CN105213153A - Based on the lower limb rehabilitation robot control method of brain flesh information impedance - Google Patents
Based on the lower limb rehabilitation robot control method of brain flesh information impedance Download PDFInfo
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Abstract
The invention discloses a kind of lower limb rehabilitation robot control method based on brain flesh information impedance, taked brain electricity and the surface electromyogram signal of patient in real time by brain electricity, surface electromyogram signal acquisition instrument, the rehabilitation degree of monitoring, assess patient.Then, different rehabilitation training strategies is taked accordingly.When rehabilitation degree is low, implements passive exercise and control, adopt PD position servo control method, control recovery set for lower limbs and make patient with correct physiology's gait orbiting motion.During rehabilitation degree height, control model of taking the initiative, by the characteristic vector of extract real-time patient EEG signals and surface electromyogram signal, makes a prediction to the motion intention of patient.With fuzzy neural network algorithm, EEG signals and surface electromyogram signal are merged again, produce the motion gait geometric locus that patient expects in real time.Then, impedance control method is utilized to realize active, the real-time collaborative control of lower limb rehabilitation robot man-machine system.
Description
Technical field
The present invention relates to Robot Control Technology, particularly a kind of control method of lower limb rehabilitation robot.
Background technology
In recent years, the lower extremity motor function impaired patients that the central nervous system disease such as spinal cord injury, apoplexy wind cause is in the trend sharply increased, and the health of the mankind in serious harm.Along with the development of society and people's medical treatment, growth in the living standard, the health of people with disability causes the concern of the whole society.Body weight support treadmill training is one of the important means for the treatment of such Disease walking rehabilitation, its effectiveness of existing a large amount of clinical research confirmations.Traditional rehabilitation therapy method mainly carries out rehabilitation training by Senior Nurse's assisting patients, and its rehabilitation training effect depends on technical merit and the love of Senior Nurse, simultaneously, Senior Nurse's quantity wretched insufficiency, training effectiveness is low, and working strength is large, so be difficult to the rehabilitation training efficiency improving rapidly patient.
, robotics is combined with rehabilitation medicine for this reason, develops intelligentized lower limb rehabilitation robot and replace Senior Nurse to complete the gait motion training of hemiplegic patient, the rehabilitation efficiency of patient can be improved significantly, alleviate the labor intensity of Senior Nurse.Many research worker are had to carry out the research work of healing robot both at home and abroad at present, but existing healing robot, training action kind is fewer, actuating range has limitation, motion amplitude is less, majority have ignored the active exercise intention of patient's lower limb, is unfavorable for exciting the active consciousness of patient and participating in the interest of rehabilitation training, is difficult to reach desirable rehabilitation training requirement.
In recent years, research institution both domestic and external have developed various types of healing robot, but mostly adopts better simply control method, could not design effective Man Machine Interface well, can not realize the Collaborative Control of man-machine system well.Application number is the Chinese patent literature of 201010561379.5 and 201310306301.2, and the angle controlled from lower limb rehabilitation robot controls for controlling source realization initiative rehabilitation to a certain degree with man-machine interaction power, surface electromyogram signal single piece of information respectively.Owing to lacking the time-variable impedance feature considering human motion characteristic, so the real-time collaborative that can not realize man-machine system well controls.In addition, lack the motor control of the Real-Time Monitoring of patient physiological information and rehabilitation scale evaluation, the Real-time Feedback realizing rehabilitation degree, look-ahead correction human body all more difficult, the active collaboration that can not realize man-machine system truly controls.
Summary of the invention
Control Problems existing for the lower limb rehabilitation robot rehabilitation exercise pointed by background technology, the object of the present invention is to provide a kind of can Real-Time Monitoring, feedback rehabilitation degree and consider patient moving compliant characteristic and impedance parameter time become the lower limb rehabilitation robot control method that feature carrys out reasonable training patient.
For achieving the above object, the present invention adopts following technical scheme to be achieved:
Based on a lower limb rehabilitation robot control method for brain flesh information impedance, it is characterized in that, comprise the steps:
(1) EEG signals of Real-time Collection brain in patients cortical rim system, and the surface electromyogram signal of quadriceps femoris and tibialis anterior;
(2) to the patient's EEG signals collected with surface electromyogram signal amplifies, the pretreatment of bandpass filtering;
(3) by feature extracting method, the time and frequency domain characteristics vector of patient's EEG signals and surface electromyogram signal is obtained;
(4) EEG signals of Healthy People and surface electromyogram signal characteristic vector and patient's EEG signals and surface electromyogram signal characteristic vector are compared, setting rehabilitation degree threshold value, when being less than this threshold value, carry out the passive rehabilitation training pattern of step (5); When being greater than this threshold value, carry out the initiative rehabilitation training mode of step (6);
(5) passive rehabilitation training pattern, adopt PD (Proportion ?Derivative) position servo control method, patient is driven by lower limb rehabilitation robot completely, carries out lower limb rehabilitation motion with physiology's gait track of standard; Meanwhile, detect angle, the angular velocity in each joint of lower limb rehabilitation robot, and as feedback signal, the movement locus of adjustment lower limb rehabilitation robot in real time;
(6) initiative rehabilitation training mode, take real-time impedance control method, specifically comprise following sub-step:
A. the impedance model of man-machine system is set up:
1. detect human-computer interaction power by man-machine interaction force transducer, and extract human-computer interaction moment by inverse dynamics model, feed back to impedance controller;
2. by the displacement of human body lower limbs and recovery set for lower limbs joint, speed, acceleration, respectively by inverse kinematics, Jacobian matrix relationship map to corresponding joint space, set up the impedance Control Model that human-computer interaction moment and recovery set for lower limbs depart from predetermined joint trajectories deviation;
3. the human body impedance model that patient departs from predetermined joint trajectories deviation and human-computer interaction moment is set up; Finally combine the impedance model setting up man-machine system:
In formula, θ
et () is for departing from the angular deviation of predetermined joint trajectories, T in human body lower limbs joint corresponding to recovery set for lower limbs joint
intt () is the man-machine interaction moment in human body lower limbs joint corresponding to recovery set for lower limbs joint; M
e, B
e, K
ebe respectively the rotary inertia of human body lower limbs, damping and rigidity; M
r, B
r, K
rbe respectively the rotary inertia of lower limb rehabilitation ESD, damping and rigidity; Wherein, M
e, B
e, K
ebecome when being;
4. adopt self adaptation UKF filtering algorithm for estimating to time-varying parameter M
e, D
e, K
ecarry out real-time online dynamic estimation, obtain the real-time impedance parameter that model is accurate, precision is higher;
B. time and frequency domain characteristics vector step (3) being got patient's EEG signals and surface electromyogram signal carries out fusion treatment by fuzzy neural network algorithm, produces the motion gait geometric locus that patient expects in real time;
C. negative feedback correction is carried out with the motion gait geometric locus of impedance model to b step 4. in a step;
D. be input in the ectoskeleton intraarticular ring position controller of recovery set for lower limbs by revised motion gait geometric locus, control the rotating angle movement in each joint, the track realizing expecting exports.
In above-mentioned steps, the feature extracting method described in step (3) is WAVELET TRANSFORM MODULUS average algorithm.
Pretreatment described in step (2), is wherein enlarged into 2000 times; The frequency of bandpass filtering be 10 ?1000Hz, and do not comprise 50Hz notched signal.
Compared with the prior art comparatively, the invention has the advantages that:
1, the present invention is according to Real-Time Monitoring, and the multi-source physiologic information (EEG signals, surface electromyogram signal) of extraction, carries out rehabilitation scale evaluation in real time.
2, by the physiologic information (EEG signals, surface electromyogram signal) that Real-Time Monitoring feeds back, the movement tendency of prediction patient, produces corresponding gait motion track expectation curve in advance, realizes rehabilitation initiatively and controls.Initiative and enthusiasm that patient participates in rehabilitation training campaign can be given full play to, strengthen its Rehabilitation confidence, alleviate the working strength of Senior Nurse.
3, under initiative rehabilitation training mode, take into full account the time-varying characteristics of human body impedance, taked impedance control method, realized the active of healing robot man-machine system, real-time collaborative has controlled, improve naturality and the compliance of man-machine interaction.
Accompanying drawing explanation
Below in conjunction with the drawings and the specific embodiments, the present invention is described in further detail
Fig. 1 is control method theory diagram of the present invention.
Fig. 2 is man-machine system's coupled impedance schematic diagram.
Fig. 3 is control hardware structural representation of the present invention.
Fig. 4 is the recovery set for lower limbs that the present invention relates to.In figure: 9, power exoskeleton; 10, medical running platform; 11, active loss of weight system; 12, movable stand.
Fig. 5 is power exoskeleton (pedipulator) structure chart in Fig. 4.In figure: 13, slider-crank mechanism; 14, ball screw linear driver; 15, servomotor; 16, gaiter mechanism.
Detailed description of the invention
See Fig. 1 and Fig. 3, motion control method of lower limb rehabilitative robot of the present invention: the physiologic information (surface electromyogram signal, EEG signals) of Real-Time Monitoring patient also carries out the assessment of Rehabilitation degree, accordingly, implement different rehabilitation training patterns: when rehabilitation degree is low, adopt passive rehabilitation training pattern, control drives patient with correct physiology's gait orbiting motion, and patient is passive, and the robot that follows does gait rehabilitation training; During rehabilitation degree height, adopt initiative rehabilitation training mode, by monitoring in real time patient physiological information (surface electromyogram signal, EEG signals) and analyze, during extraction patient moving, the feature of EEG signals and surface electromyogram signal, makes a prediction to the motion intention of patient.By the fuzzy neural network algorithm of sensing layer, these physiological signals are carried out fusion treatment, produce corresponding lower limb exoskeleton motion gait track expectation curve, realize gait TRAJECTORY CONTROL initiatively; Then, revised the gait training track of patient's expectation by impedance control method in real time, realize the real-time collaborative of lower limb rehabilitation ESD man-machine system, Shared control.Its specific implementation process comprises the steps:
1. the rehabilitation degree of couple patient is assessed, and specifically comprises:
(1) require mental skill respectively electricity, electromyographic signal collection instrument takes the EEG signals of cerebral cortex limbic system, the surface electromyogram signal of quadriceps femoris and tibialis anterior in real time.
(2) pretreatment is carried out to the EEG signals collected and surface electromyogram signal.In the present embodiment, 2000 times of amplifications are carried out to the EEG signals collected and surface electromyogram signal, and then carry out 10 ?1000Hz bandpass filtering, and do not comprise 50Hz notched signal.
(3) the time and frequency domain characteristics value of EEG signals and surface electromyogram signal is extracted.In the present embodiment, WAVELET TRANSFORM MODULUS qualitative modeling is adopted to calculate the time and frequency domain characteristics value of patient's EEG signals beta band and surface electromyogram signal, constitutive characteristic vector.
(4) using the unit vector of the characteristic vector of the EEG signals of Healthy People and surface electromyogram signal as standard, by the EEG signals of comparison patient and the difference degree of surface electromyogram signal characteristic vector and normal healthy people, carry out the rehabilitation degree of real-time assessment patient with the characteristic vector of patient and the ratio of Healthy People characteristic vector.
(5) in the present embodiment, setting judges that the threshold value of rehabilitation degree high substandard is 50%, when the characteristic vector ratio of Rehabilitation scale evaluation is less than 50%, adopt passive rehabilitation training pattern, when the characteristic vector ratio of Rehabilitation scale evaluation is greater than 50%, adopt initiative rehabilitation training mode.
2. passive rehabilitation training pattern, adopt PD (Proportion ?Derivative) position servo control method (as shown in Figure 1), its specific implementation can be subdivided into the following steps again:
(1) gather joint angle angle value during the long human body walking of different height, lower limb, the collection value of same class tester is averaged, obtains the gait data storehouse of human body standard, corresponding standard gait is chosen to different user.In gait process, choose each joint rotation angle value in corresponding gait moment according to data base
(2) detected angle, the angular velocity in each joint of lower limb rehabilitation training device in rehabilitation training by optoelectronic angle encoder in real time, feed back to PD position servo control.
(3) according to joint rotation angle value, instead separating calculating through moving, solving the motion conditions of each servomotor, controlling each servomotor and move on request.
3. initiative rehabilitation training mode takes real-time impedance control method, and its specific implementation can be subdivided into the following steps again:
(1) patient's active exercise Intention Anticipation.Obtain the EEG signals of patient and the time and frequency domain characteristics vector of surface electromyogram signal by WAVELET TRANSFORM MODULUS qualitative modeling, the motion intention of patient is made a prediction.
(2) patient's active desired trajectory generates in real time.Using patient's EEG signals of obtaining and the characteristic vector of surface electromyogram signal as input signal, input to the joint angle angle value that fuzzy neural network process obtains prediction; Initiatively, the motion gait geometric locus that patient expects is produced in real time.In the present embodiment, fuzzy neural network adopts five etale topology structures: input layer, obfuscation layer, fuzzy reasoning layer, fuzzy rule output layer and de-fuzzy layer.Specific implementation process can be subdivided into following sub-step:
A. input layer receives the characteristic vector of patient's EEG signals and surface electromyogram signal;
B. to set five artificial neurons corresponding with five kinds of Fuzzy Linguistic Variable respectively for obfuscation layer, the characteristic vector of input layer EEG signals and surface electromyogram signal is converted to negative large value respectively by Gaussian function, negative little value, null value, just little value and honest value five kinds of Fuzzy Linguistic Variable;
C. gain knowledge according to human dissection and some priori experimental results, set up corresponding Neural Fuzzy rule respectively at fuzzy reasoning layer.Then, the degree of membership of corresponding fuzzy rule is calculated by logical operator and computing.
D. method of least square is adopted to learn to optimize output membership function parameter, by calculating the weighted value obtaining every bar fuzzy rule degree of membership respectively at fuzzy rule output layer;
E. be weighted average at de-fuzzy layer to all fuzzy rule output, by last fusion, obtain the joint angle angle value of prediction.
(3) the impedance modeling of recovery set for lower limbs man-machine system, as shown in Figure 2.In the present embodiment, specific implementation process can be subdivided into following sub-step again:
A. detect human-computer interaction power by man-machine interaction force transducer, and the man-machine interaction power of measurement is extracted human-computer interaction moment by inverse dynamics model, feed back to impedance controller;
B. by the displacement of human body lower limbs and recovery set for lower limbs joint, speed, acceleration, respectively by inverse kinematics, Jacobian matrix relationship map to corresponding joint space; Set up the impedance Control Model that human-computer interaction moment and recovery set for lower limbs depart from predetermined joint trajectories deviation;
C. the human body impedance model that patient departs from predetermined joint trajectories deviation and human-computer interaction moment is set up; Finally combine the impedance model setting up man-machine system.
In formula, θ
et () is for departing from the angular deviation of predetermined joint trajectories, T in human body lower limbs joint corresponding to recovery set for lower limbs joint
intt () is the man-machine interaction moment in human body lower limbs joint corresponding to recovery set for lower limbs joint; M
e, B
e, K
ebe respectively the rotary inertia of human body lower limbs, damping and rigidity; M
r, B
r, K
rbe respectively the rotary inertia of lower limb rehabilitation ESD, damping and rigidity; Wherein, M
e, B
e, K
ebecome when being.
D. for the impedance model of above-mentioned man-machine system, utilize the error of priori noise statistics and actual observed value, real-time state covariance matrix and the gain matrix revising adjustment UKF (UnscentedKalmanFilter) filtering algorithm for estimating, build self adaptation UKF (UnscentedKalmanFilter) filtering algorithm for estimating, real-time online dynamic estimation is carried out to human body impedance time-varying parameter.
(4) the gait track expectation curve by patient's EEG signals and surface electromyogram signal active predicting is revised in real time with the accurate man-machine system's impedance model through the correction of self adaptation UKF (UnscentedKalmanFilter) filtering algorithm for estimating.
(5) be input to by revised gait geometric locus in lower limb rehabilitation ESD intraarticular ring position controller, the track that the corner controlling each joint realizes expecting exports.
(6) according to the corner value in each joint, instead separating calculating through moving, solving the motion conditions of each servomotor, control the running of each servomotor, finally realize the active of lower limb rehabilitation ESD man-machine system, real-time collaborative controls.
With reference to figure 3, based on the control method of Fig. 1, correspondence of the present invention provides a kind of lower limb rehabilitation robot, comprise: sensor assembly, data acquisition module, central processing module and motion-control module and recovery set for lower limbs etc., wherein: sensor assembly is by brain electricity, surface electromyogram signal acquisition instrument 1, man-machine interaction force transducer 2 and optoelectronic angle encoder 3 form, data acquisition module is made up of amplifier wave filter 4 and data collecting card 5, central processing module is made up of host computer 6, motion-control module is made up of motion control card 7 and servo-driver 8, recovery set for lower limbs is made up of frame for movement body and servo-controlled motor.
In this lower limb rehabilitation robot, 16 passage brain electricity cap Emotiv and 16 passage myoelectricity Acquisition Instruments selected respectively by the brain electricity in sensor assembly, surface electromyogram signal acquisition instrument 1, and piezoelectric film type sensor selected by man-machine interaction force transducer 2; Amplifier wave filter 4 is connected with each sensor in sensor assembly successively by shielding line.
After lower limb rehabilitation robot is started working, data collecting card 5 gathers EEG signals, the surface electromyogram signal of patient by brain electricity, surface electromyogram signal acquisition instrument 1, man-machine interaction force transducer 2 and optoelectronic angle encoder 3, the reciprocal force of healing robot and patient and joint angles etc., meanwhile, data collecting card 5 also carries out bandpass filtering and amplification by amplifier wave filter 4 to the original EEG signals collected and surface electromyogram signal; Then, the various signals collected are sent to host computer 6, first host computer 6 carries out signal condition to the signal collected, Rehabilitation scale evaluation is carried out again by central processing unit, determine which kind of rehabilitation exercise training pattern healing robot takes, according to joint angle displacement and the angular velocity of different motor control schema creation lower limb rehabilitation robots, then separate by motion is anti-by the physical dimension of frame for movement in recovery set for lower limbs, comprehensively draw action command; The action command that motion control card 7 accepts host computer 6 output is planned the motion of servomotor in recovery set for lower limbs, and export servo-driver 8 to, servo-driver 8 production burst signal, to the servomotor in recovery set for lower limbs, drives servomotor to realize recovery set for lower limbs and drives patient to carry out the function of rehabilitation training campaign.
With reference to figure 4, the lower limb rehabilitation training device that the present embodiment lower limb rehabilitation robot relates to is made up of power exoskeleton 9, medical running platform 10, initiatively loss of weight system 11 and movable stand 12.
With reference to figure 5, in said apparatus, core is power exoskeleton 9, power exoskeleton is designed to two exoskeleton-type pedipulators, every bar pedipulator has hip joint flexion/extension and knee joint bending/stretching, extension two degree of freedom, people's corresponding two joint rotation in sagittal plane when walking can be simulated, realize the rotation of two-freedom, at each joint, slider-crank mechanism 13 is installed, be connected on servomotor 15 by ball screw linear driver 14, for driving the rotation in each joint of ectoskeleton pedipulator.4 optoelectronic angle encoders 3 are installed for measuring the joint angles in motor process at hip joint, knee joint place respectively, in the gaiter mechanism 16 of thigh and shank, 4 personal-machine reciprocal force sensors 2 are respectively installed for detecting the contact force of people and recovery set for lower limbs in rehabilitation training motor process respectively, two kinds of information all for detecting the kinestate of rehabilitation training, and are applied in different rehabilitation training patterns.
In said apparatus, initiatively loss of weight system 11 is when patient carries out rehabilitation training campaign, can carry out active accommodation, make it meet the motion feature of gravity center of human body to the center of gravity of patient, contributes to the deviation reducing hip joint and knee joint position control.
Claims (3)
1., based on a lower limb rehabilitation robot control method for brain flesh information impedance, it is characterized in that, comprise the steps:
(1) EEG signals of Real-time Collection brain in patients cortical rim system, and the surface electromyogram signal of quadriceps femoris and tibialis anterior;
(2) to the patient's EEG signals collected with surface electromyogram signal amplifies, the pretreatment of bandpass filtering;
(3) by feature extracting method, the time and frequency domain characteristics vector of patient's EEG signals and surface electromyogram signal is obtained;
(4) EEG signals of Healthy People and surface electromyogram signal characteristic vector and patient's EEG signals and surface electromyogram signal characteristic vector are compared, setting rehabilitation degree threshold value, when being less than this threshold value, carry out the passive rehabilitation training pattern of step (5); When being greater than this threshold value, carry out the initiative rehabilitation training mode of step (6);
(5) passive rehabilitation training pattern, adopt PD position servo control method, patient is driven by lower limb rehabilitation robot completely, carries out lower limb rehabilitation motion with physiology's gait track of standard; Meanwhile, detect angle, the angular velocity in each joint of lower limb rehabilitation robot, and as feedback signal, the movement locus of adjustment lower limb rehabilitation robot in real time;
(6) initiative rehabilitation training mode, take real-time impedance control method, specifically comprise following sub-step:
A. the impedance model of man-machine system is set up:
1. detect human-computer interaction power by man-machine interaction force transducer, and extract human-computer interaction moment by inverse dynamics model, feed back to impedance controller;
2. by the displacement of human body lower limbs and recovery set for lower limbs joint, speed, acceleration, respectively by inverse kinematics, Jacobian matrix relationship map to corresponding joint space, set up the impedance Control Model that human-computer interaction moment and recovery set for lower limbs depart from predetermined joint trajectories deviation;
3. the human body impedance model that patient departs from predetermined joint trajectories deviation and human-computer interaction moment is set up; Finally combine the impedance model setting up man-machine system:
In formula, θ
et () is for departing from the angular deviation of predetermined joint trajectories, T in human body lower limbs joint corresponding to recovery set for lower limbs joint
intt () is the man-machine interaction moment in human body lower limbs joint corresponding to recovery set for lower limbs joint; M
e, B
e, K
ebe respectively the rotary inertia of human body lower limbs, damping and rigidity; M
r, B
r, K
rbe respectively the rotary inertia of lower limb rehabilitation ESD, damping and rigidity; Wherein, M
e, B
e, K
ebecome when being;
4. adopt self adaptation UKF filtering algorithm for estimating to time-varying parameter M
e, B
e, K
ecarry out real-time online dynamic estimation, obtain the real-time impedance parameter that model is accurate, precision is higher;
B. time and frequency domain characteristics vector step (3) being got patient's EEG signals and surface electromyogram signal carries out fusion treatment by fuzzy neural network algorithm, produces the motion gait geometric locus that patient expects in real time;
C. negative feedback correction is carried out with the motion gait geometric locus of impedance model to b step 4. in a step;
D. be input in the ectoskeleton intraarticular ring position controller of recovery set for lower limbs by revised motion gait geometric locus, control the rotating angle movement in each joint, the track realizing expecting exports.
2., as claimed in claim 1 based on the lower limb rehabilitation robot control method of brain flesh information impedance, it is characterized in that, the feature extracting method described in step (3) is WAVELET TRANSFORM MODULUS average algorithm.
3., as claimed in claim 1 based on the lower limb rehabilitation robot control method of brain flesh information impedance, it is characterized in that, the pretreatment described in step (2), is wherein enlarged into 2000 times; The frequency of bandpass filtering be 10 ?1000Hz, and do not comprise 50Hz notched signal.
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