CN117771631A - Rehabilitation training stepping machine based on intelligent terminal controller - Google Patents
Rehabilitation training stepping machine based on intelligent terminal controller Download PDFInfo
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Abstract
The invention discloses a rehabilitation training stepping machine based on an intelligent terminal controller, which comprises a rack, wherein two groups of driven controllable intelligent terminal controllers are arranged on the rack, a weight reducing mechanism for lifting the upper body of a patient is arranged on the rack, and a fall preventing protection device is arranged on the rack; the outside of the frame is provided with an interactive display screen and an electric cabinet component. The track of the intelligent terminal controller is obtained by adopting a DH parameter algorithm based on each joint angle curve of the standard gait. The intelligent end controller can create a transformation matrix by adopting DH parameters, coordinate systems of two adjacent joints are transformed mutually, and the transformation matrix of each joint is multiplied to calculate the positive kinematics of the whole joint chain, so that the relation between the movement of the joint and the position of the end controller is analyzed. The six-dimensional force sensor is combined with an improved DH parameter algorithm to form a lower limb walking training rehabilitation system, so that ankle joint, knee joint and hip joint rehabilitation training is driven, the walking mode of a patient is closer to a natural state, and the rehabilitation effect is better.
Description
Technical Field
The invention belongs to the technical field of rehabilitation instruments, and relates to a rehabilitation training stepping machine based on an intelligent terminal controller.
Background
Hemiplegic patients with nerve or bone damage face gait disturbances and motor function recovery challenges during rehabilitation. Traditional rehabilitation training mode is limited to manual operation, is difficult to satisfy patient's individualized demand, and the progress is slow. Therefore, the development of the intelligent robot auxiliary gait training system has important significance. In recent years, this technology has been greatly improved in cooperation with leading clinics and universities. Robotic adjuvant therapy has become an indispensable tool in neurological rehabilitation today. Numerous studies confirm clinical evidence of end effector-based gait therapies compared to traditional therapies. In clinics and medical practice, the use of gait trains greatly improves gait rehabilitation efficiency, greatly reduces costs, reduces the workload of therapists, and helps patients walk independently more effectively and purposefully.
There are two therapeutic methods in the field of gait rehabilitation:
1. the exoskeleton and the running platform are utilized to drive the joints of the lower limbs of the patient from top to bottom (from the hip joint to the ankle joint) to perform rehabilitation training (such as a Lokomat system);
2. adopts the design principle of an end effector to drive an ankle joint, a knee joint and a hip joint from bottom to top so as to carry out rehabilitation training (such as a Reha system).
Compared with an exoskeleton rehabilitation system, the Reha-like system is more in line with the physiological characteristics of lower limb walking, has a better rehabilitation effect, and is widely applied.
End effector design principle: the ankle joint plays a critical role in walking of lower limbs, and the first power of walking of a human body is from friction force generated by the ankle joint and the ground to push the body to advance. The end drive design helps the patient to actively exert force while swinging, while active motion is a key factor in achieving gait while also achieving positional sensation. The end driving function is equivalent to the lever principle, can maximally mobilize tissues such as muscles, fascia, nerves, bones and blood vessels participating in exercise, and compared with an exoskeleton robot, can maximally mobilize body functions, has the advantages of being more in line with the walking characteristics of human closed-chain exercise, has higher degrees of freedom for hip and knee joints of a patient, and is beneficial to gait rehabilitation training of the patient.
The end effector used on the existing stepping machine is used for controlling the ankle, and some of the end effector only adopts a pressure sensor to collect force between the foot and the pedal, so that the ankle is not accurately controlled in multiple directions. How to effectively process and analyze the large amount of data collected from the patient to extract meaningful features is critical to improving rehabilitation performance. Conventional data processing methods often have difficulty adapting to individual needs of different patients and complex and changeable rehabilitation environments, and therefore a data processing and feature extraction system based on force feedback and adaptive algorithms is needed.
Accordingly, there is a need for improvements in the art that overcome the shortcomings of the prior art.
Disclosure of Invention
The invention aims to provide an intelligent rehabilitation training walking machine based on force feedback and a self-adaptive algorithm, which has the capability of data processing and feature extraction, can automatically process and analyze data collected from a patient, extract features closely related to a rehabilitation process, and provide accurate data input for subsequent model training and self-adaptive adjustment.
The invention aims at realizing the following technical scheme:
the rehabilitation training stepping machine based on the intelligent terminal controllers comprises a rack, wherein two groups of driven controllable intelligent terminal controllers are arranged on the rack, and a weight reducing mechanism for lifting the upper body of a patient is arranged on the rack; the frame is provided with a fall-preventing protection device for preventing the gravity center of the patient from falling unstably during training; an interactive display screen and an electric cabinet assembly are arranged on the outer side of the frame;
the intelligent terminal controller comprises an adjusting plate and a supporting plate, a six-dimensional force sensor is arranged between the adjusting plate and the supporting plate, and a gyroscope is arranged on the adjusting plate; the adjusting plate can translate, lift and rotate under the drive of the driving mechanism;
the track of the intelligent terminal controller is obtained by DH parameter algorithm according to each joint angle curve of standard gait. The method comprises the steps of creating a human lower limb joint transformation matrix, wherein DH parameters are as follows:
defining a coordinate system: each joint (hip, knee, ankle) has a coordinate system describing the rotation and translation of the joint;
distributing DH parameters: four DH parameters are distributed to each joint, and the DH parameters are respectively:
a: translation distance of joint along x-axis of previous joint;
alpha: rotation angle of the joint about the previous joint x-axis;
d: a translation distance of the joint along the current joint z-axis;
θ: rotation angle of the joint about the current joint z-axis;
the coordinate systems of two adjacent joints are mutually converted (namely, the conversion matrix of each joint is multiplied), so that the positive kinematics of the whole joint chain can be calculated, and the relation between the movement of the joint and the position of the tail end controller is analyzed; the calculation formula of the transformation matrix T is as follows:
where a, α, d and θ are DH parameters for each joint, respectively.
Furthermore, the six-dimensional force sensor is placed right below the ankle joint, so that the force applied to the foot step of a patient can be conveniently measured when the patient walks, and data can be provided for an active training mode.
In one embodiment, the support is provided with a detachable height-adjustable knee support. When the leg supporting force of the patient is weak, the knee support is additionally arranged, so that the bending angle of the bare joint can be prevented from being excessively large.
Furthermore, the intelligent terminal controller adopts incremental PID position control through the servo motor at the ankle part, and the calculation formula is as follows:
Δu(t)＝k p (e(t)-e(t-1))+k i e(t)+k d (e(t)-2e(t-1)+e(t-2))
wherein: deltau is the increment of the control quantity at the time t; e (t) is the difference between the expected angle and the actual angle at time t; e (t-1) is the difference between the desired angle and the actual angle at time t-1; e (t-2) is the difference between the desired angle and the actual angle at time t-2; k (k) p ，k i ，k d Is an adjustable parameter.
Furthermore, the walking track control of the intelligent terminal controller is realized by adopting a self-adaptive impedance model algorithm through a translation servo motor and a vertical servo motor:
our system can be expressed by the following formula:
wherein f represents the force output by the motor;
the change formula is
F in the formula e Can be obtained from a six-dimensional force sensor;
the difference between the desired position and the actual position can be obtained from the motor of the system;
is->Is a first order derivative of (a);
is->Is a second derivative of (c).
M self-adaptive positive fixed virtual mass, B self-adaptive damping, K self-adaptive rigidity as self-adaptive parameter, its self-adaptive formula is
M＝m+α m *f
B＝b+α b *f
K＝k+α k *f
Wherein m, b, k are impedance coefficients, α m ，α b ，α k And f is the total force on the six-dimensional force sensor and is the weight coefficient of the self-adaptive parameter.
Furthermore, the driving mechanism comprises a translation driving mechanism, a lifting driving mechanism and a rotation driving mechanism, the adjusting plate swings under the drive of the rotation driving mechanism to overturn to simulate ankle movement, the translation driving mechanism can drive the lifting driving mechanism to move back and forth integrally, and the lifting driving mechanism can drive the rotation driving mechanism and the adjusting plate to move up and down integrally.
Further, a tension sensor for monitoring the tension of the rope and a stay wire encoder for sensing the actual extension length of the rope are arranged in the weight reducing mechanism.
Further, the fall-preventing protection device comprises a lifting handrail mechanism, a waist support mechanism and an emergency stop button; one end of the waist support mechanism is pivotally connected to the frame, a hollow cavity is formed in the waist support mechanism, a buffering component for absorbing vibration in three axial directions is arranged at the hollow cavity, and a lifting component for driving the buffering component to vertically move up and down is arranged on the waist support bracket; the buffer assembly is provided with a first supporting plate, a floating supporting plate is arranged on the outer side of the first supporting plate, the outer side face of the floating supporting plate is of a profiling structure, and a universal buffer assembly is arranged between the first supporting plate and the floating supporting plate.
Furthermore, a wheelchair docking platform is arranged on the stand. With waist support mechanism cooperation, the patient can take the wheel to remove to the exercise position of rehabilitation training machine of taking a step based on intelligent terminal controller under the attendant of outsider, makes things convenient for the patient to take a step based on intelligent terminal controller's rehabilitation training machine of taking a step from top to bottom fast and conveniently.
By adopting the technical scheme, the method has the following beneficial effects:
1. rehabilitation training stepping machine based on intelligent terminal controller possesses passive training, initiative training and impedance training mode. The passive training mode is suitable for patients who cannot take steps, the active training mode is suitable for patients who can take steps but take steps with abnormal postures, and the impedance training mode is suitable for patients with standard taking steps and insufficient strength. Different training modes are adopted for patients in different stages, so that the training requirements of different patients can be met.
2. The rehabilitation training stepping machine based on the intelligent terminal controller adopts a force feedback and self-adaptive impedance model algorithm, and the innovative design has remarkable advantages in improving the rehabilitation training effect of patients with walking impairment:
firstly, the motion of a patient and the interaction force with the ground are monitored in real time through a six-dimensional force sensor, and the system can rapidly capture the dynamic change of the patient and provide accurate feedback data for subsequent control.
The introduction of force feedback enables the rehabilitation training to be closer to the normal physiological function of human walking, and the patient can feel the resistance and the reaction force on the ground in the simulated walking process, so that the activity of muscle groups is better stimulated. The simulated feeling not only improves the mobility of rehabilitation training, but also helps patients quickly recover the natural control ability of leg joints and muscles.
Secondly, an intelligent terminal controller adopting an adaptive impedance model algorithm can be adjusted in real time according to individual differences and rehabilitation progress of patients. Such a personalized rehabilitation regimen helps to more effectively meet the specific needs of the patient, providing a training program that more closely conforms to their rehabilitation process. The self-adaptive impedance model algorithm enables the system to be more flexibly adapted to the physiological state of the patient, so that the rehabilitation training effect is improved to the greatest extent.
In general, the intelligent end controller adopting the force feedback and adaptive impedance model algorithm not only provides a more realistic rehabilitation experience, but also realizes personalized rehabilitation training according to individual differences of patients. The application of the technology creates a rehabilitation environment which is more close to the natural state for patients with walking disorder, and is hopeful to enhance the rehabilitation power of the patients while improving the rehabilitation effect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the invention, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present invention, should fall within the ambit of the technical disclosure.
Fig. 1 is a schematic perspective view of a usage state according to the present invention.
Fig. 2 is a schematic structural diagram of a main body in a first three-dimensional state according to the present invention.
Fig. 3 is a schematic structural diagram of a main body in a second three-dimensional state according to the present invention.
Fig. 4 is a schematic diagram of a first state structure of an intelligent end controller according to the present invention.
Fig. 5 is a schematic diagram of a second state structure of the intelligent end controller according to the present invention.
Fig. 6 is a schematic perspective view of a local intelligent end controller according to the present invention.
Fig. 7 is a schematic structural diagram of a weight-reducing mechanism according to the present invention.
Fig. 8 is a schematic diagram of a combined structure of a lifting handrail mechanism and a waist support mechanism provided by the invention.
Fig. 9 is a schematic diagram of a combination structure of a lumbar support mechanism according to the present invention.
Fig. 10 is a schematic diagram of a position control of an intelligent end controller according to the present invention.
Fig. 11 is a schematic diagram of impedance control according to the present invention.
In the figure: 1-a frame; 10a, 10 b-intelligent end controllers; 101-a support; 102-knee support; 103-six-dimensional force sensor; 104-adjusting plates; 105-a rotating electric machine; 106-a gyroscope; 107-supporting plates; 108-a translation drive mechanism; 109-lifting drive mechanism; 11-a weight-reduction mechanism; 110-rope; 111-a weight-reducing motor; 112-winding drum; 113-pulley mounting plate; 114-a tension sensor; 115-an adapter; 116-pulley means; 117-tension bracket; 118-a pull-wire encoder; 12-a wheelchair docking platform; 13-a lifting handrail mechanism; 131-an electric push rod; 132-a first carrier plate; 133-a first guide plate; 134-a guide bar; 135-a guide rod fixing plate; 136-linear bearings; 137-arm rest carrier plate; 138-armrests; 14-a lumbar support mechanism; 141-a lumbar support bracket; 142-a cushioning assembly; 143-a lifting assembly; 144-a first support plate; 145-floating support plates; 146-a first spring; 15-an interactive display screen; 16-an electric cabinet assembly; 17-patient; 18-an operation panel.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings. The description of these embodiments is provided to assist understanding of the present invention, but is not intended to limit the present invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Examples
Referring to fig. 1-3, a rehabilitation training stepping machine based on an intelligent end controller includes a frame 1, two groups of driven controllable intelligent end controllers 10a and 10b are arranged on the frame 1, the two intelligent end controllers 10a and 10b are identical in structure, and respectively aim at a left foot and a right foot of a patient 17, and support members, such as pedals with straps, are respectively arranged on the two intelligent end controllers, so that the feet of the patient 17 can be fixed in the corresponding support members (the support members can be also called as end effectors). The pedal can translate, lift and rotate under the drive of the corresponding driving mechanism, so that the simulation of all the steps of the walking machine is realized, and various motion scenes such as flat ground, upstairs, downstairs, upslopes, downslopes and the like are simulated.
The frame 1 is provided with a weight-reducing mechanism 11 for lifting the upper body of the patient and reducing the force born by the lower limbs of the patient.
The wheelchair docking platform 12 is arranged on the frame 1, so that a patient can move to the exercise position of the stepping machine under the accompanying of outsiders by taking wheels, and the patient can conveniently and quickly go up and down the stepping machine.
The lifting handrail mechanism 13 is arranged on the frame 1, the height of the handrail can be adjusted for different patients, an impetus is provided for the patient during training, and falling caused by unstable gravity center during the patient training is prevented.
The waist support mechanism 14 is arranged on the frame 1, can be clung to a patient after the patient gets on the machine, provides stable waist support, and meanwhile, the waist support plate on the waist support mechanism can move along with the up-and-down movement of the waist of the patient, so that the training comfort is improved.
The outside of the frame 1 is provided with an interactive display screen 15 for displaying game pictures specially developed with a motion system during training, realizing immersion training of patients, improving the comfort level of the experience of the patients and being interesting.
An electric cabinet assembly 16 is arranged on the outer side of the frame 1 and is a complete machine control system of the walking machine, and the electric cabinet is independent and is easy to maintain and transport. An intelligent evaluation system and a rehabilitation management system are arranged in the electric cabinet assembly 16, so that the rehabilitation condition of a patient is quantitatively evaluated, and the rehabilitation condition of the patient is conveniently known; there is a rehabilitation profile for each patient, including systematic assessment, training records, training programs, etc., which allows for a more comprehensive and systematic understanding of the patient.
Specifically, since the two intelligent end controllers 10a, 10b have the same structure, one of the intelligent end controllers 10a is taken as an example for convenience of description.
Referring to fig. 4 to 6, the intelligent terminal controller 10a includes a support 101, a knee support 102 is provided on the support 101, and the knee support 102 adopts a detachable structure, and can adjust the height up and down, so as to satisfy patients with different types and heights. The knee support 102 can prevent the bare joint from being bent too much when the leg support force of the patient is weak.
A six-dimensional force sensor 103 is arranged below the support 101, and the six-dimensional force sensor 103 is mounted on an adjusting plate 104. When the six-dimensional force sensor 103 is installed, the placement position of the six-dimensional force sensor 103 is installed right below the ankle joint, and the force condition of the foot step of a patient when the patient walks can be measured, so that data is provided for the impedance model.
The drive mechanism includes a translational drive mechanism 108, a lifting drive mechanism 109, and a rotational drive mechanism. The rotary driving mechanism comprises a bare joint rotating shaft fixed on the adjusting plate 104, the bare joint rotating shaft is connected with the rotating motor 105 through a coupler, and the adjusting plate 104 can be turned up and down under the driving of the rotating motor 105. In order to improve the accuracy of deflection of the adjustment plate 104 and make it better simulate ankle movements, a gyroscope 106 is mounted on the adjustment plate 104.
The rotary motor 105 is fixedly mounted on a support plate 107, and the support plate 107 can move up and down in the Y-axis direction by the drive of the lift drive mechanism 109. The lift drive 109 is mounted on the translation drive 108 such that the lift drive 109 can move back and forth along the X-axis direction. The translation driving mechanism 108 comprises a translation bracket, screw rod assemblies distributed in the X-axis direction are arranged on the translation bracket, screw rods of the screw rod assemblies are translation shafts, and driving ends of the translation shafts are connected with a translation servo motor through a coupler; the lifting driving mechanism 109 comprises a lifting support, a screw rod assembly distributed in the Y-axis direction is arranged on the lifting support, a screw rod of the screw rod assembly is a lifting shaft, and a driving end of the lifting shaft is connected with a vertical servo motor through a coupler. The vertical servo motor, the translation servo motor, the rotating motor, the six-dimensional force sensor 103 and the gyroscope 106 are all connected with the electric cabinet assembly 16, so that the intelligent terminal controller can automatically operate.
The intelligent terminal controller in the embodiment is a core protection point of the invention, and can capture the real-time state of the ankle joint. And according to the ankle angle curve of the normal gait, the ankle posture during walking is fitted in real time.
The intelligent end controller adopts position PID control:
in order to simplify the operation and accelerate the response speed, and meanwhile, the ankle simulator has no great requirement on the steady-state error of the speed, an incremental PID control mode is adopted, and a control schematic diagram is shown in fig. 10.
The calculation formula is as follows:
wherein:
θ is a desired angle, and the desired angle at each moment is obtained according to the ankle angle in the standard gait cycle; θ d Is the actual angle;
e (t) is t time, theta-theta d A difference between; u is the output control quantity; k (k) p Is a proportional gain; k (k) i Is the integral gain; k (k) d Is a differential gain.
In actual operation, we discretize this formula and simplify it to get the incremental PID formula:
Δu(t)＝k p (e(t)-e(t-1))+k i e(t)+k d (e(t)-2e(t-1)+e(t-2))
deltau is the increment of the control quantity at the time t;
e (t) is the difference between the expected angle and the actual angle at time t;
e (t-1) is the difference between the desired angle and the actual angle at time t-1;
e (t-2) is the difference between the desired angle and the actual angle at time t-2.
Wherein k is p ，k i ，k d K is an adjustable parameter in our system i The size of the small size of the adjustable,k d the adjustment is relatively large. Better effect can be obtained.
When the stepping machine is started, after a doctor selects a patient, the stepping machine terminal requests patient data to the server, including a sport prescription prescribed by the doctor. The step-by-step machine helps the patient to complete given training according to the setting of the exercise prescription, records training data, and transmits the training data to the server for storage after the training is completed.
In the active training mode, the stepping mechanism is characterized in that the sole of a person is fixed on the pedal through the binding belt, the six-dimensional force sensor can monitor the direction of resultant force vectors in the stepping process of the person in real time and feed back the directions to the control system, the translation servo motor and the vertical servo motor can read the specific positions of the pedal, the gyroscope can detect the actual rotation angle of the pedal, the rotating motor reads the deflection angle information of the pedal, and the three shafts cooperate, so that two intelligent terminal controllers on the stepping machine can move in cooperation with the action of the human body, and self-adaptive training is realized. So that the device has a compliant, natural motion experience.
The rehabilitation training stepping machine based on the intelligent terminal controller in the embodiment realizes various movement scenes: flat ground, upstairs, downstairs, upslope, downslope. The device for going upstairs and downslope is realized, and the control of the lifting shaft and the shifting shaft in the three shafts is determined by the position x in the step (1). The ankle rotation axis is determined by the ankle angle curve of a standard gait.
The weight-reducing mechanism 11 adopts a motor active dynamic weight-reducing system. The system can continuously provide the weight reducing moment according to the fluctuation of the human body. Referring to fig. 7, the weight reducing mechanism 11 mainly includes a weight reducing tension assembly, a gravity detecting assembly, and a wire encoder. Specifically, the weight-reducing tension assembly includes a weight-reducing motor 111 (the weight-reducing motor employs a servo motor), the weight-reducing motor 111 drives a winding drum 112 to rotate, and the rope 110 is wound on the winding drum 112. The weight-reducing motor 111 is mainly responsible for winding up, controlling the length of the rope at any time and adjusting the tension to a stable constant value.
The gravity detection assembly comprises a tension sensor 114, one end of the tension sensor 114 is fixed with the frame 1 through a connecting piece 115, and the other end of the tension sensor 114 is connected with a pulley mounting plate 113. Wherein, in order to make the pulley mounting plate 113 move smoothly, two sides of the tension sensor 114 are provided with a guide sleeve assembly, wherein, the guide sleeve is fixed on the connecting piece 115, and one end of a guide rod in the guide sleeve is fixed on the pulley mounting plate 113. A pulley arrangement 116 is provided on the pulley mounting plate 113, around which pulley arrangement 116 the rope 110 is wound. The gravity detection assembly is formed by switching the tension of the rope to the tension sensor 114 through a pulley device, detecting the tension of the rope in real time, and transmitting data to the weight-reducing motor 111 in time to form a detection closed loop. The motor for reducing weight with too small pulling force tightens the rope, and the motor for reducing weight with too large pulling force loosens the rope appropriately according to the requirement.
The tension bracket 117 is exposed outside the frame 1, is a support for lifting the upper body of the patient, corresponds to the special clothes worn by the patient, and can be fixedly connected with the patient. One end of the rope 110 is mounted on a tension bracket 117. In order to detect the actual extension length of the rope in real time and detect the actual position of the patient, a stay wire encoder 118 is further arranged on the stand 1, the rope 110 passes through the stay wire encoder 118, the stay wire encoder 118 senses the actual extension length of the rope, and the patient is guaranteed to perform rehabilitation treatment in a safe training interval.
In order to prevent the patient from falling down, the walking machine is provided with a falling-down prevention protection device, so that the patient can walk safely. The fall-preventing protection device in the embodiment mainly comprises a lifting handrail mechanism 13, a waist support mechanism 14 and an emergency stop button.
Referring to fig. 8 to 9, the lifting handrail mechanism 13 is composed of handrail components which are respectively arranged at two sides of the frame 1, and the two groups of handrail components have the same structure and respectively correspond to the left hand and the right hand of a patient, and for convenience of description, one of the handrail components will be described. The armrest assembly includes an electric pushrod 131 vertically installed on the frame, and a lower end of the electric pushrod 131 is connected with a first bearing plate 132. In order to make the first bearing plate 132 stably run up and down, a first guide plate 133 is provided on the frame, a through hole for the electric push rod 131 to pass through is provided on the first guide plate 133, two linear bearings 136 are provided on the first guide plate 133, and the two linear bearings 136 are located at two sides of the electric push rod 131. Guide rods 134 are arranged in the two linear bearings, one end of each guide rod 134 is fixed on the first bearing plate 132, and the other end of each guide rod 134 is fixed on a guide rod fixing plate 135 connected with the frame. The armrest carrying plate 137 is provided with an armrest 138, and the armrest carrying plate 137 is mounted on the first carrying plate 132 by an engagement member.
When the hand rest is used, the front and back positions and the upper and lower positions of the hand rest 138 can be adjusted according to the height and the use habit of a patient, so that an impetus is provided for the training of the patient, and the falling caused by unstable gravity center of the patient during the training is prevented. The emergency stop button is arranged on one side of the handrail grabbed by the patient, so that the patient can quickly press the emergency stop button when an emergency occurs.
One end of the waist support mechanism 14 is pivotally connected to the frame 1, so that the waist support mechanism 14 can be opened or closed as a whole. When used in conjunction with the wheelchair docking platform 12, the patient is conveniently wheeled into the walker.
The waist support mechanism 14 comprises a waist support bracket 141 with one end pivotally connected to the frame 1, a hollow cavity is formed in the waist support bracket 141, a buffer component 142 is arranged at the hollow cavity (the buffer component 142 realizes triaxial vibration reduction through three groups of springs), and a lifting component 143 (the lifting component 143 is realized by adopting screw rod transmission) for driving the buffer component 142 to vertically move up and down is arranged on the waist support bracket 141; the buffer assembly 142 is provided with a first support plate 144, a floating support plate 145 is arranged on the outer side of the first support plate 144, and the outer side surface of the floating support plate 145 is of a profiling structure. A universal buffer assembly is provided between the first support plate 144 and the floating support plate 145.
The universal buffer assembly comprises a universal ball seat fixed in the middle of the floating support plate 145, and a universal ball matched with the universal ball seat is arranged on the first support plate 144; the universal hinges are uniformly arranged on the floating support plate 145 on which the periphery of the universal ball seat is positioned, the universal hinges penetrate through the first support plate 144 through a hinge rod and are fixed with the other support plate through universal bearings, the hinge rod is provided with a first spring 146, one section of the first spring 146 is abutted to the first support plate 144, and the other end of the first spring 146 is abutted to the floating support plate 145.
The floating support plate 145 is tightly attached to the patient after the patient gets on the machine, so that stable lumbar support is provided, and as the floating support plate 145 can move along with the up-and-down movement of the lumbar part of the patient, the universal rotation of the floating support plate 145 along with the hip joint is realized, and the damping force for rotation in all directions is provided, so that the training comfort is improved.
The movement mode of the stepping machine of the embodiment is divided into three modes of passive training, active training and impedance training.
1. Passive training
The passive training is to fit the angle curve of each joint of the standard gait according to the set scene (flat ground, going upstairs and downstairs, going upstairs and downslopes) by the machine, and obtain the track curve of the terminal controller through positive kinematics analysis according to the angle and the coordinates of each joint through an improved DH parameter algorithm. By fitting this curve, the patient can be passively trained. DH parameter algorithm is a set of parameters that describe the geometrical relationship and motion transformation between joints. In gait analysis, the improved DH parameters can be used to build a joint chain model, which simulates the joint and bone structure of the human body. This helps to study the articulation of the human body while walking, running or performing other activities.
The basic principle of the improved DH parameter algorithm is as follows:
1) Defining a coordinate system: to describe the motion of joints and bones, a set of coordinate systems needs to be defined first. Each joint (hip, knee, ankle) has a local coordinate system describing the rotation and translation of the joint.
2) Distributing DH parameters: for each joint, four DH parameters need to be assigned, which are:
a: translation distance of joint along x-axis of previous joint;
alpha: rotation angle of the joint about the previous joint x-axis;
d: a translation distance of the joint along the current joint z-axis;
θ: rotation angle of the joint about the current joint z-axis.
3) Creating a conversion matrix: using DH parameters, a transformation matrix can be created that transforms the coordinate systems of two adjacent joints into each other. These matrices will describe the motion relationships between the joints.
The specific implementation formula of the improved DH parameter algorithm is as follows:
conversion matrix T:
where a, α, d and θ are DH parameters for each joint, respectively.
Positive kinematic analysis:
by multiplying the transformation matrix for each joint, the positive kinematics of the entire joint chain can be calculated, thereby analyzing the relationship of joint motion and end controller position.
2. Active training
The active training means that the machine obtains the track curve of the end controller according to the set scene (flat ground, going upstairs and downstairs, going downstairs) through the improved DH parameter algorithm and forward transport mechanical algorithm, and the machine uses the self-adaptive impedance model algorithm to adaptively adjust the force and moment of the walking machine under the condition that the patient actively exerts force, so that the patient can correct the abnormal gait under the condition that the patient exerts the initiative.
The modeling method of the adaptive impedance model is shown in fig. 11, in which:
·f e environmental forces in a tool coordinate system of the six-dimensional force sensor;
·x e ＝x d -x＝[Δv,Δw]desired position x and actual position x described for work or under a base coordinate system d A difference between;
·is x e First and second derivatives of (a);
m self-adaptive positive fixed virtual mass, B self-adaptive damping, K self-adaptive rigidity as self-adaptive parameter, its self-adaptive formula is
M＝m+α m *f
B＝b+α b *f
K＝k+α k *f
Wherein m is a number selected from the group consisting of,b, k is the impedance model coefficient, alpha m ，α b ，α k And f is the total force on the six-dimensional force sensor and is the weight coefficient of the self-adaptive parameter.
By modeling, it is possible to respond to the external force f e The force output by the translational servo motor and the vertical servo motor of the controller is adjusted in real time according to the change of (a)Thus, an active training mode is obtained. f (f) e X is obtained by six-dimensional force sensor, gyroscope and the like.
3. Impedance training
The impedance training means that the patient takes a leading step completely, and the machine applies certain resistance to the patient according to the setting to help the patient train and recover.
The rehabilitation training stepping machine of the embodiment is provided with an active training mode, and in the active training mode, the rehabilitation training stepping opportunity based on the intelligent terminal controller is combined with the walking curve of the current patient according to the walking standard curve to give one or the power or the resistance compensation to the patient. Allowing the patient to walk toward standard gait correction. The realization of the function is realized by mainly utilizing the data of a six-dimensional force sensor and a gyroscope and combining an impedance algorithm.
The stepping machine of the embodiment uses the MR immersion vision technology, so that a patient can perform game training in a realistic virtual environment, nerve remodeling is promoted, effective connection with muscles is strengthened, and training effect is improved.
The stepping machine of the embodiment can be connected with the Internet, provides management functions of pathological anatomy of a patient, patient assessment, AI diagnosis, AI plan, prescription recommendation, plan execution and the like through big data, combines a digital twin technology, simulates a human body, establishes a personal model, and enables a doctor to conduct operations of prescription preview, muscle anatomy and the like on the personal model. The system excavates the optimal prescription according with the characteristics of the patient according to different genres of rehabilitation medicine, and provides different rehabilitation suggestions for doctors.
The stepping machine of the embodiment utilizes an artificial intelligence classification algorithm and various sensor data to quantify the training rehabilitation degree of the patient, and has an objective and scientific evaluation system.
The system of the rehabilitation training stepping machine based on the intelligent terminal controller has the function of rehabilitation management, and a rehabilitation file is provided for each patient, wherein the rehabilitation file comprises system assessment, training record, training program and the like. So that doctors can more comprehensively and systematically know patients.
The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, and yet fall within the scope of the invention.
Claims (9)
1. Rehabilitation training step-by-step machine based on intelligent terminal controller, including the frame, its characterized in that: two groups of driven controllable intelligent terminal controllers are arranged on the stand, and a weight reducing mechanism for lifting the upper body of a patient is arranged on the stand; the frame is provided with a fall-preventing protection device for preventing the gravity center of the patient from falling unstably during training; an interactive display screen and an electric cabinet assembly are arranged on the outer side of the frame;
the intelligent terminal controller comprises an adjusting plate and a supporting plate, a six-dimensional force sensor is arranged between the adjusting plate and the supporting plate, and a gyroscope is arranged on the adjusting plate; the adjusting plate can translate, lift and rotate under the drive of the driving mechanism; the track of the intelligent terminal controller during walking is obtained by adopting a DH parameter algorithm according to each joint angle curve of the standard gait; the human lower limb joint transformation matrix is created as follows:
defining a coordinate system: each joint has a coordinate system describing rotation and translation of the joint;
distributing DH parameters: four DH parameters are distributed to each joint, and the DH parameters are respectively:
a: translation distance of joint along x-axis of previous joint;
alpha: rotation angle of the joint about the previous joint x-axis;
d: a translation distance of the joint along the current joint z-axis;
θ: rotation angle of the joint about the current joint z-axis;
the coordinate systems of two adjacent joints are mutually converted (namely, the conversion matrix of each joint is multiplied), so that the positive kinematics of the whole joint chain can be calculated, and the relation between the movement of the joint and the position of the tail end controller is analyzed; the calculation formula of the transformation matrix T is as follows:
where a, α, d and θ are DH parameters for each joint, respectively.
2. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 1, wherein: the six-dimensional force sensor is placed at a position right below the ankle joint.
3. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 1, wherein: the support is provided with a detachable height-adjustable knee support.
4. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 1, wherein: the intelligent terminal controller is controlled by a servo motor at the ankle part and adopts incremental PID position control, and the calculation formula is as follows:
Δu(t)＝k p (e(t)-e(t-1))+k i e(t)+k d (e(t)-2e(t-1)+e(t-2))
wherein: deltau (t) is the increment of the control quantity at the time t; e (t) is the difference between the expected angle and the actual angle at time t; e (t-1) is the difference between the desired angle and the actual angle at time t-1; e (t-2) is the difference between the desired angle and the actual angle at time t-2; k (k) p ，k i ，k d Is an adjustable parameter.
5. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 1, wherein: the walking track control of the intelligent terminal controller is realized by a translation servo motor and a vertical servo motor and by adopting a self-adaptive impedance model algorithm:
our system can be expressed by the following formula:
the change formula is
F in the formula e Can be obtained from a six-dimensional force sensor;
the difference between the desired position and the actual position can be obtained from the motor of the system;
is->Is a first order derivative of (a);
is->Is a second derivative of (c).
M self-adaptive positive fixed virtual mass, B self-adaptive damping, K self-adaptive rigidity as self-adaptive parameter, its self-adaptive formula is
M＝m+α m *f
B＝b+α b *f
K＝k+α k ·f
Wherein m, b, k are impedance coefficients, α m ，α b ，α k And f is the total force on the six-dimensional force sensor and is the weight coefficient of the self-adaptive parameter.
6. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 1, wherein: the driving mechanism comprises a translation driving mechanism, a lifting driving mechanism and a rotation driving mechanism, the adjusting plate swings under the drive of the rotation driving mechanism to overturn to simulate ankle movement, the translation driving mechanism can drive the lifting driving mechanism to move back and forth integrally, and the lifting driving mechanism can drive the rotation driving mechanism and the adjusting plate to move up and down integrally.
7. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 1, wherein: the weight-reducing mechanism is internally provided with a tension sensor for monitoring the tension of the rope and a stay wire encoder for sensing the actual extension length of the rope.
8. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 1, wherein: the fall-preventing protection device comprises a lifting handrail mechanism, a waist support mechanism and an emergency stop button; one end of the waist support mechanism is pivotally connected to the frame, a hollow cavity is formed in the waist support mechanism, a buffering component for absorbing vibration in three axial directions is arranged at the hollow cavity, and a lifting component for driving the buffering component to vertically move up and down is arranged on the waist support bracket; the buffer assembly is provided with a first supporting plate, a floating supporting plate is arranged on the outer side of the first supporting plate, the outer side face of the floating supporting plate is of a profiling structure, and a universal buffer assembly is arranged between the first supporting plate and the floating supporting plate.
9. The rehabilitation training stepping machine based on the intelligent terminal controller according to claim 8, wherein: the frame is provided with a wheelchair docking platform.
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