CN114903750A - Lower limb exoskeleton control system and lower limb exoskeleton control method for paraplegic patient - Google Patents
Lower limb exoskeleton control system and lower limb exoskeleton control method for paraplegic patient Download PDFInfo
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Abstract
The invention provides a lower limb exoskeleton control system and a lower limb exoskeleton control method for paraplegic patients, wherein the system comprises the following steps: the exoskeleton control system comprises a central controller, a power supply module, a sensor module, a human-computer interaction device, an exoskeleton power driving device and a wearable lower limb exoskeleton; collecting information data of hip joints, leg postures, back postures, ankle pressure and crutch pressure of a human body through a sensor module, and establishing a gait phase data set of a patient; according to the rehabilitation training gait phase of the paraplegic patient, the swing phase hip joint motor of the external skeleton power driving module outputs a forward movement direction assistance moment, and outputs a synchronous backward extension direction assistance moment to the support phase hip joint; the active training control on the lower limb exoskeleton is completed, and the requirement of rehabilitation training of the active muscle strength of the paraplegia patient is met. The invention can control the lower limb exoskeleton, can realize the early passive rehabilitation training of the paraplegic patient and the middle active rehabilitation training according to the posture change of the patient, and realizes the active muscle strength recovery process.
Description
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
The annual incidence rate of patients with limb mobility disorder caused by spinal cord injury is between 25 ten thousand and 50 ten thousand worldwide, the spinal cord injury is mainly young and strong, and accounts for eighty percent of the age below 40 years, which causes long-term and heavy economic burden to families and society. The traditional paraplegia orthosis is unpowered, and is most common in a bilateral hip-knee-ankle-foot orthosis (HKAFO) or a bilateral knee-ankle-foot orthosis (KAFO), and the walking stick is needed when standing or walking rehabilitation training is carried out mainly by means of forward leaning of the body gravity center and lateral leaning of the pelvis of a patient to achieve striding. With the progress of science and technology, lower limb rehabilitation robots, which are products of combining engineering such as robots, novel sensing technologies, electromechanical integration and the like with medicine and bionics, begin to appear. The lower limb exoskeleton robot integrating the robot technology and the rehabilitation medicine principle can enable paraplegic patients to achieve the purpose of rehabilitation treatment during walking, is a new means for solving the rehabilitation problem of lower limb dysfunction, and plays an important role in the medical rehabilitation process.
Disclosure of Invention
The invention aims to provide a limb exoskeleton control system and a lower limb exoskeleton control method which are effectively applied to lower limb rehabilitation training of paraplegic patients.
In order to achieve the purpose, the invention provides a lower limb exoskeleton control system for paraplegic patients, which comprises a wearable lower limb exoskeleton, a central controller, a man-machine interaction device, a power supply module, a sensor module and a power driving device, wherein the wearable lower limb exoskeleton is connected with the central controller;
the wearable lower limb exoskeleton is designed by a human body lower limb structure, and comprises: back support, leg support and plantar support;
the central controller and the power supply module are fixed on the back support, and the sensor module comprises a left/right leg posture sensor, a knee joint angle sensor, a back posture sensor and a hip joint angle sensor which are respectively arranged at the positions of corresponding joints of the leg support, and a sole pressure sensor arranged on the sole support;
the power driving device drives all parts of the wearable lower limb exoskeleton to move, and the human-computer interaction device, the power supply module, the sensor module and the power driving device are in signal connection with the central controller.
Furthermore, the walking stick is further provided with a walking stick pressure sensor, and the walking stick pressure sensor is in signal connection with the central controller.
Furthermore, the power driving device comprises a left hip motor, a right hip motor and two servo drivers;
the left hip motor and the right hip motor are respectively arranged on the left hip and the right hip of the leg support and are respectively driven by the two servo motors.
The invention also provides a lower limb exoskeleton gait following control method for the paraplegic patient, which comprises the steps of monitoring human body posture data and analyzing gait phases;
the method comprises the following steps of acquiring real-time rehabilitation exercise data by using a sensor module, processing human posture data of the sensor module by adopting a Kalman filtering algorithm, and measuring the hip joint angle of the lower limb exoskeleton to obtain:
K 1 ,K 2 the values of, respectively, Δ θ,
the gain of (a) is obtained,
and
respectively an estimated value and a predicted value of delta theta;
and the measured value of the offset at the last moment is used for obtaining more accurate human body posture data.
The gait phase analysis method comprises the following steps: and carrying out data fusion on the collected human body posture data by adopting a proportional algorithm according to the pressure values output by the 4 pressure sensors of the foot pressure sensor group at different time periods and the motion angle values at the exoskeleton hip joint, and identifying the gait phase. A general block diagram of the scaling algorithm is shown in fig. 2. And analyzing to obtain an accurate gait phase by combining the different dynamic phases, the change amplitude of the exoskeleton hip joint angle and the threshold angle during switching the gait phase.
The control method mainly comprises the following steps of motion data fusion, wherein the process mainly comprises the following steps: firstly, summing the 4 sole pressure signals to determine the pressure sum of the pressure areas selected by the soles; p 1 、P 2 、P 3 And P 4 Respectively representing the ratio of the plantar pressure sensors FSRA, FSRB, FSRC and FSRD to the sum of the pressure signals, F FSRA 、F FSRB 、F FSRC And F FSRD Respectively represent the pressure values of the sole, P, detected by the pressure sensors FSRA, FSRB, FSRC and FSRD in the selected pressure area at the same time i (i ═ 1,2,3,4) is as follows:
setting proportional value threshold value pinv FSRA 、pinv FSRB 、pinv FSRC 、pinv FSRD ；pinv FSRA 、pinv FSRB 、pinv FSRC 、pinv FSRD Are respectively P i (i ═ 1,2,3, 4); setting of threshold value according to P i The maximum occupation value (i is 1,2,3,4) in the asynchronous gait phase is combined with the actual data condition to divide and identify the threshold value of the gait phase.
The invention also provides a lower limb exoskeleton control method for the paraplegic patient, which comprises the following steps:
step 1, setting a lower limb exoskeleton rehabilitation training safety range for different patients, and recording rehabilitation movement limit posture positions through a central controller after the patients wear the lower limb exoskeleton;
and step 3, mode selection: a passive rehabilitation training mode is selected, 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 instructions to the central controller; the central controller transmits a control command to the servo motor driver;
or selecting an active gait training mode, and setting corresponding gait assistance values according to the muscle strength conditions of the lower limbs of different patients; collecting human body posture data, analyzing gait phase and movement trend of a patient, calculating corresponding motor driving assistance torque by a central controller, sending a control command to a servo motor driver, and controlling the lower limb exoskeleton to move along with the gait trend of the paraplegia patient;
step 4, acquiring and monitoring the lower limb exoskeleton state data, the human body posture data, the sole pressure and the crutch module data in real time, performing fuzzy reasoning according to a given fuzzy rule, then performing defuzzification on fuzzy parameters, and outputting PID control parameters so as to adjust control system parameters; the central controller sends out a control command to control the lower limb exoskeleton to make corresponding posture action;
step 6, after the training period is finished, sending a training stopping instruction through the crutch module, and enabling the system to enter a standing waiting state; emergency stops the current training by the emergency stop button.
Further, the passive rehabilitation training mode comprises the following steps:
step A1, selecting a passive rehabilitation training mode, setting the system in a standing initial state, and setting corresponding designated standing, left/right walking and sitting action data sets by the central controller according to inner ring limit positions set by different patients; waiting for receiving a crutch module control intention instruction;
step A2, a built-in encoder of the motor, a left/right leg posture sensor and a back posture sensor enter a state data acquisition state, and the real-time state of the lower limb exoskeleton is transmitted to a central controller;
step A3, the patient moves the left crutch module to drive the right side of the lower limb exoskeleton to move forwards as a swinging phase, and the left side of the lower limb exoskeleton is used as a supporting phase to stabilize the body balance of the patient; conversely, the patient moves the right crutch module to drive the left side of the lower limb exoskeleton to move forwards as a swing phase, and the right side of the lower limb exoskeleton is used as a support phase to stabilize the body balance of the patient;
step A4, moving the crutch modules at two sides to two sides, pressing down a standing instruction button, and enabling the equipment to enter a standing initial state;
step A5, operating the walking stick modules at two sides simultaneously, sending sitting action instructions, adjusting the posture by the lower limb exoskeleton, bending the knee joint inwards, extending the hip joint outwards, and executing the sitting action instructions;
step A6, adjusting fuzzy PID control parameters according to data changes such as passive training process attitude, motor torque and the like, and outputting corresponding motor control signals;
step a7, the passive rehabilitation training is finished, and the device enters the standing initial state.
Further, the active gait training mode comprises the steps of:
step B1: the mode selection is an active gait training mode, corresponding gait assistance values are set according to the muscle strength conditions of the lower limbs of different patients, and the gait training inner ring limit is set; the lower limb exoskeleton is in a standing initialization state;
step B2: starting an active gait training mode, and acquiring human-computer data of a patient and a lower limb exoskeleton by a sensor module, wherein the human-computer data comprises posture data of the patient, plantar pressure data and variation trend thereof, hip joint torque, angular velocity, a pressure value of a crutch module and variation trend thereof;
step B3: processing human body posture data of the sensor module by adopting a Kalman filtering algorithm, combining a hip joint angle, moment and a plantar pressure sensor group data set, carrying out data fusion, and analyzing gait phases and gait phase trends of patients;
step B4: adjusting fuzzy PID control parameters according to data changes such as the posture of the active training process, the motor moment and the like, and outputting corresponding control signals by the central controller to adjust the control parameters of the servo driver;
step B5: carrying out auxiliary rehabilitation training along with the gait intention of the patient, and carrying out active walking and standing actions;
step B6: and finishing the active gait training, and enabling the lower limb exoskeleton to enter a standing waiting state.
Compared with the prior art, the invention has the advantages that: the lower limb exoskeleton active rehabilitation training mode for the paraplegic patient can provide certain assistance torque to assist the patient to carry out rehabilitation training along with the gait change trend of the patient, monitors the hip-knee joint state, the sole region pressure and the crutch region pressure of the patient in real time, and regulates and controls the lower limb exoskeleton control parameters in real time to adapt to disturbance changes under different conditions, so that the effect of following the active rehabilitation training on the gait of the patient is realized.
Drawings
FIG. 1 is a schematic diagram of a lower extremity exoskeleton control system for a paraplegic patient in accordance with an embodiment of the present invention;
FIG. 2 is a general block diagram of a scaling algorithm in the gait phase analysis method according to the embodiment of the invention;
FIG. 3 is a system block diagram of a lower extremity exoskeleton control system for a paraplegic patient in an embodiment of the present invention;
FIG. 4 is a diagram of a CANopen network communication architecture in an embodiment of the present invention;
FIG. 5 is a block diagram of a PID closed loop control block diagram in an embodiment of the invention;
fig. 6 is a schematic view of a plantar pressure sensor group according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be further described below.
As shown in fig. 1, the present invention provides a lower extremity exoskeleton control system for paraplegic patients, which comprises a wearable lower extremity exoskeleton, a central controller 100, a human-computer interaction device, a power module 200, a sensor module and a power driving device;
wearable lower limbs ectoskeleton includes with human lower limbs structural design: back support, leg support and plantar support;
the central controller 100 and the power module 200 are fixed to 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 360 respectively provided at positions of respective joints of the leg support, and includes a plantar pressure sensor 340 and a knee joint angle sensor 320 provided at the plantar support.
The power driving device drives all parts of the wearable lower limb exoskeleton to move, and the human-computer interaction device, the power supply module 200, the sensor module and the power driving device are in signal connection with the central controller.
The walking stick is provided with a walking stick pressure sensor which is in signal connection with the central controller, and the patch film pressure sensors which are arranged at the elbow rest and the handle of the walking stick are used for judging the change of pressure values at the left side and the right side through the change of force applied to the left walking stick and the right walking stick by a human body during walking; the walking intention of a walker is predicted in advance through data processing, and when a human body wants to take a step, the hip joint motor can move automatically, so that the perception function of the exoskeleton is realized.
The power driving device comprises 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 hip and the right hip of the leg support and are respectively driven by the two servo motors. The exoskeleton power driving module comprises three-phase permanent magnet synchronous alternating current servo motors, servo drivers and speed reducers at the left hip joint and the right hip joint and provides power for the lower limb exoskeleton; the power supply module supplies power to the central controller and the exoskeleton power driving device through voltage conversion. The power supply system comprises 12V and 5V DC-DC voltage reduction circuits and a 3.3V voltage stabilizing circuit, and the power supply module hardware circuit further comprises a voltage detection and overcurrent protection part.
The left and right plantar pressure sensor groups are shown in fig. 6, and each group consists of four pressure sensors; the function is to judge the walking gait phase of the human body when the exoskeleton walks, namely the gait phase is in the supporting phase or the swinging phase.
The man-machine interaction module mainly comprises an upper computer and a crutch module, and is a main interaction means for a user to use the exoskeleton. The upper computer sends the control information to the central controller through the sending data packet, and the central controller receives and executes the data. The man-machine interaction of the crutch is realized through a circuit embedded in the crutch, control information is transmitted to the central controller mainly through wireless transmission, and the central controller transmits control data to each part through CAN communication.
The invention also provides a lower limb exoskeleton gait following control method for paraplegic patients, which comprises the steps that a sensor module acquires real-time rehabilitation movement data, wherein the human body posture data of the sensor module is processed by adopting a Kalman filtering algorithm, and the method can be obtained by combining the measurement of the angle of the hip joint of the lower limb exoskeleton:
K 1 ,K 2 the values of, respectively, Δ θ,
the gain of (a) is obtained,
and
respectively, an estimated value and a predicted value of Δ θ.
And the measured value of the offset at the last moment is used for obtaining more accurate human body posture data.
Meanwhile, the gait phase analysis method comprises the following steps: and carrying out data fusion on the collected human body posture data by adopting a proportional algorithm according to the pressure values output by the 4 pressure sensors of the foot pressure sensor group at different time periods and the motion angle values at the exoskeleton hip joint, and identifying the gait phase. A general block diagram of the scaling algorithm is shown in fig. 2. And analyzing to obtain an accurate gait phase by combining the different dynamic phases, the change amplitude of the exoskeleton hip joint angle and the threshold angle during switching the gait phase.
The control method mainly comprises the following steps of motion data fusion, wherein the process mainly comprises the following steps: the 4 sole pressure signals are summed to determine the pressure sum of the selected pressure areas of the sole. P 1 、P 2 、P 3 And P 4 Respectively representing the ratio of the plantar pressure sensors FSRA, FSRB, FSRC and FSRD to the sum of the pressure signals, F FSRA 、F FSRB 、F FSRC And F FSRD Respectively representing the detection of the selected pressure regions of the sole pressure sensors (FIG. 6) FSRA, FSRB, FSRC and FSRD at the same timePressure value, P i (i ═ 1,2,3,4) is as follows:
setting proportional value threshold value pinv FSRA 、pinv FSRB 、pinv FSRC 、pinv FSRD ；pinv FSRA 、pinv FSRB 、pinv FSRC 、pinv FSRD Are respectively P i (i is 1,2,3, 4). Setting of threshold value according to P i The value of (i ═ 1,2,3,4) has the largest proportion in the out-of-sync phases, and the threshold value for identifying the gait phase is divided according to the actual data situation.
According to the lower limb exoskeleton control system for the paraplegia patient, the control system block diagram is shown in fig. 3, rehabilitation training data are obtained, a target motion trajectory is adjusted according to the limb motion trend of the patient and the active training motion requirement, and gait following of the paraplegia patient is achieved; the motion position range is divided into an outer ring and an inner ring, the outer ring is used for mechanical limitation of the wearable lower limb exoskeleton, and the inner ring is used for position limitation of the inner ring according to the lower limb flexion and extension safety ranges of different patients; presetting a motion assistance value, and optimally adjusting the control parameters of the lower limb exoskeleton by adopting a fuzzy PID control algorithm according to the control characteristics of real-time change of the active rehabilitation training parameters; the specific control flow diagram is shown in FIG. 5;
according to one aspect of the invention, a method of controlling a lower extremity exoskeleton for a paraplegic patient, comprises the steps of:
step 1, setting a lower limb exoskeleton rehabilitation training safety range for different patients, and recording rehabilitation movement limit posture positions through a central controller after the patients wear the lower limb exoskeleton;
and step 3, mode selection: a passive rehabilitation training mode is selected, 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 instructions to the central controller through the 2.4G wireless module; the central controller transmits a control command to the servo motor driver through the CAN bus;
step 4, mode selection: selecting an active gait training mode, and setting corresponding gait assistance values according to the muscle force conditions of the lower limbs of different patients; collecting human body posture data, analyzing gait phase and movement trend of a patient, calculating corresponding motor-driven assistance torque by a central controller, sending a control command to a servo motor driver through a CAN bus, and controlling the lower limb exoskeleton to move along with the gait trend of the paraplegia patient; the effects that the exoskeleton gait of the lower limbs follows and the patient actively carries out rehabilitation training are achieved;
step 5, acquiring and monitoring the lower limb exoskeleton state data, the human body posture data, the sole pressure and the crutch module data in real time, performing fuzzy reasoning according to a given fuzzy rule, then performing defuzzification on fuzzy parameters, and outputting PID control parameters so as to adjust control system parameters; the central controller sends out a control command to control the lower limb exoskeleton to make corresponding posture action;
step 6, after the training period is finished, sending a training stopping instruction through the crutch module, and enabling the system to enter a standing waiting state; stopping the current training in emergency through an emergency stop button;
specifically, a lower limb exoskeleton passive rehabilitation training mode is selected, a patient transmits a control intention instruction to a central controller through a crutch module, the central controller sends the control instruction to a servo motor driver according to the intention of the patient, and the lower limb of the patient is subjected to passive rehabilitation exercise training of appointed action according to inner ring limit positions set by different patients. The method comprises the following steps:
step A1, selecting a passive rehabilitation training mode, setting the system in a standing initial state, and setting corresponding designated standing, left/right walking and sitting action data sets by the central controller according to inner ring limit positions set by different patients; waiting for receiving a crutch module control intention instruction;
step A2, a built-in encoder of the motor, a left/right leg posture sensor and a back posture sensor enter a state data acquisition state, and the real-time state of the lower limb exoskeleton is transmitted to a central controller;
step A3, the patient moves the left crutch module to drive the right side of the lower limb exoskeleton to move forwards as a swing phase, and the left side of the lower limb exoskeleton is used as a support phase to stabilize the body balance of the patient; on the contrary, the patient moves the right crutch module to drive the left side of the lower limb exoskeleton to move forwards as a swinging phase, and the right side of the lower limb exoskeleton is used as a supporting phase to stabilize the body balance of the patient;
step A4, moving the crutch modules at two sides to two sides, pressing down a standing instruction button, and enabling the equipment to enter a standing initial state;
step A5, operating the walking stick modules at two sides simultaneously, sending sitting action instructions, adjusting the posture by the lower limb exoskeleton, bending the knee joint inwards, extending the hip joint outwards, and executing the sitting action instructions;
step A6, adjusting fuzzy PID control parameters according to data changes such as passive training process attitude, motor torque and the like, and outputting corresponding motor control signals;
step A7, finishing the passive rehabilitation training, and enabling the equipment to enter a standing initial state;
specifically, a lower limb exoskeleton active gait training mode is selected, and after the active gait training mode is started, a sensor module collects posture data, plantar pressure data, hip joint torque and a crutch module pressure value of a patient; after data fusion, the lower limb exoskeleton equipment identifies the gait phase of the patient, judges the gait change trend of the patient, moves along with the gait change of the patient, and gives a certain assistance torque to the lower limb of the patient to assist the patient to complete corresponding rehabilitation training; the initiative rehabilitation training can greatly improve the enthusiasm and the rehabilitation effect of the patient for rehabilitation training. The method specifically comprises the following steps:
step B1: the mode selection is an active gait training mode, corresponding gait assistance values are set according to the muscle force conditions of the lower limbs of different patients, and the gait training inner ring is set for limiting; the lower limb exoskeleton is in a standing initialization state;
step B2: starting an active gait training mode, and acquiring human-computer data of a patient and a lower limb exoskeleton by a sensor module, wherein the human-computer data comprises posture data of the patient, plantar pressure data and variation trend thereof, hip joint torque, angular velocity, a pressure value of a crutch module and variation trend thereof;
step B3: processing human body posture data of the sensor module by adopting a Kalman filtering algorithm, combining a hip joint angle, moment and a plantar pressure sensor group data set, carrying out data fusion, and analyzing gait phases and gait phase trends of patients;
step B4: adjusting fuzzy PID control parameters according to data changes such as the posture of the active training process, the motor moment and the like, and outputting corresponding control signals by the central controller to adjust the control parameters of the servo driver;
step B5: carrying out auxiliary rehabilitation training along with the gait intention of the patient, and carrying out active walking and standing actions;
step B6: finishing active gait training, and enabling the lower limb exoskeleton to enter a standing waiting state;
on the other hand, compared with the advantages and the disadvantages of the common communication mode and the use condition of the hardware resources of the lower extremity exoskeleton hardware platform, the bottom layer between the modules of the invention is determined to be CAN communication, and a CANopen communication protocol is adopted; the CANopen network communication architecture of the whole control system is shown in FIG. 4; the central controller is a host, the two servo drivers of the hip joint, the sensor module system and the power supply module are slaves, and each slave is provided with a corresponding CAN network interface.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A lower limb exoskeleton control system for paraplegic patients is characterized by comprising a wearable lower limb exoskeleton, a central controller, a man-machine interaction device, a power supply module, a sensor module and a power driving device;
wearable lower limbs ectoskeleton includes with human lower limbs structural design: back support, leg support and plantar support;
the central controller and the power supply module are fixed on the back support, and the sensor module comprises a left/right leg posture sensor, a knee joint angle sensor, a back posture sensor and a hip joint angle sensor which are respectively arranged at the positions of corresponding joints of the leg support, and a sole pressure sensor arranged on the sole support;
the power driving device drives all parts of the wearable lower limb exoskeleton to move, and the human-computer interaction device, the power supply module, the sensor module and the power driving device are in signal connection with the central controller.
2. The lower extremity exoskeleton control system for paraplegic patients of claim 1, further comprising a crutch, said crutch having a crutch pressure sensor in signal communication with said central controller.
3. The lower extremity exoskeleton control system for paraplegic patients of claim 1 wherein said powered drive means 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 hip and the right hip of the leg support and are respectively driven by the two servo motors.
4. A lower extremity exoskeleton gait follow-up control method for paraplegic patients, implemented using the lower extremity exoskeleton control system of claims 1-3, comprising human body posture data monitoring and gait phase analysis;
the method comprises the following steps of acquiring real-time rehabilitation exercise data by using a sensor module, processing human body posture data of the sensor module by adopting a Kalman filtering algorithm, and measuring the angle of a hip joint of the exoskeleton of the lower limb to obtain:
K 1 ,K 2 the values of, respectively, Δ θ,
the gain of (a) is obtained,
and
respectively an estimated value and a predicted value of delta theta;
the measured value of the last moment of the offset is used for obtaining more accurate human body posture data;
the gait phase analysis method comprises the following steps: and carrying out data fusion on the collected human body posture data by adopting a proportional algorithm according to the pressure values output by the 4 pressure sensors of the foot pressure sensor group at different time periods and the motion angle values at the exoskeleton hip joint, and identifying the gait phase. A general block diagram of the scaling algorithm is shown in fig. 2. And analyzing to obtain an accurate gait phase by combining the different dynamic phases, the change amplitude of the exoskeleton hip joint angle and the threshold angle during switching the gait phase.
The control method mainly comprises the following steps of motion data fusion, wherein the process mainly comprises the following steps: firstly, summing the 4 sole pressure signals to determine the pressure sum of the pressure areas selected by the soles; p is 1 、P 2 、P 3 And P 4 Respectively representing the ratio of the plantar pressure sensors FSRA, FSRB, FSRC and FSRD to the sum of the pressure signals, F FSRA 、F FSRB 、F FSRC And F FSRD Respectively represent the pressure values of the sole, P, detected by the pressure sensors FSRA, FSRB, FSRC and FSRD in the selected pressure area at the same time i (i ═ 1,2,3,4) is as follows:
setting proportional value threshold value pinv FSRA 、pinv FSRB 、pinv FSRC 、pinv FSRD ；pinv FSRA 、pinv FSRB 、pinv FSRC 、pinv FSRD Are each P i (i ═ 1,2,3, 4); setting of threshold value according to P i The value of (i ═ 1,2,3,4) has the largest proportion in the out-of-sync phases, and the threshold value for identifying the gait phase is divided according to the actual data situation.
5. A method of lower extremity exoskeleton control for paraplegic patients using the lower extremity exoskeleton control system of any one of claims 1 to 3 and the lower extremity exoskeleton gait following control method of claim 4, comprising the steps of:
step 1, setting a lower limb exoskeleton rehabilitation training safety range for different patients, and recording rehabilitation movement limit posture positions through a central controller after the patients wear the lower limb exoskeleton;
step 2, starting the lower limb exoskeleton, initializing the system to enter a standing posture (zero position) after a patient grasps the crutch, and entering a waiting mode selection state;
and step 3, mode selection: a passive rehabilitation training mode is selected, 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 instructions to the central controller; the central controller transmits a control command to the servo motor driver;
or selecting an active gait training mode, and setting corresponding gait assistance values according to the muscle strength conditions of the lower limbs of different patients; collecting human body posture data, analyzing gait phase and movement trend of a patient, calculating corresponding motor driving assistance torque by a central controller, sending a control command to a servo motor driver, and controlling the lower limb exoskeleton to move along with the gait trend of the paraplegia patient;
step 4, acquiring and monitoring the lower limb exoskeleton state data, the human body posture data, the sole pressure and the crutch module data in real time, performing fuzzy reasoning according to a given fuzzy rule, then performing defuzzification on fuzzy parameters, and outputting PID control parameters so as to adjust control system parameters; the central controller sends out a control command to control the lower limb exoskeleton to make corresponding posture action;
step 6, after the training period is finished, sending a training stopping instruction through the crutch module, and enabling the system to enter a standing waiting state; emergency stops the current training by the emergency stop button.
6. The method of claim 5, wherein the passive rehabilitation training mode comprises the steps of:
step A1, selecting a passive rehabilitation training mode, setting the system in a standing initial state, and setting corresponding designated standing, left/right walking and sitting action data sets by the central controller according to inner ring limit positions set by different patients; waiting for receiving a crutch module control intention instruction;
step A2, a built-in encoder of the motor, a left/right leg posture sensor and a back posture sensor enter a state data acquisition state, and the real-time state of the lower limb exoskeleton is transmitted to a central controller;
step A3, the patient moves the left crutch module to drive the right side of the lower limb exoskeleton to move forwards as a swinging phase, and the left side of the lower limb exoskeleton is used as a supporting phase to stabilize the body balance of the patient; on the contrary, the patient moves the right crutch module to drive the left side of the lower limb exoskeleton to move forwards as a swinging phase, and the right side of the lower limb exoskeleton is used as a supporting phase to stabilize the body balance of the patient;
step A4, moving the crutch modules at two sides to two sides, pressing down a standing instruction button, and enabling the equipment to enter a standing initial state;
step A5, operating the walking stick modules at two sides simultaneously, sending sitting action instructions, adjusting the posture by the lower limb exoskeleton, bending the knee joint inwards, extending the hip joint outwards, and executing the sitting action instructions;
step A6, adjusting fuzzy PID control parameters according to data changes such as passive training process attitude, motor torque and the like, and outputting corresponding motor control signals;
step a7, the passive rehabilitation training is finished, and the device enters the standing initial state.
7. The method of claim 5, wherein the active gait training mode comprises the steps of:
step B1: the mode selection is an active gait training mode, corresponding gait assistance values are set according to the muscle force conditions of the lower limbs of different patients, and the gait training inner ring is set for limiting; the lower limb exoskeleton is in a standing initialization state;
step B2: starting an active gait training mode, and acquiring human-computer data of a patient and a lower limb exoskeleton by a sensor module, wherein the human-computer data comprises posture data of the patient, plantar pressure data and variation trend thereof, hip joint torque, angular velocity, a pressure value of a crutch module and variation trend thereof;
step B3: processing human body posture data of the sensor module by adopting a Kalman filtering algorithm, combining a hip joint angle, moment and a plantar pressure sensor group data set, carrying out data fusion, and analyzing gait phases and gait phase trends of patients;
step B4: adjusting fuzzy PID control parameters according to data changes such as the posture of the active training process, the motor moment and the like, and outputting corresponding control signals by the central controller to adjust the control parameters of the servo driver;
step B5: carrying out auxiliary rehabilitation training along with the gait intention of the patient, and carrying out active walking and standing actions;
step B6: and finishing the active gait training, and enabling the lower limb exoskeleton to enter a standing waiting state.
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