Foot drop electrical stimulation system and method based on multimode fusion feedback

Abstract

A foot drop electrical stimulation system based on multimode fusion feedback comprises an inertial measurement unit, a myoelectricity information acquisition module, a near-infrared muscle oxygen acquisition module, an electrical stimulation intensity adjustment module, an electrical stimulation frequency adjustment module and a functional electrical stimulation module. The inertia measurement unit and the myoelectricity information acquisition module are connected with the input side of the electrical stimulation intensity adjustment module, the output side of the electrical stimulation intensity adjustment module is connected with the input side of the functional electrical stimulation module, and the near-infrared muscle oxygen acquisition module is connected with the electrical stimulation frequency adjustment module. The electrical stimulation frequency adjusting module is connected with the input side of the functional electrical stimulation module, and the output side of the functional electrical stimulation module is a working end used for stimulating a patient. The invention further provides a foot drop electrical stimulation method based on multi-mode fusion feedback. According to the invention, the matching effect of nerve-muscle-limb multi-source information feedback and functional electrical stimulation assisted rehabilitation training is improved, and a better feedback effect of foot drop rehabilitation training is realized.

Description

Technical Field

[0001] The present invention belongs to the field of limb movement rehabilitation training, and in particular relates to a foot drop electrical stimulation system and method based on multi-mode fusion feedback. Background Art

[0002] Foot drop, as a common ankle dysfunction after stroke, is mainly manifested by paralysis or obvious weakness of the ankle dorsiflexor muscles. This symptom usually originates from dysfunction of the common peroneal nerve, which is responsible for controlling multiple muscles such as the tibialis anterior, extensor digitorum longus, extensor digitorum brevis, extensor hallucis longus, and peroneus. The typical symptoms of foot drop are: during the swing phase of gait, the sole of the foot cannot achieve the necessary dorsiflexion, resulting in the foot being unable to maintain a sufficient distance from the ground. Specifically, the patient drags the foot on the affected side when walking. In order to compensate for this action, the patient may take an abnormally high stepping action, or have foot clearance disorders and circular gait in the lower limbs. If not effectively treated for a long time, foot drop may lead to permanent changes in the tissue structure around the ankle joint, thereby causing joint contracture and deformation. Therefore, it is particularly important for patients and their families to receive timely treatment and rehabilitation training to improve gait and quality of life.

[0003] Functional electrical stimulation (FES) is an advanced rehabilitation technology used to assist in the treatment of foot drop after stroke. This technology activates target muscles by applying low-frequency pulsed current to simulate the natural movement patterns of healthy individuals. FES has significant advantages in the field of rehabilitation. It can not only enhance muscle strength, promote brain function reorganization, strengthen nerve impulses, prevent muscle atrophy, but also improve the overall health of muscles. FES devices have greatly reduced the workload of rehabilitation physicians with their small size, simple operation, easy portability, and low cost. However, FES technology has also encountered some challenges in practical applications. Differences in individual physiological characteristics make the selection of electrical stimulation parameters complicated, and usually rely on the experience of physicians and subjective feedback from patients, which may lead to excessive or insufficient stimulation and make it difficult to meet the personalized treatment needs of patients. In addition, traditional FES devices lack the ability to monitor muscle status in real time. Continuous electrical stimulation may cause muscle fatigue. If stimulation continues in a state of muscle fatigue, it may cause damage to the muscle. At the same time, the control method of these devices is usually open-loop, and the stimulation waveform is often single and fixed. There are also some defects in the actual operation of FES devices or systems, such as inconvenient operation, inability to observe changes in muscle state in real time, and inability to adjust FES parameters in real time to adapt to different medical needs. Although the current research on feedback control methods for foot drop electrical stimulation has alleviated these problems to a certain extent, there is still room for improvement in integrating muscle state monitoring and muscle autonomy. Although some functional electrical stimulation devices have added feedback signals such as joint angle, acceleration, and plantar pressure, these physical signals cannot provide direct feedback on muscle state, ignore muscle information, and easily cause muscle fatigue. Some studies have attempted to solve the problems of open-loop stimulation and lack of muscle state feedback by collecting surface electromyography signals, but only focusing on the single feedback information of surface electromyography signals will result in low accuracy in muscle state assessment and weak robustness of closed-loop control systems. In addition, most control methods fail to combine the detection of the autonomous muscle strength of the rehabilitation subjects and the degree of muscle fatigue during training. These are all problems that need to be solved in future research. Summary of the invention

[0004] In order to overcome the shortcomings of the existing technology, the present invention provides a foot drop electrical stimulation system and method based on multi-modal fusion feedback, which combines multi-modal muscle state information detection means and its response mechanism, strengthens the coupling effect of each feedback link, and improves the matching effect of nerve-muscle-limb multi-source information feedback and functional electrical stimulation assisted rehabilitation training, thereby achieving a better feedback effect of foot drop rehabilitation training. This innovative method is expected to provide a more personalized and more effective rehabilitation treatment plan for patients with foot drop.

[0005] The technical solution adopted by the present invention to solve the technical problem is:

[0006] A foot drop electrical stimulation system based on multi-mode fusion feedback comprises an inertial measurement unit, an electromyographic information acquisition module, a near-infrared muscle oxygen acquisition module, an electrical stimulation intensity adjustment module, an electrical stimulation frequency adjustment module and a functional electrical stimulation module, wherein the inertial measurement unit and the electromyographic information acquisition module are connected to the input side of the electrical stimulation intensity adjustment module, the output side of the electrical stimulation intensity adjustment module is connected to the input side of the functional electrical stimulation module, the near-infrared muscle oxygen acquisition module is connected to the electrical stimulation frequency adjustment module, the electrical stimulation frequency adjustment module is connected to the input side of the functional electrical stimulation module, and the output side of the functional electrical stimulation module is a working end for stimulating a patient.

[0007] Further, the electrical stimulation intensity adjustment module includes a pre-training model, an electrical stimulation intensity adjustment module and a controller. In the pre-training model, the expected angle curve of the affected ankle joint will be used as the input of the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle and the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint, and the expected electrical stimulation intensity I E , desired muscle activation state and voluntary muscle strength on the affected side - electrical stimulation intensity I H The muscle activation state model is used to calculate and analyze the feedback surface electromyographic signal to obtain the actual muscle activation state of the affected side; the electrical stimulation intensity model based on the muscle activation state of the affected side will be used for muscle activation state feedback to obtain the corrected electrical stimulation intensity I A The electrical stimulation intensity adjustment module calculates and analyzes the expected electrical stimulation intensity, the corrected electrical stimulation intensity, and the autonomous muscle force-electrical stimulation intensity to obtain the electrical stimulation intensity requirement value I F The controller uses the control algorithm to fine-tune the required value of the electrical stimulation intensity and obtain the output value of the electrical stimulation intensity I O .

[0008] Furthermore, the foot drop electrical stimulation system further includes a system initialization unit and a model pre-training unit. The system initialization unit is connected to the model pre-training unit, the input side of the model pre-training unit is connected to the inertial measurement unit and the electromyographic information acquisition module, and the output side of the model pre-training unit is connected to the input side of the electrical stimulation intensity adjustment module;

[0009] The system initialization unit includes wearing a foot drop electrical stimulation rehabilitation system based on multi-mode fusion feedback, initializing a functional electrical stimulation module, and calibrating various signal acquisition devices;

[0010] The model pre-training unit includes two parts: pre-experiment and model establishment. After the pre-experiment, the electrical stimulation intensity, acceleration, angular velocity and surface electromyographic signal collected in the pre-experiment are used to establish a personalized pre-training model for patients, including: using the healthy-affected side mapping relationship to determine the expected angle curve of the affected ankle joint; using a nonlinear fitting method to establish an electrical stimulation intensity model based on the affected ankle angle; using an LSTM neural network model to establish an electrical stimulation intensity model based on the affected muscle activation state; using the relationship between models to determine the establishment of a muscle activation state model based on the affected ankle angle and an electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint. After obtaining the above pre-training models, these pre-training models are output to the electrical stimulation intensity adjustment module for storage for subsequent feedback control of the electrical stimulation intensity.

[0011] Furthermore, the inertial measurement unit is used to collect acceleration and angular velocity signals of the healthy and affected calves and soles of the patient during foot drop rehabilitation training, calculate and analyze the current ankle joint angle, and then perform feedback control on the desired angle curve of the affected ankle joint;

[0012] The electromyographic information acquisition module is used for surface electromyographic signal acquisition, and is used to collect the surface electromyographic signal of the tibialis anterior muscle of the patient during foot drop rehabilitation training, calculate and analyze the current muscle activation state, and then feedback and adjust the electrical stimulation intensity;

[0013] The near-infrared muscle oxygen acquisition module uses a near-infrared light source and a detector with wavelengths of 735nm and 850nm to collect near-infrared spectrum signals of the tibialis anterior muscle of the patient during foot drop rehabilitation training, and uses the signals to feedback adjust the electrical stimulation frequency.

[0014] The electrical stimulation frequency adjustment module includes a muscle oxygen concentration change model, a muscle fatigue degree model and an electrical stimulation frequency adjustment model, wherein the muscle oxygen concentration change model is used to calculate and analyze the near-infrared spectrum signal collected by the near-infrared muscle oxygen collection module to obtain a muscle oxygen concentration change value; the muscle fatigue degree model uses the obtained muscle oxygen concentration change to further calculate the muscle fatigue degree; the electrical stimulation frequency adjustment model adjusts the electrical stimulation frequency parameters according to the muscle fatigue degree to obtain the electrical stimulation frequency demand value.

[0015] The functional electrical stimulation module is used to convert the electrical stimulation parameters sent by the electrical stimulation intensity adjustment module and the electrical stimulation frequency adjustment module into control instructions and execute them, and output electrical stimulation pulses.

[0016] A foot drop electrical stimulation control method based on multi-mode fusion feedback comprises the following steps:

[0017] Step 1, system initialization, including wearing the foot drop electrical stimulation control system based on multi-mode fusion feedback, initializing the functional electrical stimulation module and calibrating each signal acquisition device;

[0018] Step 2, model pre-training, conducting a preliminary experiment on the patient, generating a pre-trained model, and saving the obtained pre-trained model in the electrical stimulation intensity adjustment module;

[0019] Step 3, using the expected angle of the affected ankle joint in the expected angle curve of the affected ankle joint during rehabilitation training obtained in the pre-training model as an input of the control method;

[0020] Step 4, using the actual affected ankle joint angle at the current moment as angle feedback, and performing feedback adjustment on the expected angle of the affected ankle joint in step 3;

[0021] Step 5, input the expected angle of the affected ankle joint after feedback in step 4 into the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle, and the electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint, and obtain the expected electrical stimulation intensity I E , desired muscle activation state and voluntary muscle strength on the affected side - electrical stimulation intensity I H ;

[0022] Step 6, using the actual muscle activation state of the affected side at the current moment as the muscle activation state feedback, feedback adjustment is performed on the expected muscle activation state of the affected side in step 5, and the expected muscle activation state of the affected side after feedback is input into the electrical stimulation intensity model based on the muscle activation state of the affected side to obtain the corrected electrical stimulation intensity I A ;

[0023] Step 7: Input the desired electrical stimulation intensity, voluntary muscle strength-electrical stimulation intensity and corrected electrical stimulation intensity obtained in steps 5 and 6 into the electrical stimulation intensity adjustment model to obtain the electrical stimulation intensity requirement value I F , and then the electrical stimulation intensity output value I is obtained through the control algorithm O ; At the same time, the near-infrared spectrum signal of the tibialis anterior muscle is collected and input into the electrical stimulation frequency adjustment module to calculate the change in muscle oxygen concentration and muscle fatigue degree, and the muscle fatigue degree is fed back to the electrical stimulation frequency adjustment model to obtain the electrical stimulation frequency demand value;

[0024] Step 8, sending the electrical stimulation intensity output value and the electrical stimulation frequency requirement value obtained in step 7 to the functional electrical stimulation module for control instruction conversion;

[0025] Step 9: The functional electrical stimulation module executes the control instruction to output a stimulation pulse to stimulate the peroneal nerve on the affected side of the patient.

[0026] Furthermore, in step 5, the electrical stimulation intensity model based on the ankle angle of the affected side is used to find the relationship between the ankle angle and the electrical stimulation intensity of the patient's personalized needs, which is established as follows:

[0027] 5.1.1, obtaining multiple sets of electrical stimulation intensity data and multiple sets of affected-side ankle joint angles, preprocessing multiple sets of inertial measurement unit acceleration and angular velocity signals, and obtaining multiple sets of affected-side ankle joint angles;

[0028] 5.1.2. Establish an electrical stimulation intensity model based on the angle of the affected ankle joint. Use a polynomial regression model to perform nonlinear fitting on multiple sets of affected ankle joint angles and multiple sets of electrical stimulation intensity data. Use a second-order polynomial regression model for fitting. The model is expressed as:

[0029] I E =aθ H 2 +bθ H +c

[0030] Among them, I E is the expected electrical stimulation intensity; θ H is the ankle joint angle of the affected side; a, b and c are the parameters of the model, which need to be determined by data fitting;

[0031] The muscle activation state model based on the ankle joint angle of the affected side is used to detect whether the muscle activation state of the tibialis anterior muscle meets the requirements during rehabilitation training, so as to correct the electrical stimulation intensity. It is established based on the electrical stimulation intensity model based on the muscle activation state of the affected side and the electrical stimulation intensity model based on the ankle joint angle of the affected side, and is expressed as:

[0032] M LSTM (W,H)=aθ H 2 +bθ H +c

[0033] Among them, M LSTM It is a model of electrical stimulation intensity based on the activation state of the affected muscle.

[0034] The electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint is used to characterize the magnitude of the voluntary muscle strength of the tibialis anterior muscle of the patient at each moment corresponding to the expected angle curve of the affected ankle joint, so as to correct the electrical stimulation intensity. It is established as follows:

[0035] 5.2.1, obtain the ankle joint angle curve and surface electromyographic signal of the affected side during normal walking, perform data preprocessing on the acceleration and angular velocity data recorded by the two inertial measurement units on the affected side and the surface electromyographic signal of the tibialis anterior muscle on the affected side, and obtain the ankle joint angle curve and surface electromyographic signal of the tibialis anterior muscle when the affected side is walking normally;

[0036] 5.2.2, the muscle activation state is calculated according to the muscle activation state model, and the muscle activation state of the surface electromyographic signal of the tibialis anterior muscle of the affected side during normal walking is calculated in the same way as when the electrical stimulation intensity model based on the muscle activation state of the affected side is established;

[0037] 5.2.3, calculate the voluntary muscle force-electrical stimulation intensity, use the muscle activation state calculated in 5.2.2 as the input of the electrical stimulation intensity model based on the muscle activation state of the affected side, and obtain the voluntary muscle force-electrical stimulation intensity corresponding to each desired angle of the affected ankle joint;

[0038] 5.2.4, obtain the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint, and according to the autonomous muscle strength-electrical stimulation intensity corresponding to each expected angle of the affected ankle joint obtained in 5.2.3, obtain the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint.

[0039] Furthermore, in step 6, the electrical stimulation intensity model based on the muscle activation state of the affected side is used to establish an electrical stimulation intensity correction model based on the autonomous muscle strength of the affected side ankle joint and a muscle activation state model based on the angle of the affected side ankle joint, which is established as follows:

[0040] 6.1, obtaining multiple sets of electrical stimulation intensity data and multiple sets of affected-side surface electromyographic signals, performing data preprocessing on the multiple sets of surface electromyographic signals, and obtaining the surface electromyographic signals of the tibialis anterior muscle on the affected side;

[0041] 6.2. Calculate the muscle activation state according to the muscle activation state model. Perform muscle synergy analysis on multiple groups of preprocessed surface electromyographic signals. Decompose them into muscle activation weight matrix and muscle activation sequence matrix by non-negative matrix decomposition method. The muscle activation state model is as follows:

[0042] M≈W×H=M′,M∈R n×t ,W∈R n×k ,H∈R k×t

[0043] min||MM′|| 2 =∑(MM′) 2

[0044]

[0045] Where M is the preprocessed surface electromyographic signal, W is the muscle activation weight matrix, H is the muscle activation sequence matrix, M′ is the matrix reconstructed based on the muscle activation weight matrix and the activation sequence matrix, R is a real number matrix, n is the number of collected muscles, t is the number of surface electromyographic sampling points, k is the number of synergists, and W T is the transposed matrix of the muscle activation weight matrix, H Tis the transposed matrix of the muscle activation sequence matrix;

[0046] 6.3. Establish an electrical stimulation intensity model based on the activation state of the affected-side muscles. Use the multiple sets of muscle activation weight matrices and muscle activation sequences obtained in step 6.2 and multiple sets of electrical stimulation intensity data to construct a data set. Use the multiple sets of muscle activation weight matrices and muscle activation sequences as inputs to the LSTM neural network model, use the multiple sets of electrical stimulation intensity data as labels of the LSTM neural network model, train the neural network model, and obtain an electrical stimulation intensity model based on the activation state of the affected-side muscles:

[0047] I E =M LSTM (W,H)

[0048] Furthermore, in step 7, the electrical stimulation intensity is adjusted in the following manner to obtain an electrical stimulation intensity output value, and the process is as follows:

[0049] 7.1.1, the desired electrical stimulation intensity I obtained in step 5 and step 6 E , Correction of electrical stimulation intensity I A and voluntary muscle strength—electrical stimulation intensity I H Input into the electrical stimulation intensity adjustment model to obtain the electrical stimulation intensity requirement value I F , the electrical stimulation intensity regulation model is expressed as:

[0050] I F =I E -I H +αI A

[0051] Among them, I F is the required value of electrical stimulation intensity, I H is the voluntary muscle strength-electrical stimulation intensity, I A is to correct the electrical stimulation intensity, α is the proportionality coefficient;

[0052] 7.1.2, after calculating the required value of electrical stimulation intensity I F Then, the control algorithm of the controller is used to finely adjust the required value of the electrical stimulation intensity to obtain the output value of the electrical stimulation intensity I O , the PID controller is expressed as:

[0053]

[0054] Among them, I O (k) is the output value of the electrical stimulation intensity, I O (k-1) is the electrical stimulation intensity output value at the previous moment, K p , K i and K dare the proportional, integral, and differential gains of the PID controller, and e(k) is the deviation at the current moment, i.e., I O (k)-I F (k), e(k-1) is the deviation at the previous moment, I F (k) is the required value of electrical stimulation intensity at the current moment, and e(i) is the deviation at the historical moment;

[0055] In step 7, the electrical stimulation frequency adjustment module uses the near-infrared spectrum signal collected by the near-infrared muscle oxygen collection module to perform feedback adjustment to obtain the electrical stimulation frequency demand value, and the process is as follows:

[0056] 7.2.1, obtain the near infrared spectrum signals of the tibialis anterior muscle at 735nm and 850nm;

[0057] 7.2.2, calculate the concentration change values ​​of oxyhemoglobin and deoxyhemoglobin according to the muscle oxygen concentration change model, use the near infrared spectrum signal obtained in 7.2.1 to calculate the concentration change values ​​of oxyhemoglobin and deoxyhemoglobin according to the modified Lambert-Beer law, the calculation method of the muscle oxygen concentration change model is as follows:

[0058]

[0059] Where ΔA represents the optical density, V t is the voltage value measured by the detector at a certain moment during rehabilitation training, which is proportional to the light intensity value. dark is the voltage when all wavelength lamps are off, V b is the baseline voltage signal, B is the differential path factor at each wavelength, ε is the molar absorption coefficient at each wavelength, ΔC HHb is the change in deoxyhemoglobin concentration, is the change value of oxygenated hemoglobin concentration, L is the distance between the light source and the detector;

[0060] 7.2.3, calculate the degree of muscle fatigue according to the muscle fatigue degree model. The muscle fatigue degree model is established as follows: calculate the total hemoglobin change according to the change value of deoxyhemoglobin concentration and the change value of oxyhemoglobin concentration and oxygen saturation changes The degree of muscle fatigue is calculated using the change in oxygen saturation, and a linear relationship is used for calculation: F = βΔStO2, where F is the degree of muscle fatigue, β is the proportional coefficient, and β>0;

[0061] 7.2.4. The electrical stimulation frequency requirement value is obtained according to the electrical stimulation frequency adjustment model. The electrical stimulation frequency adjustment model is established as follows: the calculated muscle fatigue degree is compared with the set muscle fatigue degree threshold. The comparison rules are as follows: if the current muscle fatigue degree is greater than the set muscle fatigue degree threshold 1, the electrical stimulation frequency requirement value will be set to 0 and the electrical stimulation will be stopped; if the current muscle fatigue degree is greater than the set muscle fatigue degree threshold 2 but less than the set muscle fatigue degree threshold 1, the electrical stimulation pulse frequency needs to be reduced to relieve the muscle fatigue. For convenience, a linear relationship can be used for adjustment: f a =f1-γ(F-F2), where f a It is the required value of the electrical stimulation frequency after the feedback of the muscle fatigue degree, f1 is the electrical stimulation frequency when the muscle shows a complete tetanic contraction signal when the electrical stimulation intensity and pulse width are fixed, F2 is the set muscle fatigue degree threshold 2, γ is the proportional coefficient, γ>0; if the current muscle fatigue degree is less than the set muscle fatigue degree threshold 2, the required value of the electrical stimulation frequency will keep the current electrical stimulation frequency unchanged.

[0062] Preferably, in step 3, the expected angle curve of the affected ankle joint is used to plan the patient's personalized ankle joint rehabilitation training angle curve, which is established as follows:

[0063] 3.1, obtain the ankle joint angle curve of the healthy side during normal walking; perform data preprocessing on the acceleration and angular velocity data recorded by the two inertial measurement units of the healthy side to obtain the ankle joint angle curve of the healthy side during normal walking;

[0064] 3.2, obtain the expected angle curve of the affected-side ankle joint during rehabilitation training, according to the certain consistency of the ankle joint angles of both legs during human walking; the angle curve of the healthy-side ankle joint during normal walking is used as the expected angle curve of the affected-side ankle joint during rehabilitation training.

[0065] The technical concept of the present invention is: the present invention customizes personalized rehabilitation training programs for patients through mapping relationships, nonlinear fitting and neural network analysis of multimodal signals. Unlike the traditional method that only relies on limited stimulation mode switching, this system can adjust the electrical stimulation parameters in real time through a multi-layer feedback mechanism, accurately stimulate the peroneal nerve, trigger related muscle contraction, and thus achieve dorsiflexion of the ankle joint. This innovative design aims to improve the matching efficiency between nerve-muscle-limb multi-source information feedback and functional electrical stimulation-assisted rehabilitation training, so as to achieve the best foot drop rehabilitation training effect. The device can be used in conjunction with rehabilitation devices such as exoskeletons.

[0066] The beneficial effects of the present invention are mainly manifested in:

[0067] (1) Utilize multi-modal fusion feedback composed of inertial measurement unit, surface electromyography signal and near-infrared spectroscopy signal to adjust the functional electrical stimulation parameters in real time, effectively improve the synergy between multi-source information feedback of nerves, muscles and limbs and functional electrical stimulation-assisted rehabilitation training, and provide better rehabilitation effect and rehabilitation experience;

[0068] (2) Through muscle synergy analysis, the patient's muscle activation state can be adjusted more specifically to achieve the appropriate muscle activation state and achieve better rehabilitation effects;

[0069] (3) By obtaining the voluntary muscle strength of the affected side of the patient during autonomous walking, an electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint is established, fully considering the recovery of voluntary muscle strength during the patient's rehabilitation training, and improving the autonomy of the patient's rehabilitation training and the efficiency of functional electrical stimulation rehabilitation;

[0070] (4) By using this system, the patient's expected ankle angle curve, the electrical stimulation intensity model based on the ankle angle, the muscle activation state model based on the ankle angle, and the electrical stimulation intensity model based on the muscle activation state are obtained. This system can provide patients with personalized adaptive rehabilitation training strategies and improve the effect of functional stimulation rehabilitation in patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a structural schematic diagram of a foot drop electrical stimulation system based on multi-mode fusion feedback of the present invention;

[0072] Figure 2 This is a diagram of placing the foot drop electrical stimulation system based on multi-mode fusion feedback on the lower limbs of a human body according to the present invention;

[0073] Figure 3 A flowchart for pre-training the model of the present invention;

[0074] Figure 4 This is a flow chart of the electrical stimulation frequency adjustment module of the present invention;

[0075] Figure 5 This is a block diagram of the foot drop electrical stimulation control method based on multi-mode fusion feedback of the present invention. DETAILED DESCRIPTION

[0076] The present invention will be further described below in conjunction with the accompanying drawings.

[0077] Example 1

[0078] Reference Figure 1 to Figure 4, a foot drop electrical stimulation system based on multi-mode fusion feedback, including a system initialization unit, a model pre-training unit, an inertial measurement unit, an electromyographic information acquisition module, a near-infrared muscle oxygen acquisition module, an electrical stimulation intensity adjustment module, an electrical stimulation frequency adjustment module and a functional electrical stimulation module, wherein:

[0079] The system initialization unit includes wearing a foot drop electrical stimulation rehabilitation system based on multi-mode fusion feedback, initializing a functional electrical stimulation module and calibrating various signal acquisition devices; a model pre-training unit, which is used to determine the expected angle curve of the affected ankle joint suitable for the patient's personalized rehabilitation training, the electrical stimulation intensity model based on the affected muscle activation state, the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle, and the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint, and output these pre-trained models to the electrical stimulation intensity adjustment module for storage for subsequent feedback control of the electrical stimulation intensity; an inertial measurement unit, which is used to collect the acceleration and angular velocity signals of the healthy and affected calves and soles of the feet of the patient during the foot drop rehabilitation training, and to feedback control the electrical stimulation intensity; a surface electromyography signal acquisition module, which is used to collect the patient's The surface electromyographic signal of the tibialis anterior muscle during foot drop rehabilitation training is collected, and the signal is used for feedback control of the electrical stimulation intensity; the near-infrared muscle oxygen acquisition module uses a near-infrared light source and detector with wavelengths of 735nm and 850nm to collect the near-infrared spectral signal of the tibialis anterior muscle of the patient during foot drop rehabilitation training, and uses the signal to feedback control the electrical stimulation frequency; the electrical stimulation intensity adjustment module is used to perform feedback calculation and analysis on the information output by the pre-training model, adjust the electrical stimulation intensity parameters, and use the control algorithm to fine-tune the electrical stimulation parameters; the electrical stimulation frequency adjustment module is used to calculate and analyze the near-infrared spectral signal collected by the near-infrared muscle oxygen acquisition module, obtain the degree of muscle fatigue, and adjust the electrical stimulation frequency parameters; the functional electrical stimulation module is used to convert the electrical stimulation parameters sent by the electrical stimulation intensity adjustment module and the electrical stimulation frequency adjustment module into control instructions and execute them, and output electrical stimulation pulses.

[0080] In this embodiment, the system initialization unit is performed as follows:

[0081] 1) Wear a foot drop electrical stimulation rehabilitation system based on multi-modal fusion feedback. Figure 2Shown is a position diagram of the foot drop electrical stimulation system based on multi-mode fusion feedback in the lower limbs of the human body. 1 represents the electrodes of the functional electrical stimulation module, 2 represents the electrodes of the electromyographic information acquisition module, 3 represents the near-infrared muscle oxygen acquisition module, and 4 represents the inertial measurement unit. The functional electrical stimulation module adopts a bipolar electrode configuration, and the two electrodes are placed at the body surface projection position below the head of the fibula, where the common peroneal nerve is closer to the skin surface, which is convenient for positioning and stimulation to induce the dorsiflexion movement of the ankle joint; the two electrodes of the surface electromyographic signal acquisition device are placed on the tibialis anterior muscle on the affected side; the near-infrared muscle oxygen acquisition device is placed on the tibialis anterior muscle on the affected side; the inertial measurement unit is placed on the healthy and affected sides at 10 cm away from the ankle joint and on the dorsum of the foot, respectively, with a total of 2 groups of 4, and the inertial measurement unit-human posture alignment calibration is performed to convert the inertial measurement unit coordinate system into the human coordinate system;

[0082] 2) Initialize the functional electrical stimulation module. Initialize the electrical stimulation parameters, including initializing the electrical stimulation intensity, initializing the electrical stimulation frequency, and setting the electrical stimulation pulse width;

[0083] 3) Calibrate each signal acquisition device, including collecting the baseline voltage signal and dark voltage data of the near-infrared muscle oxygen acquisition module; calibrating the inertial measurement unit-human posture alignment of the inertial measurement unit; and converting the inertial measurement unit coordinate system into the human body coordinate system.

[0084] Model pre-training mainly includes two parts: after the system is initialized, two pre-experiments are carried out; according to the surface electromyography signals, near-infrared signals, acceleration and angular velocity collected in the two pre-experiments, the expected angle curve of the affected ankle joint suitable for the patient's personalized rehabilitation training, the electrical stimulation intensity model based on the affected muscle activation state, the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle and the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint are established in advance. After obtaining the above pre-training models, these pre-training models are output to the electrical stimulation intensity adjustment module for storage for subsequent feedback control of the electrical stimulation intensity.

[0085] like Figure 3As shown, it is a flow chart of the pre-training of the model of the present invention. After the system is initialized, two pre-experiments are required for the patient to determine the pre-training model suitable for the patient. Pre-experiment ① is carried out in the following manner: the patient wears the foot drop electrical stimulation system based on multi-mode fusion feedback of the present invention, and also wears an inertial measurement unit in the same position as the affected side on the healthy side; walks normally without the assistance of electrical stimulation; records the original signals of the acceleration and angular velocity of the inertial measurement unit on the healthy and affected sides of the patient during natural walking, as well as the surface electromyography signal of the tibialis anterior muscle on the affected side. Pre-experiment ② is carried out in the following manner: the patient wears the foot drop electrical stimulation system based on multi-mode fusion feedback of the present invention; uses a functional electrical stimulation module to stimulate the peroneal nerve on the affected side of the patient, so that the patient's ankle joint can achieve dorsiflexion, during which the electrical stimulation frequency and electrical stimulation pulse width are fixed, and the electrical stimulation intensity is gradually increased; synchronously records multiple groups of electrical stimulation intensity data, multiple groups of inertial measurement unit acceleration and angular velocity signals, and multiple groups of surface electromyography signals during the experiment.

[0086] Data preprocessing includes data preprocessing of the acceleration and angular velocity signals of the inertial measurement unit and the surface electromyography signal. Among them: the acceleration and angular velocity data recorded by each set of inertial measurement units are subjected to Kalman attitude fusion to estimate the sensor attitude, and then the bias matrix is ​​solved by analyzing the attitude quaternions of the two inertial measurement units to obtain the ankle joint angle; the surface electromyography signal is subjected to low-pass filtering, high-pass filtering and notch filtering using a Butterworth filter to remove noise, skin motion artifacts and power frequency interference.

[0087] The pre-training model is obtained by model pre-training, including the expected angle curve of the affected ankle joint, the electrical stimulation intensity model based on the muscle activation state of the affected side, the electrical stimulation intensity model based on the angle of the affected ankle joint, the muscle activation state model based on the angle of the affected ankle joint, and the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint. After obtaining these models, they are output to the electrical stimulation intensity adjustment module and saved in the electrical stimulation intensity adjustment module for subsequent electrical stimulation intensity feedback control.

[0088] The information collected by the inertial measurement unit, the electromyographic information acquisition module and the near-infrared muscle oxygen acquisition module will be input into the electrical stimulation intensity adjustment module and the electrical stimulation frequency adjustment module as feedback signals to perform feedback adjustment of the electrical stimulation parameters.

[0089] The electrical stimulation intensity adjustment module includes a pre-training model, an electrical stimulation intensity adjustment model and a controller. In the pre-training model, the expected angle curve of the affected ankle joint will be used as the input of the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle and the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint, so as to obtain the expected electrical stimulation intensity, the expected affected muscle activation state and the autonomous muscle strength-electrical stimulation intensity respectively; the muscle activation state model is used to calculate and analyze the feedback surface electromyographic signal to obtain the actual muscle activation state of the affected side; the electrical stimulation intensity model based on the affected muscle activation state will be used for muscle activation state feedback to obtain the corrected electrical stimulation intensity; the electrical stimulation intensity adjustment model will calculate and analyze the expected electrical stimulation intensity, the corrected electrical stimulation intensity and the autonomous muscle strength-electrical stimulation intensity to obtain the required electrical stimulation intensity value; the controller uses the control algorithm to finely adjust the required electrical stimulation intensity value to obtain the output electrical stimulation intensity value.

[0090] The electrical stimulation frequency adjustment module includes a muscle oxygen concentration change model, a muscle fatigue degree model and an electrical stimulation frequency adjustment model. The muscle oxygen concentration change model is used to calculate and analyze the near-infrared spectrum signal collected by the near-infrared muscle oxygen acquisition module to obtain the muscle oxygen concentration change; the muscle fatigue degree model uses the obtained muscle oxygen concentration change to calculate the muscle fatigue degree; the electrical stimulation frequency adjustment model obtains the electrical stimulation frequency demand value according to the muscle fatigue degree.

[0091] The functional electrical stimulation module is used to convert the electrical stimulation parameters sent by the electrical stimulation intensity adjustment module and the electrical stimulation frequency adjustment module into control instructions and execute them, output electrical stimulation pulses to the electrical stimulation electrodes, act on the patient's peroneal nerve, and induce dorsiflexion of the patient's affected side of the foot to achieve the target ankle joint angle.

[0092] Example 2

[0093] Reference Figure 5 , which is a block diagram of the foot drop electrical stimulation method based on multi-mode fusion feedback of the present invention. The foot drop electrical stimulation method based on multi-mode fusion feedback has been summarized, including the following steps:

[0094] Step 1: System initialization, including wearing the foot drop electrical stimulation rehabilitation system based on multi-mode fusion feedback, initializing the functional electrical stimulation module, and calibrating each signal acquisition device;

[0095] Step 2, model pre-training, conducting a preliminary experiment on the patient, generating a pre-trained model, and saving the obtained pre-trained model in the electrical stimulation intensity adjustment module;

[0096] Step 3, using the expected angle of the affected ankle joint of the expected angle curve of the affected ankle joint during rehabilitation training obtained in the pre-training model as an input of the control method;

[0097] The expected angle curve of the affected ankle joint is used to plan the patient's personalized ankle joint rehabilitation training angle curve, which is established as follows:

[0098] 3.1, obtain the ankle joint angle curve of the healthy side during normal walking. The acceleration and angular velocity data recorded by the two inertial measurement units on the healthy side in pre-experiment ① are preprocessed to obtain the ankle joint angle curve of the healthy side during normal walking;

[0099] 3.2, get the expected angle curve of the affected ankle joint during rehabilitation training. According to the certain consistency of the ankle joint angles of both legs during human walking, the angle curve of the healthy ankle joint during normal walking is used as the expected angle curve of the affected ankle joint during rehabilitation training;

[0100] Step 4, using the actual affected ankle joint angle at the current moment as angle feedback, and performing feedback adjustment on the expected angle of the affected ankle joint in step 3;

[0101] Step 5, inputting the expected angle of the affected ankle joint after feedback in step 4 into the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle, and the electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint, to obtain the expected electrical stimulation intensity, the expected voluntary muscle activation state, and the voluntary muscle strength-electrical stimulation intensity, respectively;

[0102] The electrical stimulation intensity model based on the ankle angle of the affected side is used to find the relationship between the ankle angle and the electrical stimulation intensity of the patient's personalized needs. It is established as follows:

[0103] 5.1.1, obtain multiple sets of electrical stimulation intensity data and multiple sets of affected ankle joint angles. Pre-process the multiple sets of inertial measurement unit acceleration and angular velocity signals in pre-experiment ② to obtain multiple sets of affected ankle joint angles;

[0104] 5.1.2. Establish an electrical stimulation intensity model based on the angle of the affected ankle joint. Use a polynomial regression model to perform nonlinear fitting on multiple sets of affected ankle joint angles and multiple sets of electrical stimulation intensity data. For convenience, a second-order polynomial regression model is used for fitting, and the model is expressed as:

[0105] I E =aθ H 2 +bθ H +c

[0106] Among them, I E is the expected electrical stimulation intensity; θ H is the ankle joint angle of the affected side; a, b and c are the parameters of the model, which need to be determined through data fitting.

[0107] The muscle activation state model based on the angle of the affected ankle joint is used to detect whether the muscle activation state of the tibialis anterior muscle meets the requirements during rehabilitation training, so as to correct the electrical stimulation intensity. It is established based on the electrical stimulation intensity model based on the muscle activation state of the affected side and the electrical stimulation intensity model based on the angle of the affected ankle joint, and is expressed as:

[0108] M LSTM (W,H)=aθ H 2 +bθ H +c

[0109] Among them, M LSTM It is a model of electrical stimulation intensity based on the activation state of the affected muscle.

[0110] The electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint is used to characterize the magnitude of the voluntary muscle strength of the tibialis anterior muscle of the patient at each moment corresponding to the expected angle curve of the affected ankle joint, so as to correct the electrical stimulation intensity. It is established as follows:

[0111] 5.2.1, obtain the ankle joint angle curve and surface electromyographic signal of the affected side during normal walking, perform data preprocessing on the acceleration and angular velocity data recorded by the two inertial measurement units on the affected side in pre-experiment ① and the surface electromyographic signal of the tibialis anterior muscle on the affected side, and obtain the ankle joint angle curve and surface electromyographic signal of the tibialis anterior muscle on the affected side during normal walking;

[0112] 5.2.2, calculate the muscle activation state according to the muscle activation state model. Calculate the muscle activation state of the surface electromyographic signal of the tibialis anterior muscle during normal walking on the affected side in the same way as when establishing the electrical stimulation intensity model based on the muscle activation state of the affected side;

[0113] 5.2.3, calculate the voluntary muscle force-electrical stimulation intensity, the muscle activation state calculated in 5.2.2 is used as the input of the electrical stimulation intensity model based on the muscle activation state of the affected side, and the voluntary muscle force-electrical stimulation intensity corresponding to each desired angle of the affected ankle joint is obtained;

[0114] 5.2.4, obtain the correction model of electrical stimulation intensity based on the voluntary muscle strength of the affected ankle joint, and obtain the correction model of electrical stimulation intensity based on the voluntary muscle strength of the affected ankle joint according to the voluntary muscle strength-electrical stimulation intensity corresponding to each expected angle of the affected ankle joint obtained in 5.2.3;

[0115] Step 6, using the actual muscle activation state of the affected side at the current moment as muscle activation state feedback, feedback adjustment is performed on the expected muscle activation state of the affected side in step 5, and the expected muscle activation state of the affected side after feedback is input into the electrical stimulation intensity model based on the muscle activation state of the affected side to obtain a corrected electrical stimulation intensity;

[0116] The electrical stimulation intensity model based on the muscle activation state of the affected side is used to establish the electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint and the muscle activation state model based on the angle of the affected ankle joint. It is established as follows:

[0117] 6.1, obtain multiple sets of electrical stimulation intensity data and multiple sets of affected-side surface electromyography signals. Perform data preprocessing on multiple sets of surface electromyography signals in pre-experiment ② to obtain the surface electromyography signals of the affected-side tibialis anterior muscle;

[0118] 6.2, calculate the muscle activation state according to the muscle activation state model. Perform muscle synergy analysis on multiple groups of preprocessed surface electromyographic signals, and decompose them into muscle activation weight matrix and muscle activation sequence matrix by non-negative matrix decomposition method. The muscle activation state model is as follows:

[0119] M≈W×H=M′,M∈R n×t ,W∈R n×k ,H∈R k×t

[0120] min||MM′|| 2 =∑(MM′) 2

[0121]

[0122] Where M is the preprocessed surface electromyographic signal, W is the muscle activation weight matrix, H is the muscle activation sequence matrix, M′ is the matrix reconstructed based on the muscle activation weight matrix and the activation sequence matrix, R is a real number matrix, n is the number of collected muscles, t is the number of surface electromyographic sampling points, k is the number of synergists, and W T is the transposed matrix of the muscle activation weight matrix, H T is the transposed matrix of the muscle activation sequence matrix;

[0123] 6.3, establish an electrical stimulation intensity model based on the activation state of the affected muscle. Use the multiple sets of muscle activation weight matrices and muscle activation sequences obtained in step 6.2 and multiple sets of electrical stimulation intensity data to construct a data set. Use the multiple sets of muscle activation weight matrices and muscle activation sequences as the input of the LSTM neural network model, use the multiple sets of electrical stimulation intensity data as the labels of the LSTM neural network model, train the neural network model, and obtain the electrical stimulation intensity model based on the activation state of the affected muscle:

[0124] I E =M LSTM (W,H)

[0125] Among them, M LSTM It is a model of electrical stimulation intensity based on the activation state of the affected muscle;

[0126] Step 7: Input the desired electrical stimulation intensity, voluntary muscle strength-electrical stimulation intensity and corrected electrical stimulation intensity obtained in steps 5 and 6 into the electrical stimulation intensity adjustment model to obtain the electrical stimulation intensity requirement value I F , and then the controller calculates and analyzes the output value of the electrical stimulation intensity I O At the same time, the near-infrared spectrum signal of the tibialis anterior muscle is collected and input into the electrical stimulation frequency adjustment module to calculate the changes in muscle oxygen concentration and muscle fatigue, and the muscle fatigue degree is fed back to the electrical stimulation frequency adjustment model to obtain the electrical stimulation frequency demand value;

[0127] The electrical stimulation intensity adjustment module includes a pre-training model, an electrical stimulation intensity adjustment model and a controller. The electrical stimulation intensity is adjusted in the following manner to obtain an electrical stimulation intensity output value I O , the process is as follows:

[0128] 7.1.1. Use the pre-trained model to perform steps 3 to 6 to obtain the desired electrical stimulation intensity I E , Correction of electrical stimulation intensity I A and voluntary muscle strength—electrical stimulation intensity I H

[0129] 7.1.2. Input the desired electrical stimulation intensity, corrected electrical stimulation intensity and voluntary muscle strength-electrical stimulation intensity obtained in 7.1.1 into the electrical stimulation intensity adjustment model to obtain the required value of electrical stimulation intensity. The electrical stimulation intensity adjustment model is expressed as:

[0130] I F =I E -I H +αI A

[0131] Among them, I F is the required value of electrical stimulation intensity, I H is the voluntary muscle strength-electrical stimulation intensity, I A To correct the electrical stimulation intensity, α is the proportional coefficient.

[0132] 7.1.3. After calculating the required value of the electrical stimulation intensity, use the control algorithm of the controller to fine-tune the required value of the electrical stimulation intensity to obtain the output value of the electrical stimulation intensity. Take the PID control algorithm as an example to dynamically adjust the required value of the electrical stimulation intensity. The purpose of PID control is to achieve rapid response and reduce steady-state errors, so that the system can track the required value of the electrical stimulation intensity more accurately, while reducing errors caused by changes in muscle state or external interference, ensuring the safety and effectiveness of electrical stimulation therapy. Specifically, the PID controller can be expressed as:

[0133]

[0134] Among them, IO (k) is the output value of the electrical stimulation intensity, I O (k-1) is the electrical stimulation intensity output value at the previous moment, K p , K i and K d are the proportional, integral, and differential gains of the PID controller, and e(k) is the deviation at the current moment, i.e., I O (k)-I F (k), e(k-1) is the deviation at the previous moment, I F (k) is the required value of electrical stimulation intensity at the current moment, and e(i) is the deviation at the historical moment.

[0135] The electrical stimulation frequency adjustment module uses the near-infrared spectrum signal collected by the near-infrared muscle oxygen acquisition module for feedback adjustment to obtain the required value of the electrical stimulation frequency. Near-infrared spectroscopy technology can monitor the changes in the concentration of oxygenated hemoglobin and deoxygenated hemoglobin in muscles in real time. These changes directly reflect the oxidative metabolism state of muscles and the degree of muscle fatigue, such as Figure 4 As shown in the flowchart of the electrical stimulation frequency adjustment module of the present invention, the electrical stimulation frequency requirement value is obtained by the following method:

[0136] 7.2.1, obtain the near infrared spectrum signals of the tibialis anterior muscle at 735nm and 850nm;

[0137] 7.2.2, calculate the concentration change values ​​of oxyhemoglobin and deoxyhemoglobin according to the muscle oxygen concentration change model, use the near infrared spectrum signal obtained in 7.2.1 to calculate the concentration change values ​​of oxyhemoglobin and deoxyhemoglobin according to the modified Lambert-Beer law, the calculation method of the muscle oxygen concentration change model is as follows:

[0138]

[0139] Where ΔA represents the optical density, V t is the voltage value measured by the detector at a certain moment during rehabilitation training, which is proportional to the light intensity value. dark is the voltage when all wavelength lamps are off, V b is the baseline voltage signal, B is the differential path factor at each wavelength, ε is the molar absorption coefficient at each wavelength, ΔC HHb is the change in deoxyhemoglobin concentration, is the change value of oxygenated hemoglobin concentration, L is the distance between the light source and the detector;

[0140] 7.2.3, calculate the degree of muscle fatigue according to the muscle fatigue degree model. The muscle fatigue degree model is established as follows: calculate the total hemoglobin change according to the change value of deoxyhemoglobin concentration and the change value of oxyhemoglobin concentration and oxygen saturation changes The change in oxygen saturation is used to calculate the degree of muscle fatigue. For convenience, a linear relationship can be used as an example for calculation: F=βΔStO2, where F is the degree of muscle fatigue, β is the proportional coefficient, and β>0.

[0141] 7.2.4, obtain the required value of the electrical stimulation frequency according to the electrical stimulation frequency adjustment model. The electrical stimulation frequency adjustment model is established as follows: compare the calculated muscle fatigue level with the set muscle fatigue level threshold. The comparison rules are as follows: if the current muscle fatigue level is greater than the set muscle fatigue level threshold 1, the required value of the electrical stimulation frequency will be set to 0 and the electrical stimulation will be stopped; if the current muscle fatigue level is greater than the set muscle fatigue level threshold 2 and less than the set muscle fatigue level threshold 1, it is necessary to reduce the electrical stimulation pulse frequency to relieve the muscle fatigue. For convenience, a linear relationship can be used for adjustment: f a =f1-γ(F-F2), where f a It is the required value of the electrical stimulation frequency after the feedback of the muscle fatigue degree, f1 is the electrical stimulation frequency when the muscle shows a complete tetanic contraction signal when the electrical stimulation intensity and pulse width are fixed, F2 is the set muscle fatigue degree threshold 2, γ is the proportional coefficient, γ>0; if the current muscle fatigue degree is less than the set muscle fatigue degree threshold 2, the required value of the electrical stimulation frequency will keep the current electrical stimulation frequency unchanged.

[0142] Step 8, sending the electrical stimulation intensity output value and the electrical stimulation frequency requirement value obtained in step 7 to the functional electrical stimulation module for control instruction conversion;

[0143] Step 9: The functional electrical stimulation module executes the control instruction to output a stimulation pulse to stimulate the peroneal nerve on the affected side of the patient.

[0144] The electrical stimulation signal output by the functional electrical stimulation module in the present invention adopts a low-frequency symmetrical balanced biphasic pulse, wherein the positive phase pulse acts as an electrical stimulation and the negative phase pulse acts as a balance, so that the electrical stimulation is not easy to induce pain or discomfort and delay muscle fatigue. The positive and negative phase pulses are both current-type rectangular pulses, the electrical stimulation intensity is 0-100mA, the electrical stimulation pulse width is a pulse width of 200-700us, and the pulse frequency is 0-100Hz.

[0145] The contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept and are for illustrative purposes only. The protection scope of the present invention should not be considered to be limited to the specific forms described in this embodiment, and the protection scope of the present invention also extends to equivalent technical means that can be thought of by ordinary technicians in this field based on the inventive concept.

Claims

1. A foot drop electrical stimulation system based on multi-mode fusion feedback, characterized in that: The system includes an inertial measurement unit, an electromyographic information acquisition module, a near-infrared muscle oxygen acquisition module, an electrical stimulation intensity adjustment module, an electrical stimulation frequency adjustment module and a functional electrical stimulation module. The inertial measurement unit and the electromyographic information acquisition module are connected to the input side of the electrical stimulation intensity adjustment module, the output side of the electrical stimulation intensity adjustment module is connected to the input side of the functional electrical stimulation module, the near-infrared muscle oxygen acquisition module is connected to the electrical stimulation frequency adjustment module, the electrical stimulation frequency adjustment module is connected to the input side of the functional electrical stimulation module, and the output side of the functional electrical stimulation module is a working end for stimulating a patient.

2. A foot drop electrical stimulation system based on multi-mode fusion feedback as claimed in claim 1, characterized in that: The electrical stimulation intensity adjustment module includes a pre-training model, an electrical stimulation intensity adjustment model and a controller. In the pre-training model, the expected angle curve of the affected ankle joint is used as the input of the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle and the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint, and the expected electrical stimulation intensity I E , desired muscle activation state and voluntary muscle strength on the affected side - electrical stimulation intensity I H ; The muscle activation state model is used to calculate and analyze the feedback surface electromyographic signal to obtain the actual muscle activation state of the affected side; The electrical stimulation intensity model based on the activation state of the affected muscle will be used for muscle activation state feedback to obtain the corrected electrical stimulation intensity I A The electrical stimulation intensity adjustment model calculates and analyzes the expected electrical stimulation intensity, the corrected electrical stimulation intensity, and the voluntary muscle force-electrical stimulation intensity to obtain the electrical stimulation intensity requirement value I F The controller uses the control algorithm to fine-tune the required value of the electrical stimulation intensity and obtain the output value of the electrical stimulation intensity I O .

3. A foot drop electrical stimulation system based on multi-mode fusion feedback as claimed in claim 1, characterized in that: It also includes a system initialization unit and a model pre-training unit, wherein the system initialization unit is connected to the model pre-training unit, the input side of the model pre-training unit is connected to the inertial measurement unit and the electromyographic information acquisition module, and the output side of the model pre-training unit is connected to the input side of the electrical stimulation intensity adjustment module; The system initialization unit includes wearing a foot drop electrical stimulation system based on multi-mode fusion feedback, initializing a functional electrical stimulation module, and calibrating various signal acquisition devices; The model pre-training unit includes two parts: pre-experiment and model establishment. After the pre-experiment, the electrical stimulation intensity, acceleration, angular velocity and surface electromyography signal collected in the pre-experiment are used to establish a personalized pre-training model for patients, including determining the expected angle curve of the affected ankle joint by using the healthy-affected side mapping relationship; establishing an electrical stimulation intensity model based on the affected ankle joint angle by using a nonlinear fitting method; establishing an electrical stimulation intensity model based on the affected muscle activation state by using an LSTM neural network model; determining and establishing a muscle activation state model based on the affected ankle joint angle and an electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint by using the relationship between the models; after obtaining the above-mentioned pre-training models, these pre-training models are output to an electrical stimulation intensity adjustment module for storage for subsequent feedback control of the electrical stimulation intensity.

4. A foot drop electrical stimulation system based on multi-mode fusion feedback as claimed in any one of claims 1 to 3, characterized in that: The inertial measurement unit is used to collect the acceleration and angular velocity signals of the healthy and affected calves and soles of the patient during foot drop rehabilitation training, calculate and analyze the current ankle joint angle, and then perform feedback control on the desired angle curve of the affected ankle joint; The electromyographic information acquisition module is used for surface electromyographic signal acquisition, and is used to collect the surface electromyographic signal of the tibialis anterior muscle of the patient during foot drop rehabilitation training, calculate and analyze the current muscle activation state, and then feedback and adjust the electrical stimulation intensity; The near-infrared muscle oxygen acquisition module uses a near-infrared light source and a detector with wavelengths of 735nm and 850nm to collect near-infrared spectrum signals of the tibialis anterior muscle of the patient during foot drop rehabilitation training, and uses the signals to feedback adjust the electrical stimulation frequency.

5. A foot drop electrical stimulation system based on multi-mode fusion feedback as claimed in any one of claims 1 to 3, characterized in that: The electrical stimulation frequency adjustment module includes a muscle oxygen concentration change model, a muscle fatigue degree model and an electrical stimulation frequency adjustment model, wherein the muscle oxygen concentration change model is used to calculate and analyze the near-infrared spectrum signal collected by the near-infrared muscle oxygen collection module to obtain a muscle oxygen concentration change value; the muscle fatigue degree model uses the obtained muscle oxygen concentration change to further calculate the muscle fatigue degree; the electrical stimulation frequency adjustment model adjusts the electrical stimulation frequency parameters according to the muscle fatigue degree to obtain the electrical stimulation frequency demand value.

6. A method for implementing the foot drop electrical stimulation system based on multi-mode fusion feedback as claimed in claim 1, characterized in that: The method comprises the following steps: Step 1, system initialization, including wearing the foot drop electrical stimulation control system based on multi-mode fusion feedback, initializing the functional electrical stimulation module and calibrating each signal acquisition device; Step 2, model pre-training, conducting a preliminary experiment on the patient, generating a pre-trained model, and saving the obtained pre-trained model in the electrical stimulation intensity adjustment module; Step 3, using the expected angle of the affected ankle joint in the expected angle curve of the affected ankle joint during rehabilitation training obtained in the pre-training model as an input of the control method; Step 4, using the actual affected ankle joint angle at the current moment as angle feedback, and performing feedback adjustment on the expected angle of the affected ankle joint in step 3; Step 5, input the expected angle of the affected ankle joint after feedback in step 4 into the electrical stimulation intensity model based on the affected ankle joint angle, the muscle activation state model based on the affected ankle joint angle, and the electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint, and obtain the expected electrical stimulation intensity I E , desired muscle activation state and voluntary muscle strength on the affected side - electrical stimulation intensity I H ; Step 6, using the actual muscle activation state of the affected side at the current moment as the muscle activation state feedback, feedback adjustment is performed on the expected muscle activation state of the affected side in step 5, and the expected muscle activation state of the affected side after feedback is input into the electrical stimulation intensity model based on the muscle activation state of the affected side to obtain the corrected electrical stimulation intensity I A ; Step 7: Input the desired electrical stimulation intensity, voluntary muscle strength-electrical stimulation intensity and corrected electrical stimulation intensity obtained in steps 5 and 6 into the electrical stimulation intensity adjustment model to obtain the electrical stimulation intensity requirement value I F , and then the electrical stimulation intensity output value I is obtained through the control algorithm O ; At the same time, the near-infrared spectrum signal of the tibialis anterior muscle is collected and input into the electrical stimulation frequency adjustment module to calculate the change in muscle oxygen concentration and muscle fatigue degree, and the muscle fatigue degree is fed back to the electrical stimulation frequency adjustment model to obtain the electrical stimulation frequency demand value; Step 8, sending the electrical stimulation intensity output value and the electrical stimulation frequency requirement value obtained in step 7 to the functional electrical stimulation module for control instruction conversion; Step 9: The functional electrical stimulation module executes the control instruction to output a stimulation pulse to stimulate the peroneal nerve on the affected side of the patient.

7. The method according to claim 6, characterized in that In step 5, the electrical stimulation intensity model based on the ankle angle of the affected side is used to find the relationship between the ankle angle and the electrical stimulation intensity of the patient's personalized needs, which is established as follows: 5.1.1, obtain multiple sets of electrical stimulation intensity data and multiple sets of affected ankle joint angles through preliminary experiments, pre-process multiple sets of inertial measurement unit acceleration and angular velocity signals, and obtain multiple sets of affected ankle joint angles; 5.1.

2. Establish an electrical stimulation intensity model based on the angle of the affected ankle joint. Use a polynomial regression model to perform nonlinear fitting on multiple sets of affected ankle joint angles and multiple sets of electrical stimulation intensity data. Use a second-order polynomial regression model for fitting. The model is expressed as: I E =aθ H 2 +bθ H +c in, I E is the expected electrical stimulation intensity; θ H is the ankle joint angle of the affected side; a, b and c are the parameters of the model, which need to be determined by data fitting; The muscle activation state model based on the ankle joint angle of the affected side is used to detect whether the muscle activation state of the tibialis anterior muscle meets the requirements during rehabilitation training, so as to correct the electrical stimulation intensity. It is established based on the electrical stimulation intensity model based on the muscle activation state of the affected side and the electrical stimulation intensity model based on the ankle joint angle of the affected side, and is expressed as: M LSTM (W,H)=aθ H 2 +bθ H +c Among them, M LSTM It is a model of electrical stimulation intensity based on the activation state of the affected muscle. The electrical stimulation intensity correction model based on the voluntary muscle strength of the affected ankle joint is used to characterize the magnitude of the voluntary muscle strength of the tibialis anterior muscle of the patient at each moment corresponding to the expected angle curve of the affected ankle joint, so as to correct the electrical stimulation intensity. It is established as follows: 5.2.1, obtain the ankle joint angle curve and surface electromyographic signal of the affected side during normal walking, perform data preprocessing on the acceleration and angular velocity data recorded by the two inertial measurement units on the affected side and the surface electromyographic signal of the tibialis anterior muscle on the affected side, and obtain the ankle joint angle curve and surface electromyographic signal of the tibialis anterior muscle when the affected side is walking normally; 5.2.2, the muscle activation state is calculated according to the muscle activation state model, and the muscle activation state of the surface electromyographic signal of the tibialis anterior muscle of the affected side during normal walking is calculated in the same way as when the electrical stimulation intensity model based on the muscle activation state of the affected side is established; 5.2.3, calculate the voluntary muscle force-electrical stimulation intensity, use the muscle activation state calculated in 5.2.2 as the input of the electrical stimulation intensity model based on the muscle activation state of the affected side, and obtain the voluntary muscle force-electrical stimulation intensity corresponding to each desired angle of the affected ankle joint; 5.2.4, obtain the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint, and according to the autonomous muscle strength-electrical stimulation intensity corresponding to each expected angle of the affected ankle joint obtained in 5.2.3, obtain the electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint.

8. The method according to claim 7, characterized in that In step 6, the electrical stimulation intensity model based on the muscle activation state of the affected side is used to establish an electrical stimulation intensity correction model based on the autonomous muscle strength of the affected ankle joint and a muscle activation state model based on the angle of the affected ankle joint, which is established as follows: 6.1, obtaining multiple sets of electrical stimulation intensity data and multiple sets of affected-side surface electromyographic signals, performing data preprocessing on the multiple sets of surface electromyographic signals, and obtaining the surface electromyographic signals of the tibialis anterior muscle on the affected side; 6.

2. Calculate the muscle activation state according to the muscle activation state model. Perform muscle synergy analysis on multiple groups of preprocessed surface electromyographic signals. Decompose them into muscle activation weight matrix and muscle activation sequence matrix by non-negative matrix decomposition method. The muscle activation state model is as follows: M≈W×H=M′,M∈R n×t ,W∈R n×k ,H∈R k×t min||M-M′|| 2 =∑(M-M′) 2 Where M is the preprocessed surface electromyographic signal, W is the muscle activation weight matrix, H is the muscle activation sequence matrix, M′ is the matrix reconstructed based on the muscle activation weight matrix and the activation sequence matrix, R is a real number matrix, n is the number of collected muscles, t is the number of surface electromyographic sampling points, k is the number of synergists, and W T is the transposed matrix of the muscle activation weight matrix, H T is the transposed matrix of the muscle activation sequence matrix; 6.

3. Establish an electrical stimulation intensity model based on the activation state of the affected-side muscles. Use the multiple sets of muscle activation weight matrices and muscle activation sequences obtained in step 6.2 and multiple sets of electrical stimulation intensity data to construct a data set. Use the multiple sets of muscle activation weight matrices and muscle activation sequences as inputs to the LSTM neural network model, use the multiple sets of electrical stimulation intensity data as labels of the LSTM neural network model, train the neural network model, and obtain an electrical stimulation intensity model based on the activation state of the affected-side muscles: I E =M LSTM (W,H) Among them, I E is the expected electrical stimulation intensity, M LSTM It is a model of electrical stimulation intensity based on the activation state of the affected muscle.

9. The method according to claim 6, characterized in that In step 7, the electrical stimulation intensity is adjusted in the following manner to obtain an electrical stimulation intensity output value, and the process is as follows: 7.1.1, the desired electrical stimulation intensity I obtained in step 5 and step 6 E , Correction of electrical stimulation intensity I A and voluntary muscle strength—electrical stimulation intensity I H Input into the electrical stimulation intensity adjustment model to obtain the electrical stimulation intensity requirement value I F , the electrical stimulation intensity regulation model is expressed as: I F =I E -I H +αI A in, I F is the required value of electrical stimulation intensity, I H is the voluntary muscle strength-electrical stimulation intensity, I A is to correct the electrical stimulation intensity, α is the proportionality coefficient; 7.1.2, after calculating the required value of electrical stimulation intensity I F Then, the control algorithm of the controller is used to finely adjust the required value of the electrical stimulation intensity to obtain the output value of the electrical stimulation intensity I O , the PID controller is expressed as: in, I O (k) is the output value of the electrical stimulation intensity, I O (k-1) is the electrical stimulation intensity output value at the previous moment, K p , K i and K d are the proportional, integral, and differential gains of the PID controller, and e(k) is the deviation at the current moment, i.e., I O (k)-I F (k), e(k-1) is the deviation at the previous moment, I F (k) is the required value of electrical stimulation intensity at the current moment, and e(i) is the deviation at the historical moment; The electrical stimulation frequency adjustment module uses the near-infrared spectrum signal collected by the near-infrared muscle oxygen acquisition module for feedback adjustment to obtain the electrical stimulation frequency demand value. The process is as follows: 7.2.1, obtain the near infrared spectrum signals of the tibialis anterior muscle at 735nm and 850nm; 7.2.2, calculate the concentration change values ​​of oxyhemoglobin and deoxyhemoglobin according to the muscle oxygen concentration change model, use the near infrared spectrum signal obtained in 7.2.1 to calculate the concentration change values ​​of oxyhemoglobin and deoxyhemoglobin according to the modified Lambert-Beer law, the calculation method of the muscle oxygen concentration change model is as follows: in, ΔA represents the optical density, V t is the voltage value measured by the detector at a certain moment during rehabilitation training, which is proportional to the light intensity value. darik is the voltage when all wavelength lamps are off, V b is the baseline voltage signal, B is the differential path factor at each wavelength, ε is the molar absorption coefficient at each wavelength, ΔC HHb is the change in deoxyhemoglobin concentration, is the change value of oxygenated hemoglobin concentration, L is the distance between the light source and the detector; 7.2.3, calculate the degree of muscle fatigue according to the muscle fatigue degree model. The muscle fatigue degree model is established as follows: calculate the total hemoglobin change according to the change value of deoxyhemoglobin concentration and the change value of oxyhemoglobin concentration and oxygen saturation changes The degree of muscle fatigue is calculated using the change in oxygen saturation, and a linear relationship is used for calculation: F = βΔStO2, where F is the degree of muscle fatigue, β is the proportional coefficient, and β>0; 7.2.

4. The electrical stimulation frequency requirement value is obtained according to the electrical stimulation frequency adjustment model. The electrical stimulation frequency adjustment model is established as follows: the calculated muscle fatigue degree is compared with the set muscle fatigue degree threshold. The comparison rules are as follows: if the current muscle fatigue degree is greater than the set muscle fatigue degree threshold 1, the electrical stimulation frequency requirement value will be set to 0 and the electrical stimulation will be stopped; if the current muscle fatigue degree is greater than the set muscle fatigue degree threshold 2 but less than the set muscle fatigue degree threshold 1, the electrical stimulation pulse frequency needs to be reduced to relieve the muscle fatigue. For convenience, a linear relationship can be used for adjustment: f a =f1-γ(F-F2), where f a It is the required value of the electrical stimulation frequency after the feedback of the muscle fatigue degree, f1 is the electrical stimulation frequency when the muscle shows a complete tetanic contraction signal when the electrical stimulation intensity and pulse width are fixed, F2 is the set muscle fatigue degree threshold 2, γ is the proportional coefficient, γ>0; if the current muscle fatigue degree is less than the set muscle fatigue degree threshold 2, the required value of the electrical stimulation frequency will keep the current electrical stimulation frequency unchanged.

10. The method according to any one of claims 6 to 9, characterized in that: In step 3, the expected angle curve of the affected ankle joint is used to plan the patient's personalized ankle joint rehabilitation training angle curve, which is established as follows: 3.1, obtain the ankle joint angle curve of the healthy side during normal walking; perform data preprocessing on the acceleration and angular velocity data recorded by the two inertial measurement units of the healthy side to obtain the ankle joint angle curve of the healthy side during normal walking; 3.2, obtain the expected angle curve of the affected-side ankle joint during rehabilitation training, according to the certain consistency of the ankle joint angles of both legs during human walking; the angle curve of the healthy-side ankle joint during normal walking is used as the expected angle curve of the affected-side ankle joint during rehabilitation training.

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