CN110634549A - Intelligent motion management device and method for overweight people - Google Patents
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Abstract
The invention belongs to the technical field of fitness, and particularly relates to an intelligent exercise management device and method for overweight people. The device comprises: the wearable device is worn on a human body and used for acquiring heart rate data in real time and transmitting the acquired data; the cloud server is positioned at the far end and used for receiving the collected data and calculating the optimal exercise heart rate according to the collected data; the control platform is arranged on the fitness equipment and used for receiving the collected data, calculating the optimal exercise heart rate according to the collected data, sending out prompt information according to the calculated result of the optimal exercise heart rate and controlling the running of the fitness equipment. And calculating to obtain the heart rate interval with the optimal exercise intensity. The optimal heart rate of the individual during exercise is set according to the heart rate interval, the individual is guided to perform daily exercise according to the heart rate interval standard, the weight is reduced, and the intelligent fitness exercise training device has the advantages of being high in intelligent degree, high in weight reducing efficiency and high in weight reducing scientificity.
Description
Technical Field
The invention belongs to the technical field of fitness, and particularly relates to an intelligent exercise management device and method for overweight people.
Background
At present, overweight people in China are very huge groups, the number of the overweight people accounts for more than one third of the population in China according to statistics, the groups become potential groups with metabolic syndrome and other diseases if the constitution of the groups is not controlled, and the overweight people have special significance for the groups of the overweight people developing to the early stage of diabetes (for example, JAMA issues the latest data of Chinese diabetes: four adults are in the early stage of diabetes; an investigation report issued by an authority department in 2013 shows that the diabetes prevalence rate of Chinese adults is 11.6%, the people in the early stage of diabetes accounts for 50.1% of the total population, namely, one diabetes patient exists in less than 10 adults, one diabetes patient belongs to the early stage of diabetes in every two adults, the diabetes prevalence rate of China exceeds 11.3% in the United states, the number of the patients reaches 1.14 hundred million according to the investigation developed in 2010, beyond india, it is the first major country of diabetes mellitus of misnomer).
Thus, being overweight is a serious threat to both own health and to the country. In order to match with the national policy of advocating national health, the method sets up projects from two aspects of aerobic exercise and sports drink with strong antioxidation to perform health intervention, and the projects are suitable for both the vast overweight people and all the people participating in sports (for the early stage of diabetes, medical intervention is not recommended by diagnosis and treatment guidelines of the world health organization.
Meanwhile, for the huge sports equipment/appliance market: there are no sports devices or machines currently on the market that have an optimal heart rate calculation, such as, in particular, fitness machines, treadmills, bicycles, etc. In addition, due to the huge number of overweight people in the society, a large part of the overweight people can enter the gymnasium to use the equipment or the equipment without condition, if people provide a mobile phone and a bracelet, the people can run by themselves to realize the process of completing the optimal exercise intensity according to the adjustment of the heart rate; a simple treadmill/exercise bicycle and bracelet can be designed for each family, the purpose of exercising and losing weight of each family member is achieved, and the multifunctional treadmill/exercise bicycle has a huge market.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide an intelligent exercise management apparatus and method for overweight people, which calculates a heart rate interval with optimal exercise intensity. The optimal heart rate of the individual during exercise is set according to the heart rate interval, the individual is guided to perform daily exercise according to the heart rate interval standard, the weight is reduced, and the intelligent fitness exercise training device has the advantages of being high in intelligent degree, high in weight reducing efficiency and high in weight reducing scientificity.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an intelligent motion management device for overweight people, the device comprising: the wearable device is worn on a human body and used for acquiring heart rate data in real time and transmitting the acquired data; the cloud server is positioned at the far end and used for receiving the collected data, backing up the data and calculating the optimal exercise heart rate according to the collected data; the control platform is arranged on the fitness equipment and used for receiving the collected data, calculating the optimal exercise heart rate according to the collected data, sending out prompt information according to the result of the calculated optimal exercise heart rate and controlling the running of the fitness equipment; the method for the cloud server to perform regular data analysis on the backed-up data and judge the health state according to the analysis result comprises the following steps: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing; and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible. Carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; carrying out data modeling; performing an effect analysis comprising: after the model training is finished, calculating the accuracy of the health generated by the model and the original health; and analyzing the data in the backup database according to the established model to generate an analysis result.
Further, after receiving the acquired data, the cloud server and the control platform both send the acquired data to the cloud server for backup; the cloud server is a mobile terminal such as a mobile phone or a tablet and/or a fixed terminal such as a computer/server; the cloud server also sends prompt information according to the calculated optimal exercise heart rate; the cloud server and the control platform can receive the collected heart rate data and can also acquire age data, height data and weight data of a human body.
Further, the cloud server performs regular data analysis on the backed-up data, and judges the health state according to the analysis result; the cloud server comprises: the system comprises a backup database, a timing unit and a data analysis unit; and the data analysis unit performs data analysis on the data in the backup database at regular time according to the timing information of the timing unit to generate an analysis result.
Further, the data analysis unit includes: a data processing and modeling processor; the data processing and modeling processor directly calls the data information of the backup database to perform data modeling and generate a data model; the data processing and modeling processor includes: the system comprises a data preprocessing unit, a data specification unit, a data standardization unit, an algorithm prediction unit and a modeling analysis unit; the data preprocessing is used for sequentially removing the unique attribute, processing the missing value and detecting the abnormal value from the data information; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept as much as possible; the data standardization unit scales the data processed by the protocol in proportion to make the data fall into a small specific interval; the algorithm prediction unit carries out data modeling with the data processed by the data aggregation standardization unit; the modeling analysis unit is used for carrying out precision calculation on the health data generated by the calculation model and the original health data.
Method for intelligent motion management for overweight people, the method performing the steps of:
step 1: collecting age data, height data, weight data, static heart rate data and exercise heart rate data of a human body; the collected data are sent to a cloud server for backup;
step 2: calculating to obtain the highest heart rate according to the data collected in the step 1; the highest heart rate is 220-age;
and step 3: calculating to obtain the optimal exercise heart rate according to the lowest heart rate and the highest heart rate obtained by calculation in the step 2;
and 4, step 4: prompting and/or controlling exercise intensity according to the calculated optimal exercise heart rate;
and 5: the cloud server performs regular data analysis on the backed-up data, and judges the health state according to the analysis result.
Further, in step 3, according to the lowest heart rate and the highest heart rate calculated in step 2, the method for calculating the optimal exercise heart rate includes: the optimal exercise heart rate is the highest heart rate [ 60% -75% ]; in the step 3, the method for calculating the optimal exercise heart rate according to the lowest heart rate and the highest heart rate calculated in the step 2 comprises the following steps: the optimal exercise heart rate (highest heart rate-static heart rate) ([ 60% -75% ] + static heart rate.
Further, in step 3, according to the lowest heart rate and the highest heart rate calculated in step 2, the method for calculating the optimal exercise heart rate includes:
step 3.1: calculating body mass index (weight/height) according to the weight and height data;
step 3.2: judging according to the calculated body mass index, if the body mass index is greater than or equal to 30; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 20% -100% ] + static heart rate; if the body mass index is greater than or equal to 25 and less than 30; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 40% -75% ] + static heart rate; if the body mass index is less than or equal to 25; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 40% -75% ] + static heart rate.
Further, the step 5: the method for the cloud server to perform regular data analysis on the backed-up data and judge the health state according to the analysis result comprises the following steps:
step 5.1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step 5.2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible.
Step 5.3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; the data is linearly transformed by using the following transformation function, so that the result falls in the [0,1] interval, wherein the transformation function is as follows:
step 5.4: carrying out data modeling;
step 5.5: performing an effect analysis comprising: after the model training is finished, the following formula is adopted, the health generated by the model and the original health are calculated, and the accuracy is calculated, namely R is obtained2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents model generated health data (predicted values);
nsamplesrepresenting the size of the sample size entering the model;
step 5.6: and analyzing the data in the backup database according to the established model to generate an analysis result.
The intelligent motion management device and method for overweight people of the invention have the following beneficial effects: and calculating to obtain the heart rate interval with the optimal exercise intensity. The optimal heart rate of the individual during exercise is set according to the heart rate interval, the individual is guided to perform daily exercise according to the heart rate interval standard, the weight is reduced, and the intelligent fitness exercise training device has the advantages of being high in intelligent degree, high in weight reducing efficiency and high in weight reducing scientificity.
Drawings
Fig. 1 is a schematic flow chart of a method of intelligent exercise management for overweight people according to an embodiment of the present invention;
fig. 2 is a schematic device structure diagram of an intelligent motion management device for overweight people according to an embodiment of the present invention;
fig. 3 is a graph illustrating the variation of heart rate with exercise intensity according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of an experimental effect of the data analysis accuracy of the cloud for periodic data analysis according to the embodiment of the present invention, compared with the data analysis in the prior art.
Wherein 1-best heart rate interval, 2-prior art data analysis error rate line graph, and 3-inventive data analysis error rate line graph.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
Example 1
As shown in fig. 2, an intelligent motion management device for overweight people, the device comprising: the wearable device is worn on a human body and used for acquiring heart rate data in real time and transmitting the acquired data; the cloud server is positioned at the far end and used for receiving the collected data, backing up the data and calculating the optimal exercise heart rate according to the collected data; the control platform is arranged on the fitness equipment and used for receiving the collected data, calculating the optimal exercise heart rate according to the collected data, sending out prompt information according to the result of the calculated optimal exercise heart rate and controlling the running of the fitness equipment; the method for the cloud server to perform regular data analysis on the backed-up data and judge the health state according to the analysis result comprises the following steps: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing; and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible. Carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; carrying out data modeling; performing an effect analysis comprising: after the model training is finished, calculating the accuracy of the health generated by the model and the original health; and analyzing the data in the backup database according to the established model to generate an analysis result.
Example 2
On the basis of the previous embodiment, after receiving the acquired data, the cloud server and the control platform both send the acquired data to the cloud server for backup; the cloud server is a mobile terminal such as a mobile phone or a tablet and/or a fixed terminal such as a computer/server; the cloud server also sends prompt information according to the calculated optimal exercise heart rate; the cloud server and the control platform can receive the collected heart rate data and can also acquire age data, height data and weight data of a human body.
Example 3
On the basis of the previous embodiment, the cloud server performs regular data analysis on the backed-up data, and judges the health state according to the analysis result; the cloud server comprises: the system comprises a backup database, a timing unit and a data analysis unit; and the data analysis unit performs data analysis on the data in the backup database at regular time according to the timing information of the timing unit to generate an analysis result.
Example 4
On the basis of the above embodiment, the data analysis unit includes: a data processing and modeling processor; the data processing and modeling processor directly calls the data information of the backup database to perform data modeling and generate a data model; the data processing and modeling processor includes: the system comprises a data preprocessing unit, a data specification unit, a data standardization unit, an algorithm prediction unit and a modeling analysis unit; the data preprocessing is used for sequentially removing the unique attribute, processing the missing value and detecting the abnormal value from the data information; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept as much as possible; the data standardization unit scales the data processed by the protocol in proportion to make the data fall into a small specific interval; the algorithm prediction unit carries out data modeling with the data processed by the data aggregation standardization unit; the modeling analysis unit is used for carrying out precision calculation on the health data generated by the calculation model and the original health data.
Specifically, the elements to be collected include:
height: height input is in meters. The weight can be transmitted from the intelligent electronic scale, so that the change of the body mass index per day can be dynamically reflected (the BMI is calculated as weight KG/height 2M through a formula). The calculated ratio of increasing or decreasing the exercise amount/increasing or decreasing the heart rate reserve is automatically prompted according to the change (increase or decrease) of the body mass index.
Heart rate variability: adopt motion bracelet collection, include:
static heart rate: the resting heart rate may be measured before waking in the morning
Exercise heart rate: according to curve design, the heart rate/conversion exercise intensity is dynamically recorded in real time
The data processing center and the control platform complete the following functions:
obtaining the required value from the collected elements according to the formula of controlling the exercise intensity by heart rate, programming and calculating to obtain the lowest heart rate and the highest heart rate of the exercise intensity (here, the core part needing protection), and setting the prompt (sound/language or vibration …) respectively.
And obtaining the heart rate interval of the individual exercise intensity by adopting an algorithm according to the chart for measuring the exercise intensity.
Prompting the exercise time in the exercise intensity interval, taking 15 minutes as a prompting time period, wherein the longest time period is not more than 45 minutes; after 45 minutes, the intensity was reduced, but the exercise could not be ended (start "finishing exercise" time), and the finishing exercise was maintained for 30 minutes with a warning tone every 10 minutes. And (3) timed prompting of supplementing the Vico sports beverage: the set time is after the exercise and after the meal. If the treadmill is used, the control platform also obtains the optimal running frequency output according to the optimal heart rate.
Example 5
As shown in fig. 1, an intelligent motion management method for overweight people, the method performs the following steps:
step 1: collecting age data, height data, weight data, static heart rate data and exercise heart rate data of a human body; the collected data are sent to a cloud server for backup;
step 2: calculating to obtain the highest heart rate according to the data collected in the step 1; the highest heart rate is 220-age;
and step 3: calculating to obtain the optimal exercise heart rate according to the lowest heart rate and the highest heart rate obtained by calculation in the step 2;
and 4, step 4: prompting and/or controlling exercise intensity according to the calculated optimal exercise heart rate;
and 5: the cloud server performs regular data analysis on the backed-up data, and judges the health state according to the analysis result.
Example 6
On the basis of the previous embodiment, in the step 3, according to the lowest heart rate and the highest heart rate calculated in the step 2, a method for calculating an optimal exercise heart rate includes: the optimal exercise heart rate is the highest heart rate [ 60% -75% ]; in the step 3, the method for calculating the optimal exercise heart rate according to the lowest heart rate and the highest heart rate calculated in the step 2 comprises the following steps: the optimal exercise heart rate (highest heart rate-static heart rate) ([ 60% -75% ] + static heart rate.
Example 7
On the basis of the previous embodiment, in the step 3, according to the lowest heart rate and the highest heart rate calculated in the step 2, a method for calculating an optimal exercise heart rate includes:
step 3.1: calculating body mass index (weight/height) according to the weight and height data;
step 3.2: judging according to the calculated body mass index, if the body mass index is greater than or equal to 30; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 20% -100% ] + static heart rate; if the body mass index is greater than or equal to 25 and less than 30; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 40% -75% ] + static heart rate; if the body mass index is less than or equal to 25; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 40% -75% ] + static heart rate.
Example 8
On the basis of the above embodiment, the step 5: the method for the cloud server to perform regular data analysis on the backed-up data and judge the health state according to the analysis result comprises the following steps:
step 5.1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step 5.2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible.
Step 5.3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; the data is linearly transformed by using the following transformation function, so that the result falls in the [0,1] interval, wherein the transformation function is as follows:
step 5.4: carrying out data modeling;
step 5.5: performing an effect analysis comprising: after the model training is finished, the following formula is adopted, the health generated by the model and the original health are calculated, and the accuracy is calculated, namely R is obtained2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents model generated health data (predicted values);
nsamplesrepresenting the size of the sample size entering the model;
step 5.6: and analyzing the data in the backup database according to the established model to generate an analysis result.
Specifically, through data analysis, the health status of a single user for a long time can be monitored. In addition, in the data analysis process, effect analysis is performed, including: after the model training is finished, the following formula is adopted, the health generated by the model and the original health are calculated, and the accuracy is calculated, namely R is obtained2Scoring, wherein the higher the score is, the better the model accuracy is represented;
The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and any structural changes made according to the present invention should be considered as being limited within the scope of the present invention without departing from the spirit of the present invention. .
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic 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.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (8)
1. Intelligent motion management device for overweight people, characterized in that the device comprises: the wearable device is worn on a human body and used for acquiring heart rate data in real time and transmitting the acquired data; the cloud server is positioned at the far end and used for receiving the collected data, backing up the data and calculating the optimal exercise heart rate according to the collected data; the control platform is arranged on the fitness equipment and used for receiving the collected data, calculating the optimal exercise heart rate according to the collected data, sending out prompt information according to the result of the calculated optimal exercise heart rate and controlling the running of the fitness equipment; the method for the cloud server to perform regular data analysis on the backed-up data and judge the health state according to the analysis result comprises the following steps: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing; and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible. Carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; carrying out data modeling; performing an effect analysis comprising: after the model training is finished, calculating the accuracy of the health generated by the model and the original health; and analyzing the data in the backup database according to the established model to generate an analysis result.
2. The apparatus of claim 1, wherein the cloud server and the control platform both send the collected data to the cloud server for backup after receiving the collected data; the cloud server is a mobile terminal such as a mobile phone or a tablet and/or a fixed terminal such as a computer/server; the cloud server also sends prompt information according to the calculated optimal exercise heart rate; the cloud server and the control platform can receive the collected heart rate data and can also acquire age data, height data and weight data of a human body.
3. The apparatus of claim 2, wherein the cloud server performs a periodic data analysis on the backed-up data, and determines a health status according to a result of the analysis; the cloud server comprises: the system comprises a backup database, a timing unit and a data analysis unit; and the data analysis unit performs data analysis on the data in the backup database at regular time according to the timing information of the timing unit to generate an analysis result.
4. The apparatus of claim 3, wherein the data analysis unit comprises: a data processing and modeling processor; the data processing and modeling processor directly calls the data information of the backup database to perform data modeling and generate a data model; the data processing and modeling processor includes: the system comprises a data preprocessing unit, a data specification unit, a data standardization unit, an algorithm prediction unit and a modeling analysis unit; the data preprocessing is used for sequentially removing the unique attribute, processing the missing value and detecting the abnormal value from the data information; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept as much as possible; the data standardization unit scales the data processed by the protocol in proportion to make the data fall into a small specific interval; the algorithm prediction unit carries out data modeling with the data processed by the data aggregation standardization unit; the modeling analysis unit is used for carrying out precision calculation on the health data generated by the calculation model and the original health data.
5. Intelligent motion management method for overweight people based on a device according to one of claims 1 to 4, characterized in that it performs the following steps:
step 1: collecting age data, height data, weight data, static heart rate data and exercise heart rate data of a human body; the collected data are sent to a cloud server for backup;
step 2: calculating to obtain the highest heart rate according to the data collected in the step 1; the highest heart rate is 220-age;
and step 3: calculating to obtain the optimal exercise heart rate according to the lowest heart rate and the highest heart rate obtained by calculation in the step 2;
and 4, step 4: prompting and/or controlling exercise intensity according to the calculated optimal exercise heart rate;
and 5: the cloud server performs regular data analysis on the backed-up data, and judges the health state according to the analysis result.
6. The method as claimed in claim 5, wherein in step 3, the lowest heart rate and the highest heart rate calculated in step 2 are used, and the method for calculating the optimal exercise heart rate is as follows: the optimal exercise heart rate is the highest heart rate [ 60% -75% ]; in the step 3, the method for calculating the optimal exercise heart rate according to the lowest heart rate and the highest heart rate calculated in the step 2 comprises the following steps: the optimal exercise heart rate (highest heart rate-static heart rate) ([ 60% -75% ] + static heart rate.
7. The method as claimed in claim 6, wherein in step 3, the lowest heart rate and the highest heart rate calculated in step 2 are used, and the method for calculating the optimal exercise heart rate is as follows:
step 3.1: calculating body mass index (weight/height) according to the weight and height data;
step 3.2: judging according to the calculated body mass index, if the body mass index is greater than or equal to 30; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 20% -100% ] + static heart rate; if the body mass index is greater than or equal to 25 and less than 30; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 40% -75% ] + static heart rate; if the body mass index is less than or equal to 25; the optimal exercise heart rate (highest heart rate-static heart rate) ([ 40% -75% ] + static heart rate.
8. The method of claim 6, wherein the step 5: the method for the cloud server to perform regular data analysis on the backed-up data and judge the health state according to the analysis result comprises the following steps:
step 5.1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step 5.2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible.
Step 5.3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; the data is linearly transformed by using the following transformation function, so that the result falls in the [0,1] interval, wherein the transformation function is as follows:
step 5.4: carrying out data modeling;
step 5.5: performing an effect analysis comprising: after the model training is finished, the following formula is adopted, the health generated by the model and the original health are calculated, and the accuracy is calculated, namely R is obtained2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents model generated health data (predicted values);
nsamplesrepresenting the size of the sample size entering the model;
step 5.6: and analyzing the data in the backup database according to the established model to generate an analysis result.
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