Muscle health index evaluation method and system based on lower limb muscle power analysis

Abstract

The present application belongs to the technical field of health assessment, and particularly relates to a muscle health index assessment method and system based on lower limb muscle power analysis. The present application comprises comparing the lower limb muscle health index with a preset threshold to generate a proofreading signal. In response to the proofreading signal, a preset conditioning program is executed or a retest is performed, and the execution result of the conditioning program or the retest data is defined as an evaluation parameter. Then, a comprehensive judgment is performed based on the evaluation parameter to output an overall health conclusion. After the proofreading signal is generated, the present application automatically selects and executes a corresponding conditioning program from a preset conditioning program set according to the normal state or abnormal state indicated by the proofreading signal, realizes the automation from muscle health index assessment to conditioning program execution, automatically matches and executes a suitable conditioning program according to the nature and degree of the assessed lower limb muscle state, avoids general conditioning suggestions, and improves the conditioning effect.

Description

Technical Field

[0001] This invention belongs to the field of health assessment technology, specifically relating to a method and system for assessing muscle health index based on lower limb muscle power analysis. Background Technology

[0002] As a core component supporting human activity and maintaining postural stability, the health of the lower limb muscle group directly affects an individual's mobility and is of great significance in preventing musculoskeletal injuries and delaying functional aging. Therefore, it is necessary to monitor and scientifically assess the condition of the lower limb muscles.

[0003] Currently, the assessment of lower limb muscle health primarily relies on devices such as electromyography (EMG) machines and wearable sensors. These devices are used to collect physiological signals. However, existing technologies rely on preset thresholds to determine muscle fatigue or activity, neglecting individual physiological differences. This leads to inaccurate assessments and an inability to provide truly targeted guidance. Furthermore, lower limb muscle health issues often exhibit diverse and complex characteristics, such as acute inhibition, persistent fatigue, or chronic strain. Current technologies cannot distinguish between these fundamentally different but potentially similar abnormalities, resulting in a lack of basis for subsequent rehabilitation or conditioning programs.

[0004] To address the aforementioned problems, this invention provides a method and system for assessing muscle health index based on lower limb muscle power analysis. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for assessing muscle health index based on lower limb muscle power analysis, so as to solve the problem that the existing technology cannot classify the abnormal conditions of patients' lower limb muscles.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The muscle health index assessment method based on lower limb muscle power analysis includes the following steps:

[0008] The lower limb muscle health index is calculated based on the baseline lower limb muscle power values ​​obtained from the subjects and a series of test power values.

[0009] In response to the generation of the lower limb muscle health index, the lower limb muscle health index is compared with a preset threshold to generate a calibration signal;

[0010] In response to the calibration signal, a preset treatment plan is executed or a retest is performed, and the execution result of the treatment plan or the retest data is defined as the parameter to be evaluated;

[0011] Furthermore, a comprehensive judgment is made based on the parameters to be evaluated in order to output an overall health conclusion;

[0012] The calculation of the lower limb muscle health index, based on the obtained baseline lower limb muscle power values ​​of the subjects and a series of test power values, includes: inputting a series of test power values ​​into a preset evaluation model to generate an average power parameter; calculating the evaluation difference between the baseline lower limb muscle power value and the average power parameter; and inputting the evaluation difference into a preset estimation function to generate the lower limb muscle health index. The estimation function includes at least one sub-function with an adjustable scaling factor, which is used to adjust the calculation accuracy of the lower limb muscle health index.

[0013] Preferably, the evaluation model has a maximum input tolerance; and a series of test power values ​​are input into the preset evaluation model to generate average power parameters, including:

[0014] The difference between two temporally adjacent test power values ​​in a series of test power values ​​is calculated as the input power;

[0015] Furthermore, when the input power value is determined to be within the maximum input tolerance range, the input power is input into the evaluation model to calculate the average power parameter.

[0016] Preferably, a series of test power values ​​are generated by the following steps:

[0017] The initially acquired power signal is defined as the parameter to be measured;

[0018] Perform a preset replacement operation on the parameter to be tested to generate calibration parameters;

[0019] The calibration power is calculated based on the calibration parameters, and the calibration deviation between the calibration power and the benchmark lower limb muscle power value is obtained.

[0020] Additionally, a delay operation is performed based on the calibration deviation to generate a calibration delay value, and a single test power value constituting a series of test power values ​​is generated based on the calibration power and the calibration delay value.

[0021] Preferably, in response to the calibration signal, executing a preset conditioning program includes:

[0022] Based on the calibration signal, a treatment plan is selected from the preset treatment plan set for execution. The treatment plans in the treatment plan set are pre-classified into primary treatment plans and secondary treatment plans.

[0023] When the calibration signal is a normal state signal, the first-level conditioning program, which includes soothing, hot compress and preset limb stretching operations, is selected.

[0024] Furthermore, when the calibration signal is an abnormal state signal, the secondary conditioning scheme including continuous electrical stimulation with preset electrical stimulation parameters and preset execution cycle is selected.

[0025] Preferably, performing a retest includes:

[0026] Time series monitoring of lower limb muscle power is used to determine the type of power change trend.

[0027] Furthermore, based on the determined type of power change trend, the corresponding sampling period and sampling mode are selected to collect the retest data;

[0028] The steps for performing a comprehensive judgment include:

[0029] The evaluation deviation is calculated based on the parameters to be evaluated, and the evaluation deviation is compared with the preset deviation range to perform deviation verification, thereby generating a verification signal;

[0030] Additionally, lower limb muscle status analysis is performed based on the verification signal to generate overall health conclusions.

[0031] This invention also provides a muscle health index assessment system based on lower limb muscle power analysis, comprising the following modules:

[0032] The power acquisition module is used to acquire the subject's baseline lower limb muscle power value and a series of test power values ​​generated during dynamic testing;

[0033] The health index assessment module is used to calculate the lower limb muscle health index based on the baseline lower limb muscle power value and a series of test power values, and compare the lower limb muscle health index with a preset threshold to generate a calibration signal.

[0034] The conditioning and retesting execution module is used to execute a preset conditioning plan or perform a retest in response to a calibration signal, so as to generate the execution result or retesting data of the conditioning plan, wherein the execution result or retesting data is defined as the parameter to be evaluated.

[0035] Additionally, a health conclusion generation module is used to perform a comprehensive judgment based on the parameters to be evaluated in order to output an overall health conclusion.

[0036] Preferably, the health index assessment module is used for:

[0037] A series of test power values ​​are input into a preset evaluation model to generate average power parameters;

[0038] Additionally, the evaluation difference between the baseline lower limb muscle power value and the average power parameter is calculated, and the evaluation difference is input into a preset estimation function to generate a lower limb muscle health index.

[0039] The estimation function includes at least one sub-function with an adjustable scaling factor, which is used to adjust the calculation accuracy of the lower limb muscle health index.

[0040] Preferably, the evaluation model has a maximum input tolerance; and the health index assessment module, when inputting a series of test power values ​​into the evaluation model, is used for:

[0041] The difference between two temporally adjacent test power values ​​in a series of test power values ​​is calculated as the input power;

[0042] Furthermore, when the input power value is determined to be within the maximum input tolerance range, the input power is input into the evaluation model to calculate the average power parameter.

[0043] Preferably, the power acquisition module is used to generate a series of test power values ​​in the following manner:

[0044] The initially acquired power signal is defined as the parameter to be measured;

[0045] Perform a preset replacement operation on the parameter to be tested to generate calibration parameters;

[0046] The calibration power is calculated based on the calibration parameters, and the calibration deviation between the calibration power and the benchmark lower limb muscle power value is obtained.

[0047] Additionally, a delay operation is performed based on the calibration deviation to generate a calibration delay value, and a single test power value constituting a series of test power values ​​is generated based on the calibration power and the calibration delay value.

[0048] Preferably, when executing a preset treatment plan, the treatment and retesting execution module is used to:

[0049] Based on the calibration signal, a treatment plan is selected from the preset treatment plan set for execution. The treatment plans in the treatment plan set are pre-classified into primary treatment plans and secondary treatment plans.

[0050] When the calibration signal is a normal state signal, select the first-level conditioning program, which includes soothing, hot compress and preset limb stretching operations.

[0051] In addition, when the calibration signal is an abnormal state signal, a secondary conditioning scheme including continuous electrical stimulation with preset electrical stimulation parameters and preset execution cycle is selected;

[0052] The conditioning and retesting execution module is used during retesting to:

[0053] Time series monitoring of lower limb muscle power is used to determine the type of power change trend.

[0054] Furthermore, based on the determined type of power change trend, the corresponding sampling period and sampling mode are selected to collect retest data;

[0055] The health conclusion generation module is used for:

[0056] The evaluation deviation is calculated based on the parameters to be evaluated, and the evaluation deviation is compared with the preset deviation range to perform deviation verification, thereby generating a verification signal;

[0057] Additionally, lower limb muscle status analysis is performed based on the verification signal to generate overall health conclusions.

[0058] Beneficial effects

[0059] 1. This invention compares the lower limb muscle health index with a preset threshold to generate a calibration signal. Then, it executes a treatment plan or performs a retest based on the calibration signal. The execution result of the treatment plan or the retest data is defined as the parameter to be evaluated. Based on the parameter to be evaluated, a comprehensive judgment is performed to output an overall health conclusion, thereby achieving a quantitative assessment of the lower limb muscle status. By directly linking the generated calibration signal with the subsequent treatment plan or retest, and then forming an overall health conclusion based on the actual effect of the treatment plan or retest, this invention overcomes the shortcomings of isolated information in a single assessment and the lack of subsequent follow-up verification, thereby improving the dynamics and accuracy of the overall health conclusion.

[0060] 2. In the process of generating a series of test power values, this invention performs replacement and delay operations on the initially acquired power signal. Furthermore, when inputting the test power values ​​into the evaluation model, it pre-determines whether the input power calculated from temporally adjacent test power values ​​is within the maximum input tolerance of the evaluation model. Only input power within the maximum input tolerance is received. Through this dual processing mechanism of input data, noise and sudden abnormal values ​​that may exist in the initially acquired power signal can be effectively filtered out. The power signal calibration operation ensures the data quality input into the evaluation model, while the input tolerance of the evaluation model prevents abnormal data points from interfering with the calculation of the average power parameter, thereby ensuring the purity of the evaluation basis and improving the calculation accuracy of the lower limb muscle health index and the stability of the overall health conclusion.

[0061] 3. After generating the calibration signal, the present invention automatically selects and executes the corresponding conditioning plan from the preset conditioning plan set according to the normal or abnormal state indicated by the calibration signal, thereby automating the process from muscle health index assessment to conditioning plan execution. Based on the nature and degree of the assessed lower limb muscle state, the invention automatically matches and executes the appropriate conditioning plan, avoiding general conditioning suggestions and improving the conditioning effect. Attached Figure Description

[0062] Figure 1 This is a flowchart of the method of the present invention;

[0063] Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 made by those skilled in the art under the guidance of these embodiments are within the scope of protection of the present invention.

[0065] Example 1

[0066] See Figure 1 This embodiment discloses a method for assessing muscle health index based on lower limb muscle power analysis, including the following steps:

[0067] S1. Data Acquisition and Preprocessing: Acquire benchmark data and dynamic test data for evaluation, and perform data purification to ensure their validity. Specifically:

[0068] The baseline lower limb muscle power value of the subject was obtained under preset static conditions. The preset static conditions refer to the state in which the subject does not exert force and has a fixed body posture, such as sitting or lying still on the testing equipment. The power value measured at this time reflects the basic energy consumption or inherent tension level of the muscle in the resting state, and serves as the zero-point benchmark for subsequent dynamic comparisons.

[0069] The test acquires a series of power values ​​generated by the subject performing dynamic tests within a preset unit time. The dynamic test is a standardized and repeatable action, such as a uniform knee flexion and extension movement or pedaling on a stationary bicycle with constant resistance. The raw power signal initially acquired by the sensor without any processing is defined as the parameter to be measured.

[0070] Pre-defined screening and correction processes are performed on the parameters to be tested to generate calibration parameters, thereby ensuring the physiological significance of the data. Specifically, this includes identifying and eliminating transient abnormal spikes caused by non-physiological factors, including equipment signal interference or involuntary momentary actions of the subject.

[0071] Furthermore, any instantaneous value that exceeds the preset normal physiological range is replaced with the median or average value of values ​​at several time points before and after that value to generate calibration parameters.

[0072] Furthermore, based on the calibration parameters, the calibration power is calculated and output.

[0073] Furthermore, the following steps are taken to address signal drift that may occur with long-term sensor use or minor physiological differences in the initial state of subjects before each test:

[0074] The difference between the calibration power and the baseline lower limb muscle power value is obtained as the calibration deviation. If the calibration deviation exceeds the preset stability threshold, it indicates that the initial test conditions are unstable, and the delay timer is started. The specific value of the delay time can be determined according to the magnitude of the calibration deviation according to the preset ratio. The stability threshold refers to an acceptable upper limit value set for the calibration deviation.

[0075] Pause formal data recording during the delay until the muscle condition or equipment signal stabilizes before resuming formal recording;

[0076] Based on the stabilized calibration power and the zeroed calibration delay value, individual test power values ​​that constitute a series of test power values ​​are generated one by one and recorded in chronological order.

[0077] S2, Health Index Calculation, is used to transform the collected power data sequence into a quantified health index through a calculation process. Specific steps include:

[0078] A series of test power values ​​are processed through a preset calculation procedure to generate average power parameters.

[0079] The calculation procedure includes input value constraints, namely, a maximum input tolerance, which aims to prevent sudden power fluctuations caused by the subject's sudden violent movements or muscle spasms from interfering with the overall assessment accuracy. Specifically:

[0080] Before processing the data, the difference between two temporally adjacent test power values ​​in a series of test power values ​​is calculated and defined as the input power. It is then determined whether the absolute value of the input power is within the maximum input tolerance range. If the value of the input power is within the maximum input tolerance range, the test power value at that time point is accepted and used to calculate the average power parameter. If it exceeds the tolerance range, the test power value at that time point is considered atypical physiological activity data and is not included in subsequent calculations.

[0081] After generating the average power parameters, the lower limb muscle health index is calculated and output based on the average power parameters and the baseline lower limb muscle power value. This index is used to comprehensively and quantitatively assess the subject's lower limb muscle health status. The specific calculation process includes:

[0082] The difference between the baseline lower limb muscle power value and the average power parameter is calculated, and the difference is defined as the evaluation difference. The evaluation difference objectively reflects the power output capacity of the muscle when it transitions from a resting state to a dynamic working state.

[0083] The assessment difference is quantified through a multi-dimensional scoring process to generate a lower limb muscle health index.

[0084] The scoring process includes multiple parallel computation branches, each of which scores different characteristics of the evaluation difference based on independent scoring criteria.

[0085] The aforementioned different characteristics represent different dimensions of muscle health, specifically:

[0086] One computational branch can be used to evaluate the stability of the power output, i.e., to assess the dispersion of the difference over time; another computational branch can be used to assess the peak value of the power output or the total amount of work done.

[0087] Furthermore, in practical applications, when the scores of each calculation branch are finally combined into the lower limb muscle health index, they can be multiplied by a corresponding adjustable scaling factor that can be adjusted according to individual differences such as the subject's age, weight, or past medical history. This is used to adjust the weight of different dimension scores in the total index, thereby achieving personalized weighting of different dimension scores, thus achieving personalized adaptation and improving the pertinence and accuracy of the assessment results.

[0088] The multi-dimensional scoring process is a calculation model used to quantify the assessment differences into a lower limb muscle health index through weighted summation. Its specific definition is as follows:

[0089] ;

[0090] In the formula, The lower limb muscle health index represents the final score that comprehensively quantifies the subject's lower limb muscle health status.

[0091] This indicates the total number of assessment dimensions, which means the total number of muscle health dimensions on which the score is based, such as stability, peak performance, endurance, etc.

[0092] This represents the scaling factor, which means the first... The weighting coefficients for each assessment dimension can be adjusted based on the individual circumstances of the subjects, and must meet the following requirements: ;

[0093] This represents the dimensional scoring function, which means that for the dimensional... The scoring function for each evaluation dimension is based on independent scoring criteria and inputs the evaluation difference time series. Mapped to the score of that dimension, for example, the stability dimension. It can be the reciprocal function for evaluating the variance of the difference sequence;

[0094] This represents the evaluation difference, which means that at time 10:00... The evaluation difference is the difference between the dynamic power and the reference power.

[0095] Scoring criteria are independent rules or functions used to quantify and score specific dimensions of the evaluation difference in a multi-dimensional scoring process, which are the basis for each calculation branch.

[0096] S3. Generate verification signals and execute feedback, the specific steps of which include:

[0097] Decisions are made based on the calculated health index, and corresponding follow-up processes are initiated. The lower limb muscle health index is compared with a set of preset thresholds to generate a calibration signal.

[0098] The preset threshold divides the continuous numerical range of the health index into several discrete status levels, such as normal range, warning range and abnormal range.

[0099] The generated calibration signal is output to the conditioning retest execution unit, which selects and executes the corresponding conditioning plan from the preset conditioning plan set containing multiple intervention measures, or decides to start the retest process based on the received calibration signal.

[0100] The treatment plans are pre-categorized into primary and secondary treatment plans to address different muscle conditions. Specifically:

[0101] The calibration signal includes normal state signal and abnormal state signal. When the lower limb muscle health index is within the normal range, a normal state signal is generated.

[0102] When the conditioning retest execution unit receives a normal status signal, it selects and starts the corresponding first-level conditioning plan from the conditioning plan set.

[0103] The Level 1 treatment plan is used for routine muscle maintenance and relaxation, including low-intensity vibration soothing and local heat application to the target muscle groups;

[0104] Furthermore, the Level 1 treatment plan also includes guiding subjects to complete preset limb stretching operations through visual or voice instructions in order to promote local blood circulation and relieve potential muscle tension;

[0105] When the lower limb muscle health index falls below the preset lower threshold, it enters the abnormal range and generates an abnormal state signal, which indicates that the muscles are in a state of over-fatigue, functional impairment, or require active intervention.

[0106] The secondary conditioning program is selected when the conditioning retest execution unit receives an abnormal status signal;

[0107] The secondary conditioning program is used to provide stronger physiological interventions, such as applying continuous electrical stimulation to the target muscles. The continuous electrical stimulation has preset electrical stimulation parameters and preset execution cycles. The electrical stimulation parameters include current intensity, frequency and waveform. Its purpose is to passively activate deep muscle fibers and enhance neuromuscular recruitment ability to promote muscle function recovery.

[0108] S4. Perform conditioning or retesting and define the parameters to be evaluated. Specific steps include:

[0109] Based on the calibration signal generated in the previous step, execute the preset conditioning plan or perform a retest to collect retest data;

[0110] If the conditioning program is implemented, after the conditioning process is completed, the dynamic testing method of step S1 will be used again to collect a new set of test power values, and the new lower limb muscle health index calculated from the new set of test power values ​​will be defined as the parameter to be evaluated to quantify the effect of the conditioning intervention.

[0111] If the system decides to perform a retest, that is, when the initial lower limb muscle health index is within the warning range and it is necessary to confirm its stability, the retest process will be initiated. The retest process includes:

[0112] The time series of lower limb muscle power is continuously monitored, and the type of power change trend is determined based on the change pattern of the time series over a period of time. The types of power change trends include stable output type, gradual decay type and irregular fluctuation type.

[0113] Based on the type of power change trend determined, the corresponding sampling period and sampling mode are selected from the preset sampling strategy library;

[0114] For the gradual decay trend, a sampling period that can better capture the fatigue critical point is selected, and a sampling mode with denser sampling points during that period is adopted to obtain more detailed decay curve data.

[0115] Based on the selected sampling period and sampling mode, retest data are collected, and the retest data or the new health index calculated from it is defined as the parameter to be evaluated.

[0116] The type of power change trend refers to the classification of the change pattern of lower limb muscle power over a period of time.

[0117] The sampling strategy library is a pre-set database that stores various combinations of sampling time periods and sampling modes. Each strategy corresponds to a specific type of power change trend. The sampling time period and sampling mode refer to the specific data acquisition time window and acquisition frequency mode selected from the sampling strategy library, respectively, to more accurately capture the key features of a specific power change trend.

[0118] The retest data is a sequence of newly acquired test power values ​​after the retest process is executed and the selected sampling period and sampling mode are adopted.

[0119] S5. Comprehensive Judgment and Output Conclusion: This step outputs a comprehensive conclusion on the subject's current state. Based on the parameters to be evaluated, a comprehensive judgment is performed to output an overall health conclusion. Specific steps include:

[0120] Based on the comparison between the parameter to be evaluated and the lower limb muscle health index before conditioning or retesting, the change between the two is calculated, and the change is defined as the evaluation deviation.

[0121] The assessment deviation is compared with a preset deviation range representing different degrees of improvement. The comparison process is called deviation verification, and its purpose is to confirm whether the changes in muscle state are in line with the expected physiological response.

[0122] Based on the comparison results, a verification signal characterizing the properties of the state change is generated;

[0123] Based on the verification signal and combined with the initial lower limb muscle health index, an overall health conclusion is output through a preset conclusion generation rule, specifically:

[0124] If the initial index is low, but the assessment deviation is significantly positive, that is, exceeding the preset improvement threshold, then the conclusion can be judged as an improvement in the status, and it is recommended to continue to observe.

[0125] If the initial index is within the warning range and the evaluation deviation after retesting is close to zero, that is, within the preset stable range, then the conclusion can be determined that the state is stable and there is a potential risk.

[0126] If the initial index is low and the post-treatment assessment deviation is negative, the conclusion can be determined as abnormal and requires further examination, and more in-depth medical examination is recommended.

[0127] Example 2

[0128] See Figure 2 This embodiment discloses a muscle health index assessment system based on lower limb muscle power analysis, including the following modules:

[0129] The power acquisition module is configured to acquire the baseline lower limb muscle power value measured by the subject under preset static conditions, as well as a series of test power values ​​generated by the subject performing dynamic tests within a preset unit time.

[0130] Furthermore, a series of test power values ​​are generated in the following manner:

[0131] The power signals initially acquired by the subject when performing specific actions such as squatting and jumping were collected by a surface electromyography sensor combined with a force gauge. The initially acquired power signals were defined as the parameter to be measured.

[0132] Perform preset replacement operations on the parameters to be measured, such as replacing the original data points with the sliding window mean, to generate calibration parameters, thereby filtering out signal noise or performing standardization processing;

[0133] The calibration power is calculated based on the calibration parameters;

[0134] The calibration deviation between the calibration power and the baseline lower limb muscle power value is obtained, and this calibration deviation reflects the degree of difference between the instantaneous power and the baseline state;

[0135] Based on this calibration deviation, a delay operation is performed to generate a calibration delay value, which can be used to simulate the physiological delay of muscle response;

[0136] Based on the calibration power and calibration delay values, a single test power value is generated through preset calculation logic. By repeating this process during dynamic testing, a series of test power values ​​that form the basis of the evaluation are generated.

[0137] The health index assessment module is used to calculate the lower limb muscle health index based on data provided by the power acquisition module. Specifically, it includes:

[0138] The series of test power values ​​generated by the power acquisition module are input into the preset calculation procedure to process the power fluctuation data, extract key features, and generate an average power parameter that can represent the overall level of the series of test power.

[0139] Before inputting the data, a verification step is performed: the difference between two time-adjacent test power values ​​in a series of test power values ​​is calculated and defined as the input power. Since the calculation procedure may have a maximum input tolerance to avoid interference from outliers, it is determined whether the value of the input power is within the tolerance range. Only when the condition is met will the test power value at that time point be accepted and used to calculate the average power parameter.

[0140] After obtaining the average power parameter, the assessment difference between the baseline lower limb muscle power value and the average power parameter is calculated. The assessment difference directly reflects the degree of deviation between the subject's current muscle performance and the baseline state.

[0141] The difference in this assessment is input into a preset multi-dimensional scoring process, which maps the difference in the power domain to a standardized score or index, thereby generating a lower limb muscle health index.

[0142] Furthermore, the multi-dimensional scoring process includes at least one calculation branch with an adjustable scaling factor. By adjusting this scaling factor, the calculation accuracy of the lower limb muscle health index can be fine-tuned for different populations or testing scenarios, thereby improving the flexibility and accuracy of the assessment.

[0143] The signal generation module is configured to compare the lower limb muscle health index with a preset threshold to generate a calibration signal. Based on the specific interval of the lower limb muscle health index value falling within the normal range, warning range, or abnormal range defined by the preset threshold, a calibration signal containing corresponding status instructions is generated, such as a normal status signal or an abnormal status signal, for the conditioning and retesting execution module to call.

[0144] The conditioning and retesting execution module is configured to select and execute corresponding subsequent operations based on the calibration signal to generate the parameters to be evaluated for final judgment.

[0145] When the received calibration signal indicates that a preset treatment plan needs to be executed, the corresponding plan is selected from the preset treatment plan set and executed according to the specific content of the calibration signal;

[0146] Furthermore, the set of treatment plans is pre-categorized, for example:

[0147] When the calibration signal is a normal state signal, it indicates that the muscle condition is good but can be routinely maintained. In this case, the first-level conditioning program is selected, which includes soothing the subject's lower limbs, applying heat, and guiding the subject to complete the preset limb stretching operation.

[0148] When the calibration signal is an abnormal state signal, it indicates that the muscles may be fatigued, at risk of injury, or have decreased function. In this case, a secondary conditioning program is selected. This program includes activating external equipment to apply continuous electrical stimulation with preset electrical stimulation parameters and preset execution cycles to the subject's lower limbs to promote muscle recovery or functional activation.

[0149] After the treatment plan was completed, a new set of test power values ​​was collected again. Based on the new test power values, a new lower limb muscle health index was calculated and defined as a parameter to be evaluated.

[0150] When the received calibration signal indicates that a retest is required, the retest process is initiated, specifically as follows:

[0151] By monitoring the time series of lower limb muscle power and analyzing its fluctuation patterns, frequency, or amplitude changes, the type of power change trend can be determined, such as stable, progressive fatigue, or sudden fluctuation. Based on the determined type of power change trend, the corresponding sampling period and sampling mode are selected from a preset sampling strategy library, such as high-frequency intensive sampling or low-frequency timed sampling, to collect retest data in a more targeted manner. The collected retest data, or the new health index calculated from it, is defined as the parameter to be evaluated.

[0152] The health conclusion generation module is configured to output an overall health conclusion based on the results of conditioning or retesting.

[0153] Receive the parameters to be evaluated generated by the conditioning and retesting execution module, as follows:

[0154] Based on the comparison between the parameter to be evaluated and the lower limb muscle health index before conditioning or retesting, the change between the two is calculated, and the change is defined as the evaluation deviation.

[0155] The assessment deviation is compared with the preset deviation range to perform deviation verification, thereby determining whether the treatment effect is significant or whether the retest results confirm the preliminary assessment.

[0156] The verification result will generate a verification signal. Based on this verification signal and combined with the initial lower limb muscle health index, the overall health conclusion will be output according to the preset conclusion generation rules.

[0157] The overall health conclusion is a detailed description of the current state of the subject's lower limb muscles, a warning of potential risks, and personalized training or rehabilitation recommendations.

Claims

1. A method for evaluating muscle health index based on lower extremity muscle power analysis, characterized by, Includes the following steps: Based on the obtained baseline lower limb muscle power values ​​of the subjects and a series of test power values, the lower limb muscle health index is calculated; in response to the generation of the lower limb muscle health index, the lower limb muscle health index is compared with a preset threshold to generate a calibration signal. In response to the calibration signal, a preset conditioning plan is executed or a retest is performed, and the execution result of the conditioning plan or the retest data is defined as the parameter to be evaluated; and a comprehensive judgment is performed based on the parameter to be evaluated to output an overall health conclusion; The calculation of the lower limb muscle health index, based on the obtained baseline lower limb muscle power value of the subject and a series of test power values, includes: inputting a series of test power values ​​into a preset evaluation model to generate an average power parameter; calculating the evaluation difference between the baseline lower limb muscle power value and the average power parameter; and inputting the evaluation difference into a preset estimation function to generate the lower limb muscle health index. The estimation function includes at least one sub-function with an adjustable scaling factor, which is used to adjust the calculation accuracy of the lower limb muscle health index. The evaluation model has a maximum input tolerance; and a series of test power values ​​are input into the preset evaluation model to generate average power parameters, including: calculating the difference between two time-adjacent test power values ​​in a series of test power values ​​as input power; when it is determined that the value of the input power is within the range of the maximum input tolerance, the input power is input into the evaluation model to calculate the average power parameters; A series of test power values ​​are generated by the following steps: defining the initially acquired power signal as the parameter to be tested; performing a preset replacement operation on the parameter to be tested to generate calibration parameters; calculating the calibration power based on the calibration parameters and obtaining the calibration deviation between the calibration power and the benchmark lower limb muscle power value; performing a delay operation based on the calibration deviation to generate a calibration delay value; and generating a single test power value that constitutes a series of test power values ​​based on the calibration power and the calibration delay value. When the calibration signal is in a normal or abnormal state, the preset conditioning plan is executed; when the lower limb muscle health index is within the warning range, a retest is performed.

2. The muscle health index evaluation method based on lower extremity muscle power analysis according to claim 1, characterized in that, In response to the calibration signal, the preset conditioning program is executed, including: Based on the calibration signal, a treatment plan is selected from the preset treatment plan set for execution. The treatment plans in the treatment plan set are pre-classified into primary treatment plans and secondary treatment plans. When the calibration signal is a normal state signal, the first-level conditioning program, which includes soothing, hot compress and preset limb stretching operations, is selected. Furthermore, when the calibration signal is an abnormal state signal, the secondary conditioning scheme including continuous electrical stimulation with preset electrical stimulation parameters and preset execution cycle is selected.

3. The muscle health index evaluation method based on lower extremity muscle power analysis according to claim 1, characterized in that, The retesting process includes: Time series monitoring of lower limb muscle power is used to determine the type of power change trend. Furthermore, based on the determined type of power change trend, the corresponding sampling period and sampling mode are selected to collect the retest data; The steps for performing a comprehensive judgment include: The evaluation deviation is calculated based on the parameters to be evaluated, and the evaluation deviation is compared with the preset deviation range to perform deviation verification, thereby generating a verification signal; Additionally, lower limb muscle status analysis is performed based on the verification signal to generate overall health conclusions.

4. A muscle health index evaluation system based on lower extremity muscle power analysis, characterized by, Includes the following modules: The power acquisition module is used to acquire the subject's baseline lower limb muscle power value and a series of test power values ​​generated during dynamic testing; The health index assessment module is used to calculate the lower limb muscle health index based on the baseline lower limb muscle power value and a series of test power values, and compare the lower limb muscle health index with a preset threshold to generate a calibration signal. The conditioning and retesting execution module is used to execute a preset conditioning plan or perform a retest in response to a calibration signal, so as to generate the execution result or retesting data of the conditioning plan, wherein the execution result or retesting data is defined as the parameter to be evaluated. In addition, a health conclusion generation module is used to perform a comprehensive judgment based on the parameters to be evaluated in order to output an overall health conclusion; The health index assessment module is used to: input a series of test power values ​​into a preset evaluation model to generate average power parameters; The evaluation difference between the baseline lower limb muscle power value and the average power parameter is calculated, and the evaluation difference is input into a preset estimation function to generate a lower limb muscle health index. The scoring function includes at least one sub-function with an adjustable scaling factor, which is used to adjust the calculation accuracy of the lower limb muscle health index. The evaluation model has a maximum input tolerance; and when the health index assessment module inputs a series of test power values ​​into the evaluation model, it is used to: calculate the difference between two time-adjacent test power values ​​in the series of test power values ​​as the input power; and, when it is determined that the value of the input power is within the range of the maximum input tolerance, input the input power into the evaluation model to calculate the average power parameter; The power acquisition module is used to generate a series of test power values ​​in the following ways: defining the initially acquired power signal as the parameter to be measured; performing a preset replacement operation on the parameter to be measured to generate a calibration parameter; calculating the calibration power based on the calibration parameter and obtaining the calibration deviation between the calibration power and the benchmark lower limb muscle power value; and performing a delay operation based on the calibration deviation to generate a calibration delay value, and generating a single test power value that constitutes a series of test power values ​​based on the calibration power and the calibration delay value. When the calibration signal is in a normal or abnormal state, the preset conditioning plan is executed; when the lower limb muscle health index is within the warning range, a retest is performed.

5. The muscle health index evaluation system based on lower extremity muscle power analysis according to claim 4, wherein, The conditioning and retesting execution module, when executing a preset conditioning plan, is used for: Based on the calibration signal, a treatment plan is selected from the preset treatment plan set for execution. The treatment plans in the treatment plan set are pre-classified into primary treatment plans and secondary treatment plans. When the calibration signal is a normal state signal, select the first-level conditioning program, which includes soothing, hot compress and preset limb stretching operations. In addition, when the calibration signal is an abnormal state signal, a secondary conditioning scheme including continuous electrical stimulation with preset electrical stimulation parameters and preset execution cycle is selected; The conditioning and retesting execution module is used during retesting to: Time series monitoring of lower limb muscle power is used to determine the type of power change trend. and based on the type of the determined power change trend, a corresponding sampling period and sampling mode are selected to collect the retest data; The health conclusion generation module is configured to: calculate an evaluation deviation based on the to-be-evaluated parameter, and compare the evaluation deviation with a preset deviation range to perform a deviation check, thereby generating a check signal; and based on the check signal, perform lower limb muscle state analysis to generate an overall health conclusion.

Images