CN118298992A

CN118298992A - Blood glucose management method and related electronic equipment

Info

Publication number: CN118298992A
Application number: CN202310258733.4A
Authority: CN
Inventors: 张�杰; 王念; 孙瑜宁; 解松林
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2023-01-03
Filing date: 2023-03-08
Publication date: 2024-07-05
Also published as: WO2024146486A1

Abstract

The application discloses a blood glucose management method and related electronic equipment, wherein the method can determine the meal time of a user eating event according to the blood glucose value of a user, and display the meal time and/or the blood glucose value related to the meal time on a blood glucose curve drawn by the blood glucose value of the user. Therefore, the diet event of the user can be associated with the blood sugar of the user, the change condition of the blood sugar before and after the meal intake of the user is highlighted, the user is helped to more intuitively know the influence of the diet on the blood sugar, and the user is convenient to manage and control the blood sugar from the diet.

Description

Blood glucose management method and related electronic equipment

Technical Field

The application relates to the technical field of terminals, in particular to a blood glucose management method and related electronic equipment.

Background

Diabetes is a group of metabolic diseases characterized by hyperglycemia, caused by insulin secretion defects and their biological dysfunctions. Diabetes is a long-term chronic disease, and the daily behavior and self-management ability of a user are one of key factors affecting the control condition of diabetes, so control of diabetes requires self-management of a user's system.

Disclosure of Invention

The application provides a blood sugar management method and related electronic equipment, which are used for associating an exogenous event with the blood sugar of a user by utilizing the blood sugar value of the user, helping the user to master and control the influence of the exogenous event on the blood sugar, and realizing more effective self-management of the blood sugar of the user.

In a first aspect, an embodiment of the present application provides a blood glucose management method, where the method is applied to an electronic device, and the method includes: firstly, acquiring a fasting blood glucose value; if the blood glucose value of the user in the first period exceeds the fasting blood glucose value by a first threshold value, determining a first meal time based on the blood glucose value in the first period; after determining the first meal time, and/or a blood glucose value associated with the first meal time, may be displayed in a displayed first blood glucose profile, wherein the first blood glucose profile is indicative of a change in blood glucose of the user over a first period of time.

By implementing the method provided by the embodiment of the application, the user can know the change trend of the blood sugar through the interactive interface of the electronic equipment, and the user can be helped to more intuitively and clearly know the association of diet and the blood sugar by displaying the dining time, so that the user can be helped to obtain the influence of diet events on the blood sugar, the user can better grasp and manage the change of the blood sugar concentration, and the self-management of the user on the blood sugar of the user is realized. Moreover, for diabetics, the user who manages diabetes can have more positive attitude, richer diabetes knowledge and better diabetes self-management behavior, and have more confidence of overcoming diseases.

With reference to the first aspect, in one implementation, the blood glucose value associated with the first meal time may include one or more of: the blood glucose value at the first meal time is a blood glucose value at a time point a first length of time before or after the first meal time.

The blood sugar value of key time points near the meal time is highlighted, so that the user can be helped to know the blood sugar condition before and after the meal.

With reference to the first aspect, in one implementation, the first meal time is located between a first point in time and a second point in time. The first time point is a time point where the blood glucose peak value in the first period is located, the second time point is located before the first time point, and the time length between the second time point and the first time point is a second time length; the blood glucose level at the first meal time is greater than the fasting blood glucose level, and the first meal time may refer to a first time point at which the blood glucose rise rate of the user is greater than the second threshold between the first time point and the second time point.

The relationship between the blood sugar of the user and diet is utilized, the dining time is calculated through the blood sugar, and the dining time of the diet event of the user can be accurately calculated, so that the user does not need to manually input the meal time, the dining time can be accurately calculated through the blood sugar value of the user, the operation of the user is convenient, and the condition of possible input errors when the user manually inputs the meal is avoided.

With reference to the first aspect, in one implementation, the fasting blood glucose value may be obtained in three ways:

1) The fasting blood glucose value is determined according to the blood glucose value of the user in the second period, wherein the fasting blood glucose value is the lower quartile of the blood glucose value in the second period;

2) Fasting blood glucose values are the lower quartile of blood glucose values between two meals by the user during the second period;

3) The fasting blood glucose value is determined based on a second meal time, wherein the second meal time is prior to the first meal time, and the fasting blood glucose value is an average blood glucose value for a third period of time prior to the second meal time.

Therefore, the fasting blood glucose value of the user can be accurately calculated by utilizing the blood glucose value of the user and further combining with the diet event of the user.

With reference to the first aspect, in one implementation manner, the electronic device may display a second blood glucose curve for indicating a fasting blood glucose value of each of N days and/or a third blood glucose curve for indicating a blood glucose value associated with a first meal time of each of N days, where N is greater than or equal to 1 and N is an integer.

The blood sugar change condition of the user in a period of time is displayed through the interactive interface of the electronic equipment, so that the user can be helped to view the blood sugar change condition in a period of time from a long-term angle, and the association between the diet event and the blood sugar change is convenient for the user to make integral adjustment aiming at abnormal change of blood sugar on diet.

With reference to the first aspect, in one implementation manner, the method further includes: modifying the first meal time according to user operation; the modified first meal time and/or the blood glucose value associated with the modified first meal time may then be displayed in a first blood glucose profile displayed by the electronic device.

That is, the user may manually modify the meal time so that the electronic device can display more accurate information.

With reference to the first aspect, in one implementation manner, the method further includes: the electronics can display the calories of the food and/or the nutrient ratios.

Therefore, the user can not only check the condition of blood sugar change and the dining time of the user, but also know the related information of food eaten by the user in the dining process, and the user is helped to know the dining condition more comprehensively.

In a second aspect, an embodiment of the present application further provides a blood glucose management method, where the method is applied to an electronic device, and the method includes: firstly, determining the meal quantity and the meal speed of a user in a meal process; acquiring a blood sugar actual curve of a user, wherein the blood sugar actual curve can be used for indicating the blood sugar change of the user in the meal process; furthermore, a blood glucose standard curve of the healthy people is obtained, and the blood glucose standard curve is used for indicating the blood glucose change of the healthy people in the meal process with the meal quantity and the meal speed; finally, the diet health of the user can be estimated through the blood sugar actual curve of the user and the blood sugar standard curve of the healthy population, wherein the greater the difference between the blood sugar actual curve and the blood sugar standard curve is, the less healthy the diet of the user is, and the smaller the difference between the blood sugar actual curve and the blood sugar standard curve is, the more healthy the diet of the user is.

The method provided by the embodiment of the application can quantify the difference between the blood sugar of the user and the normal blood sugar of healthy people based on the one-time dining condition of the user, help the user evaluate whether the blood sugar reaction of the user to diet is healthy or not through the blood sugar value in the dining process, help the user to know the body condition of the user more clearly, and facilitate the self management of the user to the blood sugar.

With reference to the second aspect, in one implementation manner, obtaining a blood glucose standard curve of a healthy population specifically includes: acquiring blood glucose curves of a plurality of members in a healthy crowd, wherein the blood glucose curves are used for indicating blood glucose changes of the members in the process of having meals with meal quantity and meal speed; in this way, the blood glucose standard curve for a healthy population can be determined from the blood glucose curves of a plurality of members.

It can be seen that the method can calculate the blood glucose standard curve of the healthy population through the blood glucose curves of a plurality of blood glucose healthy members, so that the blood glucose standard curve can be more representative and more accurate when representing the blood glucose condition of the healthy population.

With reference to the second aspect, in one implementation, determining a blood glucose standard curve of a healthy population according to blood glucose curves of a plurality of members may include the steps of: 1) Stretching or compressing the blood glucose curves of the plurality of members in a time domain, and/or stretching or compressing the blood glucose curves of the plurality of members in an amplitude value, so that the average values of the blood glucose curves of the plurality of members in the time domain and the amplitude value are equal; 2) And carrying out point-by-point average calculation on the blood glucose curves transformed by the members, and determining the curve formed by connecting the average values as a blood glucose standard curve.

It can be seen that the blood glucose standard curve is obtained by normalizing blood glucose curves of a plurality of members, and the blood glucose standard curve fuses the blood glucose change conditions of the plurality of members.

With reference to the second aspect, in one implementation manner, the method further includes: acquiring meal related data of a user in the meal process; wherein the meal related data comprises: heart rate data, exercise data, mood pressure data. The method for determining the dining amount and the dining speed of the user in one dining process specifically comprises the following steps: inputting meal related data of a user in a meal process into a meal model, and identifying and obtaining the meal quantity and the meal speed of the user in the meal process, wherein the meal model is trained by using the meal related data of the user in the meal process with known meal quantity and meal speed.

Therefore, the electronic equipment can calculate and obtain the accurate dining amount and the accurate dining speed of the user by acquiring the data in the dining process without counting or recording the dining amount or the dining speed of the user, and the operation of the user is convenient.

With reference to the second aspect, in one implementation manner, before acquiring the meal related data of the user during the meal, the method further includes: and detecting that the user confirms the operation of beginning to eat or that the data collected by the sensor meets the preset condition.

That is, the embodiments of the present application provide two methods for identifying the start of a meal by a user: 1) Identifying according to a user operation; 2) Is identified based on the data collected by the sensor. In this way, the meal related data in the meal process of the user can be acquired after the user is identified to begin to meal, so that the accurate meal quantity and meal speed can be calculated.

With reference to the second aspect, in one implementation, the data collected by the sensor may be used to indicate heart rate, exercise and emotional stress of the user, and the preset condition includes: the difference in heart rate of the user relative to the resting heart rate remains greater than the third threshold after excluding the effects of exercise and emotional stress.

Therefore, the method skillfully utilizes the change of the heart rate of the user in the dining process and the influence of the movement and the emotion pressure of the user on the heart rate of the user, and utilizes the data which are acquired by the sensor and are used for reflecting the heart rate, the movement and the emotion pressure of the user to identify the time node when the user begins to dining, so that the aim of identifying the dining more accurately is achieved.

With reference to the second aspect, in one implementation manner, the evaluation of the dietary health of the user through the blood glucose actual curve and the blood glucose standard curve specifically includes: extracting features of the blood glucose actual curve and the blood glucose standard curve, respectively, wherein the features can comprise one or more of the following: maximum value of single blood sugar change, blood sugar non-stable time, number of blood sugar values under different blood sugar change rate categories, number of blood sugar fluctuation times, time and amplitude of single blood sugar change and envelope of curve; the user's dietary health may then be assessed based on the differences between one or more features in the actual blood glucose profile and one or more features in the blood glucose standard profile. Wherein the greater the difference, the less healthy the user's diet, the less the difference, the more healthy the user's diet.

That is, the difference between the two curves can be compared by extracting the characteristics of the curves, the difference of the curves is quantified, and the user is helped to clearly know the difference between the blood glucose curves of the user and the healthy people.

With reference to the second aspect, in one implementation manner, the method further includes: and calculating the overall health score of the user according to the difference value of each dining process in the N times of dining processes of the user in the third period, wherein the greater the overall health score is, the less healthy the diet of the user in the third period is, the smaller the overall health score is, and the healthier the diet of the user in the third period is.

Therefore, the method can evaluate the health condition of multiple diets of the user in a period of time, and help the user to better know the physical condition of the user in a period of time.

With reference to the second aspect, in one implementation manner, the method further includes: the electronic device may display an actual blood glucose curve and a blood glucose standard curve.

Thus, the user can directly see the blood sugar condition of the user and the blood sugar difference between the user and healthy people through the interactive interface of the electronic equipment.

In a third aspect, an embodiment of the present application further provides a blood glucose management method, where the method is applied to an electronic device, and the method includes: acquiring first motion reference data before a user moves; outputting motion prompt information according to the first motion reference data, wherein the motion prompt information is used for prompting suggestion of the user on the motion; acquiring second motion reference data in the motion process of a user; outputting a motion alarm if the second motion reference data reflects that the motion risk of the user exceeds a fourth threshold; acquiring third movement reference data after the user finishes movement; outputting a motion evaluation report of the motion of the user according to the third motion reference data, wherein the motion evaluation report is used for evaluating the influence of the motion on the user; wherein the first, second and third motion reference data comprise blood glucose and parameters for reflecting the sign of the user.

By implementing the method provided by the embodiment of the application, the electronic equipment can monitor the physical condition of the user in the whole process from before the user moves to after the user moves, output a movement warning in the process of the user movement when the user moves, evaluate the movement of the user after the user movement is finished, and avoid the harm of too high or too low blood sugar to the body of the user in the process of the user movement as much as possible, thereby helping the user to move healthily and safely.

With reference to the third aspect, in one implementation, before acquiring the first motion reference data before the user moves, the method further includes: an operation that the user input is about to start the movement is detected, or that the current time reaches the movement time preset by the user is detected.

It can be seen that the electronic device may start to acquire the first motion reference data after recognizing that the user is about to start the motion, where two methods of recognizing that the user is about to start the motion are provided: 1) The user actively inputs to start movement; 2) The current time reaches the movement time preset by the user. Therefore, before the user starts to exercise, the current exercise risk of the user can be estimated according to the physical condition of the user in time, and the user can be prevented from exercising when the exercise risk is high in time, so that the user can exercise when the exercise risk is low.

With reference to the third aspect, in one implementation manner, outputting motion prompt information according to the first motion reference data specifically includes: the method comprises the steps of carrying out weighted summation calculation on the grades of all data in first motion reference data to obtain a first motion risk score, wherein the higher the data size of the data is from a normal range, the higher the grade of the data is;

when the first exercise risk score is less than lambda.TH, the exercise prompt information is a recommended exercise;

When TH > the first exercise risk score > lambda.TH, the exercise prompt information is exercise notice or an optimized exercise scheme;

When the first exercise risk score is larger than TH, the exercise prompt information is not recommended to exercise, wherein TH is determined according to basic information of the user and blood glucose characteristics reflecting blood glucose conditions of the user in a period of time.

That is, the exercise risk can be quantified through the physical condition of the user, so that the electronic equipment can evaluate the exercise risk of the user more accurately, and appropriate exercise suggestions can be obtained between different exercise risks, thereby helping the user to exercise more scientifically.

With reference to the third aspect, in one implementation manner, after acquiring the second motion reference data in the motion process of the user, the method further includes: a second athletic risk score is determined from the second athletic reference data, the second risk score being used to indicate athletic risk during the athletic event.

Similarly, in the movement process of the user, the movement risk in the movement process can be quantified according to the physical condition of the user, and the movement condition of the user can be mastered in real time.

With reference to the third aspect, in one implementation, the second athletic risk score is calculated according to the following formula:

G＝γ(t)∑c_iz_i

Wherein z _i represents the level of the ith motion reference data in the second motion reference data, c _i represents the weight of the ith motion reference data, and γ (t) represents a time attenuation factor which is a monotonically increasing function along with time t, wherein when the data size of the ith motion reference data is far from the normal range, the level of the ith motion reference data is higher, if the second motion risk score is larger, the motion risk is larger, and if the second motion risk score is smaller, the motion risk is smaller.

In the exercise process of the user, the exercise risk score is calculated by combining a time attenuation factor, and the time attenuation factor can be used for simulating the time delay high/low blood sugar risk which is gradually increased along with time, so that the exercise risk score in the exercise process of the user can reflect the exercise risk of the user more accurately and truly.

With reference to the third aspect, in one implementation, after outputting the motion alert, the method further includes: and after the user finishes the fourth time period of the movement, outputting the movement alarm again.

Therefore, after the exercise is finished, the user can be reminded of timely supplementing food, taking medicines and the like, and hyperglycemia caused by the exercise is avoided, so that the user has delayed hypoglycemia symptoms.

With reference to the third aspect, in one implementation, after the user finishes the movement, the method further includes: acquiring motion feedback information of a user; outputting a motion evaluation report of the motion of the user according to the third motion reference data, which specifically comprises the following steps: the motion estimation report of the current motion is output according to the motion feedback information and the third motion reference data, and the motion estimation report can further comprise suggestions of the next motion determined according to the motion feedback information.

The motion condition of the user is evaluated by combining the feedback data of the user, so that the content in the motion evaluation report output after the motion is finished can be more comprehensive and rich.

With reference to the third aspect, in one implementation manner, outputting a motion estimation report of the motion of the user according to third motion reference data specifically includes: and carrying out weighted summation on the grades of all the data in the third motion reference data to calculate a motion influence score, wherein when the data size of the data in the third motion reference data is far from a normal range, the grade of the data is higher, the motion influence score is larger, the influence of the motion on a user is larger, the motion influence score is smaller, and the influence of the motion on the user is smaller.

That is, when evaluating the influence of the movement on the user, the calculation can be performed by comprehensively considering a plurality of data, and the influence of the movement on the user can be evaluated as comprehensively and accurately as possible.

In a fourth aspect, an embodiment of the present application further provides a blood glucose management method, where the method is applied to an electronic device, and the method includes: firstly, identifying a first exogenous event through an event identification model, or predicting the first exogenous event through a work and rest rule of a user; the event recognition model is trained according to the historical blood glucose values and exogenous events of the user, and the first exogenous event can comprise one or more of the following: a eating event, a medication event, and a exercise event; acquiring the blood glucose value of the user in a fifth time before the current time point; finally, predicting the blood glucose value in the sixth time period after the current time point by utilizing the first exogenous event and the blood glucose value in the fifth time period.

By implementing the method provided by the embodiment of the application, the future blood glucose value of the user can be predicted based on the blood glucose value of the user and the exogenous event, the external factors which can influence the blood glucose concentration of the user are considered as much as possible, the accuracy of blood glucose prediction is improved, the association between the exogenous event and the blood glucose of the user is highlighted, so that the user can plan healthy exogenous events better, and the harm caused by hyperglycemia and hypoglycemia is effectively avoided. In addition, the user does not need to manually input exogenous events, the exogenous events happened to the user can be identified through the event identification model, the upcoming exogenous events can be predicted by utilizing the work and rest law of the user, the operation of the user is convenient, and meanwhile, the trouble of searching, inputting errors or omission of the user is avoided, so that the blood glucose prediction technology is more intelligent and perfected.

With reference to the fourth aspect, in one implementation manner, predicting the blood glucose value in the sixth duration after the current time point by using the first exogenous event and the blood glucose value in the fifth duration specifically includes: predicting the blood glucose value in the sixth time period after the current time point by using the blood glucose value in the first exogenous event and the fifth time period through a blood glucose prediction model; the blood glucose prediction model is obtained by training according to the known blood glucose value in a first historical period and the known external event in the first period, and the known blood glucose value in a second historical period, wherein the first period is an adjacent period before the second period.

That is, the future blood sugar value of the user can be predicted by using the blood sugar prediction model, and the relevance before and after the blood sugar and the influence of the exogenous event on the blood sugar are fully utilized, so that the future blood sugar can be rapidly and accurately predicted.

With reference to the fourth aspect, in one implementation manner, the event recognition model includes a motion recognition model, a medication recognition model, and a diet recognition model; the movement recognition model is used for recognizing the movement starting time, the movement quantity and the movement type of a movement event, the medication recognition model is used for recognizing the medication starting time, the medication quantity and the medicine type of a medication event, and the diet recognition model is used for recognizing the meal starting time, the meal quantity and the diet type of a diet event; if the first exogenous event comprises a first motion event identified by utilizing a motion identification model, when the blood glucose value is predicted by utilizing a blood glucose prediction model, the input of the blood glucose prediction model comprises the motion starting time, the motion quantity and the motion type of the first motion event; if the first exogenous event comprises a first drug event identified by using a drug identification model, and when the blood glucose value is predicted by using a blood glucose prediction model, the input of the blood glucose prediction model comprises the drug start time, the drug amount and the drug type of the first drug event; if the first exogenous event includes a first eating event identified by the eating identification model, the input of the blood sugar prediction model includes the eating start time, the eating amount and the eating type of the first eating event when the blood sugar value is predicted by the blood sugar prediction model.

With reference to the fourth aspect, in one implementation manner, the method further includes: the electronic device may display a fourth blood glucose profile that may be used to indicate a change in blood glucose of the user for a period of time prior to the current time point, and predict a change in blood glucose from the first exogenous event and the blood glucose value for a fifth time period after the current time point.

That is, the electronic device can display the blood sugar known by the user and the predicted blood sugar through the interactive interface, so that the user can know the past and future blood sugar change trend, grasp and manage the blood sugar change of the user, and avoid the harm caused by hyperglycemia and hyperglycemia in time.

With reference to the fourth aspect, in one implementation manner, the method further includes: the electronic device may also annotate the first exogenous event on the fourth blood glucose curve.

That is, the electronic device can display the known blood sugar and the predicted blood sugar of the user, and mark the exogenous event of the user in the electronic device, and correlate the blood sugar of the user with the exogenous event, so that the user starts from the exogenous event, timely controls the blood sugar change of the user, helps the user develop the exogenous event more scientifically and healthily, and improves the self-management capability of the user on the blood sugar.

With reference to the fourth aspect, in one implementation manner, the method further includes: adjusting the first exogenous event according to user operation; re-predicting the blood glucose value in the sixth time period by utilizing the adjusted blood glucose value in the first exogenous event and the fifth time period; and displaying a fifth blood glucose curve, wherein the fifth blood glucose curve is used for indicating the blood glucose change of the user in a period of time before the current time point and the predicted blood glucose change again in a period of time after the current time point.

That is, the user can manually adjust the exogenous event, the electronic device can synchronously display the exogenous event after adjustment, the predicted blood glucose curve is obtained, the participation of the user is enhanced, meanwhile, the association between the exogenous event and the blood glucose can be dynamically displayed, when the user needs to plan the healthy exogenous event, the exogenous event is adjusted on the premise that the future blood glucose fluctuates within a normal range, the adjusted exogenous event is used as the healthy exogenous event planned by the user, and the user is helped to manage and control the blood glucose change of the user more easily.

With reference to the fourth aspect, in an implementation manner, before the first exogenous event is identified by the event identification model or predicted by a user's action and rest law, the method further includes: and displaying a sixth blood glucose curve, wherein the sixth blood glucose curve is used for indicating the blood glucose change of the user in a period of time before the current time point and predicting the blood glucose change obtained by utilizing the blood glucose value in the fifth time period in a period of time after the current time point.

Thus, the blood glucose curve which is not predicted by considering the exogenous event and the blood glucose curve which is predicted by considering the exogenous event can be simultaneously displayed, and the difference of the exogenous event and the blood glucose prediction which is not predicted by considering the exogenous event in the method is highlighted.

With reference to the fourth aspect, in one implementation manner, before the first exogenous event is identified by the event identification model, the method further includes: it is determined that the rate of change of blood glucose of the user during the seventh time period before the current point in time is greater than a fifth threshold.

Thus, only when the blood glucose data of the user is determined to meet the preset condition, the exogenous event which has occurred by the user can be acquired, and the future blood glucose can be predicted by utilizing the exogenous event and the blood glucose data.

In a fifth aspect, embodiments of the present application provide an electronic device including a memory, one or more processors, and one or more programs; the one or more processors, when executing the one or more programs, cause the electronic device to implement the method as described in the first aspect or any one of the implementations of the first aspect, the second aspect or any one of the implementations of the second aspect, the third aspect or any one of the implementations of the third aspect, the fourth aspect or any one of the implementations of the fourth aspect.

In a sixth aspect, an embodiment of the present application provides a computer readable storage medium, including instructions, which when executed on an electronic device, cause the electronic device to perform a method as described in any one of the first aspect or any one of the implementations of the first aspect, any one of the second aspect or any one of the implementations of the third aspect, any one of the implementations of the fourth aspect or any one of the implementations of the fourth aspect.

In a seventh aspect, embodiments of the present application provide a computer program product which, when run on a computer, causes the computer to perform a method as described in any one of the first aspect or any one of the implementation manners of the first aspect, any one of the second aspect or any one of the implementation manners of the third aspect or any one of the fourth aspect.

Drawings

Fig. 1 is a schematic diagram of a communication system 1000 according to an embodiment of the present application;

Fig. 2 is a schematic flow chart of a blood glucose management method according to an embodiment of the present application;

FIG. 3 is an exemplary graphical illustration of a blood glucose profile for a user over a 24 hour period provided by an embodiment of the present application;

FIG. 4 is an exemplary graph of a blood glucose curve obtained by connecting key blood glucose values in a dietary event of a user within one month according to an embodiment of the present application;

FIG. 5 is an exemplary graphical illustration of a blood glucose profile for a user over a 24 hour period provided by an embodiment of the present application;

FIG. 6 is a flowchart of another blood glucose management method according to an embodiment of the present application;

FIG. 7 is a flowchart of a method for identifying a start of a meal by a user according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a process for normalizing two blood glucose curves to a blood glucose standard curve according to an embodiment of the present application;

Fig. 9 is a schematic flow chart of evaluating exercise risk before exercise of a user according to an embodiment of the present application;

Fig. 10 is a schematic flow chart of evaluating exercise risk in a user exercise process according to an embodiment of the present application;

FIG. 11 is a schematic flow chart of exercise risk assessment after exercise of a user according to an embodiment of the present application;

FIG. 12 is a flowchart of another blood glucose management method according to an embodiment of the present application;

FIG. 13 is a graph showing a blood glucose curve when an exogenous event is a currently occurring event according to an embodiment of the present application;

FIG. 14 is a graph showing a blood glucose curve when an exogenous event is a currently non-occurring event according to an embodiment of the present application;

fig. 15 is a schematic hardware structure of an electronic device 100 according to an embodiment of the present application;

Fig. 16 is a software architecture block diagram of an electronic device 100 according to an embodiment of the present application;

fig. 17 is a schematic diagram of a hardware structure of an electronic device 200 according to an embodiment of the present application;

Fig. 18 is a schematic hardware structure of an electronic device 300 according to an embodiment of the present application.

Detailed Description

The technical solutions of the embodiments of the present application will be clearly and thoroughly described below with reference to the accompanying drawings. Wherein, in the description of the embodiments of the present application, unless otherwise indicated, "/" means or, for example, a/B may represent a or B; the text "and/or" is merely an association relation describing the associated object, and indicates that three relations may exist, for example, a and/or B may indicate: the three cases where a exists alone, a and B exist together, and B exists alone, and furthermore, in the description of the embodiments of the present application, "plural" means two or more than two.

The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the application, unless otherwise indicated, the meaning of "a plurality" is two or more.

The term "User Interface (UI)" in the following embodiments of the present application is a media interface for interaction and information exchange between an application program or an operating system and a user, which enables conversion between an internal form of information and a form acceptable to the user. The user interface is a source code written in a specific computer language such as java, extensible markup language (extensible markup language, XML) and the like, and the interface source code is analyzed and rendered on the electronic equipment to finally be presented as content which can be identified by a user. A commonly used presentation form of a user interface is a graphical user interface (graphic user interface, GUI), which refers to a graphically displayed user interface that is related to computer operations. It may be a visual interface element of text, icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, widgets, etc., displayed in a display of the electronic device.

Diabetes is a metabolic disease characterized by hyperglycemia. The long-standing hyperglycemia can cause chronic damage and dysfunction of various tissues, especially eyes, kidneys, heart, blood vessels and nerves. It can be seen that in managing diabetes, it is necessary to monitor the blood glucose concentration in the patient in real time. Among other things, exogenous events such as exercise, diet, medication, etc. affect the change in blood glucose in a patient, and therefore monitoring of exogenous events is important for controlling the blood glucose concentration of a patient.

How to utilize the blood glucose level of the user to help the user to better grasp and control the influence of the exogenous event on the blood glucose is a problem to be solved at present.

In a first aspect, an embodiment of the present application provides a blood glucose management method, which is capable of determining a meal time of a user's eating event according to a blood glucose value of a user, and displaying the meal time and/or a blood glucose value related to the meal time on a blood glucose curve drawn from the blood glucose value of the user while displaying the meal time on the blood glucose curve.

Therefore, the user can know the change trend of the blood sugar concentration of the user in a period of time through the blood sugar curve, the diet event of the user is associated with the blood sugar of the user, the blood sugar change condition before and after meal intake of the user is highlighted, the user is helped to more intuitively know the influence of the diet event on the blood sugar, the user can grasp and manage the blood sugar concentration better, and the self-management capability of the user on diabetes can be enhanced for diabetics.

In a second aspect, the embodiment of the application further provides a blood glucose management method, which can obtain a blood glucose actual curve drawn by a blood glucose value of a user in a meal process, calculate the meal amount and the meal speed of the user in the meal process, obtain a blood glucose standard curve of a healthy crowd with the same meal amount and meal speed, and evaluate the diet health of the user by comparing the blood glucose actual curve with the blood glucose standard curve.

Therefore, the difference between the blood sugar of the user and the normal blood sugar of healthy people can be quantified based on the one-time meal condition of the user, the blood sugar value of the user in the meal process is helped, whether the blood sugar response of the user to the diet is healthy or not is estimated, and the body condition of the user is better known.

In a third aspect, the embodiment of the application further provides a blood glucose management method, which can calculate a exercise risk score of the exercise of the user based on exercise reference data before the exercise of the user, and output corresponding exercise prompt information according to the value of the exercise risk score; in addition, the method can also calculate the exercise risk score in the user exercise process based on the exercise reference data in the user exercise process, judge whether to output exercise warning according to the exercise risk score and interrupt the exercise of the user; finally, the method can also output a motion evaluation report of the motion according to the motion reference data of the user motion based on the motion reference data of the user motion after the user motion is finished. Wherein the exercise reference data includes blood glucose of the user, parameters reflecting signs of the user, and the like.

According to the method, the exercise risk of the user can be estimated around the whole process from the front of exercise to the end of exercise of the user according to the data related to the exercise of the user, the harm of too high or too low blood sugar to the physical health of the user is avoided as much as possible, and the user is helped to exercise healthier and safer.

In a fourth aspect, the embodiment of the present application further provides a blood glucose management method, where the method is capable of identifying an exogenous event through an event identification model or predicting an exogenous event through a user's work and rest law, where the event identification model is trained according to an exogenous event that occurs in a user's history and a historical blood glucose value, and the exogenous event may include one or more of the following: dietary events, medication events, exercise events, and then predicting future blood glucose data from the exogenous event and the blood glucose data for a period of time prior to the current time point.

It can be seen that the exogenous event which can influence the blood sugar change of the user is taken as a consideration factor for predicting the blood sugar, the accuracy of future blood sugar prediction can be increased, and the association between the blood sugar of the user and the exogenous event is highlighted, so that the user can start from the exogenous event better, and the change of the blood sugar of the user is regulated and controlled through regulating the exogenous event, so that the harm caused by overhigh or overlow blood sugar is avoided. In addition, the exogenous event does not need manual input of a user, so that the operation of the user is convenient, and the experience of the user is improved.

Fig. 1 is a schematic diagram of a communication system 1000 according to an embodiment of the present application.

In an embodiment of the present application, the communication system 1000 may include: display device and data acquisition device.

Wherein the display device may display text and/or picture information reflecting the user's blood glucose profile, such as a user exercise risk score, diet, exercise, medication advice, a user's blood glucose profile, etc., which may include a blood glucose profile of a user's blood glucose values connected over a period of time. The data acquisition device can be used for acquiring data of blood glucose value, heart rate, respiration, limb movement frequency, limb movement direction, limb movement amplitude, movement times and the like of a user.

The display device may include one or more devices, which may refer to a cell phone, a computer, a tablet, a wearable device, etc., that contains a display screen. The data acquisition device may include one or more devices, and the data acquisition device may acquire data through sensors included in the data acquisition device, or may acquire data input by a user or generate data by itself. Wherein, when the data acquisition device comprises a plurality of devices, different data acquisition devices can be used to acquire different data of the user. For example, the device for collecting blood glucose values of a user may refer to a continuous blood glucose monitoring (continuous glucose monitoring, CGM) device, which may implant a biosensor (microneedle sensor) subcutaneously in contact with tissue fluid to determine tissue fluid glucose concentration, and then obtain the blood glucose values by compensating for the delay between tissue fluid glucose and blood glucose.

It will be appreciated that the communication system 1000 may also include other devices, such as a storage device, which may be used to store data collected by the data collection device, text and/or picture information displayed by the display device, and so forth. The storage device may be referred to as a server, for example.

Illustratively, the communication system 1000 shown in fig. 1 comprises: electronic device 100, electronic device 200, and electronic device 300. As shown in fig. 1, the electronic device 100 may be a mobile phone, the electronic device 200 may be a CGM device, and the electronic device 300 may be a smart watch. The electronic device 100 may refer to the aforementioned display device, and the electronic device 200 and the electronic device 300 may refer to the aforementioned data collection device.

Communication connection can be established between any two devices of the electronic device 100, the electronic device 200 and the electronic device 300. Specifically, the communication connection may refer to a wired connection or a wireless connection. The wireless connection may refer to a near-range connection such as a high-fidelity wireless communication (WIRELESS FIDELITY, wi-Fi) connection, a bluetooth connection, an infrared connection, an NFC connection, a ZigBee connection, etc., or a far-range connection including, but not limited to, a far-range connection of a mobile network based on 2g,3g,4g,5g, and subsequent standard protocols. For example, the electronic device 100 and the electronic device 300 may establish a communication connection through bluetooth.

In the communication system 1000 shown in fig. 1, the electronic device 200 may collect a blood glucose level of a user and send the blood glucose level to the electronic device 100, and the electronic device 300 may be configured to collect data of a heart rate, respiration, a limb movement frequency, a limb movement direction, a limb movement amplitude, a movement number, and the like of the user and send the data to the electronic device 100, where the electronic device 100 processes the obtained data and displays text and/or picture information for reflecting the blood glucose level of the user.

It will be appreciated that more or fewer devices may be included in the communication system 1000 shown in fig. 1, e.g., the communication system 1000 may include only the electronic device 200 and the electronic device 300, the electronic device 200 may transmit the collected blood glucose values of the user to the electronic device 300, and the electronic device 300 may display text and/or picture information reflecting the blood glucose conditions of the user based on the data collected by itself and the received blood glucose values. In this case, the electronic device 200 may refer to the aforementioned display device and data collection device, and the electronic device 200 may refer to the aforementioned data collection device.

It should be noted that in the communication system 1000, one device may be a display device or a data acquisition device, or one device may be both a display device and a data acquisition device, which is not limited by the embodiment of the present application.

In order to help a user to better master and control the influence of an exogenous event on blood sugar, the application provides a plurality of blood sugar management methods around the blood sugar value of the user, helps the user to effectively control the change of the blood sugar as much as possible in daily life, and ensures the healthy life of the user.

These various blood glucose management methods are described below by way of example one to example four, respectively.

Examples 1

In order to monitor the blood sugar change of the user in real time, so that the user can adjust the blood sugar in time, the harm of diabetes is controlled, and the blood sugar condition of the user in a period of time can be reflected by displaying the blood sugar curve of the user in a period of time to the user.

However, the blood glucose curve is not marked with the diet condition of the user, or only the diet event of the user is marked in the blood glucose curve, and the intake of food directly affects the change of the blood glucose concentration of the user, only the blood glucose curve is displayed or only the fuzzy information of the diet event is displayed on the blood glucose curve, so that the user cannot be well helped to intervene or manage the change of the blood glucose, and the influence of diabetes on the health of the user is effectively controlled.

The embodiment of the application provides a blood sugar management method, which can calculate the meal time of a diet event of a user according to the blood sugar value of the user, and display the meal time on a blood sugar curve, and/or the blood sugar value related to the meal time, so that the user can correlate the blood sugar with the diet event, better know the blood sugar change before and after the meal, and facilitate the user to start from the diet, intervene and manage the change of the blood sugar concentration.

Fig. 2 shows a flow chart of a blood glucose management method according to an embodiment of the present application.

As shown in fig. 2, the method includes:

S101, acquiring the fasting blood glucose value of a user.

Fasting blood glucose level refers to the blood glucose level of a user in a fasting condition.

Wherein, can obtain the fasting blood glucose value of the user through any one of the following modes:

1) Determining the fasting blood glucose value based on the blood glucose value of the user over a period of time

Illustratively, the blood glucose value of the user may be collected over a period of time by a CGM device, such as the electronic device 200. The blood glucose values over the period of time include blood glucose values at a plurality of time points over the period of time.

Specific exemplary determination methods for determining fasting blood glucose values from blood glucose values may include, but are not limited to, the following three:

a) Determining the lower quartile of blood glucose values collected over a period of time as the fasting blood glucose value of the user

For example, the period of time may refer to 24 hours. That is, the blood glucose values collected within 24 hours of the user are ranked from small to large, and the blood glucose values ranked in one fourth are fasting blood glucose values of the user.

B) Determining the lower quartile in blood glucose values between two meals of the user as the fasting blood glucose value of the user over a period of time

For example, the period of time may refer to 24 hours, and the two meals may refer to the last meal a day before and the first meal a day after the 24 hours. That is, the blood glucose values collected from the last feeding of the user to the first feeding of the user in the following day are sorted from small to large, and the blood glucose values arranged in a quarter are the fasting blood glucose values of the user.

C) Determining the smaller value of the values obtained in the above a) and b) as the fasting blood glucose value of the user

It can be seen that, since the fasting blood glucose value is the blood glucose value of the user in the fasting condition, determining the lower quartile of the blood glucose values collected in a period of time as the fasting blood glucose value of the user can exclude the influence of the over-high blood glucose or the over-low blood glucose on the fasting blood glucose value of the user, and the fasting blood glucose value of the user can be obtained directly and accurately through the blood glucose value of the user in a period of time.

In addition, it is understood that the period of time may refer to the last period of time currently satisfactory, for example, 24 hours in method a is 24 hours back from the current point of time. In embodiments of the present application, this period of time may also be referred to as a second period of time.

2) Obtaining fasting blood glucose value determined at last time of meal

Because the blood glucose management method provided by the embodiment of the application can be repeatedly executed, a user can determine the dining time in different time periods by using the method for multiple times. Thus, when determining a meal time (e.g., a first meal time) for a period of time this time, the fasting blood glucose value determined when determining a meal time (e.g., a second meal time) for another period of time last time may be utilized.

Specifically, the fasting blood glucose value determined according to the last time of determining the meal time can be directly determined as the fasting blood glucose value which needs to be acquired before the meal time is determined. That is, when the meal time is determined by the blood glucose level management method this time, the fasting blood glucose level to be obtained in step S101 is the fasting blood glucose level determined by step S105 when the meal time was determined by the blood glucose level management method last time.

In particular, reference is made to the following step S105, in which the fasting blood glucose level determined from the meal time is not developed.

In some embodiments, the method of obtaining a fasting blood glucose value of method 1 may be applied in the first determination of a fasting blood glucose value, and the method of obtaining a fasting blood glucose value of method 2 may be applied in the non-first determination of a fasting blood glucose value. That is, in the process that the user continuously determines the meal time a plurality of times, the blood glucose management method is firstly applied to the user to determine the meal time, the blood glucose value of the user for a period of time is collected, and the fasting blood glucose value of the user is determined by using the blood glucose values, so that the meal time of one eating event of the user is determined by using the fasting blood glucose value, and the fasting blood glucose value of the user is updated according to the meal time.

It is to be understood that the obtained fasting blood glucose value may also be a smaller value of the fasting blood glucose values obtained in the above-described modes 1 and 2, respectively, and the mode of obtaining the fasting blood glucose value is not limited in the embodiment of the present application.

S102, judging whether a diet event exists in a preset period according to the fasting blood glucose value.

Whether the blood glucose value in the preset period meets the preset condition can be judged according to the fasting blood glucose value, if so, whether a diet event exists in the preset period is determined, and if not, the diet event does not exist in the preset period.

For example, the preset condition may refer to a blood glucose value within the preset period (e.g., the first period) exceeding a threshold (e.g., the first threshold) compared to a fasting blood glucose value. For example, the preset period may refer to a period having a time length of 1 hour, and further, the preset period may refer to a period in which 1 hour is traced back from the current point of time. The threshold may refer to 2mmol/L.

It will be appreciated that the length of the preset time period may also be other values, such as 2 hours, as embodiments of the present application are not limited in this regard. The threshold may also be other values, for example, the threshold may be adaptively modified according to different users, because different physical conditions of different users may cause different relative fasting blood glucose values of different users, and the embodiment of the present application does not limit the threshold.

When it is determined that there is a dietary event within the preset period according to the fasting blood glucose value, step S103 is performed.

S103, determining the meal time of the dietary event.

The meal time may refer to a period of time or a point of time, which is not limited in the embodiment of the present application.

The embodiment of the application provides two ways for determining the dining time:

1) The meal time of the dietary event can be determined based on the blood glucose level during the predetermined period

Wherein the meal time may be traced back from the blood glucose peak value within the preset period.

Specifically, determining the meal time may include: determining a time point (hereinafter, first time point) where the blood glucose peak value is located in the preset time period, and tracing back a preset duration (for example, second time period) from the time point to find another time point (hereinafter, second time point), wherein the meal time is located between the first time point and the second time point. A blood glucose value is obtained between the first time point and the second time point. Wherein, the blood glucose level G _t at the meal time t is the blood glucose level between these two time points, satisfying the earliest time point among the blood glucose levels in the following formula 1:

Wherein G ₀ represents a fasting blood glucose value, Δt represents an interval time for collecting a blood glucose value, t represents a meal time, blood glucose value G _t represents a blood glucose value of a user collected at the meal time, and G _t+Δt represents a blood glucose value of a user collected last time after the meal time.

As can be seen from equation 1, the time interval of the meal time from the time point at which the blood glucose peak is located is less than the preset time period (for example, 3 hours), and the blood glucose value of the meal time is greater than the fasting blood glucose value, and the blood glucose rise rate of the meal time is greater than 0.06.

It will be appreciated that the rate of rise of blood glucose at the meal time may also be greater than other thresholds, which are not limiting to embodiments of the present application, and may also be referred to as a second threshold in embodiments of the present application.

2) The meal time of the eating event can be determined according to the shooting time of the user for shooting food

That is, the shooting time point when the user shoots food is the meal time of the eating event.

In a specific implementation, in the process of taking a photo by a user, if the shot object in the shot picture is identified as food, the shooting time point of the shot photo is recorded, and further, when a dietary event is determined to exist in a preset time according to the fasting blood glucose value, the shooting time point is determined to be the dining time of the dietary event.

It will be appreciated that the manner in which the meal time is determined is not limited in the embodiments of the present application, for example, the meal time may also be entered by the user, as the embodiments of the present application are not limited in this regard.

S104, changing the meal time according to the operation of a user.

That is, after the meal time is calculated by the blood glucose value known to the user, the user can also manually change the meal time, correct the meal time, and ensure the accuracy of the meal time of the user.

It is understood that step S104 is an optional step.

S105, updating the fasting blood glucose value of the user according to the meal time.

For example, the updated fasting blood glucose value of the user may be an average of blood glucose values collected over a predetermined period of time (e.g., 30 minutes) prior to the meal time.

In the embodiment of the present application, the preset time period may also be referred to as a third time period.

S106, displaying a blood glucose curve formed by connecting blood glucose values of a plurality of time points of a user, and displaying meal time and/or blood glucose values related to the meal time on the blood glucose curve.

Wherein the blood glucose profile may include: blood glucose values collected at a plurality of consecutive time points including the preset time period are connected to form a blood glucose curve (e.g., a first blood glucose curve). The blood glucose profile may indicate a change in blood glucose of the user over the preset period of time.

For example, the blood glucose profile including the preset period may be a blood glucose profile in which blood glucose values having a time span of 24 hours are connected. The meal time related blood glucose values may include, but are not limited to: the blood glucose level at the meal time is a blood glucose level at a point in time that is a predetermined period of time (e.g., a first period of time) before or after the meal time, e.g., a blood glucose level 1 hour after the meal time, a blood glucose level 2 hours after the meal time, a blood glucose level 3 hours after the meal time, etc.

Fig. 3 is an exemplary schematic diagram of a blood glucose profile for a user within 24 hours according to an embodiment of the present application.

As shown in FIG. 3, the normal fasting blood glucose level was 3.9-6.1mmol/L, and the blood glucose profile also showed a fasting blood glucose level of 6.4mmol/L for breakfast of the user, a meal time of 7:00 for breakfast of the user, a blood glucose level of 6.5mmol/L for breakfast of the user, a blood glucose level of 9.5mmol/L for 1 hour after breakfast, a blood glucose level of 8.3mmol/L for 2 hours after breakfast, and a blood glucose level of 6.7mmol/L for 3 hours after breakfast.

From fig. 3, it can be seen that, through the blood glucose curve, the user can know the change condition of the blood glucose concentration of the user within 24 hours, and the blood glucose values of key time points of the dining event are marked, so that the user can conveniently identify the blood glucose values before and after the meal, and can conveniently and timely propose a coping strategy aiming at abnormal change of the blood glucose in the eating process of the user.

In addition, the blood glucose profile may further include: in a predetermined number of days (for example, N days, N is equal to or greater than 1, and N is a positive integer), in each day of eating events (for example, breakfast events), a blood glucose profile (for example, a second blood glucose profile) in which fasting blood glucose values are linked, a blood glucose profile (for example, a third blood glucose profile) in which blood glucose values at a time point before or after a meal time point are linked, that is, a blood glucose profile (for example, a third blood glucose profile) in which blood glucose values at a predetermined time point before or after a meal are linked, for example, a blood glucose profile (for example, a blood glucose profile) in which blood glucose values at 1 hour after a meal are linked, a blood glucose profile (for example, a glucose profile) in which blood glucose values at 2 hours after a meal are linked, a blood glucose profile (for 3 hours after a meal), and the like.

Taking a preset day as an example of one month, fig. 4 is an exemplary schematic diagram of a blood glucose curve formed by connecting key blood glucose values in a diet event of a user within one month according to an embodiment of the present application.

Wherein (a) in fig. 4 illustrates a blood glucose graph in which fasting blood glucose values of the same eating event in one month of the user are connected, (b) in fig. 4 illustrates a blood glucose graph in which blood glucose values of 1 hour after meal are connected in the same eating event in one month of the user, (c) in fig. 4 illustrates a blood glucose graph in which blood glucose values of 2 hours after meal are connected in the same eating event in one month of the user, and (d) in fig. 4 illustrates a blood glucose graph in which blood glucose values of 3 hours after meal are connected in the same eating event in one month of the user.

The change of the fasting blood glucose of the user for one month before meal is seen in fig. 4 (a), the change of the blood glucose of the user for one month after meal for 1h is seen in fig. 4 (b), the change of the blood glucose of the user for one month after meal for 2h is seen in fig. 4 (c), and the change of the blood glucose of the user for one month after meal for 3h is seen in fig. 4 (d). It can be seen that fig. 4 can help the user to view the blood glucose change condition of the user in a long term and the association between the diet event and the blood glucose change, so that the user can make an overall adjustment for the abnormal change of blood glucose on diet.

In some embodiments, in addition to displaying the blood glucose profile, dietary information of the dietary event may be displayed, for example, which may include, but is not limited to, one or more of the following: the type of food, the total calories of the food, the ratio of nutrients, etc. The diet information may be obtained by identifying a picture by an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) algorithm according to a picture taken by the user for the diet event, or may be obtained by user input. The embodiment of the application does not limit the way of obtaining the diet information.

Fig. 5 is an exemplary schematic diagram of a blood glucose profile for a user within 24 hours according to an embodiment of the present application.

In contrast to fig. 3, fig. 5 displays, in addition to the blood glucose profile, and the key blood glucose values on the blood glucose profile, the dietary information of the user's breakfast event, including, but not limited to, one or more of the following: calories of food, nutrient ratios, etc. Illustratively, as shown in fig. 5, the diet information includes: total calories 272 kcal, protein 9g, fat 12 g, carbohydrate 36 g, dietary fiber 2 g.

It can be seen that the user can learn about the detailed information of the meal of the user through fig. 5, in addition to the change in blood glucose concentration of the user and the association of the meal with blood glucose within 24 hours.

By way of example, a schematic representation of a blood glucose profile as shown in fig. 3, 4, 5 can be displayed by means of the aforementioned electronic device 100. In this way, the user can understand the blood glucose level of the user by viewing the content displayed by the electronic device 100.

It is understood that step S106 may be performed after step S103. That is, after the meal time is calculated based on the blood glucose level known to the user, a blood glucose curve is displayed, and the blood glucose level associated with the meal time is displayed on the blood glucose curve. Further, the user may manually modify the meal time, at which time the meal time displayed on the blood glucose profile and the blood glucose level associated with the meal time may be synchronously updated, and the fasting blood glucose level of the user may be updated based on the modified fasting blood glucose level.

In general, the blood glucose management method provided in the first embodiment can calculate the meal time of the user according to the blood glucose value known by the user, help the user to know the change condition of the blood glucose before and after the meal, further help the user to find the causal relationship between the meal, insulin and other various events affecting the blood glucose concentration, so that the change of the blood glucose concentration of the user and the adjustment of the diet can form positive feedback, and the management and control capability of the diabetes patient on the diabetes is improved. For example, when the user knows through a blood glucose profile that hypoglycemia occurs 2-3 hours after a meal of the user, the user can consider whether the symptoms of hypoglycemia are alleviated by adjusting the diet or oral medication, whether the diet or medication time, the type and amount of eating and medication need to be adjusted, and so forth.

Examples 2

Diabetes is a common disease, but only a small number of people who have specialized cognition. It is important for the general public how to evaluate the health of the public by blood glucose level. For example, if the user measures a self fasting blood glucose value of 7.3mmol/L, the user cannot know whether the fasting blood glucose value is normal or not only by the fasting blood glucose value if the user does not know the normal fasting blood glucose range.

Therefore, in order to help a user to use a blood glucose value and have clearer cognition on self health, the embodiment of the application provides a blood glucose management method, which can determine the meal size and the meal speed of the user in one meal process, acquire the blood glucose actual curve of the user in the meal process, and then acquire the blood glucose standard curve of healthy people with the same meal size and meal speed, and evaluate the diet health of the user through the blood glucose actual curve and the blood glucose standard curve, wherein the greater the difference between the blood glucose actual curve and the blood glucose standard curve is, the less healthy the diet of the user is, and the smaller the difference between the blood glucose actual curve and the blood glucose standard curve is, the healthier the diet of the user is.

Therefore, the method can quantify the difference between the blood sugar of the user and the standard blood sugar of the healthy people in the meal process, and evaluate the postprandial blood sugar health of the user. Therefore, even if the user does not have specialized knowledge on the expertise of diabetes, blood sugar and the like, the influence of dining on the blood sugar of the user can be easily known, and the user can conveniently intervene or manage and control the change of the blood sugar.

Fig. 6 is a schematic flow chart of another blood glucose management method according to an embodiment of the present application.

As shown in fig. 6, the method includes:

s201, acquiring meal related data acquired by a sensor in the process of dining of a user.

Meal related data refers to vital sign data related to a meal by a user. For example, the meal related data may include, but is not limited to: heart rate data, exercise data, mood pressure data, and the like.

Illustratively, meal related data of the user may be collected by the aforementioned sensors in the electronic device 100 and/or the electronic device 300.

It is noted that during the process of collecting meal related data through the sensor, the time nodes of beginning and ending the meal need to be identified and judged, so that the meal related data during the meal process can be obtained.

Wherein identifying the beginning of a meal may include, but is not limited to, the following two ways:

1) Detecting operation of user confirming start of meal

In this case, the user may actively input an operation to start a meal to an electronic device (e.g., electronic device 100) so that the electronic device recognizes that the user starts a meal and collects meal-related data during the user's meal through a data collection device (e.g., electronic device 300).

2) The data collected by the sensor meets the preset condition

In this case, the electronic device may collect all or part of the meal related data in real time, and determine that the user starts to eat the meal when it is determined that the all or part of the data meets the preset condition, and acquire the meal related data of the user during the process of eating the meal.

Wherein the data collected by the sensor is used to indicate heart rate, exercise, emotional stress, etc. data of the user. Since the heart rate of the user varies before and after eating, whether the user begins to eat can be determined by the difference of the heart rate of the user relative to the resting heart rate, and meanwhile, the heart rate variation of the user can be influenced by factors such as exercise, emotional stress and the like, so that the influence of exercise and emotional stress on the heart rate of the user needs to be eliminated before the user begins to eat is determined by the heart rate of the user.

That is, the preset condition may include: the difference in heart rate of the user relative to the resting heart rate remains greater than a threshold (e.g., a third threshold) after excluding the effects of exercise and emotional stress.

The detailed process of identifying the beginning of a meal from the data collected by the sensor is described below with respect to fig. 7.

Fig. 7 is a flowchart of a method for identifying a start of a meal by a user according to an embodiment of the present application.

As shown in fig. 7, the detailed process of identifying a user to begin a meal may include:

S301, acquiring a current heart rate value of a user, and determining a heart rate difference value of the current heart rate value and the resting heart rate value of the user.

Since the heart rate value changes when the user starts to eat the meal, it is possible to judge whether the user starts to eat the meal or not through the heart rate value of the user.

Wherein the heart rate value of the user and the resting heart rate value of the user may be obtained by a sensor (e.g., bone conduction sensor) in an electronic device (e.g., electronic device 100). Resting heart rate is also referred to as resting heart rate, and refers to heart rate in a resting, inactive, awake state.

The heart rate difference between the current heart rate value and the resting heart rate value of the user may be determined by the following equation 2:

Δhr=hr _N-HR₀ formula 2

Where ΔHR represents the heart rate difference, HR _N represents the user's current heart rate value, and HR ₀ represents the user's resting heart rate value.

S302, judging whether the heart rate difference value is larger than or equal to a threshold value 1.

When it is determined that the heart rate difference is greater than or equal to the threshold 1 (e.g., 10 times, 20 times, etc.), step S302 is performed.

S303, acquiring motion data of a user before the current time point.

The motion data of the user in a preset time before the current time point can be acquired. Illustratively, the preset duration may refer to 30 minutes, 1 hour, and so on.

That is, when the difference between the current heart rate value and the resting heart rate value of the user is greater than or equal to the threshold value 1, the motion data of the user in a previous period of time is acquired. The movement data reflects the movement of the user, which after movement affects the heart rate variation of the user.

Illustratively, the user's motion data may include, but is not limited to, one or more of the following: frequency of movement, speed of movement, distance of movement, etc. For example, when the movement performed by the user during a preset time before the current moment includes walking, the movement data of the user may include: step frequency, pace, walking distance.

By way of example, the user's motion data may be collected by a sensor (e.g., a gyroscope sensor, an acceleration sensor, etc.) in an electronic device (e.g., electronic device 300).

S304, calculating a movement influence factor of the meal of the user according to the movement data.

The heart rate of the user can be changed due to the movement of the user, so that the influence of the movement of the user on the heart rate is avoided, and the recognition of dining of the user is further influenced. Thus, the impact of the user's motion on meal recognition is removed prior to determining whether the user begins to eat.

Wherein the motion influencing factor can be calculated by the following formula 3:

P= Σa _ix_i equation 3

P denotes a motion influencing factor, x _i denotes motion data of a motion type, and a _i denotes a weight of the motion type.

S305, obtaining emotion pressure factors of dining of the user.

Similar to the user's movements, the heart rate changes are also affected by changes in the user's mood and stress in order to avoid that the user's mood and stress affect the recognition of the user's meal. Therefore, the influence of the emotion and pressure of the user on meal recognition is removed before judging whether the user starts to eat or not.

The mood stress factor of the user may include, but is not limited to, stress of the user, and variation Δr of mood. Illustratively, the amount of change Δr in the stress, emotion of the user may be measured by a sensor (e.g., a piezoelectric sensor).

S306, judging whether the heart rate difference value is still greater than or equal to a threshold value 2 after removing the influence of the exercise influence factor and the emotion pressure factor.

Illustratively, the determination of whether the user begins to eat may be accomplished by the following equation 4:

ΔHR- αP- βΔR is greater than or equal to TH2 equation 4

Where Δhr represents heart rate difference, P represents exercise influencing factor, α represents weight of exercise influencing factor, Δr represents mood stress factor, β represents weight of mood stress factor, TH2 represents threshold 2.

It can be seen that the current heart rate variation of the user relative to the resting heart rate may be derived from the influence of the exercise and the emotion pressure of the user and the dining effect, and the influence of the exercise and the emotion pressure of the user on the heart rate variation of the user can be removed through the formula 4, so that when the influence of the exercise and the emotion pressure of the user is removed and the heart rate variation of the user still exceeds a certain value, the user is informed of eating at present, otherwise, the user does not eat.

That is, when the heart rate difference value is still greater than or equal to the threshold value 2 after removing the influence of the exercise influence factor and the mood pressure factor, step S307 is performed, i.e., it is determined that the user starts to eat, otherwise step S308 is performed, i.e., it is determined that the user does not eat.

S307, determining that the user begins to eat.

And if the current user begins to eat, after the current moment and until the user finishes eating, the data acquired by the sensor are the data in the eating process of the user.

S308, determining that the user does not eat.

If it is determined that the user is not eating, steps S301-S308 may be repeated, i.e. it is determined in real time whether the user is beginning to eat.

As can be seen from steps S301-S308, since the heart rate changes due to the user taking meals, the time point when the user starts to take meals can also be identified by the heart rate of the user during the process of acquiring the heart rate value of the user in real time.

Correspondingly, identifying the end of the meal may include, but is not limited to, the following two ways:

1) User manually confirms that dining is finished

In this case, the user may actively input an operation of ending the meal to the electronic device (e.g., the electronic device 100) so that the electronic device recognizes that the user ends the meal, ending the acquisition of the meal related data.

2) Judging whether to finish dining according to the collected data

Since the end of a meal by a user may be accompanied by changes in the cardiovascular characteristics, exercise patterns, etc. of the user, it may be determined whether to end the meal by collecting data reflecting the cardiovascular characteristics, exercise patterns, etc. of the user.

The manner of determining to end the meal according to the collected different data may include, but is not limited to, the following ways:

a) Judging whether the user finishes dining or not through hand motion data of the user

The hand motion data of the user may be acquired by a wearable device (e.g., electronic device 300) and the user uses the hand wearing the wearable device to conduct a meal.

The user hand movement data may include, but is not limited to, one or more of the following: amplitude of hand movement, speed of hand movement, frequency of hand movement, etc.

For example, when the magnitude of the user's hand motion is less than a threshold and the magnitude of the decrease in the motion speed is less than a threshold, then it is determined that the user is finished eating.

B) Judging whether the user finishes dining or not through the body movement data of the user

Body movement data of the user may be acquired by a wearable device (e.g., electronic device 300).

The user body movement data may include, but is not limited to, one or more of the following: motion time, motion distance, motion match rate, motion frequency, motion speed, etc. These body movement data are used to indicate a movement pattern of the user, including, but not limited to: sitting, walking, running, jumping, etc.

For example, when the body movement data of the user reflects that the movement pattern of the user is changed from sitting to walking and the movement time of walking exceeds a threshold, it is determined that the user ends dining.

C) Judging whether the user finishes dining or not according to the related data of the cardiovascular characteristics of the user

Wherein the data related to the cardiovascular characteristics may include, but is not limited to, one or more of the following: heart rate, ventricular beat interval, blood oxygen saturation, and the like.

For example, when the relevant data reflects that the cardiovascular characteristics of the user are slowed, it is determined that the user has finished eating.

It will be appreciated that the data collected is not limited to the data mentioned above, nor is the manner in which the user is identified as ending a meal limited to the content mentioned above, as embodiments of the present application are not limited in this regard.

S202, calculating the dining amount and the dining speed of the user according to the dining related data acquired by the sensor.

After identifying the time point when the user begins to eat and ends to eat, the eating amount and the eating speed of the user in the eating process can be calculated according to the eating related data of the user in the eating process.

Specifically, the meal quantity and the meal speed of the user in the meal process can be identified and obtained by inputting meal related data of the user in the meal process into the meal model. The meal model is trained by using meal related data in the meal process with known meal quantity and meal speed.

Illustratively, meal size and meal speed may be calculated by the following equation 5:

q _meal,V_meal＝F(ΔHR,ΔR,fh_and,h_and,rh_and K) equation 5

Wherein, Q _meal represents meal volume, V _meal represents meal speed, Δhr represents heart rate variation, Δr represents skin electrical variation, fh _and represents hand motion frequency, nh _and represents hand motion times, rh _and represents hand motion amplitude, and F (·) represents meal model, the input of which comprises: Δhr, Δr, fh _and,nh_and,rh_and, the output comprising: q _meal,V_meal. For example, the meal model may be derived by training meal related data during a meal of a number of known meal sizes and meal speeds using a multiple linear regression algorithm.

As can be seen from equation 5, the meal related data may include heart rate values, skin electrical values, hand movement frequency, number of hand movements, hand movement amplitude, etc. acquired by the sensor from when the user starts to eat to when the user ends to eat. And calculating the dining amount and the dining speed through the related data of the user and the dining model. The heart rate value can be acquired through a bone conduction sensor, the skin electricity value can be acquired through a skin electric sensor, and the hand movement frequency, the hand movement times and the hand movement amplitude can be acquired through inertial sensors such as a gyroscope sensor and an acceleration sensor.

S203, acquiring a blood sugar actual curve of the user at the meal size and the meal speed.

The actual blood glucose curve may be formed by connecting a plurality of blood glucose values, where the plurality of blood glucose values are collected by an electronic device (e.g., the electronic device 200) from the beginning to the end of the meal. The actual blood glucose profile is used to indicate the change in blood glucose of the user during this meal.

For example, assuming that the user spends 30 minutes at this meal, from the beginning of the meal to the end of the meal, the actual blood glucose profile includes a plurality of blood glucose values collected over these 30 minutes.

S204, obtaining blood glucose curves of a plurality of members with the same meal quantity and meal speed in healthy people.

In the blood glucose management method, a diet database may be managed and maintained, in which a plurality of blood glucose values during meals of healthy people, i.e., people not suffering from diabetes, may be stored. That is, the blood glucose profile for each member of the healthy population at different meal size and different meal speed can be obtained from the diet database.

In a specific implementation, according to the dining amount and the dining speed of the user, a plurality of blood glucose curves of members with the same dining amount and the same dining speed as the user in the healthy crowd can be obtained from the diet database.

S205, determining a blood sugar standard curve of healthy people according to the plurality of blood sugar curves.

Because of the differences in the body of different members of a healthy population, there is a difference in the blood glucose profile between different members even if the members do not have diabetes. The difference may be manifested by different blood glucose levels at a single time point, and different lengths of time (different meal durations). Therefore, there is a need to adjust the multiple blood glucose curves to a blood glucose standard curve that characterizes the healthy population during meals at the same meal size and meal speed by means of a blood glucose normalization method. The blood glucose standard curve is used to indicate the change in blood glucose of a healthy population during a meal of the same meal size and meal speed of the user.

Illustratively, the blood glucose normalization method may comprise the following two steps:

Step 1: stretching or compressing the multiple blood glucose curves in the time domain and/or stretching or compressing the multiple blood glucose curves in the amplitude such that the average values of the multiple blood glucose curves in the time domain and the amplitude are equal

When the multiple blood glucose curves are subjected to lifting or compression transformation in the time domain, the transformation can be performed by taking the average value of the time lengths of the multiple blood glucose curves in the time domain as a reference, so that the time lengths of the multiple transformed blood glucose curves in the time domain are equal.

For example, assume that there are two blood glucose curves, one blood glucose curve describing a blood glucose change over 30 minutes, i.e., the blood glucose curve has a time period of 30 minutes in the time domain, and the other blood glucose curve describing a blood glucose change over 20 minutes, i.e., the blood glucose curve has a time period of 20 minutes in the time domain. Then the mean value of the time durations of the two blood glucose curves in the time domain is (30+25)/2=25 minutes. Therefore, when converting the blood glucose curve, it is necessary to compress the previous blood glucose curve from 30 minutes to 25 minutes in the time domain and stretch the next blood glucose curve from 30 minutes to 25 minutes in the time domain.

In addition, when the plurality of blood glucose curves are subjected to stretching or compression transformation in the amplitude, the transformation can be performed by taking the average value of the blood glucose values of the plurality of blood glucose curves in the amplitude as a reference, so that the blood glucose average values of the plurality of transformed blood glucose curves in the amplitude are equal.

For example, it is assumed that there are two blood glucose curves, one of which contains a plurality of blood glucose values having a mean value of 6.2mmol/L and the other of which contains a plurality of blood glucose values having a mean value of 6.8mmol/L. The mean value of the blood glucose in the amplitude of the two blood glucose curves is then (6.2+6.8)/2=6.5 mmol/L. Therefore, when the blood glucose curves are transformed, the former blood glucose curve needs to be stretched in amplitude, so that the average value of blood glucose in the amplitude is adjusted to 6.5mmol/L from 6.2mmol/L, and the latter blood glucose curve is compressed in amplitude, so that the average value of blood glucose in the amplitude is adjusted to 6.5mmol/L from 6.8mmol/L.

It will be appreciated that if the blood glucose profile meets the requirements in the time domain before transformation, there is no need to stretch or compress it in the time domain, and similarly, if the blood glucose profile meets the requirements in the amplitude before transformation, there is no need to stretch or compress it in the amplitude.

Step 2: calculating the point-by-point average value of the transformed multiple blood glucose curves, and taking the curve formed by connecting the average values as a blood glucose standard curve

That is, the blood glucose value at any time point on the blood glucose standard curve is the average of the blood glucose values at that time point on the plurality of transformed blood glucose curves.

For ease of understanding, the blood glucose normalization method is described below by way of a specific example.

Fig. 8 schematically illustrates a process of normalizing two blood glucose curves to a blood glucose standard curve.

Therein, (a) in fig. 8 illustrates a blood glucose curve 1 and a blood glucose curve 2 of a member having the same meal size and meal speed as the user in a healthy population. The blood glucose profile 1 and the blood glucose profile 2 describe the change in blood glucose over time during a member meal. As can be seen from blood glucose curve 1, the member spends 30 minutes eating time, with a blood glucose average of 6.2mmol/L. As can be seen from blood glucose curve 2, this meal time took 20 minutes, with a blood glucose average of 6.8mmol/L.

Fig. 8 (b) schematically shows a front-rear curve comparison chart (left) after time-domain (lateral) compression conversion of the blood glucose curve 1, and a front-rear curve comparison chart (right) after time-domain (lateral) stretch conversion of the blood glucose curve 2. Thus, the time-domain-transformed blood glucose level curve 1 and the time length of the blood glucose level curve 1 in the time domain are both 20 minutes.

On the basis of fig. 8 (b), fig. 8 (c) schematically shows a front-rear curve comparison chart (left) after the amplitude (longitudinal) stretch conversion of the blood glucose curve 1 after the time-domain conversion, and a front-rear curve comparison chart (right) after the amplitude (longitudinal) compression conversion of the blood glucose curve 2 after the time-domain conversion. Thus, the average value of the blood glucose levels of the blood glucose level curve 1 and the blood glucose level curve 2 after the amplitude conversion is 6.5mmol/L.

Fig. 8 (d) illustrates a transformed blood glucose curve 1, a blood glucose curve 2 and a blood glucose standard curve on the same coordinate axis, where the transformed blood glucose curve 1 and the blood glucose curve 2 are the blood glucose curves after time domain and amplitude transformation shown in fig. 8 (c), the blood glucose standard curve is obtained according to the transformed blood glucose curve 1 and the blood glucose curve 2, and the blood glucose value of a certain time point of the blood glucose standard curve is the average value of the blood glucose values of the transformed blood glucose curve 1 and the blood glucose curve 2 at the same time point.

It will be appreciated that fig. 8 illustrates only two blood glucose curves for determining a blood glucose standard curve, and in an embodiment of the present application, the blood glucose standard curve may be obtained according to 4 or 10 blood glucose curves, which is not limited in this embodiment of the present application.

S206, evaluating the diet health of the user through the blood glucose actual curve and the blood glucose standard curve.

Specifically, the user's dietary health may be assessed by comparing the difference in the actual blood glucose profile to the blood glucose standard profile. The more the actual blood glucose curve differs from the blood glucose standard curve, the less healthy the user's diet, the less the actual blood glucose curve differs from the blood glucose standard curve, and the more healthy the user's diet.

For example, the characteristics of the curve may be quantified by extracting features in the curve, by which the differences between the actual blood glucose curve and the blood glucose standard curve are compared.

Wherein the characteristics of the curve may include, but are not limited to, one or more of the following:

1) Maximum value Kmax of single blood sugar change

The maximum blood glucose change Kmax may refer to the maximum slope on the curve. Illustratively, the slope K may be determined by the following equation 6:

Where Δx represents the time variation, Δy represents the blood glucose variation, and (x ₁,y₁) and (x ₁,y₁) are two coordinate points on the curve. (x ₁,y₁) represents the blood glucose level on the curve at time point x ₁, and y ₁,(x₂,y₂ represents the blood glucose level on the curve at time point x ₂, and y ₂.

2) Non-stationary time T of blood sugar _N

The glycemic non-plateau time T _N can be determined by the following equation 7:

T _N＝T_ALL-T_S equation 7

Where T _ALL represents the total time of the curve in the time domain and T _s represents the glycemic plateau time on the curve. Illustratively, the glucose plateau time may be determined by determining the glucose plateau time on the curve from the glucose excursion coefficient.

3) Number of blood glucose values in different blood glucose change rate categories

That is, the blood glucose level included in the curve may be divided according to the blood glucose change rate, and the number of blood glucose levels in different blood glucose change rate categories may be counted.

Illustratively, the blood glucose rate of change c may be determined by the following equation 8:

where std represents the standard deviation of the blood glucose level contained in the curve, and avg represents the average value of the blood glucose level contained in the curve.

4) Number of blood glucose excursions F

The number of blood glucose excursions F may be determined according to the following equation 9:

F=f _up+F_down equation 9

Wherein F _up represents the number of blood glucose rises in the curve, and F _down represents the number of blood glucose drops in the curve.

5) Time x and amplitude y of a single blood glucose change

Where (x, y) is a point in the curve, which represents the blood glucose level y on the curve at time point x.

6) Envelope of curve

It will be appreciated that the curve may also include other features for characterizing the same, as the embodiments of the present application, without limitation.

After extracting the curve characteristics of the blood glucose actual curve and the blood glucose standard curve, the difference Diff between them can be calculated by the following equation 10:

Where, (q ₁-q₀)_i represents the difference between the actual blood glucose curve and the blood glucose standard curve for the same curve characteristic, q ₁ represents the curve characteristic in the actual blood glucose curve, q ₀ represents the curve characteristic in the blood glucose standard curve, the difference is used to indicate the health of the user's diet, the greater the difference, the less healthy the user's diet, the curve characteristic may include, for example, the aforementioned single blood glucose variation maximum Kmax, blood glucose non-plateau time T _N, the number of blood glucose values in different blood glucose variation rate categories, the number of blood glucose fluctuations F, the times x and magnitudes y of single blood glucose variation, the envelope of the curve, etc., n represents the number of curve characteristics, and λ _i represents the weight of the curve characteristic.

It can be seen that the actual blood glucose curve of the single diet of the user can be calculated by the formula 10, and the difference between the actual blood glucose curve and the standard blood glucose curve is obtained. The larger the Diff is, the larger the difference between the actual blood glucose curve of the diet of the user and the blood glucose standard curve is, which indicates that the blood glucose health condition of the diet of the user is less optimistic.

Further, for multiple diets of the user, the difference Diff _i between the actual blood glucose curve and the standard blood glucose curve of each diet of the user can be calculated by the method, and the blood glucose condition of multiple diets of the user in a period of time can be estimated by the following formula 11:

S= Σα _iβ(t)Diff_i equation 11

Where S represents the overall health score of the user 'S multiple diets over a period of time, α _i represents the weight for a single diet i over a period of time, β (t) is a time decay factor that makes the early statistical diet less weighted, diff _i represents the difference between the actual profile of the user' S blood glucose and the blood glucose standard profile for a single diet i over a period of time.

It can be seen that the greater the S, the greater the difference between the actual profile of blood glucose and the standard profile of blood glucose over a period of time for a plurality of diets of the user, which is indicative of the less optimistic diet health of the user over that period of time, and the lesser the S, the greater the difference between the actual profile of blood glucose and the standard profile of blood glucose over a period of time for a plurality of diets of the user, which is indicative of the more optimistic diet health of the user over that period of time.

In some embodiments, the actual blood glucose curve and the blood glucose standard curve may also be displayed, and, for example, the actual blood glucose curve and the blood glucose standard curve may be displayed in the same coordinate axis. Thus, the user can directly learn the health difference between the user and the healthy crowd by comparing the difference of the curves.

In general, the blood glucose management method provided in the second embodiment can quantify the difference between the blood glucose of the user and the blood glucose of the healthy population by using the blood glucose value of the user and taking the single diet of the user as a unit, evaluate the postprandial blood glucose health of the user, and help the user to better understand the physical condition of the user.

Example (III)

In daily life, exercise also affects the change in blood glucose concentration in the user's body. For patients with diabetes, improper exercise may aggravate the patient's condition and endanger the patient's physical health due to its lack of effective regulation of blood glucose.

The embodiment of the application provides a blood sugar management method, which can comprehensively evaluate the exercise risk of a user before, during and after exercise by combining various reference factors including blood sugar, heart rate, respiration and the like, helps the user to exercise more safely and healthily, and provides a guarantee for the exercise of the user to be safe and healthier.

Specifically, the blood glucose management method can be combined with a movement reference factor before movement of the user to determine a movement risk score of the user, and the suggestion of the user on the movement is output before the movement of the user; the blood sugar management method can also evaluate whether the high and low blood sugar risks exist in the movement process or after the movement of the user for movement reference factors in the movement process of the user, and output movement alarms in the movement process or after the movement of the user; the blood glucose management method can also be used for analyzing the exercise process of the user and outputting an exercise evaluation report by combining the exercise reference factors after the user exercises. Wherein the exercise reference factors include blood glucose and parameters for reflecting the user's signs, such as heart rate, respiration, blood pressure, etc.

The detailed process of assessing the risk of exercise before the exercise of the user is described below in connection with fig. 9.

Fig. 9 is a schematic flow chart of assessing exercise risk before exercise of a user according to an embodiment of the present application.

As shown in fig. 9, the evaluation of the exercise risk before the exercise of the user mainly includes:

s401, recognizing that the user is about to start moving.

Illustratively, the impending movement of the user may be identified by the following two means:

1) Detecting operation of user input about to start movement

For example, the electronic device 100 may detect that a user inputs an operation on the electronic device 100 that is about to start movement, and in response to the operation, the electronic device 100 recognizes that the user is about to start movement.

2) Detecting that the current time reaches the movement time preset by the user

In this case, the preset exercise time of the user needs to be obtained in advance, for example, the user sets to perform rope skipping exercise in the afternoon of 5:00 in advance, when the time reaches the afternoon of 5:00, the user is determined to start exercising, or the exercise time of the user is determined in advance according to the exercise habit of the user, for example, when the user is identified to perform running exercise in the morning of 9:00 every day, when the time reaches the morning of 9:00, the user can be pre-determined to start exercising.

It will be appreciated that embodiments of the present application are not limited in the manner in which the user is identified as about to initiate movement.

S402, acquiring motion reference data before the user moves.

The method comprises the step of acquiring motion reference data of a preset duration before a user moves. Illustratively, the preset duration may be 20 minutes, 30 minutes, etc., which embodiments of the present application do not limit.

The motion reference data is used to predict a risk of motion of the user, and may include, but is not limited to: blood glucose data, heart rate, blood pressure, respiration, etc. Wherein the blood glucose data may include, but is not limited to, one or more of the following: blood glucose level, number of blood glucose excursions, magnitude of blood glucose excursions, etc.

S403, calculating a sports risk score according to the sports reference data.

The athletic risk score is used to predict the risk of an upcoming athletic event for the user. For example, the greater the athletic risk score, the greater the risk that indicates the user's upcoming athletic activity may be, and the lesser the athletic risk score, the lesser the risk that indicates the user's upcoming athletic activity may be.

Illustratively, the athletic risk score G may be calculated by the following equation 12:

G= Σa _ix_i equation 12

Where x _i represents the level of the ith motion reference data, and a _i represents the weight of the ith motion reference data.

The level of the motion reference data may be determined according to the magnitude of the motion reference data. For example, the level of the motion reference data may be determined according to a mapping table of the motion reference data and the level, the mapping table including levels corresponding to the motion reference data of different numerical sizes. The motion reference data comprises: for example, blood pressure, heart rate, and blood glucose, table 1 illustrates a mapping table of exercise reference data and levels.

TABLE 1

As can be seen from table 1, the motion reference data corresponds to different levels in different numerical intervals, wherein the higher the value deviates from the normal range of the motion reference data, the higher the level thereof indicates that the user is at greater risk of a possible motion to be started.

Specifically, when calculating the exercise risk score, the average value of each data in the acquired exercise reference data of a period of time before exercise may be calculated first, then the grade of each data in the exercise reference data is determined through a mapping table similar to the values and grades shown in table 1, and finally the exercise risk score is calculated by using the above formula 12.

In some implementations, when the user's athletic performance includes multiple athletic items, an athletic risk score may also be determined in conjunction with athletic information for the multiple athletic items entered by the user, which may include, but is not limited to, one or more of the following: type of movement, intensity of movement, duration of movement, etc.

Illustratively, the athletic risk score may be determined according to the following equation 13:

G= Σa _ix_i·∑b_jy_j equation 13

Where x _i represents the level of the ith motion reference data, a _i represents the weight of the ith motion reference data, y _j represents the motion information of the jth motion item, and b _j represents the weight of the jth motion item.

S404, calculating a movement risk threshold value.

The exercise risk threshold may be determined based on basic information of the user, blood glucose characteristics reflecting the blood glucose of the user over a period of time, and the like.

Wherein the user's underlying information may include, but is not limited to, one or more of the following: age, sex, body Mass Index (BMI), type of disease, whether or not complications such as hypertension/hyperlipidemia, etc. exist. Body mass index is used for the degree of fitness and fitness of the population with sound volume and whether it is healthy.

The glycemic characteristics may include, but are not limited to, one or more of the following: average blood glucose level over a period of time, average blood glucose rate of change, coefficient of variation. The coefficient of variation is the percentage of standard deviation of blood glucose and average value of blood glucose. The coefficient of variation is used to reflect fluctuations in the blood glucose of the user over a period of time.

Illustratively, with the user's index, the exercise risk threshold TH may be calculated by the following equation 14:

Th= Σc _kt_k equation 14

Where t _k represents the level of the kth index of the user, which index level may be determined according to a mapping table of indexes and levels, and c _k represents the weight of the kth index.

It should be understood that the index-to-grade mapping includes different numerical intervals of the index or different instances of the index, and grades corresponding to different numerical intervals or different instances. Illustratively, the mapping table may refer to the aforementioned table 1. For example, the user has a body quality index of greater than 30 or less than 15, a rating of 3, a rating of 2 between 25 and 30 or between 15 and 20, a rating of 1 between 20 and 25, and for example, the user has a gender of male, a rating of 2, and a gender of female, a rating of 1.

S405, outputting exercise prompt information according to the exercise risk score and the exercise risk threshold.

The output exercise prompt information can be determined by comparing the exercise risk score G with the exercise risk threshold TH. The exercise prompt information is used for prompting suggestion of the user on the exercise.

By way of example, the following lists motion cues in several different situations:

1) When G < lambda.TH (lambda < 1), the output motion prompt information is the recommended motion

When the athletic risk score G is less than λ times the athletic risk threshold, indicating that there is little or no risk of currently performing the athletic activity, suggesting that the user perform the athletic activity.

2) When TH > G > lambda.TH, the output prompt information is exercise notice or optimized exercise scheme

When the exercise risk score G is greater than λ times and less than the exercise risk threshold, it indicates that there may be a risk in performing exercise currently, so that the user may be reminded of exercise caution matters related to the user, or the exercise scheme of the user may be optimized, so that the risk of exercise of the user is reduced as much as possible.

Among other things, exercise notes may include: prompting the user to perform a warming exercise before exercise, prompting the user to pay attention to the change of heart rate, paying attention to drinking water, performing a recovery and arrangement exercise after exercise, prompting the user to take medicine in advance when hyperglycemia risk exists or supplementing sugar in advance when hypoglycemia risk exists according to the blood sugar level of the user in a preset time before exercise, and the like.

The exercise scheme for optimizing the user is mainly applied to the case that the user inputs exercise information of a plurality of exercise items, and at this time, an exercise risk score is determined according to exercise reference data and the exercise information of the exercise items, specifically referring to the foregoing formula 13. Wherein optimizing the motion profile may include: the exercise intensity and/or the exercise duration of one or more of the exercise items input by the user are reduced. The adjusted athletic item may refer to an athletic item that contributes more to the athletic risk score.

For example, the user may be about to start exercise including rope skipping for 30 minutes and running for 30 minutes, and the product of the exercise information and the weight of the rope skipping is greater than the product of the exercise information and the weight of the running in the calculated exercise risk score, so that the rope skipping contributes more to the exercise risk score. When the motion scheme is optimized, the motion time of the rope skipping can be reduced, and the motion time of the rope skipping is shortened to 10 minutes. Thus, the output motion estimation information may include: "to ensure your safety, it is recommended to shorten the rope jump time to 10 minutes".

3) When G is more than TH, the output motion prompt information is not recommended motion

When the exercise risk score is larger than the exercise risk threshold, the risk of the current exercise is larger, and the user is not recommended to exercise.

As can be seen from steps S401-S405, before the user exercises, the physical condition before the user exercises can be comprehensively considered to evaluate whether the user is suitable for exercises at present, so that the user can exercise as healthy and safe as possible.

The detailed process of assessing exercise risk during user exercise is described below in connection with fig. 10.

Fig. 10 is a schematic flow chart of evaluating exercise risk in a user exercise process according to an embodiment of the present application.

As shown in fig. 10, the evaluation of exercise risk during exercise of the user mainly includes:

S501. determining that the user starts moving.

For example, the user's movement data may be collected by a sensor, and when the movement data indicates that the user changes from a non-movement state to a movement state, it is determined that the user starts movement.

S502, acquiring motion reference data in a user motion process.

The motion reference data is used to characterize the motion situation of the user. Wherein the motion reference data may include, but is not limited to, one or more of the following: blood glucose, heart rate, blood pressure, respiration, etc.

Illustratively, the user's blood glucose may be collected by the electronic device 200 and the user's heart rate, blood pressure, and respiration may be collected by the electronic device 300.

S503, calculating a sports risk score according to the sports reference data.

The athletic risk score is used to indicate athletic risk during exercise. Illustratively, the athletic risk score may be determined according to the following equation 15:

g=γ (t) Σc _iz_i formula 15

Where z _i denotes the level of the ith motion reference data, c _i denotes the weight of the ith motion reference data, and γ (t) denotes a time decay factor that is a monotonically increasing function following time t, which is used to simulate a delayed high/low glycemic risk that increases gradually over time.

The level of the motion reference data may be determined from the value of the motion reference data. For example, the level of the motion reference data may be determined according to a mapping table of the motion reference data and the level, and the mapping table may include levels corresponding to the motion reference data of different numerical sizes.

It can be seen that the greater the athletic risk score, the greater the athletic risk, and the lesser the athletic risk score, the lesser the athletic risk.

In some embodiments, the athletic risk score may also be determined in conjunction with user base information, which may include information about the user's height, weight, medical history, etc., and the manner in which the athletic risk score is calculated in accordance with the present application is not limited.

S504, judging whether the exercise risk score exceeds a threshold value.

The exercise risk score is a parameter calculated in real time according to exercise reference data acquired in the exercise process of the user. Thus, the athletic risk score may be constantly changing with time.

When the athletic risk score exceeds the threshold, it indicates that there is an athletic risk in the athletic activity being performed by the user, and step S505 is performed, otherwise, it continues to determine whether the athletic risk score exceeds the threshold.

In an embodiment of the present application, this threshold may also be referred to as a fourth threshold.

S505, outputting a motion alarm.

The motion alert may include an audible alert, a vibration alert, or a text alert, among others. The exercise alert is used to prompt the user to terminate the current exercise, and prompt the user to timely supplement food, take medicine, perform physical rehabilitation exercise, etc.

In addition, optionally, after the user finishes the exercise for a preset time (for example, a fourth time), the exercise alarm can be output again to remind the user to supplement food, take medicine and the like in time, so that hyperglycemia caused by exercise is avoided, and delayed hypoglycemia symptoms are caused to the user.

From steps S501-S505, it can be seen that in the process of user exercise, the exercise condition of the user can be monitored in real time, the exercise risk of the user is estimated by comprehensively considering the multi-item sign data in the process of user exercise, and when the exercise risk is large, the user is reminded to interrupt exercise in time, so that the exercise health and safety of the user are ensured.

The detailed process of performing exercise risk assessment after user exercise is described below in connection with fig. 11.

Fig. 11 is a schematic flow chart of exercise risk assessment after exercise of a user according to an embodiment of the present application.

As shown in fig. 11, performing exercise risk assessment after a user exercise may include the steps of:

s601, acquiring motion reference data of single motion of a user after the motion is finished.

The exercise reference data may include blood glucose, heart rate, blood pressure, respiration, exercise time, exercise distance, etc. data during a single exercise of the user. Wherein the blood glucose data may include: maximum or minimum of a plurality of blood glucose values collected during a single exercise of the user, blood glucose fluctuation amplitude, number of blood glucose fluctuations, number of times of exceeding a high/low blood glucose threshold, and the like.

It should be understood that embodiments of the present application are not limited to motion reference data.

S602, acquiring motion feedback information of a user.

The motion feedback information may include a user's motion perception that is fed back after the motion is completed. For example, the athletic feedback information may include a user's score for athletic strength, feedback for physical discomfort, and so forth.

It should be noted that step S602 is an optional step.

S603, outputting a motion estimation report of the motion.

Wherein the motion assessment report may be determined from motion reference data of the user, the motion assessment report being usable to assess the impact of this motion on the user.

The influence of the motion on the user can be represented by the motion influence score, and the higher the motion influence score is, the larger the influence of the motion on the user is, the lower the motion influence score is, and the smaller the influence of the motion on the user is.

Illustratively, the athletic impact score may be determined by the following equation 16:

G= Σd _iw_i equation 16

Where w _i denotes the level of the i-th motion reference data, and c _i denotes the weight of the i-th motion reference data.

Further, the athletic assessment report may also include a suggestion for the next athletic movement, which may be determined based on the user's athletic feedback information.

The advice of the next movement can comprise the adjustment advice of the movement type, the movement duration and the movement intensity of the next movement. For example, when the motion feedback information of the user indicates that the motion intensity is too high, the motion intensity of the next motion may be appropriately reduced based on the motion intensity of the next motion, when the motion feedback information of the user indicates that the motion duration is too short, the motion duration of the next motion may be appropriately increased based on the motion duration of the next motion, and when the motion feedback information of the user indicates that the user moves the arm ache after the motion of the next motion, the motion type of the next motion may be selected to avoid the arm from moving too much.

In some embodiments, a motion evaluation report for the motion of the user within a period of time can be output according to the motion reference data of the multiple motions of the user, the motion condition of the user within a period of time is reflected, the motion risk of the user within a period of time is evaluated, and the user is helped to more comprehensively know the physical condition of the user.

From steps S601-S603, it can be seen that, when the user finishes the exercise, the exercise situation of the user is summarized by comprehensively considering the multiple sign data in the exercise process of the user, the exercise is taken as a reference, advice is provided for the next exercise of the user, and assistance is provided for the user to perform long-term healthy exercise.

Example (IV)

Diabetes can make the patient's body have no way to control blood glucose concentration normally. High blood sugar is likely to cause metabolic disorders in the patient's body, and low blood sugar is likely to cause damage to the central nerve of the patient. Thus, it is important for diabetics to pay attention to their own blood glucose conditions in real time. The patient can take medicines or diets in time according to the change of blood sugar, and the blood sugar concentration can be controlled to fluctuate in a normal range, so that the harm caused by too high or too low blood sugar is reduced.

It is further envisaged that if the trend of future blood glucose changes can be known in advance, the method is more effective for the user to control the blood glucose of the user, and the user can take corresponding measures in time before the blood glucose is too high or too low, so that the harm caused by the too high or too low blood glucose can be avoided in time while the blood glucose concentration can be controlled to fluctuate within a normal range.

In order to predict the future blood glucose concentration, a blood glucose prediction method is to obtain a blood glucose prediction model capable of predicting the future blood glucose concentration through model training according to historical blood glucose data known by a user. Thus, when the user inputs the collected blood glucose level into the blood glucose prediction model, the blood glucose prediction model can output the future blood glucose level of the user.

However, although the blood glucose prediction method can predict the future blood glucose level of the user, the blood glucose level of the user may be affected by exogenous events such as exercise, diet, medication, etc., and it is not accurate to predict the future blood glucose only by the historical blood glucose level of the user.

The embodiment of the application provides a blood glucose management method, which can identify an exogenous event through an event identification model, or predict the exogenous event through a work and rest rule of a user, and predict the blood glucose value of a period of time before a current time point and the blood glucose value of a period of time after the current time point through the exogenous event, so that the effect of predicting the future blood glucose value by utilizing the exogenous event and the historical blood glucose value is realized.

It can be seen that the blood glucose management method provided by the embodiment of the application can predict the future blood glucose value of the user based on the blood glucose value of the user and the exogenous event, considers the external factors which can influence the blood glucose concentration of the user as much as possible, improves the accuracy of blood glucose prediction, highlights the association between the exogenous event and the blood glucose of the user, and helps the user to plan healthy exogenous events better, thereby effectively avoiding the harm caused by hyperglycemia and hypoglycemia. In addition, the user does not need to manually input an exogenous event, so that the operation of the user is convenient, and meanwhile, the problem of inaccurate model prediction caused by incorrect input of the user is avoided.

Fig. 12 is a flowchart of another blood glucose management method according to an embodiment of the present application.

As shown in fig. 12, the blood glucose management method may include:

s701, identifying an exogenous event through an event identification model, or predicting the exogenous event through a work and rest rule of a user.

Exogenous events include one or more of the following: eating events, medication events, exercise events. Wherein, the occurrence of the exogenous event can cause the blood sugar of the user to fluctuate. Therefore, the blood glucose management method provided by the embodiment of the application introduces exogenous events which occur before the current time point or exogenous events which can occur after the current time point to predict the future blood glucose value.

In embodiments of the present application, this exogenous time may also be referred to as a first exogenous event.

Wherein, the exogenous event can be obtained by the following two ways:

1) For the exogenous event which has occurred before the current time point, the exogenous event can be obtained through recognition of an event recognition model

The event recognition model is obtained through training according to exogenous events and historical blood glucose values which occur in the history of a user.

Since the blood glucose concentration of a user is related to an exogenous event, the change in blood glucose concentration reflects to some extent the exogenous event that occurred on the user. Therefore, the event recognition model can be trained through the blood glucose values and the exogenous events of which the user history is known, and the rule between the exogenous events and the blood glucose can be found. Thus, the exogenous event can be identified based on the event identification model and the known blood glucose data.

Wherein, according to different exogenous events, the event recognition model can comprise: a movement recognition model, a medication recognition model and a diet recognition model.

The following describes the generation modes of the movement recognition model, the medication recognition model and the diet recognition model respectively:

a) Motion recognition model

The motion recognition model can be used to recognize the motion start time, the motion amount, and the motion type of the motion event.

Illustratively, the motion recognition model may include: a motion start time recognition model, a motion amount recognition model, and a motion type recognition model.

The motion start time identification model can identify the motion start time according to the values of the characteristics such as variability, mean value, change rate and the like of blood glucose values in a period of time. For example, when the variability and the mean value of the blood glucose values of the user over a period of time satisfy a preset condition, the movement start time is determined to be a certain designated time associated with the preset period of time.

The preset condition may be determined based on the blood glucose characteristics of the historical blood glucose data and the movement start time of the historical movement event. By way of example, the preset condition may refer to a condition that a majority (e.g., 80%) of the historical exercise events satisfy a characteristic of blood glucose during a period of time before and after exercise. For example, according to the historical exercise event and the historical blood sugar data, the variability of blood sugar of the user is large within 1 hour of starting exercise, so if the variability of blood sugar of the user within 1 hour is recognized to be large, the user is determined to have an exercise event, wherein the starting time of 1 hour is the exercise starting time of the exercise event.

The motion amount recognition model may refer to a regression model, such as a support vector classification (Support Vector Classification, SVC), random forest, linear regression, etc. model. Wherein, the model Φ1 (·) can be represented by the following equation 17:

L1=Φ1 (G) formula 17

Where G represents a continuous blood glucose value recorded over a period of time and L1 represents the amount of movement of a user-triggered exercise event over the period of time, which may refer to the amount of heat consumed by the user to perform the exercise event, e.g., 100kcal,1000kj.

That is, the exercise amount recognition model may be obtained by training a model using the blood glucose values known over the history period and the exercise amounts of the exercise events known to occur over the history period. Thus, given the exercise amount recognition model and the continuous blood glucose values recorded over a period of time, the amount of exercise that resulted in an exercise event occurring over that period of time can be recognized.

The motion class recognition model may refer to a classification model, such as a support vector machine (Support Vector Machine, SVM), random forest, logistic regression, etc. model. Wherein, the model X1 (·) can be represented by the following equation 18:

s1=x1 (g 1, g2, …, gn) equation 18

Where g1, g2, …, gn denote blood glucose characteristics, such as average, root mean square, etc., extracted from a plurality of blood glucose values over a period of time, S1 denotes a type of movement of a user-triggered movement event over the period of time, for example, the type of movement may include: high intensity motion, low intensity motion, etc.

That is, the exercise category identification model may be derived by training the model using blood glucose values known over a historical period of time and exercise types of exercise events known to occur over the historical period of time. Thus, given the motion type recognition model and the continuous blood glucose values recorded over a period of time, the type of motion that resulted in the motion event occurring over the time period can be recognized.

B) Drug administration identification model

The medication identification model can be used to identify the start time, dosage, and type of medication of a medication event.

Illustratively, the medication intake identification model may include: a medication start time identification model, a medication amount identification model, and a medication type identification model.

The medication start time identification model can identify the medication start time according to the values of the characteristics such as variability, average value, change rate and the like of blood sugar values in a period of time. For example, when variability and average of blood glucose values of a user over a period of time satisfy a preset condition, it is determined that the administration start time is a certain specified time associated with a preset relationship of the period of time.

The preset condition may be determined based on the blood glucose characteristics of the historical blood glucose data and the medication start time of the historical medication event. Illustratively, the preset condition may refer to a condition that a majority (e.g., 80%) of the historical medication events satisfy a characteristic of blood glucose during a period of time before and after the medication. For example, according to the historical medication event and the historical blood sugar data, the change rate of the blood sugar of the user is larger than a certain threshold value in half an hour after the medication is started, so that if the change rate of the blood sugar of the user in half an hour is recognized to be larger than the threshold value, the medication event of the user is determined to exist, wherein the start time of the half an hour is the medication start time of the medication event.

The dose recognition model may refer to a regression model, such as an SVC, random forest, linear regression, etc. model. Wherein, the model Φ2 (·) can be represented by the following formula 19:

L2=Φ2 (G) formula 19

Where G represents a continuous blood glucose value recorded over a period of time and L2 represents a medication amount of a medication event triggered by a user over the period of time, which may refer to a medication amount taken by the user to perform the medication event, such as 1u (1 unit), 50mg, and so forth.

That is, the medication quantity identification model may be obtained by training the model using the known blood glucose values over the historical period and the medication quantity of medication events that are known to occur over the historical period. Thus, given the medication quantity identification model and the continuous blood glucose values recorded over a period of time, the medication quantity of the medication event occurring over the period of time can be identified.

The drug class identification model may refer to a classification model, such as an SVM, random forest, logistic regression, etc. model. Wherein, the model X2 (·) can be represented by the following formula 20:

s2=x2 (g 1, g2, …, gn) equation 20

Where g1, g2, …, gn denote blood glucose characteristics, such as average, root mean square, etc., extracted from a plurality of blood glucose values over a period of time, and S2 denotes the type of drug, such as metformin, acarbose, fast acting insulin, etc., of the drug administered by the user during the user-triggered medication event over the period of time.

That is, the drug class identification model may be obtained by training a model using a known blood glucose value over a history period and a drug class of a medication event known to occur over the history period. Thus, when the drug type identification model and the continuous blood glucose level recorded over a period of time are known, the drug type of the medication event occurring over the period of time can be identified.

C) Diet identification model

The diet identification model can be used to identify the start time of administration, meal size, and diet type of a diet event.

Illustratively, the diet identification model may include: a meal start time identification model, a meal quantity identification model and a diet type identification model.

The meal start time identification model can identify the meal start time according to the values of the characteristics such as variability, mean value, change rate and the like of blood glucose values in a period of time. For example, when the variability and average of the blood glucose values of the user over a period of time satisfy a preset condition, the meal start time is determined to be a certain specified time associated with the preset of the period of time.

The preset condition may be determined based on the glycemic characteristics of the historical glycemic data and the meal start time of the historical dietary event. By way of example, the preset condition may refer to a condition that a majority (e.g., 80%) of the historical dietary events satisfy a characteristic of blood glucose during a period of time before and after a meal. For example, from historical eating events and historical blood glucose data, it is known that the average of blood glucose of a user within 1 hour after a half hour from the beginning of a meal would be more than 7.2mmol/L, so if the average of blood glucose of a user within 1 hour is identified to be more than 7.2mmol/L, then it is determined that a eating event exists for the user, wherein the time point at which the half hour is traced back from the starting time of the 1 hour is the eating starting time of the eating event.

The meal recognition model may refer to a regression model, such as an SVC, random forest, linear regression, etc. model. Wherein, the model Φ3 (·) can be represented by the following equation 21:

L3=Φ3 (G) formula 21

Where G represents a continuous blood glucose value recorded over a period of time and L3 represents the meal size of a user-triggered eating event over the period of time, which may refer to the calories ingested by the user to perform the eating event, e.g., 100KCal, 1000KJ, etc.

That is, the meal size identification model may be derived by training the model using known blood glucose values over a historical period of time and meal sizes of dietary events known to occur over the historical period of time. Thus, given the meal size identification model and the continuous blood glucose values recorded over a period of time, the meal size of the eating event occurring over the period of time can be identified.

The diet category recognition model may refer to a classification model, such as an SVM, random forest, logistic regression, etc. model. Wherein, the model X3 (·) can be represented by the following formula 22:

s3=x3 (g 1, g2, …, gn) equation 22

Where g1, g2, …, gn represents the blood glucose characteristics extracted from a plurality of blood glucose values over a period of time, such as average, root mean square, etc., and S3 represents the dietary category of the diet ingested by the user during the user-triggered dietary event over the period of time, such as high glycemic index foods, medium glycemic index foods, low glycemic index foods, etc.

That is, the model may be trained to derive the diet category identification model using known blood glucose values over a historical period and the diet category of the diet event known to occur over the historical period. Thus, given the diet type recognition model and the continuous blood glucose values recorded over a period of time, the diet type of the diet event occurring over the period of time can be recognized.

2) Exogenous events possibly occurring after the current time point can be obtained through prediction of the work and rest rules of the user

Under the condition, the daily work and rest rules of the user can be summarized according to a large amount of information of the user in a period of time, and further the exogenous event is predicted in advance. The bulk information may include, but is not limited to: user-set calendar activities, data collected by various sensors (e.g., respiration, blood glucose, heart rate, blood pressure, exercise time, amount of exercise, etc.), user browsing and searching records on an application, and so forth.

For example, a user may be determined to have a movement habit of jogging 9:00 a day for half an hour from a large amount of movement data of the user over a period of time, and thus, based on this movement habit, a movement event of the user 9:00 a day in the morning may be identified.

It will be appreciated that the electronic device 100 may also learn of the exogenous event by other means, such as, for example, the user may actively input the exogenous event, including inputting information related to the exogenous event.

Illustratively, when a user inputs a sporting event, it may include inputting a sporting start time, a sporting type, a quantity of exercise, etc. of the sporting event, when a user inputs a medication event, it may include inputting a medication start time, a medication type, a medication quantity, etc. of the medication event, and when a user inputs a eating event, it may include inputting a meal start time, a eating type, a meal quantity, etc. of the eating event.

For example, the user may input to have a meal at 12:00 pm each day. For another example, the user may input that the hypoglycemic agent was taken 6:00 pm the previous day. For another example, the user may input that running exercise is required in the morning 9:00.

In some implementations, the user can alter the exogenous event. For example, for a sporting event, the user may alter the athletic start time, amount of movement, type of movement, etc. for a sporting event, for a medication event, the user may alter the medication start time, amount of medication, type of medication, etc. for a eating event, the user may alter the meal start time, amount of meal, type of diet, etc. for the eating event.

Therefore, the user can manually adjust the exogenous event for predicting the future blood sugar, the participation degree of the user is enhanced, and the predicted future blood sugar can be more accurate for the exogenous event manually corrected by the user, so that the accuracy of predicting the future blood sugar is improved.

S702, acquiring the blood glucose value of the user for a period of time before the current time point.

Illustratively, the blood glucose value of the user may be collected by an electronic device (e.g., electronic device 200) for a period of time (e.g., a fifth time period) prior to the current point in time. The blood glucose values over the period of time may include a plurality of consecutive blood glucose values.

S703, predicting the blood glucose value within a period of time after the current time point by using the exogenous event and the blood glucose value.

Illustratively, with the exogenous event and the blood glucose value, the blood glucose value within a period of time (e.g., a sixth time period) after the current point in time may be predicted by a blood glucose prediction model.

The blood sugar prediction model is input into an exogenous event and a historical blood sugar value, and is input into a future blood sugar value, and is obtained by training according to the known blood sugar value in the historical period and the known exogenous event in the previous period. Wherein during the training, the known blood glucose value and the exogenous event in a previous period (e.g., a first period) are input data of the model, and the known blood glucose value in a subsequent period (e.g., a second period) is output data of the model.

The acquisition mode of the exogenous event involved in the model training process can comprise: 1) The event identification model is used for identification; 2) Is entered by the user. The specific manner of acquiring the exogenous event may refer to the content related to step S701, which is not described herein.

It can be seen that the blood glucose prediction model can predict a plurality of blood glucose values for a future period of time based on the plurality of blood glucose values for the period of time and the exogenous event for the period of time.

The blood glucose prediction model may refer to a time series prediction model, for example, an autoregressive model, a linear regression model and other traditional time series prediction models, and may also be a random forest, support vector regression (Support Vector Regression, SVR) and other machine learning models.

Illustratively, the blood glucose prediction model ψ (·) can be represented by the following equation 23:

G _after＝Ψ(G_before, C) equation 23

Wherein G _before represents the blood glucose level of the user for a period of time before time point T0, G _after represents the blood glucose level of the user for a period of time after time point T0, and C represents an exogenous event that occurs for a period of time before time point T0, the exogenous event comprising one or more of: exercise event C1, medication event C2, diet event C3. Wherein, C1 can be represented by three parameters of exercise start time T1, exercise amount L1 and exercise type S1, C2 can be represented by three parameters of administration start time T2, administration amount L2 and medicine type S2, and C3 can be represented by meal start time T3, meal amount L3 and diet type S3.

In the process of training the blood sugar prediction model, the blood sugar value input by the blood sugar prediction model and the blood sugar value output by the blood sugar prediction model are both collected blood sugar values. The training process of the model is specifically that after a blood glucose value collected for a period of time and an exogenous event are input into a blood glucose prediction model, parameters of the blood glucose prediction model are adjusted so that the output of the blood glucose prediction model is the blood glucose value collected for a preset time period after the period of time, and the blood glucose prediction model after parameter adjustment is the blood glucose prediction model obtained through training.

In addition, in the process of training the model, when the blood glucose value input by the model does not generate an exogenous event within a period of time, the input does not contain the exogenous event. It can be seen that the blood glucose prediction model ψ (·) can also be expressed as: g _after＝Ψ(G_before), the input of the blood glucose prediction model is the blood glucose value G _before of the user for a period of time before the time point T0, and the output is the blood glucose value G _after of the user for a period of time after the time point T0.

It can be seen that, for the blood glucose prediction model, only the blood glucose value of the user for a period of time is input, and the blood glucose value of the future period of time can be predicted, but if the exogenous event occurring in the period of time is not input into the blood glucose prediction model, the predicted blood glucose value is not accurate after the exogenous event is input. Therefore, the exogenous event generated by the user is also taken as a factor for predicting the blood sugar, so that the accuracy of blood sugar prediction can be improved.

In the process of predicting the future blood sugar by utilizing the blood sugar prediction model, specifically, the acquired exogenous event and the acquired blood sugar value in a period of time before the current time point are taken as input parameters of the blood sugar prediction model, the input parameters are input into the blood sugar prediction model for prediction, and the output parameters of the blood sugar prediction model are the blood sugar value in a period of time after the current time point obtained by prediction.

Wherein, when the exogenous event comprises a motion event (e.g. a first motion event), the input of the blood glucose prediction model comprises a motion start time, a motion amount, a motion type of the motion event when predicting blood glucose using the blood glucose prediction model; when the exogenous event includes a medication event (e.g., a first medication event), the input of the blood glucose prediction model includes a medication start time, a medication amount, a medication type of the medication event when the blood glucose is predicted using the blood glucose prediction model; when the exogenous event comprises a dietary event (e.g., a first dietary event), the inputs to the blood glucose prediction model include a meal start time, a meal size, a dietary category of the dietary event when the blood glucose is predicted using the blood glucose prediction model.

In addition, it should be noted that, when the external event is an event that does not occur after the current time point, when the blood glucose prediction model is used to predict the blood glucose, the blood glucose value predicted from the current time point to the event start time of the external event is predicted from the blood glucose data before the current time point by the blood glucose prediction model, and the blood glucose value after the event start time is predicted from the blood glucose predicted from the event start time and the collected blood glucose value by the blood glucose prediction model.

It will be appreciated that when no exogenous event is present, only the blood glucose data is used, and that blood glucose data for a period of time after the current point in time can also be predicted by the blood glucose prediction model.

In some embodiments, since the external event may cause the blood glucose of the user to change greatly in a short time, before the external event is obtained, the external event may be determined by the blood glucose data of a period of time before the current time point, so as to determine whether to identify and obtain the external event. For example, in the case where the rate of change of blood glucose in an adjacent period (e.g., 10 minutes) before the current time point is greater than a threshold (e.g., a fifth threshold), an exogenous event that has occurred before the current time point may be triggered to be acquired, and then future blood glucose may be predicted in combination with the exogenous event and the known blood glucose data, otherwise only the known blood glucose data may be used to predict future blood glucose. Thus, only when the blood glucose data of the user is determined to meet the preset condition, the exogenous event which has occurred by the user can be acquired, and the future blood glucose can be predicted by utilizing the exogenous event and the blood glucose data.

In some embodiments, after predicting the blood glucose for a period of time after the current time point using the exogenous event and the blood glucose value for a period of time before the current time point, a blood glucose profile (e.g., a fourth blood glucose profile) may be displayed indicating a change in blood glucose for the user for a period of time before the current time point, and predicting the change in blood glucose for a period of time after the current time point using the exogenous event and the blood glucose value for a period of time before the current time point. Thus, the user can know the past and future blood sugar change trend, grasp and intervene the blood sugar change of the user, and avoid the harm caused by hyperglycemia in time. The blood glucose profile may be displayed by the electronic device 100, for example.

In some embodiments, the exogenous event may also be marked on the displayed blood glucose curve. Therefore, the blood sugar of the user can be associated with the exogenous event, so that the user starts from the exogenous event, and the blood sugar change of the user can be controlled in time, thereby helping the user develop the exogenous event more scientifically and healthily, and improving the self-management capability of the user on the blood sugar.

In some embodiments, after predicting future blood glucose, the exogenous event may be further adjusted according to the user operation, the adjusted exogenous event and blood glucose value may be reused, the future blood glucose may be predicted, and the adjusted blood glucose curve (e.g., the fifth blood glucose curve) may be redisplayed, where the adjusted blood glucose curve is used to indicate a change in blood glucose for a period of time before the current time point, and the predicted blood glucose change may be re-predicted for a period of time after the current time point.

That is, the user can manually adjust the exogenous event, and after the exogenous event is adjusted, the predicted blood glucose curve is obtained, so that the participation of the user is enhanced. Meanwhile, the association between the exogenous event and the blood sugar can be known dynamically, and when the user needs to plan the healthy exogenous event, the exogenous event can be adjusted on the premise that the future blood sugar fluctuates in a normal range, and the exogenous event obtained through adjustment is used as the healthy exogenous event planned by the user, so that the user is helped to manage and control the blood sugar change of the user more easily.

In some embodiments, a predicted blood glucose profile (e.g., a sixth blood glucose profile) may also be displayed before the acquisition of the exogenous event, the predicted blood glucose profile indicating a change in blood glucose of the user over a period of time before the current time point, and a predicted change in blood glucose using the blood glucose value over a period of time before the current time point over a period of time after the current time point. Thus, the blood glucose curve which is not predicted by considering the exogenous event and the blood glucose curve which is predicted by considering the exogenous event can be simultaneously displayed, and the difference of the exogenous event and the blood glucose prediction which is not predicted by considering the exogenous event in the method is highlighted.

Fig. 13 and 14 show schematic diagrams of blood glucose curves for exogenous events in different situations, respectively.

Fig. 13 illustrates a schematic diagram of a blood glucose profile in the case where the exogenous event is a currently occurring event.

As shown in fig. 13, the exogenous event includes a eating event, which is ingestion of 100kCal of water, and an exercise event, which is a running exercise consuming 50kCal of heat, the occurrence time of the exogenous event is located before the current time point. The blood glucose curve before the current time point is drawn according to the blood glucose value of the user history, the original blood glucose curve (dotted line) after the current time point is an unidentified exogenous event, the blood glucose value predicted by the blood glucose prediction model is directly drawn according to the blood glucose value before the current time point, the corrected blood glucose curve (solid line) after the current time point is drawn according to the blood glucose value before the current time point and the exogenous event identified before the current time point after the exogenous event is identified, and the blood glucose value predicted by the blood glucose prediction model is drawn. As can be seen from the comparison of the original blood glucose curve and the corrected blood glucose curve after the current time point, the blood glucose value in the blood glucose curve is predicted without the recognition of the exogenous event, which is generally lower than the blood glucose value in the blood glucose curve predicted after the recognition of the exogenous event, compared with the case that the exogenous event is not recognized, the recognition of the exogenous event and the prediction of the future blood glucose by utilizing the exogenous event are more accurate.

Fig. 14 illustrates a schematic diagram of a blood glucose profile in the case where the exogenous event is an event that does not currently occur.

As shown in fig. 14, the exogenous event includes a dietary event, which is ingestion of 200kCal of water, and a medication event, which is ingestion of 10u of insulin, the occurrence of which is located after the current time point. The blood glucose curve before the current time point is drawn according to the blood glucose value of the user history, the original blood glucose curve (dotted line) after the current time point is drawn according to the blood glucose value predicted according to the blood glucose value before the current time point, the corrected blood glucose curve (solid line) after the current time point is drawn according to the blood glucose value before the current time point and the external event predicted after the current time point, the blood glucose value predicted by the blood glucose prediction model is drawn, and the original blood glucose curve and the corrected blood glucose curve between the current time point and the occurrence time point of the external event coincide. Compared with the original blood glucose curve and the corrected blood glucose curve after the current time point, the blood glucose value in the blood glucose curve predicted by the unidentified exogenous event is generally lower than the blood glucose value in the blood glucose curve predicted by the unidentified exogenous event, and compared with the blood glucose value in the blood glucose curve predicted by the unidentified exogenous event, the blood glucose value predicted by the unidentified exogenous event identifies the exogenous event possibly occurring in the future, and the influence of the exogenous event on the blood glucose of the user is considered, so that the prediction of the future blood glucose value can be more accurate.

As can be seen from steps S701-S703, when predicting future blood glucose, taking exogenous events that can affect the change in blood glucose as external considerations when predicting blood glucose can improve the accuracy of blood glucose prediction. Further, exogenous events are identified through the event identification model and the work and rest law of the user, so that the trouble of manual input of the user can be reduced, the trouble of searching, input error or omission of the user is avoided, and the blood sugar prediction technology is more intelligent and perfected.

It can be appreciated that the methods of blood glucose management in the above four embodiments provided by the present application may be mutually fused. For example, the electronic device 100 may calculate the meal time of the user, display the meal time and the relevant blood glucose value of the meal time on the blood glucose curve, find a blood glucose standard curve of the healthy population, which has the same meal amount and meal speed as the user, according to the meal amount and meal speed of the current eating event of the user, and display the blood glucose standard curve on a colleague displaying the blood glucose curve of the user, or the electronic device 100 may display the meal time of the user on the blood glucose curve while predicting the future blood glucose value and displaying the blood glucose curve including the past and future blood glucose. Therefore, the interactive interface of the electronic equipment can be used for simultaneously displaying a plurality of items of information to the user, so that the user can know the blood sugar condition of the user from multiple aspects, and the user can better control the blood sugar change of the user from an exogenous event.

Fig. 15 is a schematic hardware structure of an electronic device 100 according to an embodiment of the present application.

The electronic device 100 may be a cell phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, and cellular telephone, personal Digital Assistant (PDA), augmented reality (augmented reality, AR) device, virtual Reality (VR) device, artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) device, wearable device, vehicle-mounted device, smart home device, and/or smart city device, and the specific type of the electronic device is not particularly limited by the embodiments of the present application.

The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (universal serial bus, USB) interface 130, a charge management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, keys 190, a motor 191, an indicator 192, a camera 193, a display 194, and a subscriber identity module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyro sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, and the like.

It should be understood that the illustrated structure of the embodiment of the present application does not constitute a specific limitation on the electronic device 100. In other embodiments of the application, electronic device 100 may include more or fewer components than shown, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, such as: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (IMAGE SIGNAL processor, ISP), a controller, a video codec, a digital signal processor (DIGITAL SIGNAL processor, DSP), a baseband processor, and/or a neural-Network Processor (NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.

In some embodiments, the processor 110 may be configured to perform the correlation steps of the foregoing embodiments one-four. For example, in one embodiment, the processor 110 may be configured to obtain a fasting blood glucose value of the user, determine whether a dietary event exists within a predetermined period of time, determine a meal time for the dietary event, update the fasting blood glucose value, and the like. For example, in the second embodiment, the processor 110 may be configured to determine a heart rate difference, determine whether the heart rate difference is greater than or equal to a threshold 1, calculate a exercise influence factor, determine whether the user begins to eat according to the heart rate difference, the exercise influence factor, and the mood pressure factor, and further, the processor 110 may be configured to obtain data related to eating collected by the sensor during the eating of the user, calculate an eating amount and an eating speed, obtain a blood glucose actual curve and a blood glucose standard curve, and evaluate the eating health of the user according to the blood glucose actual curve and the blood glucose standard curve. For example, in the third embodiment, the processor 110 may be configured to obtain the exercise reference data before the exercise of the user after identifying that the user is about to start exercise, calculate the exercise risk score and the exercise risk threshold, determine the exercise prompt information, and the processor 110 may be further configured to obtain the exercise reference data during the exercise of the user after the user starts exercise, calculate the exercise risk score to determine whether to input the exercise alert, and the processor 110 may be further configured to obtain the exercise reference data of the single exercise of the user after the user finishes exercise, and exercise feedback information, determine the exercise evaluation report, and so on. For another example, in embodiment four, the processor 110 may be configured to train a blood glucose prediction model, predict future blood glucose using user blood glucose data and exogenous events, and so forth. The details can be found in the descriptions of the first embodiment to the fourth embodiment, and the details are not repeated here.

The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Repeated accesses are avoided and the latency of the processor 110 is reduced, thereby improving the efficiency of the system.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charge management module 140 may receive a charging input of a wired charger through the USB interface 130. In some wireless charging embodiments, the charge management module 140 may receive wireless charging input through a wireless charging coil of the electronic device 100. The charging management module 140 may also supply power to the electronic device through the power management module 141 while charging the battery 142.

The power management module 141 is used for connecting the battery 142, and the charge management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140 to power the processor 110, the internal memory 121, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be configured to monitor battery capacity, battery cycle number, battery health (leakage, impedance) and other parameters. In other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charge management module 140 may be disposed in the same device.

The wireless communication function of the electronic device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, a modem processor, a baseband processor, and the like.

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the electronic device 100 may be used to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed into a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution for wireless communication including 2G/3G/4G/5G, etc., applied to the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc. The mobile communication module 150 may receive electromagnetic waves from the antenna 1, perform processes such as filtering, amplifying, and the like on the received electromagnetic waves, and transmit the processed electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can amplify the signal modulated by the modem processor, and convert the signal into electromagnetic waves through the antenna 1 to radiate. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be provided in the same device as at least some of the modules of the processor 110.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low frequency baseband signal to the baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs sound signals through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays images or video through the display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 150 or other functional module, independent of the processor 110.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (WIRELESS FIDELITY, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation SATELLITE SYSTEM, GNSS), frequency modulation (frequency modulation, FM), near field communication (NEAR FIELD communication, NFC), infrared (IR), etc., applied to the electronic device 100. The wireless communication module 160 may be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, demodulates and filters the electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, frequency modulate it, amplify it, and convert it to electromagnetic waves for radiation via the antenna 2.

In some embodiments, the electronic device 100 may obtain data collected by other devices, such as the electronic device 200, the electronic device 300, including blood glucose data, heart rate, exercise data, etc., through the mobile communication module 150 or the wireless communication module 160.

In some embodiments, antenna 1 and mobile communication module 150 of electronic device 100 are coupled, and antenna 2 and wireless communication module 160 are coupled, such that electronic device 100 may communicate with a network and other devices through wireless communication techniques. The wireless communication techniques can include the Global System for Mobile communications (global system for mobile communications, GSM), general packet radio service (GENERAL PACKET radio service, GPRS), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), BT, GNSS, WLAN, NFC, FM, and/or IR techniques, among others. The GNSS may include a global satellite positioning system (global positioning system, GPS), a global navigation satellite system (global navigation SATELLITE SYSTEM, GLONASS), a beidou satellite navigation system (beidou navigation SATELLITE SYSTEM, BDS), a quasi zenith satellite system (quasi-zenith SATELLITE SYSTEM, QZSS) and/or a satellite based augmentation system (SATELLITE BASED AUGMENTATION SYSTEMS, SBAS).

The electronic device 100 implements display functions through a GPU, a display screen 194, an application processor, and the like. The GPU is a microprocessor for image processing, and is connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display 194 includes a display panel. The display panel may employ a Liquid Crystal Display (LCD). The display panel may also be manufactured using organic light-emitting diodes (OLED), active-matrix organic LIGHT EMITTING diode (AMOLED), flexible light-emitting diodes (FLED), miniled, microled, micro-OLED, quantum dot LIGHT EMITTING diodes (QLED), or the like. In some embodiments, the electronic device may include 1 or N display screens 194, N being a positive integer greater than 1.

In some embodiments, the display 194 may be used to display blood glucose profiles, motor prompts before a user moves, motor alarms during movement, motor assessment reports after movement has ended, and the like. The blood glucose curve may be referred to as the curve shown in fig. 3,4, 5, 13 or 14, among others, for example.

The electronic device 100 may implement photographing functions through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The ISP is used to process data fed back by the camera 193. For example, when photographing, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electric signal, and the camera photosensitive element transmits the electric signal to the ISP for processing and is converted into an image visible to naked eyes. ISP can also perform algorithm optimization on noise and brightness of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (charge coupled device, CCD) or a Complementary Metal Oxide Semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then transferred to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV, or the like format. In some embodiments, electronic device 100 may include 1 or N cameras 193, N being a positive integer greater than 1.

In some embodiments, camera 193 may be used to take pictures of food while the user is dining. So that the processor 110 determines the time at which the user starts to eat based on the photographing time.

The digital signal processor is used for processing digital signals, and can process other digital signals besides digital image signals. For example, when the electronic device 100 selects a frequency bin, the digital signal processor is used to fourier transform the frequency bin energy, or the like.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 may play or record video in a variety of encoding formats, such as: dynamic picture experts group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

The NPU is a neural-network (NN) computing processor, and can rapidly process input information by referencing a biological neural network structure, for example, referencing a transmission mode between human brain neurons, and can also continuously perform self-learning. Applications such as intelligent awareness of the electronic device 100 may be implemented through the NPU, for example: image recognition, speech recognition, text understanding, etc.

The internal memory 121 may include one or more random access memories (random access memory, RAM) and one or more non-volatile memories (NVM).

The random access memory may include a static random-access memory (SRAM), a dynamic random-access memory (dynamic random access memory, DRAM), a synchronous dynamic random-access memory (synchronous dynamic random access memory, SDRAM), a double data rate synchronous dynamic random-access memory (double data rate synchronous dynamic random access memory, DDR SDRAM, such as fifth generation DDR SDRAM is commonly referred to as DDR5 SDRAM), etc.; the nonvolatile memory may include a disk storage device, a flash memory (flash memory).

The FLASH memory may include NOR FLASH, NAND FLASH, 3D NAND FLASH, etc. divided according to an operation principle, may include single-level memory cells (SLC-LEVEL CELL), multi-level memory cells (multi-LEVEL CELL, MLC), triple-level memory cells (LEVEL CELL, TLC), quad-LEVEL CELL, QLC), etc. divided according to a memory cell potential order, may include general FLASH memory (english: universal FLASH storage, UFS), embedded multimedia memory card (eMMC) MEDIA CARD, eMMC), etc. divided according to a memory specification.

The random access memory may be read directly from and written to by the processor 110, may be used to store executable programs (e.g., machine instructions) for an operating system or other on-the-fly programs, may also be used to store data for users and applications, and the like.

The nonvolatile memory may store executable programs, store data of users and applications, and the like, and may be loaded into the random access memory in advance for the processor 110 to directly read and write.

In some embodiments, the internal memory 121 may be used to store a user's blood glucose values, exercise data, blood glucose profiles, event recognition models, blood glucose prediction models, and the like.

The external memory interface 120 may be used to connect external non-volatile memory to enable expansion of the memory capabilities of the electronic device 100. The external nonvolatile memory communicates with the processor 110 through the external memory interface 120 to implement a data storage function. For example, files such as music and video are stored in an external nonvolatile memory.

The electronic device 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. Such as music playing, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal output and also to convert an analog audio input into a digital audio signal. The audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be disposed in the processor 110, or a portion of the functional modules of the audio module 170 may be disposed in the processor 110.

The speaker 170A, also referred to as a "horn," is used to convert audio electrical signals into sound signals. The electronic device 100 may listen to music, or to hands-free conversations, through the speaker 170A.

A receiver 170B, also referred to as a "earpiece", is used to convert the audio electrical signal into a sound signal. When electronic device 100 is answering a telephone call or voice message, voice may be received by placing receiver 170B in close proximity to the human ear.

Microphone 170C, also referred to as a "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can sound near the microphone 170C through the mouth, inputting a sound signal to the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C.

The earphone interface 170D is used to connect a wired earphone.

The pressure sensor 180A is used to sense a pressure signal, and may convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A is of various types, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a capacitive pressure sensor comprising at least two parallel plates with conductive material. The capacitance between the electrodes changes when a force is applied to the pressure sensor 180A. The electronic device 100 determines the strength of the pressure from the change in capacitance. When a touch operation is applied to the display screen 194, the electronic apparatus 100 detects the touch operation intensity according to the pressure sensor 180A. The electronic device 100 may also calculate the location of the touch based on the detection signal of the pressure sensor 180A.

The gyro sensor 180B may be used to determine a motion gesture of the electronic device 100. In some embodiments, the angular velocity of electronic device 100 about three axes (i.e., x, y, and z axes) may be determined by gyro sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. For example, when the shutter is pressed, the gyro sensor 180B detects the shake angle of the electronic device 100, calculates the distance to be compensated by the lens module according to the angle, and makes the lens counteract the shake of the electronic device 100 through the reverse motion, so as to realize anti-shake. The gyro sensor 180B may also be used for navigating, somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, electronic device 100 calculates altitude from barometric pressure values measured by barometric pressure sensor 180C, aiding in positioning and navigation.

The magnetic sensor 180D includes a hall sensor. The electronic device 100 may detect the opening and closing of the flip cover using the magnetic sensor 180D. In some embodiments, when the electronic device 100 is a flip machine, the electronic device 100 may detect the opening and closing of the flip according to the magnetic sensor 180D. And then according to the detected opening and closing state of the leather sheath or the opening and closing state of the flip, the characteristics of automatic unlocking of the flip and the like are set.

The acceleration sensor 180E may detect the magnitude of acceleration of the electronic device 100 in various directions (typically three axes). The magnitude and direction of gravity may be detected when the electronic device 100 is stationary. The electronic equipment gesture recognition method can also be used for recognizing the gesture of the electronic equipment, and is applied to horizontal and vertical screen switching, pedometers and other applications.

A distance sensor 180F for measuring a distance. The electronic device 100 may measure the distance by infrared or laser. In some embodiments, the electronic device 100 may range using the distance sensor 180F to achieve quick focus.

The proximity light sensor 180G may include, for example, a Light Emitting Diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The electronic device 100 emits infrared light outward through the light emitting diode. The electronic device 100 detects infrared reflected light from nearby objects using a photodiode. When sufficient reflected light is detected, it may be determined that there is an object in the vicinity of the electronic device 100. When insufficient reflected light is detected, the electronic device 100 may determine that there is no object in the vicinity of the electronic device 100.

The ambient light sensor 180L is used to sense ambient light level. The electronic device 100 may adaptively adjust the brightness of the display 194 based on the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust white balance when taking a photograph. Ambient light sensor 180L may also cooperate with proximity light sensor 180G to detect whether electronic device 100 is in a pocket to prevent false touches.

The fingerprint sensor 180H is used to collect a fingerprint. The electronic device 100 may utilize the collected fingerprint feature to unlock the fingerprint, access the application lock, photograph the fingerprint, answer the incoming call, etc.

The temperature sensor 180J is for detecting temperature. In some embodiments, the electronic device 100 performs a temperature processing strategy using the temperature detected by the temperature sensor 180J. For example, when the temperature reported by temperature sensor 180J exceeds a threshold, electronic device 100 performs a reduction in the performance of a processor located in the vicinity of temperature sensor 180J in order to reduce power consumption to implement thermal protection.

The touch sensor 180K, also referred to as a "touch device". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is for detecting a touch operation acting thereon or thereabout. The touch sensor may communicate the detected touch operation to the application processor to determine the touch event type. Visual output related to touch operations may be provided through the display 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100 at a different location than the display 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, bone conduction sensor 180M may acquire a vibration signal of a human vocal tract vibrating bone pieces. The bone conduction sensor 180M may also contact the pulse of the human body to receive the blood pressure pulsation signal. In some embodiments, bone conduction sensor 180M may also be provided in a headset, in combination with an osteoinductive headset. The audio module 170 may analyze the voice signal based on the vibration signal of the sound portion vibration bone block obtained by the bone conduction sensor 180M, so as to implement a voice function. The application processor may analyze the heart rate information based on the blood pressure beat signal acquired by the bone conduction sensor 180M, so as to implement a heart rate detection function.

The keys 190 include a power-on key, a volume key, etc. The keys 190 may be mechanical keys. Or may be a touch key. The electronic device 100 may receive key inputs, generating key signal inputs related to user settings and function controls of the electronic device 100.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration alerting as well as for touch vibration feedback. For example, touch operations acting on different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 191 may also correspond to different vibration feedback effects by touching different areas of the display screen 194. Different application scenarios (such as time reminding, receiving information, alarm clock, game, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization. In some embodiments, motor 191 may be used to generate a vibration alert when a user is performing a motion alert during a motion.

The indicator 192 may be an indicator light, may be used to indicate a state of charge, a change in charge, a message indicating a missed call, a notification, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card may be inserted into the SIM card interface 195, or removed from the SIM card interface 195 to enable contact and separation with the electronic device 100.

The electronic device may be a portable terminal device such as a mobile phone, tablet computer, wearable device, etc. that carries iOS, android, microsoft or other operating systems, or may be a non-portable terminal device such as a Laptop computer (Laptop) with a touch-sensitive surface or touch-sensitive panel, a desktop computer with a touch-sensitive surface or touch-sensitive panel, etc. The software system of the electronic device 100 may employ a layered architecture, an event driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. In the embodiment of the invention, taking an Android system with a layered architecture as an example, a software structure of the electronic device 100 is illustrated.

Fig. 16 is a software architecture block diagram of an electronic device 100 according to an embodiment of the present application.

The layered architecture divides the software into several layers, each with distinct roles and branches. The layers communicate with each other through a software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, an application layer, an application framework layer, an Zhuoyun rows (Android runtime) and system libraries, and a kernel layer, respectively.

The application layer may include a series of application packages.

As shown in fig. 16, the application package may include applications for cameras, gallery, calendar, phone calls, maps, navigation, WLAN, bluetooth, music, video, short messages, etc.

The application framework layer provides an application programming interface (application programming interface, API) and programming framework for the application of the application layer. The application framework layer includes a number of predefined functions.

As shown in fig. 16, the application framework layer may include a window manager, a content provider, a view system, a phone manager, a resource manager, a notification manager, and the like.

The window manager is used for managing window programs. The window manager can acquire the size of the display screen, judge whether a status bar exists, lock the screen, intercept the screen and the like.

The content provider is used to store and retrieve data and make such data accessible to applications. The data may include video, images, audio, calls made and received, browsing history and bookmarks, phonebooks, etc.

The view system includes visual controls, such as controls to display text, controls to display pictures, and the like. The view system may be used to build applications. The display interface may be composed of one or more views. For example, a display interface including a text message notification icon may include a view displaying text and a view displaying a picture.

The telephony manager is used to provide the communication functions of the electronic device 100. Such as the management of call status (including on, hung-up, etc.).

The resource manager provides various resources for the application program, such as localization strings, icons, pictures, layout files, video files, and the like.

The notification manager allows the application to display notification information in a status bar, can be used to communicate notification type messages, can automatically disappear after a short dwell, and does not require user interaction. Such as notification manager is used to inform that the download is complete, message alerts, etc. The notification manager may also be a notification in the form of a chart or scroll bar text that appears on the system top status bar, such as a notification of a background running application, or a notification that appears on the screen in the form of a dialog window. For example, a text message is prompted in a status bar, a prompt tone is emitted, the electronic device vibrates, and an indicator light blinks, etc.

Android run time includes a core library and virtual machines. Android runtime is responsible for scheduling and management of the android system.

The core library consists of two parts: one part is a function which needs to be called by java language, and the other part is a core library of android.

The application layer and the application framework layer run in a virtual machine. The virtual machine executes java files of the application program layer and the application program framework layer as binary files. The virtual machine is used for executing the functions of object life cycle management, stack management, thread management, security and exception management, garbage collection and the like.

The system library may include a plurality of functional modules. For example: surface manager (surface manager), media Libraries (Media Libraries), three-dimensional graphics processing Libraries (e.g., openGL ES), 2D graphics engines (e.g., SGL), etc.

The surface manager is used to manage the display subsystem and provides a fusion of 2D and 3D layers for multiple applications.

Media libraries support a variety of commonly used audio, video format playback and recording, still image files, and the like. The media library may support a variety of audio and video encoding formats, such as MPEG4, h.264, MP3, AAC, AMR, JPG, PNG, etc.

The three-dimensional graphic processing library is used for realizing three-dimensional graphic drawing, image rendering, synthesis, layer processing and the like.

The 2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is a layer between hardware and software. The inner core layer at least comprises a display driver, a camera driver, an audio driver and a sensor driver.

Fig. 17 is a schematic diagram of a hardware structure of an electronic device 200 according to an embodiment of the present application.

The electronic device 200 is used for measuring blood glucose of a user, and in particular, the electronic device 200 can calculate a glucose concentration (blood glucose) in plasma (blood) by measuring a glucose concentration (tissue fluid glucose) in tissue fluid.

As shown in fig. 17, the electronic device 200 includes a processor 201, a memory 202, a sensor 203, a wireless communication module 204, and the like.

It should be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the application, the electronic device may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 201 may include one or more processing units, such as: the processor 201 may be a modem processor, a digital signal processor, a controller, a baseband processor, and/or a neural network processor, etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors. The processor 201 may also be referred to as a micro-control processing unit.

A memory may also be provided in the processor 201 for storing instructions and data. In some embodiments, the memory in the processor 201 is a cache memory. The memory may hold instructions or data that the processor 201 has just used or recycled. If the processor 201 needs to reuse the instruction or data, it can be called directly from the memory. Repeated accesses are avoided and the latency of the processor 201 is reduced, thus improving the efficiency of the system.

The wireless communication module 204 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (WIRELESS FIDELITY, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation SATELLITE SYSTEM, GNSS), frequency modulation (frequency modulation, FM), near field communication (NEAR FIELD communication, NFC), infrared (IR), etc., as applied to electronic devices. The wireless communication module 204 may be one or more devices that integrate at least one communication processing module. The wireless communication module 204 receives electromagnetic waves via an antenna, modulates the electromagnetic wave signals, filters the electromagnetic wave signals, and transmits the processed signals to the processor 201. The wireless communication module 204 may also receive a signal to be transmitted from the processor 201, frequency modulate it, amplify it, and convert it to electromagnetic waves for radiation via an antenna.

Memory 202 may include one or more random access memories and one or more nonvolatile memories.

The non-volatile memory may include a disk storage device, a flash memory.

The random access memory may be read directly from and written to by the processor 201, may be used to store executable programs (e.g., machine instructions) for an operating system or other on-the-fly programs, may also be used to store data for users and applications, and the like.

The nonvolatile memory may store executable programs, store data of users and applications, and the like, and may be loaded into the random access memory in advance for the processor 201 to directly read and write.

The temperature sensor 2031 is for detecting a temperature. In some embodiments, the electronic device 100 utilizes the temperature detected by the temperature sensor 2031 to determine the skin temperature and/or the ambient temperature of the user.

The glucose sensor 2032 is for detecting the concentration of glucose. In some embodiments, glucose sensor 2032 may determine glucose concentration by detecting the consumption of oxygen catalyzed by glucose oxidase or H ₂O₂ generated by the oxidation of glucose in interstitial fluid. In some embodiments, glucose detection sensor 2032 determines glucose concentration by attaching glucose oxidase to the electrode surface using an electron mediator, such as nanomaterials, osmium metals, ferrocenes, benzoquinones, and the like, and then effecting electron transfer through a series of redox reactions.

In the process that the user measures blood glucose using the electronic device 200, the user may implant the electronic device 200 into subcutaneous tissue, and the electronic device 200 measures the glucose concentration in the tissue fluid through the electrode of the glucose detection sensor 2032, thereby determining the blood glucose concentration of the user.

In some embodiments, the electronic device 200 may send the collected blood glucose values to other devices, such as the electronic device 100, via the wireless communication module 204. The memory 202 may be used to store blood glucose values collected by the electronic device 200.

It should be understood that fig. 17 illustrates only an exemplary hardware configuration of the electronic device 200, and that in other embodiments of the application, the electronic device 200 may include more or fewer components, as the embodiments of the application are not limited in this respect.

As shown in fig. 18, the electronic device 300 may include a processor 301, a memory 302, a sensor 303, a display 304, a motor 305, a wireless communication module 306. The processor 301 may refer to the foregoing text description of the processor 110, and will not be described herein again; the memory 302 may refer to the above description of the internal memory 121, and will not be repeated here; the sensor 303 may include: the gyro sensor 3031, the acceleration sensor 3032, the skin sensor 3033, the touch sensor 3034 and the bone conduction sensor 3035 may refer to the text description of the gyro sensor 180B, which is not repeated here, the acceleration sensor 3032 may refer to the text description of the acceleration sensor 180E, which is not repeated here, and the touch sensor 3034 may refer to the text description of the touch sensor 180K, which is not repeated here. The bone conduction sensor 3035 may refer to the text description of the bone conduction sensor 180M described above, and will not be described again here. The piezoelectric sensor 3033 is used to measure the user's galvanic skin data reflecting changes in the user's stress, emotion. The display 304 may refer to the text description of the display 194, which is not described herein. The motor 305 may refer to the text description of the motor 191, and will not be described herein. The wireless communication module 306 may refer to the text description of the foregoing wireless communication module 160, which is not repeated herein.

In some embodiments, the electronic device 300 may send the data collected by the sensor 303 to other electronic devices, such as the electronic device 100, through the wireless communication module 306, obtain a blood glucose profile sent by the other electronic devices, such as the electronic device 100, through the wireless communication module 306, and display the blood glucose profile through the display 304. The memory 302 may be used to store data collected by the sensor 303.

It should be understood that fig. 18 illustrates only an exemplary hardware configuration of electronic device 300, and that electronic device 300 may include more or fewer components in other embodiments of the application, and that embodiments of the application are not limited in this respect.

It should be understood that the steps in the above-described method embodiments may be accomplished by integrated logic circuitry in hardware in a processor or instructions in the form of software. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution.

The present application also provides an electronic device, which may include: memory and a processor. Wherein the memory is operable to store a computer program; the processor may be configured to invoke the computer program in the memory to cause the electronic device to perform the method performed by the electronic device 100 or the electronic device 200 or the electronic device 300 in any of the embodiments described above.

The present application also provides a chip system comprising at least one processor for implementing the functions involved in the method performed by the electronic device 100 or the electronic device 200 or the electronic device 300 in any of the above embodiments.

In one possible design, the system on a chip further includes a memory to hold program instructions and data, the memory being located either within the processor or external to the processor.

The chip system may be formed of a chip or may include a chip and other discrete devices.

Alternatively, the processor in the system-on-chip may be one or more. The processor may be implemented in hardware or in software. When implemented in hardware, the processor may be a logic circuit, an integrated circuit, or the like. When implemented in software, the processor may be a general purpose processor, implemented by reading software code stored in a memory.

Alternatively, the memory in the system-on-chip may be one or more. The memory may be integral with the processor or separate from the processor, and embodiments of the present application are not limited. The memory may be a non-transitory processor, such as a ROM, which may be integrated on the same chip as the processor, or may be separately provided on different chips, and the type of memory and the manner of providing the memory and the processor are not particularly limited in the embodiments of the present application.

Illustratively, the chip system may be a field programmable gate array (field programmable GATE ARRAY, FPGA), an Application Specific Integrated Chip (ASIC), a system on chip (SoC), a central processing unit (central processor unit, CPU), a network processor (network processor, NP), a digital signal processing circuit (DIGITAL SIGNAL processor, DSP), a microcontroller (micro controller unit, MCU), a programmable controller (programmable logic device, PLD) or other integrated chip.

The present application also provides a computer program product comprising: a computer program (which may also be referred to as code, or instructions), which when executed, causes a computer to perform the method performed by any of the electronic devices 100 or 200 or 300 in any of the embodiments described above.

The present application also provides a computer-readable storage medium storing a computer program (which may also be referred to as code, or instructions). The computer program, when executed, causes a computer to perform the method performed by any of the electronic devices 100 or 200 or 300 in any of the embodiments described above.

It should be appreciated that the processor in embodiments of the present application may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a digital signal processor (DIGITAL SIGNAL processor, DSP), an application specific integrated circuit (AP 800plication specific integrated circuit,ASIC), a field programmable gate array (field programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.

In addition, the embodiment of the application also provides a device. The apparatus may be a component or module in particular, and may comprise one or more processors and memory coupled. Wherein the memory is for storing a computer program. The computer program, when executed by one or more processors, causes an apparatus to perform the methods of the method embodiments described above.

Wherein an apparatus, a computer-readable storage medium, a computer program product, or a chip provided by embodiments of the application are for performing the corresponding methods provided above. Therefore, the advantages achieved by the method can be referred to as the advantages in the corresponding method provided above, and will not be described herein.

The embodiments of the present application may be arbitrarily combined to achieve different technical effects.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Drive (SSD)), etc.

Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. And the aforementioned storage medium includes: ROM or random access memory RAM, magnetic or optical disk, etc.

In summary, the foregoing description is only exemplary embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made according to the disclosure of the present invention should be included in the protection scope of the present invention.

Claims

1. A method of glycemic management, the method being applied to an electronic device, the method comprising:

Acquiring a fasting blood glucose value;

If the blood glucose value of the user in the first period exceeds a first threshold value compared with the fasting blood glucose value, determining a first meal time according to the blood glucose value in the first period;

The first meal time is displayed in a displayed first blood glucose profile and/or a blood glucose value associated with the first meal time is displayed, the first blood glucose profile being indicative of a change in blood glucose of a user over the first period of time.

2. The method of claim 1, wherein the blood glucose value associated with the first meal time comprises one or more of: the blood glucose value for the first meal time is a blood glucose value at a time point a first length of time before or after the first meal time.

3. The method according to claim 1 or 2, wherein the first meal time is located between a first point in time at which a blood glucose peak is located within the first period of time and a second point in time before the first point in time, and wherein a time period between the first point in time and the first point in time is a second time period;

the blood glucose value of the first meal time is larger than the fasting blood glucose value, and the first meal time is the first time point when the blood glucose rising rate of the user is larger than the second threshold value between the first time point and the second time point.

4. A method according to any one of claim 1 to 3, wherein,

The fasting blood glucose value is determined according to the blood glucose value of the user in a second period, wherein the fasting blood glucose value is the lower quartile of the blood glucose value in the second period, or the fasting blood glucose value is the lower quartile of the blood glucose value of the user between two meals in the second period;

Or alternatively

The fasting blood glucose value is determined according to a second meal time, wherein the second meal time is positioned before the first meal time, and the fasting blood glucose value is an average blood glucose value in a third period before the second meal time.

5. The method according to any one of claim 1 to 4, wherein,

Displaying a second blood glucose curve for indicating the fasting blood glucose value for each of N days and/or a third blood glucose curve for indicating the blood glucose value associated with the first meal time for each of N days, N being greater than or equal to 1 and N being an integer.

6. The method according to any one of claims 1-5, further comprising:

Modifying the first meal time according to user operation;

displaying the modified first meal time in the first blood glucose profile and/or a blood glucose value associated with the modified first meal time.

7. The method according to any one of claims 1-6, further comprising:

Displaying the calories of the food and/or the nutrient fractions.

8. A method of glycemic management, the method being applied to an electronic device, the method comprising:

determining the meal quantity and the meal speed of a user during one meal process;

Acquiring a blood sugar actual curve of a user, wherein the blood sugar actual curve is used for indicating the blood sugar change of the user in the meal process;

acquiring a blood glucose standard curve of a healthy crowd, wherein the blood glucose standard curve is used for indicating blood glucose changes of the healthy crowd in a meal process with the meal quantity and the meal speed;

And evaluating the diet health of the user through the blood glucose actual curve and the blood glucose standard curve, wherein the larger the difference between the blood glucose actual curve and the blood glucose standard curve is, the less healthy the diet of the user is, and the smaller the difference between the blood glucose actual curve and the blood glucose standard curve is, the more healthy the diet of the user is.

9. The method of claim 8, wherein obtaining a blood glucose standard curve for a healthy population, specifically comprises:

obtaining blood glucose curves of a plurality of members in a healthy crowd, wherein the blood glucose curves are used for indicating blood glucose changes of the members in the process of having the meal size and the meal speed;

Determining the blood glucose standard curve of the healthy population according to the blood glucose curves of the plurality of members.

10. The method of claim 9, wherein determining a blood glucose standard curve for a healthy population from the blood glucose curves for the plurality of members, specifically comprises:

Stretching or compressing the blood glucose curves of the members in the time domain, and/or stretching or compressing the blood glucose curves of the members in the amplitude so that the average values of the blood glucose curves of the members in the time domain and the amplitude are equal;

and carrying out point-by-point average calculation on the blood glucose curves transformed by the members, and determining the curve formed by connecting the average values as the blood glucose standard curve.

11. The method according to any one of claims 8-10, further comprising:

Acquiring meal related data of a user in the meal process; wherein the meal related data comprises: heart rate data, exercise data, mood pressure data;

determining the meal quantity and the meal speed of a user during one meal process specifically comprises the following steps:

Inputting the meal related data in the process of one meal of the user into a meal model, and identifying and obtaining the meal quantity and the meal speed of the user in the process of one meal, wherein the meal model is trained by utilizing the meal related data in the process of the known meal quantity and the meal speed.

12. The method of claim 11, wherein prior to obtaining meal related data for the user during the meal, the method further comprises:

And detecting that the user confirms the operation of beginning to eat or that the data collected by the sensor meets the preset condition.

13. The method of claim 12, wherein the data collected by the sensor is used to indicate heart rate, exercise and emotional stress of the user, and the preset conditions include: the difference in heart rate of the user relative to the resting heart rate remains greater than the third threshold after excluding the effects of exercise and emotional stress.

14. The method according to any one of claims 8-13, characterized in that the user's dietary health is assessed by means of the blood glucose actual curve and the blood glucose standard curve, in particular comprising:

Extracting features of the blood glucose actual curve and the blood glucose standard curve respectively, wherein the features comprise one or more of the following: maximum value of single blood sugar change, blood sugar non-stable time, number of blood sugar values under different blood sugar change rate categories, number of blood sugar fluctuation times, time and amplitude of single blood sugar change and envelope of curve;

Evaluating the dietary health of the user based on the difference between one or more characteristics in the blood glucose real curve and the one or more characteristics in the blood glucose standard curve; wherein the greater the difference, the less healthy the user's diet, and the less the difference, the more healthy the user's diet.

15. The method of claim 14, wherein the method further comprises:

And calculating the overall health score of the user according to the difference value of each dining process in the N times of dining processes in the third period, wherein the greater the overall health score is, the less healthy the diet of the user in the third period is, and the less the overall health score is, the healthier the diet of the user in the third period is.

16. The method according to any one of claims 8-15, further comprising:

And displaying the actual blood glucose curve and the blood glucose standard curve.

17. A method of glycemic management, the method being applied to an electronic device, the method comprising:

acquiring first motion reference data before a user moves;

Outputting motion prompt information according to the first motion reference data, wherein the motion prompt information is used for prompting suggestion of the user on the motion;

acquiring second motion reference data in the motion process of a user;

outputting a motion alarm if the second motion reference data reflects that the motion risk of the user exceeds a fourth threshold;

acquiring third movement reference data after the user finishes movement;

outputting a motion evaluation report of the motion of the user according to the third motion reference data, wherein the motion evaluation report is used for evaluating the influence of the motion on the user;

wherein the first, second, and third motion reference data include blood glucose and parameters for reflecting a user's sign.

18. The method of claim 17, wherein prior to obtaining the first motion reference data prior to the user's motion, the method further comprises:

an operation that the user input is about to start the movement is detected, or that the current time reaches the movement time preset by the user is detected.

19. The method according to claim 17 or 18, wherein outputting motion cues according to the first motion reference data comprises:

the grades of all the data in the first motion reference data are weighted and summed to obtain a first motion risk score, wherein the higher the data size of the data is from the normal range, the higher the grade of the data is;

When the first exercise risk score is larger than TH, the exercise prompt information is an un-recommended exercise, wherein TH is determined according to basic information of the user and blood sugar characteristics reflecting blood sugar conditions of the user in a period of time.

20. The method according to any one of claims 17-19, wherein after obtaining the second motion reference data during the user's motion, the method further comprises:

And determining a second exercise risk score according to the second exercise reference data, wherein the second risk score is used for indicating exercise risk in the exercise process.

21. The method of claim 20, wherein the second athletic risk score is calculated according to the following equation:

G＝γ(t)∑c_iz_i

Wherein z _i represents the level of the ith motion reference data in the second motion reference data, c _i represents the weight of the ith motion reference data, and γ (t) represents a time attenuation factor, wherein the time attenuation factor is a monotonically increasing function along with time t, and when the data size of the ith motion reference data is far from the normal range, the level of the ith motion reference data is higher, if the second motion risk score is higher, the motion risk is higher, and if the second motion risk score is smaller, the motion risk is smaller.

22. The method of any one of claims 17-21, wherein after outputting the motion alert, the method further comprises:

And after the user finishes the fourth time period of the movement, outputting the movement alarm again.

23. The method according to any one of claims 17-22, wherein after the user has finished the movement, the method further comprises:

Acquiring motion feedback information of a user;

Outputting a motion evaluation report of the motion of the user according to the third motion reference data, wherein the motion evaluation report specifically comprises:

And outputting a motion estimation report of the motion according to the motion feedback information and the third motion reference data, wherein the motion estimation report can also comprise suggestions of the next motion determined according to the motion feedback information.

24. The method according to any one of claims 17-23, wherein outputting a motion assessment report of the user's motion based on the third motion reference data, in particular comprises:

And carrying out weighted summation on the grades of all the data in the third motion reference data to calculate a motion influence score, wherein when the data size of the data in the third motion reference data is far from a normal range, the grade of the data is higher, the motion influence score is larger, the influence of the motion on a user is larger, the motion influence score is smaller, and the influence of the motion on the user is smaller.

25. A method of glycemic management, the method being applied to an electronic device, the method comprising:

Identifying a first exogenous event through an event identification model, or predicting the first exogenous event through a work and rest rule of a user; the event recognition model is trained according to exogenous events and historical blood glucose values which occur according to the history of a user, and the first exogenous event comprises one or more of the following: a eating event, a medication event, and a exercise event;

Acquiring the blood glucose value of the user in a fifth time before the current time point;

and predicting the blood glucose value in a sixth time period after the current time point by utilizing the blood glucose values in the first exogenous event and the fifth time period.

26. The method of claim 25, wherein predicting the blood glucose level for a sixth time period after the current time point using the blood glucose levels for the first exogenous event and the fifth time period, specifically comprises:

Predicting the blood glucose value in a sixth time period after the current time point by using the blood glucose values in the first exogenous event and the fifth time period through a blood glucose prediction model; the blood glucose prediction model is obtained by training a known blood glucose value in a history second period according to the known blood glucose value in the history first period and the known external event in the first period, and the first period is a period adjacent to the second period before the second period.

27. The method of claim 26, wherein the event recognition model comprises a motion recognition model, a medication recognition model, a diet recognition model;

The movement recognition model is used for recognizing the movement starting time, the movement quantity and the movement type of a movement event, the medication recognition model is used for recognizing the medication starting time, the medication quantity and the medicine type of a medication event, and the diet recognition model is used for recognizing the meal starting time, the meal quantity and the diet type of a diet event;

if the first exogenous event comprises a first motion event identified by utilizing the motion identification model, when the blood glucose value is predicted by utilizing the blood glucose prediction model, the input of the blood glucose prediction model comprises the motion starting time, the motion quantity and the motion type of the first motion event;

If the first exogenous event comprises a first drug event identified by the drug identification model, and when the blood glucose value is predicted by the blood glucose prediction model, the input of the blood glucose prediction model comprises the drug start time, the drug amount and the drug type of the first drug event;

If the first exogenous event includes a first dietary event identified by the dietary identification model, and the blood glucose value is predicted by the blood glucose prediction model, the input of the blood glucose prediction model includes a meal start time, a meal amount, and a diet type of the first dietary event.

28. The method according to any one of claims 25-27, further comprising:

And displaying a fourth blood glucose curve, wherein the fourth blood glucose curve is used for indicating the blood glucose change of the user in a period of time before the current time point and predicting the obtained blood glucose change by utilizing the blood glucose values in the first exogenous event and the fifth time period in a period of time after the current time point.

29. The method of claim 28, wherein the method further comprises:

the first exogenous event is noted on the fourth blood glucose curve.

30. The method according to any one of claims 25-29, further comprising:

adjusting the first exogenous event according to user operation;

Re-predicting the blood glucose level in the sixth time period by using the adjusted first exogenous event and the blood glucose level in the fifth time period;

A fifth blood glucose profile is displayed indicating a change in blood glucose of the user over a period of time prior to the current point in time, and a re-predicted change in blood glucose over a period of time after the current point in time.

31. The method of any one of claims 25-30, wherein prior to identifying the first exogenous event by the event identification model, the method further comprises:

And displaying a sixth blood glucose curve, wherein the sixth blood glucose curve is used for indicating the blood glucose change of the user in a period of time before the current time point and predicting the blood glucose change obtained by utilizing the blood glucose value in the fifth time period in a period of time after the current time point.

32. The method of any one of claims 25-31, wherein prior to identifying a first exogenous event by an event identification model or predicting the first exogenous event by a user's law of rest, the method further comprises:

It is determined that the rate of change of blood glucose of the user during the seventh time period before the current point in time is greater than a fifth threshold.

33. An electronic device comprising a memory, one or more processors, and one or more programs; the one or more processors, when executing the one or more programs, cause the electronic device to implement the method of any of claims 1-7, 8-16, 17-24, 25-32.

34. A computer readable storage medium comprising instructions which, when run on an electronic device, cause the electronic device to perform the method of any one of claims 1 to 7, 8 to 16, 17 to 24, 25 to 32.

35. A computer program product, characterized in that the computer program product, when run on a computer, causes the computer to perform the method of any one of claims 1 to 7, 8 to 16, 17 to 24, 25 to 32.