CN113426007A - Closed-loop epidural electrical stimulation system for upper limb functional recovery - Google Patents
Closed-loop epidural electrical stimulation system for upper limb functional recovery Download PDFInfo
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Abstract
本发明公开了一种用于上肢功能恢复的闭环硬脊膜外电刺激系统。动作捕捉装置，实时获取多角度人体姿态图像并发送控制装置；脑电采集装置，实时获取脑电信号，脑电信号经放大和滤波发送控制装置；控制装置，接收人体姿态图像和脑电信号获得上肢姿态数据和脑电信号特征，生成电刺激指令并发送脊髓电刺激装置；脊髓电刺激装置，接收指令向人体脊髓发电刺激脉冲。本发明系统能够基于人体自身意志，主动调制支配人体上肢功能的神经活动，更有效地协助康复训练，促进人体上肢相关的神经环路的功能恢复。
The invention discloses a closed-loop epidural electrical stimulation system for upper limb function recovery. The motion capture device obtains multi-angle human body posture images in real time and sends the control device; the EEG acquisition device obtains the EEG signal in real time, and the EEG signal is amplified and filtered to send the control device; the control device receives the human body posture image and the EEG signal to obtain Upper limb posture data and EEG signal characteristics, generate electrical stimulation commands and send spinal cord electrical stimulation devices; spinal cord electrical stimulation devices receive commands to generate stimulation pulses to the human spinal cord. The system of the invention can actively modulate the nerve activity governing the upper limb function of the human body based on the human body's own will, more effectively assists the rehabilitation training, and promotes the functional recovery of the neural circuit related to the upper limb of the human body.
Description
Technical Field
The invention relates to a spinal cord electrical stimulation system in the medical field, in particular to a closed loop epidural electrical stimulation system for upper limb function recovery after spinal cord injury or cerebral apoplexy.
Background
The damage of the central nervous system caused by stroke or spinal cord injury can cause that a human body can not normally generate central nervous system commands for controlling movement or can not transmit the commands to skeletal muscles to cause upper limb movement dysfunction. The epidural electrical stimulation can apply stimulation current to the spinal cord dorsal dura mater to excite a central mode generation network on the human spinal cord and enhance excitability of a spinal cord circuit, thereby mobilizing skeletal muscles to contract, effectively relieving muscle fatigue and promoting remodeling of damaged nerve circuits, but the current time-space resolution is limited in practical application, and accurate control on upper limbs cannot be realized like functional electrical stimulation.
Disclosure of Invention
The invention aims to develop a closed-loop epidural electrical stimulation technology aiming at the conditions that the existing upper limb functional electrical stimulation is easy to cause muscle fatigue and the time-space resolution of the upper limb epidural electrical stimulation technology is insufficient, and the electrical stimulation strategy is changed in real time by taking real-time electroencephalogram signals and motion states of a human body as the basis, so that the activity of corresponding neural circuits is induced, and the motion of target skeletal muscles is compensated.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the closed-loop epidural electrical stimulation system comprises a motion capture device, an electroencephalogram acquisition device, a control device and a spinal cord electrical stimulation device, wherein:
the motion capture device is used for acquiring multi-angle human body posture images in real time and sending the human body posture images to the control device;
the electroencephalogram acquisition device is used for acquiring electroencephalogram signals of a human body in real time, and the electroencephalogram signals are amplified and filtered and then sent to the control device;
the control device receives the human body posture image from the motion capture device and the electroencephalogram signal from the electroencephalogram acquisition device, further processes and analyzes the upper limb posture data in the human body posture image and the electroencephalogram signal characteristics in the electroencephalogram signal in real time, generates an instruction containing an electrical stimulation parameter and sends the instruction to the spinal cord electrical stimulation device;
the spinal cord electrical stimulation device receives the instruction containing the electrical stimulation parameters sent by the control device and sends electrical stimulation pulses to the spinal cord of the human body.
The motion capture device mainly comprises at least two cameras, and the cameras collect images of human upper limbs from multiple visual angles to serve as human posture images.
The electroencephalogram acquisition device at least comprises a plurality of acquisition channels, a reference channel and an electroencephalogram electrode array, wherein the electroencephalogram electrode array comprises a plurality of electrodes arranged in the brain of the head of a human body, each acquisition channel is respectively connected with one electrode, the reference channel is connected with one electrode and is grounded, and each electrode of the acquisition channel is arranged on different brain areas to acquire electroencephalogram changes of the brain areas.
The electroencephalogram acquisition device comprises a preamplifier, a differential amplifier and an analog filter, wherein the preamplifier is used for processing the original electric signals acquired by each channel, and the original electric signals acquired by each channel are output to the control device after being subjected to preamplifier, differential amplification and filtering in sequence.
The control device receives real-time posture data and original electroencephalogram signals of a human body, carries out preprocessing on the posture data and the original electroencephalogram signals, generates processed upper limb posture data and electroencephalogram signal characteristics, obtains the difference between the current motion state and the target state of the human body according to the upper limb posture data and the electroencephalogram signal characteristics, generates an electrical stimulation instruction, and sends the electrical stimulation instruction to the spinal cord electrical stimulation device.
The control device stores a human body posture estimation program based on deep learning, key points of the upper limbs of the human body can be marked through a deep learning model according to the multi-view human body posture image transmitted by the action capturing device, the space coordinates of each key point are three-dimensionally reconstructed, and kinematic data of the upper limbs of the human body are generated to serve as upper limb posture data;
the control device is stored with an electroencephalogram signal processing program, and the electroencephalogram signal processing program performs band-pass filtering and spatial filtering on the original electroencephalogram signals transmitted by the electroencephalogram acquisition device to obtain characteristics of the electroencephalogram signals;
the control device stores a motion state classification program constructed based on a neural network, and the motion state classification program judges the current motion state of the human body according to the kinematics data and the electroencephalogram signal characteristics so as to obtain the difference between the motion state and the target state;
the control device is stored with an electrical stimulation control program, the electrical stimulation control program is internally provided with predefined anatomical mapping according to the kinematic function of each muscle of an upper limb of a human and the distribution condition of motor neurons which govern each muscle of the upper limb between a second cervical segment and a first thoracic segment of a spinal cord, the electrical stimulation control program firstly determines an electrical stimulation site according to the predefined anatomical mapping, then obtains specific electrical stimulation parameters including the frequency, the amplitude, the width, the number and other parameters of electrical stimulation pulses according to the difference processing between the current motion state and the target state, and finally codes the electrical stimulation site and the electrical stimulation parameters together into an electrical stimulation command to be sent to the spinal cord electrical stimulation device.
The spinal cord electrical stimulation device receives the electrical stimulation instruction from the control device, applies electrical stimulation pulse sequences with corresponding parameters to electrical stimulation sites on the dura mater of the spinal cord in the electrical stimulation instruction, activates motor neurons located at the electrical stimulation sites, then innervates muscles to contract so that upper limbs do corresponding actions, then is collected by the action capturing device and fed back to the control device, and is continuously circulated to perform reciprocating closed-loop control for electrical stimulation.
The invention has the beneficial effects that:
the system can actively modulate the neural activity dominating the functions of the upper limbs of the human body based on the mind of the human body, more effectively assist the rehabilitation training and promote the function recovery of the neural circuits related to the upper limbs of the human body.
The invention can avoid faster muscle fatigue caused by electrical stimulation, and can apply electrical stimulation to corresponding regions of the spinal cord to mobilize corresponding skeletal muscles by combining real-time electroencephalogram signals and motion states of a human body, thereby improving the spatial-temporal resolution of an electrical stimulation strategy.
Drawings
The invention is further explained below with reference to the figures and examples;
FIG. 1 is a schematic diagram of a closed-loop epidural electrical stimulation system according to an embodiment of the invention.
FIG. 2 is a flowchart illustrating a method for controlling a closed-loop epidural electrical stimulation system according to an embodiment of the invention.
FIG. 3 is a data processing flow diagram of an attitude estimation system according to an embodiment of the invention.
FIG. 4 is a schematic diagram of a key point structure of a human limb according to an embodiment of the invention.
Fig. 5 is a schematic diagram of a neural network structure for feature extraction and fusion and motion state classification according to an embodiment of the present invention.
FIG. 6 is a schematic diagram of electroencephalogram signal characteristics and gesture characteristics when a person performs a grasping action according to an embodiment of the present invention.
Fig. 7 is an electrical stimulation parameter corresponding to fig. 6 of an electrical stimulation control routine when a person performs a grasping action according to an embodiment of the present invention.
Fig. 8 is a predefined anatomical mapping according to an embodiment of the invention.
FIG. 9 is a schematic diagram of closed loop control logic according to an embodiment of the present invention.
In the figure: the system comprises a pair of cameras 1, an electroencephalogram electrode array 2, a computer 3, upper limb posture data 4, electroencephalogram signal characteristics 5, a neural network 6 and a spinal cord stimulation device 7; face center 8, neck center 9, shoulder joints 10, elbow joints 11, wrist joints 12, and palm joint assembly 13.
Detailed Description
The present invention will be described in further detail with reference to the following description, which is provided for describing an embodiment of the present invention with reference to the accompanying drawings, and the present invention is not limited to the scope of the present invention.
The drawings are not necessarily to scale, and certain details may be omitted or exaggerated in the interest of clarity of construction.
As shown in fig. 1, the embodied system comprises:
the closed-loop epidural electrical stimulation system comprises a motion capture device, an electroencephalogram acquisition device, a control device and a spinal cord electrical stimulation device, wherein:
the motion capture device is used for acquiring human body posture images at multiple angles in real time and sending the human body posture images to the control device;
the electroencephalogram acquisition device is used for acquiring electroencephalogram (EEG) of a human body in real time, and the EEG is pre-amplified, filtered and sent to the control device;
the control device receives the human body posture image from the motion capture device and the electroencephalogram signal from the electroencephalogram acquisition device, further processes and analyzes the upper limb posture data 4 in the human body posture image and the electroencephalogram signal characteristics 5 in the electroencephalogram signal in real time, calculates electrical stimulation parameters, generates an instruction containing the electrical stimulation parameters and sends the instruction to the spinal cord electrical stimulation device;
and the spinal cord electrical stimulation device 7 receives the instruction containing the electrical stimulation parameters sent by the control device and sends electrical stimulation pulses to the dura mater spinalis of the spinal cord of the human body so as to realize the function recovery of the upper limbs.
The upper limb function recovery of the invention is the upper limb function recovery after spinal cord injury or cerebral apoplexy.
The motion capture device mainly comprises at least two cameras 1, wherein the cameras 1 collect images of human upper limbs from multiple visual angles to serve as human posture images, and space coordinates of joints of the human upper limbs are obtained according to the human posture images through recovery.
The brain electricity collecting device at least comprises a plurality of collecting channels, a reference channel and a brain electricity electrode array 2, the brain electricity electrode array 2 comprises a plurality of electrodes which are arranged on the brain of the head of a human body, the number of the electrodes is the same as the total number of the collecting channels and the reference channel, each collecting channel is respectively connected with one electrode, the reference channel is connected with one electrode and is grounded, and each electrode of the collecting channels is arranged on different brain areas to collect brain electricity changes of the brain areas. In specific implementation, eight acquisition channels are provided, and can be composed of one or more pain-relieving electrodes commonly used in clinic at present.
The electroencephalogram acquisition device comprises a preamplifier, a differential amplifier and an analog filter, wherein the preamplifier is used for processing the original electric signals acquired by each channel, and the original electric signals acquired by each channel are output to the control device after being subjected to preamplifier, differential amplification and filtering in sequence.
The electroencephalogram acquisition device acquires original head electrophysiological signals as electroencephalogram signals through the electrode array of the electroencephalogram electrode array 2.
The control device can adopt a computer 3, receives real-time posture data and original electroencephalogram signals of a human body and carries out preprocessing, generates processed upper limb posture data 4 with kinematic data and electroencephalogram signal characteristics 5, obtains the difference between the current motion state and the target state of the human body according to the upper limb posture data 4 and the electroencephalogram signal characteristics 5, generates an electrical stimulation instruction, and sends the electrical stimulation instruction to a spinal cord electrical stimulation device 7.
The upper limb posture data refers to three-dimensional space coordinates of each joint of the upper limb of the human body, and the kinematic data refers to physical quantities such as angles, angular velocities and angular accelerations of each joint of the upper limb of the human body calculated according to the three-dimensional space coordinates. The motion state refers to a predefined state for executing various actions on the upper limb of the human body, such as arm stretching, palm grasping and the like, a series of continuous motion states form a motion state sequence, a patient who carries out rehabilitation training is in one motion state in the sequence every moment and is defined as a current motion state, and the next motion state in the sequence is defined as a target motion state.
The control device stores a human body posture estimation program based on deep learning, key points of the upper limbs of a human body can be marked through a deep learning model according to a multi-view human body posture image transmitted by the action capturing device, the space coordinates of each key point are three-dimensionally reconstructed, and kinematic data of the upper limbs of the human body are generated to serve as upper limb posture data 4;
the control device stores an electroencephalogram signal processing program, and the electroencephalogram signal processing program performs band-pass filtering and spatial filtering on an original electroencephalogram signal transmitted by the electroencephalogram acquisition device to obtain electroencephalogram signal characteristics 5; the method specifically comprises the steps of collecting original head electrophysiological signals, carrying out digital band-pass filtering, removing electromyographic signals, and preprocessing by using a spatial filter.
The control device stores a motion state classification program constructed based on a neural network 6, and the motion state classification program judges the current motion state of the human body according to the kinematics data and the electroencephalogram signal characteristics 5 so as to obtain the difference between the motion state and the target state; the target state is a preset human motion state.
The control device stores an electrical stimulation control program, the electrical stimulation control program is internally provided with an anatomical mapping predefined according to the kinematic function of each muscle of an upper limb of a human and the distribution condition of motor neurons dominating each muscle of the upper limb between a second cervical segment and a first thoracic segment of a spinal cord, the electrical stimulation control program firstly determines an electrical stimulation site according to the predefined anatomical mapping, the electrical stimulation site is arranged on a dura mater of the spinal cord, then specific electrical stimulation parameters including parameters such as frequency, amplitude, width and number of electrical stimulation pulses are obtained according to the difference processing between the current motion state and the target state, and finally the electrical stimulation site and the electrical stimulation parameters are jointly coded into an electrical stimulation command to be sent to the spinal cord electrical stimulation device.
Specifically, in the electrical stimulation control program, the difference information between the current motion state and the target state is simultaneously input to the PID controller and the dynamic inverse dynamics model, the output results of the PID controller and the dynamic inverse dynamics model are added and then subjected to amplitude limiting processing to obtain the setting parameters of the electrical stimulation pulse, and then the setting parameters are input to the spinal cord electrical stimulation device 7. The electrical stimulation control program continuously repeats the above process, and updates the current motion state and the target state until assisting the human body to complete the training action.
The spinal cord electrical stimulation device 7 receives an electrical stimulation command from the control device, applies an electrical stimulation pulse sequence with corresponding parameters to electrical stimulation sites on the dura mater of the spinal cord in the electrical stimulation command, activates motor neurons located at the electrical stimulation sites, and then controls muscles to contract so that upper limbs do corresponding actions, and then the action capturing device and the action capturing device collect and feed back the actions to the control device, so that new posture data and original electroencephalogram signals are monitored, and continuous circulation reciprocating closed-loop control is performed for electrical stimulation.
For example:
A) when the current motion state is anteflexion and the target state is arm extension, an electrical stimulation instruction is generated, specifically at the outside of C5 on the spinal cord, at a frequency of 20Hz, with an amplitude of 0.5mA, and a width of 0.2 ms.
B) When the current motion state is arm extension and the target state is palm opening, electrical stimulation commands are generated, specifically at the center and outside of C5 on the spinal cord, at a frequency of 20Hz, at an amplitude of 0.5mA, and at a width of 0.2 ms.
C) When the exercise current state is palm open and the target state is palm grip, electrical stimulation commands are generated, specifically outside C5 and outside C6 on the spinal cord, at a frequency of 30Hz, an amplitude of 0.3mA, and a width of 0.2 ms.
D) When the current motion state is palm grasping and the target state is arm retraction, electrical stimulation commands are generated, specifically outside C5 and outside C8 on the spinal cord, at a frequency of 20Hz, 0.3mA amplitude, 0.2ms width.
E) When the current motion state is arm adduction and the target state is arm relaxation, electrical stimulation commands are generated, specifically, at the center and outside of C2 and outside of C8 on the spinal cord, with the frequency of 20Hz, the amplitude of 0.5mA and the width of 0.2 ms.
The embodiment may be, for example, but not limited to, that the human body performs a task such as grasping an object in human body rehabilitation training.
In a specific implementation, the upper limbs of the human body are divided into a face center 8, a neck center 9, shoulder joints 10, elbow joints 11, wrist joints 12 and palm joint sets 13.
In specific implementation, a plurality of sites are arranged on the spinal cord, a plurality of sites arranged in an array are arranged on the back of a human body between joints C2-T1 of the spine, the column direction of the site array is parallel to the spine direction, and the row direction of the site array is perpendicular to the spine direction. In one embodiment, there are three columns and six rows of 18 sites, with the sites in the middle column being located on the back of the spine at the centerline.
In specific implementation, the closed-loop electrical stimulation system consists of a camera 1, an electroencephalogram acquisition device, a computer 3 and a spinal cord electrical stimulation device 7. The camera 1 is communicated with the computer 3 through a network, the electroencephalogram acquisition device is communicated with the computer 3 through Bluetooth, a human body posture estimation program, an electroencephalogram signal processing program, an electrical stimulation control program and a human-computer interaction interface are arranged on the computer 3, current parameters of all channels of the spinal cord electrical stimulation device 7 are controlled by a control module of the spinal cord electrical stimulation device 7, and the control module of the spinal cord electrical stimulation device 7 is communicated with the computer 3 through Bluetooth. There are many wireless communication modes that can be selected, such as Zig-Bee, Bluetooth or wifi. The lithium battery of the spinal cord stimulation device 7 is charged by a short-range wireless charger.
The camera 1 is composed of 1-3 cameras, and transmits real-time human motion images to a computer through a local network. 2 common 720P 30FPS office cameras can be selected and communicated with a computer through a local area network.
The human posture estimation program arranged in the computer 3 can load a pre-trained deep learning model, analyze the human posture image in real time, determine the parameters of electrical stimulation, including the area of the electrical stimulation, the intensity and the waveform of current, and send the parameters to the spinal cord electrical stimulation device 7 through the Bluetooth communication device. The body pose estimation program employs a convolutional neural network model, trains using the open multi-view body pose dataset, and stores the trained model.
The human-computer interaction interface on the computer 3 provides a visual monitoring window of the current posture data and the motion state of the human body and is provided with a control interface for adjusting an electric stimulation strategy.
The spinal cord stimulation device 7 is implanted below a cervical vertebra plate and above a spinal cord dura mater of a human body through a surgical operation and is powered by a built-in lithium battery.
As shown in figure 2, when the system works, a patient autonomously controls the arm to do small-amplitude motion through the residual sensorimotor function, correspondingly, electroencephalogram signals related to movement intentions are sent to the computer 3, simultaneously, the human body shape of the patient is captured by the camera 1 and the images are sent to the computer 3, the current posture information of the human body is analyzed by a human body posture estimation program on the computer 3, the coordinates of the elbow joint and each finger joint of the upper limb of the human body are calculated, and the angular velocity and the angular acceleration are transmitted to the movement state classification program.
And the motion state classification program judges the current motion state and the target state thereof according to the electroencephalogram signals and the joint kinematics data, wherein the current motion state comprises anteflexion and lifting, arm stretching, palm holding, arm adduction and arm relaxation. The electrical stimulation control program formulates an electrical stimulation strategy according to the current motion state and the target state, wherein the electrical stimulation strategy comprises a stimulation area, and the waveform, the frequency and the amplitude of current, and the communication program codes the electrical stimulation strategy into an instruction and sends the instruction to the spinal cord stimulation device 7 to execute corresponding electrical stimulation.
As shown in fig. 3, in the preparation stage of rehabilitation training, the camera is calibrated, and the internal parameters and the external parameters of the camera, including the relative position and the focal length of the camera, are determined. Meanwhile, the attitude estimation program based on the convolutional neural network is trained by using the disclosed multi-view human body attitude data set, and the effectiveness of the human body is verified when the human body performs a gripping training action.
In the rehabilitation training stage, the camera transmits the data stream of the body image of the patient to the computer in real time through the network, the posture estimation program labels key points of the upper human body, three-dimensional reconstruction is carried out by utilizing multi-view images according to the internal and external parameters of the camera, and the three-dimensional coordinates of the joint points of the upper limbs of the human body are calculated and transmitted to the electrical stimulation control program.
As shown in fig. 4, a common set of human key points is mainly composed of basic anatomical joint positions, and the system mainly uses the following key points: face center 8, neck center 9, shoulder joints 10, elbow joints 11, wrist joints 12, and palm joint assembly 13.
Alternatively, the palm joint set may be resized according to the degree of refinement of the motion state. For example, when setting the motion state of the palm only concerning the opening and the gripping of the palm, the palm joint set may include only the joints of the palm center and the finger ends; when setting the shape regarding the motion state of the palm and focusing on the palm closure, the palm joint set should contain all the joints of the human fingers.
As shown in fig. 5, which is a schematic diagram of a network structure of a deep learning-based motion state classification program, electroencephalogram signals and kinematic data are used as inputs of a neural network, are subjected to preliminary feature extraction and fusion, are transmitted to a full-connection network-based classifier, and finally, current motion states and target states thereof are output. Based on an end-to-end learning framework, collected electroencephalogram signals and collected kinematics data are respectively input into two one-dimensional convolutional neural networks for feature extraction after being preprocessed, feature vectors respectively extracted from the two signals are combined into a compatible vector space through a single-layer neural network and are transmitted through a three-layer fully-connected network, and finally a motion classifier is obtained through training data, so that a motion state and a corresponding target state can be judged and output according to corresponding input data.
As shown in fig. 6, the subject is performing a gripping action, the electroencephalogram characteristic is represented by a spatial hot spot having a specific position, and when the subject is in the S1 state, the electroencephalogram characteristic represents that the subject produces an intention to move, and at this time, the body posture characteristic is not obvious; when the tested person is in the state of S2, the movement intention of the extending forearm and the corresponding posture characteristic are generated; when the tested person is in the S3 state, the movement intention of the open palm and the corresponding posture characteristic are generated; when the tested person is in the state of S4, the tested person generates the movement intention of the grip and the corresponding posture characteristic; when the tested person is in the state of S5, the movement intention of the forearm to be retracted and the corresponding posture characteristic are generated; when the subject is in the state of S6, there is no movement intention and posture characteristics with respect to the grip. The plurality of testees are enabled to repeatedly carry out the grasping action, corresponding electroencephalogram signals and human body posture data are collected, the neural network shown in the figure 5 is trained, and the neural network can be used for judging the current motion state and the target state of the human body according to the electroencephalogram signal characteristics and the posture characteristics of the human body in rehabilitation training and used as the basis for formulating the electrical stimulation strategy.
As shown in fig. 7, the subject made an electrical stimulation strategy S1, S2, S3, S4, S5, S6 corresponding to 6 states during the execution of 6 states of the grasping action as shown in fig. 6, the electrical stimulation site is represented as a shaded rectangle with a specific position in the electrode array in the figure, and the waveform, pulse width, amplitude and frequency of the electrical stimulation are represented as the upper fold-off and frequency values in the figure.
In the figure, C2, C3, C5, C6, C7, C8 and T1 represent corresponding sections of the spinal cord, motor neurons for dominating each muscle of forelimbs are distributed on different sections in a concentrated mode, and when the corresponding sections are electrically stimulated, the motor neurons in the area are activated to dominate corresponding skeletal muscle movement, and the forearms and the palms are driven to do corresponding actions.
As shown in fig. 8, an anatomical map, which is built into the electrical stimulation control program, is predefined according to the function of the muscles of the human upper limb and the distribution of the motor neurons that innervate these muscles between the second cervical segment of the spinal cord to the first thoracic segment.
The electrical stimulation strategy is determined by a set of electrical stimulation parameters, including site, waveform, pulse width, frequency, amplitude, wherein the waveform and pulse width are determined by known clinical studies and pre-experiments, the site is determined by anatomical mapping as shown in fig. 8, the frequency, amplitude and time are calculated and updated by a closed-loop control loop as shown in fig. 9, the feature fusion module in the figure is implemented by a motion state classification program as shown in fig. 5, and the computer 3 executes closed-loop spinal cord electrical stimulation control according to the current motion state and the target motion state of the human body judged by electroencephalogram and kinematic features. .
The length of the electrical stimulation is related to the duration of the motion state; the electrical stimulation frequency is related to the angle of the target action joint, and can be calibrated according to the human condition in the first rounds of each rehabilitation training, and the calibrator is preferably a clinician or a physical therapist. In the embodiment, the electrical stimulation frequency and amplitude are selected as closed-loop control self-setting parameters, a control method combining PID and an inverse dynamics model is adopted, an actual motion state is compared with a target motion state judged according to electroencephalogram and kinematics characteristics to obtain a motion state deviation, the electrical stimulation frequency and amplitude deviation is calculated through PID and is used as an output error of the inverse dynamics model, meanwhile, real-time kinematic data such as angular velocity of joint motion and the like are used as negative feedback input, then, a connection weight coefficient of the inverse dynamics model is updated by using an error propagation method as shown by a dotted line in the figure, and finally, the sum of the output of the inverse dynamics model and PID output is subjected to amplitude limiting and output to the spinal cord stimulation device. During rehabilitation training, after several rounds of model updating, the inverse dynamics model connection weight coefficient reaches a stable state. The dynamic inverse dynamics model can be built by using a neural network, and the relation between the electrical stimulation frequency and amplitude and the target motion state is built by the model.
And for the parameters of the dynamic inverse dynamics model, carrying out a system simulation experiment by setting different numerical values, and comparing the experimental effect to determine. In the PID feedback control part, PID parameters can be determined by a Cohen-Coon method and a CHR method.
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