WO2023160149A1

WO2023160149A1 - Body fat percentage measurement method and apparatus, and computer-readable storage medium

Info

Publication number: WO2023160149A1
Application number: PCT/CN2022/139310
Authority: WO
Inventors: 游志鹏; 赵学良
Original assignee: 深圳市伊欧乐科技有限公司
Priority date: 2022-02-25
Filing date: 2022-12-15
Publication date: 2023-08-31
Also published as: CN114699063A

Abstract

A body fat percentage measurement method and apparatus, and a computer-readable storage medium. The method comprises: when a measurement instruction is received, acquiring first body data corresponding to a target object, the first body data comprising a preset height, age, gender, and posture label (S10); measuring second body data of the target object, the second body data comprising weight and lower limb impedance data (S20); and determining a body fat percentage corresponding to the target object according to the first body data and the second body data (S30). The measurement method reduces the measurement cost of the body fat percentage.

Description

Method, device and computer-readable storage medium for measuring body fat percentage

related application

This application claims priority to a Chinese patent application with application number 202210181394.X filed on February 25, 2022, the entire contents of which are incorporated herein by reference.

technical field

The present application relates to the technical field of data processing, in particular to a method, device and computer-readable storage medium for measuring body fat percentage.

Background technique

With the continuous development and optimization of the learning algorithm, the existing eight-electrode body fat scale can already fit the result close to the body fat rate measured by DEXA through the BIA algorithm. However, compared with the eight-electrode body fat scale, the four-electrode body fat scale only detects the impedance of the lower limbs, and lacks the impedance of the upper limbs and trunk. Due to the lack of detection data, the four-electrode BIA cannot fit the DEXA detection results as highly as the eight-electrode BIA algorithm. If you want to obtain the impedance data of the upper body, you will undoubtedly need to add additional equipment to measure the upper body, which will lead to an increase in the cost of measuring body fat percentage.

technical problem

The embodiments of the present application aim to solve the technical problem of how to reduce the cost of measuring body fat percentage by providing a method, device, and computer-readable storage medium for measuring body fat percentage.

technical solution

The embodiment of the present application provides a method for measuring body fat percentage, the method for measuring body fat percentage includes the following steps:

When the measurement instruction is received, the first body data corresponding to the target object is acquired, and the first body data includes preset height, age, gender and body shape tags;

Measuring second physical data of the target subject, the second physical data including body weight and lower limb impedance data;

A body fat percentage corresponding to the target object is determined according to the first body data and the second body data.

In one embodiment, the step of determining the body fat percentage corresponding to the target subject according to the first body data and the second body data includes:

Using the first body data and the second body data as input parameters of a pre-trained body fat percentage measurement model, wherein the body fat percentage measurement model is based on the first body data and the second body data Output the body fat percentage.

In one embodiment, before the step of using the first body data and the second body data as input parameters of a pre-trained body fat percentage measurement model, the method further includes:

Obtain a training set and a test set, the training set and the test set include an input set and an output set, the characteristic attributes of the input set include lower limb impedance, body weight, height, age, gender and posture labels, and the output set's Feature attributes include body fat percentage;

Model training is performed according to the training set and the test set to obtain the body fat percentage measurement model.

In one embodiment, the step of obtaining a training set and a test set includes:

Acquiring pre-collected body data samples of multiple users, the characteristic attributes of the body data samples include lower limb impedance, weight, height, age, gender, and body fat percentage;

Marking the body data sample, so that the body data sample corresponds to a body label attribute;

Divide each of the body data samples marked with body shape label attributes into the training set and the test set.

In one embodiment, the step of performing model training according to the training set and the test set includes:

Determine the loss function, and substitute the training set into the model for training;

When the loss value of the loss function converges to an optimal value, it is determined that the model training is completed;

Substituting the test set into the model for testing to obtain the performance index of the model;

determining whether the performance index is greater than or equal to a preset performance index;

When the performance index is greater than the preset performance index, the model is used as the body fat percentage measurement model.

In one embodiment, the step of acquiring the first body data corresponding to the target object when receiving the measurement instruction includes:

When the measurement instruction is received, the physical data associated with the current login account is acquired as the first physical data corresponding to the target object.

In one embodiment, the step of obtaining the physical data associated with the current login account as the first physical data corresponding to the target object when receiving the measurement instruction includes:

When receiving the measurement instruction, determine whether the target object has logged in;

When the target object has logged in, perform the step of acquiring the physical data associated with the current login account as the first physical data corresponding to the target object;

When the target object has not logged in, output prompt information that needs to log in.

In one embodiment, the step of measuring the second physical data of the target object includes:

The control measurement device measures the lower limb impedance and body weight of the target subject to obtain the second body data.

The embodiment of the present application also provides a device for measuring body fat percentage. The device for measuring body fat percentage includes: a memory, a processor, and a computer program stored in the memory and operable on the processor. The processing Each step of the method for measuring body fat percentage as described above is realized when the computer program is executed by the computer.

The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each step of the method for measuring body fat percentage as described above is realized .

In the technical solution of this embodiment, when the device for measuring body fat percentage receives the measurement instruction, it acquires the first body data corresponding to the target object, and the first body data includes preset height, age, gender and body shape tags ; Measuring second body data of the target subject, the second body data including body weight and lower limb impedance data; determining the body fat percentage corresponding to the target subject according to the first body data and the second body data. Since the measuring device of body fat percentage combines the preset first body data with the measured second body data to realize the measurement of body fat percentage, the target object only needs to cooperate with the measurement part of the second body data during the measurement process. , compared with the need to use expensive equipment to obtain the full amount of measurement in conventional technical means, this application reduces the cost of body fat percentage measurement.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present application. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

FIG. 1 is a schematic diagram of the hardware architecture of a measuring device for body fat percentage involved in an embodiment of the present application;

Fig. 2 is a schematic flow chart of the first embodiment of the method for measuring body fat percentage of the present application;

FIG. 3 is a reference diagram of the first embodiment of the method for measuring body fat percentage of the present application;

FIG. 4 is a reference diagram of the first embodiment of the method for measuring body fat percentage of the present application;

5 is a schematic flow diagram of the second embodiment of the method for measuring body fat percentage of the present application;

Fig. 6 is a reference diagram of the second embodiment of the method for measuring body fat percentage of the present application;

FIG. 7 is a reference diagram of the second embodiment of the method for measuring body fat percentage of the present application;

Fig. 8 is a reference diagram of the second embodiment of the method for measuring body fat percentage of the present application;

FIG. 9 is a schematic flowchart of the third embodiment of the method for measuring body fat percentage of the present application.

Embodiments of the present invention

In order to better understand the above-mentioned technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

The main solution of this application is: when the body fat percentage measuring device receives the measurement instruction, it acquires the first body data corresponding to the target object, and the first body data includes preset height, age, gender and body shape tags; Measuring second body data of the target object, the second body data including body weight and lower limb impedance data; determining the body fat percentage corresponding to the target object according to the first body data and the second body data.

Since the measuring device of body fat percentage combines the preset first body data with the measured second body data to realize the measurement of body fat percentage, the target object only needs to cooperate with the measurement part of the second body data during the measurement process. , compared with the need to use expensive equipment to obtain the full amount of measurement in conventional technical means, this application reduces the cost of body fat percentage measurement.

As an implementation, the device for measuring body fat percentage can be as shown in Figure 1 .

The embodiment of the present application relates to a device for measuring body fat percentage. The device for measuring body fat percentage includes: a processor 101 , such as a CPU, a memory 102 , and a communication bus 103 . Wherein, the communication bus 103 is used to realize connection and communication between these components.

The memory 102 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. As shown in Fig. 1, the detection program may be included in the memory 103 as a computer-readable storage medium; and the processor 101 may be used to call the detection program stored in the memory 102, and perform the following operations:

In one embodiment, the processor 101 can be used to call the detection program stored in the memory 102, and perform the following operations:

In order to better understand the above-mentioned technical solution, the above-mentioned technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

With reference to Fig. 2, Fig. 2 is the first embodiment of the measuring method of body fat percentage of the present application, and method comprises the following steps:

Step S10, when the measurement instruction is received, first body data corresponding to the target object is acquired, and the first body data includes preset height, age, gender and body shape tags.

Body fat scales have been widely used in daily life. Users are very concerned about the body fat percentage and other body composition indicators measured and analyzed by them, and regard them as an important reference data for adjusting their diet. Household body fat scales are often four-electrode body fat scales, which are light and handy, easy to operate and cheap, and have become the first choice of body fat measurement products for users. Although it has these advantages, the body fat rate calculated by it is compared with the industry gold standard DEXA (dual-energy X-ray absorptiometry) test results, there is still a big gap in accuracy.

Body fat percentage refers to the proportion of body fat weight in the total body weight, also known as body fat percentage, which reflects the amount of fat in the body. The body fat rate of normal adults is 15% to 18% for men and 25% to 28% for women. Body fat percentage should be kept in the normal range. If the body fat rate is too high, more than 20% of the normal body weight can be considered obese. Obesity indicates insufficient exercise, overnutrition, or some endocrine system disease, and often complicated by hypertension, hyperlipidemia, arteriosclerosis, coronary heart disease, diabetes, cholecystitis and other diseases; The safe lower limit of fat content, that is, 5% for men and 13% to 15% for women, may cause dysfunction.

In this embodiment, the user can input measurement instructions based on the body fat percentage measurement device. When the body fat percentage material device receives the instruction, it will trigger the operation of acquiring the first body data of the target object (user), wherein the first The first body data is the body data entered by the user in the registered body fat percentage measurement app, and the first body data includes preset height, age, gender and body shape tags.

In one embodiment, when the device for measuring body fat percentage detects a target object, the device for measuring body fat percentage outputs a questionnaire for the target user to fill in the above-mentioned first identity data.

In one embodiment, the image information of the target object is obtained, and the body shape of the target object is recognized by image recognition technology, and the posture is judged to obtain the posture tag in the above-mentioned first identity data.

In one embodiment, the user is prompted to input body circumference information, which is converted into user posture information by using a preset formula.

Step S20, measuring the second body data of the target subject, the second body data including body weight and lower limb impedance data.

In this embodiment, after obtaining the first body data, the device for measuring body fat percentage will measure the second body data of the target subject, wherein the second body data includes but not limited to frequently changing data such as lower body impedance and body weight.

In an embodiment, the control measurement device measures the lower limb impedance and body weight of the target subject to obtain the second body data.

Step S30, determining the body fat percentage corresponding to the target object according to the first body data and the second body data.

In this embodiment, compared with the body fat calculation results of the ordinary four-electrode BIA algorithm, the BIA algorithm after adding body data (weight, height, age, gender, and body posture) greatly reduces the body fat calculation error. Taking people with high body fat rate as an example, through the consistency analysis of the body fat rate calculation results with the eight-electrode BIA algorithm, it can be seen that the BIA algorithm with body data (refer to Figure 3) is compared with the BIA algorithm without body data (refer to Figure 4), the average error is reduced from -1.5 to -0.36, the maximum absolute value of the consistency limit is reduced from 3.08 to 1.80, the accuracy is significantly improved, and it is clinically acceptable. The method for measuring the body fat percentage of the present application does not need to improve the device hardware, it only needs to obtain the preset first body data and the measured second body data.

In one embodiment, this embodiment uses an eight-electrode body fat scale with similar data to DEXA measurement results for data collection, which is easy to operate and can acquire a large amount of data at low cost.

In the technical solution of this embodiment, since the measuring device of body fat percentage combines the preset first body data with the measured second body data to realize the measurement of body fat percentage, the target object only needs to It is enough to cooperate with the second body data of the measurement part. Compared with the conventional technical means that need to use expensive equipment to obtain the full quantity for measurement, this application reduces the cost of body fat percentage measurement.

Referring to Fig. 5, Fig. 5 is the second embodiment of the method for measuring body fat percentage of the present application, based on the first embodiment, step S30 includes:

Step S31, using the first body data and the second body data as input parameters of a pre-trained body fat percentage measurement model, wherein the body fat percentage measurement model is based on the first body data and the second body fat percentage measurement model Two body data output the body fat percentage.

In the present embodiment, the body fat rate measurement model is a neural network model, and neural networks (Neural Networks, NN) are complex network systems formed by extensive interconnection of a large number of simple processing units (called neurons), It reflects many basic features of human brain function and is a highly complex nonlinear dynamic learning system. Neural network has large-scale parallelism, distributed storage and processing, self-organization, self-adaptation and self-learning capabilities, and is especially suitable for dealing with imprecise and fuzzy information processing problems that need to consider many factors and conditions at the same time. The basis of a neural network lies in neurons. A neuron is a biological model based on the nerve cells of the biological nervous system. When people study the biological nervous system to explore the mechanism of artificial intelligence, neurons are mathematicized, thus generating a neuron mathematical model. A large number of neurons of the same form are connected together to form a neural network. A neural network is a highly nonlinear dynamical system. Although the structure and function of each neuron are not complicated, the dynamic behavior of the neural network is very complex; therefore, the neural network can express various phenomena in the actual physical world. The neural network model is described based on the mathematical model of neurons.

In this embodiment, the first body data including the height, age, gender, and body shape tags corresponding to the target object and the second body data including the target object's weight and lower limb impedance are substituted into the body fat percentage measurement model to obtain body Body fat percentage measurement model output body fat percentage.

With the development of impedance measurement technology and BIA algorithm for many years, the eight-electrode body fat algorithm can be close to the DEXA measurement results in terms of body fat percentage measurement, and the maximum absolute value of the consistency limit between the two is 1.41 (refer to Figure 6). The magnitude of the difference is clinically acceptable. Considering the degree of data correlation and the cost of data acquisition, this algorithm scheme uses the measurement results of the eight-electrode body fat scale as the standard output, and the data required for fitting will also be provided by the eight-electrode body fat scale. By learning and fitting the measurement data of various posture groups in advance, a fitting algorithm model that can adapt to each posture group is obtained, and then the posture discrimination method is used to distinguish the user's posture, and finally the user's height, age, gender, posture, Body weight and lower extremity impedance data were substituted into the model for data calculation.

Specifically, in this embodiment, the steps of model training are as follows:

Step 1: Obtain a training set and a test set, the training set and the test set include an input set and an output set, the characteristic attributes of the input set include lower limb impedance, weight, height, age, gender, and posture labels, the The characteristic attributes of the output set include body fat percentage; model training is performed according to the training set and the test set to obtain the body fat percentage measurement model. Specifically, collect a large number of body data samples measured by eight-electrode body fat scales, statistically analyze the body fat distribution rules of each body data sample, and measure results based on lower limb impedance, weight, height, age, gender, body fat percentage, and body shape label attributes Categorize people.

In one embodiment, a large number of body data samples of multiple users pre-collected by the eight-electrode body fat scale are obtained, and the characteristic attributes of the body data samples include lower limb impedance, weight, height, age, gender, and body fat percentage; The physical data samples are marked so that the physical data samples correspond to body label attributes; and each of the body data samples marked with body label attributes is divided into the training set and the test set.

In one embodiment, the training process of the body fat percentage measurement model includes: determining the loss function, and substituting the training set into the model for training; when the loss value of the loss function converges to the optimal value, it is determined that the The model training is completed; the test set is substituted into the model for testing to obtain the performance index of the model; determine whether the performance index is greater than or equal to the preset performance index; when the performance index is greater than the preset performance index , using the model as the body fat percentage measurement model.

Specifically, the body category needs to have a meaning that can be understood in common sense, and at the same time be able to effectively distinguish the measurement data, including the following steps:

Step 1.1. Divide the body fat percentage attribute in the body data sample into multiple subsections, and count the distribution of the height attribute, weight attribute, gender attribute, and age attribute of the body data sample corresponding to each subdivision. Each attribute is also divided into intervals.

Step 1.2. According to the large intervals of the body shape separation rules in step 1.1, set body labels for each interval, such as: thin, normal, strong, obese...

Step 2. Collect the measurement data of the four-electrode body fat scale corresponding to each tag category, and fit the algorithm results of the eight-electrode body fat scale to obtain a fitting algorithm model containing body attributes. Specifically, the difference in body impedance obtained by a four-electrode body fat scale and an eight-electrode body fat scale is as follows:

The four-electrode body fat scale can only measure the lower extremity impedance of the target subject, as shown in Figure 7.

The eight-electrode body fat scale can not only measure the impedance of the lower limbs of the target object, but also the impedance of the upper limbs of the target object, as shown in Figure 8.

The reason why the four-electrode algorithm is different from the eight-electrode algorithm is because the four-electrode algorithm lacks the input of upper limb impedance and trunk impedance data, so it cannot fit the output of the eight-electrode algorithm well, and the final calculated body fat percentage Will be biased towards the lower body fat rate. The body fat distribution of people with different body shapes is different, so if body attributes are added to the fitting parameters, the algorithm can automatically optimize the calculation results of the overall body fat percentage of the target object. The specific steps are as follows:

Step 2.1. Use the eight-electrode body fat scale to collect a large number of user's body data samples, and at the same time put body labels on these body data samples (data cleaning). Since the body data samples measured by the eight-electrode body fat scale include body weight and lower limb impedance, there is no need to additionally collect the measurement data of the user’s four-electrode body fat scale. If there is a significant difference in the body fat scale, the impedance data of the lower extremities of the four-electrode body fat scale must be collected separately and the impedance data of the four-electrode body fat scale shall prevail.

Step 2.2, and then convert the body label to body attribute parameters in one-hot encoding format, as shown in the following example of the conversion rule:

Assume that the set body shape labels are: thin, normal, strong, fat..., for example, the body shape evaluation result of the current target object is: strong. The body attribute parameter information corresponding to the target object is: thin (corresponding to 0), normal (corresponding to 0), strong (corresponding to 1), and obese (corresponding to 0).

That is, each evaluation result label is regarded as an attribute, and the value is 1 if the attribute meets the attribute, and the value is 0 if the attribute does not meet the attribute, and it is allowed to meet the situation of multiple evaluation result labels.

Step 2.3. Divide all marked body data samples into training set and test set, and use the lower limb impedance, weight, height, age, gender, and posture fields of the training set as input parameters, where the lower limb impedance can be measured at different current frequencies The multiple impedance data obtained, and the body fat rate is used as the output result for fitting training.

The formal model fitting training method can use deep learning to build a multi-layer neural network, use the training set for model training, and obtain an algorithm model whose input parameters are lower limb impedance, weight, height, age, gender, and body shape, and the output result is body fat percentage . Even if the body label definition is not completely accurate, the algorithm model will automatically adjust the distribution weights during the training and learning process to obtain the optimal effect. Finally, test the model with the test set to verify the actual calculation effect of the model and obtain the algorithm model.

In one embodiment, the specific steps of the method for measuring the body fat percentage of the target object are as follows:

Step 3.1. Acquisition of the body label of the target object.

Specifically, the target body body label needs to be collected before obtaining the user's second body data, and used as preset data. The acquisition method is different from the body data samples used by the training model. The specific methods include but are not limited to any of the following:

In one embodiment, the image information of the target object is obtained, and the body shape of the target object is recognized by image recognition technology, and the posture is judged to obtain a posture label.

In one embodiment, the form of questionnaire prompts the user to input body circumference information, which is converted into the user's body shape label by using a preset formula.

Step 3.2, the calculation output of the body fat percentage of the target object.

Specifically, the input parameters of the neural network model constructed in step 2 include lower limb impedance, weight, height, age, gender, and body label, so before using the model, you need to obtain the lower body impedance, weight, height, age, gender, and body label of the target object . Therefore, this embodiment uses the preset first body data (height, age, gender, and posture) and the measured second body data (weight and lower limb impedance) to measure the body fat percentage. Input the height, age, gender, body shape of the target object, and the weight and lower limb impedance data measured by the four-electrode body fat scale into the trained neural network model, and the model calculates and outputs the body fat percentage of the target object.

In the technical solution of this embodiment, through the trained neural network model, the body fat percentage output by the neural network model can be directly obtained based on the first body data and the second body data, which improves the measurement efficiency of the body fat percentage.

Referring to FIG. 9, FIG. 9 is the third embodiment of the method for measuring body fat percentage of the present application. Based on any one of the first to second embodiments, step S10 includes:

Step S11, when a measurement instruction is received, acquire the physical data associated with the current login account as the first physical data corresponding to the target object.

In an embodiment, when the measurement instruction is received, it is determined whether the target object has logged in; when the target object has logged in, the acquisition of the physical data associated with the current login account is performed as the target object corresponding The step of the first body data; when the target object is not logged in, output a prompt message that needs to log in.

In the technical solution of this embodiment, when the target object has not logged in the app account, the body fat percentage measurement device cannot obtain the first body data. Therefore, when receiving the instruction, first determine whether the target object is logged in, which can prevent the measurement progress Stuck, improving the stability of body fat percentage measurement.

In order to achieve the above purpose, an embodiment of the present application also provides a body fat percentage measurement device, the body fat percentage measurement device includes: a memory, a processor, and a computer stored on the memory and capable of running on the processor program, when the processor executes the computer program, each step of the method for measuring body fat percentage as described above is realized.

In order to achieve the above purpose, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned body fat rate measurement can be realized. The individual steps of the measurement method.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may employ the implementation of a network configuration product program embodied on one or more computer-usable computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein. form.

The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and combinations of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data-processing device for measuring body fat percentage to produce a machine, so that the computer or other programmable data-processing body fat percentage Instructions executed by the processor of the measurement device produce means for implementing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart flow or flows and/or block diagram block or blocks.

It should be noted that, in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.

While preferred embodiments of the present application have been described, additional changes and modifications to these embodiments can be made by those skilled in the art once the basic inventive concept is appreciated. Therefore, the appended claims are intended to be construed to cover the preferred embodiment and all changes and modifications which fall within the scope of the application.

Obviously, those skilled in the art can make various changes and modifications to the application without departing from the spirit and scope of the application. In this way, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

A method for measuring body fat percentage, wherein, the method for measuring body fat percentage comprises the following steps:

When the measurement instruction is received, the first body data corresponding to the target object is acquired, and the first body data includes preset height, age, gender and body shape tags;

Measuring second physical data of the target subject, the second physical data including body weight and lower limb impedance data;

A body fat percentage corresponding to the target object is determined according to the first body data and the second body data.
The method for measuring body fat percentage according to claim 1, wherein the step of determining the body fat percentage corresponding to the target object according to the first body data and the second body data comprises:

Using the first body data and the second body data as input parameters of a pre-trained body fat percentage measurement model, wherein the body fat percentage measurement model is based on the first body data and the second body data Output the body fat percentage.
The method for measuring body fat percentage as claimed in claim 2, wherein, before the step of using said first body data and said second body data as input parameters of a pre-trained body fat percentage measurement model, said Methods also include:

Obtain a training set and a test set, the training set and the test set include an input set and an output set, the characteristic attributes of the input set include lower limb impedance, body weight, height, age, gender and posture labels, and the output set's Feature attributes include body fat percentage;

Model training is performed according to the training set and the test set to obtain the body fat percentage measurement model.
The measuring method of body fat percentage as claimed in claim 3, wherein, the step of described obtaining training set and test set comprises:

Acquiring pre-collected body data samples of multiple users, the characteristic attributes of the body data samples include lower limb impedance, weight, height, age, gender, and body fat percentage;

Marking the body data sample, so that the body data sample corresponds to a body label attribute;

Divide each of the body data samples marked with body shape label attributes into the training set and the test set.
The measuring method of body fat percentage as claimed in claim 3, wherein, the described step of carrying out model training according to described training set and described test set comprises:

Determine the loss function, and substitute the training set into the model for training;

When the loss value of the loss function converges to an optimal value, it is determined that the model training is completed;

Substituting the test set into the model for testing to obtain the performance index of the model;

determining whether the performance index is greater than or equal to a preset performance index;

When the performance index is greater than the preset performance index, the model is used as the body fat percentage measurement model.
The method for measuring body fat percentage according to claim 1, wherein, when the measurement instruction is received, the step of obtaining the first body data corresponding to the target object comprises:

When the measurement instruction is received, the physical data associated with the current login account is acquired as the first physical data corresponding to the target object.
The method for measuring body fat percentage according to claim 6, wherein, when the measurement instruction is received, the step of obtaining the physical data associated with the current login account as the first physical data corresponding to the target object comprises:

When receiving the measurement instruction, determine whether the target object has logged in;

When the target object has logged in, perform the step of acquiring the physical data associated with the current login account as the first physical data corresponding to the target object;

When the target object has not logged in, output prompt information that needs to log in.
The method for measuring body fat percentage as claimed in claim 1, wherein, the step of measuring the second physical data of the target object comprises:

The control measurement device measures the lower limb impedance and body weight of the target subject to obtain the second body data.
A device for measuring body fat percentage, wherein the device for measuring body fat percentage includes: a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor executes the The computer program realizes the steps of the method for measuring body fat percentage as described in any one of claims 1 to 8.
A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the body fat percentage according to any one of claims 1 to 8 is realized. The steps of the measurement method.