A rehabilitation manipulator based on electromyographic feedback and an adaptive control method for electrical stimulation

Abstract

The present invention discloses a rehabilitation manipulator and an electric stimulation adaptive control method based on electromyographic feedback, comprising the following steps: S1, constructing an electromyographic acquisition-rehabilitation manipulator-neuromuscular electrical stimulation combined rehabilitation system; S2, performing data preprocessing on surface electromyographic signals; S3, using the frost optimization algorithm to cyclically iterate long short-term memory network parameters to construct a long short-term memory network model with optimal parameters; S4, using the constructed long short-term memory network model to predict surface electromyographic signals; S5, constructing a three-dimensional electromyographic adaptive control system based on the predicted signals; S6, generating fuzzy control rules based on an adaptive fuzzy control system; S7, controlling the combined rehabilitation system based on a control strategy. The present invention adopts the above-mentioned rehabilitation manipulator and electric stimulation adaptive control method based on electromyographic feedback, which can solve the secondary injuries caused by the untimely regulation of subjective parameters in the existing rehabilitation process, and helps to improve rehabilitation efficiency and rehabilitation effects.

Description

Technical Field

[0001] The present invention relates to the technical field of adaptive control of rehabilitation medical robots, and in particular to a rehabilitation manipulator based on myoelectric feedback and an electric stimulation adaptive control method. Background Art

[0002] Currently, traditional treatments rely primarily on physical rehabilitation, with exercise therapy being the most common. This approach requires the therapist to manually pull the patient's affected limb with the assistance of rehabilitation equipment and assistive devices to assist in completing motor tasks. Repeated assisted exercise can help patients avoid the degeneration of their motor organs caused by motor dysfunction and significantly aid in the recovery of their motor function.

[0003] This therapy has certain drawbacks. It requires a large number of professionally trained therapists with extensive clinical experience, which not only increases the difficulty of training clinical therapists but also leads to tremendous workload pressure for them. This traditional treatment method cannot meet the growing demand for stroke patients and post-stroke rehabilitation. Therefore, it is imperative to explore reliable and intelligent treatment methods to alleviate post-stroke motor dysfunction.

[0004] In response to the damaged neural pathways caused by stroke, functional electrical stimulation (FES) is applied to the post-operative movement disorder rehabilitation training process. By applying continuous electrical stimulation to the muscles innervated by the patient's damaged nerves, the muscles contract and produce limb movements, thereby restoring the patient's limb motor function. At the same time, sensory feedback stimulates the brain's motor relearning and promotes the repair of the entire damaged motor neural pathway.

[0005] The emergence of wearable rehabilitation exoskeletons for limb movement has led to the development of passive limb rehabilitation. In the field of hand function rehabilitation, wearable rehabilitation manipulators are primarily driven by pneumatics, rope drives, and motor actuators, driving the patient's affected hand for rehabilitation training.

[0006] Both of the above rehabilitation methods can achieve relatively good results in hand function rehabilitation training. However, combining rehabilitation robots with functional electrical stimulation can effectively combine the advantages of both, solving the clinical problems of inaccurate open-loop control of electrical stimulation and low activation of specific muscles in rehabilitation robotic hands.

[0007] Throughout the rehabilitation process, muscle conditions constantly change, necessitating real-time monitoring by experienced therapists. When muscle conditions become unsuitable for the current rehabilitation model, necessary adjustments are necessary to prevent secondary injury. This significantly increases the therapist's workload. Predicting muscle conditions and adjusting control strategies in advance is the key to integrating these two approaches.

[0008] Electrical stimulation alone is often prone to muscle fatigue and, without a rigid structure to guide finger extension, can easily lead to injury. Furthermore, robotic control alone cannot fully establish a muscle-extremity control loop. Compensating FES-driven muscles with a robotic hand can reduce FES drive intensity, while FES stimulation can provide enhanced muscle activation. Combining these two approaches offers a superior treatment option for post-stroke rehabilitation. Summary of the Invention

[0009] The purpose of the present invention is to provide a rehabilitation manipulator based on electromyographic feedback and an electric stimulation adaptive control method, which can ensure the safety of the rehabilitation process, reduce the possibility of muscle damage, help improve the rehabilitation training effect, and accelerate the recovery of neuroplasticity function and the rehabilitation process.

[0010] To achieve the above objectives, the present invention provides a rehabilitation manipulator based on electromyographic feedback and an electric stimulation adaptive control method, comprising the following steps:

[0011] S1. Construct a combined rehabilitation system of myoelectric acquisition, rehabilitation manipulator, and neuromuscular electrical stimulation;

[0012] S2, using complete set empirical mode decomposition and adaptive noise algorithm to preprocess the surface electromyography signal;

[0013] S3. Use the Frost Ice optimization algorithm to iterate the long short-term memory network parameters and build a long short-term memory network model with optimal parameters;

[0014] S4. Using the constructed long short-term memory network model to predict each decomposition mode of the surface electromyography signal;

[0015] S5. Construct a three-dimensional myoelectric adaptive control system based on the predicted signal as feedback;

[0016] S6. Generate fuzzy control rules based on adaptive fuzzy control system and myoelectric feedback;

[0017] S7. Control the joint rehabilitation system based on the control strategy.

[0018] Preferably, in S1, electromyographic signals are collected using electromyographic acquisition equipment, and the collected electromyographic signals are uploaded to the host computer for processing and analysis; RS485 one-to-two serial port communication is used to establish communication between the host computer and the rehabilitation manipulator and functional electrical stimulation, and the rehabilitation manipulator is used to provide passive rehabilitation of the hand; and active activation is provided through functional electrical stimulation.

[0019] Preferably, in S2, DC component detection is used to remove the DC component, the power frequency interference is removed by an adaptive 50Hz notch filter, and the high and low frequency noise in the signal are removed by fourth-order Butterworth high-pass and low-pass filters. The processed surface electromyography signal is decomposed into modal components of different frequency bands using a complete set empirical mode decomposition and an adaptive noise model.

[0020] Preferably, the processed surface electromyographic signal is decomposed from high to low using the complete set empirical mode decomposition and the adaptive noise model to obtain the corresponding intrinsic mode components IMF1, IMF2...IMF n ; Decompose the one-dimensional surface electromyography signal data into multidimensional data in different frequency ranges so that the eigenvalues ​​fall within different frequency ranges. During the decomposition process, the first-order IMF components are calculated on an overall average basis to obtain the final first-order IMF components, and then the above operation is repeated on the residual parts.

[0021] Preferably, in S3, the intrinsic mode components of each surface electromyographic signal are reorganized and normalized to form a long short-term memory network model input data set;

[0022] The initialized long short-term memory network model parameters are used to form random frost particles. The frost particles are iteratively optimized according to the particle update calculation formula to detect whether the particles are in the optimal position. If they are, the optimal parameters are output. Otherwise, the iterative optimization is continued. The particle update calculation formula is:

[0023]

[0024] Preferably, in S4, an optimized long short-term memory network model is constructed using the optimal parameters, and each modal component after reorganization and normalization is predicted at time Δt to obtain modal components IMF1(T+Δt), IMF2(T+Δt) ... IMF3(T+Δt) after time Δt;

[0025] Each modal component IMF1(T+Δt), IMF2(T+Δt)…IMF3(T+Δt) is superimposed according to the coefficient to obtain the predicted surface electromyography signal.

[0026] Preferably, in S5, feature extraction is performed on the predicted surface electromyographic signal and a three-dimensional model based on the predicted surface electromyographic signal as feedback is constructed; the median frequency MF, the mean power frequency MPF, the integrated electromyographic value iEMG, and the electromyographic root mean square value RMS are extracted respectively.

[0027]

[0028] The three-dimensional model (X, Y, Z) is constructed using the following formula:

[0029]

[0030] Where, X f is the fatigue degree change in the frequency domain, X Am is the change in fatigue degree in the time domain, n is the number of modes, Y is the degree of muscle activation, Z is the muscle activation state, iEMGA is the integrated electromyographic value of the agonist muscle under the corresponding action, and iEMGB is the integrated electromyographic value of the antagonist muscle under the corresponding action; the system adaptively adjusts parameter changes based on the feedback formed by the predicted muscle state.

[0031] Preferably, in S6, the predicted three-dimensional model is used as the input of the fuzzy rule: when the predicted muscle fatigue level X increases, the movement frequency of the rehabilitation manipulator decreases, and the stimulation voltage, current, and frequency of the functional electrical stimulation all decrease; when the predicted muscle fatigue level X decreases, the movement frequency of the rehabilitation manipulator increases, and the stimulation voltage, current, and frequency of the functional electrical stimulation all increase; when the predicted muscle activation level Y increases, the movement frequency of the rehabilitation manipulator increases, and the stimulation voltage, current, and frequency of the functional electrical stimulation all decrease; when the predicted muscle activation level Y decreases, the movement frequency of the rehabilitation manipulator decreases, and the stimulation voltage, current, and frequency of the functional electrical stimulation all increase; when the predicted activation state Z is normal, the joint rehabilitation system continues to work; when the predicted activation state Z is abnormal, the joint rehabilitation system stops working.

[0032] Preferably, in S7, the three-dimensional input (X, Y, Z) of the predicted state is fuzzified by the fuzzy controller, and fuzzy decision-making is performed according to the fuzzy control table constructed based on the three-dimensional electromyography fuzzy rules. The required output result is obtained after fuzzy reasoning, and it is defuzzified. According to the adaptive rules, it is judged whether the current muscle state matches the rehabilitation system parameters, and the system is adaptively adjusted to finally obtain the parameters (F, U, I, H), where F represents the movement frequency of the rehabilitation manipulator, U represents the electrical stimulation voltage, I represents the electrical stimulation current, and H represents the electrical stimulation frequency; the parameters of the combined rehabilitation system are adjusted to match the subsequent muscle state.

[0033] Therefore, the beneficial effects of the present invention using the above-mentioned rehabilitation manipulator based on electromyographic feedback and the electric stimulation adaptive control method are as follows:

[0034] (1) The combined rehabilitation system of the present invention fully combines the advantages of passive rehabilitation of rehabilitation robots and active activation of nerves under active rehabilitation of functional electrical stimulation.

[0035] (2) In the present invention, CEEMDAN is used to decompose the electromyographic signal and effectively extract the frequency band characteristics of the signal; the RIME algorithm is used to optimize the parameters of the LSTM model to improve the model prediction ability; by combining CEEMDAN-RIME-LSTM, the surface electromyographic signal is jointly predicted in time and frequency to improve the prediction accuracy of the electromyographic signal and provide effective muscle state for system adaptation.

[0036] (3) The present invention uses adaptive fuzzy control as the control framework of the joint rehabilitation system, fully considering the time-varying and complexity of the muscle state, and making the control process more natural and smooth based on sufficient fuzzy rules, thereby solving the problems of simple training and unsatisfactory rehabilitation effects in existing hand rehabilitation strategies and systems.

[0037] (4) The present invention constructs three-dimensional electromyographic fuzzy rules, fully considering the three muscle states during the rehabilitation process, and makes real-time adjustments based on fatigue level, activation level, and activation state. This allows the rehabilitation system to adaptively adjust parameters based on the time-varying nature of the patient's muscles during rehabilitation, making the entire joint rehabilitation system individually adaptable and significantly improving system safety.

[0038] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a step diagram of a rehabilitation manipulator based on myoelectric feedback and an electric stimulation adaptive control method of the present invention;

[0040] Figure 2 This is a structural block diagram of the myoelectric acquisition-rehabilitation manipulator-neuromuscular electrical stimulation combined rehabilitation system according to an embodiment of the present invention;

[0041] Figure 3 It is a collaborative control framework of the myoelectric acquisition-rehabilitation manipulator-neuromuscular electrical stimulation combined rehabilitation system provided by an embodiment of the present invention;

[0042] Figure 4 is a flow chart of preprocessing of surface electromyography signals provided by an embodiment of the present invention;

[0043] Figure 5 This is a flowchart of myoelectric prediction based on CEEMDAN-RIME-LSTM provided by an embodiment of the present invention;

[0044] Figure 6 This is a block diagram of a three-dimensional myoelectric adaptive control system constructed based on prediction signals provided by an embodiment of the present invention;

[0045] Figure 7 3D myoelectric fuzzy rule block diagram constructed based on the adaptive fuzzy control system of the 3D myoelectric embodiment of the present invention;

[0046] Figure 8 This is a block diagram of controlling a combined rehabilitation system based on a control strategy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0048] Example 1

[0049] like Figure 1 As shown, the present invention provides a rehabilitation manipulator based on myoelectric feedback and an electric stimulation adaptive control method, comprising the following steps:

[0050] S1. Construct a combined rehabilitation system of myoelectric acquisition, rehabilitation manipulator and neuromuscular electrical stimulation.

[0051] like Figure 2 As shown in Figure 1, the system includes a device side, a data side, a control side, and myoelectric feedback. The device side includes a four-channel functional electrical stimulation system, a rehabilitation manipulator, and a Delsys myoelectric acquisition device. The data side is divided into two parts: surface electromyographic signal preprocessing and surface electromyographic signal prediction. The control side uses fuzzy adaptive control.

[0052] The combined rehabilitation system constructs electromyographic feedback based on the muscle change status of the rehabilitation patient, avoiding secondary damage caused by the mismatch between the current muscle status and the rehabilitation equipment parameters during the rehabilitation process, ensuring the safety of the rehabilitation process while improving rehabilitation efficiency.

[0053] like Figure 3 As shown, in this embodiment, the Delsys Trigno Wireless surface electromyography acquisition device is used and pasted on the muscle position required for rehabilitation movement. Functional electrical stimulation patches are pasted on both sides in a cross-cross manner to reduce the interference of electrical stimulation on the electromyographic signal while producing good stimulation to the muscles. A rehabilitation manipulator is worn on the affected hand. The collected surface electromyographic signal is uploaded to the host computer using Bluetooth for subsequent processing. The processed surface electromyographic signal is predicted, and the predicted signal is used as the input of the fuzzy adaptive control system to obtain the required system parameters, and the system parameters are communicated in a one-to-two manner using RS485.

[0054] S2. Use complete set empirical mode decomposition and adaptive noise algorithm to preprocess the surface electromyography signal.

[0055] like Figure 4As shown in the figure, the surface electromyography signals collected from the affected hand are processed to remove the DC component to reduce the risk of signal amplitude and zero-frequency roll-off; an adaptive 50Hz notch filter is used to eliminate the 50Hz power frequency interference in the environment, further improving the accuracy of the signal; finally, a fourth-order Butterworth filter is used to perform high-pass and low-pass filtering on the original surface electromyography signals to filter out high-frequency and low-frequency noise signals, thereby improving and retaining the useful frequency band signal.

[0056] The preprocessed signal is decomposed using CEEMDAN. CEEMDAN decomposition calculates the overall average of the first-order IMF components to obtain the final first-order IMF components, and then repeats the above operation on the residual part, which effectively solves the problem of white noise transfer from high frequency to low frequency.

[0057] The relevant calculation formula is:

[0058] E i (·) is the i-th eigenmode component obtained after EMD decomposition, and the i-th eigenmode component obtained by CEEMDAN decomposition is v j is a Gaussian white noise signal that satisfies the standard normal distribution, j = 1, 2, ..., N is the number of times white noise is added, ε is the standard table of white noise, and y(t) is the signal to be decomposed. Adding Gaussian white noise to the signal to be decomposed y(t) yields the signal y(t) + (-1) q εv j (t), where q = 1, 2, ... Perform EMD decomposition on the new signal to obtain the first-order eigenmode component C 1 .

[0059] The first intrinsic mode component of CEEMDAN decomposition is obtained by averaging the N generated mode components.

[0060] The formula is:

[0061] Compute the residual after removing the first modal component:

[0062] Adding positive and negative paired Gaussian white noise to r1(t) to obtain a new signal, and performing EMD decomposition with the new signal as the carrier to obtain the first-order modal component D1, from which the second eigenmode component of CEEMDAN decomposition can be obtained:

[0063] Compute the residual after removing the second modal component:

[0064] Repeat the above steps until the residual signal is a monotonic function and cannot be decomposed further, and the algorithm ends. At this time, the number of intrinsic mode components obtained is K, and the original signal is decomposed into:

[0065] After CEEMDAN decomposition, the corresponding intrinsic mode components IMF1, IMF2...IMF n Decompose one-dimensional data into multi-dimensional data in different frequency ranges so that the eigenvalues ​​fall within different frequency ranges.

[0066] The decomposed signal is subjected to feature extraction, and characteristic values ​​such as power spectrum density, integrated electromyographic value, root mean square value, average power frequency, and median frequency are extracted for the modal components in different frequency ranges.

[0067] Integral EMG is the sum of the areas under the curve per unit time of the rectified and filtered EMG signal. It reflects the changes in EMG strength over time. The integral EMG value can be used to analyze the contraction characteristics of muscles per unit time.

[0068] The formula is:

[0069] The root mean square (RMS) value refers to the square root of the average amplitude of the sEMG signal within a certain time window. Generally, when the muscle is subjected to a large load or fatigue, the RMS will decrease, and when the muscle is under a light load or is more relaxed, the RMS will increase.

[0070] The formula is:

[0071] Where N is the number of sEMG data points, x i is the i-th data in the signal data sequence.

[0072] Mean power frequency (MPF) is the average value of the power frequency distribution of the electromyographic signal generated during muscle contraction. It can be used to assess the strength and fatigue of muscle contraction. Even if the signal is mixed with some interference noise, this feature can effectively identify useful information in the signal.

[0073] The formula is:

[0074] Where f is the frequency and PSD(f) is the power spectrum of the current EMG data.

[0075] The median frequency (MF) refers to the median frequency of the electromyographic signal generated during muscle contraction and is often used to assess the force and fatigue of muscle contraction. Generally, MF decreases when a muscle is under a heavy load or fatigued, and increases when the muscle is under a light load or is more relaxed. Its calculation formula is as follows:

[0076]

[0077] S3. Use the Frost Ice optimization algorithm to cyclically iterate the long short-term memory network parameters and construct a long short-term memory network model with optimal parameters.

[0078] like Figure 5 As shown in Figure 1, the decomposed intrinsic mode components are reconstructed into a dataset and the LSTM model parameters are initialized. The model consists of a memory unit and three gates (input gate, forget gate, and output gate).

[0079] Forget gate: determines which information in the memory unit needs to be forgotten.

[0080] The formula is: t =σ(W f ·[h t-1 ,x t ]+b f ).

[0081] Input gate: determines how the current input information affects the memory unit.

[0082] The formula is: t =σ(W f ·[h t-1 ,x t ]+b i ).

[0083] Candidate memory: Generate candidate memory for updating memory units.

[0084] The formula is:

[0085] Update memory unit: Update the memory unit according to the output of the forget gate and the input gate.

[0086] The formula is:

[0087] Output gate: determines what information to output as the hidden state at the current moment.

[0088] The formula is: t =σ(W o ·[h t-1 ,x t ]+b o ).

[0089] Formula is o t =σ(W o ·[h t-1 ,x t ]+b o ).

[0090] The decision to perform LSTM prediction is based on whether the parameters are optimal. If not, the parameters are optimized using the RIME algorithm. Initialized parameters are used to construct random frost particles, which are then iteratively optimized to determine if they are in the optimal position. If so, the optimal parameters are output, and if not, the iterative optimization continues.

[0091] The particle update calculation formula is:

[0092]

[0093] Where:

[0094] S4. Use the constructed long short-term memory network model to predict the various decomposition modes of the surface electromyography signal.

[0095] The optimized long short-term memory network model is constructed using the optimal parameters, and each modal component after reorganization and normalization is predicted at time Δt to obtain the modal components IMF1(T+Δt), IMF2(T+Δt)…IMF3(T+Δt) after time Δt.

[0096] Each modal component IMF1(T+Δt), IMF2(T+Δt)…IMF3(T+Δt) is superimposed according to the coefficient to obtain the predicted surface electromyography signal.

[0097] S5. Construct a three-dimensional myoelectric adaptive control system based on the predicted signal as feedback.

[0098] like Figure 6 As shown in the figure, the surface electromyography signals collected from the affected hand are processed to obtain characteristic values ​​such as the integrated electromyography value, root mean square value, average power frequency, and median frequency. The initial fatigue degree is calculated using the following formula:

[0099] x1=k1MPF+k2MF

[0100]

[0101] Where x1 is the initial fatigue quantization value in the frequency domain, and x2 is the initial fatigue quantization value in the time domain. The coefficient k1 is the proportion of MPF in the calculation of fatigue level, and similarly k2 is the proportion of MF. RMS k The root mean square value in the kth time window. The initial muscle activation state is calculated by the following formula:

[0102]

[0103] Where y is the initial muscle activation level, ω1 is the percentage of integrated EMG values ​​below the average activation threshold, ω2 is the percentage of integrated EMG values ​​above the average activation threshold, iEMG1 is the integrated EMG value below the average activation threshold, and iEMG2 is the integrated EMG value above the average activation threshold. The threshold is set to 0.4 times the average integrated EMG value.

[0104] The surface electromyography collected from the affected hand is decomposed by CEEMDAN, and the modal data set is reconstructed. The LSTM model with optimal parameter optimization is used to predict the time Δt, and the modal components IMF1(T+Δt), IMF2(T+Δt)...IMF3(T+Δt) after time Δt are obtained.

[0105] The predicted surface electromyographic signal is feature extracted and a three-dimensional model based on the predicted surface electromyographic signal is constructed as the feedback quantity. The median frequency MF, mean power frequency MPF, integrated electromyographic value iEMG, and electromyographic root mean square value RMS are extracted respectively to obtain the following data:

[0106]

[0107] The three-dimensional model (X, Y, Z) is constructed using the following formula:

[0108]

[0109]

[0110] Where, X f is the fatigue degree change in the frequency domain, X Am is the change in fatigue level over time, n is the number of modal states, Y is the degree of muscle activation, Z is the muscle activation state, iEMGA is the integrated EMG value of the agonist muscle under the corresponding action, and iEMGB is the integrated EMG value of the antagonist muscle under the corresponding action. The above formula calculates the predicted muscle's three-dimensional model (X, Y, Z). The system adaptively adjusts parameter changes based on the feedback generated by the predicted muscle state.

[0111] S6. Generate fuzzy control rules based on adaptive fuzzy control system and electromyographic feedback.

[0112] like Figure 7 As shown in the figure, based on different predicted muscle states, the three-dimensional model (X, Y, Z) is used as input parameters. The change in fatigue level corresponds to different parameter adjustments of functional electrical stimulation and rehabilitation manipulator.

[0113] When X increases, that is, the patient's muscle fatigue increases, by reducing the working frequency of the rehabilitation robot and reducing the voltage, current intensity and stimulation frequency of functional electrical stimulation to slow down muscle fatigue and reduce risks.

[0114] When X decreases, that is, the patient's muscle fatigue level decreases, by increasing the working frequency of the rehabilitation manipulator and increasing the voltage, current intensity and stimulation frequency of functional electrical stimulation, the rehabilitation intensity is improved, the muscles are more activated, and the rehabilitation effect is improved.

[0115] When Y increases, that is, the patient's muscle activation level increases, and the patient's muscles are in good condition. By increasing the working frequency of the rehabilitation robot, the patient can complete the movement better, and reducing the voltage, current intensity and stimulation frequency of functional electrical stimulation can reduce the occurrence of muscle fatigue, so as to better adapt to the current muscle state.

[0116] When Y decreases, that is, the patient's muscle activation level is low, and the patient's muscle performance is poor. By reducing the working frequency of the rehabilitation robot, only providing movement assistance, and increasing the voltage, current intensity and stimulation frequency of functional electrical stimulation, the nerves can be better actively activated.

[0117] When Z = 1, the agonist and antagonist muscles are co-activated, and their integrated EMG fluctuations are almost identical. This often indicates that stroke patients have abnormally increased muscle tone and spasticity. In this case, the main focus should be on relieving spasticity and stopping the corresponding working state. When Z = 0, the agonist and antagonist muscles are not co-activated, and their integrated EMG fluctuations are not simultaneous, then rehabilitation training can continue.

[0118] S7. Control the joint rehabilitation system based on the control strategy.

[0119] like Figure 8 As shown in the figure, the three-dimensional input (X, Y, Z) of the predicted state is fuzzified by a fuzzy controller. Fuzzy decision-making is performed based on a fuzzy control table constructed based on three-dimensional electromyographic fuzzy rules. The desired output is obtained through fuzzy inference, which is then defuzzified. Adaptive rules are used to determine whether the current muscle state matches the rehabilitation system parameters, and the system is adaptively adjusted. Finally, the parameters (F, U, I, H) are obtained, and the parameters of the rehabilitation manipulator and functional electrical stimulation system are adjusted to match the subsequent muscle state. Here, F represents the movement frequency of the rehabilitation manipulator, U represents the electrical stimulation voltage, I represents the electrical stimulation current, and H represents the electrical stimulation frequency.

[0120] Therefore, the present invention adopts the above-mentioned rehabilitation manipulator based on electromyographic feedback and electrical stimulation adaptive control method, which can ensure the safety of the rehabilitation process, reduce the possibility of muscle damage, help improve the rehabilitation training effect, and accelerate the recovery of neuroplasticity function and the rehabilitation process.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A rehabilitation manipulator based on electromyographic feedback and an adaptive control method for electrical stimulation, characterized in that: The following steps are involved: S1. Construct a combined rehabilitation system of myoelectric acquisition, rehabilitation manipulator, and neuromuscular electrical stimulation; S2, using complete set empirical mode decomposition and adaptive noise algorithm to preprocess the surface electromyography signal; S3. Use the Frost Ice optimization algorithm to iterate the long short-term memory network parameters and build a long short-term memory network model with optimal parameters; In S3, the intrinsic mode components of each surface electromyographic signal are reorganized and normalized to form a long short-term memory network model input data set; The initialized long short-term memory network model parameters are used to form random frost particles. The frost particles are iteratively optimized according to the particle update calculation formula to detect whether the particles are in the optimal position. If they are, the optimal parameters are output. Otherwise, the iterative optimization is continued. The particle update calculation formula is: ； S4. Using the constructed long short-term memory network model to predict each decomposition mode of the surface electromyography signal; In S4, the optimized long short-term memory network model is constructed using the optimal parameters, and each modal component after reorganization and normalization is Time prediction, get Modal components after time ; For each modal component Superimpose the coefficients to obtain the predicted surface electromyographic signal; S5. Construct a three-dimensional myoelectric adaptive control system based on the predicted signal as feedback; In S5, the predicted surface electromyographic signal is feature extracted and a three-dimensional model based on the predicted surface electromyographic signal is constructed as the feedback quantity; the median frequency MF, mean power frequency MPF, integrated electromyographic value iEMG, and electromyographic root mean square value RMS are extracted respectively. ； The three-dimensional model is constructed by the following formula : ； ； ； ； Where, is the change of fatigue degree in frequency domain, is the change of fatigue degree in time domain, is the number of modes, Y is the degree of muscle activation, Z The muscle is activated. is the integrated EMG value of the active muscle under the corresponding action, is the integrated electromyographic value of the antagonist muscle under the corresponding action; the system adaptively adjusts the parameter changes based on the feedback amount formed by the predicted muscle state; S6. Generate fuzzy control rules based on adaptive fuzzy control system and myoelectric feedback; S7. Control the joint rehabilitation system based on the control strategy.

2. The method for adaptive control of a rehabilitation manipulator and electrical stimulation based on myoelectric feedback according to claim 1, characterized in that: In S1, electromyographic signals are collected using electromyographic acquisition equipment and uploaded to the host computer for processing and analysis; RS485 one-to-two serial port communication is used to establish communication between the host computer, the rehabilitation manipulator, and functional electrical stimulation, and the rehabilitation manipulator provides passive rehabilitation of the hand; active activation is provided through functional electrical stimulation.

3. The myoelectric feedback-based rehabilitation manipulator and electrical stimulation adaptive control method according to claim 1, characterized in that: In S2, DC component detection is used to remove the DC component, an adaptive 50Hz notch filter is used to remove power frequency interference, and fourth-order Butterworth high- and low-pass filters are used to remove high- and low-frequency noise in the signal. The processed surface electromyography signal is decomposed into modal components of different frequency bands using complete set empirical mode decomposition and an adaptive noise model.

4. The myoelectric feedback-based rehabilitation manipulator and electric stimulation adaptive control method according to claim 3, characterized in that: Using the complete set empirical mode decomposition and adaptive noise model, the processed surface electromyography signal is decomposed from high to low to obtain the corresponding intrinsic mode components IMF1, IMF2...IMF n ; Decompose the one-dimensional surface electromyography signal data into multidimensional data in different frequency ranges so that the eigenvalues ​​fall within different frequency ranges. During the decomposition process, the first-order IMF components are calculated on an overall average basis to obtain the final first-order IMF components, and then the above operation is repeated on the residual parts.

5. The myoelectric feedback-based rehabilitation manipulator and electric stimulation adaptive control method according to claim 1, characterized in that: In S6, the predicted 3D model is used as the input of fuzzy rules: predicting muscle fatigue degree X When the muscle size increases, the movement frequency of the rehabilitation robot decreases, and the stimulation voltage, current, and frequency of functional electrical stimulation decrease; predict the degree of muscle fatigue X When the muscle activation degree is reduced, the movement frequency of the rehabilitation robot increases, and the stimulation voltage, current and frequency of functional electrical stimulation increase; Y When the predicted muscle activation level Y increases, the movement frequency of the rehabilitation manipulator increases, and the stimulation voltage, current, and frequency of the functional electrical stimulation decrease; when the predicted muscle activation level Y decreases, the movement frequency of the rehabilitation manipulator decreases, and the stimulation voltage, current, and frequency of the functional electrical stimulation increase; when the predicted activation state Z is normal, the joint rehabilitation system continues to work; when the predicted activation state Z In case of abnormality, the joint rehabilitation system stops working.

6. The myoelectric feedback-based rehabilitation manipulator and electric stimulation adaptive control method according to claim 1, characterized in that: In S7, the three-dimensional input of the predicted state After fuzzification by the fuzzy controller, fuzzy decision making is performed based on the fuzzy control table constructed according to the three-dimensional electromyography fuzzy rules. The required output result is obtained through fuzzy reasoning, and the fuzzification is performed. According to the adaptive rules, it is judged whether the current muscle state matches the rehabilitation system parameters, and the system is adaptively adjusted. Finally, the parameters are obtained. ,in, represents the movement frequency of the rehabilitation robot, represents the electrical stimulation voltage, represents the electrical stimulation current, Represents the electrical stimulation frequency; the parameters of the joint rehabilitation system are adjusted to match the subsequent muscle state.

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