CN114569144A - Knee joint tendon reflex state evaluation method based on myoelectricity-inertia sensing - Google Patents
Knee joint tendon reflex state evaluation method based on myoelectricity-inertia sensing Download PDFInfo
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Abstract
The invention discloses a knee joint tendon reflex state evaluation method based on myoelectricity-inertia sensing. The invention is respectively used for acquiring myoelectric information of muscle groups causing knee joint stretching and bending movement and inertia information of limb movement for detecting shank movement in the rehabilitation training process through the myoelectric acquisition module and the movement acquisition module, lags behind the myoelectric information according to the inertia information change, and establishes the acquired signal window time; for a specific training object, establishing a multiple linear regression model as an individualized tendon reflex state description equation, solving by using a least square estimation and other regression algorithms to obtain a coefficient of the equation, wherein the coefficient is used for evaluating the tendon reflex state of the training object induced by a rehabilitation training device in the rehabilitation training process; the invention combines the electromyographic signals and the inertia signals, considers both the real-time property of the electromyographic signals and the robustness of the inertia signals, and improves the effectiveness and the accuracy of the detection of the tendon reflection state.
Description
Technical Field
The invention relates to the field of limb rehabilitation training, in particular to a knee joint tendon reflex state evaluation method based on myoelectricity-inertia sensing.
Background
The knee joint is one of the important joints for realizing stable walking and standing support of the lower limbs of the human body, and is also one of the main distal joints of the lower limbs of the human body. In diseases such as stroke, the recovery speed and effect of the distal joint of the human body are more difficult than those of the proximal joint. In the traditional manual rehabilitation practice, a clinician trains the knee joint of a patient and promotes the rehabilitation of the knee joint by using a proprioceptive neuromuscular promotion technology clinical treatment method. The key operational point of the clinical treatment method is to induce the tendon reflex of the knee joint muscle group of the patient by using a manipulation and induce the patient to carry out active control on the basis of the tendon reflex. The neurophysiologic basis of this clinical treatment is that the strong proprioceptive stimulation caused by tendon reflex can be uploaded to the sensory cortex of the brain, and the neuron action potential threshold initiated by the movement of the motor cortex of the brain is reduced by the sensory-motor combined cortex of the cerebral cortex, thereby promoting the patient to initiate joint movement control more easily.
The tendon reflex action of the gonadal muscle group is classified into two types, one is the extensional tendon reflex and the other is the flexor tendon reflex. In the current application practice, the traditional method training mode is mainly to judge the muscle state by hand feeling after inducing tendon reflex stimulation, and the hand feeling judgment strategy has the problem that the judgment accuracy is easy to deviate along with factors such as experience degree and fatigue state of a doctor; the novel robot rehabilitation training mode is mainly characterized in that fixed parameters are set for the rehabilitation robot to induce tendon reflex stimulation based on experience, the motion interaction force amplitude of a shank is detected through a force sensor, the tendon reflex state is detected, however, the sudden change of the limb action caused by tendon reflex not only relates to the sudden change of the motion amplitude and leads to the obvious increase of the interaction force amplitude, but also the sudden change process is obviously represented as the rapid change of the motion speed, and therefore the strategy for detecting the interaction force amplitude based on the force sensor is not enough.
Therefore, in order to improve the rehabilitation effect of the knee joint, it is necessary to perform rapid real-time evaluation on the tendon reflex state of the knee joint by using a detection device, and use the evaluation result in the actual rehabilitation training process as important reference information of the rehabilitation training implementation strategy.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a knee joint tendon reflex state evaluation method based on myoelectricity-inertia sensing, which is used for evaluating tendon reflex limb response states induced by rehabilitation training equipment in a rehabilitation training process.
A training object sits on the seat, a first myoelectric sensor is attached to the surface of the front thigh muscle corresponding to quadriceps femoris of the training object, and a second myoelectric sensor is attached to the surface of the back thigh muscle corresponding to popliteal muscle; the thigh of the training object is positioned on the seat and relatively kept static, and the shank is driven by the knee joint to realize the stretching and flexing motion; an inertial sensor is attached to the front surface of the lower leg of the training subject.
The invention discloses a knee joint tendon reflex state evaluation method based on myoelectricity-inertia sensing, which comprises the following steps of:
1) and (4) connecting an evaluation system:
the evaluation system comprises a myoelectricity acquisition module, a motion acquisition module, a data acquisition card and an upper computer, wherein the myoelectricity acquisition module comprises a first myoelectricity sensor and a second myoelectricity sensor, and the motion acquisition module comprises an inertial sensor; the first electromyographic sensor attached to the surface of the anterior thigh muscle corresponding to quadriceps femoris of a training object and the second electromyographic sensor attached to the surface of the dorsal thigh muscle corresponding to popliteal muscle are respectively used for acquiring myoelectric information of muscle groups, wherein the myoelectric information of the muscle groups causes knee joint extension movement when the quadriceps femoris contracts as a motive muscle and the popliteal muscle relaxes as an antagonistic muscle, and causes knee joint flexion movement when the popliteal muscle contracts as the motive muscle and the quadriceps femoris muscle relaxes as the antagonistic muscle; the inertial sensor is attached to the surface of the front part of the lower leg of the training object and is used for detecting the inertial information of limb movement of the lower leg movement and detecting whether the lower leg movement can be further analyzed to obtain effective induction; the first myoelectric sensor, the second myoelectric sensor and the inertial sensor are respectively connected to a data acquisition card, and the data acquisition card is connected to an upper computer;
2) in the rehabilitation training process, muscle group electromyographic information acquired by the electromyographic acquisition module and limb movement inertia information acquired by the movement acquisition module are respectively transmitted to the data acquisition card in a wired or wireless mode, and the data acquisition card synchronizes the muscle group electromyographic information and the limb movement inertia information and then uploads the muscle group electromyographic information and the limb movement inertia information to the upper computer in a wired or wireless mode, wherein delta t is the time difference between the muscle group electromyographic information and the limb movement inertia information1Synchronously sampling cycle time for a data acquisition card;
3) dividing myoelectric information of muscle groups into motive myoelectric information and antagonistic myoelectric information according to training action; removing low-frequency and high-frequency signals in the electromyographic signals by adopting band-pass filtering on the electromyographic acquisition information; further rectifying the electromyographic information, namely keeping the electromyographic signal positive and keeping the electromyographic signal unchanged, and taking the negative absolute value of the electromyographic signal; marking the electromyographic information after the band-pass filtering and rectifying treatment, wherein the electromyographic information of the primary electromyographic information is s at the sampling time iagonist(i) Antagonistic myoelectric information is santagonist(i)；
4) For the electromyographic information of the primary muscle, adopting a time domain integral electromyographic value as an electromyographic amplitude characteristic and a frequency domain average power frequency as an electromyographic change speed characteristic; the calculation process of the myoelectric amplitude characteristic is that a positive integer T of sampling period time is taken as signal window time, the calculation is carried out on the sampling data of the myoelectric information of the original myoelectric in the signal window, the myoelectric amplitude characteristic continuously slides to the next calculation time after the calculation is finished, and the calculation formula of the myoelectric amplitude characteristic is as follows:
wherein,agonistEMGjcontinuously calculating and storing the myoelectric amplitude characteristic of the jth prime motor;
the calculation formula of the electromyographic change speed characteristic is as follows:
wherein f is frequency, and p (f) is a power spectral density function of the electromyographic signal;agonistMPFjcontinuously calculating and storing the jth electromyogram change speed characteristic;
5) for the inertia information, at the sampling time i, the inertia information output by the inertia sensor comprises a calf joint angle theta (i) and a joint angular velocity
And angular acceleration of joint
Adopting an angular velocity mean square value in a time domain as an inertia information amplitude characteristic and an angular acceleration mean square value as an inertia information change speed characteristic; inertia information amplitude feature RMS _ VjThe calculation formula of (2) is as follows:
inertial information change speed feature RMS _ AjThe calculation formula of (2) is as follows:
6) considering that myoelectric information of muscle groups can be changed quickly after the tendon reflex of the knee joint is induced, the change of limb movement slightly lags behind the myoelectric change, namely the change of inertia information lags behind the myoelectric information of the muscle groups; because the limb movement induced by tendon reflex has a latency period, in order to ensure that the myoelectric information change and the inertial information change of the muscle group are in the same signal window, the signal window time is T.DELTA.t1(ii) a In addition, it takes time for the human brain to initiate movement intention and finally for the limbs to generate movement, and therefore, the signal window time satisfies:
(3～5)·Δt0≤T·Δt1≤(100～200)ms
the setting of the signal window time ensures that the signal window can cover effective myoelectric information and inertia information of muscle groups, and simultaneously ensures that tendon reflex state evaluation is carried out by using effective data as less as possible before the active movement intention of a human body is transmitted to a training limb; thus providing support for subsequent training decisions after evaluating tendon reflex states;
7) for a specific training object, establishing a multiple linear regression model as an individualized tendon reflex state description equation:
F＝α0+α1·agonistEMGj+α2·agonistMPFj+α3·RMS_Vj+α4·RMS_Aj
wherein F is the tendon reflex limb response clinical score, alpha0Is a regression constant, α1、α2、α3And alpha4Corresponding coefficients of the myoelectricity amplitude characteristic, the myoelectricity change speed characteristic, the inertia information amplitude characteristic and the inertia information change speed characteristic are respectively set;
8) grading limb responses induced by the knee joint of the training subject through clinically applying tendon reflex stimulation through a tool into multiple response grades, and endowing the tendon reflex limb responses with clinical scores for the corresponding response grades;
9) the training patients were recorded for multiple sets of random stimuli applied by the clinical pass tool: in the k-th group of stimuli, tendon reflex stimuli were applied at time 0, and the clinical score for tendon reflex limb response corresponding to the level of response to the training subjects was recorded as Fk(ii) a At the time of the first signal window after the stimulation is applied, the myoelectric amplitude characteristic, the myoelectric change speed characteristic, the inertia information amplitude characteristic and the inertia information change speed characteristic at the moment are recorded and marked asagonistEMGj_k、agonistMPFj_k、RMS_Vj_kAnd RMS _ Vj_kThe subscript k represents the kth set of stimulation results, k ═ 1,2,3,4, … …;
10) and totally finishing N groups of stimulation, wherein N is the number of the stimulation groups to obtain N groups of data, and substituting the data into an individualized tendon reflex state description equation to obtain:
solving the equation set to obtain a coefficient alpha by using a regression algorithm0、α1、α2、α3And alpha4；
11) The coefficient alpha is established0、α1、α2、α3And alpha4Then, an individualized tendon reflex state description equation is established and is further used for evaluating the tendon reflex state induced by the rehabilitation training equipment in the rehabilitation training process of the training object:
in the rehabilitation training process, the state of antagonistic muscles needs to be considered; according to the training requirement, the antagonistic muscle is in a relaxed state, and the myoelectric amplitude of the antagonistic muscle is zero; the calculation formula of the myoelectric amplitude characteristic of the antagonistic muscle is as follows:
wherein,antagonistEMGjrepresenting continuously calculated and stored jth antagonistic myoelectric amplitude characteristics; thus, the real-time tendon reflex state estimation equation is:
Fη＝[1-sign(antagonistEMGj)]·
[α0+α1·agonistEMGj+α2·agonistMPFj+α3·RMS_Vj+α4·RMS_Aj]
wherein, FηEffectively score tendon reflex limb response due toantagonistEMGjIs greater than or equal to 0 whenantagonistEMGjWhen 0, sign: (antagonistEMGj) 0; when in useantagonistEMGj>At 0, sign: (antagonistEMGj)＝1；
12) And grading the tendon reflex limb response effective scores into a plurality of grades, and enabling each grade to correspond to the tendon reflex limb response state induced by the rehabilitation training equipment, so that the evaluation on the tendon reflex limb response state induced by the rehabilitation training equipment is completed.
In the step 3), band-pass filtering of 10-200 Hz is adopted for the myoelectric acquisition information.
In step 6), the latency period Δ t0About 30-50 ms, and the signal window time is selected to be 3-5 times of the latency period; the time from the intention of the human brain to the final limb movement is about 100-200 ms.
In step 8), the limb responses are divided into four levels, namely a limb response without tendon reflex, a tendon reflex limb response lower than normal, a tendon reflex limb response slightly lower than half of the normal range, and a tendon reflex limb response slightly higher than half of the normal range; and assigning a tendon reflex limb response clinical score to limb response: respectively as follows: 0 min, no tendon reflex limb response; 1 minute, tendon reflex limb response is lower than normal; 2 min, tendon reflex limb response is slightly lower than half of the normal range; for 3 minutes, the tendon reflex limb response was slightly higher than half of the normal range.
In step 10), the number of stimulated groups N ≧ 12. And solving by using a least square estimation regression algorithm to obtain a coefficient.
In step 12), the tendon reflex limb response is effectively scored into four grades, Fη<0.5、0.5≤Fη<1.5、1.5≤Fη<2.5 and FηNot less than 2.5; and each class corresponds to a tendon reflex limb response state induced by the rehabilitation training device: when F isη<0.5, indicating no tendon reflex limb response; when F is more than or equal to 0.5η<1.5, indicating that tendon reflex limb response is less than normal; when F is more than or equal to 1.5η<2.5, representing a slightly lower half of the tendon reflex limb response in the normal range; when F isηA value of 2.5 or more indicates that the tendon reflex limb response is a slightly higher half of the normal range.
The invention has the advantages that:
the invention is respectively used for acquiring myoelectric information of muscle groups causing knee joint stretching and bending movement and inertia information of limb movement for detecting shank movement in the rehabilitation training process through the myoelectric acquisition module and the movement acquisition module, lags behind the myoelectric information according to the inertia information change, and establishes the acquired signal window time; for a specific training object, establishing a multiple linear regression model as an individualized tendon reflex state description equation, solving by using a least square estimation and other regression algorithms to obtain a coefficient of the equation, wherein the coefficient is used for evaluating the tendon reflex state of the training object induced by a rehabilitation training device in the rehabilitation training process; the invention combines the electromyographic signal and the inertial signal, considers both the real-time property of the electromyographic signal and the robustness of the inertial signal, and improves the effectiveness and the accuracy of the detection of the tendon reflection state.
Drawings
FIG. 1 is a schematic diagram of an evaluation system used in the knee joint tendon reflex state evaluation method based on myoelectric-inertial sensing according to the present invention;
fig. 2 is a flowchart of a knee joint tendon reflex state evaluation method based on myoelectricity-inertia sensing according to the present invention.
Detailed Description
The invention will be further elucidated by means of specific embodiments in the following with reference to the drawing.
As shown in fig. 1, a training subject 1 sits on a seat 5, and a first myoelectric sensor 2 is attached to a surface of a muscle of a front thigh corresponding to quadriceps of the thigh of the training subject, and a second myoelectric sensor 3 is attached to a surface of a muscle of a back thigh corresponding to popliteal muscle; the thigh of the training object is positioned on the seat and relatively kept static, and the shank is driven by the knee joint to realize extension and flexion movement; attaching an inertial sensor 4 to the surface of the front part of the shank of the training object; the first myoelectric sensor, the second myoelectric sensor and the inertial sensor are respectively connected to a data acquisition card 6, and the data acquisition card is connected to an upper computer 7.
The knee joint tendon reflex state evaluation method based on myoelectric-inertial sensing of the embodiment, as shown in fig. 2, includes the following steps:
1) and (3) evaluating system connection:
the evaluation system comprises a myoelectricity acquisition module, a motion acquisition module, a data acquisition card and an upper computer, wherein the myoelectricity acquisition module comprises a first myoelectricity sensor and a second myoelectricity sensor, and the motion acquisition module comprises an inertial sensor; the first electromyographic sensor attached to the surface of the anterior thigh muscle corresponding to quadriceps femoris of a training object and the second electromyographic sensor attached to the surface of the back thigh muscle corresponding to popliteal cord muscle are respectively used for acquiring myoelectric information of muscle groups, wherein the myoelectric information of the muscle groups is used for acquiring the myoelectric information of knee joint extension movement when the quadriceps femoris is contracted as a motive muscle and the popliteal cord muscle is relaxed as an antagonistic muscle, and the myoelectric information of the knee joint flexion movement when the quadriceps femoris is contracted as the motive muscle and the quadriceps femoris muscle is relaxed as the antagonistic muscle; the inertial sensor is attached to the surface of the front part of the lower leg of the training object and is used for detecting the inertial information of limb movement of the lower leg movement and detecting whether the lower leg movement can be further analyzed to obtain effective induction;
2) in the rehabilitation training process, muscle group electromyographic information acquired by the electromyographic acquisition module and limb movement inertia information acquired by the movement acquisition module are respectively transmitted to the data acquisition card in a wired or wireless mode, and the data acquisition card synchronizes the muscle group electromyographic information and the limb movement inertia information and then uploads the muscle group electromyographic information and the limb movement inertia information to the upper computer in a wired or wireless mode, wherein delta t is the time difference between the muscle group electromyographic information and the limb movement inertia information1Synchronously sampling cycle time for a data acquisition card;
3) dividing myoelectric information of muscle groups into motive myoelectric information and antagonistic myoelectric information according to training action; removing low-frequency and high-frequency signals in the electromyographic signals by adopting band-pass filtering of 10-200 Hz on the electromyographic acquisition information; further rectifying the electromyographic information, namely keeping the electromyographic signal positive and keeping the electromyographic signal unchanged, and taking the negative absolute value of the electromyographic signal; marking the electromyographic information after the band-pass filtering and rectifying treatment, wherein the electromyographic information of the primary electromyographic information is s at the sampling time iagonist(i) Antagonistic myoelectric information is santagonist(i)；
4) For the electromyographic information of the primary muscle, adopting a time domain integral electromyographic value as an electromyographic amplitude characteristic and a frequency domain average power frequency as an electromyographic change speed characteristic; the calculation process of the myoelectric amplitude characteristic is that positive integer T (T value is determined by inequality in step 6) sampling period time is taken as signal window time, the sampling data of the original myoelectric information in the signal window is calculated, the myoelectric amplitude characteristic continuously slides to the next calculation time after the calculation is finished, and the calculation formula of the myoelectric amplitude characteristic is as follows:
wherein,agonistEMGjcontinuously calculating and storing the myoelectric amplitude characteristic of the jth prime motor;
the calculation formula of the electromyographic change speed characteristic is as follows:
wherein f is frequency, and p (f) is a power spectral density function of the electromyographic signal;agonistMPFjcontinuously calculating and storing the jth electromyogram change speed characteristic;
5) for the inertia information, at the sampling time i, the inertia information output by the inertia sensor comprises a crus joint angle theta (i) and a joint angular velocity
And angular acceleration of joint
Adopting an angular velocity mean square value in a time domain as an inertia information amplitude characteristic and an angular acceleration mean square value as an inertia information change speed characteristic; amplitude feature of inertial information RMS _ VjThe calculation formula of (2) is as follows:
inertial information change speed feature RMS _ AjThe calculation formula of (2) is as follows:
6) considering myoelectric signaling after tendon reflex induction of knee jointThe limb movement change is slightly lagged behind the myoelectric change, namely the inertia information change lags behind the myoelectric information of muscle groups; the limb movement induced by tendon reflex has a latency period, and the latency period is delta t0About 30-50 ms, so that the myoelectric information change and the inertia information change of the muscle group are ensured to be in the same signal window, and the signal window time is T.DELTA.t1Selecting the signal window time to be 3-5 times of the latency period; in addition, the time length is about 100-200 ms because the human brain initiates movement intention to finally generate movement of limbs; thus, the signal window time satisfies:
(3～5)·Δt0≤T·Δt1≤(100～200)ms
the setting of the signal window time ensures that the signal window can cover effective myoelectric information and inertia information of muscle groups, and simultaneously ensures that tendon reflex state evaluation is carried out by using effective data as less as possible before the active movement intention of a human body is transmitted to a training limb; thus providing support for subsequent training decisions after evaluating tendon reflex states;
7) for a specific training object, establishing a multiple linear regression model as an individualized tendon reflex state description equation:
F＝α0+α1·agonistEMGj+α2·agonistMPFj+α3·RMS_Vj+α4·RMS_Aj
wherein F is the tendon reflex limb response clinical score, alpha0Is a regression constant, α1、α2、α3And alpha4Corresponding coefficients of the myoelectricity amplitude characteristic, the myoelectricity change speed characteristic, the inertia information amplitude characteristic and the inertia information change speed characteristic are respectively set;
8) the limb response induced by the knee joint of the training subject through clinically applying tendon reflex stimulation through a tool is graded into a plurality of response grades, and the tendon reflex limb response clinical scores are given to the corresponding response grades, and are respectively: 0 min, no tendon reflex limb response; 1 minute, tendon reflex limb response is lower than normal; 2 min, tendon reflex limb response is slightly lower than half of the normal range; divide by 3, tendon reflex limb responses are slightly higher than half of the normal range;
9) multiple sets of random stimuli from the clinical pass tool were recorded for the trained patients, and the stimuli should cover multiple response levels on average: in the k-th group of stimuli, tendon reflex stimuli were applied at time 0, and the clinical score for tendon reflex limb response corresponding to the level of response to the training subjects was recorded as Fk(ii) a At the time of the first signal window after the stimulation is applied, the myoelectric amplitude characteristic, the myoelectric change speed characteristic, the inertia information amplitude characteristic and the inertia information change speed characteristic at the moment are recorded and marked asagonistEMGj_k、agonistMPFj_k、RMS_Vj_kAnd RMS _ Vj_kThe subscript k represents the kth set of stimulation results, k ═ 1,2,3,4, … …;
10) and (3) completing N (N-12) groups of stimulation to obtain N groups of data, and substituting the data into a personalized tendon reflex state description equation to obtain:
solving the equation set to obtain a coefficient alpha by using a least square estimation equal regression algorithm0、α1、α2、α3And alpha4；
11) The coefficient alpha is established0、α1、α2、α3And alpha4Then, an individualized tendon reflex state description equation is established, and is further used for evaluating the tendon reflex state induced by the rehabilitation training device in the rehabilitation training process of the training object:
in the rehabilitation training process, the state of antagonistic muscles needs to be considered; according to the training requirement, the antagonistic muscle is in a relaxed state, and the myoelectric amplitude of the antagonistic muscle is zero; the calculation formula of the myoelectric amplitude characteristic of the antagonistic muscle is as follows:
wherein,antagonistEMGjrepresenting continuously calculated and stored jth antagonistic myoelectric amplitude characteristics; thus, the real-time tendon reflex state estimation equation is:
Fη＝[1-sign(antagonistEMGj)]·
[α0+α1·agonistEMGj+α2·agonistMPFj+α3·RMS_Vj+α4·RMS_Aj]
wherein, FηEffectively score tendon reflex limb response due toantagonistEMGjIs greater than or equal to 0 whenantagonistEMGjWhen 0, sign: (antagonistEMGj) 0; when in useantagonistEMGj>At 0, sign: (antagonistEMGj)＝1；
12) The tendon reflex limb response effectiveness scores are ranked into a plurality of ranks, and each rank corresponds to a tendon reflex limb response state induced by the rehabilitation training device: when F is presentη<0.5, indicating no tendon reflex limb response; when F is more than or equal to 0.5η<1.5, indicating that tendon reflex limb response is less than normal; when F is more than or equal to 1.5η<2.5, representing a slightly lower half of the tendon reflex limb response in the normal range; when F is presentηAnd the response of the tendon reflex limb is more than or equal to 2.5, which represents that the response of the tendon reflex limb is a half of a normal range, so that the response state of the tendon reflex limb induced by the rehabilitation training equipment is evaluated.
Finally, it is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (8)
1. A knee joint tendon reflection state evaluation method based on myoelectricity-inertia sensing is used for evaluating tendon reflection limb response states induced by rehabilitation training equipment in a rehabilitation training process, a training object sits on a seat, a first myoelectricity sensor is attached to the surface of front thigh muscles corresponding to quadriceps of a thigh of the training object, and a second myoelectricity sensor is attached to the surface of back thigh muscles corresponding to popliteal muscles; the thigh of the training object is positioned on the seat and relatively kept static, and the shank is driven by the knee joint to realize extension and flexion movement; the inertial sensor is attached to the surface of the front part of the calf of a training object, and the knee joint tendon reflex state evaluation method based on myoelectricity-inertial sensing is characterized by comprising the following steps of:
1) and (3) evaluating system connection:
the evaluation system comprises a myoelectricity acquisition module, a motion acquisition module, a data acquisition card and an upper computer, wherein the myoelectricity acquisition module comprises a first myoelectricity sensor and a second myoelectricity sensor, and the motion acquisition module comprises an inertial sensor; the first electromyographic sensor attached to the surface of the anterior thigh muscle corresponding to quadriceps femoris of a training object and the second electromyographic sensor attached to the surface of the back thigh muscle corresponding to popliteal cord muscle are respectively used for acquiring myoelectric information of muscle groups, wherein the myoelectric information of the muscle groups is used for acquiring the myoelectric information of knee joint extension movement when the quadriceps femoris is contracted as a motive muscle and the popliteal cord muscle is relaxed as an antagonistic muscle, and the myoelectric information of the knee joint flexion movement when the quadriceps femoris is contracted as the motive muscle and the quadriceps femoris muscle is relaxed as the antagonistic muscle; the inertial sensor is attached to the surface of the front part of the lower leg of the training object and is used for detecting the inertial information of limb movement of the lower leg movement and detecting whether the lower leg movement can be further analyzed to obtain effective induction; the first myoelectric sensor, the second myoelectric sensor and the inertial sensor are respectively connected to a data acquisition card, and the data acquisition card is connected to an upper computer;
2) in the rehabilitation training process, muscle group electromyographic information acquired by the electromyographic acquisition module and limb movement inertia information acquired by the movement acquisition module are respectively transmitted to the data acquisition card in a wired or wireless mode, and the data acquisition card synchronizes the muscle group electromyographic information and the limb movement inertia information and then uploads the muscle group electromyographic information and the limb movement inertia information to the upper computer in a wired or wireless mode, wherein delta t is the time difference between the muscle group electromyographic information and the limb movement inertia information1Synchronously sampling cycle time for a data acquisition card;
3) dividing myoelectric information of muscle groups into motive myoelectric information and antagonistic myoelectric information according to training action; removing low-frequency and high-frequency signals in the electromyographic signals by adopting band-pass filtering on the electromyographic acquisition information; further rectifying the electromyographic information, namely keeping the electromyographic signal positive and keeping the electromyographic signal unchanged, and taking the negative absolute value of the electromyographic signal; marking the electromyographic information after the band-pass filtering and rectifying processing, wherein the electromyographic information of the prime motor is s at the sampling time iagonist(i) Antagonistic myoelectric information is santagonist(i)；
4) For the myoelectric information of the motive muscle, adopting a time domain integral myoelectric value as a myoelectric amplitude characteristic and a frequency domain average power frequency as a myoelectric change speed characteristic; the calculation process of the myoelectric amplitude characteristic is that a positive integer T of sampling period time is taken as signal window time, the calculation is carried out on the sampling data of the myoelectric information of the original myoelectric in the signal window, the myoelectric amplitude characteristic continuously slides to the next calculation time after the calculation is finished, and the calculation formula of the myoelectric amplitude characteristic is as follows:
wherein,agonistEMGjcontinuously calculating and storing the myoelectric amplitude characteristic of the jth prime motor;
the calculation formula of the electromyographic change speed characteristic is as follows:
wherein f is frequency, and p (f) is a power spectral density function of the electromyographic signal;agonistMPFjcontinuously calculating and storing the jth electromyogram change speed characteristic;
5) for the inertia information, at the sampling time i, the inertia information output by the inertia sensor comprises a crus joint angle theta (i) and a joint angular velocity
And angular acceleration of joint
Adopting an angular velocity mean square value in a time domain as an inertia information amplitude characteristic and an angular acceleration mean square value as an inertia information change speed characteristic; amplitude feature of inertial information RMS _ VjThe calculation formula of (2) is as follows:
inertial information change speed feature RMS _ AjThe calculation formula of (2) is as follows:
6) considering that myoelectric information of muscle groups can be changed quickly after the tendon reflex of the knee joint is induced, the change of limb movement slightly lags behind the myoelectric change, namely the change of inertia information lags behind the myoelectric information of the muscle groups; because the limb movement induced by tendon reflex has a latency period, in order to ensure that the myoelectric information change and the inertial information change of the muscle group are in the same signal window, the signal window time is T.DELTA.t1(ii) a In addition, it takes time for the human brain to initiate movement intention and finally for the limbs to generate movement, and therefore, the signal window time satisfies:
(3～5)·Δt0≤T·Δt1≤(100～200)ms
the setting of the signal window time ensures that the signal window can cover effective myoelectric information and inertia information of muscle groups, and simultaneously ensures that tendon reflex state evaluation is carried out by using effective data as less as possible before the active movement intention of a human body is transmitted to a training limb; thus providing support for subsequent training decisions after evaluating tendon reflex states;
7) for a specific training object, establishing a multiple linear regression model as an individualized tendon reflex state description equation:
F＝α0+α1·agonistEMGj+α2·agonistMPFj+α3·RMS_Vj+α4·RMS_Aj
wherein F is the tendon reflex limb response clinical score, alpha0Is a regression constant, α1、α2、α3And alpha4Corresponding coefficients of the myoelectricity amplitude characteristic, the myoelectricity change speed characteristic, the inertia information amplitude characteristic and the inertia information change speed characteristic are respectively set;
8) grading limb responses induced by the knee joint of the training subject through clinically applying tendon reflex stimulation through a tool into multiple response grades, and endowing the tendon reflex limb responses with clinical scores for the corresponding response grades;
9) the training patients were recorded for multiple sets of random stimuli applied by the clinical pass tool: in the k-th group of stimuli, tendon reflex stimuli were applied at time 0, and the clinical score for tendon reflex limb response corresponding to the level of response to the training subjects was recorded as Fk(ii) a At the time of the first signal window after the stimulation is applied, the myoelectric amplitude characteristic, the myoelectric change speed characteristic, the inertia information amplitude characteristic and the inertia information change speed characteristic at the moment are recorded and marked asagonistEMGj_k、agonistMPFj_k、RMS_Vj_kAnd RMS _ Vj_kThe subscript k represents the kth set of stimulation results, k ═ 1,2,3,4, … …;
10) and totally finishing N groups of stimulation, wherein N is the number of the stimulation groups to obtain N groups of data, and substituting the data into an individualized tendon reflex state description equation to obtain:
solving the equation set to obtain a coefficient alpha0、α1、α2、α3And alpha4；
11) The coefficient alpha is established0、α1、α2、α3And alpha4Then, individualize the tendon reflex stateThe description equation is established and is further used for evaluating the tendon reflex state induced by the rehabilitation training device during the rehabilitation training of the training object:
in the rehabilitation training process, the state of antagonistic muscles needs to be considered; according to the training requirement, the antagonistic muscle is in a relaxed state, and the myoelectric amplitude of the antagonistic muscle is zero; the calculation formula of the myoelectric amplitude characteristic of the antagonistic muscle is as follows:
wherein,antagonistEMGjcontinuously calculating and storing the jth antagonistic myoelectric amplitude characteristic; thus, the real-time tendon reflex state estimation equation is:
Fη＝[1-sign(antagonistEMGj)]·
[α0+α1·agonistEMGj+α2·agonistMPFj+α3·RMS_Vj+α4·RMS_Aj]
wherein, FηEffectively score tendon reflex limb response due toantagonistEMGjIs greater than or equal to 0 whenantagonistEMGjWhen 0, sign: (antagonistEMGj) 0; when in useantagonistEMGj>At 0, sign: (antagonistEMGj)＝1；
12) And grading the tendon reflex limb response effective scores into a plurality of grades, and enabling each grade to correspond to the tendon reflex limb response state induced by the rehabilitation training equipment, so that the evaluation on the tendon reflex limb response state induced by the rehabilitation training equipment is completed.
2. The assessment method according to claim 1, wherein in step 3), band-pass filtering of 10-200 Hz is applied to the electromyographic acquisition information.
3. The method of claim 1Characterised in that, in step 6), the latency period duration Δ t0The time of the signal window is 30-50 ms, and the time of the signal window is selected to be 3-5 times of the duration of the latency period.
4. The assessment method according to claim 1, wherein in step 6), the time period from the intention of the human brain to the final limb movement is 100-200 ms.
5. The assessment method according to claim 1, wherein in step 8) the limb response is divided into four levels, respectively no tendon reflex limb response, tendon reflex limb response lower than normal, tendon reflex limb response half low in the normal range and tendon reflex limb response half high in the normal range; and assigning a tendon reflex limb response clinical score to limb response: respectively as follows: 0 min, no tendon reflex limb response; 1 minute, tendon reflex limb response is lower than normal; 2 min, tendon reflex limb response is half lower than normal; divide by 3, tendon reflex limb responses are half as high as normal.
6. The assessment method according to claim 1, wherein in step 10), the number of stimulated groups N.gtoreq.12.
7. The evaluation method according to claim 1, wherein in step 10), the coefficients are solved using a least squares estimation regression algorithm.
8. The method of claim 1, wherein in step 12) the effective tendon reflex limb response is scored into four grades, each grade being Fη<0.5、0.5≤Fη<1.5、1.5≤Fη<2.5 and FηNot less than 2.5; and each class corresponds to a tendon reflex limb response state induced by the rehabilitation training device: when F is presentη<0.5, indicating no tendon reflex limb response; when F is more than or equal to 0.5η<1.5, indicating that tendon reflex limb response is less than normal; when F is more than or equal to 1.5η<2.5, tendon reflex limb responseHalf lower than normal; when F is presentηGreater than or equal to 2.5, which means that the tendon reflex limb response is half as high as the normal range.
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| CN115399791A (en) * | 2022-06-28 | 2022-11-29 | 天津大学 | Stroke lower limb function assessment method and system based on myoelectric motion multi-data fusion |
| CN117563135A (en) * | 2023-12-19 | 2024-02-20 | 燕山大学 | Multi-mode information visual functional electric stimulation closed-loop regulation and control system and method |
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| CN115399791A (en) * | 2022-06-28 | 2022-11-29 | 天津大学 | Stroke lower limb function assessment method and system based on myoelectric motion multi-data fusion |
| CN117563135A (en) * | 2023-12-19 | 2024-02-20 | 燕山大学 | Multi-mode information visual functional electric stimulation closed-loop regulation and control system and method |
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