CN111938991A - Hand rehabilitation training device and training method in double active control modes - Google Patents
Hand rehabilitation training device and training method in double active control modes Download PDFInfo
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Abstract
The invention provides a hand rehabilitation training device and a training method in a double-active control mode.A brain electrical signal of a brain motor imagination area is acquired in real time through brain electrical acquisition equipment, and the result is sent to a virtual scene for interaction after signal preprocessing, feature extraction and classification identification, so that the movement of a model hand in the scene is controlled; when the model hand reaches the designated position and then becomes a fist-making state, the electromyographic signal of the healthy side of the patient is collected, the result is sent to the wearable rehabilitation manipulator in a command form through the Bluetooth after signal preprocessing, feature extraction and classification recognition, the electromyographic signal of the healthy side drives the wearable rehabilitation manipulator of the affected side to grasp, the examinee is assisted in performing rehabilitation training, and the hand rehabilitation training is performed in a mode of combining electroencephalogram and electromyography. The invention accelerates the function remodeling of the brain damaged motion area of the testee and the hand rehabilitation speed and rehabilitation effect through the active and passive rehabilitation training, and improves the activity of the hand rehabilitation training of the patient.
Description
Technical Field
The invention relates to the technical field of rehabilitation training, in particular to a hand rehabilitation training device with double active control modes and a training method.
Background
Stroke is a high-grade disease in which brain tissue is damaged due to the inability of blood to flow into the brain caused by sudden rupture of cerebral vessels or by blockage of blood vessels. Cerebral apoplexy includes ischemic stroke and hemorrhagic stroke, and has the features of high morbidity, high mortality and high disability rate. According to the reports of the world health organization, cerebral apoplexy has become the second cause of death of cancer and coronary heart disease worldwide. In recent years, China has become the first major world with stroke 1 new case every 12 seconds. Approximately 700 million patients exist nationwide and grow at a rate of 8.7% per year, with 3177 million people predicted to be present in stroke patients nationwide by 2030. Clinical findings show that more than 75% of stroke patients present with different degrees of limb movement dysfunction, which seriously affects the quality of life of the patients and causes heavy burden to the patients, families and society. The upper limb function of the human body accounts for 60% of the whole body function, and the hand function accounts for 90% of the upper limb function. 55-75% of the patients with the stroke survive leave limb dysfunction, wherein about 80% of the patients with the hand dysfunction become one of the main diseases caused by the stroke, and the life quality and the activity of the patients are seriously influenced. The finger joint is difficult to bend and stretch, and normal grasping and stretching actions cannot be performed. In accordance with the current medical level there is no ability to complete repair of the damaged nervous system, and only some other auxiliary means can improve or replace the function of the damaged nervous system. According to the relevant practice, the hemiplegia patient can recover the simple exercise ability of the limbs to a certain extent, even recover the limb to a certain extent through timely and active exercise rehabilitation training.
However, the traditional rehabilitation training for patients with hand motor dysfunction mainly adopts one-to-one rehabilitation therapy of doctors or simple rehabilitation instruments, which not only has high labor intensity and high cost, but also has a strong training effect, and particularly for patients in later period of rehabilitation, the rehabilitation process is boring and tedious, and the participation initiative of the patients is poor, so that the rehabilitation period is prolonged. In recent years, rehabilitation techniques for hand motor dysfunction after stroke, such as compulsive motor therapy, robot-assisted therapy, transcranial magnetic stimulation, motor imagery therapy, etc., are continuously developed. The motor imagery and the physical therapy are combined to remarkably improve the hand motor function of the stroke patient. However, the rehabilitation training mode is single, the interactivity is poor, the rehabilitation training requirements of different patients and different rehabilitation stages cannot be met, and the problems of poor individual adaptability and poor patient activity exist.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses a hand rehabilitation training device and a training method in a double-active control mode, and aims to combine electroencephalogram and myoelectricity, combine motor imagery therapy and myoelectricity control mechanical arm to assist a subject to grasp training, perform man-machine interaction in a scene through Leap Motion, and provide a hand rehabilitation training method in a double-active control mode for a stroke patient.
Specifically, the invention adopts the following technical scheme:
the invention provides a hand rehabilitation training device with double active control modes, which comprises an electroencephalogram acquisition device, a myoelectricity acquisition device, a wearable rehabilitation manipulator, a virtual rehabilitation training system and a Leap Motion hand tracker,
the electroencephalogram acquisition device comprises an electroencephalogram cap and an electroencephalogram amplifier, the electroencephalogram cap is worn on the top of the head of a subject to acquire motor imagery electroencephalogram signals, and the electroencephalogram amplifier is connected with an upper computer to send and receive the signals;
the myoelectric acquisition device comprises a myoelectric acquisition module and a signal receiver, the myoelectric acquisition module is worn at the extensor muscles of the healthy side arm and the flexor carpi ulnaris of the subject to acquire myoelectric signals, and the myoelectric signals are transmitted to the upper computer through the signal receiver;
the wearable rehabilitation manipulator can realize the independent motion of each finger of the hand and can assist the extension and the bending of each finger joint, and the wearable rehabilitation manipulator is combined with the virtual rehabilitation training system and used for the rehabilitation training of a testee;
the virtual rehabilitation training system comprises a brain-muscle-electricity acquisition unit, wherein the brain-muscle-electricity acquisition unit can be used for preprocessing brain-muscle-electricity signals, extracting features and carrying out classification and identification;
the Leap Motion hand tracker is high-precision finger recognition equipment for tracking hand movement, can acquire hand movements of a subject in real time, displays the hand movements in a virtual scene in a real-time movement form of a virtual hand, and stimulates a brain movement functional area of the subject to improve the nerve activation degree of the brain of the subject by serving as visual feedback and performing virtual-real interaction.
Preferably, wherein: the wearable rehabilitation manipulator comprises a palm bone plate, a motor push rod, a palm bone connecting piece, a joint push rod, a joint angle plate and a hinge pin, wherein the palm bone plate is a fixing base of the wearable rehabilitation manipulator, the first end of the joint push rod is connected with the palm bone plate, the second end of the joint push rod is connected with the palm bone connecting piece through the hinge pin, the first end of the motor push rod is connected with a drive, the second end of the motor push rod is connected with the joint angle plate, and a slide bar on the joint angle plate can slide along a slide groove on the joint push rod.
The invention also provides a hand rehabilitation training method in a double-active control mode, which comprises the following steps:
s1, selecting a quiet and comfortable experimental environment, starting an electroencephalogram amplifier to be connected with a brain-muscle electricity acquisition unit, wearing an electroencephalogram cap for a subject, smearing conductive paste on a corresponding electrode channel, selecting C3, C4, FC3, FC4, CP3, CP4, C5 and C6 electrode channels of a brain movement area, starting an electromyography acquisition module to be connected with an upper computer through wireless transmission, switching on a power supply of a wearable rehabilitation manipulator, connecting the wearable rehabilitation manipulator with the upper computer through Bluetooth, and wearing the rehabilitation manipulator and electromyography acquisition equipment for the subject;
the electroencephalogram cap is worn on a subject and is coated with electroencephalogram paste, the electroencephalogram amplifier is connected with a electroencephalogram and electromyogram collection unit, the electromyogram collection module is worn on a side arm of the subject and is connected with an upper computer through wireless transmission, and meanwhile, a power supply of the wearable rehabilitation manipulator is connected with the upper computer through Bluetooth;
s2, logging in the virtual rehabilitation training system, carrying out motor imagery training on a subject, acquiring electroencephalogram signals of the subject for limb motor imagery, selecting 8 electrode channels of a brain motor functional area C3, C4, FC3, FC4, CP3, CP4, C5 and C6 from a signal acquisition channel, preprocessing the acquired electroencephalogram signals, extracting features, carrying out classification and identification, and establishing a PSO-SVM motor imagery classification model;
s3, classifying the PSO-SVM motor imagery classification model established in the step S2, and displaying a classification result to a subject in a mode that a model hand moves leftwards or rightwards in a virtual scene by a Leap Motion hand tracker so as to improve the neural activation degree of a brain Motion area of the subject;
s4, myoelectric training and collecting myoelectric signals: wearing a rehabilitation manipulator on the affected side of a testee, wearing a myoelectric acquisition module on the healthy side of the testee, enabling the model hand to be in a fist-making state when the model hand reaches a first position in a scene, acquiring myoelectric signals of the healthy side of the testee at the moment, carrying out active rehabilitation training in a mode of driving the affected side to train through the healthy side movement, and carrying out pretreatment, feature extraction, classification and recognition on the acquired myoelectric signals and establishing a PSO-SVM (particle swarm optimization-support vector machine) myoelectric signal classification model;
s5, classifying the PSO-SVM electromyographic signal classification model established in the step S4, sending classification results to the wearable rehabilitation manipulator through a Bluetooth module in the form of two hexadecimal instructions, and respectively controlling the gripping and stretching of the wearable rehabilitation manipulator to assist a subject in performing hand rehabilitation training;
then, the Motion of the hands of the testee is collected through the Leap Motion hand tracker, and is displayed in a virtual scene in a real-time Motion form of a virtual hand to serve as visual feedback to deepen the neural activation degree of the brain motor area of the testee;
and S6, setting training time according to the condition of the subject, generating a training report and printing and archiving the training report after the training is finished, and quitting the rehabilitation training system.
Preferably, the motor imagery training is specifically: the brain wave cap is worn by a subject and coated with brain wave paste, the rehabilitation training system is connected, motor imagery starts when a display screen of an upper computer prompts the subject to concentrate on mind, after a period of time, a left or right prompt appears in the center of the display screen of the upper computer, the subject performs left or right motor imagery along with a virtual scene prompt, in the process, the subject performs motor imagery, then the motor imagery prompt disappears, the motor imagery process of the subject is finished, and the second motor imagery training is repeatedly performed after a certain time of rest until the motor imagery training of set times is finished.
Preferably, the preprocessing and feature extraction performed on the acquired electroencephalogram signals in step S2 includes:
intercepting electroencephalogram signal data between the 2 nd to the 6 th of each electrode channel, performing down-sampling to 128Hz, performing 0.5 Hz-2 Hz high-pass filtering to remove baseline drift, and then performing self-adaptive notch to remove 50Hz power frequency interference;
carrying out wavelet packet decomposition on an EEG signal by 6 layers to extract an EEG characteristic frequency band, selecting a 0-4 Hz frequency band in a4 th layer of wavelet packet decomposition to correspond to a wave in an EEG signal, selecting a 4-8 Hz frequency band in the 4 th layer to correspond to a theta wave in the EEG signal, selecting an 8-12 Hz frequency band in the 4 th layer to correspond to an alpha wave in the EEG signal after combining with a 12-13 Hz frequency band in the 6 th layer, and selecting a 14-16 Hz frequency band in a5 th layer to correspond to a beta wave in the EEG signal after combining with a 28-30 Hz frequency band;
respectively carrying out CSP common space mode and multi-lead space filtering on EEG data of alpha wave frequency bands and beta wave frequency bands in each electrode channel so as to generate a time sequence capable of optimally distinguishing the grabbing motion motor imagery;
and extracting power spectral density of the alpha/beta frequency band by adopting a periodogram method, and further extracting the wavelet packet node energy and the characteristics of the wavelet entropy to obtain the wavelet packet entropy as the characteristic points of the electroencephalogram signal.
Preferably, the preprocessing and the feature extraction of the collected electromyographic signals in step S4 include:
the original electromyographic signals have high-frequency noise, power frequency interference and the like, and the low-pass filtering is carried out by adopting a 0-200Hz Butterworth filter. And (3) performing active segment detection on the signals by using a threshold method, and extracting time domain characteristics including a root mean square value, slope sign change and an electromyographic integral value from the active segment signals, and frequency domain characteristics of a central frequency and an average frequency domain to form a characteristic matrix. And finally, obtaining an optimal punishment coefficient C and a parameter G by using a Support Vector Machine (SVM) and a cross validation method, further obtaining a classification model, and predicting the test data through the model.
Preferably, in step S2, the power spectral density extraction is performed on the α/β band by using a periodogram method, N observation data of the random sequence x (N) is regarded as an energy-limited sequence, the discrete fourier transform of x (N) is directly calculated to obtain x (k), then the square of the amplitude of x (N) is taken and divided by N to obtain a sequence, x (N) is an estimate of the true power spectrum, and assuming that the finite-length random signal sequence is x (N), the power spectral density estimation of the random sequence is calculated by the following formula:
wherein,
representative spectral Density, FFT [ x (n)]Represents the fourier transform of the random sequence x (N), N represents the number of observations, and N represents the sequence number of the discrete sequence in the random signal.
Preferably, the extracting the wavelet packet node energy feature specifically comprises: the signal x (t) is decomposed into 2 layers by N layersNSubspace n (n ═ 1,2, 3.., 2)N) Energy E corresponding to subspace reconstruction signalnThe calculation formula is calculated by the sum of squares of the space wavelet packet coefficients as follows:
where t represents time, j represents a scale factor, k represents a translation factor,
are wavelet coefficients.
Preferably, the wavelet entropy feature is extracted, and the specific steps are as follows:
first, the signal is reconstructed
M equal divisions were made and the total energy per time period was expressed as:
second, the probability density distribution P of each band energymkThe total energy per time interval is normalized to
Thirdly, calculating band spectrum entropy values corresponding to different time periods
The band spectrum entropy value is called wavelet packet frequency band local entropy, and the matrix is expressed as
Finally, the wavelet packet entropy S calculation formula is obtained as follows:
wherein E iskRepresenting the energy of the time interval when the translation factor is time, PkRepresenting the probability density of the energy of the time frequency band, k representing the shift factor, P representing the probability density of the energy of all frequency bands, Pi representing the probability density of the energy of the ith frequency band, i representing the number of frequency bands, and m representing the fraction of signal equal.
Preferably, the electromyographic signal acquisition and feature extraction in step S4 specifically includes: root mean square value RMS is used for representing muscle contribution rate omega after normalizationj(ii) a Calculating the muscle activity alpha by using the surface electromyogram signalj(t); using muscle contribution rate omegajAnd degree of muscle activity alphaj(t) calculating the degree of upper limb movement eta, thereby adjusting the variable impedance equation coefficient;
the impedance setting equation is as follows:
in the formula, xd、xrRespectively an expected track and a reference track of the tail end position of the hand rehabilitation robot; b isdIs a damping coefficient; kdIs the stiffness coefficient; introducing the upper limb activity eta into impedance equation parameters so as to realize the self-adaptive adjustment of the impedance parameters; the impedance parameter BdAnd KdIs represented as follows: b isd＝sig(λB·η)·B0，Kd＝sig(λK·η)·K0In the formula, λBAnd λKGain coefficients of damping term and stiffness term, B0And K0Is an initial impedance coefficient, BdAnd KdFor the modified impedance coefficients, sig (. gamma.) is the sigmoid function, and as the clipping function, B isdAnd KdThe range of variation is
The invention has the following advantages:
1. the motor imagery signals of the testee are collected to carry out preprocessing, feature extraction and pattern recognition, the recognition result is used for controlling the movement of the model hand in the scene so as to enhance the brain nerve activation degree of the patient, when the model hand is in a fist making state after reaching a designated position, the healthy side electromyographic signals of the testee are collected, and after the preprocessing, the feature extraction and the pattern recognition are carried out on the healthy side electromyographic signals of the testee, the recognition result is sent to a wearable rehabilitation manipulator to assist the testee to carry out gripping action training.
2. The invention combines the motor imagery therapy and myoelectricity control manipulator assisted holding training of a testee, and carries out man-machine interaction in a scene through Leap Motion, thereby providing a hand rehabilitation training method with a double active control mode for stroke patients.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a diagram of an EEG signal acquisition electrode distribution according to the present invention;
FIG. 4 is a schematic diagram of an electromyographic signal acquisition location of the present invention;
FIG. 5 is a schematic diagram of an experimental paradigm during a training phase of the present invention;
FIG. 6 is a diagram of a wearable rehabilitation manipulator mechanism of the present invention;
FIG. 7 is a diagram of a virtual scene according to the present invention; and
fig. 8 is a flow chart of the rehabilitation training method according to the present invention.
Detailed Description
The technical contents, structural features, attained objects and effects of the present invention are explained in detail below with reference to the accompanying drawings.
Specifically, the invention adopts the following technical scheme:
as shown in fig. 1 to 8, the present invention provides a hand rehabilitation training device and a training method with dual active control modes. The training device comprises an electroencephalogram acquisition device 10, an electromyogram acquisition device 11, a wearable rehabilitation manipulator 13, a virtual rehabilitation training system 14 and a Leap Motion hand tracker 15, the electroencephalogram acquisition device 10 comprises an electroencephalogram cap 101 and an electroencephalogram amplifier 102, the electroencephalogram cap 101 is worn on the top of the head of a subject to acquire an electroencephalogram signal of motor imagery, and the signal is transmitted and received through the electroencephalogram amplifier 102 and an upper computer.
The myoelectricity collecting device 11 comprises a myoelectricity collecting module 111 and a signal receiver 112, wherein the myoelectricity collecting module 111 is worn on extensor digitorum muscles and ulnar wrist flexor muscles of a healthy side arm of a subject to collect myoelectricity signals, and the myoelectricity signals are transmitted to an upper computer through the signal receiver 112.
The wearable rehabilitation manipulator 13 can realize independent movement of each finger of the hand and can assist extension and bending of each finger joint, and the wearable rehabilitation manipulator 13 is combined with the motor imagery rehabilitation training system 14 for rehabilitation training of the testee.
The rehabilitation training system 14 comprises a brain and muscle electrical acquisition unit, and can perform preprocessing, feature extraction and classification identification of brain and muscle electrical signals. The rehabilitation training system 14 induces the subject to perform limb movement imagination in multiple directions and deeply through the design of a virtual scene, improves the interestingness of training and the nerve activation degree of the brain of the subject, and improves the activity of the subject training through the training of driving the affected side mode by the healthy side of the subject.
The Leap Motion hand tracker 15 is a high-precision finger recognition device that performs hand movement tracking. The Leap Motion hand tracker can collect the hand Motion of a testee in real time, and shows the hand Motion in a virtual scene in a real-time Motion form of a virtual hand so as to be used as visual feedback and carry out virtual-real interaction, stimulate a brain Motion functional area of the testee and improve the nerve activation degree of the brain of the testee.
As the skeleton structure of the human hand mainly comprises the carpal bones, the metacarpal bones and the phalanges, and the phalanges comprise the proximal phalanx, the middle phalanx and the distal phalanx. As shown in fig. 5, the wearable rehabilitation manipulator of the present invention includes a metacarpal plate 1, a motor push rod 2, a metacarpal connection member 3, a joint push rod 4, a joint angle plate 5, and a hinge pin 6. The palm bone plate 1 is used as a fixing base of the whole manipulator, the palm bone plate 1 is connected with a near phalanx shell and a middle phalanx shell which are mutually connected through a palm bone connecting piece, and a near phalanx joint angle plate is fixed on the palm bone plate through a fixing piece; the joints among the proximal finger joint push rod, the proximal finger joint angle plate, the middle finger joint push rod and the middle finger bone shell are all provided with a shoulder retaining ring fixing hinge pin; the back of the hand contact surface of whole manipulator is the cambered surface design, increases user's wearing comfort.
Preferably, the wearable rehabilitation manipulator adopts a linear motor as a drive, can directly convert electric energy into mechanical energy of linear motion, does not need an intermediate conversion mechanism, and can meet the requirements of the finger rehabilitation robot on flexibility, portability and safety.
When manufacturing, the wearable equipment of patient's individual customization can be realized to accessible 3D scanning reverse engineering, and 3D prints the part and adopts the high tenacity resin of import.
The invention also provides a training method, which deeply induces the subject to perform limb movement imagination by designing various virtual scenes such as character voice, picture voice and the like. The electroencephalogram signal of the brain motor imagery area is acquired in real time through electroencephalogram acquisition equipment, and the result is sent to a virtual scene for interaction after signal preprocessing, feature extraction and classification identification, so that the movement of a model hand in the scene is controlled. When the model hand reaches the designated position and then becomes a fist-making state, at the moment, the electromyographic signal of the healthy side of the patient is collected, the result is sent to the wearable rehabilitation manipulator in a command form through Bluetooth after signal preprocessing, feature extraction and classification recognition, the wearable rehabilitation manipulator is used for assisting the testee to perform gripping training, and the hand rehabilitation training is performed in a brain electricity and electromyography combined mode. Meanwhile, the hand motions of the testee are collected through the Leap Motion and synchronized to the virtual hand in the virtual scene, so that the virtual-real interaction between the wearable rehabilitation manipulator and the virtual hand is realized, the testee is stimulated to carry out active rehabilitation training, the remodeling of the brain damaged Motion area function of the testee and the hand rehabilitation speed and rehabilitation effect are accelerated, the initiative of the hand rehabilitation training of the patient is improved, and the specific training process is shown in fig. 8.
The specific first training example is shown below:
firstly, selecting a quiet and comfortable experimental environment, positioning a subject in a position about one meter in front of a computer screen, starting an electroencephalogram amplifier to be connected with a brain-muscle electricity acquisition unit, wearing an electroencephalogram cap on the subject, and coating conductive paste on a corresponding electrode channel, as shown in fig. 3, selecting brain motion areas C3 and C4 as the electrode channels, alternatively selecting FC3, FC4, CP3, CP4, C5 and C6 as the electrode channels in order to better analyze electroencephalogram signals of the brain motion areas, simultaneously starting a muscle electricity acquisition module to be connected with an upper computer through wireless transmission, switching on a power supply of a wearable rehabilitation manipulator, connecting the wearable rehabilitation manipulator with the upper computer through Bluetooth, and wearing the rehabilitation manipulator and muscle electricity acquisition equipment for the subject.
Secondly, logging in a virtual rehabilitation training system, and starting motor imagery training and hand grasping action training of the testee, wherein the training comprises the steps of collecting, preprocessing, feature extraction, classification and recognition of motor signals and brain and muscle electrical signals and PSO-SVM classification model establishment.
The motor imagery signal acquisition specifically comprises the following steps: as shown in fig. 3, L represents a left brain area, R represents a right brain area, a 64-lead wireless electroencephalogram acquisition system is selected for electroencephalogram data acquisition, electrode positions are positioned by adopting international standard 10-20 electrode leads, and a reference electrode is arranged in a central area of the top of the head. The sampling frequency of the amplifier is 1000Hz, the acquisition channels are 8 electrode channels related to the motion area, and the acquisition channels comprise: c3, C4, FC3, FC4, CP3, CP4, C5, and C6.
The motor imagery training stage specifically comprises: selecting a quiet and comfortable experimental environment, wearing the electroencephalogram acquisition equipment by a subject, keeping the body in a relaxed state, and avoiding eye movement and other actual actions as much as possible in the exercise imagination training process. As shown in fig. 5a, the subject was asked to perform limb motor imagery according to the screen prompts. Firstly, the screen prompts the subject to concentrate on the fact that the motor imagery is about to start, the process lasts for 2s, when the 2s is reached, a left (right) prompt appears in the center of the screen, the subject follows the virtual scene prompt to perform the left (right) motor imagery, the duration is 4s, the motor imagery prompt disappears when the 6s is reached, the motor imagery process of the subject is ended, then the time of 2s is provided for the subject to rest, the second motor imagery training is repeatedly performed until the designated number of times of motor imagery training is completed, and in the embodiment, the number of times of the motor imagery training is 10.
The preprocessing of the data of the motor brain electrical signals comprises the following specific steps: the collected training data is divided into 10 segments, and data between 2s and 6s (4 s in total) of each channel in 8 channels is intercepted respectively for analysis and processing. Firstly, down-sampling an intercepted motor imagery signal to 128Hz, and then carrying out 0.5-2 Hz high-pass filtering to remove baseline drift and self-adaptive notch to remove 50Hz power frequency interference.
The extraction and classification identification of the motor intention characteristics of the motor brain electrical signals are specifically as follows: because the characteristics of the electroencephalogram signals are mainly reflected in an alpha (alpha) frequency band (8-13 Hz) and a beta (beta) frequency band (13-30 Hz), CSP common space mode is respectively carried out on alpha frequency band (8-13 Hz) and beta frequency band (13-30 Hz) data of the electroencephalogram signals acquired in 10 times of experiments for multi-lead spatial filtering, and the electroencephalogram signals generate new time sequences capable of optimally distinguishing the grabbing motion motor imagery after filtering processing.
Further, the power spectral density extraction of the alpha/beta frequency band is realized by adopting a periodogram method, N observation data of a random sequence x (N) are regarded as a sequence with limited energy, the discrete Fourier transform of x (N) is directly calculated to obtain X (k), then the square of the amplitude of the sequence is taken and divided by N to be used as the sequence, and x (N) the estimation of a real power spectrum. Assuming that the finite random signal sequence is x (n), the power spectral density estimation of the finite random signal sequence is in relation to the estimation
Wherein,
representative spectral Density, FFT [ x (n)]Represents the fourier transform of the random sequence x (N), N represents the number of observations, and N represents the sequence number of the discrete sequence in the random signal.
Further, the extraction of the characteristics of the electroencephalogram signals also comprises the extraction of wavelet packet node energy characteristics, and specifically comprises the following steps: the signal x (t) is decomposed by N layers and can be divided into 2NSubspace n (n ═ 1,2, 3.., 2)N) Energy E corresponding to subspace reconstruction signalnCan be calculated from the sum of the squares of the spatial wavelet packet coefficients, as:
where t represents time, j represents a scale factor, k represents a translation factor,
are wavelet coefficients.
Further, the extraction of the characteristics of the motion brain electrical signals also comprises the extraction of wavelet entropy characteristics, firstly, the reconstructed signals are
Making m equal divisions, the total energy per epoch can be expressed as:
second, the probability density distribution P of each band energymkCan be normalized by the total energy per time interval
Thirdly, the energy distribution situation of the signal in different frequency bands of different time periods can be reflected, and band spectrum entropy values corresponding to different time periods are calculated
The band spectrum entropy value is called wavelet packet frequency band local entropy, and the matrix can be expressed as
Finally, the calculation formula of the wavelet packet entropy S is as follows:
Ekrepresenting the energy of the time interval when the translation factor is time, PkRepresenting the probability density of the energy of the time frequency band, k representing the shift factor, P representing the probability density of the energy of all frequency bands, Pi representing the probability density of the energy of the ith frequency band, i representing the number of frequency bands, and m representing the fraction of signal equal.
Collecting electromyographic signals: as shown in FIG. 4, the left arm of FIG. 4 is with the palm facing outward, the myoelectric collection module patch is attached to the flexor carpi ulnaris at position 100, the right arm of FIG. 4 is with the palm facing inward, the patch is attached to the extensor digitorum at position 200, and the reference electrode patch is attached to the inner side of the wrist.
The myoelectricity training stage specifically comprises the following steps: selecting a quiet and comfortable experimental environment, wearing a rehabilitation manipulator for the affected side of a subject, wearing a myoelectricity acquisition module on the healthy side of the subject, and avoiding other actual actions except prompting as much as possible in the model building stage. As shown in fig. 5b, the subject was asked to perform grip training according to the screen prompts. And (3) prompting a static keeping stage by a screen at the 2 nd s, keeping the testee in a static state as much as possible in the process, maintaining the stability of the electromyographic signals, making a fist making action at a constant speed on the screen at the 6 th s, and finishing the establishment stage of the electromyographic training model at the 18 th s.
Electromyographic signal preprocessing: because the original electromyographic signal has high-frequency noise, power frequency interference and the like, the electromyographic signal needs to be preprocessed. The project adopts a Butterworth filter of 0-200Hz to carry out low-pass filtering. Secondly, performing active segment detection on the signals by using a threshold method, and then extracting time domain characteristics including a root mean square value, slope sign change and an electromyographic integral value from the active segment signals, and forming a characteristic matrix by the frequency domain characteristics of the central frequency and the average frequency domain. And finally, obtaining the optimal C and G by using a Support Vector Machine (SVM) and a cross validation method so as to obtain a classification model.
Electromyographic signal acquisition and feature extraction: root mean square value RMS is used for representing muscle contribution rate omega after normalizationj(ii) a Calculating the muscle activity alpha by using the surface electromyogram signalj(t); using muscle contribution rate omegajAnd degree of muscle activity alphaj(t) calculating the degree of upper limb movement eta, thereby adjusting the variable impedance equation coefficient;
the impedance setting equation is as follows:
in the formula, xd、xrRespectively an expected track and a reference track of the tail end position of the hand rehabilitation robot; b isdIs a damping coefficient; kdIs the stiffness coefficient; introducing the upper limb activity eta into impedance equation parameters so as to realize the self-adaptive adjustment of the impedance parameters; impedance parameter BdAnd KdIs represented as follows: b isd＝sig(λB·η)·B0，Kd＝sig(λK·η)·K0In the formula, λBAnd λKAre respectively provided withGain factors for damping and stiffness terms, B0And K0Is an initial impedance coefficient, BdAnd KdFor the modified impedance coefficients, sig (. gamma.) is the sigmoid function, and as the clipping function, B isdAnd KdThe range of variation is
Establishing a PSO-SVM classification model: the SVM (support vector machine) can realize the construction of an optimal segmentation hyperplane in a feature space, thereby linearly separating samples of different classes. And inputting the CSP characteristic matrix into the SVM for classification model training, and simultaneously carrying out self-adaptive optimization adjustment on a punishment parameter C and a kernel parameter g in the SVM modeling process by utilizing the global search capability of a Particle Swarm Optimization (PSO) algorithm so as to obtain the SVM classifier with better classification effect and more accurate identification.
Wearable rehabilitation manipulator control: the motor imagery classification result of the testee is sent to the wearable rehabilitation manipulator in an instruction form through the upper computer, the wearable rehabilitation manipulator is controlled to assist the patient to carry out rehabilitation training, as shown in fig. 4, the wearable rehabilitation manipulator can realize independent movement of each finger of the hand, can assist each finger joint to stretch and bend, and receives two hexadecimal instructions of the upper computer through Bluetooth 4.2.
The motor imagery on-line training stage: and performing current motor imagery classification recognition by using the established PSO-SVM motor imagery classification model, performing virtual interaction on classification results in a mode that the model hand moves leftwards (rightwards) in a scene, and changing the model hand into a fist-making state when the model hand reaches two ends of a screen and presses a designated position.
Myoelectric on-line training stage: when the model hand in the virtual scene is in a fist-making state, the testee carries out current action classification and identification through the trained PSO-SVM electromyographic signal classification model, sends the classification result to the wearable rehabilitation manipulator in an instruction form, drives the training of the affected hand part through the action of the healthy hand part, and controls the wearable rehabilitation manipulator to assist the patient with hand dysfunction to carry out rehabilitation training.
Motor imagery virtual scene: as shown in fig. 7a, 7b and 7c, a Unity3D game development engine is used to build three task-based virtual scenes including audio, text, pictures, video and virtual movements, so as to comprehensively induce the subject to perform limb movement imagination.
Further, in the sound text scene shown in fig. 7a, the subject is induced to perform the corresponding body movement imagery by text prompt such as "moving imagery to the left" and simultaneously playing the voice corresponding to the text content. If the classification recognition result of the PSO-SVM on the motor imagery signals of the subject is left, the model hand in the virtual scene moves to the left side of the apple, and then the model hand changes into a fist-making state; and if the classification recognition result is right, the model hand in the virtual scene moves to the left apple position and then changes into a fist-making state. Wherein a1 is training timing time, a2 is character prompt, a3 is voice start, a4 is model hand, a5 is apple model, and a6 is virtual hand.
The upper computer sends a grabbing command to the wearable rehabilitation manipulator, if the classification result is not grabbing, the upper computer sends a command to enable the wearable rehabilitation manipulator to be in an extending posture, wherein a1 is training timing time, a2 is character prompt, a3 is voice on, and a4 is a virtual hand.
In the picture speech scene shown in fig. 7b, the subject is prompted to perform limb motor imagery of the corresponding action by a scene such as "right motor imagery".
In the virtual game scenario shown in fig. 7c, the subject can make a limb grasping motor imagery through grasping left or right object cues of the virtual arm in the scenario.
Further, when the model hand becomes the fist state, the healthy side hand of testee carries out the gripping action, through the host computer to healthy side electromyographic signal's categorised discernment back, with the gripping action with the form of hexadecimal order send to wearable rehabilitation manipulator, drive the rehabilitation training of the sick side hand of testee. Through the active rehabilitation training of this kind of side of being good for driving affected side, improved hand function patient's rehabilitation training effect greatly.
Furthermore, the invention adopts the Leap Motion hand tracker to collect the hand motions of the testee in real time, completes gesture recognition after analysis and processing, displays the motions of the virtual hand to the testee, generates a visual nerve feedback to the testee, achieves the effect of simultaneously performing closed-loop nerve rehabilitation training and physical therapy, and improves the initiative and the efficiency of the rehabilitation training of patients with hand dysfunction.
And when the target training time is reached, the rehabilitation training system automatically generates a training report, prints and archives the training information of the testee, and finally quits the rehabilitation training system to complete the rehabilitation training.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.
Claims (10)
1. The utility model provides a hand rehabilitation training device of two active control modes which characterized in that: which comprises an electroencephalogram acquisition device, a myoelectricity acquisition device, a wearable rehabilitation manipulator, a virtual rehabilitation training system and a Leap Motion hand tracker,
the electroencephalogram acquisition device comprises an electroencephalogram cap and an electroencephalogram amplifier, the electroencephalogram cap is worn on the top of the head of a subject and is used for acquiring electroencephalogram signals of motor imagery, and the electroencephalogram amplifier is connected with an upper computer to send and receive the signals;
the myoelectric acquisition device comprises a myoelectric acquisition module and a signal receiver, wherein the myoelectric acquisition module is worn at extensor muscles and ulnar wrist flexor muscles of a healthy side arm of a subject to acquire myoelectric signals, and the myoelectric signals are transmitted to an upper computer through the signal receiver;
the wearable rehabilitation manipulator can realize the independent motion of each finger of the hand and can assist the extension and the bending of each finger joint, and the wearable rehabilitation manipulator is combined with the virtual rehabilitation training system and used for the rehabilitation training of a testee;
the virtual rehabilitation training system comprises a brain-muscle-electricity acquisition unit, wherein the brain-muscle-electricity acquisition unit can be used for preprocessing brain-muscle-electricity signals, extracting features and carrying out classification and identification;
the Leap Motion hand tracker is high-precision finger recognition equipment for tracking hand movement, can acquire hand movements of a subject in real time, displays the hand movements in a virtual scene in a real-time movement form of a virtual hand, and stimulates a brain movement functional area of the subject to improve the nerve activation degree of the brain of the subject by serving as visual feedback and performing virtual-real interaction.
2. The dual active control mode hand rehabilitation training device of claim 1, wherein: wherein: the wearable rehabilitation manipulator comprises a palm bone plate, a motor push rod, a palm bone connecting piece, a joint push rod, a joint angle plate and a hinge pin, wherein the palm bone plate is a fixing base of the wearable rehabilitation manipulator, the first end of the joint push rod is connected with the palm bone plate, the second end of the joint push rod is connected with the palm bone connecting piece through the hinge pin, the first end of the motor push rod is connected with a drive, the second end of the motor push rod is connected with the joint angle plate, and a slide bar on the joint angle plate can slide along a slide groove on the joint push rod.
3. A method of performing dual active control mode hand rehabilitation training by the dual active control mode hand rehabilitation training device of claim 1, wherein: which comprises the following steps:
s1, selecting a quiet and comfortable experimental environment, starting an electroencephalogram amplifier to be connected with a brain-muscle electricity acquisition unit, wearing an electroencephalogram cap for a subject, smearing conductive paste on a corresponding electrode channel, selecting C3, C4, FC3, FC4, CP3, CP4, C5 and C6 electrode channels of a brain movement area, starting an electromyography acquisition module to be connected with an upper computer through wireless transmission, switching on a power supply of a wearable rehabilitation manipulator, connecting the wearable rehabilitation manipulator with the upper computer through Bluetooth, and wearing the rehabilitation manipulator and electromyography acquisition equipment for the subject;
the electroencephalogram cap is worn on a subject and is coated with electroencephalogram paste, the electroencephalogram amplifier is connected with a electroencephalogram and electromyogram collection unit, the electromyogram collection module is worn on a side arm of the subject and is connected with an upper computer through wireless transmission, and meanwhile, a power supply of the wearable rehabilitation manipulator is connected with the upper computer through Bluetooth;
s2, logging in the virtual rehabilitation training system, carrying out motor imagery training on a subject, acquiring electroencephalogram signals of the subject for limb motor imagery, selecting 8 electrode channels of a brain motor functional area C3, C4, FC3, FC4, CP3, CP4, C5 and C6 from a signal acquisition channel, preprocessing the acquired electroencephalogram signals, extracting features, carrying out classification and identification, and establishing a PSO-SVM motor imagery classification model;
s3, classifying the PSO-SVM motor imagery classification model established in the step S2, displaying the classification result to the subject in a mode that the hand of the model moves leftwards or rightwards in the virtual scene, and improving the neural activation degree of the brain motor area of the subject;
s4, myoelectric training and collecting myoelectric signals: wearing a rehabilitation manipulator on the affected side of a testee, wearing a myoelectric acquisition module on the healthy side of the testee, enabling the model hand to be in a fist-making state when the model hand reaches a first position in a scene, acquiring myoelectric signals of the healthy side of the testee at the moment, carrying out active rehabilitation training in a mode of driving the affected side to train through the healthy side movement, and carrying out pretreatment, feature extraction, classification and recognition on the acquired myoelectric signals and establishing a PSO-SVM (particle swarm optimization-support vector machine) myoelectric signal classification model;
s5, classifying the PSO-SVM electromyographic signal classification model established in the step S4, sending classification results to the wearable rehabilitation manipulator through a Bluetooth module in the form of two hexadecimal instructions, and respectively controlling the gripping and stretching of the wearable rehabilitation manipulator to assist a subject in performing hand rehabilitation training;
then, the Motion of the hands of the testee is collected through the Leap Motion hand tracker, and is displayed in a virtual scene in a real-time Motion form of a virtual hand to serve as visual feedback to deepen the neural activation degree of the brain motor area of the testee;
and S6, setting training time according to the condition of the subject, generating a training report and printing and archiving the training report after the training is finished, and quitting the rehabilitation training system.
4. The dual active control mode hand rehabilitation training method of claim 3, wherein: the motor imagery training specifically comprises the following steps: the brain wave cap is worn by a subject and coated with brain wave paste, the rehabilitation training system is connected, motor imagery starts when a display screen of an upper computer prompts the subject to concentrate on mind, after a period of time, a left or right prompt appears in the center of the display screen of the upper computer, the subject performs left or right motor imagery along with a virtual scene prompt, in the process, the subject performs motor imagery, then the motor imagery prompt disappears, the motor imagery process of the subject is finished, and the second motor imagery training is repeatedly performed after a certain time of rest until the motor imagery training of set times is finished.
5. The dual active control mode hand rehabilitation training method of claim 3, wherein: the preprocessing and feature extraction of the acquired electroencephalogram signals in step S2 includes:
intercepting electroencephalogram signal data between the 2 nd to the 6 th of each electrode channel, performing down-sampling to 128Hz, performing 0.5 Hz-2 Hz high-pass filtering to remove baseline drift, and then performing self-adaptive notch to remove 50Hz power frequency interference;
carrying out wavelet packet decomposition on an EEG signal by 6 layers to extract an EEG characteristic frequency band, selecting a 0-4 Hz frequency band in a4 th layer of wavelet packet decomposition to correspond to a wave in an EEG signal, selecting a 4-8H frequency band in the 4 th layer to correspond to a theta wave in the EEG signal, selecting an 8-12 Hz frequency band in the 4 th layer to correspond to an alpha wave in the EEG signal after combining with a 12-13 Hz frequency band in the 6 th layer, and selecting a 14-16 Hz frequency band in a5 th layer to correspond to a beta wave in the EEG signal after combining with a 28-30 Hz frequency band;
respectively carrying out CSP common space mode and multi-lead space filtering on EEG data of alpha wave frequency bands and beta wave frequency bands in each electrode channel so as to generate a time sequence capable of optimally distinguishing the grabbing motion motor imagery;
and extracting power spectral density of the alpha/beta frequency band by adopting a periodogram method, and further extracting the wavelet packet node energy and the characteristics of the wavelet entropy to obtain the wavelet packet entropy as the characteristic points of the electroencephalogram signal.
6. The dual active control mode hand rehabilitation training method of claim 3, wherein: the preprocessing and feature extraction of the collected electromyographic signals in step S4 includes:
the method comprises the steps of performing low-pass filtering by using a 0-200Hz Butterworth filter, performing active segment detection on signals by using a threshold method, extracting time domain characteristics including a root mean square value, slope sign change and a myoelectricity integral value from the active segment signals, and forming a characteristic matrix by using frequency domain characteristics of a central frequency and an average frequency domain, and finally obtaining an optimal punishment coefficient C and a parameter G by using a support vector machine and a cross validation method to further obtain a classification model, and predicting test data through the model.
7. The dual active control mode hand rehabilitation training method of claim 3, wherein: in step S2, a periodogram method is used to extract the power spectral density of the α/β band, N observation data of a random sequence x (N) is regarded as an energy-limited sequence, the discrete fourier transform of x (N) is directly calculated to obtain x (k), then the square of the amplitude is taken and divided by N as a sequence, x (N) is the estimation of the real power spectrum, and assuming that the finite-length random signal sequence is x (N), the power spectral density estimation calculation formula is:
wherein,
representative spectral Density, FFT [ x (n)]Represents the fourier transform of the random sequence x (N), N represents the number of observations, and N represents the sequence number of the discrete sequence in the random signal.
8. The dual active control mode hand rehabilitation training method of claim 7, wherein: the specific method for extracting the wavelet packet node energy features is as follows: the signal x (t) is decomposed into 2 layers by N layersNSubspace n (n ═ 1,2, 3.., 2)N) Energy E corresponding to subspace reconstruction signalnThe calculation formula is calculated by the sum of squares of the space wavelet packet coefficients as follows:
where t represents time, j represents a scale factor, k represents a translation factor,
are wavelet coefficients.
9. The dual active control mode hand rehabilitation training method of claim 7, wherein: extracting wavelet entropy characteristics, and specifically comprising the following steps:
first, the signal is reconstructed
M equal divisions were made and the total energy per time period was expressed as:
second, the probability density distribution P of each band energymkThe total energy per time interval is normalized to
Thirdly, calculating band spectrum entropy values corresponding to different time periods
The band spectrum entropy value is called wavelet packet frequency band local entropy, and the matrix is expressed as
Finally, the wavelet packet entropy S calculation formula is obtained as follows:
wherein E iskRepresenting the energy of the time interval when the translation factor is time, PkRepresenting the probability density of the energy of the time frequency band, k representing the shift factor, P representing the probability density of the energy of all frequency bands, Pi representing the probability density of the energy of the ith frequency band, i representing the number of frequency bands, and m representing the fraction of signal equal.
10. The dual active control mode hand rehabilitation training method of claim 6, wherein: the electromyographic signal acquisition and feature extraction in step S4 specifically includes: root mean square value RMS is used for representing muscle contribution rate omega after normalizationj(ii) a Calculating the muscle activity alpha by using the surface electromyogram signalj(t); using muscle contribution rate omegajAnd degree of muscle activity alphaj(t) calculating the degree of upper limb movement eta, thereby adjusting the variable impedance equation coefficient;
the impedance setting equation is as follows:
in the formula, xd、xrRespectively an expected track and a reference track of the tail end position of the hand rehabilitation robot; b isdIs a damping coefficient; kdIs the stiffness coefficient; introducing the upper limb activity eta into impedance equation parameters so as to realize the self-adaptive adjustment of the impedance parameters; the impedance parameter BdAnd KdIs represented as follows: b isd＝sig(λB·η)·B0，Kd＝sig(λK·η)·K0In the formula, λBAnd λKGain coefficients of damping term and stiffness term, B0And K0Is an initial impedance coefficient, BdAnd KdFor the modified impedance coefficients, sig (. gamma.) is the sigmoid function, and as the clipping function, B isdAnd KdThe range of variation is
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