CN113440828B - Running state monitoring method and system based on cloud computing and storage medium - Google Patents
Running state monitoring method and system based on cloud computing and storage medium Download PDFInfo
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- A63B2230/04—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations
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Abstract
The application relates to a running state monitoring method, a running state monitoring system and a storage medium based on cloud computing, wherein the method comprises the steps of identifying a current user and a current running machine corresponding to the current user after a login request signal is received; acquiring a personal health profile of a current user; acquiring static body condition information of a current user; according to the personal health file and the static body condition information of the current user, selecting a personal motion energy consumption distinguishing model corresponding to the current user from a currently stored human motion energy consumption distinguishing model library; acquiring real-time dynamic motion state information of a current user; generating a real-time motion report corresponding to the current user according to the personal motion state discrimination model and the real-time dynamic motion state information; and feeding back a real-time motion report. The exercise device has the effects of improving the body building effect of people and reducing the possibility of excessive exercise.
Description
Technical Field
The application relates to the field of treadmill intellectualization, in particular to a running state monitoring method and system based on cloud computing and a storage medium.
Background
Along with the development of economic society and the improvement of life quality, people pay more and more attention to self health, accompany with the vigorous development of the indoor fitness industry, the treadmill is used as the most aerobic fitness equipment and widely exists in fitness halls and families, and a user controls the motion process and the motion intensity of the user by starting or stopping the treadmill through keys on a control panel of the treadmill, adjusting the speed, the gradient and the like, thereby achieving the purpose of exercising the body. How to scientifically, safely and effectively exercise becomes a core problem concerned in national fitness and is also a key point and a focus of current researchers.
As is known, excessive exercise cannot achieve the effect of body-building exercise, but increases the load on the human body, and causes serious injury to the safety of the human body. For example, excessive movement may cause dehydration and syncope of the human body.
However, when people use the treadmill to exercise, due to the lack of effective guidance or judgment standards for a long time, their exercise status often cannot be confirmed in time, so that it is difficult to achieve the best exercise effect, and even there is a risk of excessive exercise.
Disclosure of Invention
In order to help improve the body building effect of people and reduce the possibility of excessive movement, the application provides a device.
In a first aspect, the application provides a running state monitoring method based on cloud computing, which adopts the following technical scheme:
a running state monitoring method based on cloud computing comprises the following steps:
after receiving a login request signal, identifying a current user and a current treadmill corresponding to the current user;
acquiring a personal health file of the current user, wherein the personal health file comprises the age, the sex and the height of the current user;
acquiring static body condition information of the current user, wherein the static body condition information comprises the weight, the resting heart rate, the static blood pressure and the static body temperature of the current user;
according to the personal health file and the static body condition information of the current user, selecting a personal exercise energy consumption distinguishing model corresponding to the current user from a currently stored human exercise energy consumption distinguishing model library;
acquiring real-time dynamic motion state information of the current user;
generating a real-time motion report corresponding to the current user according to the personal motion state discrimination model and the real-time dynamic motion state information;
feeding back the real-time motion report.
By adopting the technical scheme, when the current user starts to use the running machine, the running machine acquires and feeds back the static body condition information of the current user to the server, in the process of the current user exercise, the treadmill acquires and feeds back real-time dynamic exercise condition information in real time, the server selects a proper personal exercise energy consumption distinguishing model according to the personal health file and the static physical condition information of the current user, continuously comparing the real-time dynamic motion state information with the personal motion energy consumption distinguishing model in the current motion process of the user, thereby generating a real-time exercise report, the server feeds the real-time exercise report back to the treadmill in real time, the current user can conveniently check the real-time exercise report, and the current user can make adjustment in time by referring to the real-time exercise report, thereby being beneficial to improving the body-building effect of people and reducing the possibility of excessive exercise.
Optionally, the real-time dynamic exercise status information includes a real-time heart rate, and after the obtaining of the static physical status information of the current user, the method further includes:
acquiring the expected amount of motion of the current user;
calculating and generating the human body optimal heart rate of the current user;
after the real-time dynamic motion state information of the current user is obtained, the method further comprises the following steps:
comparing the real-time heart rate of the current user with the optimal human body heart rate, and if the real-time heart rate is larger than the optimal human body heart rate and the difference value is larger than a preset value, generating a voice alarm signal;
and feeding back the voice alarm signal to the current treadmill.
By adopting the technical scheme, the server continuously compares the real-time heart rate of the current user with the optimal heart rate of the human body, and timely generates and feeds back the voice alarm signal when the real-time heart rate is higher, so that the treadmill sends out prompt and alarm to the current user, and the possibility of excessive exercise is reduced.
Optionally, the human motion energy consumption discrimination model library includes a plurality of human motion energy consumption discrimination models, each human motion energy consumption discrimination model corresponds to a plurality of feature tags, and each feature tag corresponds to one of data in the personal health record or static body condition information; the method for selecting the personal exercise energy consumption distinguishing model corresponding to the current user from the currently stored human exercise energy consumption distinguishing model library specifically comprises the following steps:
screening all human motion energy consumption distinguishing models according to a preset screening sequence according to the personal health files and the static body condition information of the current user;
and marking the screened unique human motion energy consumption distinguishing model as a personal motion energy consumption distinguishing model corresponding to the current user.
By adopting the technical scheme, the server screens out the human body movement energy consumption discrimination model which is most suitable for the current user according to the actual condition of the current user, and the monitoring and pre-discrimination effects are improved.
Optionally, after feeding back the real-time motion report, the method further includes:
judging whether a movement stopping signal is received or not, and if the movement stopping signal is received, generating a final movement report;
and feeding back the final motion report to the intelligent mobile terminal of the current user.
By adopting the technical scheme, the current user can check the final motion report through the intelligent mobile terminal after the motion is finished, so that the current user can know the motion condition of the current user.
Optionally, the human motion energy consumption discrimination model library is generated in the following manner:
extracting all the final motion reports at a preset time node;
dividing all the final motion reports into a plurality of to-be-processed data groups;
performing machine learning processing on each to-be-processed data group to generate a human motion energy consumption distinguishing model;
and uniformly storing all the human motion energy consumption distinguishing models to generate the human motion energy consumption distinguishing model library.
By adopting the technical scheme, the server generates or updates the human body movement energy consumption distinguishing model library regularly, thereby facilitating the monitoring and prejudging of the running state of the user.
Optionally, the real-time state information includes real-time blood pressure, real-time body temperature, real-time heart rate, real-time exercise duration and real-time running speed of the current user, and a mapping relationship between each item of data, such as ideal blood pressure, ideal body temperature, ideal heart rate and ideal exercise total energy consumption, formed in a machine learning manner and the exercise duration is recorded in the personal exercise energy consumption discrimination model; the generating a real-time motion report corresponding to the current user according to the personal motion state discrimination model and the real-time dynamic motion state information specifically includes:
and comparing the real-time dynamic motion state information with corresponding data in the personal motion energy consumption distinguishing model according to the real-time motion duration, and generating a real-time motion report corresponding to the current user according to a comparison result.
By adopting the technical scheme, the server can generate the real-time motion report in real time by comparing the real-time dynamic motion state information with the personal motion energy consumption judging model, thereby being beneficial to realizing the effects of improving the body-building effect of people and reducing the possibility of excessive motion.
In a second aspect, the present application provides a server, which adopts the following technical solutions:
a server, comprising:
the receiving module is used for receiving login request signals, static body condition information and real-time dynamic motion condition information;
the storage module is used for storing the personal health file and the human motion energy consumption distinguishing model library;
the processing module is used for calling data from the storage module, extracting a personal exercise energy consumption distinguishing model corresponding to the current user and generating a real-time exercise report;
and the sending module is used for feeding back the real-time motion report.
By adopting the technical scheme, the body building effect of people is improved, and the possibility of excessive movement is reduced.
In a third aspect, the present application provides a treadmill terminal, which adopts the following technical solution:
a treadmill terminal, comprising:
the Internet connection module is used for connecting the Internet;
the information acquisition module is used for acquiring various data in the static body condition information and various data in the real-time dynamic movement condition information of the current user;
the data transmission module is used for establishing connection with the information acquisition module and acquiring data acquired by the information acquisition module;
the processor module is used for controlling the running of the treadmill;
the data sending module is used for sending the acquired various data;
and the data receiving module is used for receiving various data sent by the server, including a real-time motion report.
By adopting the technical scheme, the body building effect of people is improved, and the possibility of excessive movement is reduced.
In a fourth aspect, the application provides a running state monitoring system based on cloud computing, which adopts the following technical scheme:
a running state monitoring system based on cloud computing comprises an intelligent mobile terminal, the server and the running machine, wherein the server is as described in the second aspect, and the running machine is as described in the third aspect.
In a fifth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium comprising a computer program stored thereon which is loadable by a processor and adapted to carry out the method of the first aspect.
By adopting the above technical solution, after the computer-readable storage medium is loaded into any computer, the computer can execute the running state monitoring method based on cloud computing according to the first aspect.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the real-time exercise report is fed back to the current user in real time, so that the body building effect of people is improved, and the possibility of excessive exercise is reduced;
2. by generating the personal optimal heart rate and comparing the current real-time heart rate of the user with the human optimal heart rate, the voice alarm signal is generated and fed back in time when the real-time heart rate is higher, and the possibility of excessive exercise is reduced.
Drawings
Fig. 1 is a block diagram of a running state monitoring system based on cloud computing in an embodiment of the present application.
Fig. 2 is a block diagram of the structure of the treadmill terminal in the embodiment of the present application.
Fig. 3 is a flowchart of a running state monitoring method based on cloud computing in an embodiment of the present application.
Fig. 4 is a flowchart of a process of generating a discriminant model library for human motion energy consumption by speech in the embodiment of the present application.
Description of reference numerals: 1. a treadmill terminal; 101. an internet connection module; 102. a data receiving module; 103. an information acquisition module; 104. a data transmission module; 105. a data transmission module; 106. a voice acquisition module; 107. an AEC module; 108. a voice processing module; 109. a touch screen display module; 110. a processor module; 111. a microphone module; 2. a server; 21. a receiving module; 22. a storage module; 23. a processing module; 24. a big data processing module; 25. a sending module; 3. an intelligent mobile terminal.
Detailed Description
The present application is described in further detail below with reference to figures 1-4.
The embodiment of the application discloses running state monitoring system based on cloud computing, referring to fig. 1, running state monitoring system based on cloud computing includes treadmill terminal 1, server 2 and intelligent mobile terminal 3.
Referring to fig. 2, the treadmill terminal 1 includes:
the internet connection module 101 is used for establishing connection with the intelligent mobile terminal and the server;
the data receiving module 102 is used for receiving various data sent by the server and the intelligent mobile terminal;
an information acquisition module 103 comprising a speed sensor, a gravity sensor and a smart band for wearing on the wrist of the user to acquire data;
a data transmission module 104, which may be an Spp/ble data transmission module, for establishing connection with the information acquisition module and acquiring data acquired by the information acquisition module;
the data sending module 105 is used for feeding back various data obtained by the server to the server;
the voice acquisition module 106 is used for acquiring voice information sent by a user;
the AEC module 107 is configured to perform echo cancellation on the acquired voice information;
the voice processing module 108 is used for recognizing the collected voice information and converting the voice information into an instruction which can be recognized and processed;
the touch screen display module 109 comprises a touch screen display screen and is used for displaying various information for the user to view;
a processor module 110 for controlling the operation of the treadmill;
the microphone module 111 includes a microphone array for performing voice broadcasting.
Based on the system, the embodiment of the application further discloses a running state monitoring method based on cloud computing. Referring to fig. 3, the running state monitoring method based on cloud computing includes:
s100: a login request signal is acquired.
Wherein, the login request signal is sent out by the following modes: the current personnel use intelligent Mobile terminal 3 and current treadmill to join in marriage the net to establish the connection, later, generate and send the login request signal through intelligent Mobile terminal 3, carry the address code of current treadmill and the identity information of current personnel in the login request signal, wherein, identity information includes name, sex, height and age. The server 2 identifies whether the current person is a registered user, if not, the step goes to step S110, otherwise, the step goes to step S120.
S110: a new user is registered.
Specifically, a personal health profile corresponding to the current person is established according to the identity information of the current person, a user number corresponding to the personal health profile is distributed to the current person, meanwhile, the current person is marked as the current user, and the current user is associated with the current treadmill. The personal health record comprises name, gender, age and height information of the current user.
S120: and acquiring a corresponding personal health profile.
Specifically, when the current person is a registered user, the corresponding identity information also carries a corresponding user number, the server 2 identifies the user number, extracts a personal health profile corresponding to the user number, marks the current person as the current user, and associates the current user with the current treadmill.
S200: obtaining static body condition information of the current user.
Specifically, after the current user wears the intelligent bracelet and stands on the current treadmill, the current treadmill is in a non-running state, and at the moment, the gravity sensor in the current treadmill acquires the weight of the current user, and the intelligent bracelet worn on the wrist of the current user collects the quiet heart rate, the static blood pressure and the static body temperature of the user. Thereafter, all the above data are integrated in the current treadmill, thereby generating static physical condition information corresponding to the current user and transmitting it to the server 2. The server 2 acquires the static physical condition information of the current user and stores the static physical condition information. Wherein the static body condition information includes body weight, resting heart rate, static blood pressure, and static body temperature.
S210: the desired amount of motion of the current user is acquired.
Specifically, the intelligent mobile terminal 3 provides a selection item of the desired amount of exercise for the current user to fill in, and feeds back the filling result of the user to the server 2, thereby obtaining the desired amount of exercise of the current user. In the present embodiment, the desired motion amount selection item includes the following selections: the user can select the large amount of exercise, the medium amount of exercise and the small amount of exercise according to the actual demands of the user.
S220: and calculating to obtain the current human body optimal heart rate of the user.
Specifically, the calculation formula of the optimal heart rate of the human body is as follows: human best heart rate = (maximum heart rate-resting heart rate-age) × Q + resting heart rate, in units of times/second. Wherein, the maximum heart rate takes 220 times/second, which represents the limit value of the heart rate of the human body; the resting heart rate is measured by S200, and Q represents the amount of exercise, and is generated by the server 2 according to the result of filling out the selection item of the desired amount of exercise by the current user in S210. For example, when the desired amount of exercise is a large amount of exercise, the corresponding value of Q is 80%, when the desired amount of exercise is a medium amount of exercise, the corresponding value of Q is 60%, and when the desired amount of exercise is a small amount of exercise, the corresponding value of Q is 40%.
S300: and acquiring a personal exercise energy consumption distinguishing model.
Specifically, according to the acquired personal health file and static body condition information of the current user, a personal exercise energy consumption distinguishing model corresponding to the current client is selected from a currently stored human exercise energy consumption distinguishing model library, wherein the human exercise energy consumption distinguishing model library is formed by processing historical data stored in an exercise report database periodically by the server 2, and the specific steps are shown in S10-S13. The human motion energy consumption distinguishing model library comprises a plurality of human motion energy consumption distinguishing models.
Specifically, each human motion energy consumption distinguishing model corresponds to a plurality of feature labels. The feature label includes one-level feature label and second grade feature label, and one-level feature label includes: age label, sex label, etc. each one-level feature label has a definite value correspondingly. The secondary feature labels include: the system comprises a static body temperature label, a static blood pressure label, a static heart rate label, a body mass index label and the like, wherein each secondary characteristic label corresponds to a value interval. Wherein, the value of the body mass index in the body mass index label is calculated by the following formula: body mass index = weight (kg) ÷ height (m ^ 2).
And during selection, identifying the value of each item in the personal health file and the static body condition information of the current user, and screening all human motion energy consumption distinguishing models. And during screening, sequentially screening according to a preset screening sequence. For example, if the predetermined screening order is: age, sex, body mass index …, the screening procedure is as follows:
firstly, identifying the age of a current user as a, screening out all human motion energy consumption discrimination models with the age labels of a, and forming a first screening group; then, identifying that the gender of the current user is b, screening all human motion energy consumption discrimination models in the first screening group, screening the human motion energy consumption discrimination models of which all gender labels have the value of b, and forming a second screening group; then, identifying the body mass index c of the current user, screening all the human body movement energy consumption distinguishing models in the second screening group, and screening out the human body movement energy consumption distinguishing models with the value intervals of all the body mass index labels covering c; and repeating the steps until a unique human body movement energy consumption distinguishing model is screened out and marked as a personal movement energy consumption distinguishing model corresponding to the current user.
S400: and acquiring real-time dynamic motion state information.
And after the current user starts running, acquiring the real-time dynamic motion state information of the current user. Specifically, the real-time dynamic exercise status information includes real-time status information of the current user and total exercise energy consumption, and the real-time status information includes real-time blood pressure, real-time body temperature, real-time heart rate, real-time exercise duration and real-time running speed of the current user. The real-time blood pressure, the real-time body temperature and the real-time heart rate are acquired by an intelligent bracelet worn on the wrist of a current user in real time, the real-time exercise duration is automatically identified by the current running machine, and the real-time running speed is measured by a speed sensor on the current running machine. After the server 2 acquires the real-time condition information uploaded by the current running machine, the total energy consumption of the current exercise is calculated by the following formula: total energy consumption for exercise (kcal) = weight (kg) × exercise time period (h) × K, where K =30 ÷ average running speed (min/400 m), where average running speed refers to the average running speed of the current user over the real-time exercise time period, as calculated by the server 2.
S500: and generating a real-time motion report and performing data comparison.
Specifically, the server 2 processes and generates a real-time motion report according to the acquired real-time dynamic motion state information of the current user and the corresponding personal motion energy consumption discrimination model. The personal exercise energy consumption distinguishing model records the mapping relation between each item of data such as ideal blood pressure, ideal body temperature, ideal heart rate, ideal exercise total energy consumption and the like and exercise duration, which are formed in a machine learning mode.
After the server 2 acquires the real-time dynamic motion state information, firstly, the current motion time length is identified, and values of ideal blood pressure, ideal body temperature, ideal heart rate and ideal motion total energy consumption corresponding to the current motion time length are extracted from the personal motion energy consumption distinguishing model; then, comparing the real-time blood pressure with the ideal blood pressure, comparing the real-time body temperature with the ideal body temperature, comparing the real-time heart rate with the ideal heart rate, and comparing the total energy consumption of the ideal exercise with the total energy consumption of the current exercise so as to generate a real-time exercise report, wherein the real-time exercise duration, the real-time running speed and the following data are recorded in the real-time exercise report: real-time blood pressure, real-time body temperature, real-time heart rate, current total exercise energy consumption and the difference between the above items and the corresponding ideal data. And when any real-time data is larger than the corresponding ideal data and the difference value is larger than the corresponding preset threshold value, simultaneously entering S510 and S600, otherwise, only entering S600. Taking the real-time heart rate and the ideal heart rate as examples, the corresponding preset threshold value is 10 times/second, and when the ideal heart rate is 108 times/second and the real-time heart rate is 120 times/second, the steps are simultaneously performed in S510 and S600.
S510: the current user's real-time heart rate is compared with the human optimum heart rate.
And when the real-time heart rate is larger than the human body optimal heart rate and the difference value is larger than 10 times/second, the step is executed to S520, and otherwise, the step is executed to S530.
S520: and generating and feeding back voice alarm signals.
Specifically, the server 2 generates a voice alarm signal and feeds the voice alarm signal back to the current treadmill, and the voice alarm signal may be: "your heart rate is too fast, this motion will stop automatically, please pay attention to rest. "and the like. After the current treadmill receives the voice alarm signal, voice broadcasting is carried out through the microphone array, and meanwhile, the current treadmill automatically decelerates step by step until stopping.
S530: and generating and feeding back voice prompt signals.
Specifically, when any real-time data is larger than the corresponding ideal data, it indicates that there is a risk of the exercise intensity of the current user being too large, and at this time, the server 2 generates a voice prompt signal, which may be "you have a large current exercise intensity, suggest a speed reduction, and scientifically exercise. "etc. are suggestive statements. And sending the voice prompt signal to the current treadmill, and after receiving the voice prompt signal, the current treadmill carries out voice broadcast through the microphone array so as to remind the current user.
S600: and feeding back the real-time exercise report to the current treadmill.
Specifically, server 2 feeds back the real-time motion report to current treadmill, and current treadmill acquires above-mentioned real-time motion report back, carries out visual processing with it and shows on touch screen display to make things convenient for current user to look over and confirm, and then can follow the running speed of actual conditions adjustment self. Meanwhile, because personnel are in the running state, the finger operation keys are inconvenient, so the current treadmill can also be controlled by the current user through a mode of sending instructions by voice, after voice information sent by the current user is collected by a voice collection module of the current treadmill, echo cancellation processing is carried out by an AEC module 107, the voice information collected is identified by a voice processing module 108 and is converted into instructions which can be identified and processed by a processor module 110 of the current treadmill, and thus the current treadmill can perform corresponding operations, for example: start, accelerate, decelerate, stop, etc.
S700: and generating and feeding back a final motion report after receiving the motion stop signal.
Specifically, the server 2 generates a final exercise report after receiving an exercise stop signal sent by the current treadmill, and the final exercise report records a time variation trend graph of the total exercise duration, the total exercise energy consumption, the real-time heart rate, the real-time blood pressure, the real-time body temperature and other data of the current user. The change trend function is generated by processing each real-time data by the server 2, for example, a coordinate system is established by taking time as an abscissa and real-time heart rate as an ordinate, and all values of the real-time heart rate acquired in the exercise are put into the coordinate system according to the acquired time, so that a change trend graph of the real-time heart rate along with time is generated. The server 2 feeds the final motion report back to the intelligent mobile terminal 3 of the current user, so that the current user can conveniently check the final motion report.
S800: and correspondingly storing the final motion report.
And storing the final exercise report in an exercise report database corresponding to the current user.
On the other hand, the present embodiment further includes a process of generating a human motion energy consumption discriminant model library, and with reference to fig. 4, the process specifically includes:
s10: and extracting all final motion reports at a preset time node.
Wherein the final motion report is stored in the motion report database in the server 2 according to the registered user to which it belongs. The server 2 extracts all final motion reports before each time node of the generation period according to a preset generation period.
S11: the final motion reports are grouped.
Specifically, each registered user corresponds to a personal health profile (generated by S110) and an average static sign tag, where the average static sign tag includes an average static heart rate, an average static blood pressure, an average static body temperature, and an average weight, and the average static sign is generated by the server 2 performing average calculation on the static signs of the registered user moving all the time between the current time node and the adjacent time node.
The registered users are grouped according to the gender, the age and the height in the personal health record, and are further grouped according to the data of each item of the label in the average static physical sign, so that the registered users in the same group have the same gender, the same age and the same height, and meanwhile, the average static heart rate value, the average static blood pressure value and the average static body temperature value of the registered users in the same group are all located in the same value interval. All final sport reports of all registered users of the same group are sorted into a pending data group.
S12: and performing machine learning processing to generate a human body motion energy consumption distinguishing model.
Specifically, for each to-be-processed data group, the server 2 performs machine learning processing on all data in the to-be-processed data group, so as to establish a human motion characteristic discrimination model corresponding to the to-be-processed data group, and each human motion characteristic discrimination model records mapping relations between each item of data, such as ideal blood pressure, ideal body temperature, ideal heart rate, ideal motion total energy consumption and the like, and motion duration, formed in a machine learning manner. And according to the corresponding sex, age and height of each data group to be processed, assigning a primary characteristic label as described in S300 to the human motion characteristic discrimination model corresponding to the data group to be processed. Similarly, according to the value interval of each average static physical sign corresponding to each to-be-processed data group, the secondary feature label as described in S300 is assigned to the human motion feature discrimination model corresponding to the to-be-processed data group.
S13: and generating a human motion energy consumption distinguishing model library.
Specifically, all human motion energy consumption distinguishing models are uniformly stored as a human motion energy consumption distinguishing model library.
Based on the above method, an embodiment of the present application further discloses a server, and with reference to fig. 1, the server includes:
the receiving module 21 is used for receiving various information and signals fed back by the treadmill terminal 1 and the intelligent mobile terminal 3;
the storage module 22 is used for storing personal health files, a human body motion energy consumption distinguishing model library, a motion report database and the like and storing various calculation or processing rules;
the processing module 23 is configured to register a new user; for retrieving corresponding information and data from the storage module 22; the method is used for calculating various data such as the optimal heart rate, the body mass index and the like of the human body; the system is used for screening to select the personal exercise energy consumption distinguishing model for processing and generating a real-time exercise report; the voice alarm device is used for carrying out data comparison processing and generating a voice alarm signal or a voice prompt signal;
the big data processing module 24 is used for performing machine learning processing and generating a human body movement energy consumption distinguishing model base;
and the sending module 25 is configured to feed back the real-time motion report and the final motion report, and send various signals such as a voice prompt signal and a voice alarm signal.
The embodiment of the present application further discloses a computer-readable storage medium, which stores a computer program that can be loaded by a processor and executes the running state monitoring method based on cloud computing, and the computer-readable storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above examples are only used to illustrate the technical solutions of the present application, and do not limit the scope of protection of the application. It is to be understood that the embodiments described are only some of the embodiments of the present application and not all of them. All other embodiments, which can be derived by a person skilled in the art from these embodiments without making any inventive step, are within the scope of the present application.
Claims (8)
1. A running state monitoring method based on cloud computing is characterized by comprising the following steps:
after receiving a login request signal, identifying a current user and a current treadmill corresponding to the current user;
acquiring a personal health file of the current user, wherein the personal health file comprises the age, the sex and the height of the current user;
acquiring static body condition information of the current user, wherein the static body condition information comprises the weight, the resting heart rate, the static blood pressure and the static body temperature of the current user;
according to the personal health file and the static body condition information of the current user, selecting a personal exercise energy consumption distinguishing model corresponding to the current user from a currently stored human exercise energy consumption distinguishing model library, wherein the personal exercise energy consumption distinguishing model records the mapping relation between each of ideal blood pressure, ideal body temperature, ideal heart rate and ideal exercise total energy consumption and exercise duration formed in a machine learning mode;
acquiring real-time dynamic motion state information of the current user, wherein the real-time dynamic motion state information comprises real-time state information of the current user and total energy consumption of current motion, and the real-time state information comprises real-time blood pressure, real-time body temperature, real-time heart rate, real-time motion duration and real-time running speed of the current user;
comparing the real-time dynamic motion state information with corresponding data in the personal motion energy consumption distinguishing model according to real-time motion duration, and generating a real-time motion report corresponding to the current user according to a comparison result;
feeding back the real-time exercise report to the current treadmill.
2. A running state monitoring method based on cloud computing according to claim 1, wherein the real-time dynamic exercise condition information includes a real-time heart rate, and after obtaining the static physical condition information of the current user, further comprising:
acquiring the expected amount of motion of the current user;
calculating and generating the human body optimal heart rate of the current user;
after the real-time dynamic motion state information of the current user is obtained, the method further comprises the following steps:
comparing the real-time heart rate of the current user with the optimal human body heart rate, and if the real-time heart rate is larger than the optimal human body heart rate and the difference value is larger than a preset value, generating a voice alarm signal;
and feeding back the voice alarm signal to the current treadmill.
3. The running state monitoring method based on cloud computing of claim 1, wherein the human motion energy consumption discriminant model library comprises a plurality of human motion energy consumption discriminant models, each of the human motion energy consumption discriminant models corresponds to a plurality of feature tags, and each of the feature tags corresponds to one of data in a personal health profile or static body condition information; the method for selecting the personal exercise energy consumption distinguishing model corresponding to the current user from the currently stored human exercise energy consumption distinguishing model library specifically comprises the following steps:
screening all human motion energy consumption distinguishing models according to a preset screening sequence according to the personal health file and the static body condition information of the current user;
and marking the screened unique human motion energy consumption distinguishing model as a personal motion energy consumption distinguishing model corresponding to the current user.
4. The running state monitoring method based on cloud computing according to claim 1, wherein after the feeding back the real-time exercise report, further comprising:
judging whether a movement stopping signal is received or not, and if the movement stopping signal is received, generating a final movement report;
feeding back the final motion report to the intelligent mobile terminal (3) of the current user.
5. The running state monitoring method based on cloud computing of claim 4, wherein the human motion energy consumption discriminant model library is generated by:
extracting all the final motion reports at a preset time node;
dividing all the final motion reports into a plurality of to-be-processed data groups;
performing machine learning processing on each to-be-processed data group to generate a human motion energy consumption distinguishing model;
and uniformly storing all the human motion energy consumption distinguishing models to generate the human motion energy consumption distinguishing model library.
6. A server, comprising:
the system comprises a user identification module, a user identification module and a user identification module, wherein the user identification module is used for identifying a current user and a current treadmill corresponding to the current user after receiving a login request signal;
the personal health record acquisition module is used for acquiring a personal health record of the current user, wherein the personal health record comprises the age, the sex and the height of the current user;
the static body condition information acquisition module is used for acquiring static body condition information of the current user, and the static body condition information comprises the weight, the resting heart rate, the static blood pressure and the static body temperature of the current user;
the personal exercise energy consumption distinguishing model selecting module is used for selecting a personal exercise energy consumption distinguishing model corresponding to the current user from a currently stored human exercise energy consumption distinguishing model library according to the personal health file and the static body condition information of the current user, and the mapping relation between each of ideal blood pressure, ideal body temperature, ideal heart rate and ideal exercise total energy consumption and exercise duration formed in a machine learning mode is recorded in the personal exercise energy consumption distinguishing model;
the real-time dynamic exercise condition information acquisition module is used for acquiring real-time dynamic exercise condition information of the current user, wherein the real-time dynamic exercise condition information comprises real-time state information of the current user and total energy consumption of current exercise, and the real-time state information comprises real-time blood pressure, real-time body temperature, real-time heart rate, real-time exercise duration and real-time running speed of the current user;
the real-time motion report generation module is used for comparing the real-time dynamic motion state information with corresponding data in the personal motion energy consumption discrimination model according to real-time motion duration, and generating a real-time motion report corresponding to the current user according to a comparison result;
and the sending module is used for feeding back the real-time exercise report to the current treadmill.
7. The utility model provides a running state monitoring system based on cloud calculates which characterized in that: comprising an intelligent mobile terminal (3), a server (2) according to claim 6 and a treadmill terminal (1) signally connected to the server (2).
8. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 5.
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