A gait control system of a lower limb rehabilitation robot

Abstract

The application discloses a gait control system of a lower limb rehabilitation robot, which comprises a robot body module, a data monitoring module, a trajectory tracking module and a gait generation module; the robot body module assists the lower limb dysfunction population to carry out rehabilitation exercise training; the data monitoring module collects and obtains comprehensive data of the patient; the trajectory tracking module suppresses interference in the training process and tracks the joint angle to ensure the accuracy of the gait trajectory; the gait generation module is used for processing and analyzing the comprehensive data index in the rehabilitation training, maintaining the balance of the patient in the walking process, and generating the actual gait. The gait control system of the lower limb rehabilitation robot provided by the application realizes real-time monitoring of the motion data in the lower limb rehabilitation training, interference suppression, trajectory tracking and gait generation, can generate and predict the correct gait suitable for the patient, and further restores the motion function of the lower limb dysfunction population.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of lower limb rehabilitation, in particular to a gait control system of a lower limb rehabilitation robot. BACKGROUND

[0002] The number of stroke patients is large, the incidence rate and the disability rate are high, lower limb motor dysfunction is a common sequelae of stroke, and the decline of lower limb function will seriously affect the daily life of patients. The recovery training of walking function is an important link for patients to return to normal life, but the contradiction between the large number of stroke patients and the serious shortage of rehabilitation therapists has caused the gap and demand of rehabilitation training. Compared with traditional rehabilitation training, lower limb rehabilitation robot improves the efficiency and effect of rehabilitation training, and has a huge clinical application prospect.

[0003] However, in the process of assisting lower limb dysfunction population to conduct gait training by the lower limb rehabilitation robot, the existence of interference caused by internal and external factors such as vibration, mechanical and ground interaction seriously affects the trajectory accuracy of the lower limb rehabilitation robot. At the same time, because the standard gait is commonly used for training, there is a lack of real-time gait planning and prediction for patients, which leads to the problems of imbalance of center of gravity in the walking process of patients and poor man-machine coupling, which further leads to the decline of rehabilitation training effect. SUMMARY

[0004] In order to at least solve one of the defects existing in the prior art, the present application discloses a gait control system of a lower limb rehabilitation robot, which comprises a robot body module, a data monitoring module, a trajectory tracking module and a gait generation module. The lower limb rehabilitation robot gait control system provided by the present application assists hemiplegic lower limbs to conduct rehabilitation exercise training through the robot body module; the robot body module assists lower limb dysfunction population to conduct rehabilitation exercise training; the data monitoring module collects and feeds back real-time data of the subjects; the trajectory tracking module suppresses the interference in the training process and tracks the joint angle to ensure the accuracy of the gait trajectory; and the gait generation module is used for processing and analyzing the comprehensive data index in the rehabilitation training, preventing imbalance of patients in the training process, and generating actual gait according to the intention recognition of patients.

[0005] To achieve the above-mentioned purpose, the present application provides a gait control system of a lower limb rehabilitation robot, which comprises a robot body module, a data monitoring module, a trajectory tracking module and a gait generation module,

[0006] The robot body module is used for assisting lower limb dysfunction population to complete rehabilitation exercise training;

[0007] The data monitoring module is used to obtain real-time signals of various sensors, and feed back data generated by patient movement in the rehabilitation training process, including hip and knee joint movement data, human-machine interaction torque and plantar pressure;

[0008] The trajectory tracking module is used to track the angle and angular velocity kinematics data of the hip and knee joints in real time during the lower limb rehabilitation training process, and control the movement of the lower limb exoskeleton after self-anti-interference processing;

[0009] The gait generation module is used to switch in real time during active and passive training through healthy side intention, generate lower limb movement trajectory in real time, and adjust gait curve according to plantar pressure to ensure the stability of the center of gravity during walking.

[0010] Further, the robot body module is used to drive the patient to perform lower limb rehabilitation exercise training, and adjust the joint motor speed according to the implementation parameters fed back by the trajectory tracking module, so as to realize the gait trajectory and correct the deviation between the actual gait and the theoretical gait.

[0011] Further, the robot body module is used to drive the patient to perform lower limb rehabilitation exercise training, and obtain real-time comprehensive data of the training according to the data monitoring module, and the trajectory tracking module suppresses interference and tracks the trajectory according to the obtained data, and simultaneously serves as a position controller of the gait generation module, and the gait generation module plans the gait and ensures the balance of the patient during walking according to the patient's intention and plantar pressure.

[0012] Further, the data monitoring module includes a motor signal acquisition system, a force sensor and a plantar pressure acquisition system; the motor signal acquisition system is used to obtain the current angle and angular velocity of the motor, the force sensor obtains the human-machine interaction force F, and the plantar pressure acquisition system is used to acquire plantar pressure data; the hip and knee joint movement parameters include hip joint angle θ1, hip joint angular velocity v1, hip joint angular acceleration a1, hip joint torque τ1, knee joint angle θ2, knee joint angular velocity v2, knee joint angular acceleration a2, knee joint torque τ2 and healthy side intention recognition torque τ3.

[0013] Further, the trajectory tracking module performs parameter identification and state estimation on the model of the lower limb rehabilitation robot, suppresses the periodic disturbance existing in the lower limb rehabilitation training process, realizes accurate tracking of the normal gait trajectory, and drives the patient to accurately reproduce the gait trajectory.

[0014] Further, in the trajectory tracking module, the input joint angle q d , joint angular velocity The robust controller outputs torque τ to the robot and the observer, the robot moves under the joint of torque driving and external disturbance, and the current joint angle is obtained through the angle encoder and fed back to the robust controller and the observer, and the observer estimates the joint angle estimation value and the disturbance torque estimation value The input robust controller realizes the closed-loop control process of trajectory tracking.

[0015] Further, the gait generation module obtains the foot pressure F f and the joint torque M, the hip joint angle θ1, the hip joint angle θ2, the distance L G from the center of the human body to the shoulder, the length of the thigh L1, the length of the lower leg L2, and the distance d i from the pressure point to the center of the foot, to obtain the imbalance force of the human body

[0016]

[0017] Further, the trajectory of the ankle joint during walking of the normal person with different leg lengths is collected to obtain the function of the angle β between the ankle joint and the vertical direction and the distance ρ from the ankle joint to the hip joint, wherein A is the coefficient matrix of the fitting function.

[0018] ρ=A(β 3 (L1, L2), β 2 (L1, L2), β (L1, L2), 1)

[0019] Obtain the hip-knee joint angle, wherein x is the horizontal coordinate of the ankle joint, and y is the vertical coordinate of the ankle joint.

[0020]

[0021] According to the human-machine coupling force constraint, the imbalance force and the intention recognition force, the real-time joint angle θ i is obtained, wherein θ s (ΔF) is the modified angle obtained according to the intention recognition force, and Δθ(F) is the bias angle generated by the admittance controller

[0022] θ i =θ i-1 +θ s (ΔF)+Δθ(F)

[0023] According to the plurality of hip joint angle θ1 values collected at the beginning of the movement, the expression of the hip-knee joint is fitted, wherein θ is the hip-knee joint angle matrix.

[0024]

[0025] Further, a real-time gait trajectory suitable for the patient is obtained, the trajectory is obtained under the constraint of keeping the patient's body balance, and the optimization target is to minimize the human-machine interaction force, so as to improve the human-machine coupling and the training effect of the patient's lower limbs in the lower limb rehabilitation training.

[0026] Compared with the prior art, the present application has the following advantages and technical effects:

[0027] (1) The gait control system of the lower limb rehabilitation robot provided by the present application aims to solve the problem that the standard gait is used as the target trajectory in the existing lower limb rehabilitation training process, but the gait is not suitable for the corresponding patient, and the human-machine coupling is poor. The robot body module assists the lower limb dysfunction population to perform rehabilitation exercise training, the data monitoring module collects and obtains comprehensive data of the lower limb training process, the trajectory tracking module suppresses interference in the training process and tracks the joint angle to ensure the accuracy of the gait trajectory, and the gait generation module processes and analyzes the comprehensive data index in the rehabilitation training to keep the balance of the patient in the walking process and generate the actual gait.

[0028] (2) The present application realizes the monitoring and evaluation of the lower limb rehabilitation training in the rehabilitation training process, which is beneficial to the patient to learn the correct movement mode and further promote the recovery of the movement function of the lower limbs. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings:

[0030] Figure 1 It is a schematic diagram of the gait control system of the lower limb rehabilitation robot provided in the embodiment of the present application.

[0031] Figure 2 It is a schematic diagram of the trajectory tracking process in the embodiment of the present application.

[0032] Figure 3 It is a schematic diagram of the gait trajectory generation system in the embodiment of the present application.

[0033] Shown in the figure: 1-robot body module, 2-data monitoring module, 3-trajectory tracking module, 4-gait generation module. DETAILED DESCRIPTION

[0034] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts are within the protection scope of the present application.

[0035] The terms used in the present specification are merely for the purpose of describing particular embodiments and are not intended to limit the present application. The singular forms "a", "an" and "the" used in one or more embodiments of the present specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present specification means and includes any or all possible combinations of one or more associated listed items.

[0036] As shown in Figure 1 The present application provides a gait control system of a lower limb rehabilitation robot, which comprises a robot body module 1, a data monitoring module 2, a trajectory tracking module 3 and a gait generation module 4.

[0037] The robot body module 1 serves as an actuator to assist the lower limb dysfunction population to complete the rehabilitation exercise training.

[0038] The data monitoring module 2 is used to obtain real-time signals of various sensors and feed back the data generated by the patient's movement during the rehabilitation training, which includes hip and knee joint movement data, human-machine interaction torque and plantar pressure.

[0039] The trajectory tracking module 3 is used to track the angle, angular velocity kinematics data of the hip and knee joints in real time during the lower limb rehabilitation training, estimate the disturbance torque in the movement process, and provide corresponding torque by the joint motor to offset the disturbance, so as to realize self-anti-interference processing, control the movement of the lower limb exoskeleton, and ensure the consistency of the actual gait and the standard gait.

[0040] The gait generation module 4 is used to switch in real time during the active and passive training through the healthy side intention, generate the lower limb movement trajectory in real time, and adjust the gait curve according to the plantar pressure, so as to ensure the stability of the center of gravity during walking.

[0041] In some embodiments of the present application, the gait generation module 4 judges the movement intention of the patient according to the obtained human-machine interaction torque, calculates the torque acting on the trainer according to the plantar pressure and joint torque, and judges whether the walking process is in a gait stable state.

[0042] In some embodiments of the present application, the robot body module 1 is used to drive the patient to perform lower limb rehabilitation exercise training, and the joint motor speed is adjusted according to the real-time parameters fed back by the trajectory tracking module 3 to realize the gait trajectory and correct the deviation between the actual gait and the theoretical gait.

[0043] In some embodiments of the present application, the data monitoring module 2 includes a motor signal acquisition system, a force sensor and a plantar pressure acquisition system, wherein the motor signal acquisition system is used to obtain the current angle and angular velocity of the motor; the force sensor is used to obtain the characteristic parameters of the human-machine interaction force F, and the plantar pressure acquisition system is used to acquire the plantar pressure data.

[0044] Among them, the hip and knee joint motion data includes hip joint angle θ1, hip joint angular velocity v1, hip joint angular acceleration a1, hip joint torque τ1, knee joint angle θ2, knee joint angular velocity v2, knee joint angular acceleration a2, knee joint torque τ2, and healthy side intention recognition torque τ3.

[0045] The trajectory tracking module 3 obtains the mass, length, center of mass and moment of inertia of the lower limbs by parameter identification of the model of the lower limb rehabilitation robot, and obtains the estimated value of the state variable by taking the joint angular velocity and the unmodeled disturbance d as state variables, so as to accurately track the normal gait trajectory by providing corresponding torque by the joint motor to offset the disturbance and suppress the periodic disturbance existing in the lower limb rehabilitation training process, drive the patient to accurately reproduce the gait trajectory.

[0046] The gait generation module 4 obtains the human imbalance force F f and the joint torque M, the hip joint angle θ1, the hip joint angle θ2, the distance L G from the center of gravity of the human body to the shoulder center, the length L1 of the thigh, the length L2 of the calf, the distance d i from the pressure point to the center of the foot, and the angle β of the calf joint to the vertical direction, to obtain the human imbalance force F L

[0047]

[0048] Further, the trajectory of the ankle joint when a normal person with different leg lengths walks is collected to obtain the function of the angle β of the ankle joint to the vertical direction and the distance ρ from the ankle joint to the hip joint, wherein A is the coefficient matrix of the fitting function.

[0049] ρ=A(β 3 (L1,L2),β 2 (L1,L2),β(L1,L2),1)′

[0050] Obtain the hip-knee angle, wherein x is the transverse coordinate of the ankle joint, and y is the longitudinal coordinate of the ankle joint.

[0051]

[0052] The maximum and minimum values of the human-robot interaction force are allowed as human-robot coupling force constraints, the human-robot interaction force F is obtained according to the human-robot coupling force constraints, the unbalance force and the force sensor, and the real-time joint angles θ1 and θ2 are obtained, wherein θ1 and θ2 are the real-time joint angles. s (ΔF) is the correction angle obtained according to the human-robot interaction force, and Δθ(F) is the bias angle generated by the admittance controller

[0053] θ i = θ i-1 + θ s (ΔF) + Δθ(F)

[0054] According to the plurality of hip joint angle θ1 values collected at the beginning of the movement, the expression of the hip-knee joint is fitted, wherein θ is the hip-knee joint angle matrix.

[0055]

[0056] The real-time gait trajectory suitable for the patient is obtained, which is obtained under the constraint of maintaining the balance of the patient's body and with the optimization target of minimizing the human-robot interaction force, so as to improve the human-robot coupling and the training effect of the patient's lower limb rehabilitation in the lower limb rehabilitation training.

[0057] The optimization formula of the real-time gait trajectory is as follows:

[0058]

[0059] Wherein F Lmax is the maximum value of the allowed human unbalance force.

[0060] The present application assists the lower limb dysfunction population to carry out rehabilitation exercise training through the robot body module; the data monitoring module collects and obtains the comprehensive data of the patient; the trajectory tracking module suppresses the interference in the training process, tracks the joint angle, so as to ensure the accuracy of the gait trajectory; the gait generation module is used for processing and analyzing according to the comprehensive data index in the rehabilitation training, maintaining the balance of the patient in the walking process, and generating the actual gait. The gait control system of the lower limb rehabilitation robot provided by the present application realizes the real-time monitoring of the motion data, the interference suppression, the trajectory tracking and the gait generation in the lower limb rehabilitation training, can generate and predict the correct gait suitable for the patient, and then restore the motor function of the lower limb dysfunction population.

[0061] The use flow of the embodiment is as follows:

[0062] In a feasible embodiment, the robot body module 1 is connected with the left and right lower limbs of the patient respectively, and assists the patient to perform rehabilitation training, and the data acquisition module 2 collects the training data as input parameters and transmits them to the gait generation module 4; in the process of assisting the hemiplegic lower limb to perform rehabilitation training, the trajectory tracking module 3 estimates the joint angular velocity and unmodeled disturbance through an observer according to the joint angle data and joint torque data fed back by the data acquisition module 2, suppresses the disturbance through a robust controller, and transmits the obtained joint angle to the robot body module 1 as input to correct the deviation between the expected joint angle and the actual joint angle; the gait generation module 4 obtains the body imbalance force and the patient's intention recognition force according to the joint angle, the human-computer interaction force and the plantar pressure fed back by the data acquisition module 2, takes the human-computer coupling force as the optimization target, the body imbalance force as the constraint, and the body ankle joint trajectory as the prior parameter, fits the real-time patient gait trajectory, and generates the accurate gait conforming to the patient in real time to assist the patient to perform lower limb rehabilitation training.

[0063] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0064] According to the disclosure and teaching of the above description, those skilled in the art of the present application can also make changes and modifications to the above embodiments. Therefore, the present application is not limited to the specific embodiments disclosed and described above, and some modifications and changes of the present application should fall within the protection scope of the claims of the present application. According to the disclosure and teaching of the above description, those skilled in the art of the present application can also make changes and modifications to the above embodiments. Therefore, the present application is not limited to the specific embodiments disclosed and described above, and some modifications and changes of the present application should fall within the protection scope of the claims of the present application.

Claims

1. A gait control system of a lower extremity rehabilitation robot, characterized by, The lower limb rehabilitation robot training system comprises a robot body module (1), a data monitoring module (2), a trajectory tracking module (3) and a gait generation module (4), The robot body module (1) is used for assisting the lower limb dysfunction population to complete rehabilitation exercise training. The data monitoring module (2) is used for acquiring real-time signals of various sensors, feeding back data generated by patient movement in the rehabilitation training process, including hip and knee joint movement data, human-machine interaction torque and foot pressure; The trajectory tracking module (3) is used for real-time tracking of the angle, angular velocity kinematics data of the hip and knee joints in the lower limb rehabilitation training process, controlling the movement of the lower limb exoskeleton after self-anti-interference processing; The gait generation module (4) is used for real-time switching of the healthy side intention in the active and passive training, real-time generation of the lower limb movement trajectory, and adjustment of the gait curve according to the foot pressure, so as to ensure the stability of the center of gravity in the walking process; The gait generation module (4) obtains the body imbalance force by plantar pressure and joint torque , hip joint angle , knee joint angle , distance from body center of gravity to shoulder center , thigh length , shank length , distance from pressure point to plantar center . According to the trajectories of the ankle joint of the normal people with different leg lengths during walking, the angle between the ankle joint and the vertical direction is obtained and the function of the distance between the ankle joint and the hip joint ​ wherein is a coefficient matrix; Further, hip-knee angle is obtained, wherein is the transversal coordinate of the ankle joint, is the longitudinal coordinate of the ankle joint. According to the human-machine coupling force constraint, the unbalanced force and the human-machine interaction force, the real-time joint angle is obtained wherein is the modified angle obtained according to the intention recognition force, is the bias angle generated by the admittance controller According to the multiple hip joint angles collected at the initial stage of the movement Values, to obtain an expression of the hip-knee joint, wherein Is a hip-knee joint angle matrix: A real-time gait trajectory suitable for the patient is obtained, which is obtained under the constraint of maintaining the balance of the patient's body and with the optimization objective of minimizing human-machine interaction force; The optimization formula of the real-time gait trajectory is: wherein, is the maximum allowable human imbalance force, is the bias angle generated by the admittance controller, is the correction angle obtained from the human-robot interaction force, is the human imbalance force.

2. The lower limb rehabilitation robot gait control system according to claim 1, characterized in that, The robot body module (1) is used for driving the patient to perform lower limb rehabilitation exercise training, and according to the real-time comprehensive data of the training acquired by the data monitoring module (2), the trajectory tracking module (3) acquires data to suppress interference and track the trajectory, and simultaneously serves as a position controller of the gait generation module (4), and the gait generation module (4) plans a gait according to the intention of the patient and the foot pressure and ensures the balance of the patient in the walking process.

3. The lower limb rehabilitation robot gait control system according to claim 1, characterized in that, The data monitoring module (2) comprises a motor signal acquisition system, a force sensor and a foot pressure acquisition system. The motor signal acquisition system is used to obtain the current angle and angular velocity of the motor, and the force sensor is used to obtain the human-machine interaction force The characteristic parameters are obtained by the plantar pressure acquisition system.

4. The lower limb rehabilitation robot gait control system according to claim 1, characterized in that, The hip-knee-ankle motion data includes a hip joint angle , a hip joint angular velocity , a hip joint angular acceleration , a hip joint torque , a knee joint angle , a knee joint angular velocity , a knee joint angular acceleration , a knee joint torque , and a healthy side intention recognition moment .

5. The lower limb rehabilitation robot gait control system according to claim 1, characterized in that, The trajectory tracking module (3) obtains the mass, length, center of mass and moment of inertia of the lower limbs by parameter identification on the model of the lower limb rehabilitation robot, obtains the joint angular velocity and unmodeled disturbance The estimation value of the state variable is obtained as the state variable, the periodic disturbance existing in the lower limb rehabilitation training process is estimated by estimating the disturbance torque in the motion process, the joint motor provides the corresponding torque to offset the disturbance, the disturbance is inhibited, the anti-interference ability of the system is improved, and the accurate tracking of the normal gait trajectory is realized.

6. The lower limb rehabilitation robot gait control system according to claim 5, characterized in that, The trajectory tracking module (3) has input joint angle , joint angular velocity to the robust controller, the robust controller outputs torque to the prototype and the observer, the prototype moves under the joint action of torque driving and external disturbance, the current joint angle is obtained through the angle encoder and fed back to the robust controller and the observer, the observer estimates joint angle estimation value and disturbance torque estimation value , which are input to the robust controller to realize the closed-loop control process of trajectory tracking.

7. The gait control system of lower limb rehabilitation robot according to claim 1, wherein, The gait generation module (4) judges the movement intention of the patient according to the acquired human-machine interaction torque.

8. The gait control system of lower extremity rehabilitation robot according to claim 1, characterized in that, The gait generation module (4) calculates the torque acting on the trainer according to the foot pressure and joint torque, and judges whether the walking process is in a gait stable state.

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