A user behavior feedback method and system based on energy state change

Abstract

The present application discloses a user behavior feedback method and system based on energy state change, the method comprising: obtaining at least two impedance values ​​and at least two body parameters of the user's body measured within a predetermined period, wherein the body parameters are physiological parameters of the user; determining the energy state change of the user based on at least the at least two impedance values ​​and at least two body parameters; determining the corresponding behavior characteristics of the user based on predetermined information, and feeding back to the user, wherein the behavior characteristics are the behavior characteristics that cause the energy state change of the user, wherein the predetermined information at least includes the energy state change of the user. The present application solves the problems caused by relying on manual supervision of weight loss in the prior art, achieves the effect of scientific supervision, and is conducive to improving people's physical health.

Description

Technical Field

[0001] The present application relates to the field of software, and specifically, to a method and system for user behavior feedback based on energy state changes. Background Art

[0002] Obesity has become a public health problem, and the medical community has long proven that lifestyle management is the only way to lose weight. But when people think of lifestyle management, their first reaction is to stay hungry, exercise a lot, and have negative emotions such as difficulty persisting in the face of temptation, so not many people choose it.

[0003] Users who need to lose weight generally hope to get more professional guidance and supervision so that they can stick to it for a long time.

[0004] Some professional weight loss agencies can provide humane and personalized help to clients change their lifestyles through the services of a large number of professionals, but such services are costly and not everyone can enjoy them.

[0005] Moreover, this method relies on the professionalism of service personnel and has relatively high requirements for them. Summary of the invention

[0006] The embodiments of the present application provide a method and system for user behavior feedback based on energy state changes, so as to at least solve the problems caused by relying on manual supervision of weight loss in the prior art.

[0007] According to one aspect of the present application, a user behavior feedback method based on energy state change is provided, comprising: obtaining at least two impedance values ​​and at least two body parameters of the user's body measured within a predetermined period, wherein the body parameters are physiological parameters of the user; determining the energy state change of the user based on at least the at least two impedance values ​​and the at least two body parameters, wherein the energy state change is used to indicate at least one of the following: energy intake, energy intake structure, energy consumption, and energy consumption method; determining the corresponding behavior characteristics of the user based on predetermined information, and feeding back to the user, wherein the behavior characteristics are behavior characteristics that cause the energy state change of the user, wherein the predetermined information at least includes the energy state change of the user.

[0008] Furthermore, the predetermined period is in days, and the predetermined period includes at least two consecutive days. The at least two impedance values ​​and at least two body parameters of the user's body measured within the predetermined period include: at least two impedance values ​​and / or at least two body parameters of the user's body are measured at fixed time points and / or at time points when fixed behaviors occur on each day within the predetermined period; and / or, when two impedance values ​​and two body parameters are needed, the two impedance values ​​and the two body parameters are measured in the same time period of two adjacent days within the predetermined period and after the same fixed behavior occurs to obtain the two impedance values ​​and the two body parameters.

[0009] Furthermore, the fixed time point of each day for obtaining the impedance value and the body parameters / or the measurement point at which the fixed behavior occurs is after getting up in the morning and defecating; more measurement points of the impedance value and the body parameters may also include: before getting up in the morning and defecating and before going to bed.

[0010] Furthermore, it also includes: obtaining two consecutive sets of body impedance values ​​and / or at least two sets of body parameters of the user, and the body impedance value and / or body parameters of the user before going to bed on the first day, to obtain three sets of measurement results; determining the behavioral characteristics of the user on the first day based on at least the three sets of measurement results, wherein the behavioral characteristics are behavioral characteristics made by the user that affect the change of the energy state.

[0011] Furthermore, the behavioral characteristics of the first day determined at least based on the three sets of measurement results and fed back to the user include at least one of the following: the amount of food eaten by the user; the eating structure of the user; the cooking method of the user; the quality of the food eaten by the user; the eating behavior habits of the user; the work and rest conditions of the user; the amount of activity of the user; the user's activity type preference; the degree of physical fatigue of the user; and an analysis of the metabolism patterns of energy substances in the user's body.

[0012] Furthermore, the predetermined information also includes at least one of the following: the impedance value, the body parameter; and / or the body parameter includes at least one of the following: weight, age, gender, height information.

[0013] According to another aspect of the present application, an electronic device is also provided, including: a screen; used to display at least one of the following obtained by the above measurement: body parameters, impedance values, behavioral characteristics; software for executing the above method; a memory for storing the software; and a processor for running the software.

[0014] According to another aspect of the present application, a user behavior feedback system based on energy state changes is also provided, including: an acquisition module, used to obtain at least two impedance values ​​and at least two body parameters of the user's body measured within a predetermined period, wherein the body parameters are physiological parameters of the user; a determination module, used to determine the energy state change of the user based on at least the at least two impedance values ​​and the at least two body parameters, wherein the energy state change is used to indicate at least one of the following: energy intake, energy intake structure, energy consumption, and energy consumption method; a feedback module, determines the corresponding behavior characteristics of the user according to predetermined information, and feeds back to the user, wherein the behavior characteristics are behavior characteristics that cause the user's energy state change, and the predetermined information includes: the user's energy state change.

[0015] Further, the predetermined period is in days, and the predetermined period includes at least two days, and the acquisition module is used to measure at least two impedance values ​​and / or at least two body parameters of the user's body at fixed time points and / or at fixed behavior occurrence time points of each day in the predetermined period; and / or, the acquisition module is used to measure the two impedance values ​​and the two body parameters in the same time period of two adjacent days in the predetermined period and after the same fixed behavior occurs when two impedance values ​​and two body parameters are needed; and / or, the acquisition module is also used to obtain two consecutive groups of the body impedance values ​​and / or at least two groups of the body parameters of the user, and the body impedance value and / or the body parameters of the user before going to bed measured on the first day, to obtain three groups of measurement results; the feedback module is also used to determine the user's behavioral characteristics on the first day based on at least the three groups of measurement results, wherein the behavioral characteristics are behavioral characteristics made by the user that affect the change of the energy state; and / or, the predetermined information also includes at least one of the following: the impedance value, the body parameter; and / or, the body parameter includes at least one of the following: weight, age, gender, height information.

[0016] Furthermore, the fixed time point of each day for obtaining the impedance value and the body parameters / or the measurement point at which the fixed behavior occurs is after getting up in the morning and defecating; more measurement time points of the impedance value and the body parameters may also include: before getting up in the morning and defecating and before going to bed; and / or, the first day's behavioral characteristics fed back to the user by the feedback module include at least one of the following: the amount of food eaten by the user; the user's eating structure; the user's cooking method; the quality of the food eaten by the user; the user's eating behavior habits; the user's work and rest conditions; the amount of activity of the user; the user's activity type preference; the user's physical fatigue level; and analysis of the user's body energy substance metabolism rules.

[0017] In the embodiment of the present application, at least two impedance values ​​and at least two body parameters of the user's body measured within a predetermined period are obtained, wherein the body parameters are physiological parameters of the user; the energy state change of the user is determined at least based on the at least two impedance values ​​and the at least two body parameters, wherein the energy state change is used to indicate at least one of the following: energy intake, energy intake structure, energy consumption, and energy consumption method; the corresponding behavior characteristics of the user are determined according to predetermined information, and fed back to the user, wherein the behavior characteristics are behavior characteristics that cause the energy state change of the user, wherein the predetermined information at least includes the energy state change of the user. The present application solves the problems caused by relying on manual supervision of weight loss in the prior art, achieves the effect of scientific supervision, and is conducive to improving people's physical health. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a flowchart of a user behavior feedback method based on energy state changes according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] The reason why a healthy lifestyle is easier said than done is that people usually only pay attention to and take action on timely feedback, but the impact of an unhealthy lifestyle on health is extremely delayed and subtle, so people do not pay enough attention to some bad habits in their lifestyle; at the same time, because everyone's age, gender, genes, and living environment are different, the same lifestyle has different impacts on everyone, so although many people know the general principles of a healthy lifestyle, when it comes to the details of their own lives, there is often no particularly clear method, which makes it difficult for everyone to start. In this embodiment, a method is provided that can be scaled up and low-cost, allowing ordinary people to easily, naturally, and effortlessly adjust their lifestyles and gain health.

[0023] This embodiment provides a user behavior feedback method based on energy state changes. Figure 1 is a flow chart of a user behavior feedback method based on energy state change according to an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0024] Step S102, obtaining at least two resistance values ​​and at least two body parameters of the user's body measured within a predetermined period, wherein the body parameters are physiological parameters of the user;

[0025] Step S104, determining a change in the energy state of the user based on at least two resistance values ​​and at least two body parameters, wherein the change in the energy state is used to indicate at least one of the following: energy intake, energy intake structure, energy consumption, and energy consumption mode;

[0026] The resistance value is used in the above steps because different physical states of each person correspond to different resistance values. The user's physical state can be determined by the resistance value. In order to ensure a more accurate determination of the energy state, a body parameter can be added. The body parameter may include but is not limited to at least one of the following: weight, age, gender, height information. As an optional method, the above resistance value is the impedance value of the human body. More preferably, it can be further said that the impedance value of the body below the waist and abdomen (inclusive) or the change of the body impedance value.

[0027] In the above steps, the energy state can be obtained according to the physiological parameters and the resistance value, and then the changes between the multiple energy states can be obtained according to the multiple energy states.

[0028] In an optional manner, the collected user data (for example, data stored in a medical institution and authorized by the user) can be used as training data, and each set of training data includes a user's physiological parameters such as height and weight, resistance value, and fat parameters corresponding to the physiological parameters and resistance value, and the fat parameters are used to indicate the user's fat content (for example, body fat percentage and / or muscle mass). When the amount of training data exceeds a threshold, the training data can be sent to a machine learning engine for training.

[0029] The machine learning engine can use an existing machine learning engine. The specific engine construction and operation process can refer to the technical manuals of each engine. Those skilled in the art can build a corresponding machine learning engine based on these technical manuals. After the machine learning engine is trained using multiple sets of training data, a first machine learning model can be obtained. After obtaining the model, the model can be used to obtain the user's fat parameters. As long as the user's physiological parameters and resistance values ​​are input into the first model, the first model can output the corresponding fat parameters.

[0030] Then the energy state of the user is obtained according to the fat parameter and the above physiological parameters. In the above example, the fat parameter is used, and the fat parameter can also be considered as a kind of physiological parameter. If the user directly inputs the fat parameter, the energy state can be obtained according to the following example.

[0031] As another optional implementation, since a machine learning engine has been used, a second model can be trained using the machine learning engine, and each of the multiple sets of training data used to train the model includes the user's fat parameters and physiological parameters, and the energy state corresponding to the fat parameters and physiological parameters. After the second model is obtained through training, the corresponding energy state can be obtained by simply inputting the fat parameters and physiological parameters.

[0032] The energy status obtained can also be displayed to the user so that the user can perceive it himself. If the energy status does not need to be displayed to the user, the third model can be used. Each of the multiple sets of training data used to train the model includes the user's fat parameters and physiological parameters, as well as the behavioral suggestions corresponding to the fat parameters and physiological parameters. The difference between the third model and the second model is that the model directly outputs the behavioral suggestions. After the second model is obtained through training, the corresponding behavioral suggestions can be obtained by simply inputting the fat parameters and physiological parameters.

[0033] After the energy state is obtained, the change of the energy state can be obtained according to at least two energy states.

[0034] Step S106, determining the behavior characteristics corresponding to the user according to the predetermined information, and feeding back to the user, wherein the behavior characteristics are the behavior characteristics that cause the energy state of the user to change; wherein the predetermined information at least includes: the energy state change of the user. Optionally, the predetermined information may also include: physiological parameters and / or resistance values.

[0035] The model based on the machine learning engine in the above optional implementation is used after accumulating a large amount of training data. If the amount of training data is insufficient, a pre-configured correspondence can be used. For example, the energy state can be obtained according to the pre-configured correspondence between resistance value, body parameter and energy state. These correspondences can be measured in advance by accurate measurement means. Alternatively, the various measured data can be fitted into a function through software, and the resistance value and body parameter can be used as the input of the function to obtain the energy state output by the function.

[0036] The determination of the behavioral characteristics can also be carried out based on the relationship between the pre-configured predetermined information and the behavioral characteristics. The relationship between these pre-configured predetermined information and the behavioral characteristics is obtained by statistics of samples. When the number of samples reaches a level that can be used for machine learning training, a machine learning model can be trained to obtain the behavioral characteristics based on the predetermined information. The model can be called a behavioral characteristic model. The behavioral characteristic model is trained using multiple groups of training data, each group of training data includes predetermined information and labels, and the labels are used to identify the behavioral characteristics under the predetermined information. After training, a converged behavioral characteristic model is obtained, which can be used after verification by verification data, and the verification data also comes from the sample data. The predetermined information in step S106 is input into the behavioral characteristic model, and the behavioral characteristic model can input a label for indicating the behavioral characteristics.

[0037] Regardless of the implementation method used, in the above steps, as long as the user's energy (or heat) processing capacity is determined by the combination of resistance value and body parameters, behavioral suggestions can be provided to the user through the measurement of each cycle. The processing capacity can also be understood as including how much calories the user has consumed and how much calories he has consumed. The above behavioral characteristic model can directly obtain behavioral characteristics. For changes in energy states, the changes in energy states can be determined by comparison after obtaining the energy states. The above has been explained and will not be repeated here.

[0038] In this embodiment, the processing can also be performed by a classification method. For example, at least one of the following can be sorted and classified: the direction and / or amplitude of the body impedance change at a predetermined time, the direction and / or amplitude combination of the body parameter change, and the classification basis is that each change combination can correspond to a certain specific behavioral feature that causes this change. For example, the calorie intake exceeds the predetermined standard value, and this behavior corresponds to a combination of multiple groups of impedance changes and / or body parameter changes. Therefore, by measuring the changes in body impedance and body parameters at a predetermined time, the behavioral characteristics of the user's current lifestyle can be inferred.

[0039] There are many classification methods, for example, classification can be performed through machine learning.

[0040] The model trained by the machine learning can be called a classification model, which is obtained by training multiple sets of training data, each set of training data including specific behavioral features and changes in body impedance and / or body parameters caused by the behavioral features. The model trained by taking the specific behavioral features as input and the changes in body impedance and / or body parameters as output can be used to determine the changes in body impedance and / or body parameters based on the behavioral features; the model trained by taking the changes in body impedance and / or body parameters as input and the specific behavioral features as output can be used to output behavioral features based on changes in body impedance and / or body parameters.

[0041] When classifying, the correspondence with behavioral characteristics can be further classified according to gender, age, height, living environment, etc.

[0042] As another way, the changes in body composition can be deduced by combining the changes in body impedance with the changes in body parameters, and then the behavioral characteristics of the lifestyle can be deduced through the changes in body composition; this derivation method can also be understood as a classification method. The difference from the above embodiment is that two classifications are performed. The specific method used is the same as the above embodiment and will not be repeated here. The body composition here may include at least one of the following: body fat percentage, muscle mass, skeletal muscle percentage, internal fat, BMI, body water, protein, bone mass; in addition to body weight, the variables in the body parameters may also include blood pressure, blood sugar, etc. Other parameters are not listed here one by one.

[0043] The above steps can be run in a program, which can be an application installed in a mobile device, or a software installed in a personal computer, or a service running on a server. In the following embodiments, software, applications, and services are all referred to as software. By using the software to make judgments and push behavioral suggestions, large-scale applications can be achieved without being limited by the number and capabilities of professional service personnel. In addition, for users, on the one hand, it is very easy to get behavioral suggestions, and on the other hand, the software is based on objective physical values ​​to obtain behavioral suggestions, which is more scientific. Therefore, the above steps solve the problems caused by relying on manual supervision of weight loss in the prior art, achieve the effect of scientific supervision, and are conducive to improving people's physical health.

[0044] In an optional embodiment, the above-mentioned predetermined period can be in units of days, for example, the predetermined period can include at least two consecutive days. If the measurement is performed on a daily basis, at least two impedance values ​​and / or at least two body parameters of the user's body are measured at fixed time points and / or fixed behavior occurrence points of each day within the predetermined period; For example, measurements are performed at 7 am, 9 am and 11 pm every day. This measurement method can establish the relationship between time, resistance value and body parameters. However, this measurement is not a very accurate value, because the behavior of each user at different time points is different. In order to find a more reasonable measurement point, this embodiment can choose to get up in the morning after defecation every day. Optionally, if multiple data in a day are required, the following behavior can be added for measurement: the resistance value and / or body parameters of the user's body before defecation and before going to bed after getting up in the morning of each day are measured. If only two impedance values ​​and two body parameters are needed, the two impedance values ​​and the two body parameters are measured in the same time period of two adjacent days within the predetermined period and after the same fixed behavior occurs.

[0045] As another optional implementation, when the user uses it for the first time, three sets of measurement results can be obtained based on obtaining two consecutive sets of body impedance values ​​and / or at least two sets of body parameters of the user, and the body impedance value and / or body parameters of the user before going to bed on the first day; the behavioral characteristics of the user on the first day are determined based on at least the three sets of measurement results, wherein the behavioral characteristics are behavioral characteristics of the user that affect the change in the energy state.

[0046] As another optional implementation, since the measurement is performed every day, the user's behavior on that day can be determined based on the measurement results obtained every day. For example, the user's body resistance value and / or at least one body parameter measured on that day after getting up in the morning, after getting up in the morning and defecating, and before going to bed are obtained to obtain three groups of measurement results; the user's behavior characteristics on that day are determined based on at least the three groups of measurement results.

[0047] In one example, the user's physical condition can be determined based on the fat difference and muscle difference of the next day. Specifically, when the fat difference of the next day is not a negative value and the muscle rate difference of the next day is not a positive value, it means that the user's body fat has not increased and the muscle has not decreased, so the user's physical condition is determined to be normal; otherwise, the user's physical condition is determined to be abnormal, and then the cause of the abnormal physical condition of the user is determined to be excessive drinking or lack of drinking water based on the resistance value.

[0048] In order to obtain more accurate reasons, the user's behavior on the day can be determined based on the three sets of measurement results; feedback from the user on the actual behavior on the day can be received; and the user's behavior on the day can be determined based on the user's first behavior and feedback on the day. For example, the user can be asked to input the amount of water consumed, and further judgment can be made based on the amount of water consumed input by the user.

[0049] In the above example, the behavior characteristics determined according to the change of the user's energy state can be displayed to the user. The above behavior characteristics may include at least one of the following: the amount of food eaten by the user; the eating structure of the user; the cooking method of the user; the quality of the food eaten by the user; the eating behavior habits of the user; the work and rest status of the user; the amount of activity of the user; the preference of the user's activity type; the degree of physical fatigue of the user; and the analysis of the metabolism law of the user's body energy substances.

[0050] The above steps and / or functions are described below in conjunction with a preferred embodiment. The preferred embodiment combines part or all of the above optional embodiments.

[0051] The behavior feedback software includes a measuring device that can measure weight and body impedance at one time, and a data sending module that can send the measured weight and body impedance values ​​to a data processing module. The data processing module will give corresponding feedback information based on the user's measurements at different periods, times, states, and contexts.

[0052] The physical state of the human body at any point in time is the result of all the accumulated lifestyles over the past days and months. Therefore, after the user's first measurement, the data analysis module will deduce the characteristics of the user's past behavior based on the user's weight, impedance, age, gender and height information, and give it as the first feedback to the user. Through this feedback, the user will be aware of the direct relationship between his current physical state and lifestyle behavior. Next, the software will give the user the best and second best weight, body fat and internal fat standard range according to the user's age and gender, and give the time required to reach each stage according to general standards, giving the user hope and guiding him to make a commitment to himself.

[0053] Next, the software will give users a practical plan based on the national standard dietary guidelines, allowing users to adjust their diet to create a calorie gap based on their own understanding. At this time, the data collection module will count the user's weight every morning (1), after defecation (2) and before going to bed (3), and count the average and daily deviation of the difference between each value every day. The statistical cycle is 3 to 7 days, and then the user is given the second feedback point, that is, the overall state of the body's energy intake and consumption balance, specifically the user's intake capacity and consumption capacity, and the consumption capacity is divided into basic metabolic capacity, digestive capacity, consumption capacity and food thermal effect capacity.

[0054] Combined with the analysis of the user's various capabilities observed above, the system will then give a plan suggestion customized for the user. The system will have a manual coach interface. The manual coach can select a method that is easy for the user to implement and can achieve a calorie deficit from the above plan suggestions based on the user's meal check-in content, and give it to the user and supervise its execution.

[0055] After the user starts to execute, the system will automatically locate the bad behaviors that affect the body that day based on the weight and impedance measured three times a day by the user, as well as the corresponding age, gender, height and other information, and provide timely feedback through the interactive design of the system, so that the user can become aware and make adjustments consciously.

[0056] The difference between this software and traditional BIA analysis is that this software is committed to studying the precise response of data changes to daily behaviors and guiding users to consciously change their behaviors through appropriate feedback, rather than pursuing the accuracy of static body data.

[0057] The software in this preferred embodiment can accurately locate user behavior based on human body impedance and weight data, and through a series of accurate feedback point settings and subtle guidance, it can prompt users to consciously and spontaneously change their behavior. Most importantly, for users, as long as they spend a few dozen seconds every day measuring their weight and body impedance, they can unlock all feedback points. This will achieve scale and allow more people to have a certain effect of lifestyle management at a low cost.

[0058] In this embodiment, an electronic device is also provided, including a memory and / or a processor, wherein software is stored in the memory, and the processor is configured to run the software to execute the method in the above embodiment. The electronic device may also include: a screen. The screen is used to display relevant information, for example, the screen is used to display the above-measured body parameters, resistance values, behaviors and / or behavioral suggestions. If the screen is a touch screen, it can also be used as an input device, and various information can be input through the screen. Of course, the electronic device may also include other types of input devices, such as a physical or virtual keyboard, and for example, a microphone, through which voice commands can be input.

[0059] The software may also be referred to as a computer program, which may also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps of the functions specified in one or more blocks can be implemented by different modules corresponding to different steps. The software or program can also be called a device or system.

[0060] In this embodiment, a user behavior feedback system based on energy state change is also provided, including: A user behavior feedback system based on energy state change, including: an acquisition module, used to obtain at least two impedance values ​​and at least two body parameters of the user's body measured within a predetermined period, wherein the body parameters are physiological parameters of the user; a determination module, used to determine the energy state change of the user based on at least the at least two impedance values ​​and at least two body parameters, wherein the energy state change is used to indicate at least one of the following: energy intake, energy intake structure, energy consumption, energy consumption mode; a feedback module, which determines the corresponding behavior characteristics of the user according to predetermined information and feeds back to the user, wherein the behavior characteristics are the behavior characteristics that cause the user's energy state change; wherein the predetermined information at least includes: the user's energy state change. Optionally, the predetermined information may also include: physiological parameters and / or resistance values.

[0061] The modules in the feedback system correspond to the steps in the above method, which have been explained in the method and will not be repeated here.

[0062] Preferably, the predetermined period is in days, and the predetermined period includes at least two consecutive days. The acquisition module is used to measure at least two impedance values ​​and / or at least two body parameters of the user's body at fixed time points and / or at the time points when fixed behaviors occur on each day within the predetermined period; and / or, the acquisition module is used to measure in the same time period of two adjacent days within the predetermined period and after the same fixed behavior occurs to obtain two impedance values ​​and two body parameters when two impedance values ​​and two body parameters are needed; and / or, the acquisition module is also used to obtain two consecutive groups of body impedance values ​​and / or at least two groups of body parameters of the user, as well as the body impedance value and / or body parameters of the user before going to bed measured on the first day, to obtain three groups of measurement results; the feedback module is also used to determine the user's behavioral characteristics on the first day based on at least the three groups of measurement results, wherein the behavioral characteristics are behavioral characteristics of the user that affect the change in energy state.

[0063] Preferably, the fixed time point of each day for obtaining the impedance value and the body parameters / or the measurement point at which the fixed behavior occurs is after getting up in the morning and defecating; more measurement time points of the impedance value and the body parameters may also include: before getting up in the morning and defecating and before going to bed; and / or, the first day's behavioral characteristics fed back to the user by the feedback module include at least one of the following: the amount of food eaten by the user; the user's eating structure; the user's cooking method; the quality of the food eaten by the user; the user's eating behavior habits; the user's work and rest conditions; the amount of activity of the user; the user's activity type preference; the user's physical fatigue level; and analysis of the user's body energy substance metabolism rules.

[0064] The above-mentioned program or software or system or device can be run in a processor, or can also be stored in a memory (or called a computer-readable medium), which includes permanent and non-permanent, removable and non-removable media. Information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0065] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A user behavior feedback method based on energy state change, characterized in that: include: Acquire at least two impedance values ​​and at least two body parameters of the user's body measured within a predetermined period, wherein the body parameters are physiological parameters of the user; wherein the at least two resistance values ​​are used to determine the user's physical state, and the at least two resistance values ​​are impedance values ​​of the waist and abdomen and / or the body below the waist and abdomen or changes in body impedance values; The energy state change of the user is determined at least according to the at least two impedance values ​​and the at least two body parameters, wherein the energy state change is used to indicate at least one of the following: energy intake, energy intake structure, energy consumption, and energy consumption method; the predetermined period is in days, and the predetermined period includes at least two consecutive days, and the at least two impedance values ​​and at least two body parameters of the user's body measured within the predetermined period include: at least two impedance values ​​and / or at least two body parameters of the user's body are measured at fixed time points and / or at time points when fixed behaviors occur on each day within the predetermined period; and / or, when two impedance values ​​and two body parameters are needed, the two impedance values ​​and the two body parameters are measured at the same time period of two adjacent days within the predetermined period and after the same fixed behaviors occur to obtain the two impedance values ​​and the two body parameters; wherein the collected user data is used as training data, and each A set of training data includes a physiological parameter and a resistance value of a user, and a fat parameter corresponding to the physiological parameter and the resistance value, and the fat parameter is used to indicate the fat content of the user; when the amount of training data exceeds a threshold, the training data is sent to a machine learning engine for training; after the machine learning engine is trained using multiple sets of training data, a first machine learning model is obtained; after obtaining the first model, the physiological parameter and the resistance value of the user are input into the first model, and the first model outputs the corresponding fat parameter; the energy state of the user is obtained according to the fat parameter and the above-mentioned physiological parameter; a second model is trained using the machine learning engine, and each set of training data in the multiple sets of training data used to train the second model includes the fat parameter and the physiological parameter of the user, and the energy state corresponding to the fat parameter and the physiological parameter; after the second model is obtained through training, the corresponding energy state is obtained by inputting the fat parameter and the physiological parameter; The behavioral characteristics corresponding to the user are determined according to the predetermined information and fed back to the user, wherein the behavioral characteristics are the behavioral characteristics that cause the change of the user's energy state, wherein the predetermined information at least includes the change of the user's energy state; wherein the behavioral characteristics determined according to the change of the user's energy state are displayed to the user, and the behavioral characteristics include at least one of the following: the amount of food eaten by the user; the eating structure of the user; the cooking method of the user; the quality of the food eaten by the user; the eating behavior habits of the user; the work and rest status of the user; the amount of activity of the user; the user's activity type preference; the degree of physical fatigue of the user; and the analysis of the metabolism law of the user's body energy substances.

2. The method according to claim 1, characterized in that The fixed time point of each day and / or the fixed behavior measurement point for obtaining the impedance value and the body parameter is after getting up and defecating in the morning; More measurement time points of the impedance values ​​and the body parameters may also include: before defecation in the morning and before going to bed.

3. The method according to claim 2, characterized in that Also includes: Obtaining two consecutive sets of the body impedance values ​​and / or at least two sets of the body parameters of the user, and the body impedance values ​​and / or the body parameters of the user before going to bed measured on the first day, to obtain three sets of measurement results; The behavior characteristics of the user on the first day are determined at least based on the three groups of measurement results, wherein the behavior characteristics are behavior characteristics made by the user that affect the change of the energy state.

4. The method according to claim 3, characterized in that: The behavioral characteristics of the first day determined at least based on the three groups of measurement results and fed back to the user include at least one of the following: The amount of food the user eats; the user's eating structure; the user's cooking method; the quality of the food the user eats; the user's eating behavior habits; the user's work and rest conditions; the amount of activity the user does; the user's activity type preference; the user's physical fatigue level; and an analysis of the user's body energy substance metabolism patterns.

5. The method according to claim 1, characterized in that The predetermined information further includes at least one of the following: the impedance value, the body parameter; and / or, The physical parameters include at least one of the following: weight, age, gender, and height information.

6. An electronic device, characterized in that: include: Screen; used to display at least one of the following measured by any one of claims 1 to 5: body parameters, impedance values, behavioral characteristics; Software for executing the method according to any one of claims 1 to 5; A memory, used for storing the software; A processor is used to run the software.

7. A user behavior feedback system based on energy state changes, characterized in that: include: An acquisition module, used to acquire at least two impedance values ​​and at least two body parameters of the user's body measured within a predetermined period, wherein the body parameters are physiological parameters of the user; wherein the at least two resistance values ​​are used to determine the user's physical state, and the at least two resistance values ​​are impedance values ​​of the waist and abdomen and / or the body below the waist and abdomen or changes in body impedance values; A determination module is used to determine the energy state change of the user at least based on the at least two impedance values ​​and the at least two body parameters, wherein the energy state change is used to indicate at least one of the following: energy intake, energy intake structure, energy consumption, and energy consumption mode; the predetermined period is in days, and the predetermined period includes at least two consecutive days. The at least two impedance values ​​and at least two body parameters of the user's body measured within the predetermined period include: at least two impedance values ​​and / or at least two body parameters of the user's body are measured at fixed time points and / or fixed behavior time points of each day within the predetermined period; and / or, when two impedance values ​​and two body parameters are needed, the two impedance values ​​and the two body parameters are measured at the same time period of two adjacent days within the predetermined period and after the same fixed behavior occurs to obtain the two impedance values ​​and the two body parameters; wherein the user data that has been collected is used as training data to train the training data. The method comprises the following steps: using a plurality of training data sets, each of which includes a physiological parameter and a resistance value of a user, and a fat parameter corresponding to the physiological parameter and the resistance value, and the fat parameter is used to indicate the fat content of the user; when the amount of training data exceeds a threshold, the training data is sent to a machine learning engine for training; after the machine learning engine is trained using a plurality of training data sets, a first machine learning model is obtained; after obtaining the first model, the physiological parameter and the resistance value of the user are input into the first model, and the first model outputs the corresponding fat parameter; the energy state of the user is obtained according to the fat parameter and the above-mentioned physiological parameter; a second model is trained using the machine learning engine, and each of the plurality of training data sets used to train the second model includes the fat parameter and the physiological parameter of the user, and the energy state corresponding to the fat parameter and the physiological parameter; after obtaining the second model through training, the corresponding energy state is obtained by inputting the fat parameter and the physiological parameter; A feedback module determines the behavior characteristics corresponding to the user according to predetermined information and feeds back to the user, wherein the behavior characteristics are the behavior characteristics that cause the change of the user's energy state, and the predetermined information includes: the change of the user's energy state; wherein the behavior characteristics determined according to the change of the user's energy state are displayed to the user, and the behavior characteristics include at least one of the following: the amount of food eaten by the user; the eating structure of the user; the cooking method of the user; the quality of the food eaten by the user; the eating behavior habits of the user; the work and rest status of the user; the amount of activity of the user; the user's activity type preference; the degree of physical fatigue of the user; and the analysis of the metabolism law of the user's body energy substances.

8. The system according to claim 7, characterized in that The acquisition module is further used to acquire two consecutive sets of the body impedance values ​​and / or at least two sets of the body parameters of the user, and the body impedance values ​​and / or the body parameters of the user before going to bed on the first day, to obtain three sets of measurement results; the feedback module is further used to determine the behavior characteristics of the user on the first day at least based on the three sets of measurement results, wherein the behavior characteristics are the behavior characteristics of the user that affect the change of the energy state; and / or, the predetermined information also includes at least one of the following: the impedance value, the body parameter; and / or, The physical parameters include at least one of the following: weight, age, gender, and height information.

9. The system according to claim 8, characterized in that The fixed time point of each day for obtaining the impedance value and the body parameter / the fixed behavior measurement point is after getting up in the morning and defecating; more measurement time points of the impedance value and the body parameter may also include: before getting up in the morning and defecating and before going to bed; and / or, The feedback module is used to provide feedback to the user on the first day's behavioral characteristics, including at least one of the following: the amount of food the user eats; the user's eating structure; the user's cooking method; the quality of the food the user eats; the user's eating behavior habits; the user's work and rest conditions; the amount of activity the user has; the user's activity type preference; the user's physical fatigue level; and an analysis of the user's body energy substance metabolism patterns.

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