CN106422172A - Speed self-adaptive control method of lower limb rehabilitation training system treadmill based on myoelectricity - Google Patents
Speed self-adaptive control method of lower limb rehabilitation training system treadmill based on myoelectricity Download PDFInfo
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B22/00—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements
- A63B22/02—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with movable endless bands, e.g. treadmills
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
- A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
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Abstract
The invention discloses a speed self-adaptive control method of a lower limb rehabilitation training system treadmill based on myoelectricity. The speed self-adaptive control method comprises the following steps: acquiring a myoelectric signal of a lower limb of a patient in real time by using a myoelectric signal acquirer; after pretreatment of filtration, noise reduction and the like, respectively extracting an amplitude characteristic and an energy characteristic of a related myoelectric signal, and predicting a step period and a step length of movement of the lower limb of the patient in real time by using a step period detection algorithm and a step length estimation algorithm; furthermore, calculating an expected speed of movement of the lower limb of the patient according to the data; meanwhile, acquiring a speed transmission function of the treadmill by using a system recognition method; and finally, with the combination of the speed transmission function of the treadmill, driving the control motor of the treadmill according to a PID speed servo control algorithm, thereby achieving self-adaptive following control on the speed of the treadmill. As the step speed of a human body is predicted according to the myoelectric signal, signals can be conveniently acquired and processed, a patient can control the speed of the treadmill to be coordinated with the speed of the patient in real time according to active movement attention of the patient, active rehabilitation training of the patient can be achieved, and the rehabilitation training velocity can be increased.
Description
Technical field
The present invention relates to lower limbs rehabilitation training robot system control technique, more particularly to a kind of initiative rehabilitation training mode
Under, lower limb rehabilitation training system treadmill speed adaptive control method.
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 causes
In the trend for sharply increasing, the health of the mankind in serious harm.Development and people's medical treatment, the carrying of living standard with society
Height, the health of people with disability causes the concern of the whole society.Body weight support treadmill training is treated for such Disease walking rehabilitation
One of important means, its effectiveness of existing substantial amounts of clinical research confirmation.Many research worker are had to open both at home and abroad at present
The research work of exhibition rehabilitation training system, but traditional rehabilitation training system, training action species is fewer, and actuating range has
Limitation, motion amplitude is less, and majority have ignored the active exercise of patient's lower limb and be intended to, and be unfavorable for exciting the active consciousness of patient
And the interest of participation rehabilitation training, it is extremely difficult to preferable rehabilitation training requirement.
In recent years, research institution both domestic and external have developed various types of rehabilitation training systems, the master that wherein patient participates in
Dynamic rehabilitation training pattern has become the widespread consensus of major trend and people.The Chinese patent of Application No. 201010561379.5
Document discloses a kind of lower limb rehabilitation training system control method, with man-machine interaction power, is realized by impedance-controlled fashion certain
The active compliance rehabilitation control of degree.The degree that patient is actively engaged in rehabilitation training can be improved, but is not carried out rehabilitation training
The self adaptation model- following control of system running motor speed.The treadmill for using during walking rehabilitation training at present is all based on greatly
One constant speed, this is not corresponded in the rule that walking medium velocity is continually changing naturally with people, is especially instructed in active
Practice under pattern, need according to patients'wT real-time adjustment gait speed.The Chinese patent text of Application No. 201510070183.9
Offer and a kind of lower limb rehabilitation training system motion control system is disclosed, in initiative rehabilitation training process, sensed using pressure
Stress value at the active role power of device Real-time Collection patient, i.e. patient's hip joint fixed constraint, thus stress value judgement is run
The corresponding acceleration or deceleration action of machine, produces the movement tendency of acceleration and deceleration on a treadmill according to patient, and treadmill is constantly autonomous
Ground governing speed, realizes the self adaptation model- following control of running motor speed.But the method has constrained human normal gait and has needed
Add a set of power apparatus；The precision of rehabilitation training control is have impact on, and, force measuring device installs and uses inconvenience, also increases
The cost of control system.Minetti et al. detects the displacement of people by ultrasonic sensor, constantly calculates and control treadmill
Speed, it is achieved that the self adaptation model- following control of lower limb rehabilitation training system running motor speed.But the method is in treadmill displacement
When big, speed governing is obvious, and displacement hour is not obvious；It is unfavorable for the real-time speed adaptive model- following control of treadmill.Above method does not have
From the active exercise intention of patient, self adaptation model- following control of the running motor speed to patient's active exercise is realized.Final real
Now running motor speed is mated with the real-time synchronization of patient's desired speed；Realize the training of patient's initiative rehabilitation.
Content of the invention
For under the lower limbs rehabilitation training robot system initiative rehabilitation motor pattern pointed by background technology, which is supporting
Treadmill speed follower control exist problem, it is an object of the invention to provide one kind can real-time estimate patient in rehabilitation
Lower limb gait motion speed the active self-adaptation control method that running motor speed real-time synchronization is followed is carried out accordingly in journey.
The present invention is adopted the following technical scheme that and is achieved：
A kind of lower limb rehabilitation training system treadmill speed adaptive control method based on myoelectricity, comprises the steps：
(1) surface electromyogram signal of the left and right Thigh bone musculus lateralis externi of difference Real-time Collection patient, bone rectus and biceps femoris；
(2) the patient surface's electromyographic signal to collecting is amplified, filters and the pretreatment such as noise reduction；
(3) pass through feature extracting method, obtain the feature parameter vectors and the amplitude Characteristics of patient surface's electromyographic signal respectively
Vector；
(4) using the patient surface's electromyographic signal the feature parameter vectors for obtaining, pre- in real time by gait cycle estimation algorithm
Survey the gait cycle of patient's lower extremity movement.Specifically include following sub-steps：
A. first, a sliding window is initialized, and chooses the long N of suitable window；
The feature parameter vectors of the surface electromyogram signal for b. being obtained using step (3) calculate the integrated value of energy feature signal
Feature；Its specific formula for calculation is：
In formula, IEMGpI () is the integrated value characteristic vector of i moment patient lower limb thigh vastus lateraliss myoelectricity energy signal
Value, EMGpJ () is the value of j moment patient lower limb vastus lateraliss myoelectricity energy signal；N is the length of sliding window；
C. real-time detection, judge the effective peak of each gait cycle, and record the corresponding moment t of effective peaki；
D. with the effective peak of two adjacent electromyographic signals, the moment real-time gait cycle for calculating patient's lower extremity movement is corresponded to
Ti, its specific formula for calculation is：
Ti=ti-ti-1
In formula, TiFor i-th gait cycle of patient's lower extremity movement, tiDuring for i-th effective peak of electromyographic signal to corresponding to
Carve, ti-1The corresponding moment for patient's the i-th -1 effective peak of electromyographic signal.
(5) using the patient surface's electromyographic signal amplitude Characteristics vector for obtaining, suffered from by step-size estimation algorithm real-time estimate
The step-length of person's lower extremity movement.Specifically include following sub-steps：
A. first, the model of step-length estimation is set up using the amplitude Characteristics vector of the left and right thigh electromyographic signal of patient；Specifically
As follows：
In formula, LiRepresent the step-length of the i-th step；WithRepresent the left lower limb electromyographic signal of the i-th step patient respectively
Characteristic vector and the characteristic vector with right lower limb electromyographic signal and aiAnd biRepresent the coefficient in model, a respectively0Represent the first of setting
Beginning constant；
B. the coefficient of step-length estimation models in step (a) is obtained with least square in training；
C. using the step-length estimation models that sets up in step (a), the step-length of real-time estimate patient's lower extremity movement.
(6) step-length that the gait cycle that predicts step (4) and step (5) are predicted, is counted in real time by leg speed prediction algorithm
Calculate the desired leg speed of patient；Produce the desired speed command signal for controlling treadmill.
(7) the input-output transmission function of running motor speed is obtained with identification method.
(8) last, in conjunction with the speed transmission function of treadmill, meanwhile, detect the speed of treadmill as feedback signal, profit
With the controlled motor of PID Speed servo control algorithm drives treadmill, the self adaptation model- following control of running motor speed is finally realized.
In above-mentioned steps, pretreatment described in step (2), wherein it is enlarged into 1000 times；The frequency of bandpass filtering is 20-
500Hz, and do not include 50Hz notched signal.
The feature extracting method of the feature parameter vectors described in step (3) and amplitude Characteristics vector is respectively：
In formula, EMGPRepresent the feature parameter vectors value of electromyographic signal, EMGiRepresent value of the electromyographic signal in the i moment, N table
Show the length of signal segment；MAV represents the amplitude absolute value average of electromyographic signal.
Leg speed real-time estimate algorithm described in step (6) is：
In formula, vi-dRepresent the desired speed in treadmill i moment, LiRepresent the step-length of real-time estimate, TiRepresent real-time estimate
Gait cycle.
Compared with prior art, it is an advantage of the current invention that：
1st, the present invention is according to surface electromyogram signal real-time estimate patient's lower limb active exercise of patient's lower limb gait motion
Desired speed, and then followed according to the self adaptation of the desired speed controlling running motor speed of patient's active, realize running motor speed
With the synchronized Coordinative Control of patient's desired speed, the demand of patient's initiative rehabilitation training is met.
2nd, motion intention and movement tendency of the present invention by human body lower limbs surface electromyogram signal real-time estimate patient, can
Overcome constraint of the treadmill self adaptation follower method based on pressure sensing to human normal gait, it is to avoid force measuring device is pacified
The inconvenience that dress is used, it is also possible to save the cost of control system.
Description of the drawings
Below in conjunction with the accompanying drawings and specific embodiment is described in further detail to the present invention
Fig. 1 is control method theory diagram of the present invention.
Fig. 2 is human body lower limbs gait cycle prediction flow chart.
Fig. 3 is lower limb rehabilitation training system involved in the present invention.
In figure：1st, power exoskeleton；2nd, treadmill；3rd, active loss of weight system；4th, movable stand.
Fig. 4 is present invention control hardware architecture diagram.
In figure：5th, electromyographic signal collection instrument；6th, signal processing circuit；7th, data collecting card；8th, host computer；9th, serial ports；10、
Treadmill speed control.
Specific embodiment
Referring to Fig. 1 and Fig. 4, lower limb rehabilitation training system treadmill speed adaptive controlling party of the present invention based on myoelectricity
Method：The surface electromyogram signal related to patient's lower limb gait motion is taken in real time by electromyographic signal collection instrument, through amplifying, filtering
After the pretreatment such as ripple, noise reduction, extract amplitude Characteristics and the energy feature of related electromyographic signal using temporal analysiss respectively, pass through
Gait cycle detection algorithm and step-size estimation algorithm real-time estimate go out gait cycle and the step-length of patient's lower extremity movement；Pre- accordingly again
The desired speed of patient's lower limb active exercise is surveyed, produces the desired speed command signal for controlling treadmill；At the same time, profit
The speed transfer function model of lower limb rehabilitation training system treadmill is obtained with identification method；Finally, in conjunction with the speed of treadmill
Degree transmission function, realizes the self adaptation model- following control of running motor speed using PID servo control algorithm；Final realization is completely by trouble
The initiative rehabilitation training of person's motion intention.Its specific implementation process comprises the steps：
(1) surface electromyogram signal of the left and right Thigh bone musculus lateralis externi of difference Real-time Collection patient, bone rectus and biceps femoris.
(2) the patient surface's electromyographic signal to collecting is amplified, filters and the pretreatment such as noise reduction.In the present embodiment
In, the surface electromyogram signal to collecting carries out 1000 times of amplifications, then carries out 20-500Hz bandpass filtering again, and does not include
50Hz notched signal.
(3) pass through feature extracting method, obtain the feature parameter vectors and the amplitude Characteristics of patient surface's electromyographic signal respectively
Vector.In the present embodiment, using squared magnitude and its energy feature of calculating of electromyographic signal then exhausted with the amplitude of electromyographic signal
To being worth its amplitude Characteristics of mean value computation.
(4) using the patient surface's electromyographic signal the feature parameter vectors for obtaining, pre- in real time by gait cycle estimation algorithm
Survey the gait cycle of patient's lower extremity movement.Its prediction algorithm be embodied as flow process, as shown in Figure 2.In the present embodiment, concrete real
The process of applying can be subdivided into following sub-step：
A. first, a sliding window is initialized, and chooses the long N of suitable window；
The feature parameter vectors of the surface electromyogram signal for b. being obtained using step (3) calculate the integrated value of energy feature signal
Feature；Its specific formula for calculation is：
The feature parameter vectors of the surface electromyogram signal for c. being obtained using step (3) calculate the integrated value of energy feature signal
Feature；Its specific formula for calculation is：
In formula, IEMGpI () is the integrated value characteristic vector of i moment patient lower limb thigh vastus lateraliss myoelectricity energy signal
Value, EMGpJ () is the value of j moment patient lower limb vastus lateraliss myoelectricity energy signal；N is the length of sliding window；
D. real-time detection, judge the effective peak of each gait cycle, and record the corresponding moment t of effective peaki；This reality
Apply in example, the detection of effective peak, judge that specific implementation process is as follows：
First, it is determined that whether the signal value of sliding window middle position is maximum；
Secondly, will determine that the sliding window centre position maximum for obtaining is compared with threshold value, more than given threshold be then
The effective peak of gait cycle；
Finally, mobile sliding window, repeats said process until the ending of signal sequence；Detect each gait cycle has
Effect peak value.
E. with the effective peak of two adjacent electromyographic signals, the moment real-time gait cycle for calculating patient's lower extremity movement is corresponded to
Ti, its specific formula for calculation is：
Ti=ti-ti-1
In formula, TiFor i-th gait cycle of patient's lower extremity movement, tiDuring for i-th effective peak of electromyographic signal to corresponding to
Carve, ti-1The corresponding moment for patient's the i-th -1 effective peak of electromyographic signal.
(5) using the patient surface's electromyographic signal amplitude Characteristics vector for obtaining, suffered from by step-size estimation algorithm real-time estimate
The step-length of person's lower extremity movement.In the present embodiment, the specific implementation process of step-size estimation algorithm can be subdivided into following sub-step again：
A. first, the model of step-length estimation is set up using the amplitude Characteristics vector of the left and right thigh electromyographic signal of patient；Specifically
As follows：
In formula, LiRepresent the step-length of the i-th step；WithRepresent the left lower limb electromyographic signal of the i-th step patient respectively
Characteristic vector and the characteristic vector with right lower limb electromyographic signal and aiAnd biRepresent the coefficient in model, a respectively0Represent the first of setting
Beginning constant；
B. the coefficient of step-length estimation models in step (a) is obtained with least square in training；
C. using the step-length estimation models that sets up in step (a), the step-length of real-time estimate patient's lower extremity movement.
(6) step-length that the gait cycle that predicts step (4) and step (5) are predicted, is counted in real time by leg speed prediction algorithm
Calculate the desired leg speed of patient；Produce the desired speed command signal for controlling treadmill；In the present embodiment, leg speed is pre- in real time
Method of determining and calculating is：
In formula, vi-dRepresent the desired speed in treadmill i moment, LiRepresent the step-length of real-time estimate, TiRepresent real-time estimate
Gait cycle.
(7) the input-output transmission function of running motor speed is obtained with identification method.In the present embodiment, using race
The time response that step machine is input into unit step picks out structure and the design parameter of running motor speed transmission function.
(8) last, in conjunction with the speed transmission function of treadmill, meanwhile, detect the speed of treadmill as feedback signal, profit
With the controlled motor of PID Speed servo control algorithm drives treadmill, the self adaptation model- following control of running motor speed is finally realized.
With reference to Fig. 3, the lower limb rehabilitation training system involved by the present embodiment is by power exoskeleton 1, treadmill 2, actively
Loss of weight system 3 and movable stand 4 constitute.
With reference to Fig. 4, lower limb rehabilitation training system treadmill speed adaptive control system hardware of the present invention based on myoelectricity
It is made up of treadmill body, sensing data acquisition module, central control module and treadmill speed control etc.；Wherein：Sensing
Data acquisition module is made up of electromyographic signal collection instrument 5,6 data capture card 7 of signal processing circuit, and central control module is by upper
Position machine (industrial computer) 8 and serial ports 9 constitute.
In the treadmill speed adaptive control system, treadmill 2 is from the internal commercial race comprising speed control motor
Step Paragon 508；16 passage myoelectricity Acquisition Instruments selected by electromyographic signal collection instrument 5 in sensing data acquisition module；At signal
Reason circuit 6 is sequentially connected with 5 data capture card 7 of electromyographic signal collection instrument by shielding line, to the patient's lower limb table for collecting
The facial muscle signal of telecommunication such as is amplified, filters at the pretreatment.
After lower limb rehabilitation training system starts, when treadmill is using automatic speed regulation pattern, data collecting card 7 passes through
The surface electromyogram signal of 5 Real-time Collection patient's lower limb of electromyographic signal collection instrument, at the same time, data collecting card 5 is also by signal
The initial surface electromyographic signal that process circuit 6 pairs is collected carries out bandpass filtering and amplification, noise reduction etc. are processed；Then, collection
To various signals be sent to host computer 8, signal of the host computer 8 first to collecting carries out the signal conditions such as feature extraction, then
Patient's gait cycle prediction algorithm and step-ahead prediction algorithm are run by central processing unit, then, by leg speed prediction algorithm reality
When calculate the desired leg speed of patient；By serial ports 9, desired for patient leg speed is sent in treadmill speed control 10, most again
Input for controller；Treadmill speed control 10 combines the speed transmission function of treadmill, meanwhile, detect the speed of treadmill
Degree realizes running motor speed as feedback signal using the controlled motor of PID Speed servo control algorithm drives treadmill 2
Self Adaptive Control.The real-time synchronization coordination exercise of running motor speed and patient's desired speed is finally realized, meets patient's active health
The demand that refreshment is practiced.
Claims (6)
1. a kind of lower limb rehabilitation training system treadmill speed adaptive control method based on myoelectricity, it is characterised in that include
Following step：
(1) surface electromyogram signal of the left and right Thigh bone musculus lateralis externi of difference Real-time Collection patient, bone rectus and biceps femoris；
(2) the patient surface's electromyographic signal to collecting is amplified, filters and noise reduction pretreatment；
(3) pass through feature extracting method, obtain the feature parameter vectors and the amplitude Characteristics vector of patient surface's electromyographic signal respectively；
(4) using the patient surface's electromyographic signal the feature parameter vectors for obtaining, suffered from by gait cycle estimation algorithm real-time estimate
The reasonable gait cycle of person's lower extremity movement；
(5) using the patient surface's electromyographic signal amplitude Characteristics vector for obtaining, by under step-size estimation algorithm real-time estimate patient
The step-length of limb motion；
(6) step-length that the gait cycle that predicts step (4) and step (5) are predicted, calculates trouble in real time by leg speed prediction algorithm
The desired leg speed of person；Produce the desired speed command signal for controlling treadmill；
(7) the input-output transmission function of running motor speed is obtained with identification method；
(8) last, in conjunction with the speed transmission function of treadmill, meanwhile, detect that the speed of treadmill, as feedback signal, is utilized
The controlled motor of PID Speed servo control algorithm drives treadmill, finally realizes the self adaptation model- following control of running motor speed.
2. the lower limb rehabilitation training system treadmill speed adaptive control method based on myoelectricity according to claim 1,
Characterized in that, the pretreatment described in step (2), amplification be；The frequency of bandpass filtering is 20-500Hz, and not
Comprising 50Hz notched signal.
3. the lower limb rehabilitation training system treadmill speed adaptive control method based on myoelectricity according to claim 1,
Characterized in that, the feature extracting method of the feature parameter vectors described in step (3) and amplitude Characteristics vector is respectively：
In formula, EMGPRepresent the feature parameter vectors value of electromyographic signal, EMGiRepresent value of the electromyographic signal in the i moment, N represents letter
The length of number section；MAV represents the amplitude absolute value average of electromyographic signal.
4. the lower limb rehabilitation training system treadmill speed adaptive control method based on myoelectricity according to claim 1,
Characterized in that, step (4) specifically includes following step：
A. first, a sliding window is initialized, and chooses the long N of suitable window；
The feature parameter vectors of the surface electromyogram signal for b. being obtained using step (3) calculate the integrated value spy of energy feature signal
Levy；Its specific formula for calculation is：
In formula, IEMGpI () is the integrated value characteristic vector value of i moment patient lower limb thigh vastus lateraliss myoelectricity energy signal,
EMGpJ () is the value of j moment patient lower limb vastus lateraliss myoelectricity energy signal；N is the length of sliding window；
C. real-time detection, judge the effective peak of each gait cycle, and record the corresponding moment t of effective peaki；
D. with the effective peak of two adjacent electromyographic signals, the moment real-time gait cycle T for calculating patient's lower extremity movement is corresponded toi, its
Specific formula for calculation is：
Ti=ti-ti-1
In formula, TiFor i-th gait cycle of patient's lower extremity movement, tiFor the corresponding moment of i-th effective peak of electromyographic signal,
ti-1The corresponding moment for patient's the i-th -1 effective peak of electromyographic signal.
5. the lower limb rehabilitation training system treadmill speed adaptive control method based on myoelectricity according to claim 1,
Characterized in that, step (5) specifically includes following step：
A. first, the model of step-length estimation is set up using the amplitude Characteristics vector of the left and right thigh electromyographic signal of patient；Specifically such as
Under：
Li=a0+aiMAVi l+biMAVi r
In formula, LiRepresent the step-length of the i-th step；MAVi lWithRepresent respectively the feature of the left lower limb electromyographic signal of the i-th step patient to
Amount and the characteristic vector with right lower limb electromyographic signal and aiAnd biRepresent the coefficient in model, a respectively0Represent the initial normal of setting
Amount；
B. the coefficient of step-length estimation models in step (a) is obtained with least square in training；
C. using the step-length estimation models that sets up in step (a), the step-length of real-time estimate patient's lower extremity movement.
6. the lower limb rehabilitation training system treadmill speed adaptive control method based on myoelectricity according to claim 1,
Characterized in that, the leg speed real-time estimate algorithm described in step (6) is：
In formula, vi-dRepresent the desired speed in treadmill i moment, LiRepresent the step-length of real-time estimate, TiRepresent the step of real-time estimate
The state cycle.
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| CN112842825A (en) * | 2021-02-24 | 2021-05-28 | 郑州铁路职业技术学院 | Training device for lower limb rehabilitation recovery |
| CN113577747A (en) * | 2021-08-06 | 2021-11-02 | 青岛迈金智能科技有限公司 | Heart rate belt equipment and step length calculating method thereof |
| CN113730190A (en) * | 2021-09-18 | 2021-12-03 | 上海交通大学 | Upper limb rehabilitation robot system with three-dimensional space motion |
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