CN116439713A - Physical load assessment method based on hemodynamic parameters and BP neural network - Google Patents
Physical load assessment method based on hemodynamic parameters and BP neural network Download PDFInfo
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Abstract
The invention discloses an evaluation method based on hemodynamic parameters and BP neural network, which comprises the following steps: s1, establishing a prediction model through a BP neural network; s2, training a preset BP neural network model by utilizing the multiple values of the hemodynamic parameters to obtain a sample result; s3, measuring hemodynamic parameters of a tested person in a quiet state, and extracting features to obtain an input vector G1; s4, carrying out feature extraction on hemodynamic parameters measured by a tested person immediately after the load movement is finished to obtain an input vector G2; s5, respectively inputting the G1 and the G2 into a prediction model to obtain a test evaluation result; s6, a tested person completes the Borg fatigue evaluation scale, evaluates the physical load value of the tested person in the test, and corresponds to the evaluation result to obtain a final evaluation report; the method can be combined objectively and objectively, improves the accuracy and reliability of the test, and enables non-professional staff to clearly and definitely know the self-body energy adaptation condition.
Description
Technical Field
The invention relates to the technical field of fitness training, in particular to a physical load assessment method based on hemodynamic parameters and BP neural network.
Background
Body fit is a necessary condition of the body to enable an individual to properly use the necessary muscle endurance, cardiopulmonary ability, flexibility, coordination, agility, strength, balance, speed and accuracy to achieve the necessary acquisition of tasks without creating an irrecoverable feeling of excessive fatigue and fatigue. Cardiopulmonary function is an important indicator in physical training. Cardiopulmonary function refers to the force by which the body ingests and converts oxygen into energy, and this overall process involves the functions of the heart's blood production and pumping, the functions of the lungs' oxygen uptake and gas exchange, the oxygen carrying efficiency of the blood circulatory system, and the function of muscle oxygen utilization. If the physical load is excessive during physical training, secondary injuries are easily generated. Therefore, it is necessary to conduct a related study with respect to the evaluation that the day of the physical training of the tested person, particularly soldiers and athletes, is testable.
At present, objective evaluation and subjective evaluation are common methods in the field of fitness evaluation. The objective physiological data is a popular body fit energy assessment method, but the operation is complex, and the current experiment stage is adopted, and the relation between various indexes and body fit energy is not proved forcefully; the subjective evaluation scale method is the most popular method for evaluating the fitness, is simple to operate, and is easily influenced by subjective factors of testees.
Disclosure of Invention
In view of the above, the present invention aims at overcoming the drawbacks of the prior art, and its main objective is to provide a physical load assessment method based on hemodynamic parameters and BP neural network, which makes the identification result of the fitness status of the tested person simpler and more intuitive, and the assessment result can make the non-professional person clearly and definitely know the fitness status.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a physical load assessment method based on hemodynamic parameters and BP neural network adopts the following steps:
s1, determining a plurality of hemodynamic parameters of a tested person, and establishing a prediction model through a BP neural network; performing multiple values for each hemodynamic parameter according to historical data;
s2, training a preset BP neural network model by utilizing the multiple values of the hemodynamic parameters to obtain a sample result;
s3, allowing a tested person to sit still for a period of time before detection, measuring hemodynamic parameters of the tested person in a quiet state by using ICON hemodynamic equipment, respectively performing time-frequency domain and nonlinear analysis on the parameters, and extracting features to obtain an input vector G1;
s4, allowing the tested person to perform load movement for a period of time, immediately measuring hemodynamic parameters of the tested person after the load movement is finished, respectively performing time-frequency domain and nonlinear analysis on the parameters, and performing feature extraction to obtain an input vector G2;
s5, respectively inputting the G1 and the G2 into a prediction model to identify the health condition of the tested person, and obtaining a test evaluation result of the tested person;
s6, the tested person completes the Borg fatigue evaluation scale, evaluates the physical load value of the tested person in the test, and corresponds to the evaluation result to obtain a final evaluation report.
As a preferred embodiment, the parameters measured by the ICON hemodynamic equipment are heart rate, stroke volume, heart displacement, chest fluid level, myocardial contraction index, corrected ejection time, output per stroke index, and heart rate index, and the parameters are used as independent variables, and the physical load value is used as a dependent variable to perform multi-factor analysis of variance.
As a preferred option, the load movement includes military fitness test system (ACFT) including hard pull, rear projection in standing position, push-up, 25 meter sprint/drag/carry, horizontal bar unwind, 2 mile run.
As a preferred option, the sex, age, weight, height and BMI index of the tested person are documented prior to the measured hemodynamic parameters.
As a preferred solution, a database is included, which is used to record the weight, height, BMI index and the evaluation result obtained from each test of the same person tested at different times.
As a preferable mode, the evaluation result is 1 to 10.
As a preferred scheme, the BP neural network algorithm in the prediction model includes an input layer, a hidden layer and an output layer, in the training process, the input layer is used for inputting the hemodynamic parameter and the expected value, the hidden layer is used for comparing the hemodynamic parameter to be analyzed with the expected value, and adjusting the weight coefficient according to the error between the hemodynamic parameter to be analyzed and the expected value, and the output layer is used for outputting the corresponding sample result.
The invention mainly completes an incremental exercise test by a tested person to evaluate the physical load limit value, and acquires hemodynamic parameters of the tested person, including the SV, the HR, the CO and the like of each stroke volume, records the sex, the height and the weight of the tested person, takes a plurality of variables as input variables and inputs the variables into a BP artificial neural network detection model to obtain a tested person fitness evaluation report based on an indirect measurement method by constructing a detection model.
Compared with the prior art, the invention has obvious advantages and beneficial effects, the invention obtains the hemodynamic parameters with higher correlation with the fitness test through multi-factor variance analysis, establishes a detection model through the BP neural network, evaluates the physical load of the tested person under exercise by using the parameters measured by the ICON hemodynamic equipment, combines the Borg scale with higher authority to judge the physical load value, comprehensively obtains the fitness evaluation report of the tested person, can be combined objectively and objectively, and improves the accuracy and reliability of the test. The invention effectively fuses the hemodynamic parameters reflecting the human index values, gives the body fit condition identification result through the BP neural network model, not only ensures that the identification result of the body fit condition of the tested person is simpler and more visual, but also ensures that the non-professional person can clearly and definitely know the body fit condition, and has important guiding significance for the adjustment, recovery and treatment of the body fit of the tested person.
In order to more clearly illustrate the structural features and efficacy of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a block diagram of a test flow of an embodiment of the present invention;
FIG. 2 is a Table showing the Borg index of an embodiment of the present invention.
Detailed Description
For the purpose of making the technical solution and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and examples of implementation. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that when an element is referred to as being "fixed to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Referring to fig. 1 and 2, an embodiment of the present invention provides a physical load assessment method based on hemodynamic parameters and a BP neural network, which includes the following steps:
s1, determining a plurality of hemodynamic parameters of a tested person, and establishing a prediction model through a BP neural network; performing multiple values for each hemodynamic parameter according to historical data;
s2, training a preset BP neural network model by utilizing the multiple values of the hemodynamic parameters to obtain a sample result;
s3, allowing a tested person to sit still for a period of time before detection, measuring hemodynamic parameters of the tested person in a quiet state by using ICON hemodynamic equipment, respectively performing time-frequency domain and nonlinear analysis on the parameters, and extracting features to obtain an input vector G1;
s4, allowing the tested person to perform load movement for a period of time, immediately measuring hemodynamic parameters of the tested person after the load movement is finished, respectively performing time-frequency domain and nonlinear analysis on the parameters, and performing feature extraction to obtain an input vector G2;
s5, respectively inputting the G1 and the G2 into a prediction model to identify the health condition of the tested person, and obtaining a test evaluation result of the tested person;
s6, the tested person completes the Borg fatigue evaluation scale, evaluates the physical load value of the tested person in the test, and corresponds to the evaluation result to obtain a final evaluation report.
Specifically, parameters measured by the ICON hemodynamic equipment are heart rate, stroke volume, heart displacement, thoracic fluid level, myocardial contraction index, corrected ejection time, output per stroke index and heart discharge index, the parameters are taken as independent variables, and the physical load value is taken as the dependent variable to carry out multi-factor analysis of variance. The load motions include military physical fitness test system (ACFT) which includes hard pull, rear projection in standing position, push-up, 25 meter sprint/drag/transport, horizontal bar hoist unwind, 2 mile run.
The sex, age, weight, height and BMI index of the tested person were documented prior to the measured hemodynamic parameters. The system also comprises a database, wherein the database is used for recording the weight, the height and the BMI index of the same tested person tested at different times and the evaluation result obtained by each test. The evaluation result was 1 to 10.
The BP neural network algorithm in the prediction model comprises an input layer, a hidden layer and an output layer, wherein in the training process, the input layer is used for inputting hemodynamic parameters and expected values, the hidden layer is used for comparing the hemodynamic parameters to be analyzed with the expected values, weight coefficients are adjusted according to errors between the hemodynamic parameters to be analyzed and the expected values, and the output layer is used for outputting corresponding sample results.
Compared with the prior art, the invention has obvious advantages and beneficial effects that the incremental exercise test is completed by a tested person, the physical load limit value is evaluated, the BP neural network detection model is constructed, the hemodynamic parameters of the tested person, including the SV, the HR, the CO and the like of each stroke volume, are collected and fixed, the gender, the height and the weight of the tested person are recorded, the multivariate is used as input variables to be input into the BP artificial neural network detection model, the hemodynamic parameters with higher correlation with the physical load are obtained through multi-factor variance analysis, the hemodynamic parameters are respectively the chest fluid level, the myocardial contraction index, the corrected ejection time, the output index of each stroke and the heart displacement index, the physical load of the tested person under exercise is evaluated by using the parameters measured by ICON hemodynamic equipment, the determination of the physical load value is carried out by combining with the Borg meter with higher authority, the body adaptation evaluation report of the tested person is comprehensively obtained, and the invention can be combined with the main and objective of the accuracy and the reliability of the test. The subjective evaluation data is proved by students for years, and the subjective evaluation data has certain authority and credibility, so that the objective evaluation data can be supplemented in credibility; the objective evaluation data is a real reflection of the physiological change of the tested person, and can weaken subjectivity of a subjective evaluation method.
The invention effectively fuses the hemodynamic parameters reflecting the human index values, gives the body fit condition identification result through the BP neural network model, not only ensures that the identification result of the body fit condition of the tested person is simpler and more visual, but also ensures that the non-professional person can clearly and definitely know the body fit condition, and has important guiding significance for the adjustment, recovery and treatment of the body fit of the tested person.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalents, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (7)
1. A physical load assessment method based on hemodynamic parameters and BP neural network algorithm is characterized by comprising the following steps:
s1, determining a plurality of hemodynamic parameters of a tested person, and establishing a prediction model through a BP neural network; performing multiple values for each hemodynamic parameter according to historical data;
s2, training a preset BP neural network model by utilizing the multiple values of the hemodynamic parameters to obtain a sample result;
s3, allowing a tested person to sit still for a period of time before detection, measuring hemodynamic parameters of the tested person in a quiet state by using ICON hemodynamic equipment, respectively performing time-frequency domain and nonlinear analysis on the parameters, and extracting features to obtain an input vector G1;
s4, allowing the tested person to perform load movement for a period of time, immediately measuring hemodynamic parameters of the tested person after the load movement is finished, respectively performing time-frequency domain and nonlinear analysis on the parameters, and performing feature extraction to obtain an input vector G2;
s5, respectively inputting the G1 and the G2 into a prediction model to obtain a test evaluation result of a tested person;
s6, the tested person completes the Borg fatigue evaluation scale, evaluates the physical load value of the tested person in the test, and corresponds to the evaluation result to obtain a final evaluation report.
2. The method for estimating physical load based on hemodynamic parameters and a BP neural network according to claim 1, wherein: parameters measured by the ICON hemodynamic equipment are heart rate, stroke volume, heart displacement, chest fluid level, myocardial contraction index, corrected ejection time, output per stroke index and heart discharge index, the parameters are used as independent variables, and the physical load value is used as the dependent variables to carry out multi-factor analysis of variance.
3. The method for estimating physical load based on hemodynamic parameters and a BP neural network according to claim 1, wherein: the load motions include military physical fitness test system (ACFT) which includes hard pull, rear projection in standing position, push-up, 25 meter sprint/drag/transport, horizontal bar hoist unwind, 2 mile run.
4. Physical load assessment based on hemodynamic parameters and BP neural network as claimed in claim 1
The estimation method is characterized in that: the sex, age, weight, height and BMI index of the tested person were documented prior to the measured hemodynamic parameters.
5. The method for estimating physical load based on hemodynamic parameters and a BP neural network according to claim 4, wherein: the system comprises a database, wherein the database is used for recording the weight, the height and the BMI index of the same tested person tested at different times and the evaluation result obtained by each test.
6. The method for estimating physical load based on hemodynamic parameters and a BP neural network according to claim 1, wherein: the evaluation result was 1 to 10.
7. The method for estimating physical load based on hemodynamic parameters and a BP neural network according to claim 1, wherein: the BP neural network algorithm in the prediction model comprises an input layer, a hidden layer and an output layer, wherein in the training process, the input layer is used for inputting hemodynamic parameters and expected values, the hidden layer is used for comparing the hemodynamic parameters to be analyzed with the expected values, weight coefficients are adjusted according to errors between the hemodynamic parameters to be analyzed and the expected values, and the output layer is used for outputting corresponding sample results.
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| US20210000412A1 (en) * | 2016-12-29 | 2021-01-07 | Thomas Mengden | Device, system and method for generating biofeedback |
| CN115736851A (en) * | 2022-09-19 | 2023-03-07 | 中国人民解放军总医院 | Fitness evaluation system, control method thereof and storage medium |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104055496A (en) * | 2014-01-15 | 2014-09-24 | 中国航天员科研训练中心 | Method for estimating exercise load level based on cardiogenic signals |
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| US20210000412A1 (en) * | 2016-12-29 | 2021-01-07 | Thomas Mengden | Device, system and method for generating biofeedback |
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