CN111543970A - Heart rate data processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to a heart rate data processing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring age information of a user; acquiring a bio-impedance value of a user; determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value; and calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information. By adopting the method, the calculation accuracy of the maximum heart rate value can be effectively improved.
Description
Technical Field
The present application relates to the field of electronic technologies, and in particular, to a heart rate data processing method and apparatus, an electronic device, and a storage medium.
Background
Along with domestic electronic equipment's technical development and popularization, intelligent equipment such as a lot of intelligence personal scales have appeared, and traditional personal scales advance to measure the weight, and more intelligence personal scales can also realize body fat measurement, heart rate measurement etc.. The maximum heart rate is the heart rate corresponding to the maximum load intensity that can be borne when exercise load is performed. The maximum heart rate is an important reference in determining maximum working capacity and maximum oxygen consumption.
However, the conventional maximum heart rate determination method usually estimates the maximum heart rate according to a fixed constant, and the method has universality and universality, and the accuracy of the obtained maximum heart rate is low.
Disclosure of Invention
In view of the above, it is necessary to provide a heart rate data processing method, a device, an electronic device and a storage medium, which can effectively improve the accuracy of calculating the maximum heart rate value.
A method of heart rate data processing, the method comprising:
acquiring age information of a user;
acquiring a bio-impedance value of a user;
determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value;
and calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information.
In one embodiment, the step of determining a maximum heart rate calculation parameter corresponding to the bio-impedance value based on the bio-impedance value comprises: determining a target body type corresponding to the biological impedance value of the user from a plurality of preset candidate body type types according to the biological impedance value; and acquiring a maximum heart rate calculation parameter corresponding to the target body type.
In one embodiment, the determining a target body type category corresponding to the bio-impedance value of the user from a plurality of preset candidate body type categories comprises: acquiring physiological parameter information of the user; calculating the body fat rate and the body mass index of the user according to the biological impedance value and the physiological parameter information; determining the target body type category according to the body fat rate and the body mass index.
In one embodiment, the candidate body type categories include obesity type, health type, robustness type and exercise deficiency type, the obesity type, the health type, the robustness type and the exercise deficiency type respectively correspond to different value conditions, and the value conditions include at least one of a body fat rate value condition and a body mass index value condition; the step of determining the body type category of the user according to the body fat rate and the body mass index comprises: obtaining the value taking condition corresponding to each candidate body type; and determining a target value condition matched with the body fat rate and the body mass index from each value condition, and determining a candidate body type corresponding to the target value condition as a target body type.
In one embodiment, the maximum heart rate calculation parameters include a body type correlation coefficient and an age correlation coefficient; the calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information comprises: acquiring a preset corresponding relation between a maximum heart rate value and a body type correlation coefficient as well as between the age correlation coefficient and the age of a user; acquiring a target body type correlation coefficient and a target age correlation coefficient corresponding to the target body type category; and calculating the maximum heart rate value of the user according to the target body type correlation coefficient, the target age correlation coefficient, the age information and the preset corresponding relation.
In one embodiment, the method further comprises: determining a corresponding heart rate early warning value according to the maximum heart rate value; collecting a current heart rate value of the user; and when the current heart rate value reaches the heart rate early warning value, generating corresponding heart rate prompt information.
A heart rate data processing apparatus, the apparatus comprising:
the data acquisition module is used for acquiring age information of a user;
the biological impedance obtaining module is used for obtaining a biological impedance value of a user;
the calculation module is used for determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value; and calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information.
In one embodiment, the apparatus further comprises a heart rate warning module for determining a corresponding heart rate warning value according to the maximum heart rate value; collecting a current heart rate value of the user; and when the current heart rate value reaches the heart rate early warning value, generating corresponding heart rate prompt information.
An electronic device comprising a memory storing a computer program and a processor implementing the steps of the heart rate data processing method provided in any one of the embodiments of the present application when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the heart rate data processing method as provided in any one of the embodiments of the application.
According to the heart rate data processing method, the age information of the user is obtained, the biological impedance value of the user is obtained, the body fat rate parameter and the quality index of the user can be determined through the biological impedance value, the body fat rate parameter and the quality index can reflect the body type of the user, and therefore the maximum heart rate calculation parameter corresponding to the biological impedance value related to the body type of the user can be determined through the biological impedance value. The maximum heart rate value of the user is further calculated according to the maximum heart rate calculation parameter and the age information, so that the maximum heart rate value of the user can be effectively calculated according to the body type and the age information of the user, the maximum heart rate values of the users with different body types can be accurately calculated, and the calculation accuracy of the maximum heart rate value is effectively improved.
Drawings
FIG. 1 is a diagram of an embodiment of a heart rate data processing method;
FIG. 2 is a schematic flow chart of a method for processing heart rate data according to an embodiment;
FIG. 3 is a flow diagram illustrating the steps of calculating a maximum heart rate value for a user in one embodiment;
FIG. 4 is a flow chart illustrating the steps of heart rate value warning in one embodiment;
FIG. 5 is a block diagram of a heart rate data processing apparatus according to an embodiment;
FIG. 6 is a diagram of the internal structure of an electronic device in one embodiment;
fig. 7 is an internal configuration diagram of an electronic apparatus based on an electronic scale in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The heart rate data processing method provided by the application can be applied to the application environment shown in fig. 1. As shown in fig. 1, the electronic device 100 includes a processor 110. Specifically, the processor 110 in the electronic device 100 acquires age information of the user; acquiring a bio-impedance value of a user; determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value; and calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information. The electronic device 100 may be, but is not limited to, various electronic scales, wearable devices, mobile terminals, intelligent toilets, and the like.
In one embodiment, as shown in fig. 2, a heart rate data processing method is provided, which is exemplified by the application of the method to the electronic device in fig. 1, and includes the following steps:
The electronic device may store the age information of the user in advance, or may directly obtain the age information of the user to be tested from other devices or a server. The age information is one of user information, and besides the age information, the electronic device can also acquire user information such as the identity, height, weight, sex and the like of the user. The user information can be obtained from local or other devices, and can also be obtained by instant measurement of the electronic device.
For example, the electronic device may be a body scale, and may collect weight information of the user at the time of measurement.
The bio-impedance value may be an impedance value corresponding to a human bio-impedance signal acquired by the electronic device. The bio-impedance signal can be obtained by calculation according to the current signal and the voltage drop of the current signal generated by the human body, for example, the bio-impedance signal can be obtained by a bio-impedance method, and the impedance can be approximately replaced by a resistance value. The electronic device may include a contact electrode and a bio-impedance measurement circuit for acquiring a bio-impedance value of the user.
And step 206, determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value.
The bio-impedance value can be used for determining a body fat rate parameter and a quality index of the user, and then determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the body fat rate parameter and the quality index.
Specifically, the electronic device may calculate the body fat rate parameter according to the obtained impedance value, the weight parameter, the height parameter, the age parameter, and the gender parameter, and the body fat rate parameter and the quality index of the user may further reflect the body type of the user. The electronic device can also calculate the body mass index of the user according to the weight parameter and the height parameter, so that the maximum heart rate calculation parameter corresponding to the bio-impedance value related to the body type of the user can be determined by using the bio-impedance value.
And step 208, calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information.
In the exercise load, the oxygen consumption and the heart rate of the user increase with the amount of exercise. The maximum heart rate is the highest level that the heart rate reaches at the maximum load intensity when oxygen consumption and the heart rate cannot be increased further. The maximum heart rate is therefore an important reference in determining the maximum working capacity and the maximum oxygen consumption.
The electronic equipment can calculate the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information of the user so as to determine the maximum heart rate value which is consistent with the body parameter information of the user. Therefore, the maximum heart rate values corresponding to different body parameters of the user can be accurately and effectively calculated, and the calculation accuracy of the maximum heart rate values is effectively improved.
According to the heart rate data processing method, the electronic equipment acquires the age information of the user, and after the biological impedance value of the user is acquired, the body fat rate parameter and the quality index of the user can be determined through the biological impedance value, the body fat rate parameter and the quality index can reflect various body parameters of the user, and therefore the maximum heart rate calculation parameter corresponding to the biological impedance value related to the body parameters of the user can be determined by utilizing the biological impedance value. The maximum heart rate value of the user is further calculated according to the maximum heart rate calculation parameter and the age information, so that the maximum heart rate value of the user can be effectively calculated according to the body type and the age information of the user, the maximum heart rate values aiming at different body parameters of the user can be accurately calculated, and the calculation accuracy of the maximum heart rate value is effectively improved.
In one embodiment, the electronic device can further perform body composition analysis according to the physiological parameter values and the body impedance values of the user to obtain a plurality of body composition parameters, and generate a body composition report by using the plurality of body composition parameters, so that the user can conveniently and effectively know the self condition. The parameters of the human body composition can comprise a plurality of parameters such as body moisture content, body fat rate, muscle mass and the like.
In one embodiment, the step of determining, from the bio-impedance values, a maximum heart rate calculation parameter corresponding to the bio-impedance values comprises: determining a target body type corresponding to the biological impedance value of the user from a plurality of preset candidate body type types according to the biological impedance value; and acquiring a maximum heart rate calculation parameter corresponding to the target body type.
The body type is the general description and evaluation of the shape of the human body, and has a certain relation with the reaction of the motor ability and other functions of the human body. For example, body types can be classified into three categories, endodermal or round, mesodermal or muscular, ectodermal or thin, according to the Sherden human classification. In another example, body types can be classified into multiple body type categories, such as obese, healthy, robust, and hypokinetic.
In this embodiment, the body fat rate parameter and the quality index of the user may be determined by the bio-impedance value, and the body fat rate parameter and the quality index may reflect the body type of the user, so that the target body type corresponding to the bio-impedance value of the user may be determined from a plurality of preset candidate body type types by using the bio-impedance value. The electronic equipment can further determine the maximum heart rate calculation parameter corresponding to the target body type of the user, so that the maximum heart rate value of the user can be accurately and effectively calculated according to the target body type and age information of the user. Compared with the prior art that the maximum heart rate value is estimated only according to the age information and the fixed constant of the user, the maximum heart rate value of the individual user in different body types can be accurately and effectively calculated by determining the target body type of the user according to the bio-impedance value, and the calculation accuracy of the maximum heart rate value is effectively improved.
In one embodiment, determining a target body type category corresponding to the bio-impedance value of the user from a plurality of preset candidate body type categories comprises: acquiring physiological parameter information of a user; calculating the body fat rate and the body mass index of the user according to the biological impedance value and the physiological parameter information; and determining the classification of the target body type according to the body fat rate and the body mass index.
After the electronic device acquires the age information and the bio-impedance value of the user, the physiological parameter information of the user can be acquired, and the physiological parameter values can include parameter information such as height, weight, sex and the like. The electronic equipment calculates the body fat rate and the body mass index of the user according to the biological impedance value and the physiological parameter information, and determines a target body type corresponding to the biological impedance value of the user from a plurality of preset candidate body type types according to the body fat rate and the body mass index.
In particular, the body mass index may be obtained according to the following formula: BMI ═ W/H2。
Wherein BMI represents body mass index, W represents weight parameter, H represents height parameter, H represents weight index2I.e. the square of the height parameter. Thereby, the body quality index of the user can be effectively calculated according to the physiological parameter value.
After the body fat rate and the body mass index are calculated by the electronic equipment according to the physiological parameter value and the biological impedance value, body type classification is further carried out according to the body fat rate and the body mass index, and therefore the target body type of the user is obtained. The electronic equipment further acquires the maximum heart rate calculation parameter corresponding to the target body type, and then calculates the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information, so that the maximum heart rate values corresponding to different body types can be accurately and effectively calculated.
As an implementation manner, the electronic device may obtain a preset body type classification model, where the body type classification model is obtained by utilizing sample data to train in advance; and inputting the body fat rate parameters and the quality index of the user into a preset body type classification model, calculating the probability that the user belongs to each body type, and determining the body type with the highest probability or the body type meeting a certain condition as a target body type.
The body type classification model can be a classification model constructed based on a neural network model, a Markov model or a decision model. The body type classification model can be obtained by utilizing a large amount of sample data to train in advance. For example, a large amount of sample data is acquired in advance, and the sample data may be collected body type data of a plurality of different users. The method comprises the steps of carrying out big data analysis on a large amount of sample data, for example, carrying out cluster analysis and feature extraction on the sample data, extracting a plurality of corresponding attribute values and evaluation factors in the sample data, analyzing the relation between the evaluation factors and various body types, and further constructing a classification model according to the evaluation factors by using a preset algorithm. The method can further verify the constructed classification model by using verification sample data until the accuracy of the verification sample data meets a preset condition, and then the trained body type classification model is obtained, so that the body type classification model with high accuracy can be effectively constructed.
After the electronic equipment calculates and obtains the corresponding body fat rate and body mass index according to the physiological parameter value and the biological impedance value of the user, a preset body type classification model is called, the body fat rate parameter and the mass index of the user are input into the body type classification model, and then the probability of a plurality of body type categories corresponding to the body fat rate parameter and the mass index of the user is calculated through the body type classification model. The electronic device obtains the body type with the probability meeting the condition, specifically the body type corresponding to the maximum probability value, and further determines the body type as the target body type of the user. The body type classification model is used for classifying the body type of the user according to the body fat rate and the body quality index, so that the body type of the user can be rapidly and accurately classified, and the efficiency and the accuracy of body type classification of the user can be effectively improved.
In one embodiment, the candidate body type includes obesity type, health type, robustness type and exercise deficiency type, the obesity type, the health type, the robustness type and the exercise deficiency type respectively correspond to different value conditions, and the value condition includes at least one of a body fat rate value condition and a body mass index value condition, that is, each candidate body type respectively corresponds to a body fat rate value condition, or each candidate body type respectively corresponds to a body mass index value condition, or each candidate body type respectively corresponds to a body fat rate value condition and a body mass index value condition; accordingly, the step of determining the body type category of the user according to the body fat rate and the body mass index comprises: obtaining the value taking condition corresponding to each candidate body type; and determining target value conditions matched with the body fat rate and the body mass index from the value conditions, and determining candidate body type corresponding to the target value conditions as the target body type.
Specifically, for example, the body fat rate may be expressed as P and the body mass index may be expressed as BMI. The normal range of body fat percentage is assumed to be P1-P2, and the normal range of BMI is assumed to be 18.5-24. As an example, the value ranges corresponding to the four body types of obesity, healthy, robust and hypokinesia are as follows:
obesity type: body fat rate greater than P2; alternatively, BMI greater than 24 and body fat ratio greater than (P1+ P2)/2.
Health type: body fat rate is in the normal range and BMI is in the normal range.
And (3) robust: the body fat rate is less than (P1+ P2)/2 and the BMI is greater than 24, or the body fat rate is less than P1 and the BMI is in the normal range.
Exercise-deficient type: body fat ratio less than P2 and BMI less than 24.
Therefore, the candidate body type can be classified into 4 types according to different value conditions corresponding to the body fat rate and the body mass index: obesity, healthy, robust, and exercise deficient. Therefore, the body type of the user can be effectively determined according to the body fat rate parameter and the quality index.
In one embodiment, as shown in fig. 3, the maximum heart rate calculation parameters include a body type correlation coefficient and an age correlation coefficient; the method comprises the following steps of calculating a maximum heart rate value of a user according to a maximum heart rate calculation parameter and age information, and specifically comprises the following steps:
And step 304, acquiring a target body type association coefficient and a target age association coefficient corresponding to the target body type category.
And step 306, calculating the maximum heart rate value of the user according to the target body type correlation coefficient, the target age correlation coefficient, the age information and the preset corresponding relation.
Wherein the maximum heart rate calculation parameters comprise a body type correlation coefficient and an age correlation coefficient. The body type correlation coefficient is a coefficient of a preset correlation relationship existing between the body type and the maximum heart rate, and the age correlation coefficient is a coefficient of a preset correlation relationship existing between the age and the maximum heart rate.
The electronic equipment acquires the age information and the biological impedance value of the user, determines the body fat rate and the body mass index of the user according to the biological impedance value, and determines the target body type of the user according to the body fat rate and the body mass index. The electronic equipment further obtains preset corresponding relations between the maximum heart rate value and the body type correlation coefficient, between the age correlation coefficient and the user age, and obtains a target body type correlation coefficient and a target age correlation coefficient corresponding to the target body type category. The electronic device can further determine a maximum heart rate calculation parameter according to the target body type correlation coefficient, the target age correlation coefficient, the age information and the preset corresponding relation. The electronic equipment calculates the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information by using a preset algorithm. Therefore, the maximum heart rate calculation parameter corresponding to the bio-impedance value related to the body type category of the user can be accurately calculated, the maximum heart rate value of the user can be accurately calculated, and the calculation accuracy of the maximum heart rate value is effectively improved.
Furthermore, the electronic equipment can also acquire a preset body type incidence coefficient matrix; determining a target body type correlation coefficient and a target age correlation coefficient corresponding to the target body type according to the body type correlation coefficient matrix; and determining the target body type correlation coefficient and the target age correlation coefficient as heart rate correlation coefficients.
The coefficient matrix is often used to represent mathematical relationships of some items, such as the direct and inverse relationships of the items are proved by the coefficient matrix of such relationships. The body type correlation coefficient matrix may be obtained by analyzing a large amount of body type correlation data in advance. The body type correlation coefficient matrix may be represented as a matrix composed of a plurality of correlation coefficients, where each correlation coefficient is used to characterize the degree of influence of the corresponding factor on the body type. The heart rate correlation coefficient can be used for representing the correlation degree of the type class and the heart rate. The heart rate correlation coefficient for the maximum heart rate that the user can tolerate is also different for different body type categories.
After the target body type of the user is determined by the electronic equipment, a body type association coefficient matrix is obtained, a target body type association coefficient and a target age association coefficient corresponding to the target body type are calculated according to the body type association coefficient matrix, and therefore the heart rate association coefficient corresponding to the target body type of the user can be obtained according to the preset body type association coefficient matrix. The heart rate correlation coefficient may include a first heart rate correlation coefficient and a second heart rate correlation coefficient, for example, the first heart rate correlation coefficient may be a target body type correlation coefficient, and the second heart rate correlation coefficient may be a target age correlation coefficient. By using the heart rate correlation coefficient, the maximum heart rate value of the user can be calculated quickly and accurately.
In one embodiment, the heart rate calculation model may also be a calculation model that is constructed in advance by using a preset algorithm. After the target body type of the user is identified and the corresponding heart rate correlation coefficient is determined by the electronic equipment, the physiological parameter value and the heart rate correlation coefficient of the user are input into the heart rate calculation model, and then the target heart rate value is calculated by the heart rate calculation model according to the physiological parameter value and the heart rate correlation coefficient by using a heart rate calculation algorithm. The electronic device then determines the calculated target heart rate value as the maximum heart rate value for the user.
For example, the expression for the heart rate calculation model may be: HR (human HR)m＝b-a*Age。
Wherein HR ismAnd a is the maximum calculated heart rate of the measured object, a is the Age correlation coefficient, b is the body type correlation coefficient, and Age is the Age parameter value of the user. By determining the target body type of the user and calculating the corresponding maximum heart rate value according to the heart rate correlation coefficient and the physiological parameter value corresponding to the target body type, the maximum heart rate values corresponding to different body types can be accurately and effectively calculated, and the calculation accuracy of the maximum heart rate value is effectively improved.
In one embodiment, the electronic device may further obtain heart rate guidance information matched with the maximum heart rate value, and push the heart rate guidance information to a user terminal corresponding to the user. The heart rate guidance information may include exercise intensity guidance information, attention information, heart rate prevention guidance information, and the like corresponding to the maximum heart rate value. The database stores heart rate guidance information corresponding to different maximum heart rate values and body types in advance. The electronic equipment acquires the heart rate guidance information with the highest matching degree according to the maximum heart rate value, and pushes the matched heart rate guidance information to the user terminal corresponding to the user identifier. Since heart rate is related to the exercise load, the heart rate data may reflect exercise intensity, etc., and thus the exercise intensity may be controlled according to the maximum heart rate. The maximum heart rate can be expressed as the heart rate which cannot continuously rise along with the increase of the exercise intensity when the most strenuous exercise is performed, and can be expressed as the limit heart rate of the exercise load which can be born. The user can obtain matched heart rate guide information according to the corresponding maximum heart rate value of the user, and the required exercise heart rate and exercise intensity of the user are determined according to the heart rate guide information so as to prompt the user to carry out reasonable exercise.
The user terminal can be a smart phone or a smart watch, for example, when the electronic device is the smart watch, the obtained heart rate guidance information can be directly pushed to be displayed on the electronic device, and the matched heart rate guidance information can also be pushed to be displayed on the smart phone bound by the user identifier. The heart rate guidance information matched with the maximum heart rate value is obtained and pushed to the user, so that the user can effectively know the heart rate condition, attention and other information, and the accuracy of pushing the heart rate guidance information is effectively improved.
In one embodiment, as shown in fig. 4, the method further includes a step of heart rate value early warning, which specifically includes the following steps:
and step 402, determining a corresponding heart rate early warning value according to the maximum heart rate value.
And 406, generating corresponding heart rate prompt information when the current heart rate value reaches the heart rate early warning value.
The heart rate early warning value can be represented as a heart rate value close to the maximum heart rate value and used for early warning prompt, and the heart rate prompt information is used for prompting that the current heart rate of the user is too high and the maximum heart rate value is about to be reached.
The electronic equipment collects the biological impedance value of the user identification, calculates the body fat rate parameter and the quality index according to the physiological parameter value and the biological impedance value, and classifies the body type according to the body fat rate parameter and the quality index; and then determining a corresponding heart rate correlation coefficient and a corresponding physiological parameter value according to the target body type, calculating to obtain a maximum heart rate value corresponding to the user identifier, and then determining a corresponding heart rate early warning value according to the maximum heart rate value. For example, a preset critical threshold value may be subtracted from the maximum heart rate value to obtain a corresponding heart rate early warning value.
The electronic device can acquire the current heart rate value of the user according to a preset detection frequency and monitor the acquired current heart rate value. When the current heart rate value of the user reaches the heart rate early warning threshold value of the user, the current heart rate of the user is indicated to be higher, and the electronic equipment immediately generates corresponding heart rate prompt information to prompt the user to carry out early warning. For example, when the user is exercising violently, the user can be prompted to be in danger when exercising violently, so that the user can lighten the current exercise load according to the prompt to perform early warning and the like. The heart rate of the user is monitored and prompted by the early warning heart rate value, so that the heart rate condition of the user can be effectively monitored and early warned.
In one embodiment, the exercise heart rate may represent the heart rate state interval of the user while exercising, and maintaining a good exercise heart rate during exercise is important for both exercise performance and exercise safety. The exercise intensity is one of the main factors that determine the exercise load, and is the amount of effort and the degree of physical strain during exercise. The proper exercise intensity can effectively promote the improvement of physical functions and strengthen physique. If the strength is too high and exceeds the bearing capacity of the body, the body can be damaged or even the health of the body is damaged. For example, the exercise intensity may include a plurality of intensity ranges, such as low intensity exercise, medium intensity exercise, and high intensity exercise, and the intensity range may be different for each type of exercise.
The electronic equipment can further calculate the corresponding exercise heart rate and exercise intensity according to the maximum heart rate value of the user after accurately calculating the maximum heart rate value corresponding to the body type of the user by using a preset algorithm according to the physiological parameter value and the heart rate correlation coefficient corresponding to the body type of the user, and then obtains matched target exercise push information according to the exercise heart rate and the exercise intensity, and pushes the target exercise push information to a user terminal corresponding to a user identifier.
For example, the target movement push information may include movement push information such as a movement type and a movement duration. For example, the type of exercise may include a variety of recommended exercises such as walking, jogging, running, cycling, aerobics, fat burning, cardio-pulmonary reinforcement, muscle strength reinforcement, and the like. The electronic equipment can acquire matched target motion pushing information according to the motion heart rate and the motion intensity corresponding to the maximum heart rate value of the user, so that the motion pushing information according with the self condition of the user can be accurately and effectively pushed for the user.
It should be understood that although the various steps in the flow charts of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided a heart rate data processing apparatus comprising: a data acquisition module 502, a bio-impedance acquisition module 504, and a heart rate calculation module 506, wherein:
a data obtaining module 502, configured to obtain age information of a user;
a bio-impedance obtaining module 504, configured to obtain a bio-impedance value of the user;
a heart rate calculation module 506, configured to determine a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value; and calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information.
In one embodiment, the heart rate calculation module 506 is further configured to determine a target body type category corresponding to the bio-impedance value of the user from a plurality of preset candidate body type categories according to the bio-impedance value; and acquiring a maximum heart rate calculation parameter corresponding to the target body type.
In one embodiment, the heart rate calculation module 506 is further configured to obtain physiological parameter information of the user; calculating the body fat rate and the body mass index of the user according to the biological impedance value and the physiological parameter information; and determining the target body type category of the user according to the body fat rate and the body mass index.
In one embodiment, the candidate body type categories include obesity type, healthy type, robust type and exercise deficiency type, the obesity type, the healthy type, the robust type and the exercise deficiency type respectively correspond to different value conditions, and the value conditions include at least one of body fat rate value conditions and body mass index value conditions; the heart rate calculation module 506 is further configured to obtain a value condition corresponding to each candidate body type; and determining target value conditions matched with the body fat rate and the body mass index from the value conditions, and determining candidate body type corresponding to the target value conditions as the target body type.
In one embodiment, the maximum heart rate calculation parameters include a body type correlation coefficient and an age correlation coefficient; the heart rate calculation module 506 is further configured to obtain a preset corresponding relationship between the maximum heart rate value and the body type correlation coefficient, and between the age correlation coefficient and the user age; acquiring a target body type correlation coefficient and a target age correlation coefficient corresponding to the target body type; and calculating the maximum heart rate value of the user according to the target body type correlation coefficient, the target age correlation coefficient, the age information and the preset corresponding relation.
In one embodiment, the apparatus further includes a heart rate early warning module, configured to determine a corresponding heart rate early warning value according to the maximum heart rate value, collect a current heart rate value of the user, and generate corresponding heart rate prompt information when the current heart rate value reaches the heart rate early warning value.
For specific limitations of the heart rate data processing apparatus, reference may be made to the above limitations of the heart rate data processing method, which are not described herein again. The modules in the heart rate data processing device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, an electronic device is provided, the internal structure of which may be as shown in FIG. 6. The electronic equipment comprises a detection sensor, an impedance detection electrode, a signal conditioning unit, an analog-to-digital conversion unit, a processor, a network interface, a memory and a display screen which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method of heart rate data processing. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the electronic device may be any one of an electronic scale, a wearable device, a mobile terminal, and an intelligent toilet.
In one embodiment, the electronic device may be an electronic scale, and the internal structure thereof may be as shown in fig. 7. The electronic scale includes a scale body 700, a load cell 702, a plurality of contact electrodes 704, and a circuit board 706. The scale body 700 is used for fixing and supporting and can be made of engineering plastics, toughened glass, metal brackets and the like; the weighing sensor 702 is used for collecting a weight signal of a measuring object; the number of the contact electrodes 704 may be 4, and specifically includes two excitation electrodes 7041 and two measurement electrodes 7042 for acquiring bio-impedance signals of the measurement object; the circuit board 706 is connected to the contact electrodes 704 for mounting circuit components, for example, a signal conditioning unit, an analog-to-digital conversion unit, and a microcontroller as a central processing module are disposed in the circuit board 706. In addition, the electronic scale may further include a display unit 708, and the display unit 708 may be a liquid crystal display or a light emitting diode display, and is mainly used for displaying measurement information such as weight, body fat rate, heart rate, and the like.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the heart rate data processing method as provided in any one of the embodiments of the application.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of heart rate data processing, the method comprising:
acquiring age information of a user;
acquiring a bio-impedance value of a user;
determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value;
and calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information.
2. The method of claim 1, wherein the step of determining a maximum heart rate calculation parameter corresponding to the bio-impedance value based on the bio-impedance value comprises:
determining a target body type corresponding to the biological impedance value of the user from a plurality of preset candidate body type types according to the biological impedance value;
and acquiring a maximum heart rate calculation parameter corresponding to the target body type.
3. The method according to claim 2, wherein the determining a target body type category corresponding to the bio-impedance value of the user from a plurality of preset candidate body type categories comprises:
acquiring physiological parameter information of the user;
calculating the body fat rate and the body mass index of the user according to the biological impedance value and the physiological parameter information;
determining the target body type category according to the body fat rate and the body mass index.
4. The method according to claim 3, wherein the candidate body type categories comprise obesity type, health type, robust type and hypokinesia type, the obesity type, the health type, the robust type and the hypokinesia type respectively correspond to different value conditions, and the value conditions comprise at least one of a body fat rate value condition and a body mass index value condition;
the step of determining the body type category of the user according to the body fat rate and the body mass index comprises:
obtaining the value taking condition corresponding to each candidate body type;
and determining a target value condition matched with the body fat rate and the body mass index from each value condition, and determining a candidate body type corresponding to the target value condition as a target body type.
5. The method of claim 2, wherein the maximum heart rate calculation parameters include a body type correlation coefficient and an age correlation coefficient;
the calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information comprises:
acquiring a preset corresponding relation between a maximum heart rate value and a body type correlation coefficient as well as between the age correlation coefficient and the age of a user;
acquiring a target body type correlation coefficient and a target age correlation coefficient corresponding to the target body type category;
and calculating the maximum heart rate value of the user according to the target body type correlation coefficient, the target age correlation coefficient, the age information and the preset corresponding relation.
6. The method according to any one of claims 1 to 5, further comprising:
determining a corresponding heart rate early warning value according to the maximum heart rate value;
collecting a current heart rate value of the user;
and when the current heart rate value reaches the heart rate early warning value, generating corresponding heart rate prompt information.
7. A heart rate data processing apparatus, characterized in that the apparatus comprises:
the data acquisition module is used for acquiring age information of a user;
the biological impedance obtaining module is used for obtaining a biological impedance value of a user;
the calculation module is used for determining a maximum heart rate calculation parameter corresponding to the bio-impedance value according to the bio-impedance value; and calculating the maximum heart rate value of the user according to the maximum heart rate calculation parameter and the age information.
8. The apparatus of claim 7, further comprising a heart rate pre-warning module configured to determine a corresponding heart rate pre-warning value according to the maximum heart rate value; collecting a current heart rate value of the user; and when the current heart rate value reaches the heart rate early warning value, generating corresponding heart rate prompt information.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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Application publication date: 20200818 |