Stroke patient upper limb rehabilitation evaluation system

Abstract

The application relates to a stroke patient upper limb rehabilitation evaluation system, which at least comprises a collection module for collecting upper limb information of a stroke patient; a processing module for processing the upper limb information collected by the collection module; a training module for assisting the stroke patient in upper limb movement; and a display module for showing the upper limb movement state of the stroke patient; the display module shows the difference between the virtual upper limb and the target state to the stroke patient so that the stroke patient drives the real upper limb to move; the processing module judges the movement ability of the real upper limb based on the change of the difference between the virtual upper limb and the target state, and controls the training module to assist movement according to the movement ability, wherein the processing module adjusts the driving torque of the training module applied to the bilateral upper limbs based on the electromyographic signal difference between the bilateral upper limbs collected by the collection module. The processing module evaluates the movement ability and coordination of the bilateral upper limbs and gives targeted rehabilitation assistance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of stroke rehabilitation, and particularly relates to a stroke patient upper limb rehabilitation evaluation system. BACKGROUND

[0002] There are hemiplegic patients with partial paralysis of left and right limbs among stroke patients, and systems for helping these patients to perform rehabilitation training are increasingly popular. Existing upper limb paralysis rehabilitation training at least includes assisted movement rehabilitation training by using upper limb rehabilitation robots, exoskeletons and the like. The rehabilitation robot can provide high-intensity, repetitive, task-specific, interactive treatment for the paralyzed upper limbs, and at the same time provide an objective evaluation method for the recovery of motor function, measure kinematics and kinematics changes, but the upper limb rehabilitation robot cannot improve the upper limb muscle strength. The motor function evaluation is the basis for doctors to develop rehabilitation training plans for stroke hemiplegic patients, and it can also be used as an evaluation means for the efficacy of various treatment schemes, and is crucial in the fields of motor rehabilitation and neuroscience. Traditional motor rehabilitation evaluation is mainly based on a scale and is performed by a professional therapist. The scale evaluation method was developed early and its feasibility has been clinically verified, but it has three shortcomings: the evaluation result is greatly affected by the therapist's subjectivity, different therapists evaluate according to experience and different standards, and different therapists may give different evaluation results for the same patient; the scale evaluation method gives a corresponding score according to the different performances of the patient's corresponding movements, the score gradient is small, and the evaluation result cannot reflect the true situation of the patient, in addition, the therapist may be difficult to capture the patient's subtle movements visually, resulting in an incomplete evaluation result; the existing scale evaluation method requires a therapist to guide the patient to perform a series of movements, and at least one patient needs to be matched with one therapist, so the evaluation efficiency is low; at the same time, a set of evaluation process is time-consuming and labor-intensive.

[0003] The prior art such as the patent document with publication number CN110755085B proposes a movement function evaluation method and device based on joint activity and movement coordination, the method comprising: collecting limb movement signals of a target object; extracting joint activity data and movement coordination data in the limb movement signals, and evaluating the movement function of the limb, wherein the joint activity data includes active action data and passive action data. The training standard of the existing rehabilitation training evaluation system is usually the limb reflection value of a normal person. However, for stroke patients, the limb training response cannot reach the normal standard in a short time. Especially for most stroke patients, there is a left-right hemiplegia situation, which leads to a relatively obvious difference in the training response of the left and right limbs. If the general standard is directly used for training and evaluation, the same degree of training intensity is provided for the left and right limbs of the patient, which will make the limb with poor response bear excessive training pressure, which not only cannot achieve the purpose of rehabilitation, but also may cause greater damage. At the same time, such non-differential training slows down the rehabilitation process of the patient, which hinders the patient's normal life.

[0004] In addition, on the one hand, there are differences in the understanding of those skilled in the art; on the other hand, the inventors have studied a large number of literatures and patents when making the present invention, but due to the limited space, all the details and contents are not listed in detail, which does not mean that the present invention does not have these characteristics of the prior art. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art in the background art. SUMMARY

[0005] In view of the deficiencies of the technical solutions proposed by the prior art, the present application proposes a stroke patient upper limb rehabilitation evaluation system, at least comprising: a collection module for collecting upper limb information of a stroke patient; a processing module for processing the upper limb information collected by the collection module; a training module for assisting the stroke patient in upper limb movement; a display module for displaying the upper limb movement state of the stroke patient; the display module displays the gap between the virtual upper limb and the target state to the stroke patient to make the stroke patient drive the real upper limb to move; the processing module judges the movement ability of the real upper limb based on the gap change between the virtual upper limb and the target state, and controls the training module to assist movement according to the movement ability, wherein the processing module adjusts the driving torque of the training module applied to the bilateral upper limbs based on the difference in electromyographic signals between the bilateral upper limbs collected by the collection module. The processing module evaluates the movement ability and coordination of the bilateral upper limbs and gives targeted rehabilitation assistance.

[0006] In the above scheme, the virtual upper limb refers to the upper limb model constructed in the display module, the real upper limb refers to the upper limb of the patient in the actual environment, the target state refers to the upper limb pattern in which the patient is expected to move the upper limb to the target position and target posture, which is randomly generated in the display module, and the gap between the virtual upper limb and the target state refers to the gap between the current position of the virtual upper limb and the target position and the gap between the current posture of the virtual upper limb and the target posture.

[0007] The present application takes into account the difference in limb movement between the left and right sides of the patient, and provides different and targeted training parameters for the two sides, thereby improving the training effect and training efficiency, avoiding the occurrence of uncoordinated conditions of the left and right limb muscles in the rehabilitation process due to the application of unsuitable training intensity, solving the problem of hindering the patient's normal life caused by the uncoordinated movement of the two sides of the body, and having a certain positive effect on the quality of life of stroke patients.

[0008] Preferably, the processing module controls the driving torque of the training module to gradually and slowly increase, and the training module stops increasing when the real upper limb has a movement trend, and assists the driving of the real upper limb with the current driving torque plus a surplus value of a preset proportion of the current driving torque. In this way, the actual driving force of the real upper limb is still mainly driven by the stroke patient himself, and the training module is driven in an auxiliary manner, and the increased surplus value can avoid overuse of muscles and protect the health of the muscles of the stroke patient.

[0009] Preferably, the processing module adjusts the size of the surplus value in a preset proportion based on the difference between the first and second electromyographic signals of the bilateral upper limbs collected by the collection module, and then adjusts the total value of the driving torque applied to one of the upper limbs. The first and second electromyographic signals refer to the electromyographic signals obtained by the electromyographic collection unit of the collection module under the movement of one of the upper limbs of the patient. Specifically, the first electromyographic signal refers to the electromyographic signal generated by the movement of the left upper limb, and the second electromyographic signal refers to the electromyographic signal generated by the movement of the right upper limb. The total value of the driving torque is equal to the driving torque when the movement trend occurs and a surplus value is added according to the preset proportion (for example, one tenth) of the driving torque.

[0010] If the intensity of the first myoelectric signal of the left upper limb is greater than the intensity of the second myoelectric signal of the right upper limb, it indicates that the recovery degree of the left upper limb is better than that of the right upper limb, and thus, when the driving torque is applied by the training module, the driving torque applied to the right upper limb is smaller than that of the left upper limb. The design logic of this way is that a smaller driving torque is applied to the right upper limb with a poor recovery degree, so that the right upper limb is exercised more, and because a smaller driving torque is applied, the stroke patient's attention is more focused on driving the right upper limb to move during the movement of the right upper limb, thereby making the recovery effect of the right upper limb gradually level with that of the left upper limb, so as to achieve the effect of keeping the bilateral upper limbs coordinated during the rehabilitation training of the bilateral upper limbs.

[0011] Preferably, the processing module can construct a virtual upper limb in the virtual environment of the display module based on at least the myoelectric signal and the motion information of the real upper limb obtained by the acquisition module, wherein the acquisition module at least includes a myoelectric acquisition unit for acquiring myoelectric signals and a motion capture unit for capturing actions.

[0012] Preferably, the step of mapping the real upper limb to the virtual upper limb in the virtual environment by the processing module includes:

[0013] building a model of the virtual upper limb in the virtual environment;

[0014] acquiring myoelectric signals and posture information of the real upper limb;

[0015] extracting features of the myoelectric signals, the motion information, and the posture information of the real upper limb, and classifying them in a stepwise manner according to the feature strengths;

[0016] fusing the myoelectric features and the angular features of the motion and inputting them into a classifier for pattern recognition;

[0017] dynamically recognizing the actions and motions of the real upper limb and synchronously adjusting the actions and motions of the virtual upper limb.

[0018] Preferably, the display module is configured in a manner that can be observed by the stroke patient, so that the stroke patient constantly imagines the virtual upper limb moving to the target state when observing the picture of the virtual upper limb in the display module moving to the target state.

[0019] Preferably, the processing module can analyze the time and motion state of the virtual upper limb moving to the target state in the display module, and judge the motion ability of the real upper limb in the actual environment according to the time and motion state.

[0020] Preferably, the processing module is capable of analyzing the driving angle and driving torque required by the training module to assist the movement based on inverse kinematics, wherein the driving torque calculated by the processing module is the maximum driving torque that the training module can provide under the next assistance movement.

[0021] Preferably, the step of calculating the driving angle and driving torque by the processing module comprises:

[0022] acquiring the current position of the virtual upper limb end and the target position of the target state, and calculating the driving angle of each joint based on the inverse kinematics algorithm from the current position and the target position;

[0023] adjusting the arm support torque of each joint dynamically based on iterative learning, calculating the arm support torque required by each joint from the target driving angle, the actual joint angle corresponding to the target driving angle, the target joint angular velocity and the actual joint angular velocity;

[0024] performing inverse dynamics control based on feedback linearization, calculating the output torque of each joint from the target motion parameters and the actual motion parameters of each joint, and calculating the driving amount of each joint according to the output torque of each joint;

[0025] controlling the driving torque applied by the joint to be trained according to the driving amount of each joint to perform rehabilitation training.

[0026] Preferably, the electromyography acquisition part of the acquisition module is configured at the bilateral upper limbs of the stroke patient together with the training module, and the motion capture part of the acquisition module is configured together with the display module.

[0027] According to a preferred embodiment, the application provides a stroke patient upper limb rehabilitation evaluation system which can be applied to the evaluation of the rehabilitation effect of the upper limbs of a stroke patient in an individual family. In the existing technical solutions for evaluating the rehabilitation effect of stroke patients, the patient needs to go to a specific professional medical staff to perform various instrument and motion analysis tests to know the rehabilitation condition, which is a serious time and economic burden for ordinary families. Therefore, the application provides a portable and household stroke patient upper limb rehabilitation evaluation system which can display various movements of the upper limbs through the display module, and then the patient can imagine the same movement of the upper limbs according to the displayed movement, and then evaluate the rehabilitation effect and the coordination between the upper limbs during the rehabilitation process by analyzing and comparing the electromyography signals generated by the upper limb movement. By setting the rehabilitation evaluation system in an individual family, the stroke patient can evaluate the rehabilitation condition at any time and perform targeted rehabilitation training. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a simplified relationship structure diagram of a stroke patient upper limb rehabilitation evaluation system of the present application;

[0029] Figure 2 is a simplified structure diagram of a display module of a stroke patient upper limb rehabilitation evaluation system of the present application;

[0030] Figure 3 is a simplified structure diagram of a training module of a stroke patient upper limb rehabilitation evaluation system of the present application.

[0031] List of reference signs

[0032] 100: acquisition module; 200: processing module; 300: training module; 400: display module; 110: electromyographic acquisition unit; 120: motion capture unit. DETAILED DESCRIPTION

[0033] The following will be described in conjunction with the accompanying Figures 1-3 The present application will be described in detail.

[0034] Figure 1 A simplified relationship structure diagram of a stroke patient upper limb rehabilitation evaluation system of the present application is shown, which at least includes a training module 300 for applying auxiliary movement, an acquisition module 100 for acquiring physiological information and movement state of bilateral upper limbs, a display module 400 for showing target state, and a processing module 200 for analysis and processing. The acquisition module 100 can at least acquire movement state of bilateral upper limbs and electromyographic signal after movement, the processing module 200 virtually moves the upper limbs of the patient to the display module 400 based on the movement state so as to be observed by the stroke patient, and the display module 400 internally displays the target state, and the stroke patient needs to move the actual bilateral upper limbs to the target state according to the observed target state under the assistance of the training module 300.

[0035] The technical scheme of enabling the movement state of the bilateral upper limbs to be observed by the stroke patient by using the display module 400 is an extension of the motor imagery therapy and the mirror therapy. The motor imagery therapy refers to repeated motor imagery without any motor output, and a specific region of an activity in the brain is activated according to the motor memory, and compared with the actual movement, the motor imagery activates more the prefrontal lobe and the posterior parietal lobe. The mirror therapy is to place a mirror in a position that is convenient for the patient to observe, and the non-paralyzed upper limb is placed in front of the mirror, and the paralyzed upper limb is placed behind the mirror. The non-paralyzed upper limb performs the flexion and extension movement of the wrist and fingers, and the patient simultaneously observes the mirror image of the non-paralyzed upper limb, and the paralyzed upper limb performs the same movement of the non-paralyzed upper limb. Through the adjustment of the excitability of the cortex-muscle, the mirror therapy can directly promote the recovery of the motor function, and the mirror therapy belongs to the motor imagery therapy and is based on repeated motor function imagination.

[0036] In the present application, according to Figure 2 The display module 400 of the stroke patient upper limb rehabilitation evaluation system of the present application is shown in the simplified structure schematic diagram, the movement of the non-paralyzed upper limb used for comparison in the mirror therapy is set as the target state, that is, the display module 400 is equivalent to the mirror in the mirror therapy, and the target state in the display module 400 is the non-paralyzed upper limb in the mirror therapy. The stroke patient imagines how the actual bilateral upper limbs should move in the brain to enable the simulated virtual upper limb in the display module 400 to move from the current position to the target state position, thereby promoting the excitability and correlation between the cortex and the upper limb muscle, and assisting the stroke upper limb paralyzed patient in the rehabilitation training.

[0037] Preferably, the acquisition module 100 can acquire the movement state information of the upper limb in the actual environment, and after being processed by the processing module 200, the virtual upper limb is displayed in the display module 400, and the virtual upper limb in the display module 400 can be synchronized with the state of the virtual upper limb based on the actual upper limb movement state information acquired by the acquisition module 100 in real time.

[0038] Preferably, the processing module 200 can accurately analyze the movement trajectory of the virtual upper limb in the display module 400. Specifically, the processing module 200 can obtain the trajectory movement required for the virtual upper limb to move from the current position to the target state position, and further obtain the auxiliary movement parameters of the training module 300 to assist the movement of the upper limb in the actual environment, so that the upper limb can complete the target state.

[0039] Preferably, the target state in the display module 400 can be static or dynamic. The static target state refers to a specific posture, such as arm extension, arm flexion, fist clenching, etc. The dynamic target state refers to the action that the arm can perform, for example:

[0040] finger extension: achieved by the cooperation of the palmaris extensor, the finger extensor and the thumb extensor;

[0041] finger flexion: achieved by the cooperation of the finger flexor, the thumb flexor and the wrist flexor;

[0042] fist clenching: achieved by the cooperation of the palmaris flexor, the finger extensor and the thumb extensor;

[0043] finger spreading: achieved by the cooperation of the palmaris extensor, the finger extensor and the thumb extensor;

[0044] palm rotation: achieved by the cooperation of the forearm rotator, the wrist flexor and the wrist extensor;

[0045] wrist lifting: achieved by the cooperation of the wrist flexor, the radial wrist extensor and the ulnar wrist extensor;

[0046] The myoelectric signals of the real upper limbs for the above-mentioned movements and postures as the target state are respectively acquired by the myoelectric sensors arranged at the corresponding muscle parts.

[0047] Preferably, the acquisition module 100 comprises a myoelectric acquisition part 110 and a motion capture part 120, wherein the myoelectric acquisition part 110 is used to acquire the myoelectric signals and other information of different movement states and send them to the processing module 200, and the processing module 200 constructs the muscle change of the virtual upper limb inside the display module 400 based on the myoelectric signals and other information acquired by the myoelectric acquisition part 110; the motion capture part 120 is used to acquire the joint coordinates and spatial posture information of the upper limb and send them to the processing module 200, and the processing module 200 adjusts the coordinates and posture of the virtual upper limb in the display module 400 based on the changes of the joint coordinates and spatial posture information.

[0048] Preferably, the myoelectric acquisition part 110 adopts a surface myoelectric sensor, a myoelectric electrode and / or a myoelectric arm ring, the myoelectric acquisition part 110 can be used to acquire the myoelectric signals and other information of the bilateral upper limbs in different movement states in the actual environment, and the portable myoelectric arm ring can also acquire the displacement signals of the upper limbs of the stroke patients, specifically, the myoelectric arm ring can be at least worn on the forearm and upper arm of the stroke patient respectively, and can acquire the bioelectric signals generated by the muscles of the forearm and upper arm, the motion acceleration and displacement signals generated by the motion.

[0049] Preferably, the display module 400 substantially provides a virtual environment, wherein the virtual upper limb and the target state position are both located inside the virtual environment of the display module 400, and the virtual environment of the display module 400 is built by using the existing technology, such as Unity3D or other software technology capable of three-dimensional modeling, which has been widely used in the field of virtual modeling, therefore, the present application will not be described here.

[0050] Preferably, the processing module 200 maps the upper limb information acquired by the acquisition module 100 on the virtual upper limb of the display module 400, specifically including mapping for the shoulder, elbow and finger joints in the upper limb.

[0051] Preferably, in the present application, the processing module 200 constructs a virtual upper limb capable of synchronizing the upper limb movement in the actual environment in the virtual environment of the display module 400 based on the upper limb movement information and posture information acquired by the electromyography acquisition part 110 and the motion capture part 120 of the acquisition module 100, specifically including the following steps:

[0052] S110: building a model of the virtual upper limb in the virtual environment; mapping the real upper limb in the actual environment to the virtual upper limb in the virtual environment of the display module 400, and mapping the movement information of the real upper limb acquired by the electromyography acquisition part 110 and the motion capture part 120 to the virtual upper limb in the virtual environment;

[0053] S120: acquiring the electromyography signal and the posture information of the real upper limb; acquiring the electromyography signal and the posture information of the upper limb through the electromyography acquisition part 110 and the motion capture part 120;

[0054] S130: extracting features of the electromyography signal, the movement information and the posture information of the real upper limb, and classifying them in a ladder according to the feature strength;

[0055] S140: inputting the electromyography features and the angle features of the movement into the classifier after fusion for pattern recognition;

[0056] S150: dynamically recognizing the action and movement of the real upper limb and synchronously adjusting the action and movement of the virtual upper limb; establishing communication between the virtual environment and mathematical software such as MATLAB, recognizing the action and movement of the real upper limb online, and synchronously adjusting the action and movement of the virtual upper limb in the display module 400.

[0057] Preferably, in step S110, the mapping of the virtual upper limb includes mapping the movement information of the hand and forearm, upper arm of the real upper limb captured by the motion capture part 120 to the hand and forearm, upper arm of the virtual upper limb in the virtual environment, assigning the preset part of the electromyography arm ring to the upper arm in the virtual upper limb in the virtual environment, synchronizing the movement information of the upper arm, assigning the posture position information of the joints of the fingers and the wrist joint captured by the hand image motion capture part 120 to the fingers and forearm in the virtual upper limb, synchronizing the movement information of the fingers and forearm, calibrating the initial direction of the upper arm and forearm in the virtual environment, and adjusting the virtual upper limb to synchronize the real upper limb.

[0058] Preferably, in step S120, the acquisition module 100 includes the electromyography signal acquired by the electromyography acquisition unit 110 or the electromyography electrode, and the motion capture unit 120 of the acquisition module 100 is arranged at the display module 400 to acquire gesture information and posture information of the forearm and upper arm by the motion capture unit 120, the portable electromyography arm ring communicates with the processing module 200 through the Bluetooth receiver, the processing module 200 sets the way of acquiring electromyography in the virtual environment to acquire the forearm electromyography information when different gestures are performed, establishes a plurality of gesture actions of the gesture library, and the acquisition way includes the process of preparing gesture action countdown-performing gesture action-repeating action, the posture position information of the fingers and the forearm captured by the hand image motion capture unit 120 is calculated to obtain the finger bending angle, elbow joint activity degree and other angle information, and the upper arm movement information acquired by the portable electromyography arm ring is calculated to obtain the shoulder joint activity degree.

[0059] Preferably, in step S130, the feature extraction includes extracting the time domain features of the electromyography signal, including the average absolute value, the number of zero crossings and the coefficients of the autoregressive model, extracting the gesture information features such as joint activity degree, and extracting the posture information of the upper limb such as position information, and setting the spatial motion trajectory and calculating the trajectory deviation degree; the electromyography features and posture information of normal people are used as templates, and are classified in a ladder according to the feature strength, and the electromyography features and posture information of the user are obtained according to the corresponding ladder classification to obtain the corresponding score.

[0060] Preferably, in step S140, the pattern recognition includes fusing the electromyography features and the angle features of the gesture, and then inputting a BP neural network or other classifier for pattern classification, saving the parameters and threshold values of the virtual upper limb, and taking the position information and other posture information as conditions to judge the direction of movement.

[0061] Preferably, in step S150, the online recognition specifically includes: calling the function of MATLAB in the processing module 200 or establishing communication between the virtual environment in the display module 400 and MATLAB, and then classifying the actions of the stroke patient in the processing module 200, and driving the virtual upper limb to perform synchronous movement in the virtual environment according to the classification.

[0062] Preferably, the processing module 200 can analyze the time of the virtual upper limb movement in the display module 400 to the target state, judge the movement ability of the real upper limb in the actual environment according to the time and the movement state, and give a certain amount of assistance and support through the training module 300 in the case of insufficient movement ability, so as to help the stroke patient to complete the movement from the initial state to the target state.

[0063] Embodiment 2

[0064] This embodiment is an improvement and supplement of Embodiment 1, and the repeated contents will not be described again.

[0065] As shown in Figure 1 A stroke patient upper limb rehabilitation evaluation system at least includes a collection module 100, a processing module 200, a training module 300 and a display module 400, wherein the display module 400 is configured in a manner that can be observed by the stroke patient and displays the target state of the upper limb movement and the virtual upper limb in the display module 400, wherein the virtual upper limb is mapped by the processing module 200 based on the upper limb information obtained by the collection module 100 in the virtual environment of the display module 400, in the training process, the processing module 200 can judge the movement ability of the real upper limb in the actual environment according to the movement time and state of the virtual upper limb in the virtual environment of the display module 400, when the processing module 200 considers that the movement ability of the real upper limb is insufficient to complete the movement from the initial state to the target state, the processing module 200 will send a control signal to the training module 300, and the training module 300 will assist the real upper limb to complete the movement in the actual environment.

[0066] Preferably, the processing module 200 mainly judges according to the gap between the virtual upper limb and the target state in the virtual environment of the display module 400 and the time, for example, the virtual upper limb in the display module 400 does not move to the preset position of the target state within a certain preset time period, or the gesture is not accurate enough after moving to the preset position, etc., specifically, the movement ability of the virtual upper limb (the virtual upper limb is synchronized with the real upper limb, essentially refers to the real upper limb) can be rated according to the gap between the virtual upper limb and the target state, more specifically, in the rating process, the processing module 200 can also refer to the electromyographic signal of the real upper limb obtained by the electromyographic acquisition part 110 of the collection module 100, and through the analysis of the electromyographic signal, the movement ability of the real upper limb can be judged to a certain extent.

[0067] Preferably, when the processing module 200 considers that the movement ability of the real upper limb (virtual upper limb) is insufficient, the processing module 200 analyzes the driving angle and driving torque that the training module 300 needs to provide based on inverse kinematics.

[0068] Preferably, for the movement state of the upper limb, the various movements of the entire upper limb can be essentially decomposed into the rotation movements of each joint, specifically, no matter what state and form of normal movement the upper limb performs, the length and shape of the skeleton will not change, and the flexibility of the upper limb mainly depends on the multi-angle flexible rotation of the connecting joints between each skeleton, therefore, when applied to each skeleton in the form of the exoskeleton of the training module 300, only the rotation angle and rotation direction of the joint connection between each skeleton need to be controlled to achieve the auxiliary movement of the upper limb.

[0069] Preferably, the step of calculating the driving angle and the driving torque of the training module 300 according to the distribution of the virtual upper limbs and the target state in the virtual environment in the display module 400 mainly includes:

[0070] S210: obtaining the current position of the virtual upper limb end and the target position of the target state corresponding to the rehabilitation training task, and calculating the driving angle of each joint based on the inverse kinematics algorithm from the current position and the target position;

[0071] S220: adjusting the arm support torque of each joint of the training module 300 online based on iteration learning, and calculating the arm support torque required by each joint from the target driving angle, the actual joint angle corresponding to the target driving angle, the target joint angular velocity and the actual joint angular velocity;

[0072] S230: performing inverse dynamics control based on feedback linearization, calculating the output torque of each joint from the target motion parameters and the actual motion parameters of each joint, and calculating the driving amount of each joint according to the output torque of each joint;

[0073] S240: controlling the driving torque applied by the trained joint according to the driving amount of each joint to perform rehabilitation training.

[0074] Preferably, the training module 300 is based on the driving angle and the driving torque calculated by the processing module 200, wherein the calculated driving torque is the maximum driving torque that the training module 300 can provide under this auxiliary movement, thereby assisting the upper limbs of the stroke patient to move.

[0075] Preferably, the driving torque of the training module 300 is configured to be enhanced in a step-by-step and slow manner, and stopped when the real upper limbs have a movement trend, and the real upper limbs are driven by the current driving torque plus a surplus value of the preset proportion of the current driving torque. For patients who are not completely paralyzed, a better rehabilitation training method is to let the stroke patients drive the upper limbs to move by themselves, and the technical solution of the present application is designed based on this method, that is, the driving torque applied by the training module 300 is increased from zero, and when the real upper limbs move by themselves, the driving torque applied by the training module 300 is unchanged. Only when the real upper limbs still do not move for a period of time, the driving torque of the training module 300 starts to increase gradually, and the increasing speed is relatively slow. If the real upper limbs have a movement trend during the increasing process, the driving torque of the training module 300 stops increasing, and increases by a surplus value according to the preset proportion (for example, one tenth) of the driving torque when the movement trend appears. In this way, the actual movement of the real upper limbs is still mainly driven by the stroke patients themselves, and the training module 300 is auxiliary. The driving torque of the training module 300 is increased by a surplus value to avoid overuse of muscles and protect the health of the muscles of the stroke patients.

[0076] Compared with the existing driving scheme that completely provides driving force, the training module 300 of the present application focuses on assisting the stroke patients to move the bilateral upper limbs, and the stroke patients watch the movement state and target state of the virtual upper limbs in the display module 400 during the movement process, and constantly imagine the movement mode of moving the virtual upper limbs to the target state in their minds. Thus, the electroencephalogram continuously sends driving signals to the neurons connected to the muscles, so that the muscles contract as much as possible, and the training module 300 is used to assist the upper limbs to move when the muscles contract to the maximum extent that can be contracted at present, so that the brain can remember the feedback signals of muscle movement, strengthen the control connection between the brain and the upper limb muscles during rehabilitation training, and thus improve the efficiency of rehabilitation training.

[0077] Embodiment 3

[0078] This embodiment is improved and supplemented on the basis of embodiments 1 and 2, and the repeated contents will not be described again.

[0079] In actual situations, most daily activities require the participation of both upper limbs, and the paralysis of one upper limb seriously affects the patient's participation in functional activities that require the participation of both upper limbs. The activity of one upper limb and the activity of both upper limbs have different neural control mechanisms. Only through simultaneous training of both upper limbs can the functional activities of both upper limbs be obtained, for example, one upper limb fixes an object, and one upper limb operates the object, such as twisting a bottle cap, cutting vegetables, etc. Simultaneous training of both upper limbs can improve the function of the paralyzed upper limb, especially the proximal function of the upper limb. The reason may be that the trunk and proximal limb muscles are bilaterally innervated, or it may be that bilateral symmetric training produces greater electromyography of the trunk muscles, thereby promoting the stability of the trunk, which is important for the control of the proximal limb. When training, different upper limb exercises need to be selected according to the specific situation of the patient.

[0080] Therefore, in the actual process of rehabilitation treatment, the difference in the recovery of both upper limbs needs to be considered. If the same driving torque is used for auxiliary training, the recovery degree of both upper limbs may differ, and whether the difference occurs can be determined by the electromyography signal obtained by the acquisition module 100. If the difference still exists, the use of the same driving torque for auxiliary rehabilitation training of both upper limbs may result in a larger difference in the recovery degree between both upper limbs, thereby causing the occurrence of uncoordination between both upper limbs.

[0081] Therefore, the present application provides a stroke patient upper limb rehabilitation evaluation system as shown in Figure 1 The system at least includes an acquisition module 100, a processing module 200, a training module 300, and a display module 400. The processing module 200 can calculate the driving angle and driving torque required by the training module 300 to apply to the real upper limb for assisting movement according to the distance between the target state in the display module 400 and the virtual upper limb. In addition, the processing module 200 can also adjust the driving torque applied to both upper limbs according to the difference between the first electromyography signal and the second electromyography signal of both upper limbs obtained by the electromyography acquisition part 110 of the acquisition module 100.

[0082] Specifically, it is assumed that the electromyography signal of the left upper limb of the stroke patient obtained by the electromyography acquisition part 110 of the acquisition module 100 is recorded as the first electromyography signal, and the electromyography signal of the right upper limb of the stroke patient obtained by the electromyography acquisition part 110 is recorded as the second electromyography signal. The processing module 200 analyzes the first electromyography signal and the second electromyography signal, calculates the difference between the first electromyography signal and the second electromyography signal, and adjusts the driving torque applied to both upper limbs by the training module 300 according to the difference.

[0083] Preferably, the analysis of the electromyography signal by the processing module 200 mainly includes raw surface electromyography signal analysis and processed data analysis, which mainly focuses on time domain and frequency domain analysis, and the purpose of signal analysis is to study the correlation between the time-frequency characteristics of surface electromyography signal and muscle structure, muscle activity state and functional state, to explore the possible reasons for the change of surface signal, and to effectively apply the change of electromyography signal to reflect muscle activity and function. The raw surface electromyography signal is the most direct form of display of the occurrence and resting state of electromyography activity. Without considering the amplitude, the starting relationship of the electromyography signal can be analyzed, that is, the density and height of the raw electromyography signal during muscle activity, which can reflect the amplitude and strength of muscle contraction to a certain extent. The higher the density and height, the stronger the surface electromyography signal, and the stronger the contraction. The processed data analysis is to directly record the raw surface electromyography signal, use the signal processing system in the software, rectify, smooth and normalize the raw signal, and further calculate and analyze. For example, the analysis software ErgoLAB provides raw surface electromyography signal analysis and processed data analysis methods such as time domain analysis, frequency domain analysis and segmented analysis.

[0084] Preferably, the processing module 200 can at least analyze the electromyography signal obtained by the acquisition module 100 to determine the muscle contraction state, and determine whether the muscle movement is normal according to the pre-set muscle contraction threshold. Specifically, the processing module 200 mainly monitors the abnormal muscle contraction state, determines whether the muscle is in an abnormal movement state by calculating the difference between the muscle contraction value obtained from the real-time collected electromyography signal and the pre-set abnormal muscle contraction threshold, and monitors the abnormal muscle contraction pattern during the rehabilitation training process. The abnormal muscle contraction pattern includes muscle overexcitation, muscle overinhibition, abnormal muscle synergy, abnormal co-contraction of agonist and antagonist muscles, and muscle fatigue.

[0085] Preferably, the processing module 200 is also capable of determining the connection response of different muscles to the brain electrical signal during the movement based on the muscle electrical signal sizes of different muscles of the same upper limb obtained by the plurality of muscle electrical acquisition units 100 of the acquisition module 100. Specifically, the upper limb includes the deltoid muscle, the biceps brachii, the triceps brachii, the brachialis, the pronator teres, the brachioradialis, the extensor carpi radialis longus, the extensor carpi radialis brevis, the flexor carpi radialis, the palmaris longus, the flexor digitorum superficialis, and the extensor carpi ulnaris, etc. from the shoulder to the finger, and the stretching and contraction conditions of the muscles at each position are not consistent when the upper limb is moving. The processing module 200 determines the muscle electrical signal range in which the muscles of the upper limb should be in according to the analysis of the current movement of the upper limb by the motion capture unit 120, and determines the activity of each muscle during the rehabilitation process according to the muscle electrical signals of different positions actually acquired by the muscle electrical acquisition unit 110, for example, in the arm flexion movement, the biceps brachii, the triceps brachii, and the brachialis should be in a relatively active state, if the muscle electrical signal of the triceps brachii is too weak during this process, the processing module 200 considers that the connection of the triceps brachii to the brain is more, and gives a weak evaluation of the activity of the triceps brachii, and in the subsequent rehabilitation process, the movement posture that can train the triceps brachii is focused on as much as possible to improve the rehabilitation effect.

[0086] Preferably, the processing module 200 analyzes the difference value between the first muscle electrical signal and the second muscle electrical signal based on the above analysis method, and specifically, the processing module 200 evaluates the coordination of the bilateral upper limbs of the stroke patient based on the similarity of the first muscle electrical signal and the second muscle electrical signal, wherein the first muscle electrical signal and the second muscle electrical signal are muscle electrical signals synchronously acquired under the same movement of the bilateral upper limbs.

[0087] Preferably, the processing module 200 can evaluate the rehabilitation coordination of the bilateral upper limbs of the stroke patient based on the similarity value between the first electromyography signal and the second electromyography signal, and the processing module 200 can divide the coordination degree of the bilateral upper limbs of the stroke patient into at least five levels, i.e., a first level, a second level, a third level, a fourth level, and a fifth level, based on the similarity value between the first electromyography signal and the second electromyography signal. Specifically, the first level: the similarity value between the first electromyography signal and the second electromyography signal reaches 91% to 100% in a statistical sense, indicating that the coordination training effect of the patient during the rehabilitation training is good; the second level: the similarity value between the first electromyography signal and the second electromyography signal reaches 81% to 90% in a statistical sense, indicating that the coordination training effect of the patient during the rehabilitation training needs to be improved; the third level: the similarity value between the first electromyography signal and the second electromyography signal reaches 61% to 80% in a statistical sense, indicating that the coordination training effect of the patient during the rehabilitation training is poor; the fourth level: the similarity value between the first electromyography signal and the second electromyography signal reaches 41% to 60% in a statistical sense, indicating that the coordination training effect of the patient during the rehabilitation training is unqualified; and the fifth level: the similarity value between the first electromyography signal and the second electromyography signal is less than or equal to 40% in a statistical sense, indicating that the coordination training effect of the patient during the rehabilitation training is very poor.

[0088] In the calculation of the driving torque of the training module 300 described above, the driving torque of the training module 300 is configured to be enhanced in a step-by-step and slow manner, and the enhancement is stopped when the real upper limb has a movement trend, and the real upper limb is driven with the current driving torque plus a surplus value of the current driving torque preset proportion. In the case of combining the difference value between the first electromyography signal and the second electromyography signal, the calculation of the driving torque applied to the bilateral upper limbs needs to be further improved, and at least the preset proportion for calculating the surplus value can be changed based on the difference value between the first electromyography signal and the second electromyography signal. Referring to the foregoing content, the preset proportion refers to the ratio between the surplus value and the current driving torque, which is usually represented and calculated in the form of the surplus value being a fraction of the current driving torque.

[0089] Preferably, the processing module 200 adjusts the size of the surplus value in the manner of adjusting the preset proportion based on the difference value between the first electromyography signal and the second electromyography signal, and then adjusts the total value of the driving torque applied to one of the bilateral upper limbs.

[0090] Specifically, it is assumed that the intensity of the first electromyographic signal of the left upper limb is greater than the intensity of the second electromyographic signal of the right upper limb, i.e. the recovery degree of the left upper limb is better than that of the right upper limb, and thus, when the driving torque is applied by the training module 300, the driving torque applied to the right upper limb is smaller than that of the left upper limb. The design logic of this kind of way is that a smaller driving torque is applied to the right upper limb with a poorer recovery degree, so that the right upper limb is exercised more, and because a smaller driving torque is applied, the stroke patient's attention is more focused on driving the right upper limb to move during the movement of the right upper limb, thereby gradually leveling the recovery effect of the right upper limb with the left upper limb, thereby achieving the effect of keeping the bilateral upper limbs coordinated during the rehabilitation training of the bilateral upper limbs.

[0091] Preferably, the way of determining the preset proportion according to the difference value between the first electromyographic signal and the second electromyographic signal can be a stepwise corresponding way. For example, in the aforementioned patient coordination training grading way in the rehabilitation training process, level one indicates that the preset proportion for calculating the surplus value in the driving torque applied to the left upper limb and the right upper limb remains consistent, level two indicates that the preset proportion for calculating the surplus value in the driving torque applied to the left upper limb adopts a default value (0.1, ten percent), and the preset proportion for calculating the surplus value in the driving torque applied to the right upper limb can be set to 0.08, i.e. eight percent; level three indicates that the preset proportion for calculating the surplus value in the driving torque applied to the left upper limb adopts a default value (0.1, ten percent), and the preset proportion for calculating the surplus value in the driving torque applied to the right upper limb can be set to 0.06, i.e. six percent; level four indicates that the preset proportion for calculating the surplus value in the driving torque applied to the left upper limb adopts a default value (0.1, ten percent), and the preset proportion for calculating the surplus value in the driving torque applied to the right upper limb can be set to 0.04, i.e. four percent; level five indicates that the preset proportion for calculating the surplus value in the driving torque applied to the left upper limb adopts a default value (0.1, ten percent), and the preset proportion for calculating the surplus value in the driving torque applied to the right upper limb can be set to 0.02, i.e. two percent.

[0092] Embodiment 4

[0093] This embodiment is improved and supplemented on the basis of embodiments 1-3, and repeated contents will not be described again.

[0094] The present application provides a stroke patient upper limb rehabilitation evaluation system, which at least includes an acquisition module 100, a processing module 200, a training module 300 and a display module 400, wherein the training module 300 is generally in the form of an exoskeleton, and the display module 400 is a regular display screen.

[0095] Preferably, the acquisition module 100 is mainly divided into an electromyographic acquisition part 110 and a motion capture part 120, the electromyographic acquisition part 110 and the motion capture part 120 are separately configured, wherein the electromyographic acquisition part 110 is configured together with the training module 300 at the bilateral upper limbs of the stroke patient, specifically, the electrode sheet, arm ring, sensor and the like of the electromyographic acquisition part 110 are clamped in the middle by the training module 300 and the skin of the upper limbs of the stroke patient; the motion capture part 120 of the acquisition module 100 is cooperatively configured with the display module 400, specifically, the camera equipment of the motion capture part 120 is configured on the display module 400.

[0096] Preferably, the processing module 200 can be integrated in the form of software inside the display screen of the display module 400, or can be installed in the form of a chip inside the display module 400, the processing module 200 can at least receive information from the acquisition module 100, and construct a virtual upper limb in the screen (virtual environment) of the display module 400 after processing the collected motion and posture information of the real upper limb, the processing module 200 can also judge the coordination of the left and right upper limbs according to the collected electromyographic signals of the real upper limb, and output the required auxiliary motion parameters to the training module 300 in combination with the gap between the virtual upper limb and the target state in the display module 400 and the coordination of the left and right upper limbs, the auxiliary motion parameters mainly include driving angle and driving torque.

[0097] Preferably, Figure 3A simplified structural schematic diagram of the training module 300 of the stroke patient upper limb rehabilitation evaluation system of the present application is shown, which at least includes a first exoskeleton, a second exoskeleton, a third exoskeleton and a fourth exoskeleton, wherein the first exoskeleton and the second exoskeleton are movably connected through a first exo-joint, the second exoskeleton and the third exoskeleton are movably connected through a second exo-joint, and the third exoskeleton and the fourth exoskeleton are movably connected through a third exo-joint. Specifically, the first exoskeleton is substantially structured according to the shape of the human shoulder and is used to cover the human shoulder, the second exoskeleton is structured according to the shape of the human upper arm and is used to cover the human upper arm, the third exoskeleton is structured according to the shape of the human lower arm and is used to cover the human lower arm, and the fourth exoskeleton is structured according to the shape of the human palm and fingers and is used to cover the human palm and fingers, wherein the first exo-joint between the first exoskeleton and the second exoskeleton is similar to the human shoulder joint, the second exo-joint between the second exoskeleton and the third exoskeleton is similar to the human elbow joint, and the third exo-joint between the third exoskeleton and the fourth exoskeleton is similar to the human wrist joint. In the prior art, relatively mature technologies have been designed for exoskeletons, rehabilitation robots, etc. The specific driving principle of the training module 300 can refer to the existing technical principles. The advantage of the training module 300 of the present application lies in that it can provide a driving torque only for slightly assisting the movement of the upper limbs of the stroke patient according to the control of the processing module 200, and is not actively driven to move the upper limbs, and the training module 300 of the present application can also analyze the coordination between the left and right upper limbs based on the processing module 200, and adjust the size of the auxiliary driving torque at the left and right upper limbs to make the upper limb with poorer rehabilitation effect receive more training, so that the stroke patient's attention is more focused on the upper limb with poorer rehabilitation effect, thereby gradually reducing the difference in rehabilitation degree between the left and right upper limbs and making the left and right upper limbs coordinate.

[0098] Based on the above scheme, the present application focuses on the movement difference between the left and right limbs of the patient, and provides different and targeted training parameters for both sides, which improves the training effect and training efficiency, avoids the occurrence of uncoordinated conditions of the left and right limb muscles in the rehabilitation process due to the application of unsuitable training intensity, solves the problem of hindering the patient's normal life caused by the uncoordinated movement of the two limbs, and has a certain positive effect on the life quality of the stroke patient.

[0099] It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to devise modifications which, though perhaps not explicitly described or shown herein, nonetheless fall within the scope of the application. Accordingly, the patent application includes all modifications encompassed within the scope of the claims and their equivalents. The patent application should be limited only by the claims and their equivalents.

Claims

1. A stroke patient upper limb rehabilitation evaluation system, comprising at least: a collection module (100) for collecting upper limb information of a stroke patient; a processing module (200) for processing the upper limb information collected by the collection module (100); a training module (300) for assisting the stroke patient in upper limb movement; a display module (400) for displaying the upper limb movement state of the stroke patient; characterized in that: the display module (400) displays the gap between the virtual upper limb and the target state to the stroke patient to enable the stroke patient to drive the real upper limb to move; the processing module (200) judges the movement ability of the real upper limb based on the change of the gap between the virtual upper limb and the target state, and controls the training module (300) to assist driving according to the movement ability, wherein the processing module (200) adjusts the driving torque applied to the bilateral upper limbs by the training module (300) based on the difference between the electromyographic signals of the bilateral upper limbs collected by the collection module (100), the processing module (200) controls the driving torque of the training module (300) to gradually increase, and the training module (300) stops increasing when the real upper limb has a movement trend, and assists driving the real upper limb with the current driving torque plus a surplus value of a preset proportion of the current driving torque, the processing module (200) adjusts the size of the surplus value in the form of adjusting the preset proportion based on the difference between the first electromyographic signal and the second electromyographic signal of the bilateral upper limbs collected by the collection module (100), and then adjusts the total value of the driving torque applied to one of the bilateral upper limbs.

2. The stroke patient upper limb rehabilitation assessment system according to claim 1, wherein, The processing module (200) can construct a virtual upper limb in the virtual environment of the display module (400) based on at least the electromyographic signals and movement information of the real upper limb obtained by the collection module (100), wherein the collection module (100) comprises at least an electromyographic collection unit (110) for collecting electromyographic signals and a motion capture unit (120) for capturing motion.

3. The stroke patient upper limb rehabilitation assessment system according to claim 2, wherein, The steps of the processing module (200) for mapping the real upper limb to the virtual upper limb in the virtual environment include: building a model of the virtual upper limb in the virtual environment; collecting electromyographic signals and posture information of the real upper limb; extracting features of the electromyographic signals, movement information, and posture information of the real upper limb, and classifying them in a stepwise manner according to the strength of the features; fusing the electromyographic features and the angular features of the movement and inputting them into a classifier for pattern recognition; dynamically recognizing the motion and movement of the real upper limb and synchronously adjusting the motion and movement of the virtual upper limb.

4. The stroke patient upper limb rehabilitation assessment system according to claim 1, wherein, The display module (400) is configured in a manner that can be observed by the stroke patient, so that the stroke patient constantly imagines the virtual upper limb moving to the target state when observing the picture of the virtual upper limb and the target state in the display module (400).

5. The stroke patient upper limb rehabilitation assessment system according to claim 1, wherein, The processing module (200) can analyze the time and movement state of the virtual upper limb moving to the target state in the display module (400), and judge the movement ability of the real upper limb in the actual environment according to the time and movement state.

6. The stroke patient upper limb rehabilitation assessment system according to claim 1, wherein, The processing module (200) can analyze the driving angle and the driving torque required for the training module (300) to be assisted to drive based on inverse kinematics, wherein the driving torque calculated by the processing module (200) is the maximum driving torque that the training module (300) can provide under the next assistance driving.

7. The stroke patient upper limb rehabilitation assessment system according to claim 6, wherein, The step of calculating the driving angle and the driving torque by the processing module (200) comprises: obtaining the current position of the virtual upper limb end and the target position of the target state, and calculating the driving angle of each joint from the current position and the target position based on the inverse kinematics algorithm; adjusting the arm support torque of each joint dynamically based on the iterative learning, calculating the arm support torque required for each joint from the target driving angle, the actual joint angle corresponding to the target driving angle, the target joint angular velocity and the actual joint angular velocity; performing inverse dynamics control based on feedback linearization, calculating the output torque of each joint from the target motion parameters and the actual motion parameters of each joint, and calculating the driving amount of each joint according to the output torque of each joint; controlling the driving torque applied by the joint to be trained according to the driving amount of each joint to perform rehabilitation training.

8. The upper extremity rehabilitation assessment system for stroke patients according to any one of claims 1 to 7, characterized in that, The stroke patient upper limb rehabilitation evaluation system can be applied to the upper limb rehabilitation effect evaluation of a stroke patient in an individual family. The stroke patient upper limb rehabilitation evaluation system can be applied to the upper limb rehabilitation effect evaluation of a stroke patient in an individual family.

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