CN115429262B - Lactic acid threshold prediction method, device, equipment and medium for motion data analysis - Google Patents
Lactic acid threshold prediction method, device, equipment and medium for motion data analysis Download PDFInfo
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Abstract
The invention discloses a lactic acid threshold prediction method, a device, equipment and a medium for motion data analysis, wherein the method comprises the following steps: and acquiring the motion data acquired by the client in real time, judging whether the accumulated motion data accords with the admission condition, if so, calculating an efficiency coefficient set corresponding to the motion data, generating a scattered point data set, inputting the scattered point data set into a function model, performing logistic regression calculation to obtain a predicted value set, performing weighted calculation on the predicted value of the predicted value set to obtain a preliminary lactic acid threshold, correcting the preliminary lactic acid threshold according to a correction formula, the predicted value set and the historical lactic acid threshold to obtain a corrected lactic acid threshold, and transmitting the corrected lactic acid threshold to the client. The invention belongs to the technical field of data analysis, and can perform intelligent analysis based on the motion data and the historical lactic acid threshold value of a user so as to acquire the corrected lactic acid threshold value corresponding to the individual and the current state of the user, thereby greatly improving the speed and accuracy of predicting the lactic acid threshold value corresponding to the individual.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a lactic acid threshold prediction method, a device, equipment and a medium for motion data analysis.
Background
The energy supply of the human body during exercise is derived from three systems of aerobic metabolism, glycolysis and ATP-CP. The proportion of the three types of systems that are powered depends on the exercise load. When the exercise load is lower and the energy demand is lower, the human body is mainly metabolized by oxygen; as the exercise load is increased, the energy demand of the human body is increased, so that the human body breathes faster and deepens on the one hand, and the heartbeat is accelerated to increase the oxygen supply rate; on the other hand, the glycolysis energy supply proportion is increased due to the slower aerobic metabolism mobilization speed.
Pyruvic acid produced during glycolysis reaction produces lactic acid under the condition of insufficient oxygen supply. At lower exercise loads, the lactic acid production rate is lower; when the load reaches a certain level, the lactic acid production rate exceeds the elimination rate, and lactic acid in blood rapidly accumulates, causing an increase in fatigue of the human body. Researchers refer to the inflection point at which lactic acid begins to accumulate rapidly during such increasing load movement as the actual threshold of lactic acid. Because of its close relationship to the exercise load and the energy supply system, it is also known as the critical point for both aerobic and anaerobic energy supply.
In the practical application process, the lactic acid threshold is usually displayed more directly in the form of the maximum oxygen intake percentage or heart rate, and has very important significance for evaluating endurance exercise capacity, for example, the lactic acid threshold is obtained to judge the exercise state of the user, so that the user is reminded to rest at proper time in time, and the influence on physical health caused by transitional fatigue is avoided. Although the method has great significance on exercise training, the measurement of the lactic acid threshold is difficult, the blood of a sporter is required to be extracted at fixed time through a complex instrument to obtain the lactic acid value, information such as real-time exercise oxygen uptake, real-time pulse and the like is collected through the instrument, a bleeding lactic acid content-oxygen uptake curve or a heart rate-oxygen uptake curve is drawn, and inflection points in the bleeding lactic acid content-oxygen uptake curve are positioned to determine the corresponding lactic acid threshold.
And the exercise function and the load bearing capacity of each person are different, the method for directly measuring the lactic acid threshold value in the laboratory cannot be widely applied to people with exercise preference, and the lactic acid threshold value which is directly measured is used as an empirical value and cannot be accurately matched with the person, so that the lactic acid threshold value applicable to different persons is difficult to obtain. Therefore, the lactic acid threshold value corresponding to the individual cannot be obtained quickly in the related art method.
Disclosure of Invention
The embodiment of the invention provides a lactic acid threshold prediction method, a device, equipment and a medium for motion data analysis, which aim to solve the problem that a lactic acid threshold corresponding to a person cannot be obtained rapidly in the prior art method.
In a first aspect, an embodiment of the present invention provides a lactic acid threshold prediction method for motion data analysis, where the method includes:
acquiring the motion data acquired by the client in real time, and judging whether the current accumulated motion data accords with preset admittance conditions or not; the exercise data comprises heart rate and pace;
if the currently accumulated motion data accords with the admission condition, calculating an efficiency coefficient set corresponding to the currently accumulated motion data;
generating a scatter data set according to the corresponding relation between the motion data and the efficiency coefficient set;
inputting the scattered point data set into a preset function model for logistic regression calculation to obtain a predicted value set corresponding to the scattered point data set;
carrying out weight calculation on the pre-estimated values contained in the pre-estimated value set according to a preset weight formula to obtain a corresponding preliminary lactic acid threshold value;
correcting the preliminary lactic acid threshold according to a preset correction formula, the preset value set and the historical lactic acid threshold of the user to which the client belongs to obtain a corresponding corrected lactic acid threshold;
And sending the corrected lactic acid threshold value to the client as a motion state control threshold value, so that the client displays the motion state control threshold value and monitors the motion state of the user according to the motion state control threshold value.
In a second aspect, an embodiment of the present invention provides a lactic acid threshold prediction apparatus for motion data analysis, including:
the admission judging unit is used for acquiring the motion data acquired by the client in real time and judging whether the current accumulated motion data accords with preset admission conditions or not; the exercise data comprises heart rate and pace;
an efficiency coefficient set obtaining unit, configured to calculate an efficiency coefficient set corresponding to the currently accumulated motion data if the currently accumulated motion data meets the admission condition;
the scattered point data set generating unit is used for generating a scattered point data set according to the corresponding relation between the motion data and the efficiency coefficient set;
the estimated value set acquisition unit is used for inputting the scattered point data set into a preset function model to perform logistic regression calculation to obtain an estimated value set corresponding to the scattered point data set;
the preliminary lactic acid threshold value acquisition unit is used for carrying out weight calculation on the predicted values contained in the predicted value set according to a preset weight formula to obtain a corresponding preliminary lactic acid threshold value;
The preliminary lactic acid threshold correction unit is used for correcting the preliminary lactic acid threshold according to a preset correction formula, the preset value set and the historical lactic acid threshold of the user to which the client belongs to obtain a corresponding corrected lactic acid threshold;
and the threshold sending unit is used for sending the corrected lactic acid threshold as a motion state control threshold to the client so that the client displays the motion state control threshold and monitors the motion state of the user according to the motion state control threshold.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the lactic acid threshold prediction method for motion data analysis according to the first aspect when the processor executes the computer program.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, where the computer program when executed by a processor causes the processor to perform the lactic acid threshold prediction method for motion data analysis according to the first aspect.
The embodiment of the invention provides a lactic acid threshold prediction method, a device, equipment and a medium for motion data analysis. And acquiring the motion data acquired by the client in real time, judging whether the accumulated motion data accords with the admission condition, if so, calculating an efficiency coefficient set corresponding to the motion data, generating a scattered point data set, inputting the scattered point data set into a function model, performing logistic regression calculation to obtain a predicted value set, performing weighted calculation on the predicted value of the predicted value set to obtain a preliminary lactic acid threshold, correcting the preliminary lactic acid threshold according to a correction formula, the predicted value set and the historical lactic acid threshold to obtain a corrected lactic acid threshold, and transmitting the corrected lactic acid threshold to the client. By the method, intelligent analysis can be performed based on the motion data and the historical lactic acid threshold value of the user, so that the corrected lactic acid threshold value corresponding to the individual and the current state of the user is obtained, the speed and accuracy of predicting the lactic acid threshold value corresponding to the individual are greatly improved, and the motion state of the user can be accurately monitored through the obtained lactic acid threshold value.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and 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 lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an application scenario of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
FIG. 3 is another flow chart of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
fig. 4 is a schematic sub-flowchart of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of another sub-flowchart of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of another sub-flowchart of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of another sub-flowchart of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a later sub-flowchart of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention;
FIG. 9 is a schematic block diagram of a lactic acid threshold prediction apparatus for motion data analysis provided by an embodiment of the present invention;
fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all 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 should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic flow chart of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention, and fig. 2 is a schematic application scenario diagram of a lactic acid threshold prediction method for motion data analysis according to an embodiment of the present invention; the lactic acid threshold prediction method for the motion data analysis is applied to the management server 10, and the management server 10 and the client 20 are connected in a network manner so as to realize the transmission of data information; the lactic acid threshold prediction method for motion data analysis is executed through application software installed in the management server 10, the management server 10 is a server for executing the lactic acid threshold prediction method for motion data analysis to analyze motion data from the client 20 to predict and obtain a lactic acid threshold, such as a server configured in an enterprise, the client 20 is a terminal device for collecting user motion data and uploading the user motion data to the management server 10, the client 20 can be configured in various intelligent wearing devices, such as a smart watch, a smart eye, a smart earphone and the like, and the motion data collected by the client 20 at least comprises heart rate and matching speed.
The client 20 and the management server 10 can directly establish wireless communication connection through 2G/3G/4G/5G communication; the client 20 may also establish a wireless communication connection with the management server 10 through other terminal devices, for example, after the client 20 establishes a connection with a terminal device such as a mobile phone, the bridging between the client 20 and the management server is implemented through the connection between the mobile phone and the management server 10, and data information is transmitted.
Where heart rate is the frequency of the user's heart beat, typically in units of beats/minute. The speed matching, specifically meaning the time required per kilometer, is a concept commonly used in the training of running exercises. The client 20 may periodically detect the pulse of the user and obtain the heart rate according to the pulse beat of the user; meanwhile, the client 20 may obtain the real-time position of the user through a position detector (such as a Beidou positioning chip), and obtain the matching speed of the user motion according to the position change condition of the user.
As shown in fig. 1, the method includes steps S110 to S170.
S110, acquiring the motion data acquired by the client in real time, and judging whether the current accumulated motion data accords with preset admittance conditions.
Acquiring the motion data acquired by the client in real time, and judging whether the current accumulated motion data accords with preset admittance conditions or not; the exercise data includes heart rate and pace. The client can acquire the motion data of the user in real time and judge whether the user enters a motion mode according to the motion data, or the user can input a control instruction for entering the motion mode on the client so that the client can acquire the information of the user entering the motion mode according to the control instruction, the client can send the information of the user entering the motion mode to the management server, and the management server can determine that the user is in the motion mode according to the received information. After the user enters the sport mode, the client can send the acquired sport data to the management server, and the management server can judge whether the sport data accords with the admission condition or not after receiving the sport data. The client can periodically collect the motion data of the user, the collected frequency can be 0.2-1Hz, and each time the client collects the motion data of a group of users, the motion data is uploaded to the management server in real time.
The management server acquires the motion data of the client, accumulates and stores the received motion data in the current motion mode, and comprehensively judges whether the current accumulated and stored motion data accords with the admission condition.
In one embodiment, as shown in fig. 3, step S110 is preceded by steps S1101 and S1102.
S1101, acquiring historical motion data of a user to which the client belongs; and S1102, calculating the historical motion data according to a preset calculation formula, and taking a calculation result as an intensity judgment threshold value in the admission condition.
The management server stores the historical motion data of the user to which the client belongs, for example, the historical motion data can be stored in a database configured by the management server, and the historical motion data corresponding to the user identifier can be obtained from the database according to the user identifier corresponding to the user to which the client belongs. The historical motion data comprises parameter information such as the matching speed, the heart rate and the like acquired in the historical motion process of the user, the historical motion data can be calculated according to a calculation formula to obtain corresponding calculation results, the calculation results can be used as intensity judgment thresholds in the admission conditions, and whether the currently accumulated motion data accords with preset admission conditions or not is judged.
In one embodiment, as shown in fig. 5, step S1102 includes sub-steps S1103 and S1104.
S1103, calculating a heart rate difference value between a maximum heart rate and a resting heart rate in the historical exercise data; s1104, multiplying the heart rate difference value by the heart rate coefficient according to the calculation formula, and adding the product value and the resting heart rate to obtain a corresponding calculation result.
Specifically, a heart rate difference between a maximum heart rate and a resting heart rate can be obtained from historical exercise data, the resting heart rate can be a heart rate value acquired when a user is in a resting state, the maximum heart rate is a value with the maximum value of the center of exercise of the user, and the heart rate difference can be obtained by subtracting the resting heart rate from the maximum heart rate.
And multiplying the heart rate difference value by the heart rate coefficient according to a calculation formula, and adding the obtained product value to the resting heart rate to obtain a corresponding calculation result, wherein the calculation result can be used as an intensity judgment threshold value for subsequent judgment. The intensity judgment threshold value can be used as a judgment basis for the user to enter the medium exercise intensity corresponding to the user, and if the heart rate of the user is larger than the intensity judgment threshold value, the user enters the medium exercise intensity at the moment.
For example, a specific calculation process may be represented by equation (1):
hrm＝(hr max -hr rest )×α+hr rest ，α∈(0,1) (1)；
Wherein hrm is the calculated intensity determination threshold, hr max Maximum heart rate, hr rest For resting heart rate, a is heart rate coefficient, and the value range of a is (0, 1).
In one embodiment, as shown in FIG. 4, step S110 includes sub-steps S111, S112, S113, S114, and S115.
And S111, judging whether the total duration of the currently accumulated motion data is greater than the default duration of the admission condition.
When the admission judgment is carried out, the whole duration of the currently accumulated motion data can be acquired firstly, namely the time interval between the reception of the first group of motion data and the reception of the latest group of motion data, and whether the whole duration is greater than the default duration in the admission condition is judged. For example, the default time period may be 10 minutes or 15 minutes.
And S112, if the integral time length is longer than the default time length, acquiring the load time length with the heart rate greater than the strength judgment threshold value in the admission condition.
If the integral time length is longer than the default time length, further acquiring the load time length with the heart rate greater than the judgment intensity threshold value in the admission condition, wherein the acquired coincidence time length, namely the accumulated time with the heart rate greater than the judgment intensity threshold value, is acquired.
S113, judging whether the load duration is greater than a load duration threshold of the admission condition; and S114, if the load time length is greater than the load time length threshold, judging that the currently accumulated motion data accords with the admission condition.
And judging whether the load duration is greater than a load duration threshold of the admission condition. For example, the load duration threshold may be 2 minutes, and when the load duration is greater than 2 minutes, it is determined that the currently accumulated motion data meets the admission condition.
S115, if the whole duration is not greater than the default duration or the load duration is not greater than the load duration threshold, determining that the currently accumulated motion data does not meet the admission condition.
If the overall duration is not greater than the default duration or the load duration is not greater than the load duration threshold, determining that the currently accumulated motion data does not meet the admission condition; then a determination may be made again as to whether the currently accumulated motion data meets the admission condition after receiving the new motion data.
And S120, if the currently accumulated motion data accords with the admission condition, calculating an efficiency coefficient set corresponding to the currently accumulated motion data.
And if the currently accumulated motion data accords with the access condition, calculating an efficiency coefficient set corresponding to the currently accumulated motion data. If the current accumulated motion data accords with the admission condition, an efficiency coefficient set corresponding to the accumulated motion data can be calculated, and the efficiency coefficient set comprises an efficiency coefficient corresponding to each motion data. The efficiency coefficient may be used as an efficiency indicator RE (Running Efficiency) of the user's running exercise. RE can reflect the accumulation of fatigue: the larger the value, the higher the load of the runner is, and the higher the possibility of being in a fatigue state is
Specifically, the calculation process of the efficiency coefficient can be expressed by using the formula (2):
RE＝ln(hr-hr 0 /v-v 0 ) (2)；
wherein hr and v are respectively the heart rate and pace of the runner (the heart rate and pace of the runner collected at a certain moment) in a certain group of exercise data, hr 0 、v 0 Respectively, the correction parameters preset in the formulas.
S130, generating a scattered point data set according to the corresponding relation between the motion data and the efficiency coefficient set.
And generating a scattered point data set according to the corresponding relation between the motion data and the efficiency coefficient set. The motion data may be integrated with the set of efficiency coefficients to generate a scatter data set.
In one embodiment, as shown in FIG. 6, step S130 includes sub-steps S131 and S132.
S131, obtaining a heart rate value corresponding to each efficiency coefficient in the efficiency coefficient set in the motion data; s132, pairing each efficiency coefficient and the corresponding heart rate value into two-dimensional scattered point data to generate a scattered point data set containing the two-dimensional scattered point data corresponding to each efficiency coefficient.
And obtaining the heart rate value corresponding to each efficiency coefficient in the exercise data, and pairing the efficiency coefficient with the corresponding heart rate value to obtain the two-dimensional scatter data. And combining all the obtained two-dimensional scattered point data to generate a scattered point data set. That is, RE value (efficiency coefficient) statistics are performed from the heart rate perspective to obtain a scatter data set, which may be represented as one data set containing a plurality of two-dimensional scatter data: x= [ (hr) 1 ,re 1 ),(hr 2 ,re 2 ),(hr i ,re i )...(hr n ,re n )]The method comprises the steps of carrying out a first treatment on the surface of the Wherein n is the total number of two-dimensional scattered data contained in the data set, namely the total number of data points, hr i 、re i The heart rate value and RE value of the i-th point therein, respectively.
And S140, inputting the scattered point data set into a preset function model for logistic regression calculation to obtain a predicted value set corresponding to the scattered point data set.
And inputting the scattered point data set into a preset function model for logistic regression calculation to obtain a predicted value set corresponding to the scattered point data set. The obtained scattered point data set is input into a function model for logistic regression calculation, specifically, two-dimensional scattered point data contained in the scattered point data set can be normalized and respectively input into a function space { logic (hr) } formed by logistic regression functions, a plurality of different lactic acid threshold estimation values corresponding to the logistic regression functions are obtained, and the obtained lactic acid threshold estimation values are combined into a pre-estimation value set corresponding to the scattered point data set.
In one embodiment, as shown in FIG. 7, step S140 includes sub-steps S141 and S142.
S141, carrying out normalized calculation on two-dimensional scattered data in the scattered data set through parameters corresponding to each logistic regression function in the function model to obtain normalized numerical values corresponding to each logistic regression function; s142, inputting normalized numerical values corresponding to the logistic regression functions into the logistic regression functions to perform logistic regression calculation, and obtaining the estimated value set formed by lactic acid threshold value estimated values corresponding to the logistic regression functions.
Specifically, the two-dimensional scattered data in the scattered data set can be normalized by the parameters corresponding to each logistic regression function, and the logistic regression function comprises the parameters for normalized calculation of the two-dimensional scattered data.
The efficiency coefficient in the two-dimensional scattered point data can be converted into the numerical value range of [0,1 ] through the combination of parameters and a normalized calculation formula]For example, the normalized calculation formula may be y=s×ln re i +u, where s and u are parameters in the logistic regression function, re i And (3) the efficiency coefficient in the ith two-dimensional scattered point data, and y is the normalized efficiency coefficient obtained by calculation. And combining the normalized efficiency coefficient and the corresponding heart rate value into a corresponding normalized numerical value. The efficiency of the subsequent logistic regression calculation can be improved by performing normalized calculation on the efficiency coefficient.
Inputting normalized numerical values corresponding to the logistic regression functions into the logistic regression functions for logistic regression calculation, so that probability that the data center value of each two-dimensional scattered point is a threshold value is calculated, a corresponding function image is generated, the abscissa in the function image is a heart rate value, and the numerical range is 20-200; the ordinate is the probability that the heart rate value is the threshold value, and the value range is 0-1.
And counting the probability corresponding to the heart rate value according to the obtained function image, and obtaining the heart rate value corresponding to the point where the probability value is mutated as the corresponding lactic acid threshold value estimated value. By the method, one lactic acid threshold value estimated value can be correspondingly obtained by one logistic regression function, a plurality of lactic acid threshold value estimated values can be correspondingly obtained by a plurality of logistic regression functions, and a set of estimated values can be obtained by combining all the obtained lactic acid threshold value estimated values.
And S150, carrying out weight calculation on the predicted values contained in the predicted value set according to a preset weight formula to obtain a corresponding preliminary lactic acid threshold.
And carrying out weighted calculation on the predicted values contained in the predicted value set according to a preset weighted formula to obtain a corresponding preliminary lactic acid threshold value. In order to further improve accuracy of lactic acid threshold prediction, the pre-estimation value (i.e. lactic acid threshold estimation value) included in the pre-estimation value set may be weighted, and different logistic regression functions correspond to different weights, so that a process of weighting calculation may be represented by using formula (3):
wherein hr lactate For calculating the preliminary lactic acid threshold value, w j Weight hr corresponding to the jth logistic regression function lactatej N is the total number of logistic regression functions, which is the predicted value corresponding to the jth logistic regression function.
In the above steps, RE indexes are processed through a mathematical function model and a weighted formula, so that the heart rate and the speed distribution range in which a runner is most severely converted from a 'fatigue free' state to a 'fatigue' state in the movement process can be calculated, wherein the range is a predicted initial lactic acid threshold, and the initial lactic acid threshold is a threshold for representing the heart rate in times per minute.
And S160, correcting the preliminary lactic acid threshold according to a preset correction formula, the preset value set and the historical lactic acid threshold of the user to which the client belongs, so as to obtain a corresponding corrected lactic acid threshold.
And correcting the preliminary lactic acid threshold according to a preset correction formula, the preset value set and the historical lactic acid threshold of the user to which the client belongs, so as to obtain a corresponding corrected lactic acid threshold. In order to further improve accuracy of lactic acid threshold prediction, the obtained preliminary lactic acid threshold can be corrected through a correction formula and a predicted value and a historical lactic acid threshold of a user to which the client belongs, so as to obtain a corrected lactic acid threshold.
For a particular runner, its lactic acid threshold depends on the physical characteristics of the individual's exercise, the exercise state of the day, etc. Predictions made from one motion record tend to fluctuate around the true value. The method further improves the reliability of lactic acid threshold prediction by correcting the preliminary lactic acid threshold through the historical lactic acid threshold.
In one embodiment, as shown in FIG. 8, step S160 includes sub-steps S161, S162, and S163.
S161, acquiring a plurality of historical data values which are the same as the cycle number in the historical lactic acid threshold according to the preset cycle number.
The management server stores a historical lactic acid threshold value corresponding to each user in advance, and the historical lactic acid threshold value can be one lactic acid threshold value corresponding to each user daily, weekly or monthly. A plurality of historical data values of the historical lactic acid threshold that are the same as the number of cycles may be obtained based on the number of cycles.
For example, if the cycle is "week" and the cycle number is 5, the historical lactic acid threshold corresponding to each week may be obtained as the historical data value within 5 weeks before the current time.
S162, calculating a variation value corresponding to the predicted value set.
The preliminary lactic acid threshold values are included in the pre-estimated value set, the preliminary lactic acid threshold values can be calculated to obtain variation values, the variation values can be used for representing the credibility of the preliminary lactic acid threshold values in the pre-estimated value set, the smaller the variation values are, the smaller the deviation between the preliminary lactic acid threshold values is, and the higher the result credibility is; the larger the variation value, the larger the deviation between the preliminary lactic acid thresholds, and the lower the reliability of the result. Specifically, the variability value may be the standard deviation SD of the preliminary lactic acid threshold.
S163, inputting the preliminary lactic acid threshold value, the variability value and the plurality of historical data values into the correction formula for calculation to obtain a corresponding corrected lactic acid threshold value.
And simultaneously inputting the obtained variation value, a plurality of historical data values and the preliminary lactic acid threshold value into a correction formula for calculation, so as to obtain the corresponding corrected lactic acid threshold value. Specifically, the correction formula may be expressed by formula (4):
wherein hr ln For the final calculated corrected lactic acid threshold, sdhr lr For the mutation degree value corresponding to the predicted value set, f (sdhr lr ) As a function of calculating the mutation degree value, f (sdhr lr ) The value range of (2) is [0,1 ]]The method comprises the steps of carrying out a first treatment on the surface of the The smaller the variation value, the corresponding calculated f (sdhr lr ) The larger the contribution degree of the preliminary lactic acid threshold value to the correction value is, the larger the contribution degree is; the greater the degree of variation, the corresponding calculated f (sdhr lr ) The smaller the contribution degree of the preliminary lactic acid threshold value to the correction value is, the smaller the contribution degree is; hr (hr) lhi For the ith historical data value, v is the total number of historical data values, w i The weight value corresponding to the i-th historical data value in the formula is hr as i is smaller lhi The smaller the interval time between the current date and the corresponding weight value w i The larger; the greater i is, hr lhi The larger the interval time between the current date and the corresponding weight value w i The smaller.
And S170, sending the corrected lactic acid threshold value to the client as a motion state control threshold value, so that the client displays the motion state control threshold value and monitors the motion state of the user according to the motion state control threshold value.
And sending the corrected lactic acid threshold value to the client as a motion state control threshold value, so that the client displays the motion state control threshold value and monitors the motion state of the user according to the motion state control threshold value. After acquiring the corrected lactic acid threshold, the management server 10 may send the corrected lactic acid threshold as a movement state control threshold to the client, where the corrected lactic acid threshold is a threshold for representing the heart rate, and the unit is a time/minute. After receiving the exercise state control threshold, the client 20 may display the exercise state control threshold, that is, may display a heart rate (times/min) corresponding to the lactic acid threshold in the client 20. And the displayed heart rate value is used as a motion state control threshold value, and the motion state of the user is monitored by combining with the training load of the current motion of the user.
For example, when the training purpose is aerobic endurance, the client may determine whether the heart rate value in the current exercise data of the user is below the exercise state control threshold, and if the heart rate value in the current exercise data exceeds the exercise state control threshold, may send a prompt message to the user to prompt the user to pay attention to adjust the exercise state and take a proper rest; when the training aim is anaerobic endurance, the client can judge whether the heart rate value in the current exercise data of the user is not in a certain range near the exercise state control threshold, if the heart rate value in the current exercise data is higher than the exercise state control threshold by a certain range, prompt information can be sent out to prompt the user to rest, namely the heart rate of the user exceeds the exercise state control threshold too much, and the user can be exhausted very quickly; if the heart rate value in the current exercise data is lower than the exercise state control threshold value by a certain range, prompt information can be sent out to prompt the user to pay attention to adjusting the exercise state, namely if the heart rate value of the user is lower than the exercise state control threshold value too much, the lactic acid reflecting fatigue cannot be accumulated rapidly, and the training effect can be influenced.
For the acquired motion state control threshold, the fatigue degree of the user at different moments and different matching speeds can be detected by combining the RE index with the motion state control threshold, and the fatigue degree of the user is fed back to prompt the user, so that the method has good practical significance in the practical application process.
In the lactic acid threshold prediction method for motion data analysis provided by the embodiment of the invention, motion data acquired by a client is acquired in real time, whether the accumulated motion data accords with an admission condition is judged, if so, an efficiency coefficient set corresponding to the motion data is calculated, a scattered point data set is generated, the scattered point data set is input into a function model to carry out logistic regression calculation to obtain a predicted value set, the predicted value of the predicted value set is weighted and calculated to obtain a preliminary lactic acid threshold, and the preliminary lactic acid threshold is corrected according to a correction formula, the predicted value set and a historical lactic acid threshold to obtain a corrected lactic acid threshold and is sent to the client. By the method, intelligent analysis can be performed based on the motion data and the historical lactic acid threshold value of the user, so that the corrected lactic acid threshold value corresponding to the individual and the current state of the user is obtained, the speed and accuracy of predicting the lactic acid threshold value corresponding to the individual are greatly improved, and the motion state of the user can be accurately monitored through the obtained lactic acid threshold value.
The embodiment of the present invention further provides a lactic acid threshold prediction device for motion data analysis, which may be configured in the management server 10, and the lactic acid threshold prediction device for motion data analysis is used for executing any embodiment of the lactic acid threshold prediction method for motion data analysis. Specifically, referring to fig. 9, fig. 9 is a schematic block diagram of a lactic acid threshold prediction apparatus for motion data analysis according to an embodiment of the present invention.
As shown in fig. 9, the lactic acid threshold prediction apparatus 100 for motion data analysis includes an admission judgment unit 110, an efficiency coefficient set acquisition unit 120, a scatter data set generation unit 130, an estimated value set acquisition unit 140, a preliminary lactic acid threshold acquisition unit 150, a preliminary lactic acid threshold correction unit 160, and a threshold transmission unit 170.
An admission judgment unit 110, configured to acquire, in real time, motion data acquired by the client, and judge whether the currently accumulated motion data meets a preset admission condition; the exercise data includes heart rate and pace.
In a specific embodiment, the lactic acid threshold prediction apparatus 100 for motion data analysis further includes a subunit: the historical motion data acquisition unit is used for acquiring historical motion data of a user to which the client belongs; and the intensity judgment threshold calculating unit is used for calculating the historical motion data according to a preset calculation formula so as to take a calculation result as an intensity judgment threshold in the admission condition.
In a specific embodiment, the intensity judgment threshold calculation unit includes a subunit: the heart rate difference value calculation unit is used for calculating the heart rate difference value between the maximum heart rate and the resting heart rate in the historical exercise data; and the calculation result acquisition unit is used for multiplying the heart rate difference value by the heart rate coefficient according to the calculation formula, and adding the product value and the resting heart rate to obtain a corresponding calculation result.
In a specific embodiment, the admission judgment unit 110 includes a subunit: the overall duration judging unit is used for judging whether the overall duration of the currently accumulated motion data is greater than the default duration of the access condition; the load duration obtaining unit is used for obtaining the load duration with the heart rate larger than the strength judgment threshold value in the admission condition if the integral time duration is longer than the default time duration; the load duration judging unit is used for judging whether the load duration is greater than a load duration threshold value of the admission condition; the first judging unit is used for judging that the currently accumulated motion data accords with the admission condition if the load time length is larger than the load time length threshold; and the second judging unit is used for judging that the currently accumulated motion data does not accord with the admission condition if the whole duration is not greater than the default duration or the load duration is not greater than the load duration threshold.
And the efficiency coefficient set obtaining unit 120 is configured to calculate an efficiency coefficient set corresponding to the currently accumulated motion data if the currently accumulated motion data meets the admission condition.
And the scatter data set generating unit 130 is configured to generate a scatter data set according to the correspondence between the motion data and the efficiency coefficient set.
In a specific embodiment, the scatter data set generating unit 130 includes a subunit: the heart rate value acquisition unit is used for acquiring a heart rate value corresponding to each efficiency coefficient in the efficiency coefficient set in the exercise data; and the pairing unit is used for pairing each efficiency coefficient and the corresponding heart rate value into two-dimensional scattered point data so as to generate a scattered point data set containing the two-dimensional scattered point data corresponding to each efficiency coefficient.
And the estimated value set obtaining unit 140 is configured to input the scattered point data set into a preset function model to perform logistic regression calculation, so as to obtain an estimated value set corresponding to the scattered point data set.
In a specific embodiment, the pre-estimation value set obtaining unit 140 includes a subunit: the normalized value acquisition unit is used for carrying out normalized calculation on the two-dimensional scattered data in the scattered data set through parameters corresponding to each logistic regression function in the function model to obtain normalized values corresponding to each logistic regression function; and the logistic regression calculation unit is used for inputting normalized numerical values corresponding to the logistic regression functions into the logistic regression functions to carry out logistic regression calculation to obtain the estimated value set consisting of lactic acid threshold estimated values corresponding to the logistic regression functions.
The preliminary lactic acid threshold value obtaining unit 150 is configured to perform a weighted calculation on the predicted values included in the predicted value set according to a preset weighted formula, so as to obtain a corresponding preliminary lactic acid threshold value.
The preliminary lactic acid threshold value correcting unit 160 is configured to correct the preliminary lactic acid threshold value according to a preset correction formula, the preset value set, and a historical lactic acid threshold value of a user to which the client belongs, so as to obtain a corresponding corrected lactic acid threshold value.
In a specific embodiment, the preliminary lactic acid threshold correction unit 160 includes a subunit: a historical data value obtaining unit, configured to obtain a plurality of historical data values with the same cycle number in the historical lactic acid threshold according to a preset cycle number; the variation value calculation unit is used for calculating a variation value corresponding to the preset value set; and the correction calculation unit is used for inputting the preliminary lactic acid threshold value, the variability value and the plurality of historical data values into the correction formula for calculation to obtain a corresponding correction lactic acid threshold value.
And a threshold sending unit 170, configured to send the corrected lactic acid threshold as a motion state control threshold to the client, so that the client displays the motion state control threshold and monitors the motion state of the user according to the motion state control threshold.
The lactic acid threshold prediction device for motion data analysis provided by the embodiment of the invention is used for acquiring the motion data acquired by the client in real time by applying the lactic acid threshold prediction method for motion data analysis, judging whether the accumulated motion data accords with the admission condition, calculating an efficiency coefficient set corresponding to the motion data and generating a scattered point data set if the accumulated motion data accords with the admission condition, inputting the scattered point data set into a function model for logistic regression calculation to obtain a predicted value set, weighting the predicted value of the predicted value set to obtain a preliminary lactic acid threshold, correcting the preliminary lactic acid threshold according to a correction formula, the predicted value set and the historical lactic acid threshold, and transmitting the corrected lactic acid threshold to the client. By the method, intelligent analysis can be performed based on the motion data and the historical lactic acid threshold value of the user, so that the corrected lactic acid threshold value corresponding to the individual and the current state of the user is obtained, the speed and accuracy of predicting the lactic acid threshold value corresponding to the individual are greatly improved, and the motion state of the user can be accurately monitored through the obtained lactic acid threshold value.
The lactic acid threshold prediction means of motion data analysis described above may be implemented in the form of a computer program which may be run on a computer device as shown in fig. 10.
Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device may be a management server 10 for performing a lactic acid threshold prediction method for motion data analysis to predict a lactic acid threshold.
With reference to fig. 10, the computer device 500 includes a processor 502, a memory, and a network interface 505, which are connected by a system bus 501, wherein the memory may include a storage medium 503 and an internal memory 504.
The storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, may cause the processor 502 to perform a lactic acid threshold prediction method of motion data analysis, wherein the storage medium 503 may be a volatile storage medium or a non-volatile storage medium.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform a lactic acid threshold prediction method of motion data analysis.
The network interface 505 is used for network communication, such as providing for transmission of data information, etc. It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and does not constitute a limitation of the computer device 500 to which the present inventive arrangements may be applied, and that a particular computer device 500 may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
The processor 502 is configured to execute a computer program 5032 stored in a memory to implement the corresponding functions in the lactic acid threshold prediction method for motion data analysis.
Those skilled in the art will appreciate that the embodiment of the computer device shown in fig. 10 is not limiting of the specific construction of the computer device, and in other embodiments, the computer device may include more or less components than those shown, or certain components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may include only a memory and a processor, and in such embodiments, the structure and function of the memory and the processor are consistent with the embodiment shown in fig. 10, and will not be described again.
It should be appreciated that in an embodiment of the invention, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a volatile or nonvolatile computer readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps included in the lactic acid threshold prediction method of motion data analysis described above.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein. Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units is merely a logical function division, there may be another division manner in actual implementation, or units having the same function may be integrated into one unit, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or part of what contributes to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a computer-readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. A lactic acid threshold prediction method for motion data analysis, wherein the method is applied to a management server, and the management server establishes network connection with a client to realize transmission of data information, and the method comprises the following steps:
acquiring the motion data acquired by the client in real time, and judging whether the current accumulated motion data accords with preset admittance conditions or not; the exercise data comprises heart rate and pace;
if the currently accumulated motion data accords with the admission condition, calculating an efficiency coefficient set corresponding to the currently accumulated motion data; the calculation formula of the efficiency coefficient in the efficiency coefficient set is as follows: re=ln (hr-hr) 0 /v-v 0 ) Wherein hr and v are heart rate and pace of the runner in a certain group of exercise data respectively, hr 0 、v 0 Respectively correcting parameters preset in the formula;
generating a scatter data set according to the corresponding relation between the motion data and the efficiency coefficient set;
inputting the scattered point data set into a preset function model for logistic regression calculation to obtain a predicted value set corresponding to the scattered point data set;
carrying out weight calculation on the pre-estimated values contained in the pre-estimated value set according to a preset weight formula to obtain a corresponding preliminary lactic acid threshold value;
correcting the preliminary lactic acid threshold according to a preset correction formula, the preset value set and the historical lactic acid threshold of the user to which the client belongs to obtain a corresponding corrected lactic acid threshold;
sending the corrected lactic acid threshold value to the client as a motion state control threshold value, so that the client displays the motion state control threshold value and monitors the motion state of a user according to the motion state control threshold value;
the generating a scatter data set according to the correspondence between the motion data and the efficiency coefficient set includes:
acquiring a heart rate value corresponding to each efficiency coefficient in the efficiency coefficient set in the motion data;
pairing each efficiency coefficient and the corresponding heart rate value into two-dimensional scattered point data to generate a scattered point data set containing the two-dimensional scattered point data corresponding to each efficiency coefficient;
Inputting the scattered point data set into a preset function model for logistic regression calculation to obtain a predicted value set corresponding to the scattered point data set, wherein the method comprises the following steps:
carrying out normalized calculation on two-dimensional scattered data in the scattered data set through parameters corresponding to each logistic regression function in the function model to obtain normalized numerical values corresponding to each logistic regression function;
inputting normalized numerical values corresponding to the logistic regression functions into the logistic regression functions to perform logistic regression calculation to obtain the estimated value set consisting of lactic acid threshold estimated values corresponding to the logistic regression functions, wherein the method comprises the following steps: performing logistic regression calculation according to one logistic regression function to obtain the probability that each two-dimensional scattered data center value is a threshold value, and generating a corresponding function image according to the probability that each two-dimensional scattered data center value is the threshold value; counting the probability corresponding to the heart rate value according to the function image to obtain the heart rate value corresponding to the point where the probability value is mutated as a corresponding lactic acid threshold value estimated value; and correspondingly obtaining a plurality of lactic acid threshold estimation values by the plurality of logistic regression functions to serve as the pre-estimation value set.
2. The lactic acid threshold prediction method for motion data analysis according to claim 1, wherein before the determining whether the motion data currently accumulated meets a preset admission condition, further comprising:
Acquiring historical motion data of a user to which the client belongs;
and calculating the historical motion data according to a preset calculation formula, so as to take a calculation result as an intensity judgment threshold value in the admission condition.
3. The lactic acid threshold prediction method for motion data analysis according to claim 2, wherein the determining whether the motion data currently accumulated meets a preset admission condition includes:
judging whether the total duration of the currently accumulated motion data is greater than the default duration of the admission condition;
if the integral time length is longer than the default time length, acquiring a load time length with a heart rate greater than an intensity judgment threshold value in the admission condition;
judging whether the load duration is greater than a load duration threshold of the admission condition;
if the load time length is greater than the load time length threshold, judging that the currently accumulated motion data accords with the admission condition;
and if the overall duration is not greater than the default duration or the load duration is not greater than the load duration threshold, determining that the currently accumulated motion data does not meet the admission condition.
4. The lactic acid threshold prediction method for motion data analysis according to claim 2, wherein the calculating the historical motion data according to a preset calculation formula to use a calculation result as an intensity judgment threshold in the admission condition includes:
Calculating a heart rate difference between a maximum heart rate and a resting heart rate in the historical motion data;
and multiplying the heart rate difference value by the heart rate coefficient according to the calculation formula, and adding the product value and the resting heart rate to obtain a corresponding calculation result.
5. The lactic acid threshold prediction method for motion data analysis according to claim 1, wherein the correcting the preliminary lactic acid threshold according to a preset correction formula, the set of estimated values, and a historical lactic acid threshold of a user to which the client belongs to obtain a corresponding corrected lactic acid threshold includes:
acquiring a plurality of historical data values which are the same as the cycle number in the historical lactic acid threshold according to a preset cycle number;
calculating a variation value corresponding to the predicted value set;
and inputting the preliminary lactic acid threshold value, the variation degree value and the plurality of historical data values into the correction formula for calculation to obtain a corresponding corrected lactic acid threshold value.
6. A lactic acid threshold prediction device for motion data analysis, wherein the device is configured in a management server, and the management server establishes a network connection with a client to realize transmission of data information, and the device comprises:
The admission judging unit is used for acquiring the motion data acquired by the client in real time and judging whether the current accumulated motion data accords with preset admission conditions or not; the exercise data comprises heart rate and pace;
an efficiency coefficient set obtaining unit, configured to calculate an efficiency coefficient set corresponding to the currently accumulated motion data if the currently accumulated motion data meets the admission condition; the calculation formula of the efficiency coefficient in the efficiency coefficient set is as follows: re=ln (hr-hr) 0 /v-v 0 ) Wherein hr and v are heart rate and pace of the runner in a certain group of exercise data respectively, hr 0 、v 0 Respectively correcting parameters preset in the formula;
the scattered point data set generating unit is used for generating a scattered point data set according to the corresponding relation between the motion data and the efficiency coefficient set;
the estimated value set acquisition unit is used for inputting the scattered point data set into a preset function model to perform logistic regression calculation to obtain an estimated value set corresponding to the scattered point data set;
the preliminary lactic acid threshold value acquisition unit is used for carrying out weight calculation on the predicted values contained in the predicted value set according to a preset weight formula to obtain a corresponding preliminary lactic acid threshold value;
The preliminary lactic acid threshold correction unit is used for correcting the preliminary lactic acid threshold according to a preset correction formula, the preset value set and the historical lactic acid threshold of the user to which the client belongs to obtain a corresponding corrected lactic acid threshold;
the threshold sending unit is used for sending the corrected lactic acid threshold to the client as a motion state control threshold, so that the client displays the motion state control threshold and monitors the motion state of a user according to the motion state control threshold;
the scatter data set generating unit includes a subunit: the heart rate value acquisition unit is used for acquiring a heart rate value corresponding to each efficiency coefficient in the efficiency coefficient set in the exercise data; the pairing unit is used for pairing each efficiency coefficient and the corresponding heart rate value into two-dimensional scattered point data so as to generate a scattered point data set containing the two-dimensional scattered point data corresponding to each efficiency coefficient;
the pre-estimation value set acquisition unit includes a subunit: the normalized value acquisition unit is used for carrying out normalized calculation on the two-dimensional scattered data in the scattered data set through parameters corresponding to each logistic regression function in the function model to obtain normalized values corresponding to each logistic regression function; the logistic regression calculation unit is used for inputting normalized numerical values corresponding to the logistic regression functions into the logistic regression functions to carry out logistic regression calculation to obtain the estimated value set formed by lactic acid threshold value estimated values corresponding to the logistic regression functions;
Inputting normalized values corresponding to the logistic regression functions into the logistic regression functions to perform logistic regression calculation, and obtaining the set of estimated values formed by lactic acid threshold estimated values corresponding to the logistic regression functions, wherein the method comprises the following steps: performing logistic regression calculation according to one logistic regression function to obtain the probability that each two-dimensional scattered data center value is a threshold value, and generating a corresponding function image according to the probability that each two-dimensional scattered data center value is the threshold value; counting the probability corresponding to the heart rate value according to the function image to obtain the heart rate value corresponding to the point where the probability value is mutated as a corresponding lactic acid threshold value estimated value; and correspondingly obtaining a plurality of lactic acid threshold estimation values by the plurality of logistic regression functions to serve as the pre-estimation value set.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the lactic acid threshold prediction method of motion data analysis according to any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which when executed by a processor implements a lactic acid threshold prediction method of motion data analysis according to any one of claims 1 to 5.
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