CN118526767B - Motion load measurement method, system and equipment based on motion load entropy - Google Patents
Motion load measurement method, system and equipment based on motion load entropy Download PDFInfo
- Publication number
- CN118526767B CN118526767B CN202411009180.XA CN202411009180A CN118526767B CN 118526767 B CN118526767 B CN 118526767B CN 202411009180 A CN202411009180 A CN 202411009180A CN 118526767 B CN118526767 B CN 118526767B
- Authority
- CN
- China
- Prior art keywords
- stimulus
- perception
- load
- difference
- comparison
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000691 measurement method Methods 0.000 title abstract description 16
- 230000008447 perception Effects 0.000 claims abstract description 81
- 238000000034 method Methods 0.000 claims abstract description 31
- 230000000638 stimulation Effects 0.000 claims abstract description 29
- 238000005259 measurement Methods 0.000 claims abstract description 27
- 238000012360 testing method Methods 0.000 claims description 47
- 238000004364 calculation method Methods 0.000 claims description 19
- 230000000052 comparative effect Effects 0.000 claims description 11
- 238000010248 power generation Methods 0.000 claims description 6
- 101100134058 Caenorhabditis elegans nth-1 gene Proteins 0.000 claims description 4
- 230000008859 change Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 4
- 238000007476 Maximum Likelihood Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 230000014509 gene expression Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000011325 biochemical measurement Methods 0.000 description 1
- 230000004071 biological effect Effects 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000000053 physical method Methods 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Landscapes
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention provides a motion load measurement method, a motion load measurement system and motion load measurement equipment based on motion load entropy, wherein the method comprises the following steps: setting a reference stimulus and a plurality of comparison stimuli; applying the reference stimulus and the plurality of comparison stimuli to the subject and recording difference perception data; calculating motion load entropy based on the difference perception difference data; determining a minimum effective stimulation of the exercise load and an exercise load progression based on the exercise load entropy; the method for determining the minimum effective stimulation of the exercise load is to find a comparison stimulation value corresponding to the maximum exercise load entropy; the number of exercise load stages reflects the magnitude of the exercise load. The scheme realizes the body and mind body integrated measurement, provides a new measurement method and effectively improves the measurement accuracy of the movement load.
Description
Technical Field
The invention relates to the fields of motion data measurement and motion data processing, in particular to a motion load measurement method, a motion load measurement system and motion load measurement equipment based on motion load entropy.
Background
Accurate determination and proper placement of exercise load is the most central and fundamental content in exercise training. The proper exercise load can lead the body of the athlete to have good biological adaptation, and the body can be degraded due to excessive load. Accurate determination of exercise dosage is a key problem in promoting health and is a necessary core technology for developing active health medicine.
Currently, the measurement method of the motion load is still in a simple linear science stage.
In the prior art, different students have different views about exercise load, and the views generally comprise the following steps: (1) is an external stimulus, i.e., an external load; (2) Is the adaptation, reaction or stress of the organism in physiology, biochemistry and psychology, namely the internal load; (3) consider a combination of both internal and external loads.
The exercise load measurement methods according to the above viewpoints are roughly classified into the following four types:
Class 1 physical measurement method. The method has the characteristics of high precision and good repeatability, and is more convenient for physical index acquisition along with the wide use of wearable equipment such as GPS, IMU and the like, and the method is mainly based on parameters such as speed, force, power, distance and the like. However, this approach ignores individual responses and internal load monitoring, ultimately resulting in a loss of accuracy in the application.
The 2 nd is the physiological measurement method, and the 3 rd is the biochemical measurement method. The nature of both methods is that of the biological effects brought about by the motor stimulus, with the advantage of incorporating individualised features. However, they are "amounts" of stimulation that are replaced by "effects" of stimulation. More importantly, the application of the method often indirectly expresses the motion load by establishing a simple linear 'quantity-effect' regression equation, neglects the complexity of nonlinearity, fractal and the like of living substances and dynamics, and makes the measurement method inaccurate.
Class 4 psychological methods. The method mainly adopts scale tests, such as RPE and the like. Such methods often fail to explicitly determine the same exercise intensity using the RPE scale due to individual-to-individual differences.
Based on analysis of the nature of the exercise load, limitations are unavoidable with either of the above approaches alone. It can be seen that any simple, linear, single factor measurement method has limitations, and that the motion load should be measured by a host-guest nonlinear unified measurement method.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a motion load measurement method, a system and equipment based on the motion load subject and object body and mind unification viewpoint, which are based on the motion load entropy theory,
Specifically, the invention provides the following technical scheme:
in one aspect, the present invention provides a motion load measurement method based on motion load entropy, the method comprising:
s1, setting a reference stimulus and a plurality of comparison stimuli of a certain level of test; applying the reference stimulus and a plurality of comparison stimuli to a subject, performing a perception test, and recording obtained difference perception difference data, wherein the perception in the difference perception difference data is recorded as1, the non-perception is recorded as 0, and all the difference perception difference data obtained by the test of the present stage form a sequence data set of the present stage; a plurality of comparison stimuli form a current level power set;
S2, calculating the motion load entropy of the current stage based on a sequence data set formed by the difference perception difference data; the motion load entropy is obtained by carrying out information entropy calculation and solving on a sequence data set based on the sensing test results of the current-stage reference stimulus and a plurality of comparison stimuli, and is used for measuring the uncertainty of the sensing process;
determining the corresponding comparison stimulus as the minimum effective stimulus of the exercise load according to the maximum value of the exercise load entropy; the corresponding trial series is taken as the exercise load series.
Preferably, in the step S1, the reference stimulus and the plurality of comparison stimuli are set in the following manner:
Initializing a level 1 reference stimulus value, wherein the level n reference stimulus value is set to be the level n-1 exercise load minimum effective stimulus value;
Initializing a plurality of comparison stimuli of the level 1 exercise load to form a power value set formed by the plurality of comparison stimuli; the update of the power set of the nth stage comparison stimulus is determined by the least significant motor load stimulus of the nth-1 stage.
Preferably, the exercise load progression refers to calculating the exercise load least effective stimulus based on the reference stimulus and the corresponding comparative stimulus in an iterative manner, the iterative manner being: and taking the motion load minimum effective stimulation obtained by the calculation of the stage as the reference stimulation in the next stage iteration, updating the next stage comparison stimulation based on the motion load minimum effective stimulation obtained by the calculation of the stage, and calculating the motion load minimum effective stimulation of the next stage.
Preferably, the power set of the nth stage comparison stimulus is determined in such a way that:
；
Wherein, Representing the power set of the nth stage,Indicating the least effective stimulation of the n-1 stage motor load,Indicating a comparison stimulus interval. Preferably, the number of elements of all power sets is equal.
Preferably, in S1, in order to avoid the interference of subjective cognition, the reference stimulus and the plurality of comparison stimuli are applied to the subject according to a random time length, and the plurality of comparison stimuli are applied to the subject in a randomly selected manner, in which:
s11, based on the set nth-order reference stimulus, the subject completes movement within a first random time period;
s12, randomly selecting a comparison stimulus from the nth level power set after the execution of the step S11 is completed; the power set is composed of a plurality of comparison stimuli;
S13, the subject completes movement within a second random time period, and difference perception and perception difference data from the start of reference stimulation movement to the end of comparison stimulation movement are acquired;
S14, repeating the steps S11 to S13 until all the comparison stimulus values in the power set are repeatedly tested for preset times, ending the collection of the nth level test data, and forming an nth level sequence data set based on the collected difference perception difference data;
n represents a test series; the first random time period is the same as or different from the second random time period.
Preferably, the S2 further includes:
s21, calculating motion load entropy based on an nth-level sequence data set, and calculating motion load minimum effective stimulation corresponding to the nth-level sequence data set;
S22, judging whether to enter a next-stage test; if the next stage test is carried out, n=n+1, based on the calculated motion load minimum effective stimulation corresponding to the nth stage, updating to obtain a power set of n+1 stage, and returning to S11; and when all the tests are executed, judging that the next-stage test is not performed, and terminating the test.
Preferably, in the step S2, the motion load entropy is calculated by:
；
；
=；
Where LE represents motion load entropy, i=1, 2; representing a reference stimulus, s representing a comparison stimulus, Indicating the probability of perception in the difference data,The probability of no perception in the difference data of the perception difference is represented, R represents the perception result, r=0 represents no perception, and r=1 represents perception.
Preferably, in the step S2, the means for determining the least effective stimulus of the exercise load is:
；
Wherein MES represents the minimum effective stimulus of the exercise load, LE represents the entropy of the exercise load, i=1, 2; s represents the comparative stimulus to be compared with, Indicating the probability of perception in the difference data,Indicating the probability of no perception in the difference data of the perception difference.
Preferably, the probability of perception in the level difference perception difference dataThe fitting model is established, specifically:
；
Wherein, AndIs a parameter of the model.
Preferably, parameters of the modelAndMay be determined by maximum likelihood estimation.
Preferably, the least effective stimulus for the movement load is fitted into the modelCorresponding to the comparison stimulus, i.eThe corresponding comparative stimulus; where s represents the comparison stimulus and LE represents the motion load entropy.
Preferably, the probability of perception in the difference perception difference dataEstablishing a fitting model, and evaluating the model through fitting degree:
；
Wherein, The degree of fitting is indicated and,Indicating the perceived duty ratio of the subject in the difference perception data acquired by the mth comparison stimulus value of the current level,In order to fit the probability of fit of the model,And (3) for the average value of the perception duty ratio in all the perception difference data of the current level, M represents the total comparison stimulus sample space quantity of the current level.
In another aspect, the present invention further provides a motion load measurement system based on motion load entropy, the system comprising:
a power generation device, a difference perception discriminator, and a control program module;
The power generation device is connected with the control program module and is used for generating corresponding exercise load based on the set reference stimulus and a plurality of comparison stimuli;
the difference perception discriminator is connected with the control program module and is used for collecting difference perception difference data;
the control program module is used for controlling the power generation equipment and collecting the difference perception difference data output by the difference perception discriminator;
The system is used for executing the motion load measuring method based on the motion load entropy.
Preferably, the system further comprises a data statistics analysis module for calculating motion load entropy, MES and LS values, and performing data updating and data outputting.
Preferably, the system is further provided with a blood pressure meter and a heart rate belt for acquiring relevant body data of the subject.
In yet another aspect, the present invention also provides a motion load measurement device based on motion load entropy, the device comprising a memory and a processor that invokes computer instructions in the memory to perform the motion load measurement method based on motion load entropy as described above, based on reference stimulus, comparative stimulus, and difference-of-sense difference data.
Compared with the prior art, the scheme provides definition of motion load entropy and a measurement method of the motion load entropy, provides a calculation mode of motion load perception and minimum effective stimulation quantity, unifies physical quantity applied to human bodies and tester perception into one equation, thereby realizing physical and psychological subject-body integrated measurement, improving the measurement accuracy of the motion load, and making up the defects of the prior theory in the integrated measurement and accurate measurement method.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the invention;
FIG. 2 is a detailed flow chart of the exercise load measurement according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of a motion load measurement system framework in accordance with an embodiment of the present invention;
fig. 4 is a schematic diagram of a practical use of the motion load measurement system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some, but not all, of the embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be appreciated by those of skill in the art that the following specific embodiments or implementations are provided as a series of preferred arrangements of the present invention for further explanation of the specific disclosure, and that the arrangements may be used in conjunction or association with each other, unless it is specifically contemplated that some or some of the specific embodiments or implementations may not be associated or used with other embodiments or implementations. Meanwhile, the following specific examples or embodiments are merely provided as an optimized arrangement, and are not to be construed as limiting the scope of the present invention.
The technical team of the scheme provides that the exercise load is a unified whole of the host and the object and is the perception of the change of the stimulation quantity of the host to the object on the basis of a large number of researches and verifications, and the change is obtained through comparison and identification, so that the reference stimulation quantity and the comparison stimulation quantity are needed for measuring the exercise load.
Therefore, referring to fig. 1, based on the concept of the motion load entropy proposed by the present invention, the present embodiment obtains the reference stimulus amount and the comparison stimulus amount of the motion load through the motion load measurement system, and calculates the motion load entropy based on the obtained reference stimulus amount and the comparison stimulus amount, thereby determining the effective motion load and the motion load progression, and completing the measurement of the effective motion load.
Further, in this embodiment, for the measure of the exercise load, two important measures are mainly obtained, namely, the exercise load minimum effective stimulation and the exercise load progression. First, a reference stimulus (REFERENCE STIMULUS) is to be determined. Then, a random comparative stimulus is appliedAnd performing a sense difference sensing test to obtain corresponding data. Calculation of the relative to the reference stimulus after performing multiple testsBased on the motion Load entropy (LE, load's Entropy) of the perceived distribution, the motion Load least significant stimulus (MES: MINIMAL EFFECTIVE Stimulus of Exercise Load) can be further calculated, and furthermore, the motion Load progression (LS: load's Step) can be further determined. The following describes the development of each key in this embodiment.
1. Motion load entropy
In this embodiment, the motion Load entropy (Load's Entropy, LE) is defined as a measure of the perceived result of the comparative stimulus based on the reference stimulus. The motion load entropy is premised on a reference stimulus. Specifically: for a particular reference stimulusRandomly applying a comparison stimulusThere are two possibilities of the sensing response result R of the subject to the stimulus variation, in this embodiment, let no sensing be 0, have sensing be 1, and have sensingThe probability of (2) is:
；
probability of no perception =In this embodiment, the motion load entropy is:
；
Here, there is a perceptual probability The calculation of (2) can be obtained through test data, and can also be performed by establishing a corresponding model based on the test data. The model may be established by those skilled in the art based on the characteristics of the measured data, the data accuracy requirement, the calculation complexity requirement, etc., and the model may not be unique.
2. Minimal effective stimulation of exercise load
In this embodiment, the exercise load minimum effective stimulus (MES: MINIMAL EFFECTIVE Stimulus of Exercise Load) is defined based on the exercise load entropy, the exercise load minimum effective stimulus is the comparative stimulus corresponding to the maximum exercise load entropy,Is relative to the reference stimulus amountA minimum perceived stimulus, any less thanIs ineffective exercise load stimulation. I.e.
；
In the above formula, LE represents motion load entropy, s represents comparative stimulus, i=1 or 2; when the motion load entropy reaches a maximum value,At this timeIndicating whether a sensing critical state exists or not, or suggesting that the neural network connection activity of the organism participating in sensing reaches the maximum at the moment.
3. Number of exercise load stages
In this embodiment, the exercise Load progression (LS: load' sStep) is from the reference stimulusStarting at 0, the measurement is repeated with the determined least significant exercise load stimulus (i.e., MES) set as the new reference stimulus, i.e.The iteration number of the minimum effective stimulus is solved, namely MES is solved again, and LS is added with 1.The magnitude reflects the magnitude of the exercise load on one side for the number of repeated tests.
Based on the definition, the specific solving process of LS is as follows: reference stimulus is providedStarting motion load entropy measurement according to the aboveSolving formula of (2)Re-orderAnd record. The above measurement procedure is iterated,Starting counting, repeating the above calculation, and calculating once per cycleThe count is incremented by 1 until all measures are terminated. In this way, the determination of the number of exercise load stages can be accomplished. It should be noted that the calculation method of the motion load stage number in the present embodiment is only a preferred method, and the calculation or definition of the motion load stage number may be modified or adjusted, for example, by taking the number of iterations as a variable, performing conventional mathematical calculations (for example, performing first-order weighted calculation and performing integer, second-order weighted calculation and performing integer, etc.) to obtain a corresponding stage number, and such conventional mathematical transformation calculations should be considered as falling within the scope of the present invention.
4. Motion load entropy measure
In this embodiment, the motion load entropy (LE: load Entropy) measure is obtained by randomly generating several comparison stimulus values, applying the comparison stimulus values to the subject along with the reference stimulus values, recording the difference perception difference data of the subject, calculating the motion load entropy based on the recorded data after all the measure data are completed, and further determining the motion load minimum effective stimulus and the motion load progression of the subject.
In a preferred embodiment, a measuring system is provided, wherein a power generating device is configured, and the measuring system is used for generating resistance power values with random sizes according to a certain preset scheme and controlling a power vehicle to change resistance in cooperation with control and data acquisition of a motion load. During the data acquisition, a random period of time has elapsed to acquire a comparison result of whether the subject feels a difference between the comparison stimulus and the reference stimulus, and record. And counting all test response values after the repeated test times are met, and calculating entropy values of the stimulus perception distribution of the subject. In this embodiment, we will describe a constant power bicycle as the power generating device.
Referring to fig. 3 and 4, in a preferred embodiment, the measurement system apparatus is configured as follows:
(1) A constant power bicycle is used as the resistance generating device (i.e., power generating device). The power resolution is set to, for example, ±1W, etc., and the power range may be set based on the experimental data acquisition requirements, for example, may be set to 0-1000W.
(2) A sense difference perception discriminator. During the test, selection data of the subjects for perceived perception (i.e., the judgment result of the subjects) is collected. The difference perception discriminator can be connected with the control program module through a wired or wireless mode (such as WIFI, bluetooth and the like).
(3) And a control program module. The resistance force generation device is used for randomly generating resistance force and controlling the power bicycle to change power output, and collecting result data of the difference perception discriminator.
In a more preferred embodiment, the measurement system may further be provided with a data statistics analysis module, which calculates the corresponding motion load entropy, MES and LS values from the discrimination result acquired by the measurement system, the reference stimulus of the power generating device, the comparison stimulus, and other data, and calculates and updates the model, and outputs or displays the data.
Furthermore, more preferably, the system is further provided with a sphygmomanometer and a heart rate belt, which are worn by the tester when collecting the test data.
Referring to fig. 2, the flow of data acquisition is as follows:
in this embodiment, first, initial setting is performed on each level of data, which includes the following steps (1) and (2):
(1) Set the first Reference stimulation of stage motor load. In an embodiment, the reference stimulus for the level 1 motor load ls=1. First, theOf stagesIs the firstOf the stage motion load. The saidIs relative to a reference stimulusMinimal stimulus that can be perceived.
(2) Setting the firstStage motion loadIs a power set of (a) is provided. In an embodiment, the power set of the comparison stimulus s resistance of the class 1 motor load may be set toIn the present embodiment, the above-mentioned comparison stimulus s is set to be equal difference (i.e., equal interval), namely, interval valueThe power set S is composed of multiple comparison stimuli S (S is power), which are used as initial values, and the subsequent power sets are set by referring to the MES set obtained by the previous stage calculation, namely the firstThe power set of the comparative stimulus of the level test is according to the firstOf stagesAn arrangement, for example, one preferred embodiment is: assuming that the least effective stimulus for exercise load calculated at stage n-1 is MES,Representing that the interval between two adjacent comparison stimuli is 2 watts (i.e., the load interval), then the power set S of the nth stage is set to s= { MES, mes+2, mes+4, mes+6, … }, where the number of elements S in set S is 26 as the 1 st stage S initially set, e.g., if n-1 stage mes=20,Then the set S of the nth level is set to {20,22,24,26, … }, for 26 elements. Of course, there may be other ways of setting the update mode of the power set S, for example, when updating, the value of the first element of the set S may be based on the product of the MES of the previous stage (i.e. the n-1 stage) and the scaling factor, i.e. aMES; may also beThen updated s= { aMES, aMES +，aMES+…, Where the number of elements of set S can still be set constant all the time; or the setting of the nth stage set S may be determined by a fixed mathematical relationship of the nth-1 stage MES, for example, the first element of the nth stage set S may be a ratio between the nth-1 stage MES and an average value of a plurality of previous MES, and then multiplied by the nth stage MES, and the remaining elements in the set may be determined based on the first element. In yet another embodiment, the differences between the elements in the set S may be adjustable, based on the accuracy requirements of the test, etc., may be uniformly distributed in a manner similar to an arithmetic progression, or may be non-uniformly distributed, and conventional transformations based on the concepts of the present invention should be considered as falling within the scope of the present invention.
After initial data is determined, starting to enter a test link, wherein the steps (3) - (8) are as follows:
(3) The test is initiated. According to the above settings Setting a random duration in a certain time range (for example, 20-30 seconds)And the pedaling is completed within the random time period.
(4) After (3) is finished, at the power setIs selected randomly from a comparison stimulusDown to the power car.
(5) Randomly selecting a duration within a certain time range (e.g. within 10-15 seconds)And then executing the test, finishing pedaling, collecting and recording the difference perception result data by the subject through the difference perception discriminator according to the perception condition after the pedaling is finished.
(6) Repeating steps (3) - (5) until all power values in the comparison stimulus set (i.e. power set) S are repeated for preset times (100 times, for example) to finish the level test to form the firstStage test dataset X n. Here, each comparison stimulus S in the power set S needs to be repeated a preset number of times, for example 100 times, etc.
(7) Motion load measure joint equation pair according to motion load entropyThe sequence dataset X n of the levels is modeled (i.e., a motion load measure model of motion load entropy is built), model parameters are calculated, and the corresponding motion load least significant stimulus (i.e., MES) is calculated, and the level is determined as the corresponding motion load progression.
(8) Judging whether to start the next-stage test (namely judging whether to terminate the test); if the next stage test is started, adding 1 to the stage LS, namely, letting n=n+1, modifying the value of the power set S according to the MES obtained by calculation, returning to the step (3), and continuing to execute; after all the levels are tested, the measurement test can be terminated.
In this embodiment, further, by establishing a motion load measure joint equation set as an individual motion load sensing model, the preferred establishing mode and the model evaluation mode of the model are performed according to the following modes:
In this embodiment, for a given reference stimulus For the probability of the individual being perceived as a comparative stimulus sBy the method, a motion load measurement model based on motion load entropy can be established to characterize the change rule of individual motion perception probability along with stimulation, and the model is as follows:
；
Wherein, AndThe parameters of the model can be determined by single-stage test data through maximum likelihood estimation, the parameter values of the corresponding model can be solved for different individual data, and under the setting, the calculation can be performedThe method comprises the following steps:
；
The LE represents motion load entropy, namely, comparison stimulus corresponding to the maximum motion load entropy in the model is used as MES after the model is established.
The above has the perception probability in the perception difference dataIs described as a preferred model only, and should not be construed as limiting the scope of the invention, as perceived probabilities can be determined by one skilled in the artThe model of (2) is appropriately adjusted to study the trend and the characteristics of the change, so as to describe the change rule, for example, a multi-order fitting curve is adopted. Similarly, the expression of the corresponding MES value also follows the perceived probabilityThe above-described expression of MES should not be interpreted as limiting the scope of the invention as such, as there are different expressions for the variation of the model of (a).
5. Evaluation of measurement model
In this embodiment, Q 2 is preferably used to evaluate how well the model fits to the real perceived ratio, and the closer Q 2 is to 1, the closer the model is to the real data. In this embodiment, the calculation method of Q 2 is as follows:
；
Wherein, The current tester is provided with a perceived duty cycle for the mth comparison stimulus value of the current level (e.g., nth level), i.e., a perceived proportion in a predetermined number of (e.g., 100) repeated tests,For the probability of fit of the model,For the average value of the perceived duty ratio in all the perceived difference data of the current level (e.g., the nth level), a maximum likelihood estimation method may be used to estimate two parameters α and β in the motion load measure model, and further evaluate the Q 2 of the model fitness. M represents the number of total comparison stimulus sample spaces of the current level (i.e. the nth level), e.g. the number of elements in the power set S formed by the comparison stimulus used in the above embodiment, i.e. m=26.
In this example, we performed statistical analysis of the fitness of the individual motor stimulus perception model with 32 tester data, and the results are shown in table 1.
TABLE 1 degree of fitness Q of individual motor stimulus perception models 2
From the results of the motion perception models of all testers given in table 1, the value of the individual perception model Q 2 is between 92.34% and 99.74%, which indicates that the degree of fitting of the perception measure model given in this embodiment to the individual motion load test data is very high.
In yet another embodiment, the solution of the present invention may also be implemented by means of a device comprising one or more processors and a memory, the processor may invoke computer instructions in the memory to perform a motion load measure method based on motion load entropy based on the reference stimulus, the comparison stimulus and the difference perceived difference data. The processor may be externally connected to, for example, a difference perception arbiter, to receive difference perception difference data, etc.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or information execution capabilities, and may control other components in the electronic device to perform the desired functions.
The memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program information may be stored on the computer readable storage medium, which may be executed by a processor to implement the primary mode I2C/SMBUS control method or other desired functions of the various embodiments of the present invention described above.
In one example, the apparatus may further include: input devices and output devices, which are interconnected by a bus system and/or other forms of connection mechanisms.
Logic and/or steps represented in the flowcharts or otherwise described herein may be embodied in any readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. A method for measuring motion load based on motion load entropy, the method comprising:
s1, setting a reference stimulus and a plurality of comparison stimuli of a certain level of test; applying the reference stimulus and a plurality of comparison stimuli to a subject, performing a perception test, and recording obtained difference perception difference data, wherein the perception in the difference perception difference data is recorded as1, the non-perception is recorded as 0, and all the difference perception difference data obtained by the test of the present stage form a sequence data set of the present stage; a plurality of comparison stimuli form a current level power set;
S2, calculating the motion load entropy of the current stage based on a sequence data set formed by the difference perception difference data; the motion load entropy is obtained by carrying out information entropy calculation and solving on a sequence data set based on the sensing test results of the current-stage reference stimulus and a plurality of comparison stimuli;
determining the corresponding comparison stimulus as the minimum effective stimulus of the exercise load according to the maximum value of the exercise load entropy; the corresponding test series is used as a motion load series;
In the step S1, the setting modes of the reference stimulus and the plurality of comparison stimuli are as follows:
Initializing a level 1 reference stimulus value, wherein the level n reference stimulus value is set to be the level n-1 exercise load minimum effective stimulus value;
Initializing a plurality of comparison stimuli of the level 1 exercise load to form a power value set formed by the plurality of comparison stimuli; the update of the power set of the nth stage comparison stimulus is determined by the least effective stimulus of the motor load of the nth-1 stage;
In the step S1, the reference stimulus and the plurality of comparison stimuli are applied to the subject according to a random time length, and the plurality of comparison stimuli are applied to the subject in a random selection manner, specifically in the following manner:
s11, based on the set nth-order reference stimulus, the subject completes movement within a first random time period;
s12, randomly selecting a comparison stimulus from the nth level power set after the execution of the step S11 is completed; the power set is composed of a plurality of comparison stimuli;
S13, the subject completes movement within a second random time period, and difference perception and perception difference data from the start of reference stimulation movement to the end of comparison stimulation movement are acquired;
S14, repeating the steps S11 to S13 until all the comparison stimulus values in the power set are repeatedly tested for preset times, ending the collection of the nth level test data, and forming an nth level sequence data set based on the collected difference perception difference data;
n represents a test series; the first random time length is the same as or different from the second random time length;
In the step S2, the motion load entropy is calculated by:
；
；
；
Where LE represents motion load entropy, i=1, 2; representing a reference stimulus, s representing a comparison stimulus, Indicating the probability of perception in the difference data,The probability of no perception in the difference data of the perception difference is represented, R represents the perception result, r=0 represents no perception, and r=1 represents perception.
2. The method of claim 1, wherein S2 further comprises:
s21, calculating motion load entropy based on an nth-level sequence data set, and calculating motion load minimum effective stimulation corresponding to the nth-level sequence data set;
S22, judging whether to enter a next-stage test; if the next stage test is carried out, n=n+1, based on the calculated motion load minimum effective stimulation corresponding to the nth stage, updating to obtain a power set of n+1 stage, and returning to S11; and when all the tests are executed, judging that the next-stage test is not performed, and terminating the test.
3. The method according to claim 1, wherein in S2, the manner of determining the least effective stimulus of the exercise load is:
；
Wherein MES represents the minimum effective stimulus of the exercise load, LE represents the entropy of the exercise load, i=1, 2; s represents the comparative stimulus to be compared with, Indicating the probability of perception in the difference data,Indicating the probability of no perception in the difference data of the perception difference.
4. The method of claim 1 wherein the level difference perception difference data is provided with a perceived probabilityThe fitting model is established, specifically:
；
Wherein, AndIs a parameter of the model.
5. The method of claim 1, wherein the power set of the nth stage of comparison stimulus is determined by:
；
Wherein, Representing the power set of the nth stage,Indicating the least effective stimulation of the n-1 stage motor load,Indicating a comparison stimulus interval.
6. A motion load measurement system based on motion load entropy, the system comprising:
a power generation device, a difference perception discriminator, and a control program module;
The power generation device is connected with the control program module and is used for generating corresponding exercise load based on the set reference stimulus and a plurality of comparison stimuli;
the difference perception discriminator is connected with the control program module and is used for collecting difference perception difference data;
the control program module is used for controlling the power generation equipment and collecting the difference perception difference data output by the difference perception discriminator;
The system is configured to perform the motion load measure method based on motion load entropy according to any of claims 1-5.
7. A motion load measuring device based on motion load entropy, characterized in that the device comprises a memory and a processor, which invokes computer instructions in the memory to perform the motion load measuring method based on motion load entropy according to any of claims 1-5, based on reference stimulus, comparison stimulus and difference perceived difference data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411009180.XA CN118526767B (en) | 2024-07-26 | 2024-07-26 | Motion load measurement method, system and equipment based on motion load entropy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411009180.XA CN118526767B (en) | 2024-07-26 | 2024-07-26 | Motion load measurement method, system and equipment based on motion load entropy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118526767A CN118526767A (en) | 2024-08-23 |
| CN118526767B true CN118526767B (en) | 2024-10-15 |
Family
ID=92390604
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411009180.XA Active CN118526767B (en) | 2024-07-26 | 2024-07-26 | Motion load measurement method, system and equipment based on motion load entropy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118526767B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111629667A (en) * | 2018-01-24 | 2020-09-04 | 日本电信电话株式会社 | Motion load estimation method, motion load estimation device, and recording medium |
| CN112384140A (en) * | 2018-05-25 | 2021-02-19 | 丰田自动车欧洲公司 | System and method for determining the perceptual load and stimulus perception level of the human brain |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU104852U1 (en) * | 2011-03-24 | 2011-05-27 | Александр Александрович Севастьянов | SPORT LOAD CONTROL SYSTEM AND SPORTS SIMULATOR FOR TRAINING OR COMPETITIONS |
| CN104246852B (en) * | 2012-02-22 | 2018-11-16 | 乔塞林.福伯特 | Consciousness-cognition-motor learning system and method |
| AT520374B1 (en) * | 2017-09-06 | 2020-01-15 | Dipl Ing Dr Techn Christoph Guger | Determination of the ranking of the perception intensity of stimuli from a test set of stimuli in a test subject |
| CN114886433A (en) * | 2022-05-27 | 2022-08-12 | 浙江大学 | Exercise load evaluation method with environment adaptability |
| CN116602661A (en) * | 2023-05-12 | 2023-08-18 | 国家体育总局体育科学研究所 | Motion load nonlinear measurement method and system |
| CN117972302B (en) * | 2024-03-11 | 2024-07-02 | 北京师范大学-香港浸会大学联合国际学院 | Motion load perception parameter estimation method based on Bayesian self-adaption method |
-
2024
- 2024-07-26 CN CN202411009180.XA patent/CN118526767B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111629667A (en) * | 2018-01-24 | 2020-09-04 | 日本电信电话株式会社 | Motion load estimation method, motion load estimation device, and recording medium |
| CN112384140A (en) * | 2018-05-25 | 2021-02-19 | 丰田自动车欧洲公司 | System and method for determining the perceptual load and stimulus perception level of the human brain |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118526767A (en) | 2024-08-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Intraclass correlation: Improved modeling approaches and applications for neuroimaging | |
| US10679757B2 (en) | Method and apparatus for establishing a blood pressure model and method and apparatus for determining a blood pressure | |
| Zeng et al. | Classification of neurodegenerative diseases using gait dynamics via deterministic learning | |
| CN104717921A (en) | Autodidactic cognitive training device and method thereof | |
| CN101835421B (en) | Feature value candidate generating device and feature value candidate generating method | |
| CN117972302B (en) | Motion load perception parameter estimation method based on Bayesian self-adaption method | |
| CN112582067A (en) | Age estimation model training and age estimation method and device based on big data | |
| Lombardi et al. | Piecewise constant averaging methods allow for fast and accurate hierarchical Bayesian estimation of drift diffusion models with time-varying evidence accumulation rates | |
| Wein et al. | Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures | |
| CN118526767B (en) | Motion load measurement method, system and equipment based on motion load entropy | |
| CN112884225A (en) | Body test result prediction method, body test result prediction device, electronic equipment and storage medium | |
| CN116453656B (en) | Psychological health assessment early warning system and psychological health assessment early warning method | |
| JP2005000528A (en) | Cerebral function measuring device and cerebral function measuring program | |
| CN114642424A (en) | Physical ability assessment method and device based on somatosensory interaction technology | |
| CN112768066A (en) | Neurosurgical patient comprehensive diagnosis and treatment method and system | |
| Gonzalez et al. | Modeling vo2 and vco2 with hammerstein-wiener models | |
| CZ308153B6 (en) | Method of determining the level of physical fitness | |
| CN108742538A (en) | Sign measurement method and medical robotic system based on big data and artificial intelligence | |
| Schutz | Stochastic processes: their nature and use in the study of sport and physical activity | |
| Alsammani et al. | Estimation of circular statistics in the presence of measurement bias | |
| CN116779150B (en) | Personalized medical decision method, device and application based on multi-agent interaction | |
| CN117807454B (en) | Data signal processing method for abdominal pelvic muscle recovery | |
| US20050261874A1 (en) | Method and arrangement and computer programme with programme code means and computer programme products for the analysis of neuronal activities in neuronal areas | |
| Markošová et al. | Topology of brain functional networks: towards the role of genes | |
| Tu et al. | Power analyses for correlations from clustered study designs |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |