CN113426007B - Closed-loop dura mater external electric stimulation system for upper limb function recovery - Google Patents
Closed-loop dura mater external electric stimulation system for upper limb function recovery Download PDFInfo
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Abstract
The invention discloses a closed-loop epidural electric stimulation system for upper limb function recovery. The motion capture device acquires multi-angle human body posture images in real time and sends the images to the control device; the electroencephalogram acquisition device acquires electroencephalogram signals in real time, and the electroencephalogram signals are amplified and filtered to be sent to the control device; the control device receives the human body posture image and the brain electrical signal to obtain upper limb posture data and brain electrical signal characteristics, generates an electrical stimulation instruction and sends the electrical stimulation instruction to the spinal cord electrical stimulation device; the spinal cord electric stimulation device receives instructions to generate stimulation pulses to the spinal cord of a human body. The system can actively modulate the neural activity of the upper limb function of the human body based on the own will of the human body, more effectively assist rehabilitation training and promote the functional recovery of the neural loop related to the upper limb of the human body.
Description
Technical Field
The invention relates to a spinal cord electric stimulation system in the medical field, in particular to a closed-loop epidural electric stimulation system for recovering functions of upper limbs after spinal cord injury or cerebral apoplexy.
Background
The damage of the central nervous system caused by cerebral apoplexy or spinal cord injury can cause that the human body can not normally generate central nervous system instructions for controlling movement or can not transmit the instructions to skeletal muscles to cause movement dysfunction of upper limbs, one type of commonly used solution is functional electric stimulation, and the method is to fix one or more groups of electrodes on the surface of the skin of the upper limbs of the human body to electrically stimulate related muscles so as to help the human body to exercise arms and palms, but has the defects of easily causing muscle fatigue and being incapable of working for a long time. The epidural electric stimulation can apply stimulation current on the dura mater of the dorsal spinal cord, excite a central mode on the spinal cord of a human to generate a network, strengthen the excitability of a spinal cord loop, thereby mobilizing skeletal muscles to shrink, effectively relieving muscle fatigue and promoting remodeling of injured nerve loops, but the current method has limited space-time resolution in practical application and cannot realize accurate control of upper limbs like functional electric stimulation.
Disclosure of Invention
Aiming at the conditions that the existing upper limb functional electric stimulation is easy to cause muscle fatigue and the space-time resolution of the upper limb epidural electric stimulation technology is insufficient, the invention develops a closed-loop epidural electric stimulation technology, changes an electric stimulation strategy in real time according to the real-time brain electrical signals and the motion state of a human body, induces the activity of corresponding nerve loops and compensates the motion of target skeletal muscles.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
The closed-loop epidural electric stimulation system comprises a motion capture device, an electroencephalogram acquisition device, a control device and a spinal cord electric 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 brain electrical signal from the brain electrical acquisition device, further processes and analyzes the upper limb posture data in the human body posture image and the brain electrical signal characteristics in the brain electrical signal in real time, generates an instruction containing electrical stimulation parameters and sends the instruction to the spinal cord electrical stimulation device;
the spinal cord electric stimulation device receives the instruction containing the electric stimulation parameters sent by the control device and sends electric stimulation pulses to the spinal cord of the human body.
The motion capture device mainly comprises at least two cameras, and the cameras acquire images of upper limbs of a human body from multiple views to serve as human body 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 which are arranged on the head of a human body, each acquisition channel is connected with one electrode respectively, the reference channel is connected with one electrode and grounded, the electrodes of the acquisition channels are arranged on different brain areas, and the electroencephalogram change of the brain areas is acquired.
The electroencephalogram acquisition device comprises a pre-amplifier, a differential amplifier and an analog filter, wherein the pre-amplifier, the differential amplifier and the analog filter are used for processing original electric signals acquired by all channels, and the original electric signals acquired by all channels are sequentially subjected to pre-amplification, differential amplification and filtering processing and then output to the control device.
The control device receives real-time posture data of a human body and original brain electrical signals, performs preprocessing, generates processed upper limb posture data and brain electrical signal characteristics, obtains differences between the current motion state and the target state of the human body according to the upper limb posture data and the brain electrical 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, marks key points of the upper limbs of the human body according to the multi-view human body posture image transmitted by the motion capture device through a deep learning model, reconstructs the space coordinates of each key point in a three-dimensional way, and generates kinematic data of the upper limbs of the human body as upper limb posture data;
the control device stores an electroencephalogram signal processing program, and the electroencephalogram signal processing program carries out band-pass filtering and spatial filtering on the original electroencephalogram signal transmitted by the electroencephalogram acquisition device to obtain electroencephalogram signal characteristics;
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 motion data and the electroencephalogram signal characteristics so as to obtain the difference between the motion state and the target state;
The control device stores an electric stimulation control program, wherein the electric stimulation control program is internally provided with a predefined anatomical mapping according to the kinematic function of each muscle of the upper limb of a person and the distribution condition of motor neurons of each muscle of the upper limb from a second neck section to a first chest section of a spinal cord, the electric stimulation control program firstly determines an electric stimulation site according to the predefined anatomical mapping, then obtains specific electric stimulation parameters according to the difference processing between the current motion state and the target state, and comprises parameters such as the frequency, the amplitude, the width, the number and the like of electric stimulation pulses, and finally encodes the electric stimulation site and the electric stimulation parameters together into an electric stimulation instruction to be sent to the spinal cord electric stimulation device.
The spinal cord electric stimulation device receives an electric stimulation instruction from the control device, applies an electric stimulation pulse sequence with corresponding parameters to an electric stimulation site on the dura mater of the spinal cord in the electric stimulation instruction, and the motor neuron at the electric stimulation site is activated to further control muscles to contract, so that the upper limb performs corresponding actions, and then the actions are collected and fed back to the control device by the action capturing device and the action capturing device, and the electric stimulation is continuously controlled in a circulating and reciprocating closed loop mode.
The beneficial effects of the invention are as follows:
The system can actively modulate the neural activity of the upper limb function of the human body based on the own will of the human body, more effectively assist rehabilitation training and promote the functional recovery of the neural loop related to the upper limb of the human body.
The invention can not only avoid faster muscle fatigue caused by electric stimulation, but also can apply electric stimulation to the corresponding region of the spinal cord by combining the real-time brain electrical signals and the motion state of the human body to mobilize the corresponding skeletal muscle, thereby improving the space-time resolution of the electric stimulation strategy.
Drawings
The invention is further described below with reference to the drawings and examples;
Fig. 1 is a schematic structural view of a closed-loop epidural electric stimulation system according to an embodiment of the invention.
Fig. 2 is a flowchart of a control method of the closed-loop epidural electric stimulation system according to an embodiment of the invention.
FIG. 3 is a flow chart of a pose estimation system data processing according to an embodiment of the invention.
Fig. 4 is a schematic diagram of a human limb key point structure according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a neural network structure for feature extraction, fusion and motion state classification according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of electroencephalogram signal features and gesture features when a person performs a gripping action according to one embodiment of the present invention.
Fig. 7 is an electrical stimulation parameter corresponding to fig. 6 of an electrical stimulation control program when a person performs a gripping action in accordance with an embodiment of the invention.
Fig. 8 is a predefined anatomical map 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: 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; a face center 8, a neck center 9, a shoulder joint 10, an elbow joint 11, a wrist joint 12, and a palm joint set 13.
Detailed Description
The following describes an embodiment of the present invention in further detail with reference to the accompanying drawings, and does not limit the scope of the present invention.
The matters in the drawings are not to scale and some details are omitted or exaggerated for the purpose of structural clarity.
As shown in fig. 1, the embodied system includes:
the closed-loop epidural electric stimulation system comprises a motion capture device, an electroencephalogram acquisition device, a control device and a spinal cord electric stimulation device, wherein:
the motion capture device is used for acquiring the 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 signals (EEG, electroencephalo-graph) of a human body in real time, and the electroencephalogram signals are sent to the control device after being pre-amplified and filtered;
The control device receives the human body posture image from the motion capture device and the brain electrical signal from the brain electrical acquisition device, further processes and analyzes the upper limb posture data 4 in the human body posture image and the brain electrical signal characteristics 5 in the brain electrical signal in real time, calculates the electrical stimulation parameters, generates an instruction containing the electrical stimulation parameters and sends the instruction to the spinal cord electrical stimulation device;
The spinal cord electric stimulation device 7 receives the instruction containing the electric stimulation parameters sent by the control device and sends electric stimulation pulse to the dura mater of the spinal cord of the human body so as to realize the recovery of the functions of the upper limbs.
The upper limb function recovery of the invention is that 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 upper limbs of a human body from multiple views to serve as human body posture images, and recover and obtain space coordinates of each joint of the upper limbs of the human body according to the human body posture images.
The electroencephalogram acquisition device at least comprises a plurality of acquisition channels, a reference channel and an electroencephalogram electrode array 2, wherein the electroencephalogram electrode array 2 comprises a plurality of electrodes which are arranged on the head of a human body, the number of the electrodes is the same as the total number of the acquisition channels and the reference channels, each acquisition channel is respectively connected with one electrode, the reference channel is connected with one electrode and grounded, the electrodes of the acquisition channels are arranged on different brain areas, and the electroencephalogram change of the brain areas is acquired. In specific implementation, eight acquisition channels are provided, and the device can be composed of one or more analgesic electrodes commonly used in clinic at present.
The electroencephalogram acquisition device comprises a preamplifier, a differential amplifier and an analog filter, wherein the preamplifier, the differential amplifier and the analog filter are used for processing original electric signals acquired by all channels, and the original electric signals acquired by all channels are sequentially subjected to preamplification, differential amplification and filtering processing and then output to the control device.
The electroencephalogram acquisition device is used for acquiring an original head electrophysiological signal serving as an electroencephalogram signal through an electrode array of the electroencephalogram electrode array 2.
The control device can adopt a computer 3, receives real-time posture data of a human body and original brain electrical signals, performs preprocessing, generates processed upper limb posture data 4 with kinematic data and brain electrical 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 brain electrical signal characteristics 5, generates an electrical stimulation instruction, and sends the electrical stimulation instruction to the spinal cord electrical stimulation device 7.
The upper limb posture data refer to three-dimensional space coordinates of each joint of the upper limb of the human body, and the kinematic data refer to physical quantities such as angles, angular velocities, angular accelerations and the like 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 of performing various actions on the upper limb of the human body, such as arm stretching, hand grasping, etc., a series of continuous motion states form a motion state sequence, a patient performing rehabilitation training is in one motion state in the sequence at any moment, the current motion state is defined, 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, marks key points of the upper limbs of the human body according to the multi-view human body posture image transmitted by the motion capture device through a deep learning model, reconstructs the space coordinates of each key point in a three-dimensional way, and generates kinematic data of the upper limbs of the human body as upper limb posture data 4;
The control device stores an electroencephalogram signal processing program, and the electroencephalogram signal processing program carries out band-pass filtering and spatial filtering on the original electroencephalogram signal transmitted by the electroencephalogram acquisition device to obtain an electroencephalogram signal characteristic 5; specifically, the original head electrophysiological signals are collected for digital band-pass filtering, myoelectric signals are removed, and a spatial filter is used for preprocessing.
The control device stores a motion state classification program constructed based on the neural network 6, and the motion state classification program judges the current motion state of the human body according to the motion 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 body movement state.
The control device stores an electric stimulation control program, wherein the electric stimulation control program is internally provided with a predefined anatomical mapping according to the kinematic function of each muscle of the upper limb of a person and the distribution condition of motor neurons of each muscle of the upper limb from the second cervical segment to the first thoracic segment of the spinal cord, the electric stimulation control program firstly determines electric stimulation sites according to the predefined anatomical mapping, the electric stimulation sites are on the dura mater of the spinal cord, then obtains specific electric stimulation parameters according to the difference treatment between the current motion state and the target state, and the specific electric stimulation parameters comprise parameters such as the frequency, the amplitude, the width, the number and the like of electric stimulation pulses, and finally encodes the electric stimulation sites and the electric stimulation parameters together into an electric stimulation instruction to be sent to the spinal cord electric stimulation device.
Specifically, in the electrical stimulation control program, the difference information between the current motion state and the target state is simultaneously input into the PID controller and the dynamic inverse dynamic model, the output results of the PID controller and the dynamic inverse dynamic model are added, and then the amplitude limiting processing is performed to obtain the setting parameters of the electrical stimulation pulse, and then the setting parameters are input into the spinal cord electrical stimulation device 7. The electric stimulation control program continuously repeats the process, and updates the current motion state and the target state until the human body is assisted to complete the training action.
The spinal cord electric stimulation device 7 receives an electric stimulation instruction from the control device, applies an electric stimulation pulse sequence with corresponding parameters to an electric stimulation site on the dura mater of the spinal cord in the electric stimulation instruction, and the motor neuron at the electric stimulation site is activated to further control muscles to contract so as to enable the upper limbs to make corresponding actions, then the action capturing device and the action capturing device acquire and feed back the actions to the control device, new posture data and original brain electrical signals are monitored, and electric stimulation is carried out by continuous cyclic reciprocating closed-loop control.
For example:
A) When the current motion state is anteflexion and lifting and the target state is arm extension, an electric stimulation instruction is generated, specifically, the electric stimulation instruction is generated outside C5 on the spinal cord, and the electric stimulation instruction is in 20Hz frequency, 0.5mA amplitude and 0.2ms width.
B) When the current motion state is arm extension and the target state is palm opening, an electric stimulation instruction is generated, specifically, the electric stimulation instruction is generated at the center and the outer side of C5 on the spinal cord, and the electric stimulation instruction is in 20Hz frequency, 0.5mA amplitude and 0.2ms width.
C) When the movement state is palm opening and the target state is palm holding, an electric stimulation instruction is generated, specifically, the electric stimulation instruction is arranged on the outer side of C5 and the outer side of C6 on the spinal cord, and the electric stimulation instruction is in 30Hz frequency, 0.3mA amplitude and 0.2ms width.
D) When the current motion state is palm holding and the target state is arm withdrawing, an electric stimulation instruction is generated, specifically, the electric stimulation instruction is arranged on the outer side of C5 and the outer side of C8 on the spinal cord, and the electric stimulation instruction is in 20Hz frequency, 0.3mA amplitude and 0.2ms width.
E) When the current motion state is the adduction of the arm and the target state is the relaxation of the arm, an electric stimulation instruction is generated, specifically, the electric stimulation instruction is generated at the center and the outer side of C2 and the outer side of C8 on the spinal cord, and the electric stimulation instruction is in 20Hz frequency, 0.5mA amplitude and 0.2ms width.
Specific embodiments may perform tasks such as, but not limited to, grasping an object by a human body during rehabilitation training of the human body.
In practice, 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 the implementation, a plurality of sites are arranged on the spinal cord, a plurality of sites are arranged on the back of a human body between joints C2-T1 of the spinal column in an array mode, the column direction of the site array is parallel to the spinal column direction, and the row direction of the site array is perpendicular to the spinal column direction. In a specific implementation, 18 sites of three columns and six rows are arranged, and the sites of the middle column are positioned on the back where the central line of the spine is positioned.
In specific implementation, the closed-loop electric stimulation system consists of a camera 1, an electroencephalogram acquisition device, a computer 3 and a spinal cord electric stimulation device 7. The camera 1 is communicated with the computer 3 through a network, the brain electricity acquisition device is communicated with the computer 3 through Bluetooth, the computer 3 is provided with a human body posture estimation program, an brain electricity signal processing program, an electric stimulation control program and a human-computer interaction interface, the current parameters of all channels of the spinal cord electric stimulation device 7 are controlled by the control module of the spinal cord electric stimulation device 7, and the control module of the spinal cord electric stimulation device 7 is communicated with the computer 3 through Bluetooth. There are various wireless communication modes, 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 consists of 1-3 cameras, and transmits real-time human body moving images to a computer through a local network. 2 ordinary 720P 30FPS office cameras can be selected and communicated with a computer through a local area network.
The human body posture estimation program installed in the computer 3 can load a pre-trained deep learning model, analyze human body posture images in real time, determine parameters of electric stimulation, including electric stimulation areas, current intensity and waveforms, and send the parameters to the spinal cord electric stimulation device 7 through the Bluetooth communication device. The human body posture estimation program adopts a convolutional neural network model, uses the disclosed multi-view human body posture data set to train and stores the trained model.
The man-machine interaction interface on the computer 3 provides a visual monitoring window for the current posture data and the motion state of the human body, and is provided with a control interface for adjusting the electric stimulation strategy.
The spinal cord stimulation device 7 is implanted under the cervical vertebral plate of the human body through surgery and above the spinal dura mater, and is powered by an embedded lithium battery.
As shown in fig. 2, when the system works, a patient autonomously controls an arm to make a small-amplitude motion through a residual sensory-motor function, and accordingly, an electroencephalogram signal related to a movement intention is transmitted to a computer 3, and simultaneously, the human body morphology of the patient is captured by a camera 1 and an image is transmitted to the computer 3, a human body posture estimation program on the computer 3 analyzes current posture information of the human body, coordinates, angular velocity and angular acceleration of an elbow joint and each finger joint of the human body are calculated, and the motion state classification program is transmitted.
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, and comprises forward bending and lifting, arm stretching, palm holding, arm adduction and arm relaxation. The electric stimulation control program establishes an electric stimulation strategy according to the current motion state and the target state, wherein the electric stimulation strategy comprises a stimulated region, a waveform, a frequency and an amplitude of current, and the communication program codes the electric stimulation strategy into a command and sends the command to the spinal cord stimulation device 7 to execute corresponding electric stimulation.
As shown in fig. 3, in the rehabilitation training preparation stage, the camera is first calibrated, and the internal parameters and external parameters of the camera, including the relative position and focal length of the camera, are determined. Simultaneously, the disclosed multi-view human body posture data set is used for training a posture estimation program based on a convolutional neural network, and the effectiveness of the human body is verified when the human body makes a grasping training action.
In the rehabilitation training process stage, the camera transmits a patient body image data stream to the computer in real time through a network, the gesture estimation program marks key points of the upper human body, three-dimensional reconstruction is carried out by utilizing multi-view images according to internal and external parameters of the camera, and three-dimensional coordinates of the upper limb joint points of the human body are calculated and transmitted to the electric stimulation control program.
As shown in fig. 4, the common human body key point set mainly consists of basic anatomic joint positions, and the system mainly uses the following key points: a face center 8, a neck center 9, a shoulder joint 10, an elbow joint 11, a wrist joint 12, and a palm joint set 13.
Alternatively, the set of palm joints may be resized according to the degree of refinement of the motion state. For example, when the movement state of the palm is set to focus only on the opening and the grasping of the palm, the palm joint set may include only joints at the center of the palm and the distal ends of the fingers; when setting the motion state of the palm as well as focusing on the shape of the palm closure, the set of palm joints should contain all the joints of the human finger.
As shown in fig. 5, which is a network structure diagram of a motion state classification program based on deep learning, electroencephalogram signals and kinematic data are taken as input of a neural network, are subjected to preliminary feature extraction and fusion, are transmitted to a classifier based on a fully-connected network, and finally, the current motion state and the target state thereof are output. Based on an end-to-end learning framework, the collected electroencephalogram signals and the kinematic data are preprocessed and then respectively input into two one-dimensional convolutional neural networks to perform feature extraction, then feature vectors extracted from the two signals are combined into a compatible vector space through a single-layer neural network and transmitted through a three-layer full-connection 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 the corresponding input data.
As shown in fig. 6, the tested person is performing a grasping action, the electroencephalogram signal features are represented by a spatial hot spot with a specific position, and when the tested person is in the S1 state, the electroencephalogram signal features represent that the tested person generates movement intention, and at this time, the body posture features are not obvious; when the tested person is in the S2 state, generating movement intention and corresponding posture characteristics of the extending forearm; when the tested person is in the S3 state, generating movement intention of the open palm and corresponding posture characteristics; when the tested person is in the S4 state, the tested person generates a grasping movement intention and corresponding gesture characteristics; when the tested person is in the S5 state, generating movement intention of the retracting forearm and corresponding posture characteristics; when the subject is in the S6 state, there is no movement intention and posture characteristics about the grip. The multiple tested subjects repeatedly perform the grasping action, corresponding electroencephalogram signals and human body posture data are collected, and the neural network shown in fig. 5 is trained, so that the method 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 is used as the basis for making an electrical stimulation strategy.
As shown in fig. 7, to be tested in performing the 6 states of the grip action shown in fig. 6, the electro-stimulation control program formulated electro-stimulation strategies S1, S2, S3, S4, S5, S6 corresponding to the 6 states, electro-stimulation sites represented as shaded rectangles having specific positions in the electrode array in the figure, and waveforms, pulse widths, amplitudes, and frequencies of the electro-stimulation represented as upper fold and frequency values in the figure.
In the figure, C2, C3, C5, C6, C7, C8 and T1 represent corresponding segments of spinal cord, and motor neurons which govern various muscles of the forelimbs are intensively distributed on different segments, and when the corresponding segments are electrically stimulated, the motor neurons in the region are activated to govern the movement of corresponding skeletal muscles, so that the forearms and the palms are driven to perform corresponding actions.
As shown in fig. 8, an anatomical map is predefined according to the function of the individual muscles of the upper extremities of the person and the distribution of motor neurons innervating these muscles between the second cervical section of the spinal cord to the first thoracic section, said anatomical map being built into the electrical stimulation control program.
The electrical stimulation strategy is determined by a set of electrical stimulation parameters including a site, a waveform, a pulse width, a frequency and an amplitude, wherein the waveform and the pulse width are determined by known clinical study and pre-experiment, the site is determined by anatomical mapping as shown in fig. 8, the frequency and the amplitude and the time are calculated and updated by a closed-loop control circuit as shown in fig. 9, a characteristic fusion module in the figure is realized 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 the electroencephalogram and the kinematic characteristics. .
The electrical stimulation time is related to the duration of the exercise state; the electrical stimulation frequency is related to the target action joint angle and can be calibrated according to the human condition in a few rounds before each rehabilitation training, and the calibrator is preferably a clinician or a physical therapist. In this embodiment, the electrical stimulation frequency and amplitude are selected as closed-loop control self-tuning parameters, a control method combining PID and inverse dynamic model is adopted, the actual motion state is compared with the target motion state judged according to the electroencephalogram and the kinematic characteristics to obtain motion state deviation, the electrical stimulation frequency and amplitude deviation is calculated through PID and is used as the output error of the inverse dynamic model, meanwhile, the real-time kinematic data such as the angular velocity of the joint motion is used as negative feedback input, then the connection weight coefficient of the inverse dynamic 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 dynamic model and the PID output is used as amplitude limit and is output to the spinal cord stimulation device. During rehabilitation training, after a plurality of rounds of model updating, the inverse dynamic model connection weight coefficient reaches a stable state. The dynamic inverse dynamic model can be built by using a neural network, and the relation between the frequency and amplitude of the electric stimulation and the target motion state is built by the model.
And (3) for the dynamic inverse dynamic model parameters, performing a system simulation experiment by setting different values, and comparing the experimental effects to determine. In the PID feedback control part, PID parameters can be determined by adopting a Cohen-Coon method and a CHR method.
Claims (5)
1. A closed-loop epidural electrical stimulation system for upper limb functional recovery, characterized by:
The closed-loop epidural electric stimulation system comprises a motion capture device, an electroencephalogram acquisition device, a control device and a spinal cord electric 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 brain electrical signal from the brain electrical acquisition device, further processes and analyzes upper limb posture data (4) in the human body posture image and brain electrical signal characteristics (5) in the brain electrical signal in real time, generates an instruction containing electrical stimulation parameters and sends the instruction to the spinal cord electrical stimulation device;
The spinal cord electric stimulation device (7) receives an instruction containing electric stimulation parameters sent by the control device and sends electric stimulation pulses to the spinal cord of a human body;
the control device receives real-time posture data of a human body and original brain electrical signals, performs preprocessing, generates processed upper limb posture data (4) and brain electrical signal characteristics (5), obtains differences between the current motion state and the target state of the human body according to the upper limb posture data (4) and the brain electrical signal characteristics (5), generates an electrical stimulation instruction, and sends the electrical stimulation instruction to the spinal cord electrical stimulation device (7);
the control device stores a human body posture estimation program based on deep learning, marks key points of the upper limbs of the human body according to the multi-view human body posture image transmitted by the motion capture device through a deep learning model, reconstructs the space coordinates of each key point in three dimensions, and generates kinematic data of the upper limbs of the human body as upper limb posture data (4);
The control device stores an electroencephalogram signal processing program, and the electroencephalogram signal processing program carries out band-pass filtering and spatial filtering on the original electroencephalogram signal transmitted by the electroencephalogram acquisition device to obtain an electroencephalogram signal characteristic (5);
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 motion data and the electroencephalogram signal characteristics (5), so as to obtain the difference between the motion state and the target state;
The control device stores an electric stimulation control program, wherein the electric stimulation control program is internally provided with a predefined anatomical mapping according to the kinematic function of each muscle of the upper limb of a person and the distribution condition of motor neurons of each muscle of the upper limb from a second cervical segment to a first thoracic segment of a spinal cord, the electric stimulation control program firstly determines an electric stimulation site according to the predefined anatomical mapping, then obtains specific electric stimulation parameters including frequency, amplitude, width and number parameters of electric stimulation pulses according to the difference processing between the current motion state and the target state, and finally codes the electric stimulation site and the electric stimulation parameters together into an electric stimulation instruction to be sent to the spinal cord electric stimulation device.
2. A closed loop epidural electrical stimulation system for upper limb functional recovery according to claim 1, characterized in that: the motion capture device mainly comprises at least two cameras (1), wherein the cameras (1) collect images of upper limbs of a human body from multiple views to serve as human body posture images.
3. A closed loop epidural electrical stimulation system for upper limb functional recovery according to claim 1, characterized in that: the electroencephalogram acquisition device at least comprises a plurality of acquisition channels, a reference channel and an electroencephalogram electrode array (2), wherein the electroencephalogram electrode array (2) comprises a plurality of electrodes which are arranged on the head of a human body, each acquisition channel is connected with one electrode respectively, the reference channel is connected with one electrode and grounded, the electrodes of the acquisition channels are arranged on different brain areas, and the electroencephalogram change of the brain areas is acquired.
4. A closed loop epidural electrical stimulation system for upper limb functional recovery according to claim 3, characterized in that: the electroencephalogram acquisition device comprises a pre-amplifier, a differential amplifier and an analog filter, wherein the pre-amplifier, the differential amplifier and the analog filter are used for processing original electric signals acquired by all channels, and the original electric signals acquired by all channels are sequentially subjected to pre-amplification, differential amplification and filtering processing and then output to the control device.
5. A closed loop epidural electrical stimulation system for upper limb functional recovery according to claim 1, characterized in that: the spinal cord electric stimulation device (7) receives an electric stimulation instruction from the control device, applies an electric stimulation pulse sequence with corresponding parameters to an electric stimulation site on the dura mater of the spinal cord in the electric stimulation instruction, and the motor neuron at the electric stimulation site is activated to further control muscles to contract, so that the upper limb performs corresponding actions, and then the actions are collected and fed back to the control device by the action capturing device and the action capturing device to perform electric stimulation under continuous cyclic reciprocating closed-loop control.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111973874A (en) * | 2020-08-14 | 2020-11-24 | 北京航空航天大学 | Photoelectric combined stimulation device and method |
| CN112754499A (en) * | 2021-01-25 | 2021-05-07 | 中国科学院心理研究所 | System and method for synchronously recording electrophysiological signals of brain and spinal cord |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109453462A (en) * | 2018-11-06 | 2019-03-12 | 苏州大学 | A kind of functional electrostimulation device and system |
| CN111973874A (en) * | 2020-08-14 | 2020-11-24 | 北京航空航天大学 | Photoelectric combined stimulation device and method |
| CN112754499A (en) * | 2021-01-25 | 2021-05-07 | 中国科学院心理研究所 | System and method for synchronously recording electrophysiological signals of brain and spinal cord |
Non-Patent Citations (1)
| Title |
|---|
| 王永骥等.《 硬脊膜外脊髓电刺激器的实验设计》.《中国临床康复》.2006,第10卷(第29期),118-120. * |
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