Muscle rehabilitation training intensity monitoring method and system

Abstract

The invention provides a muscle rehabilitation training intensity monitoring method and system, and the method comprises the steps: obtaining first-round joint activity data, first-round muscle training saturation data and first-round muscle fatigue tolerance data when determining to start muscle rehabilitation training; when the first-round muscle fatigue tolerance data is smaller than a preset fatigue threshold value, muscle rehabilitation training is suspended until the preset rest duration is met, and muscle rehabilitation training is recovered; the muscle rehabilitation training process is repeated until the joint motion range data difference value of two continuous rounds of muscle rehabilitation training meets the preset training requirement and the muscle training saturation data difference value of the two continuous rounds of muscle rehabilitation training meets the preset training requirement, and the muscle rehabilitation training is finished. The problem that in the prior art, the training intensity is not clear due to the fact that the training amount tolerance cannot be objectively detected is solved. According to the method provided by the invention, the training intensity can be clearly determined by objectively monitoring the training amount tolerance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of assisted rehabilitation training, in particular to a muscle rehabilitation training intensity monitoring method and system. BACKGROUND

[0002] Robot-assisted rehabilitation training intervention should be combined with continuous objective quantitative motor function evaluation to monitor rehabilitation progress, optimize the process and achieve individualized optimal rehabilitation effect.

[0003] At present, the existing technology rehabilitation content and course design and efficacy evaluation are mostly fixed or subjective courses, and the training content is based on single clinical characteristics, such as 2-3 weeks of hospitalization in a comprehensive hospital, 1 hour of upper limb training per day, or the popular early high-intensity rehabilitation intervention and research in recent years; the efficacy is determined according to the comparison of clinical scales and performance evaluation before and after the training course. Such evaluation cannot reveal the saturation and characteristic changes of the rehabilitation process, such as whether the individual patient's motor function recovery has entered the plateau period, whether the corresponding training can be stopped or alternative rehabilitation is selected, or secondary damage is caused by improper operation; at the same time, the clinical scale evaluation cannot reflect the small functional changes in the individual rehabilitation process through continuous repeated detection; at the same time, the current rehabilitation intervention has not realized the objective detection of the training tolerance of individual patients, resulting in the problem that the training intensity cannot be clearly determined whether it is insufficient or excessive. SUMMARY

[0004] Based on the above problems, the present application provides a muscle rehabilitation training intensity monitoring method and system, which solves the problem that the training tolerance cannot be objectively detected, resulting in unclear training intensity.

[0005] To achieve the above purpose, the embodiment of the present application provides a muscle rehabilitation training intensity monitoring method, which comprises:

[0006] When it is determined to start muscle rehabilitation training, the joint activity angle information of the first round of patients is collected to obtain the first round of joint activity data;

[0007] The muscle electrical signals associated with the active joints of the first round of patients are collected, the muscle saturation of the patient is evaluated, and the first round of muscle training saturation data is obtained;

[0008] Based on the preset first algorithm and the muscle electrical signals associated with the active joints of the first round of patients, the first round of muscle fatigue tolerance data is obtained;

[0009] When the first round of muscle fatigue tolerance data is less than the preset fatigue threshold, the muscle rehabilitation training is suspended until the preset rest duration is met, and the muscle rehabilitation training is resumed;

[0010] The above muscle rehabilitation training process is repeated until the difference value of the joint range of motion data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement and the difference value of the muscle training saturation data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement, and the muscle rehabilitation training is ended.

[0011] The embodiment of the present application proposes a muscle rehabilitation training intensity monitoring method. Through the objective detection of muscle training objective data, the training intensity of subsequent muscles can be obtained based on accurate data. The objective data includes joint data, electromyography data and muscle fatigue data. The data is obtained from multiple dimensions to judge the range of joint movement and the saturation and fatigue of muscles, so that more comprehensive monitoring can be obtained during rehabilitation training. At the same time, the rehabilitation training process is monitored by setting a fatigue threshold. Based on the above multi-dimensional data basis, it is determined whether the muscle needs to rest according to the training degree. The training intensity of the muscle is effectively monitored according to the data change difference before and after training to prevent the problem of secondary muscle damage caused by insufficient or excessive training. Therefore, the method proposed by the present application can realize the effect of clear training intensity through objective monitoring of training tolerance.

[0012] Further, when it is determined to start muscle rehabilitation training, the joint movement angle information of the first round of patients is collected to obtain the first round of joint range of motion data, specifically:

[0013] When it is determined to start muscle rehabilitation training, the joint movement angle information of the first round of patients is collected. Based on the digital-electric signal conversion algorithm, the joint movement angle information of the first round of patients is converted into digital-electric signal to obtain the first round of joint range of motion data.

[0014] Further, the muscle electrical signal associated with the moving joint of the first round of patients is collected, and the saturation of the muscle of the patient is evaluated to obtain the first round of muscle training saturation data, specifically:

[0015] The muscle electrical signal associated with the moving joint of the first round of patients is collected by collecting the muscle electrical signal of the first round of patients. Based on the muscle electrical signal associated with the moving joint of the first round of patients, a first muscle normalized envelope amplitude is constructed. According to the first muscle normalized envelope amplitude, the electromyography mean value of each muscle of the patient is calculated to obtain a first plurality of muscle electromyography mean values. Based on the first plurality of muscle electromyography mean values, a first muscle common contraction index is obtained by repeating value acquisition. Based on the first plurality of muscle electromyography mean values and the first muscle common contraction index, the saturation of the muscle of the patient is evaluated to obtain the first round of muscle training saturation data.

[0016] Further, the first round of muscle fatigue tolerance data is obtained based on the preset first algorithm and the muscle electrical signal associated with the joint of the patient during the activity, specifically:

[0017] The first muscle electromyography power is obtained based on the muscle electrical signal associated with the joint of the patient during the activity, the first muscle electromyography power frequency is obtained based on the first muscle electromyography power, and the average probability of the muscle of the patient is calculated through the first muscle electromyography power and the first muscle electromyography power frequency based on the preset first algorithm to obtain the first round of muscle fatigue tolerance data.

[0018] Further, when the first round of muscle fatigue tolerance data is less than the preset fatigue threshold, the muscle rehabilitation training is suspended until the preset rest duration is met, and the muscle rehabilitation training is resumed, specifically:

[0019] The first round of muscle fatigue tolerance data is monitored in real time based on the preset fatigue threshold, the muscle rehabilitation training is suspended when it is determined that the first round of muscle fatigue tolerance data is less than the preset fatigue threshold, the muscle rehabilitation training suspension duration is monitored based on the preset rest duration, and the muscle rehabilitation training is resumed when it is determined that the muscle rehabilitation training suspension duration meets the preset rest duration.

[0020] Further, the above muscle rehabilitation training process is repeated until the difference value of the joint activity degree data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement and the difference value of the muscle training saturation data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement, and the muscle rehabilitation training is ended, specifically:

[0021] When it is determined that the muscle rehabilitation training is resumed, the joint activity angle information of the second round of patients is collected, the joint activity angle information of the second round of patients is converted into electrical signals based on the electrical signal conversion algorithm to obtain the second round of joint activity degree data, the second muscle normalized envelope amplitude is constructed based on the muscle electrical signal associated with the joint during the activity of the second round, the muscle electrical mean value of the patient is calculated based on the second muscle normalized envelope amplitude to obtain the second muscle electrical mean value, the second muscle common contraction index is obtained through repeated value acquisition based on the second muscle electrical mean value, the muscle saturation of the second round is evaluated based on the second muscle electrical mean value and the second muscle common contraction index to obtain the second round of muscle training saturation, and it is determined whether the muscle rehabilitation training is ended by calculating the difference value of the first round of joint activity degree data and the second round of joint activity degree data to meet the preset training requirement and the difference value of the first round of muscle training saturation and the second round of muscle training saturation to meet the preset training requirement.

[0022] Further, the muscle rehabilitation training is ended when the difference between the first joint range of motion data and the second joint range of motion data meets the preset training requirement and the difference between the first muscle training saturation and the second muscle training saturation meets the preset training requirement.

[0023] The first range of motion difference value is obtained by calculating the difference between the first joint range of motion data and the second joint range of motion data, and the first saturation difference value is obtained by calculating the difference between the first muscle training saturation and the second muscle training saturation; the muscle rehabilitation training is ended when it is determined that the first range of motion difference value and the first saturation difference value both meet the preset training requirement.

[0024] Further, the muscle rehabilitation training is resumed when it is determined that the first range of motion difference value and the first saturation difference value both do not meet the preset training requirement.

[0025] When it is determined that the muscle rehabilitation training is resumed, the third joint range of motion data and the third muscle training saturation are obtained; the second range of motion difference value and the second saturation difference value are calculated based on the second joint range of motion data and the second muscle training saturation and the third joint range of motion data and the third muscle training saturation; it is determined whether the second range of motion difference value and the second saturation difference value meet the preset training requirement; if the preset training requirement is met, the muscle rehabilitation training is ended; if the preset training requirement is not met, the above steps are repeated and the second range of motion difference value and the second saturation difference value are updated until the preset training requirement is met, and the muscle rehabilitation training is ended.

[0026] The embodiment of the present application also provides a muscle rehabilitation training intensity monitoring system, comprising:

[0027] a first data acquisition module, a second data acquisition module, a third data acquisition module, a first monitoring module and a second monitoring module;

[0028] The first data acquisition module is used to collect the joint activity angle information of the patient in the first round when it is determined that the muscle rehabilitation training is started, and obtain the first joint range of motion data.

[0029] The second data acquisition module is used to collect the muscle electrical signal associated with the active joint of the patient in the first round, evaluate the muscle saturation of the patient, and obtain the first muscle training saturation data.

[0030] The third data acquisition module is used to obtain the first muscle fatigue endurance data based on a preset first algorithm and the muscle electrical signal associated with the active joint of the patient in the first round.

[0031] The first monitoring module is used for suspending the muscle rehabilitation training until a preset rest duration is met when the first round muscle fatigue endurance data is less than a preset fatigue threshold value, and resuming the muscle rehabilitation training.

[0032] The second monitoring module is used for repeating the above muscle rehabilitation training process until a difference value of joint range of motion data of two consecutive rounds of muscle rehabilitation training meets a preset training requirement and a difference value of muscle training saturation data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement, and ending the muscle rehabilitation training.

[0033] The embodiment of the present application provides a muscle rehabilitation training intensity monitoring system. Objective data of muscle training can be observed and detected through a first data acquisition module, a second data acquisition module and a third data acquisition module, so that accurate data basis of subsequent muscle training intensity can be obtained. The objective data includes joint data, electromyography data and muscle fatigue data. Data is obtained from multiple dimensions, and the range of joint motion and the saturation and fatigue of muscle are judged, so that more comprehensive monitoring can be obtained in the rehabilitation training process. The first monitoring module sets a fatigue threshold value to monitor the rehabilitation training process. Based on the above multi-dimensional data basis, whether the muscle needs to rest is determined by judging the training degree of the muscle. The second monitoring module effectively monitors the training intensity of the muscle according to the data change difference before and after training, so as to prevent the problem of secondary muscle damage caused by insufficient training or excessive training. Therefore, the method provided by the present application can realize the effect of clear training intensity through objective monitoring of training amount endurance.

[0034] Further, the second monitoring module is used for repeating the above muscle rehabilitation training process until a difference value of joint range of motion data of two consecutive rounds of muscle rehabilitation training meets a preset training requirement and a difference value of muscle training saturation data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement, and ending the muscle rehabilitation training, and further comprising:

[0035] A first training intensity judgment unit, a training data acquisition unit, a training data processing unit, a second training intensity judgment unit and a training data updating unit.

[0036] The first training intensity judgment unit is used for resuming the muscle rehabilitation training when it is determined that the difference value of the range of motion and the difference value of the saturation do not meet the preset training requirement.

[0037] The training data acquisition unit is used for acquiring third round joint range of motion data and third round muscle training saturation when it is determined that the muscle rehabilitation training is resumed.

[0038] The training data processing unit is configured to calculate a second range of motion difference value and a second muscle training saturation difference value based on the second round joint range of motion data and the second round muscle training saturation and the third round joint range of motion data and the third round muscle training saturation.

[0039] The second training intensity judgment unit is configured to judge whether the second range of motion difference value and the second muscle training saturation difference value meet a preset training requirement, and if the preset training requirement is met, end the muscle rehabilitation training.

[0040] The training data updating unit is configured to update the second range of motion difference value and the second muscle training saturation difference value until the preset training requirement is met if the preset training requirement is not met, and end the muscle rehabilitation training. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A step flowchart of a muscle rehabilitation training intensity monitoring method provided by an embodiment of the present application is shown in the figure.

[0042] Figure 2 A step flowchart of a data processing repetition process of a muscle rehabilitation training intensity monitoring method provided by an embodiment of the present application is shown in the figure.

[0043] Figure 3 A specific application implementation manner diagram of a muscle rehabilitation training intensity monitoring method provided by an embodiment of the present application is shown in the figure.

[0044] Figure 4 A module structure diagram of a muscle rehabilitation training intensity monitoring system provided by an embodiment of the present application is shown in the figure.

[0045] Figure 5 A second monitoring module structure diagram of a muscle rehabilitation training intensity monitoring system provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0047] In the embodiment, in order to better explain the technical solutions of the present application, the Io-ENMS system is used to execute the technical solutions of the present application as one of the embodiments of the present application, which is not limited to other embodiments of the present application, and will not be described hereinafter. Specifically, as shown in the figure, Figure 3 Figure 3 ​A specific application embodiment schematic diagram of a muscle rehabilitation training intensity monitoring method provided for an embodiment of the present application, the Io-ENMS system comprises: elbow and wrist joint fusion angle sensor (Flex sensor) and control box (control board).

[0048] Embodiment 1

[0049] Reference Figure 1 , Figure 1 A step flowchart of a muscle rehabilitation training intensity monitoring method provided for an embodiment of the present application. As shown in the figure, Figure 1 The embodiment of the present application proposes a muscle rehabilitation training intensity monitoring method, comprising steps 101 to 105, and each step is as follows:

[0050] Step 101, when determining to start muscle rehabilitation training, collect the joint activity angle information of the first round of patients, and obtain the first round of joint range of motion data;

[0051] As an example of the present embodiment, when determining to start muscle rehabilitation training, the joint activity angle information of the first round of patients is collected; based on the digital and electrical signal conversion algorithm, the joint activity angle information of the first round of patients is converted into digital and electrical signals, and the first round of joint range of motion data is obtained. Specifically, one of the implementable ways is to measure the change of joint range of motion (ROM) in the training process in real time through the elbow and wrist joint fusion angle sensor (Flex sensor), and then calculate the ROM value (equivalent to joint range of motion data) through the digital I / O circuit in the control box by MCU.

[0052] Step 102, collect the muscle electrical signal associated with the active joint of the first round of patients, evaluate the muscle saturation of the patient, and obtain the first round of muscle training saturation data;

[0053] As an example of the present embodiment, the muscle electrical signal associated with the active joint of the first round of patients is obtained by collecting the muscle electrical signal of the first round of patients; based on the muscle electrical signal associated with the active joint of the first round of patients, a first muscle normalized envelope amplitude is constructed; according to the first muscle normalized envelope amplitude, the mean value of each muscle of the patient is calculated respectively, and the first muscle mean value is obtained; based on the first muscle mean value, the first muscle common contraction index is obtained by repeating value acquisition; based on the first muscle mean value and the first muscle common contraction index, the muscle saturation of the patient is evaluated, and the first round of muscle training saturation data is obtained. Specifically, one of the implementable ways is that the muscle electrical signal (EMG) associated with the active joint of the patient is collected by the existing skin surface electrode, and the quantification of EMG level can be mathematically expressed as:

[0054]

[0055] where T is the observation length, EMG i (t) is the normalized envelope amplitude of the target muscle i, is the mean EMG level (quantifying the EMG level).

[0056] The independent contraction between the muscle pairs can be quantified by the co-contraction index (CI) (equivalent to the muscle co-contraction index):

[0057] where A ij is the overlapping part of the EMG of the two target muscles i and j.

[0058] According to the obtained characteristics of the sustained EMG changes, it is found that whether it is a patient in the early stage or a patient in the chronic stage, the patient will show a decrease in the EMG level of the spastic muscle and an increase in the independent contraction between the muscle pairs during the rehabilitation of the upper limb function. In the previous study on the rehabilitation of the upper limb after stroke, by comparing the decreasing trend of the mean EMG level in each day of training, the improvement process of the muscle tension can be reflected, and the decreasing process of the CI value represents the improvement of the independent control ability of each muscle. Therefore, based on the EMG level (equivalent to the mean EMG level of the muscle) and the CI value (equivalent to the muscle co-contraction index), the state of the muscle can be evaluated, and the muscle saturation data can be obtained.

[0059] As another example of the embodiment, the collection of the muscle electrical signal (EMG) associated with the joint movement of the patient can also be based on the extraction of the characteristic signals of the central and peripheral numbers of the electroencephalogram and the brain oxygen, the electromyogram and the muscle oxygen, and the physiological parameters of the patient, and the linear discriminant analysis (LDA) and the support vector machine (SVM) are used as the classifier to realize the wavelet transform of the EEG, the multi-motion pattern recognition based on the electromyogram, and the mode of the electrical stimulation bidirectional feedback. Based on the six time domain features, the three frequency domain features, the wavelet transform coefficients, the two nonlinear entropies, and the two fractal dimensions of the surface electromyogram, a total of 14 features are obtained, four feature combinations are obtained by feature combination, and the LDA classification method is used to compare and study the classification success rate and the classification real-time performance of the four combinations. Then, the BP neural network is used, and the BP neural network is improved, and the dimensionality reduction and classification performance of the surface electromyogram are related.

[0060] In step 103, based on the preset first algorithm and the muscle electrical signal associated with the joint movement of the patient in the first round, first round muscle fatigue resistance data is obtained.

[0061] As an example of the embodiment, a first muscle electromyography power is obtained based on the muscle electrical signal of the joint associated with the patient activity in the first round; a first muscle electromyography power frequency is obtained based on the first muscle electromyography power; and a first round muscle fatigue resistance data is obtained by performing mean probability calculation on the muscle of the patient based on the first preset algorithm and the first muscle electromyography power and the first muscle electromyography power frequency. Specifically, in one embodiment, the real-time evaluation of the fatigue resistance of the individual in each training round in the present application is monitored based on the decline amplitude of the mean frequency (MPF) of the muscle. Specifically, the mean frequency of the muscle is calculated as follows:

[0062]

[0063] wherein f j is the frequency value of the electromyography power spectrum at the frequency collection point j, P j is the electromyography power spectrum at the frequency collection point j, and M is the length of the frequency sampling interval. In electromyography signal analysis, M is usually defined as the square of the length of the time domain electromyography data. Therefore, the muscle fatigue resistance data can be obtained by calculating the mean frequency of the muscle.

[0064] Step 104, when the first round muscle fatigue resistance data is less than the preset fatigue threshold, the muscle rehabilitation training is suspended until the preset rest duration is met, and the muscle rehabilitation training is resumed.

[0065] As an example of the embodiment, the first round muscle fatigue resistance data is monitored in real time based on the preset fatigue threshold; when it is determined that the first round muscle fatigue resistance data is less than the preset fatigue threshold, the muscle rehabilitation training is suspended; the muscle rehabilitation training suspension duration is monitored based on the preset rest duration; and when it is determined that the muscle rehabilitation training suspension duration meets the preset rest duration, the muscle rehabilitation training is resumed. Specifically, in one embodiment, when the MPF of one muscle decreases to 70% or less of its baseline, it indicates that the training individual has muscle fatigue, the muscle rehabilitation training is suspended, and the individual rests for 5 minutes; when the rest time is over, the muscle rehabilitation training is resumed and the MPF data of the muscle is continuously monitored; more specifically, the target muscle fatigue degree can be monitored every ten minutes, and if it exceeds 70%, the training is suspended and the individual rests for 5 minutes.

[0066] As another example of the present embodiment, according to the previous study found in the early stage of stroke in rats as a model of hemorrhagic stroke treadmill training, the degree of peripheral muscle fatigue is controlled at a level of not more than 70% of MPF decrease, which can achieve more effective motor function reconstruction and smaller brain damage than forced continuous training. The training intensity is determined. The directed transfer function method is used to construct the motor causal brain network and estimate the effective connection between the EEG signals of each lead during the exercise. From the perspective of network measures such as node degree, clustering coefficient, average path length, local and global efficiency, the relationship between upper limb motor control ability and brain computer network is studied and analyzed, and the mechanism based on the differences in muscle signal, exercise fatigue, joint angle and brain control ability of the tester is revealed. The classification and prediction of hemiplegic limb motor dysfunction can use the GB-DD-MLP network model.

[0067] Step 105, repeat the above muscle rehabilitation training process until the difference between the joint activity data of the two consecutive muscle rehabilitation training meets the preset training requirement and the difference between the muscle training saturation data of the two consecutive muscle rehabilitation training meets the preset training requirement, and end the muscle rehabilitation training.

[0068] As an example of the present embodiment, when it is determined to resume muscle rehabilitation training, the joint activity angle information of the second round of patients is collected, the joint activity angle information of the second round of patients is converted into electrical signal based on the electrical signal conversion algorithm, and the second round of joint activity data is obtained. Based on the muscle electrical signal associated with the second active joint, the second muscle normalized envelope amplitude is constructed. According to the second muscle normalized envelope amplitude, the mean value of the muscle electrical signal of each muscle of the patient is calculated to obtain the second muscle electrical mean value. Based on the second muscle electrical mean value, the second muscle common contraction index is obtained by repeating the value acquisition. Based on the second muscle electrical mean value and the second muscle common contraction index, the muscle of the second round is evaluated for saturation to obtain the second round muscle training saturation. By calculating the difference between the first round joint activity data and the second round joint activity data, the first activity difference value is obtained. By calculating the difference between the first round muscle training saturation and the second round muscle training saturation, the first saturation difference value is obtained. When it is determined that the first activity difference value and the first saturation difference value both meet the preset training requirement, the muscle rehabilitation training is ended.

[0069] By calculating the difference between the first round joint activity data and the second round joint activity data, the first activity difference value is obtained. By calculating the difference between the first round muscle training saturation and the second round muscle training saturation, the first saturation difference value is obtained. When it is determined that the first activity difference value and the first saturation difference value both meet the preset training requirement, the muscle rehabilitation training is ended.

[0070] Referring to Figure 2 , Figure 2A step flow diagram of a data processing repeated process of a muscle rehabilitation training intensity monitoring method provided for an embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, the data processing repeated process includes the following steps: Figure 2

[0071] Step 201, when it is determined that the first activity difference value and the first saturation difference value do not meet the preset training requirements, resuming muscle rehabilitation training.

[0072] Step 202, when it is determined to resume muscle rehabilitation training, acquiring third round joint activity data and third round muscle training saturation.

[0073] Step 203, based on the second round joint activity data and the second round muscle training saturation, and the third round joint activity data and the third round muscle training saturation, calculating a second activity difference value and a second saturation difference value.

[0074] Step 204, determining whether the second activity difference value and the second saturation difference value meet the preset training requirements, and if so, ending muscle rehabilitation training.

[0075] Step 205, if not, repeating the above steps and updating the second activity difference value and the second saturation difference value until the preset training requirements are met, and ending muscle rehabilitation training.

[0076] In a specific implementation, when a round of muscle rehabilitation training is performed, joint angle (ROM) and electromyography (EMG) data of the main muscles are collected at the time of training, and the corresponding ROM value, EMG level, muscle CI and fatigue degree are calculated based on the collected data. The fatigue degree is used to determine whether to continue training and whether to rest. The ROM value, EMG level and muscle CI data change rate of two consecutive rounds are calculated. When the ROM value, EMG level and muscle CI data change rate change by less than 5% before and after, it is indicated that the individual has been saturated with training, and the training can be terminated (i.e., a course of treatment is completed). The specific data processing process is described in Embodiment 1, and will not be described here.

[0077] ​The embodiment of the present application provides a muscle rehabilitation training intensity monitoring method. Objective data of muscle training is detected, so that accurate data basis of subsequent muscle training intensity can be obtained. The objective data includes joint data, electromyography data and muscle fatigue data. Data is obtained from multiple dimensions, and the range of motion of joints and the saturation and fatigue of muscles are judged, so that more comprehensive monitoring can be obtained during rehabilitation training. A fatigue threshold is set to monitor the rehabilitation training process. Based on the above multi-dimensional data basis, whether the muscle needs to rest is determined according to the training degree of the muscle. The training intensity of the muscle is effectively monitored according to the data change difference before and after training, so that the problem of secondary muscle damage caused by insufficient training or excessive training is prevented. Therefore, the method provided by the present application can realize the effect of clear training intensity by objectively monitoring the training tolerance.

[0078] Embodiment 2

[0079] Referring to Figure 4 , Figure 4 A module structure schematic diagram of a muscle rehabilitation training intensity monitoring system provided by an embodiment of the present application is shown in the figure. Figure 4 As shown in the figure, the embodiment of the present application provides a muscle rehabilitation training intensity monitoring system, which comprises:

[0080] a first data acquisition module 401, a second data acquisition module 402, a third data acquisition module 403, a first monitoring module 404 and a second monitoring module 405;

[0081] The first data acquisition module 401 is used to collect the joint activity angle information of the patient in the first round when it is determined to start muscle rehabilitation training, and obtain the joint activity data in the first round.

[0082] The second data acquisition module 402 is used to collect the electromyography signal of the muscle associated with the active joint of the patient in the first round, evaluate the saturation of the muscle of the patient, and obtain the muscle training saturation data in the first round.

[0083] The third data acquisition module 403 is used to obtain the muscle fatigue tolerance data in the first round based on a preset first algorithm and the electromyography signal of the muscle associated with the active joint of the patient in the first round.

[0084] The first monitoring module 404 is used to pause the muscle rehabilitation training when the muscle fatigue tolerance data in the first round is less than a preset fatigue threshold until a preset rest duration is met, and then resume the muscle rehabilitation training.

[0085] The second monitoring module 405 is configured to repeat the muscle rehabilitation training process until the difference between the joint range of motion data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement and the difference between the muscle training saturation data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement, and the muscle rehabilitation training is ended.

[0086] As an example of the present embodiment, refer to Figure 5 , Figure 5 FIG. 2 is a structural schematic diagram of a second monitoring module of a muscle rehabilitation training intensity monitoring system according to an embodiment of the present application. As shown in FIG. 2, the second monitoring module 405 is configured to repeat the muscle rehabilitation training process until the difference between the joint range of motion data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement and the difference between the muscle training saturation data of two consecutive rounds of muscle rehabilitation training meets the preset training requirement, and the muscle rehabilitation training is ended, and further comprises: Figure 5

[0087] a first training intensity judging unit 501, a training data obtaining unit 502, a training data processing unit 503, a second training intensity judging unit 504, and a training data updating unit 505;

[0088] The first training intensity judging unit 501 is configured to resume the muscle rehabilitation training when it is determined that the difference between the range of motion data and the difference between the saturation data do not meet the preset training requirement.

[0089] The training data obtaining unit 502 is configured to obtain the joint range of motion data of the third round and the muscle training saturation of the third round when it is determined to resume the muscle rehabilitation training.

[0090] The training data processing unit 503 is configured to calculate the second difference between the range of motion data and the second difference between the saturation data based on the joint range of motion data of the second round, the muscle training saturation of the second round, the joint range of motion data of the third round, and the muscle training saturation of the third round.

[0091] The second training intensity judging unit 504 is configured to determine whether the second difference between the range of motion data and the second difference between the saturation data meet the preset training requirement, and end the muscle rehabilitation training if the preset training requirement is met.

[0092] The training data updating unit 505 is configured to update the second difference between the range of motion data and the second difference between the saturation data until the preset training requirement is met and the muscle rehabilitation training is ended if the preset training requirement is not met.

[0093] ​The embodiment of the application provides a muscle rehabilitation training intensity monitoring system, objective muscle training data can be observed and detected through the first data acquisition module, the second data acquisition module and the third data acquisition module, so that the training intensity of the subsequent muscle can obtain accurate data basis, and the objective data includes joint data, electromyography data and muscle fatigue data; the data is obtained from multiple dimensions, and the range of motion of the joint and the saturation and fatigue of the muscle are judged, so that more comprehensive monitoring can be obtained in the rehabilitation training process; meanwhile, the first monitoring module sets a fatigue threshold to monitor the rehabilitation training process, based on the above-mentioned multi-dimensional data basis, whether the muscle needs to rest is determined by judging the training degree of the muscle, and then the training intensity of the muscle is effectively monitored according to the data change difference before and after training through the second monitoring module, so that the problem of secondary muscle damage caused by insufficient training or excessive training is prevented. Therefore, the method provided by the application can realize the effect of clear training intensity through objective monitoring of training amount tolerance.

[0094] The above only describes the preferred embodiments of the application, and it should be pointed out that, for those skilled in the art, without departing from the technical principles of the application, a number of improvements and modifications can be made, and these improvements and modifications should also be considered as the protection scope of the application.

[0095] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" means that the specific features, structures, materials or characteristics described in combination with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0096] In addition, the terms "first", "second" are only for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one feature. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

Claims

1. A method of monitoring the intensity of a muscle rehabilitation training, characterized in that, The method comprises the steps of: When it is determined to start muscle rehabilitation training, the joint activity angle information of the first round of patients is collected, and the first round of joint activity data is obtained; The muscle electrical signals associated with the active joints of the first round of patients are collected, and the muscle saturation of the patients is evaluated to obtain the first round of muscle training saturation data; Based on the preset first algorithm and the muscle electrical signals associated with the active joints of the first round of patients, the first round of muscle fatigue resistance data is obtained; When the first round of muscle fatigue resistance data is less than the preset fatigue threshold, the muscle rehabilitation training is suspended until the preset rest duration is met, and the muscle rehabilitation training is resumed; The above muscle rehabilitation training process is repeated until the difference between the joint activity data of the last two rounds of muscle rehabilitation training meets the preset training requirement and the difference between the muscle training saturation data of the last two rounds of muscle rehabilitation training meets the preset training requirement, and the muscle rehabilitation training is ended.

2. The muscle rehabilitation training intensity monitoring method according to claim 1, wherein, When it is determined to start muscle rehabilitation training, the joint activity angle information of the first round of patients is collected, and the first round of joint activity data is obtained, specifically: When it is determined to start muscle rehabilitation training, the joint activity angle information of the first round of patients is collected; Based on the digital-electric signal conversion algorithm, the joint activity angle information of the first round of patients is converted into digital-electric signals to obtain the first round of joint activity data.

3. The muscle rehabilitation training intensity monitoring method of claim 1, wherein, The muscle electrical signals associated with the active joints of the first round of patients are collected, and the muscle saturation of the patients is evaluated to obtain the first round of muscle training saturation data, specifically: The muscle electrical signals associated with the active joints of the first round of patients are collected, and the muscle saturation of the patients is evaluated to obtain the first round of muscle training saturation data, specifically: Based on the muscle electrical signals associated with the active joints of the first round of patients, a first muscle normalized envelope amplitude is constructed; According to the first muscle normalized envelope amplitude, the mean value of the muscle electrical signals of each muscle of the patient is calculated to obtain a first plurality of muscle mean values; Based on the first plurality of muscle mean values, a first muscle common contraction index is obtained by repeating value acquisition; Based on the first plurality of muscle mean values and the first muscle common contraction index, the muscle saturation of the patient is evaluated to obtain the first round of muscle training saturation data.

4. The muscle rehabilitation training intensity monitoring method of claim 1, wherein, Based on the preset first algorithm and the muscle electrical signals associated with the active joints of the first round of patients, the first round of muscle fatigue resistance data is obtained, specifically: Based on the muscle electrical signals associated with the active joints of the first round of patients, a first muscle electromyographic power is obtained; Based on the first muscle electromyographic power, a first muscle electromyographic power frequency is obtained; Based on the preset first algorithm, the average probability of the muscle of the patient is calculated by the first muscle electromyographic power and the first muscle electromyographic power frequency to obtain the first round of muscle fatigue resistance data.

5. The muscle rehabilitation training intensity monitoring method of claim 1, wherein, When the first round of muscle fatigue resistance data is less than the preset fatigue threshold, the muscle rehabilitation training is suspended until the preset rest duration is met, and the muscle rehabilitation training is resumed, specifically: Based on the preset fatigue threshold, the first round of muscle fatigue resistance data is monitored in real time; When it is determined that the first round of muscle fatigue resistance data is less than the preset fatigue threshold, the muscle rehabilitation training is suspended; monitoring the muscle rehabilitation training pause duration based on the preset rest duration; resuming the muscle rehabilitation training when it is determined that the muscle rehabilitation training pause duration meets the preset rest duration.

6. The muscle rehabilitation training intensity monitoring method of claim 1, wherein, The above muscle rehabilitation training process is repeated until the joint range of motion data difference of two consecutive rounds of muscle rehabilitation training meets the preset training requirement and the muscle training saturation data difference of two consecutive rounds of muscle rehabilitation training meets the preset training requirement, and the muscle rehabilitation training is ended, specifically: When it is determined to resume the muscle rehabilitation training, the joint activity angle information of the second round of patients is collected, and the joint activity angle information of the second round of patients is converted into electrical signals based on the digital-electrical signal conversion algorithm to obtain the second round of joint range of motion data; Based on the second round of active joint associated muscle electrical signals, a second muscle normalized envelope amplitude is constructed; According to the second muscle normalized envelope amplitude, the electromyographic mean value of each muscle of the patient is calculated to obtain a second muscle electromyographic mean value; Based on the second muscle electromyographic mean value, a second muscle common contraction index is obtained by repeating value acquisition; Based on the second muscle electromyographic mean value and the second muscle common contraction index, the muscle of the second round is evaluated for saturation to obtain a second round muscle training saturation. Whether to end the muscle rehabilitation training is determined by calculating the difference between the first round joint range of motion data and the second round joint range of motion data meeting the preset training requirement and the difference between the first round muscle training saturation and the second round muscle training saturation meeting the preset training requirement.

7. A method of monitoring the intensity of a muscle rehabilitation exercise according to claim 6, characterized in that Whether to end the muscle rehabilitation training is determined by calculating the difference between the first round joint range of motion data and the second round joint range of motion data meeting the preset training requirement and the difference between the first round muscle training saturation and the second round muscle training saturation meeting the preset training requirement. A first range of motion difference value is obtained by calculating the difference between the first round joint range of motion data and the second round joint range of motion data. A first saturation difference value is obtained by calculating the difference between the first round muscle training saturation and the second round muscle training saturation. When it is determined that the first range of motion difference value and the first saturation difference value both meet the preset training requirement, the muscle rehabilitation training is ended.

8. A method of monitoring the intensity of a muscle rehabilitation exercise according to claim 7, characterized in that The steps further include: When it is determined that the first range of motion difference value and the first saturation difference value both do not meet the preset training requirement, the muscle rehabilitation training is resumed. When it is determined to resume the muscle rehabilitation training, the third round joint range of motion data and the third round muscle training saturation are obtained. Based on the second round joint range of motion data and the second round muscle training saturation and the third round joint range of motion data and the third round muscle training saturation, a second range of motion difference value and a second saturation difference value are calculated. Whether the second range of motion difference value and the second saturation difference value meet the preset training requirement is determined, and if they meet the preset training requirement, the muscle rehabilitation training is ended. If they do not meet the preset training requirement, the above steps are repeated and the second range of motion difference value and the second saturation difference value are updated until the preset training requirement is met, and the muscle rehabilitation training is ended.

9. A muscle rehabilitation training intensity monitoring system, characterized in that ​ The first data acquisition module, the second data acquisition module, the third data acquisition module, the first monitoring module and the second monitoring module; The first data acquisition module is used for collecting joint activity angle information of the patient in the first round when it is determined to start the muscle rehabilitation training, and acquiring joint activity data in the first round; The second data acquisition module is used for collecting muscle electrical signals associated with the active joint of the patient in the first round, performing saturation evaluation on the muscle of the patient, and obtaining muscle training saturation data in the first round; The third data acquisition module is used for acquiring muscle fatigue endurance data in the first round based on a preset first algorithm and the muscle electrical signals associated with the active joint of the patient in the first round; The first monitoring module is used for suspending the muscle rehabilitation training until a preset rest duration is met when the muscle fatigue endurance data in the first round is less than a preset fatigue threshold, and resuming the muscle rehabilitation training; The second monitoring module is used for repeating the muscle rehabilitation training process until the difference value of the joint activity data in the consecutive two rounds of muscle rehabilitation training meets the preset training requirement and the difference value of the muscle training saturation data in the consecutive two rounds of muscle rehabilitation training meets the preset training requirement, and ending the muscle rehabilitation training.

10. A muscle rehabilitation training intensity monitoring system as claimed in claim 9, characterized in that, The second monitoring module is used for repeating the muscle rehabilitation training process until the difference value of the joint activity data in the consecutive two rounds of muscle rehabilitation training meets the preset training requirement and the difference value of the muscle training saturation data in the consecutive two rounds of muscle rehabilitation training meets the preset training requirement, and ending the muscle rehabilitation training, and further comprising: A first training intensity judgment unit, a training data acquisition unit, a training data processing unit, a second training intensity judgment unit and a training data updating unit; The first training intensity judgment unit is used for resuming the muscle rehabilitation training when it is determined that the difference value of the activity and the difference value of the saturation do not meet the preset training requirement; The training data acquisition unit is used for acquiring joint activity data in the third round and muscle training saturation in the third round when it is determined to resume the muscle rehabilitation training; The training data processing unit is used for calculating a second difference value of the activity and a second difference value of the saturation based on the joint activity data in the second round and the muscle training saturation in the second round, and the joint activity data in the third round and the muscle training saturation in the third round; The second training intensity judgment unit is used for judging whether the second difference value of the activity and the second difference value of the saturation meet the preset training requirement, and ending the muscle rehabilitation training if the preset training requirement is met; The training data updating unit is used for repeating the above steps and updating the second difference value of the activity and the second difference value of the saturation until the preset training requirement is met, and ending the muscle rehabilitation training if the preset training requirement is not met.

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