the joints determined by the correlation determination means (5) ext And L d And L hm The relationship (d) increases the output of the lower extremity exoskeleton device; reducing the output of the lower extremity exoskeleton device when the muscles of the user are capable of generating sufficient torque;
the exoskeleton robot inverse dynamics model is established according to skeleton dynamic parameters and the combination of the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the relation between the moment of each joint of the lower limb, and is obtained after walking training data training of a large number of healthy users.
6. The device of claim 5 wherein: the phase determination module (3) comprises:
the pressure acquisition module (301) is used for acquiring the sole pressure of a user;
the judgment module (302) is used for enabling the lower limbs of the user to be in a swing stage when the sole pressure is zero; when the sole pressure is not zero, the lower limb of the user is in a support stage.
7. The method of claim 5, wherein the method comprises the steps of: the relationship between the angle, the angular velocity and the angular acceleration of each joint of the lower limb and the moment of each joint of the lower limb comprises:
Figure FDA0002428912920000031
wherein
Figure FDA0002428912920000032
Respectively representing the angle, angular velocity and angular acceleration of each joint;
Figure FDA0002428912920000033
is a Coriolis item;
Figure FDA0002428912920000034
is an inertia matrix;
Figure FDA0002428912920000035
is a moment of gravity; l is p The expression of the passive moment for each target connection is as follows:
Figure FDA0002428912920000036
where θ is the anatomical joint angle, i.e., the angle between the location of interest and the anatomical location of the connected segments, θ 0 And d j = (j =1,2,. 6) is a normal number.
8. The method of claim 7, wherein the method comprises the steps of: moment L of each joint ext And L d And L hm Relation of (1), L ext ＝f(L d ，L hm ) The method comprises the following steps:
when in the swing phase:
L ext ＝L d +λL hm
wherein λ is a sensitivity parameter;
when in the support stage, fixed point control is carried out:
Figure FDA0002428912920000041
kp is proportional coefficient, kd is differential coefficient, G is gravity vector, q is proportional coefficient d 、
Figure FDA0002428912920000042
The angular velocity vector, the angular acceleration vector and the angular vector of the joint are adopted when the joint is at the fixed point during the fixed point control.
9. A storage medium having a computer program stored thereon for execution by a processor, the computer program comprising: the computer program when executed implements the steps of the method of claims 1-4 for smart distribution of functional electrical stimulation and lower extremity exoskeleton.
10. An apparatus, characterized by: the device comprises a functional electric stimulation module, a lower limb exoskeleton module, an intelligent control module (6), a signal acquisition module for acquiring the angle, the angular velocity and the angular acceleration of each joint of the lower limb of a user, and a muscle force sensor attached to the leg of the user for acquiring the muscle state of the user, wherein a pressure sensor is arranged at the sole of the lower limb exoskeleton module;
the intelligent control module (6) has a processor and a memory, the memory having stored thereon a computer program for execution by the processor, the computer program when executed implementing the steps of the method for intelligent allocation of functional electrical stimulation and lower extremity exoskeleton of any of claims 1 to 4.
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