Muscle rehabilitation training intensity monitoring method and system

Abstract

This invention proposes a method and system for monitoring the intensity of muscle rehabilitation training. When muscle rehabilitation training is initiated, the system acquires data on joint range of motion, muscle training saturation, and muscle fatigue tolerance from the first round of training. If the muscle fatigue tolerance data from the first round is less than a preset fatigue threshold, the muscle rehabilitation training is paused until a preset rest period is met, then resumed. This process is repeated until the difference in joint range of motion data between two consecutive rounds of training meets the preset training requirements, and the difference in muscle training saturation data between two consecutive rounds of training meets the preset training requirements, at which point the muscle rehabilitation training ends. This addresses the problem in existing technologies where the inability to objectively detect training volume tolerance leads to unclear training intensity. The method proposed in this invention achieves a clear understanding of training intensity through objective monitoring of training volume tolerance.

Description

Technical Field

[0001] This invention relates to the field of assisted rehabilitation training, and in particular to a method and system for monitoring the intensity of muscle rehabilitation training. Background Technology

[0002] Robot-assisted rehabilitation training should be combined with continuous, objective, and quantitative assessment of motor function to monitor the rehabilitation process, optimize the process, and achieve the best individualized rehabilitation results.

[0003] Currently, most existing rehabilitation techniques, treatment designs, and efficacy assessments are based on fixed or subjective courses, with training content grounded in single clinical manifestations. Examples include 2-3 week hospital stays in general hospitals with 1 hour of upper limb training daily, or the recent trend of early high-intensity rehabilitation interventions and research. Efficacy is determined by comparing clinical scales and performance assessments before and after the training course. Such assessments cannot reveal the saturation point or characteristic changes in the rehabilitation process, such as whether an individual patient's motor function recovery has reached a plateau and training can be discontinued or alternative rehabilitation options chosen, or whether improper operation has caused secondary injury. Furthermore, clinical scale assessments, through continuous and repeated testing of an individual's rehabilitation progress, are insufficient to reflect minor functional changes. Additionally, current rehabilitation interventions lack objective testing of an individual patient's training tolerance, leading to difficulties in determining whether training intensity is insufficient or excessive. Summary of the Invention

[0004] Based on the above problems, this invention proposes a method and system for monitoring the intensity of muscle rehabilitation training, which solves the problem of unclear training intensity caused by the failure to objectively detect training volume tolerance.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for monitoring the intensity of muscle rehabilitation training, comprising:

[0006] Once muscle rehabilitation training is initiated, information on the patient's joint range of motion is collected during the first round to obtain the first round of joint range of motion data.

[0007] The muscle electrical signals associated with the patient's joint movements were collected in the first round to assess the muscle saturation and obtain muscle training saturation data for the first round.

[0008] Based on the preset first algorithm and the muscle electrical signals associated with the patient's active joints in the first round, muscle fatigue tolerance data for the first round is obtained.

[0009] When the muscle fatigue tolerance data of the first round is less than the preset fatigue threshold, muscle rehabilitation training is paused until the preset rest time is met, and then muscle rehabilitation training is resumed.

[0010] Repeat the above muscle rehabilitation training process until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, then end the muscle rehabilitation training.

[0011] This invention proposes a method for monitoring the intensity of muscle rehabilitation training. By objectively detecting muscle training data, an accurate data foundation is established for subsequent muscle training intensity. This objective data includes joint data, electromyography (EMG) data, and muscle fatigue data. Data is acquired from multiple dimensions, and the range of motion of joints, as well as muscle saturation and fatigue levels, are assessed, enabling more comprehensive monitoring during rehabilitation training. Furthermore, by setting a fatigue threshold, the rehabilitation training progress is monitored. Based on the aforementioned multidimensional data foundation, the need for rest is determined by assessing the level of muscle training. The difference in data before and after training is used to effectively monitor muscle training intensity, preventing secondary muscle damage caused by undertraining or overtraining. Therefore, the method proposed in this invention can achieve a clear understanding of training intensity by objectively monitoring training volume tolerance.

[0012] Furthermore, when it is determined that muscle rehabilitation training will begin, the joint range of motion information of the patient in the first round will be collected to obtain the joint range of motion data in the first round, specifically as follows:

[0013] Once muscle rehabilitation training is initiated, the joint range of motion information of the patients in the first round is collected. Based on the digital-to-electrical signal conversion algorithm, the joint range of motion information of the patients in the first round is converted into digital-to-electrical signals to obtain the joint range of motion data of the first round.

[0014] Furthermore, the acquisition of muscle electrical signals associated with the patient's joint movements during the first round of training, and the assessment of muscle saturation to obtain muscle training saturation data for the first round, specifically involves:

[0015] By collecting electromyographic (EMG) signals from the muscles of the patients in the first round, EMG signals associated with the joints of the patients in the first round are obtained. Based on the EMG signals associated with the joints of the patients in the first round, a first normalized envelope amplitude of the muscles is constructed. According to the first normalized envelope amplitude of the muscles, the mean EMG value of each muscle of the patient is calculated to obtain the mean EMG value of a first set of muscles. Based on the mean EMG value of the first set of muscles, a first muscle co-contraction index is obtained by obtaining repeated values. Based on the mean EMG value of the first set of muscles and the first muscle co-contraction index, the saturation of the patient's muscles is evaluated to obtain the muscle training saturation data of the first round.

[0016] Furthermore, the step of obtaining muscle fatigue endurance data for the first round based on a preset first algorithm and the muscle electrical signals associated with the patient's joint movements in the first round specifically involves:

[0017] Based on the electromyographic signals of the patient's joints in the first round of activity, the first muscle electromyographic power is obtained; based on the first muscle electromyographic power, the first muscle electromyographic power frequency is obtained; based on the preset first algorithm, the average probability of the patient's muscles is calculated using the first muscle electromyographic power and the first muscle electromyographic power frequency to obtain the muscle fatigue tolerance data for the first round.

[0018] Furthermore, when the muscle fatigue endurance data of the first round is less than a preset fatigue threshold, muscle rehabilitation training is paused until a preset rest period is met, and then muscle rehabilitation training is resumed. Specifically:

[0019] Based on a preset fatigue threshold, the muscle fatigue tolerance data of the first round is monitored in real time; when it is determined that the muscle fatigue tolerance data of the first round is less than the preset fatigue threshold, muscle rehabilitation training is paused; based on a preset rest duration, the pause duration of muscle rehabilitation training is monitored; when it is determined that the pause duration of muscle rehabilitation training meets the preset rest duration, muscle rehabilitation training is resumed.

[0020] Furthermore, the above-described muscle rehabilitation training process is repeated until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, at which point the muscle rehabilitation training ends. Specifically, this involves:

[0021] Once muscle rehabilitation training is initiated, joint range of motion information for the second round of patients is collected. Based on a digital-to-electrical signal conversion algorithm, this information is converted to obtain second-round joint range of motion data. Based on the muscle electrical signals associated with the second-round joints, a second normalized envelope amplitude for the muscles is constructed. According to this normalized envelope amplitude, the mean electromyography (EMG) values ​​for each muscle are calculated, resulting in a second set of muscle EMG mean values. Based on these mean values, a second muscle co-contraction index is obtained through repetition analysis. Based on the mean values ​​and the co-contraction index, muscle saturation is assessed in the second round to obtain the second-round muscle training saturation. The decision to terminate muscle rehabilitation training is made based on whether the difference between the first-round and second-round joint range of motion data meets the preset training requirements, and whether the difference between the first-round and second-round muscle training saturation meets the preset training requirements.

[0022] Furthermore, the step of determining whether to end muscle rehabilitation training by calculating whether the difference between the joint range of motion data of the first round and the joint range of motion data of the second round meets the preset training requirements, and whether the difference between the muscle training saturation of the first round and the muscle training saturation of the second round meets the preset training requirements, specifically involves:

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

[0024] Furthermore, when it is determined that neither the first activity difference nor the first saturation difference meets the preset training requirements, muscle rehabilitation training is resumed.

[0025] Once muscle rehabilitation training is initiated, acquire the joint range of motion data and muscle training saturation data from the third round. Based on the joint range of motion data and muscle training saturation data from the second and third rounds, calculate the second range of motion difference and the second saturation difference. Determine whether the second range of motion difference and the second saturation difference meet the preset training requirements. If they do, end the muscle rehabilitation training. If they do not meet the preset training requirements, repeat the above steps and update the second range of motion difference and the second saturation difference until they meet the preset training requirements, then end the muscle rehabilitation training.

[0026] This invention also provides a muscle rehabilitation training intensity monitoring system, comprising:

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

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

[0029] The second data acquisition module is used to collect the muscle electrical signals associated with the patient's joints during the first round of activity, to assess the saturation of the patient's muscles, and to obtain the muscle training saturation data for the first round.

[0030] The third data acquisition module is used to acquire muscle fatigue endurance data for the first round based on a preset first algorithm and the muscle electrical signals associated with the patient's active joints in the first round.

[0031] The first monitoring module is used to pause muscle rehabilitation training until the preset rest time is met when the muscle fatigue endurance data of the first round is less than the preset fatigue threshold, and then resume muscle rehabilitation training.

[0032] The second monitoring module is used to repeat the above muscle rehabilitation training process until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, and then the muscle rehabilitation training ends.

[0033] This invention proposes a muscle rehabilitation training intensity monitoring system. Through a first, second, and third data acquisition module, objective data on muscle training is objectively detected, providing an accurate data foundation for subsequent muscle training intensity. This objective data includes joint data, electromyography (EMG) data, and muscle fatigue data, acquiring data from multiple dimensions and assessing joint mobility, muscle saturation, and fatigue levels, enabling more comprehensive monitoring during rehabilitation training. Simultaneously, the first monitoring module sets a fatigue threshold to monitor the rehabilitation training progress. Based on the aforementioned multidimensional data foundation, the system determines whether rest is needed by assessing the muscle training level. The second monitoring module effectively monitors muscle training intensity based on the difference in data before and after training, preventing secondary muscle damage caused by undertraining or overtraining. Therefore, the method proposed in this invention can achieve a clear understanding of training intensity by objectively monitoring training volume tolerance.

[0034] Furthermore, the second monitoring module is used to repeat the above-mentioned muscle rehabilitation training process until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, and then the muscle rehabilitation training ends. The module also includes:

[0035] The system comprises a first training intensity determination unit, a training data acquisition unit, a training data processing unit, a second training intensity determination unit, and a training data update unit.

[0036] The first training intensity judgment unit is used to resume muscle rehabilitation training when it is determined that neither the activity difference nor the saturation difference meets the preset training requirements.

[0037] The training data acquisition unit is used to acquire the joint range of motion data and the muscle training saturation data of the third round when it is determined to resume muscle rehabilitation training.

[0038] The training data processing unit is used to calculate the second range of motion difference and the second saturation difference based on the second round of joint range of motion data and the second round of muscle training saturation, as well as the third round of joint range of motion data and the third round of muscle training saturation.

[0039] The second training intensity judgment unit is used to determine whether the second activity difference and the second saturation difference meet the preset training requirements. If the preset training requirements are met, the muscle rehabilitation training ends.

[0040] The training data update unit is used to update the second activity difference and the second saturation difference until the preset training requirements are met, and then end the muscle rehabilitation training if the preset training requirements are not met. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the steps of a method for monitoring the intensity of muscle rehabilitation training according to a certain embodiment of the present invention.

[0042] Figure 2 This is a flowchart illustrating the steps of a repetitive data processing method for monitoring muscle rehabilitation training intensity according to a certain embodiment of the present invention.

[0043] Figure 3 This is a schematic diagram illustrating one specific application of a muscle rehabilitation training intensity monitoring method provided in a certain embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the module structure of a muscle rehabilitation training intensity monitoring system according to a certain embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of the structure of the second monitoring module of a muscle rehabilitation training intensity monitoring system provided in a certain embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] In this embodiment, to better explain the technical solution of the present invention, the execution of the technical solution of the present invention using the Io-ENMS system is used as an example of one embodiment of the present invention. It is stated here that no other limitations are imposed on the possible implementations of the present invention, and further details will not be elaborated below; specifically, as... Figure 3 As shown, Figure 3This is a schematic diagram of one specific application of a muscle rehabilitation training intensity monitoring method provided in a certain embodiment of the present invention. The Io-ENMS system includes: a fusion angle sensor (Flex sensor) at the elbow and wrist joints and a control board.

[0048] Example 1

[0049] See Figure 1 , Figure 1 This is a schematic flowchart illustrating the steps of a method for monitoring muscle rehabilitation training intensity according to a certain embodiment of the present invention. Figure 1 As shown in the figure, this invention proposes a method for monitoring the intensity of muscle rehabilitation training, including steps 101 to 105, each step of which is as follows:

[0050] Step 101: Once it is determined to start muscle rehabilitation training, collect the joint range of motion information of the patient in the first round and obtain the joint range of motion data in the first round.

[0051] As an example of this embodiment, when muscle rehabilitation training is initiated, the joint range of motion (ROM) information of the patient in the first round is collected. Based on a digital-to-electrical signal conversion algorithm, the joint range of motion information of the patient in the first round is converted into digital signals to obtain the joint range of motion data for the first round. Specifically, one possible implementation method is to measure the changes in joint range of motion (ROM) in real time during training by using a fusion angle sensor (Flex sensor) at the elbow and wrist joints, and then calculate the ROM value (equivalent to joint range of motion data) by the MCU through the digital I / O circuit in the control box.

[0052] Step 102: Collect the muscle electrical signals associated with the patient's joints during the first round of activity, assess the saturation of the patient's muscles, and obtain the muscle training saturation data for the first round.

[0053] As an example of this embodiment, by collecting electromyographic (EMG) signals of the patient's muscles in the first round, the EMG signals associated with the patient's joint movements in the first round are obtained; based on the EMG signals associated with the patient's joint movements in the first round, a first normalized muscle envelope amplitude is constructed; according to the first normalized muscle envelope amplitude, the mean EMG value of each muscle of the patient is calculated to obtain a first set of muscle mean EMG values; based on the first set of muscle mean EMG values, a first muscle co-contraction index is obtained by obtaining repeated values; based on the first set of muscle mean EMG values ​​and the first muscle co-contraction index, the saturation of the patient's muscles is evaluated to obtain the muscle training saturation data for the first round. Specifically, one possible implementation is: acquiring the EMG signals associated with the patient's joint movements using existing skin surface electrodes, and quantifying the EMG level can be mathematically expressed as:

[0054]

[0055] In the formula, T is the observation duration, and EMG is the EMG value. i (t) represents the normalized envelope amplitude of the target muscle i. Mean electromyography (quantified EMG level).

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

[0057] Among them, A ij The overlapping portion of the EMG of the two target muscles i and j.

[0058] Based on the characteristics of persistent EMG changes, it was found that both early-stage and chronic-stage stroke patients exhibited decreased EMG levels in spastic muscles and increased independence of contraction between muscle pairs during upper limb functional rehabilitation. Previous studies on post-stroke upper limb rehabilitation have shown that the decline in average EMG levels during daily training reflects the improvement in muscle tone, while the decrease in co-contraction index (CI) indicates improved voluntary control over individual muscles. Therefore, muscle status can be assessed based on EMG levels (equivalent to mean muscle electromyography) and CI values ​​(equivalent to the muscle co-contraction index), thereby obtaining muscle saturation data.

[0059] As another example of this embodiment, the acquisition of electromyographic (EMG) signals associated with the patient's joint movements can also extract central and peripheral EEG and brain oxygenation, EMG and muscle oxygenation, and patient physiological parameters based on feature signals. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) are used as classifiers to achieve wavelet transform of EEG, multi-movement pattern recognition of EMG based on EMG signals, and bidirectional feedback of electrical stimulation. Based on 14 features—6 time-domain features, 3 frequency-domain features, wavelet transform coefficients, 2 nonlinear entropies, and 2 fractal dimensions—of surface EMG signals, four feature combinations are obtained. The classification success rate and real-time performance of these four combinations are compared using the LDA classification method. Furthermore, a BP neural network is employed and improved to study the dimensionality reduction and classification performance of surface EMG signals.

[0060] Step 103: Based on the preset first algorithm and the muscle electrical signals associated with the patient's active joints in the first round, obtain the muscle fatigue endurance data for the first round.

[0061] As an example of this embodiment, based on the electromyographic signals of the patient's joints during the first round of exercise, 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 patient's muscles is calculated using the first muscle electromyographic power and the first muscle electromyographic power frequency to obtain the muscle fatigue endurance data for the first round. Specifically, one possible implementation is as follows: In this invention, the real-time assessment of individual fatigue endurance in each round of training is monitored based on the decrease in the mean power frequency (MPF) of the muscle. Specifically, the method for calculating the mean power frequency of the muscle is as follows:

[0062]

[0063] Among them, f j P represents the frequency value of the electromyographic power spectrum at frequency acquisition point j. j Let J represent the electromyographic power spectrum at frequency sampling point j, and M be the length of the frequency sampling interval. In electromyographic signal analysis, M is usually defined as the second power of the length of the time-domain electromyographic data. Therefore, muscle fatigue endurance data can be obtained by calculating the average frequency of the muscle.

[0064] Step 104: When the muscle fatigue endurance data of the first round is less than the preset fatigue threshold, pause the muscle rehabilitation training until the preset rest time is met, and then resume the muscle rehabilitation training.

[0065] As an example of this embodiment, based on a preset fatigue threshold, the muscle fatigue endurance data of the first round is monitored in real time; when it is determined that the muscle fatigue endurance data of the first round is less than the preset fatigue threshold, muscle rehabilitation training is paused; based on a preset rest duration, the pause duration of muscle rehabilitation training is monitored; when it is determined that the pause duration of muscle rehabilitation training meets the preset rest duration, muscle rehabilitation training is resumed. Specifically, one possible implementation is as follows: when the MPF of one muscle drops to 70% or below its baseline, the trainee is prompted to experience muscle fatigue, muscle rehabilitation training is paused, and a 5-minute rest is initiated; when the rest period ends, muscle rehabilitation training is resumed and the MPF data of the muscle is monitored again; more specifically, the target muscle fatigue level can be continuously monitored every ten minutes, and if it exceeds 70%, a pause is prompted, and a 5-minute rest is initiated.

[0066] As another example in this embodiment, based on previous research findings in early-stage treadmill training using hemorrhagic stroke rats as a model, controlling peripheral muscle fatigue to a level where MPF decreases by no more than 70% can achieve more effective motor function reconstruction and less brain damage than forced continuous training, thus determining the training intensity. A motor causal brain network is constructed using the directed transfer function method, and the effective connections between EEG signals in each lead during movement are estimated. From the perspective of network measures such as node degree, clustering coefficient, average path length, and local and global efficiency, the relationship between upper limb motor control ability and the brain computer network is studied and analyzed, revealing the mechanism based on differences in electromyographic signals, motor fatigue, joint angles, and brain control ability among test subjects. The GB-DD-MLP network model can be used for the classification and prediction of hemiplegic limb motor dysfunction.

[0067] Step 105: Repeat the above muscle rehabilitation training process until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, then end the muscle rehabilitation training.

[0068] As an example of this embodiment, when it is determined that muscle rehabilitation training will resume, the joint range of motion information of the patient in the second round is collected. Based on the digital-to-electrical signal conversion algorithm, the joint range of motion information of the patient in the second round is converted into digital-to-electrical signals to obtain the joint range of motion data of the second round. Based on the muscle electromyography signals associated with the joints in the second round, a second normalized envelope amplitude of the muscles is constructed. According to the second normalized envelope amplitude of the muscles, the mean electromyography value of each muscle of the patient is calculated to obtain the mean electromyography value of a second set of muscles. Based on the mean electromyography value of the second set of muscles, the second muscle co-contraction index is obtained by obtaining the repetition value. Based on the mean electromyography value of the second set of muscles and the second muscle co-contraction index, the saturation of the muscles in the second round is evaluated to obtain the muscle training saturation of the second round. By calculating whether the difference between the joint range of motion data of the first round and the joint range of motion data of the second round meets the preset training requirements and whether the difference between the muscle training saturation of the first round and the muscle training saturation of the second round meets the preset training requirements, it is determined whether to end the muscle rehabilitation training.

[0069] The first range of motion difference is obtained by calculating the difference between the range of motion data of the first round and the range of motion data of the second round; the first saturation difference is obtained by calculating the difference between the muscle training saturation of the first round and the muscle training saturation of the second round; when it is determined that both the first range of motion difference and the first saturation difference meet the preset training requirements, the muscle rehabilitation training ends.

[0070] See Figure 2 , Figure 2This is a flowchart illustrating the repetitive data processing steps of a muscle rehabilitation training intensity monitoring method according to a certain embodiment of the present invention; as shown below. Figure 2 As shown, the data processing repetitive process includes the following steps:

[0071] Step 201: When it is determined that neither the first activity difference nor the first saturation difference meets the preset training requirements, muscle rehabilitation training is resumed.

[0072] Step 202: Once it is determined to resume muscle rehabilitation training, obtain the joint range of motion data and the muscle training saturation data for the third round.

[0073] Step 203: Based on the joint range of motion data of the second round and the muscle training saturation of the second round, as well as the joint range of motion data of the third round and the muscle training saturation of the third round, calculate the difference between the second range of motion and the difference between the second saturation.

[0074] Step 204: Determine whether the second activity difference and the second saturation difference meet the preset training requirements. If they meet the preset training requirements, end the muscle rehabilitation training.

[0075] Step 205: If the preset training requirements are not met, repeat the above steps and update the second activity difference and the second saturation difference until the preset training requirements are met, and end the muscle rehabilitation training.

[0076] The specific implementation method involves collecting joint range of motion (ROM) and electromyography (EMG) data of major muscles during each round of muscle rehabilitation training. Based on the collected data, the corresponding ROM value, EMG level, muscle concentration index (CI), and fatigue level for each round are calculated. Fatigue level is used to determine whether training needs to continue or rest is necessary. The rate of change between two consecutive rounds of ROM values, EMG levels, and muscle CI is calculated. When the rate of change in ROM values, EMG levels, and muscle CI is less than 5%, it indicates that the individual has reached training saturation, and training can be terminated (i.e., one course of treatment ends). The specific data processing procedures are described in Example 1 and will not be repeated here.

[0077] This invention proposes a method for monitoring the intensity of muscle rehabilitation training. By objectively detecting muscle training data, an accurate data foundation is established for subsequent muscle training intensity. This objective data includes joint data, electromyography (EMG) data, and muscle fatigue data. Data is acquired from multiple dimensions, and the range of motion of joints, as well as muscle saturation and fatigue levels, are assessed, enabling more comprehensive monitoring during rehabilitation training. Furthermore, by setting a fatigue threshold, the rehabilitation training progress is monitored. Based on the aforementioned multidimensional data foundation, the need for rest is determined by assessing the level of muscle training. The difference in data before and after training is used to effectively monitor muscle training intensity, preventing secondary muscle damage caused by undertraining or overtraining. Therefore, the method proposed in this invention can achieve a clear understanding of training intensity by objectively monitoring training volume tolerance.

[0078] Example 2

[0079] See Figure 4 , Figure 4 This is a schematic diagram of the module structure of a muscle rehabilitation training intensity monitoring system according to a certain embodiment of the present invention. Figure 4 As shown in the figure, an embodiment of the present invention proposes a muscle rehabilitation training intensity monitoring system, comprising:

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

[0081] The first data acquisition module 401 is used to collect the joint range of motion information of the patient in the first round when it is determined that muscle rehabilitation training has started, and to obtain the joint range of motion data in the first round.

[0082] The second data acquisition module 402 is used to collect the muscle electrical signals associated with the patient's joints during the first round of activity, to assess the saturation of the patient's muscles, and to obtain the muscle training saturation data for the first round.

[0083] The third data acquisition module 403 is used to acquire muscle fatigue endurance data for the first round based on a preset first algorithm and the muscle electrical signals associated with the patient's active joints in the first round.

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

[0085] The second monitoring module 405 is used to repeat the above-mentioned muscle rehabilitation training process until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, and then the muscle rehabilitation training ends.

[0086] As an example of this embodiment, see Figure 5 , Figure 5 This is a schematic diagram of the structure of the second monitoring module of a muscle rehabilitation training intensity monitoring system provided in one embodiment of the present invention. Figure 5 As shown, the second monitoring module 405 is used to repeat the above-mentioned muscle rehabilitation training process until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, and then the muscle rehabilitation training ends. It also includes:

[0087] The system comprises a first training intensity judgment unit 501, a training data acquisition unit 502, a training data processing unit 503, a second training intensity judgment unit 504, and a training data update unit 505.

[0088] The first training intensity judgment unit 501 is used to resume muscle rehabilitation training when it is determined that neither the activity difference nor the saturation difference meets the preset training requirements.

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

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

[0091] The second training intensity judgment unit 504 is used to judge whether the second activity difference and the second saturation difference meet the preset training requirements. If the preset training requirements are met, the muscle rehabilitation training ends.

[0092] The training data update unit 505 is used to update the second activity difference and the second saturation difference until the preset training requirements are met, and then end the muscle rehabilitation training if the preset training requirements are not met.

[0093] This invention proposes a muscle rehabilitation training intensity monitoring system. Through a first, second, and third data acquisition module, objective data on muscle training is objectively detected, providing an accurate data foundation for subsequent muscle training intensity. This objective data includes joint data, electromyography (EMG) data, and muscle fatigue data, acquiring data from multiple dimensions and assessing joint mobility, muscle saturation, and fatigue levels, enabling more comprehensive monitoring during rehabilitation training. Simultaneously, the first monitoring module sets a fatigue threshold to monitor the rehabilitation training progress. Based on the aforementioned multidimensional data foundation, the system determines whether rest is needed by assessing the muscle training level. The second monitoring module effectively monitors muscle training intensity based on the difference in data before and after training, preventing secondary muscle damage caused by undertraining or overtraining. Therefore, the method proposed in this invention can achieve a clear understanding of training intensity by objectively monitoring training volume tolerance.

[0094] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0095] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0096] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

Claims

1. A muscle rehabilitation training intensity monitoring system, characterized in that, include: First data acquisition module, second data acquisition module, third data acquisition module, first monitoring module, and second monitoring module; The first data acquisition module is used to collect the joint range of motion information of the patient in the first round when it is determined that muscle rehabilitation training has started, and to obtain the joint range of motion data in the first round. The second data acquisition module is used to collect the electromyographic signals of the joints associated with the patient's movements in the first round, to assess the saturation of the patient's muscles, and to obtain the muscle training saturation data for the first round. Specifically, it collects the electromyographic signals of the patient's muscles in the first round to obtain the electromyographic signals of the joints associated with the patient's movements in the first round. Based on the muscle electrical signals associated with the patient's active joints in the first round, a normalized envelope amplitude of the first muscle is constructed. Based on the normalized envelope amplitude of the first muscle, the average electromyography value of each muscle of the patient is calculated to obtain the average electromyography value of the first several muscles. Based on the average electromyographic values ​​of the first few muscles, the first muscle cocontraction index is obtained by repeating the values. Based on the average electromyographic values ​​of the first few muscles and the first muscle cocontraction index, the saturation of the patient's muscles is assessed to obtain the muscle saturation data of the first round of muscle training. The third data acquisition module is used to acquire muscle fatigue endurance data for the first round based on a preset first algorithm and the muscle electrical signals associated with the patient's active joints in the first round. The first monitoring module is used to pause muscle rehabilitation training until the preset rest time is met when the muscle fatigue endurance data of the first round is less than the preset fatigue threshold, and then resume muscle rehabilitation training. The second monitoring module is used to repeat the above muscle rehabilitation training process until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, and then the muscle rehabilitation training ends.

2. The muscle rehabilitation training intensity monitoring system as described in claim 1, characterized in that, When it is determined that muscle rehabilitation training will begin, the joint range of motion information of the patient in the first round will be collected to obtain the joint range of motion data in the first round, specifically as follows: Once muscle rehabilitation training is initiated, information on the patient's joint range of motion is collected during the first round of training. Based on the digital-to-electrical signal conversion algorithm, the joint range of motion information of the patients in the first round is converted into digital-to-electrical signals to obtain the joint range of motion data of the first round.

3. The muscle rehabilitation training intensity monitoring system according to claim 1, wherein, The method of obtaining muscle fatigue endurance data for the first round based on a preset first algorithm and the muscle electrical signals associated with the patient's joint movements in the first round specifically involves: Based on the muscle electrical signals associated with the patient's active joints in the first round, the electromyographic power of the first muscle is obtained. Based on the first muscle electromyographic power, the frequency of the first muscle electromyographic power is obtained; Based on the preset first algorithm, the average probability of the patient's muscles is calculated by using the electromyographic power of the first muscle and the frequency of the electromyographic power of the first muscle, and the muscle fatigue tolerance data of the first round is obtained.

4. The muscle rehabilitation training intensity monitoring system as claimed in claim 1, wherein, When the muscle fatigue endurance data of the first round is less than the preset fatigue threshold, muscle rehabilitation training is paused until the preset rest period is met, and then muscle rehabilitation training is resumed. Specifically: Based on a preset fatigue threshold, monitor muscle fatigue endurance data in real time during the first round. When it is determined that the muscle fatigue endurance data of the first round is less than the preset fatigue threshold, muscle rehabilitation training is suspended. Based on preset rest duration, monitor the duration of pauses in muscle rehabilitation training; Once it is determined that the pause duration of the muscle rehabilitation training meets the preset rest duration, the muscle rehabilitation training is resumed.

5. The muscle rehabilitation training intensity monitoring system as claimed in claim 1, wherein, The muscle rehabilitation training process is repeated until the difference in joint range of motion data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements and the difference in muscle training saturation data between two consecutive rounds of muscle rehabilitation training meets the preset training requirements, at which point the muscle rehabilitation training ends. Specifically: Once it is determined that muscle rehabilitation training will resume, the joint range of motion information of the patients in the second round will be collected. Based on the digital-to-electrical signal conversion algorithm, the joint range of motion information of the patients in the second round will be converted into digital-to-electrical signals to obtain the joint range of motion data of the second round. Based on the muscle electrical signals associated with the joints in the second round of activity, the normalized envelope amplitude of the second muscle is constructed. Based on the normalized envelope amplitude of the second muscle, the mean electromyography value of each muscle of the patient is calculated to obtain the mean electromyography value of the second number of muscles. Based on the average electromyographic values ​​of the second set of muscles, the second muscle cocontraction index is obtained by repeating the values. Based on the average electromyographic values ​​of the second set of muscles and the second muscle cocontraction index, the saturation of the muscles in the second round is evaluated to obtain the muscle training saturation in the second round. By calculating whether the difference between the joint range of motion data in the first round and the joint range of motion data in the second round meets the preset training requirements, and the difference between the muscle training saturation in the first round and the muscle training saturation in the second round meets the preset training requirements, it is determined whether to end the muscle rehabilitation training.

6. A muscle rehabilitation training intensity monitoring system as claimed in claim 5, characterized in that, The step of determining whether to end muscle rehabilitation training involves calculating whether the difference between the joint range of motion data from the first round and the joint range of motion data from the second round meets the preset training requirements, and whether the difference between the muscle training saturation from the first round and the muscle training saturation from the second round meets the preset training requirements. Specifically: The first range of motion difference is obtained by calculating the difference between the joint range of motion data in the first round and the joint range of motion data in the second round. The first saturation difference is obtained by calculating the difference between the muscle training saturation in the first round and the muscle training saturation in the second round. When it is determined that both the first activity difference and the first saturation difference meet the preset training requirements, the muscle rehabilitation training ends.

7. A muscle rehabilitation training intensity monitoring system as claimed in claim 6, characterized in that, It also includes the following steps: When it is determined that neither the first activity difference nor the first saturation difference meets the preset training requirements, muscle rehabilitation training is resumed. Once it is determined to resume muscle rehabilitation training, obtain the joint range of motion data and muscle training saturation data for the third round. Based on the joint range of motion data from the second round and the muscle training saturation data from the second round, as well as the joint range of motion data from the third round and the muscle training saturation data from the third round, the difference in the second range of motion and the difference in the second saturation are calculated. Determine whether the second activity difference and the second saturation difference meet the preset training requirements. If they meet the preset training requirements, end the muscle rehabilitation training. If the preset training requirements are not met, repeat the above steps and update the second activity difference and the second saturation difference until the preset training requirements are met, and end the muscle rehabilitation training.

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