CN114903750A - A lower extremity exoskeleton control system and lower extremity exoskeleton control method for paraplegic patients - Google Patents

Abstract

The present invention provides a lower limb exoskeleton control system and a lower limb exoskeleton control method for paraplegic patients. The system includes: a central controller, a power supply module, a sensor module, a human-computer interaction device, an exoskeleton power drive device, a wearable lower limb exoskeleton Skeleton; the sensor module collects the information data of human hip joint, leg posture, back posture, ankle pressure, crutch pressure, and establishes a patient gait phase data set; The swing phase hip joint motor outputs the assist torque in the forward motion direction, and outputs the synchronous backward extension direction assist torque to the support phase hip joint; completes the active training control of the lower extremity exoskeleton, and meets the active muscle strength rehabilitation training needs of paraplegic patients. The invention can control the exoskeleton of the lower limbs, and can realize the passive rehabilitation training of the paraplegic patients in the early stage, and the active rehabilitation training according to the posture changes of the patients in the middle stage, so as to realize the active muscle strength recovery process.

Description

A lower extremity exoskeleton control system and lower extremity exoskeleton control method for paraplegic patients

technical field

The invention relates to the technical field of medical rehabilitation engineering, in particular to a lower limb exoskeleton control system and a lower limb exoskeleton control method for paraplegic patients.

Background technique

The annual incidence of limb movement disorders caused by spinal cord injury in the world is between 250,000 and 500,000. Spinal cord injuries are mainly in young adults, and 80% are under the age of 40. caused a long-term and heavy economic burden. Traditional paraplegic orthoses have no active power, and bilateral hip-knee-ankle-foot orthoses (HKAFO) or bilateral knee-ankle-foot orthoses (KAFO) are the most common. walking, while standing or walking rehabilitation training requires the use of crutches. With the advancement of science and technology, the lower limb rehabilitation robot, which combines engineering such as robotics, new sensing technology, and mechatronics with medicine and bionics, has begun to appear. The lower extremity exoskeleton robot, which integrates robotics technology and the principles of rehabilitation medicine, can enable paraplegic patients to achieve the purpose of rehabilitation treatment while walking.

SUMMARY OF THE INVENTION

The purpose of the present invention is to propose a limb exoskeleton control system and a lower limb exoskeleton control method effectively applied to the lower limb rehabilitation training of paraplegic patients.

In order to achieve the above object, the present invention proposes a lower limb exoskeleton control system for paraplegic patients, including a wearable lower limb exoskeleton, a central controller, a human-computer interaction device, a power supply module, a sensor module and a power drive device;

The wearable lower extremity exoskeleton is designed with the human lower extremity structure, including: back support, leg support and sole support;

The central controller and the power module are fixed on the back support, and the sensor module includes a left/right leg posture sensor, a knee joint angle sensor, a back posture sensor and a hip sensor respectively arranged at the corresponding joints of the leg support. A joint angle sensor, and a plantar pressure sensor including a plantar support;

The power drive device drives each part of the wearable lower limb exoskeleton to move, and the human-computer interaction device, the power supply module, the sensor module and the power drive device are all signal-connected to the central controller.

Further, it also includes a crutch, the crutch is provided with a crutch pressure sensor, and the crutch pressure sensor is signally connected to the central controller.

Further, the power drive device includes a left hip motor, a right hip motor and two servo drives;

The left hip motor and the right hip motor are respectively arranged on the left and right hips of the leg support, and are respectively driven by the two servo motors.

The present invention also provides a lower limb exoskeleton gait following control method for paraplegic patients, including monitoring of human body posture data and gait phase analysis;

The sensor module is used to obtain the real-time data of rehabilitation exercise, the Kalman filter algorithm is used to process the human body posture data of the sensor module, and combined with the measurement of the hip joint angle of the lower extremity exoskeleton, we get:

的增益，

和

分别是Δθ的估计值和预测值；

作为偏移量的最后时刻的测量值，以此得到较为准确的人体姿态数据。K ₁ , K ₂ are, respectively, Δθ,

gain,

and

are the estimated and predicted values of Δθ, respectively;

As the measurement value at the last moment of the offset, more accurate human posture data can be obtained.

The gait phase analysis method is as follows: according to the pressure values output by the four pressure sensors of the plantar pressure sensor group in different time periods and the movement angle value of the exoskeleton hip joint, a proportional algorithm is used to analyze the collected human body posture data. Data fusion to identify gait phases. The overall block diagram of the proportional algorithm is shown in Figure 2. Combined with the different gait phase, the change range of the exoskeleton hip joint angle and the threshold angle when the gait phase is switched, the accurate gait phase is obtained by analysis.

The control method mainly lies in the fusion of motion data. The main process is as follows: firstly, the summation of the four plantar pressure signals is performed to determine the total pressure of the selected pressure area of the plantar; P ₁ , P ₂ , P ₃ and P ₄ are respectively It is used to represent the proportion of the plantar pressure sensors _FSRA , _FSRB , _FSRC and _FSRD to the total pressure signal. The plantar pressure value detected by the selected pressure area, P _i (i=1, 2, 3, 4) is expressed as follows:

Set the proportional value thresholds pinv _FSRA , pinv _FSRB , pinv _FSRC , pinv _FSRD ; pinv _FSRA , pinv _FSRB , pinv _FSRC , pinv _FSRD are the thresholds of Pi ( _i =1, 2, 3, 4) respectively; the setting of the thresholds According to the value of P _i (i=1, 2, 3, 4), which accounts for the largest proportion of different gait phases, and combined with actual data, the threshold for identifying gait phases is divided.

The present invention also provides a lower limb exoskeleton control method for paraplegic patients, comprising the following steps:

Step 1: Set the lower limb exoskeleton rehabilitation training safety range for different patients. After the patient wears the lower limb exoskeleton, the central controller records the position of the restricted posture of the rehabilitation exercise;

Step 2, start the lower limb exoskeleton, after the patient grasps the crutches, the system initializes and enters the standing posture (zero position), and enters the waiting mode selection state;

Step 3, mode selection: select the passive rehabilitation training mode, the paraplegic patient controls the lower limb exoskeleton to perform walking, sitting and standing training actions through the crutch module, and the crutch module transmits the instructions to the central controller; the central controller transmits the control instructions to the servo motor driver;

Or choose the active gait training mode, and set the corresponding gait assist value according to the lower limb muscle strength of different patients; collect human body posture data, analyze the patient's gait phase and movement trend, and the central controller calculates the corresponding motor drive assist torque, send control commands into the servo motor driver, and control the lower limb exoskeleton to follow the gait trend of the paraplegic patient;

Step 4: Collect and monitor the state data of the lower extremity exoskeleton, human body posture data, plantar pressure and crutch module data in real time, carry out fuzzy inference according to the given fuzzy rules, then de-fuzzy the fuzzy parameters, and output the PID control parameters to adjust accordingly. Control system parameters; the central controller issues control commands to control the lower limb exoskeleton to make corresponding posture actions;

Step 6: After the training period is over, a stop training instruction is sent through the crutch module, and the system enters a standing waiting state; in an emergency, the current training is stopped through the emergency stop button.

Further, the passive rehabilitation training mode includes the following steps:

Step A1, the mode is selected as passive rehabilitation training mode, the system is in the initial state of standing, and the central controller sets the corresponding designated standing, left/right walking, and sitting motion data sets according to the inner ring limit positions set by different patients. ;Wait to receive the crutch module control intent command;

Step A2, the built-in encoder of the motor, the left/right leg attitude sensor and the back attitude sensor enter the state data collection state, and the real-time state of the lower limb exoskeleton is transmitted to the central controller;

In step A3, the patient moves the left crutch module to drive the right side of the lower limb exoskeleton to move forward as a swing phase, and the left side of the lower limb exoskeleton acts as a support phase to stabilize the patient's body balance; on the contrary, the patient moves the right crutch module to drive the lower limb exoskeleton to the left side. The side moves forward as the swing phase, and the right side of the lower extremity exoskeleton acts as the support phase to stabilize the patient's body balance;

Step A4, move the crutch modules on both sides to both sides, press the standing command button, and the device enters the initial standing state;

Step A5, operate the crutch modules on both sides at the same time, send a sitting action command, adjust the posture of the lower limb exoskeleton, flex the knee joint inward, extend the hip joint outward, and execute the sitting action command;

Step A6, adjusting the fuzzy PID control parameters according to data changes such as attitude and motor torque in the passive training process, and outputting the corresponding motor control signal;

Step A7, the passive rehabilitation training ends, and the device enters the initial standing state.

Further, the active gait training mode includes the following steps:

Step B1: The mode is selected as the active gait training mode, the corresponding gait assist value is set according to the lower limb muscle strength of different patients, and the limit of the gait training inner ring is set; the lower limb exoskeleton is in the standing initialization state;

Step B2: Turn on the active gait training mode, and the sensor module collects the human-machine data of the patient and the lower limb exoskeleton, including the patient's posture data, plantar pressure data and its change trend, hip joint torque, angular velocity, and crutch module pressure value and its change trend ;

Step B3: use the Kalman filter algorithm to process the human body posture data of the sensor module, and combine the data sets of the hip joint angle, torque and plantar pressure sensor group to perform data fusion, and analyze the patient's gait phase and gait phase trend;

Step B4: adjust the fuzzy PID control parameters according to the data changes such as attitude and motor torque in the active training process, and the central controller outputs the corresponding control signal to adjust the control parameters of the servo driver;

Step B5: follow the patient's gait intention to carry out auxiliary rehabilitation training, and perform active walking and standing actions;

Step B6: End the active gait training, and the lower limb exoskeleton enters the standing waiting state.

Compared with the prior art, the advantage of the present invention lies in that the active rehabilitation training mode of the lower limb exoskeleton for paraplegic patients of the present invention can follow the changing trend of the patient's gait and give a certain torque assisting the patient to perform rehabilitation training, and provide the patient with a certain amount of assistance for rehabilitation training. The state of the hip and knee joints, the pressure in the sole area and the pressure in the crutch area are monitored in real time. At the same time, the control parameters of the lower limb exoskeleton are adjusted in real time to adapt to the disturbance changes in different situations, so as to achieve the effect of active rehabilitation training for the patient's gait.

Description of drawings

1 is a schematic structural diagram of a lower limb exoskeleton control system for a paraplegic patient in an embodiment of the present invention;

2 is an overall block diagram of a proportional algorithm in the gait phase analysis method simultaneously included in the embodiment of the present invention;

3 is a system block diagram of a lower limb exoskeleton control system for a paraplegic patient in an embodiment of the present invention;

Fig. 4 is the CANopen network communication architecture diagram in the embodiment of the present invention;

5 is a structural block diagram of a PID closed-loop control block diagram in an embodiment of the present invention;

6 is a schematic diagram of a plantar pressure sensor group in an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be further described below.

As shown in FIG. 1 , the present invention proposes a lower extremity exoskeleton control system for paraplegic patients, including a wearable lower extremity exoskeleton, a central controller 100, a human-computer interaction device, a power module 200, a sensor module and a power drive device;

The wearable lower extremity exoskeleton is designed with human lower extremity structure, including: back support, leg support and foot support;

The central controller 100 and the power module 200 are fixed on the back support, and the sensor module includes a left/right leg posture sensor 310, a knee joint angle sensor 320, a back posture sensor 350 and a hip joint angle sensor, which are respectively arranged at the corresponding joints of the leg support. 360, and includes a plantar pressure sensor 340 and a knee joint angle sensor 320 provided on the sole support.

The power drive device drives the activities of various parts of the wearable lower limb exoskeleton, and the human-computer interaction device, the power supply module 200 , the sensor module and the power drive device are all signal-connected to the central controller.

It also includes a crutch, a crutch pressure sensor is provided on the crutch, the crutch pressure sensor is connected with the signal of the central controller, and the patch film pressure sensor is placed at the crutch elbow support and handle, and its function is to apply it to the left and right through the human body when walking. The change of the force on the crutches is used to judge the change of the pressure value on the left and right sides; and the walking intention of the walker is predicted in advance through data processing. When the human body wants to take a step, the hip motor can move autonomously, realizing the perception function of the exoskeleton.

The power driving device includes a left hip motor 520, a right hip motor and two servo drivers 510; the left hip motor and the right hip motor are respectively arranged on the left and right hips of the leg support, and are driven by two servo motors respectively. The exoskeleton power drive module includes three-phase permanent magnet synchronous AC servo motor, servo driver and reducer at the left and right hip joints to provide power for the lower limb exoskeleton; the power module supplies the central controller and the exoskeleton power drive device through voltage conversion. The power supply system includes 12V and 5V DC-DC step-down circuit, 3.3V voltage regulator circuit, and the power module hardware circuit also includes voltage detection and overcurrent protection parts.

The left and right plantar pressure sensor groups, as shown in Figure 6, each group is composed of four pressure sensors; its function is to judge the gait phase of human walking when the exoskeleton is walking, that is, in the support phase or in the swing phase.

The human-computer interaction module mainly includes the host computer and the crutch module, which is the main interaction method for the user to use the exoskeleton. The upper computer sends the control information to the central controller by sending data packets, and the central controller receives the data and executes it. The human-computer interaction of the crutches is realized through the circuit embedded in the crutches. The control information is mainly transmitted to the central controller through wireless transmission, and the central controller transmits the control data to each part through CAN communication.

The present invention also proposes and provides a lower limb exoskeleton gait following control method for paraplegic patients, which includes a sensor module to obtain real-time rehabilitation exercise data, wherein Kalman filtering algorithm is used to process the sensor module's human body posture data, combined with the lower limb exoskeleton hip joint To measure the angle, we can get:

的增益，

和

分别是Δθ的估计值和预测值。

作为偏移量的最后时刻的测量值，以此得到较为准确的人体姿态数据。K ₁ , K ₂ are, respectively, Δθ,

gain,

and

are the estimated and predicted values of Δθ, respectively.

As the measurement value at the last moment of the offset, more accurate human posture data can be obtained.

At the same time, the gait phase analysis method is included: according to the pressure values output by the 4 pressure sensors of the plantar pressure sensor group in different time periods and the movement angle value of the exoskeleton hip joint, a proportional algorithm is used to analyze the collected human body posture data. Fusion to identify gait phases. The overall block diagram of the proportional algorithm is shown in Figure 2. Combined with the different gait phase, the change range of the exoskeleton hip joint angle and the threshold angle when the gait phase is switched, the accurate gait phase is obtained by analysis.

The control method mainly lies in the fusion of motion data, and the process is mainly as follows: firstly, the summation of the four plantar pressure signals is performed to determine the total pressure of the selected pressure area of the plantar. P ₁ , P ₂ , P ₃ and P ₄ are used to represent the proportion of the plantar pressure sensors FSRA, FSRB, FSRC and FSRD to the total pressure signal, respectively, and F _FSRA , F _FSRB , F _FSRC , and F _FSRD represent the plantar pressure, respectively The plantar pressure values detected by the sensors (Fig. 6) FSRA, FSRB, FSRC and FSRD in the selected pressure area at the same time, P _i (i=1, 2, 3, 4) are expressed as follows:

Set the proportional value thresholds pinv _FSRA , pinv _FSRB , pinv _FSRC , pinv _FSRD ; pinv _FSRA , pinv _FSRB , pinv _FSRC , and pinv _FSRD are the thresholds of P _i (i=1, 2, 3, 4) respectively. The threshold is set according to the value of P _i (i=1, 2, 3, 4) which accounts for the largest proportion of the different gait phases, and the threshold for identifying the gait phase is divided according to the actual data situation.

For the lower limb exoskeleton control system for paraplegic patients, the block diagram of the control system is shown in Figure 3. Rehabilitation training data is obtained, and the target movement trajectory is adjusted according to the patient's limb movement trend and active training movement requirements, so as to realize the paraplegic patient's gait following; The movement position range is divided into an outer ring and an inner ring. The outer ring is the mechanical limit of the wearable lower extremity exoskeleton, and the inner ring is the inner ring position limit set according to the lower limb flexion and extension safety range of different patients. The control characteristics of real-time change of active rehabilitation training parameters The fuzzy PID control algorithm is used to optimize and adjust the control parameters of the lower extremity exoskeleton; the specific control flow diagram is shown in Figure 5;

According to one aspect of the present invention, a lower limb exoskeleton control method for paraplegic patients, comprising the following steps:

Step 1: Set the lower limb exoskeleton rehabilitation training safety range for different patients. After the patient wears the lower limb exoskeleton, the central controller records the position of the restricted posture of the rehabilitation exercise;

Step 2, start the lower limb exoskeleton, after the patient grasps the crutches, the system initializes and enters the standing posture (zero position), and enters the waiting mode selection state;

Step 3, mode selection: select the passive rehabilitation training mode, the paraplegic patient controls the lower limb exoskeleton to walk, sit and stand through the crutch module, and the crutch module transmits the instructions to the central controller through the 2.4G wireless module; the central controller will The control command is transmitted to the servo motor driver through the CAN bus;

Step 4, mode selection: select the active gait training mode, set the corresponding gait assist value according to the lower limb muscle strength of different patients; collect human body posture data, analyze the patient's gait phase and movement trend, and the central controller calculates the corresponding gait assist value. The motor drives the assist torque, and sends control commands to the servo motor driver through the CAN bus to control the lower limb exoskeleton to follow the gait trend of the paraplegic patient; achieve the effect of the lower limb exoskeleton gait following and the patient's active rehabilitation training;

Step 5: Collect and monitor the state data of the lower extremity exoskeleton, human body posture data, plantar pressure and crutch module data in real time, carry out fuzzy inference according to the given fuzzy rules, then de-fuzzy the fuzzy parameters, and output the PID control parameters to adjust accordingly. Control system parameters; the central controller issues control commands to control the lower limb exoskeleton to make corresponding posture actions;

Step 6, when the training period is over, a stop training instruction is sent through the crutch module, and the system enters a standing waiting state; in an emergency, the current training is stopped through the emergency stop button;

Specifically, the passive rehabilitation training mode of the lower extremity exoskeleton is selected, and the patient transmits the control intention command to the central controller through the crutch module, and the central controller sends the control command to the servo motor driver according to the patient's intention, according to the inner ring limit position set by different patients. , passive rehabilitation exercise training for the patient's lower limbs with specified movements. It includes the following steps:

Step A1, the mode is selected as passive rehabilitation training mode, the system is in the initial state of standing, and the central controller sets the corresponding designated standing, left/right walking, and sitting motion data sets according to the inner ring limit positions set by different patients. ;Wait to receive the crutch module control intent command;

Step A2, the built-in encoder of the motor, the left/right leg attitude sensor and the back attitude sensor enter the state data collection state, and the real-time state of the lower limb exoskeleton is transmitted to the central controller;

In step A3, the patient moves the left crutch module to drive the right side of the lower limb exoskeleton to move forward as a swing phase, and the left side of the lower limb exoskeleton acts as a support phase to stabilize the patient's body balance; on the contrary, the patient moves the right crutch module to drive the lower limb exoskeleton to the left side. The side moves forward as the swing phase, and the right side of the lower extremity exoskeleton acts as the support phase to stabilize the patient's body balance;

Step A4, move the crutch modules on both sides to both sides, press the standing command button, and the device enters the initial standing state;

Step A5, operate the crutch modules on both sides at the same time, send a sitting action command, adjust the posture of the lower limb exoskeleton, flex the knee joint inward, extend the hip joint outward, and execute the sitting action command;

Step A6, adjusting the fuzzy PID control parameters according to data changes such as attitude and motor torque in the passive training process, and outputting the corresponding motor control signal;

Step A7, the passive rehabilitation training ends, and the equipment enters the initial standing state;

Specifically, the active gait training mode of the lower limb exoskeleton is selected, and after the active gait training mode is turned on, the sensor module collects the patient's posture data, plantar pressure data, hip joint torque and crutch module pressure value; after data fusion, the lower limb exoskeleton equipment The gait phase of the patient will be identified, and at the same time, the change trend of the patient's gait will be judged, followed by the movement of the patient's gait change, and a certain assist torque will be given to the patient's lower limbs to assist the patient to complete the corresponding rehabilitation training; active rehabilitation training can greatly It can improve the enthusiasm and rehabilitation effect of patients for rehabilitation training. Specifically include the following steps:

Step B1: The mode is selected as the active gait training mode, the corresponding gait assist value is set according to the lower limb muscle strength of different patients, and the limit of the gait training inner ring is set; the lower limb exoskeleton is in the standing initialization state;

Step B2: Turn on the active gait training mode, and the sensor module collects the human-machine data of the patient and the lower limb exoskeleton, including the patient's posture data, plantar pressure data and its change trend, hip joint torque, angular velocity, and crutch module pressure value and its change trend ;

Step B3: use the Kalman filter algorithm to process the human body posture data of the sensor module, and combine the data sets of the hip joint angle, torque and plantar pressure sensor group to perform data fusion, and analyze the patient's gait phase and gait phase trend;

Step B4: adjust the fuzzy PID control parameters according to the data changes such as attitude and motor torque in the active training process, and the central controller outputs the corresponding control signal to adjust the control parameters of the servo driver;

Step B5: follow the patient's gait intention to carry out auxiliary rehabilitation training, and perform active walking and standing actions;

Step B6: End the active gait training, and the lower limb exoskeleton enters the standing waiting state;

On the other hand, the present invention compares the advantages and disadvantages of common communication methods and the use of hardware resources of the lower limb exoskeleton hardware platform, and determines that the bottom layer between the modules of the present invention is CAN communication, and adopts CANopen communication protocol; Function, the CANopen network communication architecture of the entire control system is shown in Figure 4; the central controller is the master, the two servo drives of the hip joint, the sensor module system, and the power module are used as slaves, and each slave has a corresponding CAN network interface.

The above are only preferred embodiments of the present invention, and do not have any limiting effect on the present invention. Any person skilled in the art, within the scope of not departing from the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, all belong to the technical solution of the present invention. content still falls within the protection scope of the present invention.

Claims (7)

1. a lower extremity exoskeleton control system for paraplegic patients, is characterized in that, comprises wearable lower extremity exoskeleton, central controller, human-computer interaction device, power supply module, sensor module and power drive device; The wearable lower extremity exoskeleton is designed with the human lower extremity structure, including: back support, leg support and sole support; The central controller and the power module are fixed on the back support, and the sensor module includes a left/right leg posture sensor, a knee joint angle sensor, a back posture sensor and a hip sensor respectively arranged at the corresponding joints of the leg support. A joint angle sensor, and a plantar pressure sensor including a plantar support; The power drive device drives each part of the wearable lower limb exoskeleton to move, and the human-computer interaction device, the power supply module, the sensor module and the power drive device are all signal-connected to the central controller. 2 . The lower limb exoskeleton control system for paraplegic patients according to claim 1 , further comprising a crutch, a crutch pressure sensor is provided on the crutch, and the crutch pressure sensor is connected to the central control system. 3 . signal connection. 3. The lower limb exoskeleton control system for paraplegic patients according to claim 1, wherein the power drive device comprises a left hip motor, a right hip motor and two servo drives; The left hip motor and the right hip motor are respectively arranged on the left and right hips of the leg support, and are respectively driven by the two servo motors. 4. A lower extremity exoskeleton gait following control method for paraplegic patients, realized using the lower extremity exoskeleton control system as described in claims 1-3, including monitoring of human body posture data and gait phase analysis; The sensor module is used to obtain the real-time data of rehabilitation exercise, the Kalman filter algorithm is used to process the human body posture data of the sensor module, and combined with the measurement of the hip joint angle of the lower extremity exoskeleton, we get:

K ₁ , K ₂ are, respectively, Δθ,

gain,

and

are the estimated and predicted values of Δθ, respectively;

As the measurement value of the last moment of the offset, more accurate human body posture data can be obtained; The gait phase analysis method is as follows: according to the pressure values output by the four pressure sensors of the plantar pressure sensor group in different time periods and the movement angle value of the exoskeleton hip joint, a proportional algorithm is used to analyze the collected human body posture data. Data fusion to identify gait phases. The overall block diagram of the proportional algorithm is shown in Figure 2. Combined with the different gait phase, the change range of the exoskeleton hip joint angle and the threshold angle when the gait phase is switched, the accurate gait phase is obtained by analysis. The control method mainly lies in the fusion of motion data. The main process is as follows: firstly, the summation of the four plantar pressure signals is performed to determine the total pressure of the selected pressure area of the plantar; P ₁ , P ₂ , P ₃ and P ₄ are respectively It is used to represent the proportion of the plantar pressure sensors _FSRA , _FSRB , _FSRC and _FSRD to the total pressure signal. The plantar pressure value detected by the selected pressure area, P _i (i=1, 2, 3, 4) is expressed as follows:

Set the proportional value thresholds pinv _FSRA , pinv _FSRB , pinv _FSRC , pinv _FSRD ; pinv _FSRA , pinv _FSRB , pinv _FSRC , pinv _FSRD are the thresholds of Pi ( _i =1, 2, 3, 4) respectively; the setting of the thresholds According to the value of P _i (i=1, 2, 3, 4), which accounts for the largest proportion of different gait phases, and combined with actual data, the threshold for identifying gait phases is divided. 5. A lower extremity exoskeleton control method for paraplegic patients, using the lower extremity exoskeleton control system according to any one of claims 1-3, and the lower extremity exoskeleton gait following control method in claim 4 , is characterized in that, comprises the following steps: Step 1: Set the lower limb exoskeleton rehabilitation training safety range for different patients. After the patient wears the lower limb exoskeleton, the central controller records the position of the restricted posture of the rehabilitation exercise; Step 2, start the lower limb exoskeleton, after the patient grasps the crutches, the system initializes and enters the standing posture (zero position), and enters the waiting mode selection state; Step 3, mode selection: select the passive rehabilitation training mode, the paraplegic patient controls the lower limb exoskeleton to perform walking, sitting and standing training actions through the crutch module, and the crutch module transmits the instructions to the central controller; the central controller transmits the control instructions to the servo motor driver; Or choose the active gait training mode, and set the corresponding gait assist value according to the lower limb muscle strength of different patients; collect human body posture data, analyze the patient's gait phase and movement trend, and the central controller calculates the corresponding motor drive assist torque, send control commands into the servo motor driver, and control the lower limb exoskeleton to follow the gait trend of the paraplegic patient; Step 4: Collect and monitor the state data of the lower extremity exoskeleton, human body posture data, plantar pressure and crutch module data in real time, carry out fuzzy inference according to the given fuzzy rules, then de-fuzzy the fuzzy parameters, and output the PID control parameters to adjust accordingly. Control system parameters; the central controller issues control commands to control the lower limb exoskeleton to make corresponding posture actions; Step 6: After the training period is over, a stop training instruction is sent through the crutch module, and the system enters a standing waiting state; in an emergency, the current training is stopped through the emergency stop button. 6. The lower limb exoskeleton control method for paraplegic patients according to claim 5, wherein the passive rehabilitation training mode comprises the following steps: Step A1, the mode is selected as passive rehabilitation training mode, the system is in the initial state of standing, and the central controller sets the corresponding designated standing, left/right walking, and sitting motion data sets according to the inner ring limit positions set by different patients. ;Wait to receive the crutch module control intent command; Step A2, the built-in encoder of the motor, the left/right leg attitude sensor and the back attitude sensor enter the state data collection state, and the real-time state of the lower limb exoskeleton is transmitted to the central controller; In step A3, the patient moves the left crutch module to drive the right side of the lower limb exoskeleton to move forward as a swing phase, and the left side of the lower limb exoskeleton acts as a support phase to stabilize the patient's body balance; on the contrary, the patient moves the right crutch module to drive the lower limb exoskeleton to the left side. The side moves forward as the swing phase, and the right side of the lower extremity exoskeleton acts as the support phase to stabilize the patient's body balance; Step A4, move the crutch modules on both sides to both sides, press the standing command button, and the device enters the initial standing state; Step A5, operate the crutch modules on both sides at the same time, send a sitting action command, adjust the posture of the lower limb exoskeleton, flex the knee joint inward, extend the hip joint outward, and execute the sitting action command; Step A6, adjusting the fuzzy PID control parameters according to data changes such as attitude and motor torque in the passive training process, and outputting the corresponding motor control signal; Step A7, the passive rehabilitation training ends, and the device enters the initial standing state. 7. The lower limb exoskeleton control method for paraplegic patients according to claim 5, wherein the active gait training mode comprises the following steps: Step B1: The mode is selected as the active gait training mode, the corresponding gait assist value is set according to the lower limb muscle strength of different patients, and the limit of the gait training inner ring is set; the lower limb exoskeleton is in the standing initialization state; Step B2: Turn on the active gait training mode, and the sensor module collects the human-machine data of the patient and the lower limb exoskeleton, including the patient's posture data, plantar pressure data and its change trend, hip joint torque, angular velocity, and crutch module pressure value and its change trend ; Step B3: use the Kalman filter algorithm to process the human body posture data of the sensor module, and combine the data sets of the hip joint angle, torque and plantar pressure sensor group to perform data fusion, and analyze the patient's gait phase and gait phase trend; Step B4: adjust the fuzzy PID control parameters according to the data changes such as attitude and motor torque in the active training process, and the central controller outputs the corresponding control signal to adjust the control parameters of the servo driver; Step B5: follow the patient's gait intention to carry out auxiliary rehabilitation training, and perform active walking and standing actions; Step B6: End the active gait training, and the lower limb exoskeleton enters the standing waiting state.

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