CN117860531A - Spinal cord injury rehabilitation device based on myoelectricity biofeedback - Google Patents
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Abstract
The invention relates to a spinal cord injury rehabilitation device based on myoelectricity biofeedback, which comprises an upper limb movement assembly, a lower limb movement assembly, a controller, a myoelectricity detection unit and an interaction assembly, wherein the myoelectricity detection unit is connected with the upper limb movement assembly; the controller is configured with a feedback driving strategy, data detected by the myoelectricity detection unit are fed back into the controller to control the upper limb movement assembly and the lower limb movement assembly to work, through the arrangement, firstly, the myoelectricity detection and limb linkage rehabilitation technology can be combined, and different customized rehabilitation tasks corresponding to each patient are realized through algorithm support, so that the intensity is more in line with the condition of an actual patient, the rehabilitation effect is improved, pain caused by electric stimulation is reduced, along with the increase of sample data, the pertinence is stronger, and discomfort caused by experience is avoided.
Description
Technical Field
The invention relates to spinal cord injury rehabilitation medical equipment, in particular to a spinal cord injury rehabilitation device based on myoelectricity biofeedback.
Background
Spinal cord injury (spinal cord injury, SCI) is a central nervous system injury caused by factors such as low altitude fall, car accidents, etc., and patients often suffer from limb dysfunction, seriously affect the quality of life of the patients, and cause a heavy burden to society. At present, the prevalence rate of SCI in China is 13-60 per 100 ten thousand, the prevalence population is concentrated in 35-50 years old, and men are most, so that great economic burden is brought to families and society of patients. There is an urgent need to develop rehabilitation techniques that better aid in functional recovery in spinal cord injured patients.
The electrical stimulation is one of the effective treatment means aiming at spinal cord injury at present, and researches prove that the electrical stimulation technology can effectively improve the recovery of nerve tissues after spinal cord injury, reduce inflammation and promote the functional recovery of patients. However, the electrostimulation technique lacks initiative, and at the same time, electrostimulation is only performed on the local limb, and does not help the patient to recover as a whole. There are studies using epidural electrical stimulation to help patients achieve walking function, but this technique requires surgical implantation with the risk of sequelae such as electrode displacement, and is effective only for chronic patients.
Disclosure of Invention
In view of the above, the present invention aims to provide a spinal cord injury rehabilitation device based on myoelectric biofeedback.
In order to solve the technical problems, the technical scheme of the invention is as follows: a spinal cord injury rehabilitation device based on myoelectricity biofeedback comprises an upper limb movement assembly, a lower limb movement assembly, a controller, a myoelectricity detection unit and an interaction assembly;
the upper limb movement assembly comprises an upper limb control module, the lower limb movement assembly comprises a lower limb movement module, and the upper limb movement module, the lower limb movement module and the myoelectricity detection unit are all coupled with the controller;
the controller is configured with a feedback drive strategy comprising:
step S1, acquiring patient information and matching a corresponding rehabilitation task list from a preset feedback information base according to the patient information;
s2, collecting the rest feedback data from the myoelectricity detection unit, and extracting the rest feedback characteristics from the rest feedback data according to a preset rest analysis sub-strategy;
s3, bringing the rest feedback characteristic into the rehabilitation task list to adjust the sequence of the rehabilitation tasks in the rehabilitation task list, and outputting the rehabilitation tasks to the interaction assembly according to the sequence;
s4, collecting static feedback data from the myoelectricity detection unit, and extracting static feedback characteristics from the static feedback data according to a preset static analysis strategy;
s5, bringing the static feedback characteristic into a pre-trained rehabilitation learning model to obtain a corresponding action instruction, and sending the action instruction to an upper limb movement module and a lower limb movement module to drive the upper limb movement assembly and the lower limb movement assembly to work respectively;
step S6, according to the action instruction, theoretical action feedback data are called from a preset action association database, action feedback data are collected from the myoelectricity detection unit, and the action feedback data are compared with the theoretical action feedback data to generate action deviation characteristics;
step S7, the action deviation characteristic is brought into a rehabilitation task list to correct the rehabilitation task, and the rehabilitation task is output to the interaction component;
and step S8, returning to the step S4 until the rehabilitation tasks of the rehabilitation task list are completed.
Further: the myoelectricity detection unit is coupled with a myoelectricity data analysis module; the myoelectricity data analysis module is configured with a resting acquisition strategy and a static acquisition strategy, wherein the resting acquisition strategy is used for acquiring resting feedback data, and the static acquisition strategy is used for acquiring the static feedback data;
the rest acquisition strategy comprises the steps of calculating a deviation average value in each preset rest acquisition window by a rest acquisition algorithm, taking a biological myoelectric waveform corresponding to the rest acquisition window with the largest deviation average value as rest feedback data, wherein the rest acquisition algorithm comprises the following steps of
Wherein->The acquisition range for the resting acquisition window is +.>Mean value of deviation at time,/->For a preset segmentation rest weight, +.>For the preset macro window rest weight, there is +.>，/>For the bio-myoelectric waveform obtained in step S2, < >>For the bioelectric waveform obtained in step S2 +.>For average division of segment spacingSegment function (F)>For a predetermined segment interval +.>For the total number of segmentation intervals in the resting acquisition window, +.>The mean value of the bioelectricity waveform in a resting acquisition window is obtained;
the static acquisition strategy comprises the steps of acquiring a mean value piecewise function in the static feedback data, generating a static threshold breaking model according to the distribution of the mean value piecewise function, taking the bioelectricity waveform acquired in the step S4 into the static threshold breaking model to output a static threshold value in real time, taking the moment when the static threshold value is higher than a preset static upper limit reference for the first time as an acquisition starting point of the static feedback data, and taking the moment when the static threshold value is lower than a preset static line lower reference for the first time as an acquisition end point of the static feedback data.
Further: the action instruction also comprises a stimulation current for controlling the myoelectricity detection unit.
Further: the feedback information base stores a plurality of rehabilitation tasks in advance, each rehabilitation task takes a patient matching characteristic as an index, and each rehabilitation task is associated with rehabilitation value data, a rehabilitation matching algorithm is configured in step S1, the rehabilitation matching algorithm is used for calculating a rehabilitation matching value of a rehabilitation task list corresponding to each rehabilitation task, the corresponding rehabilitation task is called from the feedback information base to maximize the sum of the rehabilitation matching values under a preset rehabilitation constraint condition, and the rehabilitation matching algorithm is thatWherein->For said rehabilitation matching value, +.>Rehabilitation value data corresponding to rehabilitation tasksIs a rehabilitation effect value of->Matching the similarity value of the characteristics and the patient information for the patient corresponding to the rehabilitation task>For the exercise burden value in the rehabilitation value data corresponding to the rehabilitation task, < ->For the load conflict value of the rehabilitation task and other rehabilitation tasks in the rehabilitation task list, +.>Is the preset weight of rehabilitation effect, +.>The preset disease condition is similar in weight,preset exercise burden weight, +.>And (5) presetting a load conflict weight.
Further: the resting analysis sub-strategy comprises
S2-1, calling a historical standard waveform according to patient information;
s2-2, differentiating a rest acquisition waveform corresponding to the rest acquisition data and a historical standard waveform to generate a rest deviation waveform;
s2-3, calculating the envelope area of the resting deviation waveform to generate a resting confidence value, and if the resting confidence value exceeds a preset rehabilitation expected range, re-acquiring resting feedback data until the resting confidence value is lower than the preset rehabilitation expected range;
and step S2-4, matching corresponding rest feedback characteristics from the rest deviation waveforms through a preset rest characteristic matching database.
Further: each rehabilitation task is corresponding to a plurality of intensity options, each intensity option is corresponding to an intensity value, and the step S3 further comprises determining the corresponding intensity option from the rehabilitation tasks through a resting feedback feature, and sequencing the execution sequence of the rehabilitation tasks according to the intensity values.
Further: the static analysis strategy comprises the steps of pre-constructing a static characteristic database, wherein the static characteristic database is pre-stored with a plurality of static sub-characteristics, each static sub-characteristic is corresponding to a static trust value, and the static trust value reflects the times of the occurrence of the static sub-characteristic in historical data;
the static analysis strategy comprises a static analysis algorithm, wherein the static analysis algorithm calculates a static matching value corresponding to each combination of the static sub-features, and the static matching value comprisesWherein->For the value of the static match,for the duration of the myoelectric waveform that can be replaced by the static sub-feature, +.>Is the total duration of the myoelectric waveform, +.>For the number of static sub-features in the combination of the current static sub-features, +.>For the preset matching relation weight, +.>For a preset trust relationship weight, +.>Is->Each static sub-feature corresponds toSimilarity of myoelectric biological waveforms replaced, < >>Is->Static trust values corresponding to the static sub-features;
the static analysis strategy selects a combination of static sub-features with highest static matching values to generate the static feedback feature.
Further: each rehabilitation task corresponds to a rehabilitation training model, a plurality of nerve nodes are configured in the rehabilitation training model, each nerve node is configured with a task matching load corresponding to each static sub-feature, the total value of the corresponding task matching load is calculated by each static feedback feature after passing through one node, the position of the next nerve node is determined in the rehabilitation training model according to the total value of the task matching load until a nerve transmission path is completed, and corresponding action instructions are matched according to the nerve transmission path.
Further: in step S6, a biological force application simulation model is pre-constructed, different action instructions are constructed according to the biological force application simulation model, force application vectors of the force application points at different moments are generated according to the biological force application relationship, and theoretical biological current is calculated according to the force application vectors to generate theoretical action feedback data.
Further: each action deviation feature is configured with a rehabilitation correction value corresponding to each task type, and the corresponding rehabilitation correction value is brought into a rehabilitation task corresponding to the task type to update the corresponding intensity option.
The technical effects of the invention are mainly as follows: through setting up like this, can combine through biological myoelectricity detection and four limbs linkage rehabilitation technique at first, support through the algorithm, realize corresponding the different customization formula rehabilitation task of every patient, so the condition that is the intensity more accords with actual patient improves recovered effect, reduces the pain that the electric stimulation brought, along with sample data's increase, the pertinence is stronger moreover, avoids the uncomfortable condition that produces according to experience.
Drawings
Fig. 1: the invention relates to an axial side view I of a spinal cord injury rehabilitation device based on myoelectricity biofeedback;
fig. 2: the invention relates to a front view of a spinal cord injury rehabilitation device based on myoelectricity biofeedback;
fig. 3: the invention relates to a second axial view of a spinal cord injury rehabilitation device based on myoelectricity biofeedback;
fig. 4: the invention relates to a control topological graph of a spinal cord injury rehabilitation device based on myoelectricity biofeedback;
fig. 5: the invention discloses a control flow chart of a spinal cord injury rehabilitation device based on myoelectricity biofeedback.
Reference numerals: 10. a base; 11. a roller; 20. a base; 21. a first telescopic arm; 22. a second telescopic arm; 30. a bracket; 31. an upper limb movement assembly; 32. a lower limb movement assembly; 33. a second telescopic arm; 40. an interaction component; 100. a controller; 131. an upper limb movement module; 132. a lower limb movement module; 101. myoelectricity detection unit; 110. and the myoelectricity data analysis module.
Detailed Description
The following detailed description of the invention is provided in connection with the accompanying drawings to facilitate understanding and grasping of the technical scheme of the invention.
Firstly, in order to more clearly understand the design contribution of the invention, an extremity linkage technology and an myoelectricity biofeedback technology are introduced, wherein the extremity linkage technology is a technology for assisting a patient to control and coordinate the movement of limbs to perform rehabilitation treatment by providing assistance/resistance, and coordination of the whole movement can be improved by performing coordinated movement on the upper limbs and the lower limbs, meanwhile, muscle strength and control can be enhanced, and joint stiffness caused by long-term braking can be improved. However, the traditional four-limb linkage technology cannot change the resistance/power-assisted size in real time according to the state of a patient, and the difference of positions and postures can lead to reality, medical staff are required to manually adjust, the characteristics of personalized treatment are not achieved, and the nerve recovery at the spinal cord injury position cannot be promoted. The four-limb linkage rehabilitation device suitable for spinal cord injury is different, as shown in fig. 1-3, because the optimal scheme of the patient with spinal cord injury is a horizontal four-limb linkage device, the device comprises a base, a roller is arranged below the base, so that medical staff can conveniently move equipment to a bed tail, a base is arranged on the base, a controller and a corresponding control circuit are arranged inside the base, a lifting motor is further arranged inside the base and used for driving a first telescopic arm on the base to lift so that the legs of patients can be flush with a lower limb movement assembly, a bracket is arranged on the first telescopic arm, and an upper limb movement assembly, a lower limb movement assembly and a second telescopic arm are arranged on the bracket; the second telescopic arm is used for adjusting the distance between the upper limb movement assembly and the lower limb movement assembly so as to adapt to patients with different shapes. The upper limb movement assembly and the lower limb movement assembly are respectively provided with a movement motor, and the motors can provide assistance or resistance when the patient moves. The interaction component is provided with two, sets up respectively in the both ends of support, and one is used for being convenient for medical personnel observe data, and one is convenient for link with the disease, and myoelectricity detecting element connects out through the cable on the support, fixes with patient's health during the use, realizes the effect of stimulus and detection.
Myoelectric detection unit the principle is as follows, myoelectric biofeedback technology is a therapeutic technique that feeds back to a patient in real time by measuring and analyzing weak muscle surface electrical activity (myoelectric signals). The patient can combine its signal feedback function to carry out corresponding rehabilitation training, can improve patient's initiative participation sense and provide timely feedback, carries out individualized training according to patient's muscle strength recovery condition simultaneously. However, the traditional myoelectricity biofeedback technology cannot directly promote nerve regeneration or repair, can only treat corresponding muscles, lacks external assistance, and cannot realize limb activity and function recovery by simply relying on the myoelectricity biofeedback technology for patients with complete spinal cord injury. As the applied electrical stimulation cannot be targeted. If the horizontal limb linkage device is combined, a problem exists that the body binding part of the patient is small, and if the biological feedback waveforms of the score points are only compared, the device is only applicable to static detection, such as resting detection (the patient does not exert force) and static detection (the patient does not exert force but does not move), and the device cannot achieve the aim of better detection effect under the condition of static detection, because the position change of the human body influences current feedback, how to actually obtain reliable data and optimize the treatment scheme according to the reliable data, which is the core content of the prior art, is also difficult, and if the effect of treatment can still be mentioned under the exercise condition is further increased on the basis, the difficulty is higher.
Referring to fig. 1-4, a spinal cord injury rehabilitation device based on myoelectricity biofeedback comprises an upper limb movement assembly, a lower limb movement assembly, a controller, a myoelectricity detection unit and an interaction assembly;
referring to fig. 4, the upper limb movement assembly includes an upper limb control module, the lower limb movement assembly includes a lower limb movement module, and the upper limb movement module, the lower limb movement module, and the myoelectricity detection unit are all coupled to the controller;
referring to fig. 5, as core content of the present invention, the controller is configured with a feedback driving strategy including:
step S1, acquiring patient information and matching corresponding rehabilitation task lists from a preset feedback information base according to the patient information, wherein the rehabilitation tasks in different states are different at first, and the rehabilitation tasks in different courses of treatment are also different, so that the patient information, such as illness state, illness position, physical condition, previous rehabilitation task execution condition and the like, needs to be acquired at first when the patient information is set, and a targeted treatment strategy can be established for each rehabilitation task through the combination of past historical data; the feedback information base stores a plurality of rehabilitation tasks in advance, each rehabilitation task takes the matching characteristics of a patient as an index, for example, the disease occurrence position of the patient is suitable for the action of an upper limb with higher difficulty, the action of a lower limb with higher difficulty is not suitable for, the rehabilitation task with corresponding difficulty can be matched with the matching characteristics of the patient at the disease occurrence position, each task has curative effect, difficulty and possible conflict degree among the tasks, and the information can be as follows: each rehabilitation task is associated with a rehabilitation value numberAccording to the record, the step S1 is configured with a rehabilitation matching algorithm for calculating a rehabilitation matching value of a rehabilitation task list corresponding to each rehabilitation task, and retrieving the corresponding rehabilitation task from the feedback information base to maximize the sum of the rehabilitation matching values under the preset rehabilitation constraint condition, where the rehabilitation matching algorithm isWherein->For said rehabilitation matching value, +.>For the rehabilitation effect value in the rehabilitation value data corresponding to the rehabilitation task, the rehabilitation effect value is positively correlated with the effect of the rehabilitation task, the clinical use of different rehabilitation tasks can be evaluated and fed back through big data feedback, the rehabilitation effect value is optimized in different modes, the higher the effect is, the larger the rehabilitation effect value is, and the weight is the weight of the user>Matching the similarity value of the characteristics and the patient information for the patient corresponding to the rehabilitation task>For the exercise burden value in the rehabilitation value data corresponding to the rehabilitation task, < ->For the load conflict value of the rehabilitation task and other rehabilitation tasks in the rehabilitation task list, +.>Is the preset weight of rehabilitation effect, +.>Preset disease similarity weight, ++>Preset exercise burden weight, +.>And (5) presetting a load conflict weight. The task lists can reach the optimal matching result through the rehabilitation matching algorithm, and under the condition of ensuring the effect, the task lists have the highest pertinence and lower burden on patients, task conflicts among the task lists are smaller, and the optimal strategy for each use is obtained. The rehabilitation constraint condition may be a single condition such as that the total load cannot exceed a threshold value, or a composite condition that the number of tasks cannot exceed a preset number, and the matching value is maximized by the constraint condition.
S2, collecting the rest feedback data from the myoelectricity detection unit, namely collecting the rest feedback data under the condition that a patient does not relax forcefully, and extracting the rest feedback characteristics from the rest feedback data according to a preset rest analysis sub-strategy;
the resting analysis sub-strategy comprises
S2-1, calling a historical standard waveform according to patient information; the historical standard waveform is generated according to patient information, such as a waveform obtained when professional detection is performed before, and then is estimated according to the rehabilitation degree to obtain the historical standard waveform, or a waveform of similar patient conditions obtained through big data analysis according to the patient conditions.
S2-2, differentiating a rest acquisition waveform corresponding to the rest acquisition data and a historical standard waveform to generate a rest deviation waveform; the extraction of the characteristics is facilitated by waveform differencing, so that abnormal conditions can be found.
S2-3, calculating the envelope area of the rest deviation waveform to generate a rest confidence value, wherein the rest confidence value and the envelope area are positively correlated, calculating through a preset area-confidence value mapping function to obtain the rest confidence value, and if the rest confidence value exceeds a preset expected rehabilitation range, indicating that the installation position is incorrect or the patient is not normally relaxed, re-acquiring the rest feedback data until the rest confidence value is lower than the preset expected rehabilitation range; the value of the rehabilitation expectation range is empirically set.
And step S2-4, matching corresponding rest feedback characteristics from the rest deviation waveforms through a preset rest characteristic matching database. The corresponding rest feedback characteristics, namely the waveform characteristics of the patient in the rest state, can be obtained by matching the rest characteristic matching database, the rest characteristic matching data stores the waveform characteristics in advance, the rest data of the historical patient is analyzed by a clustering analysis algorithm, and the relation between the rest feedback characteristics and the illness state of the patient is extracted, so that the rest feedback characteristics which need to be identified and noted are extracted.
S3, bringing the rest feedback characteristic into the rehabilitation task list to adjust the sequence of the rehabilitation tasks in the rehabilitation task list, and outputting the rehabilitation tasks to the interaction assembly according to the sequence; the medical staff and the patient can be guided to operate to finish the rehabilitation task, each rehabilitation task is provided with a plurality of intensity options, each intensity option is provided with an intensity value, the step S3 further comprises the steps of determining the corresponding intensity option from the rehabilitation task through a resting feedback characteristic, determining that the intensity options of different rehabilitation tasks are different, each rehabilitation task is positively correlated with a basic intensity value and a movement load, selecting the different intensity options to obtain an intensity value coefficient, calculating to obtain a final intensity value, adjusting the different intensity value coefficient through the different resting feedback characteristic, judging the lower limb strength rehabilitation training which is obliquely upward through the resting feedback characteristic, and if the difficulty is high, changing the intensity option of the lower limb rehabilitation training in the rehabilitation task, particularly providing assistance during movement, or reducing the requirement, and sequencing the execution sequence of the rehabilitation tasks according to the intensity value. The ordering of the intensity values may be from large to small and then larger.
S4, collecting static feedback data from the myoelectricity detection unit, wherein the static feedback data is feedback waveforms of the myoelectricity detection unit under the condition that a patient exerts force, and extracting static feedback characteristics from the static feedback data according to a preset static analysis strategy; the static analysis strategy comprises the steps of pre-constructing a static characteristic database, wherein the static characteristic database is pre-stored with a plurality of static sub-characteristics, each static sub-characteristic is corresponding to a static trust value, and the static trust value reflects the times of the occurrence of the static sub-characteristic in historical data; the more times that a static sub-feature appears, the more the static trust value is accounted for
The static analysis strategy comprises a static analysis algorithm, wherein the static analysis algorithm calculates a static matching value corresponding to each combination of the static sub-features, and the static matching value comprisesWherein->For the value of the static match,for the duration of the myoelectric waveform that can be replaced by the static sub-feature, +.>Is the total duration of the myoelectric waveform, +.>For the number of static sub-features in the combination of the current static sub-features, +.>For the preset matching relation weight, +.>For a preset trust relationship weight, +.>Is->Similarity of individual static sub-features to the corresponding replaced myoelectric bio-waveforms, +.>Is->Static trust values corresponding to the static sub-features; judgment by static matching algorithmAnd (3) combining the static sub-features, selecting the most reliable combination for matching, and finally determining the corresponding myoelectricity biological waveform similarity. Because a complete waveform can be regarded as a combination of a plurality of static sub-features, if the similarity is higher, the preset static sub-waveforms can be replaced, and if the complete waveform can be combined into a corresponding actual waveform, the analysis result is closer, so that the accuracy is higher.
The static analysis strategy selects a combination of static sub-features with highest static matching values to generate the static feedback feature.
S5, bringing the static feedback characteristic into a pre-trained rehabilitation learning model to obtain a corresponding action instruction, and sending the action instruction to an upper limb movement module and a lower limb movement module to drive the upper limb movement assembly and the lower limb movement assembly to work respectively; the action instruction also comprises a stimulation current for controlling the myoelectricity detection unit. Each rehabilitation task corresponds to a rehabilitation training model, a plurality of nerve nodes are configured in the rehabilitation training model, each nerve node is configured with a task matching load corresponding to each static sub-feature, the total value of the corresponding task matching load is calculated by each static feedback feature after passing through one node, the position of the next nerve node is determined in the rehabilitation training model according to the total value of the task matching load until a nerve transmission path is completed, and corresponding action instructions are matched according to the nerve transmission path. The application of the model is the power or resistance and the gesture at each moment and the size and mode of the electric stimulation, so the combination is very complex, a plurality of nodes are preconfigured in a neural network mode, and then the sub-features are identified in a feature matching mode to quickly find the instruction set. Therefore, by indexing the nodes with higher recognition degree, an effective neural network transmission path can be rapidly determined, so that an action instruction corresponding to the path is obtained, the rehabilitation learning model continuously calls historical data to train through the pose simulation mimicry model, then the nodes are generated by simulating the theoretical stimulation condition of each position, each node corresponds to each feature and is loaded, then the path is rapidly found according to the load, and an instruction set, namely the combination of the nodes, can be obtained by selecting the path with the minimum load. The training of the model is to simulate by simulation software to provide data of a training set and a testing set.
Step S6, according to the action instruction, theoretical action feedback data are called from a preset action association database, action feedback data are collected from the myoelectricity detection unit, and the action feedback data are compared with the theoretical action feedback data to generate action deviation characteristics; in step S6, a biological force application simulation model is pre-constructed, different action instructions are constructed according to the biological force application simulation model, force application vectors of the force application points at different moments are generated according to the biological force application relationship, and theoretical biological current is calculated according to the force application vectors to generate theoretical action feedback data. And establishing a force application relation between each position of the organism and the final output through a simulation force application model, and then obtaining a theoretical neurotransmitter transmission relation, so that theoretical action feedback data can be obtained.
Step S7, the action deviation characteristic is brought into a rehabilitation task list to correct the rehabilitation task, and the rehabilitation task is output to the interaction component; each action deviation feature is configured with a rehabilitation correction value corresponding to each task type, and the corresponding rehabilitation correction value is brought into a rehabilitation task corresponding to the task type to update the corresponding intensity option. If the deviation of the motion is characterized, the deviation is detected and the force is excessively long, for example, the load is increased, and then a certain assistance force is provided or a certain resistance force is reduced.
And step S8, returning to the step S4 until the rehabilitation tasks of the rehabilitation task list are completed.
The myoelectricity detection unit is coupled with a myoelectricity data analysis module; the myoelectricity data analysis module is configured with a resting acquisition strategy and a static acquisition strategy, wherein the resting acquisition strategy is used for acquiring resting feedback data, and the static acquisition strategy is used for acquiring the static feedback data;
the rest acquisition strategy comprises the steps of calculating a deviation average value in each preset rest acquisition window by a rest acquisition algorithm, taking a biological myoelectric waveform corresponding to the rest acquisition window with the largest deviation average value as rest feedback data, wherein the rest acquisition algorithm comprises the following steps of
Wherein->The acquisition range for the resting acquisition window is +.>Mean value of deviation at time,/->For a preset segmentation rest weight, +.>For the preset macro window rest weight, there is +.>，/>For the bio-myoelectric waveform obtained in step S2, < >>For the bioelectric waveform obtained in step S2 +.>For the mean value piecewise function of the piecewise interval, +.>For a predetermined segment interval +.>For the total number of segmentation intervals in the resting acquisition window, +.>The mean value of the bioelectricity waveform in a resting acquisition window is obtained;
the static acquisition strategy comprises the steps of acquiring a mean value piecewise function in the static feedback data, generating a static threshold breaking model according to the distribution of the mean value piecewise function, taking the bioelectricity waveform acquired in the step S4 into the static threshold breaking model to output a static threshold value in real time, taking the moment when the static threshold value is higher than a preset static upper limit reference for the first time as an acquisition starting point of the static feedback data, and taking the moment when the static threshold value is lower than a preset static line lower reference for the first time as an acquisition end point of the static feedback data. By preprocessing the collected data in different states, the calculation amount can be reduced during analysis, and the efficiency can be improved.
According to the invention, the four-limb linkage technology and the myoelectricity biofeedback technology are combined, and the resistance, the posture and the position change are changed in real time through analysis of feedback information, so that an intelligent treatment effect is achieved. The specific implementation mode is as follows:
the spinal cord injury limb linkage rehabilitation training method based on the electrical stimulation-myoelectricity biofeedback can be divided into the following four steps, and fig. 1 is a design diagram of an overall scheme:
1. rest state and task state surface myoelectricity detection
The surface myoelectricity detection electrode is arranged at the myoabdomen of the muscle to be trained, and the reference electrode is arranged at the apophyma. After the surface myoelectric signals are amplified, filtered and subjected to analog-to-digital conversion, the surface myoelectric values (root mean square average) of the resting state/task state of the muscles are displayed on the interaction assembly.
2. Setting an electric stimulation control mode, confirming an electric stimulation application position according to a treatment target, and adjusting electric stimulation related parameters including stimulation frequency, stimulation intensity and stimulation waveform.
3. Setting a threshold value, setting a surface electromyographic signal value which enables the electric stimulation and the four-limb linkage device to act on based on the detected task state muscle surface electromyographic signal value, namely, after subjective exertion of force of a patient reaches the threshold value, completing static acquisition, performing nerve regulation by the electric stimulation device, and performing movement by the four-limb linkage device.
4. After the four limbs linkage module power is set and the surface myoelectricity value reaches a set threshold value, the PAD control device transmits a power value, the power value range is between 0 and 180, the initial value of the power is 0, and after the control module receives the power value, the PWM duty ratio is adjusted, so that the voltage is changed, and corresponding power is generated. As can be seen from u=ir, f=bil, where U represents voltage, I represents current intensity, R represents resistance, B represents magnetic induction intensity, I represents current intensity, and L represents length of wire perpendicular to magnetic induction line. When the voltage is larger, the generated magnetic field is stronger, thereby generating more power.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the bioelectricity feedback, the electric stimulation technology and the four-limb linkage technology are combined, the electric stimulation and the four-limb linkage can be applied according to the real-time state of a patient, the defects of passive rehabilitation and limitation of an action part of the traditional electric stimulation technology are overcome, the defects of low individuation degree and incapability of treating a spinal cord injury position of the traditional four-limb linkage technology are overcome, and the defects that the traditional myoelectric biofeedback technology can not directly promote nerve regeneration or repair and limitation of an action object are overcome.
2. According to the invention, through the cooperation of hardware and software parts, the exercise intensity can be adjusted in real time according to the myoelectric data of the muscle surface of the patient, so that the exercise intensity is convenient for the patient to use.
One specific example is as follows:
1. the limbs of the patient are kept relaxed, the surface myoelectricity detection electrodes a and b are placed at the myoabdominal bulge of the biceps of the left upper limb, and the reference electrode c is placed at the olecranon of the left upper limb; the surface myoelectricity detection electrodes d and e are placed at the left lower limb quadriceps myoabdominal bulge, and the reference electrode f is placed at the left lower limb patella. The rms average M, N of the surface myoelectricity of the left upper limb and the left lower limb of the patient in the resting state for 5 seconds was measured, and then the patient was allowed to exercise actively, and the rms average A, B of the surface myoelectricity of the left upper limb and the left lower limb of the patient in the maximum muscle contraction state for 5 seconds was measured.
2. The electrode plate is placed at the spinal cord injury position of a patient, the electric stimulation frequency is set to be 20Hz, the electric stimulation intensity is 10mA, and the electric stimulation waveform is intermittent wave.
3. According to the surface electromyographic signal value detected by the patient, setting the upper limb threshold value to be 50% A and setting the lower limb threshold value to be 500% N.
4. The upper limb and the lower limb of the patient are subjectively stressed, and the muscle exertion degree of the patient is displayed on a display screen. When the myoelectricity on the surface of the muscles of the upper and lower limbs reaches 50% A and 500% N, the four-limb linkage device starts to start immediately to drive the limbs of the upper and lower limbs of the patient to move, after the patient continues to exert force for 5 seconds, static collection is completed, the four-limb linkage device receives an action instruction to start, the limbs of the upper and lower limbs of the patient are driven to move, and after one cycle is completed, the threshold is reduced by 20% by correcting deviation characteristics.
In summary, the spinal cord injury limb linkage rehabilitation training method based on the electrical stimulation-myoelectricity biofeedback provided by the embodiment of the invention can improve individuation of the traditional limb linkage equipment on the basis of jointly recovering the local injury position and the whole function of the spinal cord of a patient, and further research can obtain a perfect spinal cord injury electrical stimulation-myoelectricity biofeedback type limb linkage training system.
Of course, the above is only a typical example of the invention, and other embodiments of the invention are also possible, and all technical solutions formed by equivalent substitution or equivalent transformation fall within the scope of the invention claimed.
Claims (10)
1. Spinal cord injury rehabilitation device based on myoelectricity biofeedback, its characterized in that: the device comprises an upper limb movement assembly, a lower limb movement assembly, a controller, an myoelectricity detection unit and an interaction assembly;
the upper limb movement assembly comprises an upper limb control module, the lower limb movement assembly comprises a lower limb movement module, and the upper limb movement module, the lower limb movement module and the myoelectricity detection unit are all coupled with the controller;
the controller is configured with a feedback drive strategy comprising:
step S1, acquiring patient information and matching a corresponding rehabilitation task list from a preset feedback information base according to the patient information;
s2, collecting the rest feedback data from the myoelectricity detection unit, and extracting the rest feedback characteristics from the rest feedback data according to a preset rest analysis sub-strategy;
s3, bringing the rest feedback characteristic into the rehabilitation task list to adjust the sequence of the rehabilitation tasks in the rehabilitation task list, and outputting the rehabilitation tasks to the interaction assembly according to the sequence;
s4, collecting static feedback data from the myoelectricity detection unit, and extracting static feedback characteristics from the static feedback data according to a preset static analysis strategy;
s5, bringing the static feedback characteristic into a pre-trained rehabilitation learning model to obtain a corresponding action instruction, and sending the action instruction to an upper limb movement module and a lower limb movement module to drive the upper limb movement assembly and the lower limb movement assembly to work respectively;
step S6, according to the action instruction, theoretical action feedback data are called from a preset action association database, action feedback data are collected from the myoelectricity detection unit, and the action feedback data are compared with the theoretical action feedback data to generate action deviation characteristics;
step S7, the action deviation characteristic is brought into a rehabilitation task list to correct the rehabilitation task, and the rehabilitation task is output to the interaction component;
and step S8, returning to the step S4 until the rehabilitation tasks of the rehabilitation task list are completed.
2. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 1, wherein: the myoelectricity detection unit is coupled with a myoelectricity data analysis module; the myoelectricity data analysis module is configured with a resting acquisition strategy and a static acquisition strategy, wherein the resting acquisition strategy is used for acquiring resting feedback data, and the static acquisition strategy is used for acquiring the static feedback data;
the rest acquisition strategy comprises the steps of calculating a deviation average value in each preset rest acquisition window by a rest acquisition algorithm, taking a biological myoelectric waveform corresponding to the rest acquisition window with the largest deviation average value as rest feedback data, wherein the rest acquisition algorithm comprises the following steps of
Wherein->The acquisition range for the resting acquisition window is +.>Mean value of deviation at time,/->For a preset segmentation rest weight, +.>For the preset macro window rest weight, there is +.>，/>For the bio-myoelectric waveform obtained in step S2, < >>For the bioelectric waveform obtained in step S2 +.>For the mean value piecewise function of the piecewise interval, +.>For a predetermined segment interval +.>For the total number of segmentation intervals in the resting acquisition window, +.>The mean value of the bioelectricity waveform in a resting acquisition window is obtained;
the static acquisition strategy comprises the steps of acquiring a mean value piecewise function in the static feedback data, generating a static threshold breaking model according to the distribution of the mean value piecewise function, taking the bioelectricity waveform acquired in the step S4 into the static threshold breaking model to output a static threshold value in real time, taking the moment when the static threshold value is higher than a preset static upper limit reference for the first time as an acquisition starting point of the static feedback data, and taking the moment when the static threshold value is lower than a preset static line lower reference for the first time as an acquisition end point of the static feedback data.
3. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 1, wherein: the action instruction also comprises a stimulation current for controlling the myoelectricity detection unit.
4. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 1, wherein: the feedback information base stores a plurality of rehabilitation tasks in advance, each rehabilitation task takes a patient matching characteristic as an index, and each rehabilitation task is associated with rehabilitation value data, a rehabilitation matching algorithm is configured in step S1, the rehabilitation matching algorithm is used for calculating a rehabilitation matching value of a rehabilitation task list corresponding to each rehabilitation task, the corresponding rehabilitation task is called from the feedback information base to maximize the sum of the rehabilitation matching values under a preset rehabilitation constraint condition, and the rehabilitation matching algorithm is thatWherein->For said rehabilitation matching value, +.>For the rehabilitation effect value in the rehabilitation value data corresponding to the rehabilitation task, < ->Matching the similarity value of the characteristics and the patient information for the patient corresponding to the rehabilitation task>For the exercise burden value in the rehabilitation value data corresponding to the rehabilitation task, < ->For the load conflict value of the rehabilitation task and other rehabilitation tasks in the rehabilitation task list, +.>Is the preset weight of rehabilitation effect, +.>Preset disease similarity weight, ++>Preset exercise burden weight, +.>And (5) presetting a load conflict weight.
5. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 1, wherein: the resting analysis sub-strategy comprises
S2-1, calling a historical standard waveform according to patient information;
s2-2, differentiating a rest acquisition waveform corresponding to the rest acquisition data and a historical standard waveform to generate a rest deviation waveform;
s2-3, calculating the envelope area of the resting deviation waveform to generate a resting confidence value, and if the resting confidence value exceeds a preset rehabilitation expected range, re-acquiring resting feedback data until the resting confidence value is lower than the preset rehabilitation expected range;
and step S2-4, matching corresponding rest feedback characteristics from the rest deviation waveforms through a preset rest characteristic matching database.
6. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 1, wherein: each rehabilitation task is corresponding to a plurality of intensity options, each intensity option is corresponding to an intensity value, and the step S3 further comprises determining the corresponding intensity option from the rehabilitation tasks through a resting feedback feature, and sequencing the execution sequence of the rehabilitation tasks according to the intensity values.
7. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 1, wherein: the static analysis strategy comprises the steps of pre-constructing a static characteristic database, wherein the static characteristic database is pre-stored with a plurality of static sub-characteristics, each static sub-characteristic is corresponding to a static trust value, and the static trust value reflects the times of the occurrence of the static sub-characteristic in historical data;
the static analysis strategy comprises a static analysis algorithm, wherein the static analysis algorithm calculates a static matching value corresponding to each combination of the static sub-features, and the static matching value comprisesWherein->For static match value, +.>For the duration of the myoelectric waveform that can be replaced by the static sub-feature, +.>Is the total duration of the myoelectric waveform, +.>For the number of static sub-features in the combination of the current static sub-features, +.>For the preset matching relation weight, +.>For a preset trust relationship weight, +.>Is->Similarity of individual static sub-features to the corresponding replaced myoelectric bio-waveforms, +.>Is->Static trust values corresponding to the static sub-features;
the static analysis strategy selects a combination of static sub-features with highest static matching values to generate the static feedback feature.
8. The spinal cord injury rehabilitation device based on myoelectric biofeedback according to claim 7, wherein: each rehabilitation task corresponds to a rehabilitation training model, a plurality of nerve nodes are configured in the rehabilitation training model, each nerve node is configured with a task matching load corresponding to each static sub-feature, the total value of the corresponding task matching load is calculated by each static feedback feature after passing through one node, the position of the next nerve node is determined in the rehabilitation training model according to the total value of the task matching load until a nerve transmission path is completed, and corresponding action instructions are matched according to the nerve transmission path.
9. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 1, wherein: in step S6, a biological force application simulation model is pre-constructed, different action instructions are constructed according to the biological force application simulation model, force application vectors of the force application points at different moments are generated according to the biological force application relationship, and theoretical biological current is calculated according to the force application vectors to generate theoretical action feedback data.
10. The spinal cord injury rehabilitation device based on myoelectricity biofeedback according to claim 6, wherein: each action deviation feature is configured with a rehabilitation correction value corresponding to each task type, and the corresponding rehabilitation correction value is brought into a rehabilitation task corresponding to the task type to update the corresponding intensity option.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102567638A (en) * | 2011-12-29 | 2012-07-11 | 无锡微感科技有限公司 | Interactive upper limb rehabilitation system based on micro-sensor |
| KR20130034896A (en) * | 2011-09-29 | 2013-04-08 | 연세대학교 원주산학협력단 | Emg feedback-based active training system for rehabilitation exercise |
| WO2015019477A1 (en) * | 2013-08-08 | 2015-02-12 | 株式会社日立製作所 | Rehab system and control method therefor |
| CN104997523A (en) * | 2014-04-18 | 2015-10-28 | 东北大学 | Upper limb rehabilitation robot rehabilitation training motion function assessment method |
| WO2018214527A1 (en) * | 2017-05-25 | 2018-11-29 | 深圳市未来健身科技有限公司 | Rehabilitation and healthcare assessment method and apparatus |
| CN111345823A (en) * | 2018-12-24 | 2020-06-30 | 中国移动通信有限公司研究院 | Remote exercise rehabilitation method and device and computer readable storage medium |
| CN113990441A (en) * | 2021-11-25 | 2022-01-28 | 杭州电子科技大学 | Lower limb knee joint active muscle myoelectric fitting method based on biodynamics |
| CN114366556A (en) * | 2021-12-31 | 2022-04-19 | 华南理工大学 | Multi-mode training control system and method for lower limb rehabilitation |
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130034896A (en) * | 2011-09-29 | 2013-04-08 | 연세대학교 원주산학협력단 | Emg feedback-based active training system for rehabilitation exercise |
| CN102567638A (en) * | 2011-12-29 | 2012-07-11 | 无锡微感科技有限公司 | Interactive upper limb rehabilitation system based on micro-sensor |
| WO2015019477A1 (en) * | 2013-08-08 | 2015-02-12 | 株式会社日立製作所 | Rehab system and control method therefor |
| CN104997523A (en) * | 2014-04-18 | 2015-10-28 | 东北大学 | Upper limb rehabilitation robot rehabilitation training motion function assessment method |
| WO2018214527A1 (en) * | 2017-05-25 | 2018-11-29 | 深圳市未来健身科技有限公司 | Rehabilitation and healthcare assessment method and apparatus |
| CN111345823A (en) * | 2018-12-24 | 2020-06-30 | 中国移动通信有限公司研究院 | Remote exercise rehabilitation method and device and computer readable storage medium |
| CN113990441A (en) * | 2021-11-25 | 2022-01-28 | 杭州电子科技大学 | Lower limb knee joint active muscle myoelectric fitting method based on biodynamics |
| CN114366556A (en) * | 2021-12-31 | 2022-04-19 | 华南理工大学 | Multi-mode training control system and method for lower limb rehabilitation |
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