Exercise load measurement method, system, and device based on exercise load entropy

Abstract

The present invention provides an exercise load measurement method, system, and device based on exercise load entropy. The method comprises: setting a reference stimulus and a plurality of comparison stimuli; applying the reference stimulus and the plurality of comparison stimuli to a subject, and recording difference data on difference perception; on the basis of the difference data on difference perception, calculating exercise load entropy; and on the basis of the exercise load entropy, determining the minimum effective stimulus of exercise load and an exercise load series. The method for determining the minimum effective stimulus of the exercise load comprises searching for a comparison stimulus value corresponding to the maximum exercise load entropy. The exercise load series reflects the magnitude of the exercise load. The present solution achieves integrated measurement of a physical and mental subject-object, and provides a new measurement method, thereby effectively improving the accuracy of measuring exercise load.

Description

Methods, systems, and equipment for measuring exercise load based on exercise load entropy. Technical Field

[0001] This invention relates to the fields of motion data measurement and motion data processing, and in particular to a method, system, and device for measuring motion load based on motion load entropy. Background Technology

[0002] Accurate measurement and appropriate scheduling of exercise load are the most core and fundamental aspects of sports training. Appropriate exercise load allows the athlete's body to undergo favorable biological adaptations, while excessive load can lead to deterioration. Precise measurement of exercise dosage is a key issue in promoting health and a necessary core technology for the development of proactive health medicine.

[0003] Currently, the methods for measuring exercise load are still at the stage of simple linear science.

[0004] In the existing technology, different scholars have different views on exercise load, which can be roughly divided into the following categories: (1) external stimuli, i.e., external load; (2) physiological, biochemical, and psychological adaptation, response, or stress of the body, i.e., internal load; (3) the combination of internal and external loads.

[0005] The methods for measuring exercise load corresponding to the above viewpoints can be roughly divided into the following four categories:

[0006] Category 1 physical measurement methods. These methods are characterized by high accuracy and good repeatability. Especially with the widespread use of wearable devices such as GPS and IMUs, the acquisition of physical indicators has become more convenient. This method is mainly based on parameters such as velocity, force, power, and distance. However, this method neglects the monitoring of individual responses and internal loads, ultimately leading to a loss of accuracy in application.

[0007] The second category is physiological measurement methods, and the third is biochemical measurement methods. The essence of these two types of methods is the biological effect of exercise stimulation, and their advantage lies in incorporating individualized characteristics. However, they both substitute the "effect" of the stimulus for the "quantity" of the stimulus. More importantly, their application often indirectly expresses exercise load by establishing a simple linear "quantity-effect" regression equation, ignoring the nonlinear and fractal complexities of biological matter and dynamics, making the measurement methods inaccurate.

[0008] The fourth type of psychological method primarily uses scale tests, such as the RPE (Responsive Physical Examination). However, due to individual differences, this type of method often makes it difficult to definitively determine the same exercise intensity using the RPE scale.

[0009] However, based on the analysis of the nature of exercise load, using any of the above methods alone will inevitably have limitations. Therefore, any simple, linear, single-factor measurement method has limitations, and exercise load should adopt a unified subject-object nonlinear measurement method. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this application provides a method, system, and device for measuring exercise load based on the concept of the unity of mind and body in relation to the subject and object of exercise load, and an exercise load entropy theory.

[0011] Specifically, the present invention provides the following technical solutions:

[0012] On one hand, the present invention provides a method for measuring motion load based on motion load entropy, the method comprising:

[0013] S1. Set up a reference stimulus and several comparison stimuli for a certain level of test; apply the reference stimulus and several comparison stimuli to the subject to conduct a perception test, and record the obtained perception difference data. The perception difference data with perception is recorded as 1, and the absence of perception is recorded as 0. All perception difference data obtained in this level of test form the sequence dataset of this level; the several comparison stimuli form the power set of this level.

[0014] S2. Calculate the current level of motor load entropy based on the sequence dataset formed by the perceptual difference data; the motor load entropy is obtained by calculating the information entropy of the sequence dataset based on the perceptual test results of the current level reference stimulus and several comparative stimuli, and is used to measure the uncertainty of the perceptual process.

[0015] The maximum value of the exercise load entropy determines the corresponding comparison stimulus as the minimum effective stimulus for the exercise load; the corresponding test level is taken as the exercise load level.

[0016] Preferably, in step S1, the reference stimulus and several comparison stimuli are configured as follows:

[0017] Initialize the value of the first-level reference stimulus, and set the value of the nth-level reference stimulus to the value of the minimum effective stimulus for the exercise load of the (n-1)th level.

[0018] Initialize several comparative stimuli for the first-level exercise load to form a power value set consisting of several comparative stimuli; update the power set of the nth-level comparative stimuli by the minimum effective stimulus of the (n-1)th-level exercise load.

[0019] Preferably, the exercise load level refers to the calculation of the minimum effective stimulus for exercise load based on the reference stimulus and the corresponding comparison stimulus in an iterative manner. The iterative manner is as follows: the minimum effective stimulus for exercise load calculated at the current level is used as the reference stimulus for the next level of iteration, and the comparison stimulus for the next level is updated based on the minimum effective stimulus for exercise load calculated at the current level, and the minimum effective stimulus for exercise load for the next level is calculated.

[0020] Preferably, the power set of the nth level comparative stimulus is determined as follows: S n ={MES n-1 MES n-1 +Δ, MES n-1 +2Δ, MES n-1 +3Δ,…}

[0021] Among them, S n Denotes the power set of the nth level, MES n-1 Let represent the minimum effective stimulus for the (n-1)th level of exercise load, and Δ represent the comparison stimulus interval. Preferably, the number of elements in all power sets is equal.

[0022] Preferably, in step S1, to avoid interference from subjective cognition, the reference stimulus and several comparison stimuli are applied to the subject at random time intervals, and the comparison stimuli are applied to the subject in a randomly selected manner, specifically as follows:

[0023] S11. Based on the set nth level reference stimulus, the subject completes the exercise within the first random duration.

[0024] S12. After S11 is completed, a comparison stimulus is randomly selected from the power set of the nth level; the power set consists of several comparison stimuli.

[0025] S13. Subjects complete the exercise within the second random duration, and data on the difference in sensory perception are collected from the start of the reference stimulus exercise to the end of the comparison stimulus exercise.

[0026] S14. Repeat steps S11 to S13 until all the comparative stimulus values ​​in the power set have been tested a preset number of times. Then, end the data collection for the nth level test and form the nth level sequence dataset based on the collected sensory difference data.

[0027] n represents the test level; the first random duration may be the same as or different from the second random duration.

[0028] Preferably, S2 further includes:

[0029] S21. Calculate the motion load entropy based on the nth level sequence dataset, and calculate the minimum effective stimulus for the motion load corresponding to this level;

[0030] S22. Determine whether to proceed to the next level of the experiment; if so, let n = n+1, and based on the calculated minimum effective stimulus of the exercise load corresponding to the nth level, update the power set of the n+1th level, and return to S11; when all experiments have been completed, determine whether to proceed to the next level of the experiment and terminate the experiment.

[0031] Preferably, in step S2, the motion load entropy is calculated as follows: p1 = P(R = 1|s) R ,s) p2=1-p1

[0032] Where LE represents the entropy of the motion load, i = 1, 2; s R s represents the reference stimulus, p1 represents the probability of perception in the difference in perception data, p2 represents the probability of no perception in the difference in perception data, and R represents the perception result, where R=0 indicates no perception and R=1 indicates perception.

[0033] Preferably, in step S2, the method for determining the minimum effective stimulus for the exercise load is as follows:

[0034] Where MES represents the minimum effective stimulus for the exercise load, LE represents the entropy of the exercise load, i = 1, 2; s represents the comparison stimulus, p1 represents the probability of perception in the difference in sensory perception data, and p2 represents the probability of no perception in the difference in sensory perception data.

[0035] Preferably, a fitting model is established for the probability p1 of perception in the data on the difference in perception at this level, specifically as follows:

[0036] Where α and β are the parameters of the model.

[0037] Preferably, the model parameters α and β can be determined by maximum likelihood estimation.

[0038] Preferably, the minimum effective stimulus for the exercise load is the comparison stimulus corresponding to p1 = 0.5 in the fitted model, i.e. The corresponding comparative stimulus; where s represents the comparative stimulus and LE represents the entropy of the exercise load.

[0039] Preferably, a fitting model is established for the probability p1 of perception in the perceived difference data, and the model is evaluated by the degree of fit:

[0040] Among them, Q 2 p represents the degree of fit. 1m This indicates the percentage of subjects who perceived the difference in perceived difference for the m-th comparative stimulus value at the current level. This represents the fit probability of the fitted model. M represents the average percentage of perceived differences in all perceptual data at the current level, and M represents the total number of comparative stimulus samples at the current level.

[0041] On the other hand, the present invention also provides a motion load measurement system based on motion load entropy, the system comprising:

[0042] Power generation equipment, perceptual difference sensor and discriminator, and control program module;

[0043] The power generation device is connected to the control program module and is used to generate a corresponding exercise load based on a set reference stimulus and several comparison stimuli.

[0044] The perceptual difference discriminator is connected to the control program module and is used to collect perceptual difference data.

[0045] The control program module is used to control the power generation device and collect the difference in perception data output by the difference in perception discriminator.

[0046] The system is used to execute the motion load measurement method based on motion load entropy as described above.

[0047] Preferably, the system further includes a data statistical analysis module for calculating the motion load entropy, MES and LS values, and for updating and outputting the data.

[0048] Preferably, the system is further equipped with a blood pressure monitor and a heart rate monitor for collecting relevant physical data of the subject.

[0049] In another aspect, the present invention also provides a motion load measurement device based on motion load entropy. The device includes a memory and a processor. The processor calls computer instructions in the memory based on reference stimuli, comparison stimuli, and perceptual difference data to execute the motion load measurement method based on motion load entropy as described above.

[0050] Compared with existing technologies, this solution provides a definition of exercise load entropy and a method for measuring exercise load entropy. It also provides a method for calculating exercise load perception and minimum effective stimulus, unifying the physical quantities applied to the human body and the test subject's perception into a single equation. This achieves integrated measurement of the mind and body, improves the accuracy of exercise load measurement, and makes up for the shortcomings of existing theories in integrated measurement and accurate measurement methods. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 is a schematic diagram of the method flow according to an embodiment of the present invention;

[0053] Figure 2 is a detailed flowchart of the exercise load measurement according to an embodiment of the present invention;

[0054] Figure 3 is a schematic diagram of the motion load measurement system framework according to an embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of the actual use of the motion load measurement system according to an embodiment of the present invention. Detailed Implementation

[0056] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0057] Those skilled in the art should understand that the following specific embodiments or implementation methods are a series of optimized configurations listed to further explain the specific content of the invention. These configuration methods can be combined or used in conjunction with each other, unless the invention explicitly states that some or a specific embodiment or implementation method cannot be associated with or used in conjunction with other embodiments or implementation methods. Furthermore, the following specific embodiments or implementation methods are merely optimized configurations and are not intended to limit the scope of protection of the invention.

[0058] Based on extensive research and verification, the technical team of this solution proposes that exercise load is a unified whole of subject and object, which is the subject's perception of changes in the amount of stimulus from the object. These changes are obtained through comparison and identification. Therefore, the measurement of exercise load must have a reference stimulus and a comparison stimulus.

[0059] Therefore, referring to Figure 1, based on the concept of exercise load entropy proposed in this invention, this embodiment obtains the reference stimulus and comparison stimulus of exercise load through the exercise load measurement system, and then calculates the exercise load entropy based on the obtained reference stimulus and comparison stimulus, thereby determining the effective exercise load and the exercise load level, and completing the measurement of the effective exercise load.

[0060] Furthermore, in this embodiment, the measurement of exercise load mainly involves two important metrics: the minimum effective stimulus for the exercise load and the number of exercise load levels. First, the reference stimuli must be determined. R Then, a random comparison stimulus s is applied to conduct a perception difference test, and the corresponding data are obtained. After multiple tests, the difference in perception relative to the reference stimulus s is calculated. R The perceived distribution of the motion load entropy (LE) is used to calculate the minimum effective stimulus of exercise load (MES). Furthermore, the load step (LS) can also be determined simultaneously. The key steps in this embodiment are described below.

[0061] I. Entropy of Exercise Load

[0062] In this embodiment, load entropy (LE) is defined as a measure of the perceptual outcome of a reference stimulus on a comparison stimulus. Load entropy is predicated on a reference stimulus. Specifically: for a specific reference stimulus s... R When a comparative stimulus s is randomly applied, the subject's perceptual response R to the change in stimulus has two possibilities. In this embodiment, let no perception be 0 and perception be 1. The probability of perception p1 is: p1 = P(R = 1|s) R ,s)#

[0063] The probability of no perception is p2 = 1 - p1. Therefore, in this embodiment, the motion load entropy is:

[0064] Here, the perceived probability p1 can be calculated using test data or by building a corresponding model based on the test data. The method of building the model can be determined by those skilled in the art based on the characteristics of the measured data, data accuracy requirements, or computational complexity requirements, and the model does not have to be unique.

[0065] II. Minimum Effective Stimulus for Exercise Load

[0066] In this embodiment, the Minimum Effective Stimulus of Exercise Load (MES) is defined based on the exercise load entropy. The minimum effective stimulus of exercise load is the comparison stimulus corresponding to the maximum exercise load entropy. The MES is relative to the reference stimulus amount s. R The minimum perceptible stimulus; any stimulus smaller than the MES is considered an ineffective motor load.

[0067] In the above formula, LE represents the exercise load entropy, s represents the comparison stimulus, and i = 1 or 2; when the exercise load entropy reaches its maximum value... At this point, p1 = p2 = 0.5, indicating a critical state between the presence and absence of perception, or suggesting that the activity of the neural network connections involved in perception in the body has reached its maximum.

[0068] III. Exercise Load Levels

[0069] In this embodiment, the load step (LS) is calculated from the reference stimulus s. R Starting from 0, the minimum effective stimulus (MES) obtained from the exercise load is set as the new reference stimulus, and the measurement is repeated for s. R =MES, which calculates the minimum number of iterations required for the minimum effective stimulus. This involves solving for MES again and incrementing LS by 1. LS represents the number of repeated tests, and its magnitude reflects the intensity of the exercise load.

[0070] Based on the above definition, the specific solution process of LS is as follows: Let the reference stimulus s R =0 to start the motion load entropy measurement, calculate the MES according to the above MES solution formula, and then let s R =MES, and record LS=1. Repeat the above measurement process iteratively, starting to count LS, repeating the above calculation, and incrementing the LS count by 1 for each iteration, until all measurements are terminated. Based on this method, the determination of the motion load level can be completed. Here, it should be noted that the calculation method of the motion load level given in this embodiment is only a preferred method. The calculation or definition of the motion load level can also be modified or adjusted. For example, the number of iterations can be used as a variable to perform conventional mathematical calculations (such as performing first-order weighted calculation and rounding, second-order weighted calculation and rounding, etc.) to obtain the corresponding level. Such conventional mathematical transformation calculations should all be considered to fall within the protection scope of this invention.

[0071] IV. Measurement of Exercise Load Entropy

[0072] In this embodiment, the load entropy (LE) measure is obtained by randomly generating several comparison stimulus values ​​and applying them to the subject along with a reference stimulus value, recording the subject's perceptual difference data, and calculating the load entropy based on the recorded data after all the measurement data is completed, and further determining the subject's minimum effective stimulus for load and the load level.

[0073] In a preferred embodiment, we set up a measurement system, which includes a power generation device. In conjunction with the control of exercise load and data acquisition, the measurement system generates resistance power values ​​of random magnitudes according to a predetermined scheme and controls the power bicycle to change the resistance. During data acquisition, the system collects and records the comparison results of whether the subject perceives a difference between the comparative stimulus and the reference stimulus at random time intervals. After a certain number of repeated tests, all test response values ​​are statistically analyzed, and the entropy value of the subject's stimulus perception distribution is calculated. In this embodiment, we use a constant-power bicycle as the power generation device for illustration.

[0074] Referring to Figures 3 and 4, in a preferred embodiment, the measurement system equipment is configured as follows:

[0075] (1) Use a constant power bicycle as a resistance generating device (i.e., a power generating device). The power resolution is set to, for example, ±1W, and the power range can be set based on the experimental data acquisition requirements, for example, 0-1000W.

[0076] (2) Perceptual difference discriminator. During the test, the subject's choice data regarding perceptual awareness (i.e., the subject's judgment results) is collected. The perceptual difference discriminator can be connected to the control program module via wired or wireless means (e.g., WIFI, Bluetooth, etc.).

[0077] (3) Control program module. Used to randomly generate resistance, control the power bicycle to change power output, and collect the result data of the perception discriminator.

[0078] In a more preferred embodiment, the measurement system may also include a data statistical analysis module, which calculates the corresponding motion load entropy, MES and LS values ​​from the discrimination results, reference stimuli and comparison stimuli of the power generation device, and other data collected by the measurement system, and performs model calculations, data updates, and data output or display.

[0079] In addition, more preferably, the system is also equipped with a blood pressure monitor and a heart rate monitor, which are worn by the test subject when collecting test data.

[0080] Referring to Figure 2, the data acquisition process is as follows:

[0081] In this embodiment, the data at each level is first initially set up, as follows: (1) and (2):

[0082] (1) Set the reference stimulus s for the nth level of exercise load. R In this embodiment, the reference stimulus s for the first-level exercise load LS=1 is... R =0w. The nth level of s R The MES is for the (n-1)th level of exercise load. The MES is relative to the reference stimulus s.R The smallest perceptible stimulus.

[0083] (2) Set the power set of the (n-1)th level exercise load s. In the embodiment, the power set of the resistance of the comparison stimulus s of the first level exercise load can be set as S = {0, 2, 4, 6, 8, ..., 50}, a total of 26 values. In this embodiment, the comparison stimuli s are set in an arithmetic progression (i.e., equal intervals), that is, the interval value Δ = 2. At this time, the power set S is composed of multiple comparison stimuli s (s is power). We take this as the initial value. The power sets of subsequent levels are set with reference to the MES set calculated at the previous level. That is, the power set of the comparison stimulus of the nth level test is arranged according to the MES of the (n-1)th level. For example, a superior The selected implementation method is as follows: Assuming that the minimum effective stimulus for the exercise load calculated at level n-1 is MES, and Δ = 2 indicates that the interval between two adjacent comparison stimuli is 2 watts (i.e., the load interval), then the power set S of level n is set as S = {MES, MES+2, MES+4, MES+6, ...}, where the number of elements s in set S is the same as that of level 1 initially set, which is 26. For example, if MES = 20 and Δ = 2 at level n-1, then the set S of level n is set as {20, 22, 24, 26, ...}, which contains 26 elements. Of course, there are other ways to update the power set S. For example, during the update, the value of the first element of set S can be based on the product of the MES of the previous level (i.e., level n-1) and the proportional coefficient, i.e., aMES; or it can be the load interval bΔ. Then the updated S = {aMES, aMES+1×bΔ, aMES+2×bΔ, ...}. Here, the number of elements in set S can still be set to remain unchanged. Alternatively, the setting of set S at level n can be determined by a fixed mathematical relationship of MES at level n-1. For example, the first element of set S at level n can be the ratio between MES at level n-1 and the average of multiple previous MES, multiplied by MES at level n. The remaining elements in the set can be determined based on the first element. In another approach, the differences between the elements in set S can be adjusted. Depending on the accuracy requirements of the test, the difference can be a uniform distribution similar to an arithmetic sequence or a non-uniform distribution. These conventional transformations based on the ideas of this invention should all be considered to fall within the protection scope of this invention.

[0084] After determining the initial data, the testing phase is initiated, as follows (3)-(8):

[0085] (3) Start the test. Follow the settings above. R Within a certain time range (e.g., 20-30 seconds), a random duration t{s} is set. R}, and complete the pedaling within that random duration.

[0086] (4) After (3) is completed, randomly select a comparison stimulus s from the power set S. i Distribute to the power vehicle.

[0087] (5) Randomly select a duration t{s} within a certain time range (e.g., a time range of 10-15 seconds). i Then, the test is performed again, and the pedaling is completed. After the test, the subject collects and records the sensory difference perception results data through the sensory difference perception discriminator based on the perception situation.

[0088] (6) Repeat steps (3) to (5) until all power values ​​in the comparison stimulus set (i.e., power set) S have been repeated a preset number of times (e.g., 100 times), then end the test at this level and form the nth level test dataset X. n Here, each comparison stimulus s in the power set S needs to be repeated a preset number of times, such as 100 times.

[0089] (7) Apply the joint equations of the motion load measure based on the motion load entropy to the sequence dataset X of the nth level. n Modeling is performed (i.e., establishing a motion load measurement model for motion load entropy), model parameters are calculated, and the corresponding minimum effective stimulus (MES) for motion load is calculated, and this level is determined as the corresponding motion load level.

[0090] (8) Determine whether to start the next level test (i.e., determine whether to terminate the test); if the next level test is started, the level LS is incremented by 1, i.e., n = n + 1, and the value of the power set S is modified according to the calculated MES, and the process is returned to step (3) to continue execution; after all levels have been tested, the test can be terminated and the measurement experiment is terminated.

[0091] In this embodiment, a joint equation set for measuring exercise load is further established as an individual exercise load perception model. The preferred method for establishing and evaluating this model is as follows:

[0092] In this embodiment, for a given reference stimulus s R The probability that an individual is affected by a comparative stimulus s is expressed as p1(s). R ,s) indicates that we can establish a motion load measurement model based on motion load entropy to characterize the variation of an individual's motion perception probability with stimuli. The model is as follows:

[0093] Where α and β are the model parameters, which can be determined from single-stage experimental data through maximum likelihood estimation. For data from different individuals, the corresponding model parameter values ​​can be solved. Under this setting, the MES can be calculated as follows:

[0094] Where LE represents the motion load entropy, which is the comparative stimulus corresponding to the maximum motion load entropy in the model after the model is established, and is used as the MES.

[0095] The above model for perceptual difference data with a perceived probability p1 is merely described as a preferred model and should not be construed as limiting the scope of protection of this invention. Those skilled in the art can appropriately adjust the model with a perceived probability p1 to study its changing trends and characteristics, thereby describing its changing patterns, for example, by using multi-order fitting curves. Similarly, the expression of the corresponding MES value will also have different expressions depending on the model with a perceived probability p1. Therefore, the above expression for MES should also not be construed as limiting the scope of protection of this invention.

[0096] V. Evaluation of the Measurement Model

[0097] In this embodiment, preferably, Q is used. 2 To evaluate how well the model fits the perceived proportions, Q 2 The closer Q is to 1, the closer the model is to the actual data. In this embodiment, Q... 2 The calculation method is as follows:

[0098] Where, p 1m This represents the percentage of test takers who perceive the m-th comparative stimulus value at the current level (e.g., level n), which is the proportion of test takers who perceive the stimulus value after a predetermined number of repeated tests (e.g., 100 times). This represents the model's fit probability. To estimate the average percentage of perceived differences across all data at the current level (e.g., level n), the maximum likelihood estimation method can be used to estimate two parameters, α and β, in the exercise load measurement model, and further evaluate the Q-factor of the model fit. 2 M represents the total number of comparison stimulus sample spaces at the current level (i.e., level n), such as the number of elements in the power set S formed by the comparison stimuli used in the above embodiment, i.e., M = 26.

[0099] In this embodiment, we conducted a statistical analysis on the fitting degree of the individual motion stimulus perception model using data from 32 test subjects, and the results are shown in Table 1.

[0100] Table 1. Fit of the Individual Motion Stimulus Perception Model (Q) 2

[0101] Table 1 shows the motion perception model results for all test takers, indicating that the individual's perception model Q... 2 The values ​​are between 92.34% and 99.74%, indicating that the perception measurement model given in this embodiment fits the individual exercise load test data very well.

[0102] In yet another embodiment, the present invention can also be implemented as a device, which includes one or more processors and a memory. The processor can invoke computer instructions in the memory based on reference stimuli, comparison stimuli, and perceptual difference data to execute a motion load measurement method based on motion load entropy. The processor can be externally connected, for example, to a perceptual difference discriminator to receive perceptual difference data.

[0103] A processor may be a central processing unit (CPU) or other form of processing unit with data processing and / or information execution capabilities, and may control other components in an electronic device to perform desired functions.

[0104] The memory may include one or more computer program products, which 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. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program information may be stored on the computer-readable storage medium, and the processor may run the program information to implement the main mode I2C / SMBUS control method of the various embodiments of the present invention described above, or other desired functions.

[0105] In one example, the device may also include input and output devices, which are interconnected via a bus system and / or other forms of connection mechanisms.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein may be specifically implemented in any readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for measuring exercise load based on exercise load entropy, characterized by, The method comprises: S1, setting a reference stimulus and a plurality of comparison stimuli of a certain level of test; applying the reference stimulus and the plurality of comparison stimuli to a subject, performing a perception test, and recording the obtained difference perception data, wherein 1 is recorded as a perception and 0 is recorded as no perception, the obtained difference perception data of the whole level of test forms a sequence data set of the level of test, and the plurality of comparison stimuli form a power set of the level of test; S2, calculating the motion load entropy based on the sequence data set formed by the difference perception data; the motion load entropy is calculated based on the sequence data set of the perception test results of the reference stimulus and the plurality of comparison stimuli of the level of test, and the sequence data set is calculated by information entropy to obtain the motion load entropy; The corresponding comparison stimulus is determined as the motion load minimum effective stimulus according to the maximum value of the motion load entropy, and the corresponding test level is determined as the motion load level.

2. The method of claim 1, wherein, In S1, the reference stimulus and the plurality of comparison stimuli are set in the following manner: The value of the first level reference stimulus is initialized, and the value of the nth level reference stimulus is set as the value of the motion load minimum effective stimulus of the (n-1)th level; A plurality of comparison stimuli of the first level motion load are initialized to form a power value set composed of the plurality of comparison stimuli, and the power set of the nth level comparison stimulus is determined by the motion load minimum effective stimulus of the (n-1)th level.

3. The method of claim 1, wherein, In S1, the reference stimulus and the plurality of comparison stimuli are applied to the subject for a random time length, and the plurality of comparison stimuli are applied to the subject in a random manner, specifically as follows: S11, based on the set nth level reference stimulus, the subject completes the motion in a first random time length; S12, after S11 is executed, a comparison stimulus is randomly selected from the nth level power set; the power set is composed of a plurality of comparison stimuli; S13, the subject completes the motion in a second random time length, and collects the difference perception data from the reference stimulus motion to the comparison stimulus motion; S14, steps S11 to S13 are repeated until all comparison stimulus values in the power set are tested for a preset number of times, the nth level test data collection is ended, and the nth level sequence data set is formed based on the collected difference perception data; n represents the test level; the first random time length is the same as or different from the second random time length.

4. The method of claim 3, wherein, S2 further comprises: S21, calculating the motion load entropy based on the nth level sequence data set, and calculating the corresponding motion load minimum effective stimulus of the level; S22, determining whether to enter the next level of test; if the next level of test is entered, n is set as n+1, the power set of the (n+1)th level is updated based on the calculated corresponding motion load minimum effective stimulus of the nth level, and S11 is returned; when all tests are executed, it is determined that the next level of test is not entered, and the test is terminated.

5. The method of claim 1, wherein, In the S2, the calculation method of the motion load entropy is: p1 = P (R = 1 | s R ,s) p2 = 1-p1 where LE represents the motion load entropy, i = 1, 2; s R where s represents the reference stimulus, s represents the comparison stimulus, pi represents the probability of perception in the just noticeable difference data, p2 represents the probability of no perception in the just noticeable difference data, R represents the perception result, R = 0 represents no perception, and R = 1 represents perception.

6. The method of claim 1, wherein, In the S2, the way to determine the motion load minimum effective stimulation is: Wherein, MES represents the motion load minimum effective stimulus, LE represents the motion load entropy, i=1, 2; s represents the comparison stimulus, p1 represents the probability of perception in the difference perception data, and p2 represents the probability of no perception in the difference perception data.

7. The method of claim 5, wherein, A fitting model is established for the probability p1 of the perceived perceptual difference in the level difference perception difference data, specifically: Wherein, α and β are parameters of the model.

8. The method of claim 2, wherein, The power set determination method of the nth level comparison stimulus is: n = {MES n-1 , MES n-1 + Δ, MES n-1 + 2Δ, MES n-1 + 3Δ,...} where S n represents the nth power set, MES n-1 represents the n-1st minimum effective stimulation of exercise load, and Δ represents the comparison stimulation interval.

9. A motion load measure system based on motion load entropy, characterized by, The system comprises: The power generation device, the sense of difference perception discriminator and the control program module; The power generation device is connected to the control program module, and is configured to generate corresponding movement load based on the set reference stimulus and a plurality of comparison stimuli; The sense of difference perception discriminator is connected to the control program module, and is configured to collect sense of difference perception difference data; The control program module is configured to control the power generation device and collect the sense of difference perception difference data output by the sense of difference perception discriminator; The system is configured to perform the movement load measurement method based on movement load entropy according to any one of claims 1-8.

10. A motion load measure device based on motion load entropy, characterized by The device comprises a memory and a processor, and the processor is configured to call computer instructions in the memory based on the reference stimulus, the comparison stimulus and the sense of difference perception difference data, so as to perform the movement load measurement method based on movement load entropy according to any one of claims 1-8.

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