Sports body temperature measuring method, device and equipment and storage medium

Abstract

The invention provides an exercise body temperature measuring method, device and equipment and a storage medium, and relates to the technical field of body temperature measurement. In some embodiments of the invention, exercise physiological parameters of a user in an exercise state and resting physiological parameters of the user in a resting state are collected; determining the calorific value of the user in the motion state according to the motion physiological parameters; according to the exercise physiological parameters and the resting physiological parameters, the heat dissipating capacity of the user in the exercise state is determined; in combination with the resting physiological parameters and the exercise physiological parameters, determining the calorific value and the heat dissipating capacity of the user in the exercise state, and correcting the human body temperature according to the calorific value and the heat dissipating capacity to obtain the exercise body temperature of the user in the exercise state; and the exercise body temperature with relatively high precision is obtained through quantified heat productivity and heat dissipating capacity.

Description

Technical Field

[0001] The present disclosure relates to the technical field of body temperature measurement, and in particular to a method, device, equipment and storage medium for measuring body temperature during exercise. Background Art

[0002] In recent years, wearable device temperature measurement has received widespread attention. Wearable devices can capture the dynamic changes of body temperature due to their continuous measurement advantages, and by establishing personalized prediction models, they are of great significance to personal health management, disease tracking, and epidemic prevention. If connected to the medical system, health data can be shared to expand application scenarios. This technology has broad application prospects and important social value.

[0003] Currently, skin temperature and heart rate are usually used to estimate the body temperature of a user in motion, but the measurement accuracy of the body temperature of a user in motion is low. Summary of the invention

[0004] The present disclosure provides a method, device, equipment and storage medium for measuring body temperature during exercise, so as to at least solve the problem of low accuracy in measuring body temperature of users during exercise.

[0005] The technical solution of the present disclosure is as follows:

[0006] The present disclosure provides a method for measuring body temperature during exercise, including:

[0007] Collecting the user's sports physiological parameters in a sports state and the resting physiological parameters in a resting state, wherein the resting physiological parameters include: human body temperature;

[0008] Determining the heat generation of the user in a state of exercise according to the exercise physiological parameters; and

[0009] Determining the heat dissipation of the user in a motion state according to the motion physiological parameter and the resting physiological parameter;

[0010] The human body temperature is corrected according to the heat generation and the heat dissipation to obtain the exercise body temperature of the user in the exercise state.

[0011] Optionally, the exercise physiological parameter includes: heart rate, and determining the heat generation of the user in an exercise state according to the exercise physiological parameter includes:

[0012] Determine the basal metabolic rate based on the height, weight, age and gender of the user, and

[0013] Determining total calorific value according to the gender, weight and heart rate of the user;

[0014] The calorific value of the user in the exercise state is determined according to the total calorific value and the basal metabolic rate.

[0015] Optionally, the sports physiological parameter includes at least one of the following parameters: sports skin temperature, sports environment temperature and sports skin humidity, and the resting physiological parameter includes at least one of the following parameters: resting skin temperature, resting environment temperature and resting skin humidity, and determining the heat dissipation of the user in a sports state according to the sports physiological parameter and the resting physiological parameter includes:

[0016] determining a skin temperature change amount according to the exercise skin temperature and the resting skin temperature;

[0017] Determining an amount of change in ambient temperature according to the exercise ambient temperature and the resting ambient temperature;

[0018] Determining a skin humidity change amount according to the exercise skin humidity and the resting skin humidity;

[0019] The heat dissipation of the user in motion is determined according to at least one of the skin temperature change, the ambient temperature change and the skin humidity change.

[0020] Optionally, determining the heat dissipation of the user in motion according to the skin temperature change, the ambient temperature change, and the skin humidity change includes:

[0021] The skin temperature change, the ambient temperature change and the skin humidity change are input into an existing heat dissipation estimation model to obtain the heat dissipation of the user in motion.

[0022] Optionally, before using the heat dissipation estimation model, the method further includes:

[0023] Collecting sample exercise skin temperature, sample resting skin temperature, sample exercise skin humidity, sample resting skin humidity, sample exercise environment temperature and sample resting environment temperature of multiple sample users and sample heat dissipation corresponding to the multiple sample users;

[0024] Determine a change in the sample skin temperature of each sample user according to the sample exercise skin temperature and the sample resting skin temperature of each sample user;

[0025] Determine a change in sample skin humidity of each sample user according to the sample exercise skin humidity and the sample resting skin humidity of each sample user;

[0026] Determining a change in the sample environment temperature of each sample user according to the sample exercise environment temperature and the sample resting environment temperature of each sample user;

[0027] The initial model is trained according to the sample skin temperature changes, the sample skin humidity changes, the sample environment temperature changes and the sample heat dissipation of the plurality of sample users to obtain a heat dissipation estimation model.

[0028] Optionally, the correcting the human body temperature according to the heat generation and the heat dissipation to obtain the exercise body temperature of the user in the exercise state includes:

[0029] Determining a body temperature correction coefficient according to the calorific value, the heat dissipation and the basal metabolic rate;

[0030] The exercise body temperature is determined according to the body temperature correction coefficient and the human body temperature.

[0031] Optionally, determining a body temperature correction coefficient according to the calorific value, the heat dissipation and the basal metabolic rate includes:

[0032] Determining a heat change amount according to the heat generation amount and the heat dissipation amount;

[0033] A body temperature correction coefficient is determined according to the heat change and the basal metabolic rate.

[0034] The present disclosure also provides a device for measuring body temperature during exercise, including:

[0035] A collection module, used to collect the user's sports physiological parameters in a sports state and the resting physiological parameters in a resting state, wherein the resting physiological parameters include: human body temperature;

[0036] A first determination module, configured to determine the heat generation of the user in a state of exercise according to the exercise physiological parameters; and

[0037] A second determination module determines the heat dissipation of the user in a motion state according to the motion physiological parameters and the resting physiological parameters;

[0038] The third determination module corrects the human body temperature according to the heat generation and the heat dissipation to obtain the exercise body temperature of the user in the exercise state.

[0039] Optionally, the exercise physiological parameter includes: heart rate, and when the first determination module determines the heat generation of the user in the exercise state according to the exercise physiological parameter, it is used to:

[0040] Determine the basal metabolic rate based on the height, weight, age and gender of the user, and

[0041] Determining total calorific value according to the gender, weight and heart rate of the user;

[0042] The calorific value of the user in the exercise state is determined according to the total calorific value and the basal metabolic rate.

[0043] Optionally, the sports physiological parameter includes at least one of the following parameters: sports skin temperature, sports environment temperature and sports skin humidity, and the resting physiological parameter includes at least one of the following parameters: resting skin temperature, resting environment temperature and resting skin humidity, and the second determination module, when determining the heat dissipation of the user in a sports state according to the sports physiological parameter and the resting physiological parameter, is used to:

[0044] determining a skin temperature change amount according to the exercise skin temperature and the resting skin temperature;

[0045] Determining an amount of change in ambient temperature according to the exercise ambient temperature and the resting ambient temperature;

[0046] Determining a skin humidity change amount according to the exercise skin humidity and the resting skin humidity;

[0047] The heat dissipation of the user in motion is determined according to at least one of the skin temperature change, the ambient temperature change and the skin humidity change.

[0048] Optionally, when determining the heat dissipation of the user in motion according to the skin temperature change, the ambient temperature change and the skin humidity change, the second determination module is configured to:

[0049] The skin temperature change, the ambient temperature change and the skin humidity change are input into an existing heat dissipation estimation model to obtain the heat dissipation of the user in motion.

[0050] Optionally, when the third determination module corrects the human body temperature according to the heat generation and the heat dissipation to obtain the exercise body temperature of the user in the exercise state, it is used to:

[0051] Determining a body temperature correction coefficient according to the calorific value, the heat dissipation and the basal metabolic rate;

[0052] The exercise body temperature is determined according to the body temperature correction coefficient and the human body temperature.

[0053] Optionally, when determining the body temperature correction coefficient according to the calorific value, the heat dissipation and the basal metabolic rate, the third determination module is used to:

[0054] Determining a heat change amount according to the heat generation amount and the heat dissipation amount;

[0055] A body temperature correction coefficient is determined according to the heat change and the basal metabolic rate.

[0056] The present disclosure also provides a smart wearable device, including:

[0057] processor;

[0058] a memory for storing instructions executable by the processor;

[0059] The processor is configured to execute the instructions to implement each step in the above method.

[0060] The present disclosure also provides an electronic device, including:

[0061] processor;

[0062] a memory for storing instructions executable by the processor;

[0063] The processor is configured to execute the instructions to implement the steps in the above method.

[0064] The embodiment of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step in the above method is implemented.

[0065] The embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which implements each step in the above method when executed by a processor.

[0066] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0067] In some embodiments of the present disclosure, the user's exercise physiological parameters in a motion state and the resting physiological parameters in a resting state are collected; the heat generation of the user in the motion state is determined based on the exercise physiological parameters; and the heat dissipation of the user in the motion state is determined based on the exercise physiological parameters and the resting physiological parameters; the heat generation and heat dissipation of the user in the motion state are determined by combining the resting physiological parameters and the exercise physiological parameters, and the human body temperature is corrected based on the heat generation and heat dissipation to obtain the exercise body temperature of the user in the exercise state; the exercise body temperature with higher accuracy is obtained through the quantified heat generation and heat dissipation.

[0068] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0070] Figure 1 A schematic flow chart of a method for measuring body temperature during exercise provided by an exemplary embodiment of the present disclosure;

[0071] Figure 2 A schematic diagram of a flow chart of a method for measuring body temperature during exercise provided by an embodiment of the present disclosure;

[0072] Figure 3 A schematic diagram of the structure of a sports body temperature measurement device provided by an exemplary embodiment of the present disclosure;

[0073] Figure 4 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0074] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0075] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0076] It should be noted that the user information involved in this disclosure includes but is not limited to: user device information and user personal information; the collection, storage, use, processing, transmission, provision and disclosure of user information in this disclosure comply with the relevant laws and regulations and do not violate public order and good morals.

[0077] In the model that estimates body temperature based on skin temperature and heart rate, skin temperature is usually proportional to core temperature. However, when exercising, the core body temperature tends to rise, and due to the influence of sweating on the skin surface, the skin temperature tends to fall, which leads to a large deviation in the body temperature prediction results. There is currently no effective solution to this problem. Usually, methods such as prohibiting measurement during exercise and using historical value estimation are used. These methods cannot effectively measure the user's exercise body temperature during exercise.

[0078] In response to the above technical problems, in some embodiments of the present disclosure, the user's exercise physiological parameters in a motion state and the resting physiological parameters in a resting state are collected; the heat generation of the user in the motion state is determined based on the exercise physiological parameters; and the heat dissipation of the user in the motion state is determined based on the exercise physiological parameters and the resting physiological parameters; the heat generation and heat dissipation of the user in the motion state are determined by combining the resting physiological parameters and the exercise physiological parameters, and the human body temperature is corrected based on the heat generation and heat dissipation to obtain the exercise body temperature of the user in the exercise state; and the exercise body temperature with higher accuracy is obtained through the quantified heat generation and heat dissipation.

[0079] The technical solutions provided by various embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0080] Figure 1 The following is a flow chart of a method for measuring body temperature during exercise provided by an exemplary embodiment of the present disclosure. Figure 1 As shown, the method includes:

[0081] S101: Collecting the user's sports physiological parameters in a sports state and the resting physiological parameters in a resting state, wherein the resting physiological parameters include: human body temperature;

[0082] S102: determining the heat generation of the user in the exercise state according to the exercise physiological parameters; and

[0083] S103: determining the heat dissipation of the user in a motion state according to the motion physiological parameters and the resting physiological parameters;

[0084] S104: Correcting the human body temperature according to the heat generation and heat dissipation to obtain the user's exercise body temperature in the exercise state.

[0085] In this embodiment, the execution subject of the above method is a smart wearable device. The disclosed embodiment does not limit the type of smart wearable devices, and smart wearable devices include but are not limited to the following: smart watches, smart bracelets, smart earplugs, smart glasses, smart clothing, wearable fitness equipment, smart wristbands, smart anklets, and smart head-mounted devices.

[0086] In this embodiment, the smart wearable device collects the user's exercise physiological parameters in a motion state and the resting physiological parameters in a resting state; the smart wearable device determines the heat generation of the user in the motion state based on the exercise physiological parameters; and determines the heat dissipation of the user in the motion state based on the exercise physiological parameters and the resting physiological parameters; the heat generation and heat dissipation of the user in the motion state are determined by combining the resting physiological parameters and the exercise physiological parameters, and the smart wearable device corrects the human body temperature based on the heat generation and heat dissipation to obtain the exercise body temperature of the user in the exercise state; the exercise body temperature with higher accuracy is obtained through the quantified heat generation and heat dissipation, and the user can obtain and understand the current exercise body temperature in a timely manner.

[0087] It should be noted that the state of motion refers to the state in which the body is active or in motion. When the body is doing physical exercise, sports training, daily activities, and other forms of physical exercise, it is in the state of motion. In the state of motion, the various systems of the body (such as respiratory, cardiovascular, muscle, etc.) will adapt and adjust accordingly to meet the needs of the body.

[0088] Resting state: refers to the state of rest or stillness of the body. In the absence of exercise or physical activity, the body enters a resting state. In the resting state, the various systems of the body are relatively inactive, and physiological indicators such as respiratory rate, heart rate, and blood pressure are generally at a low level. At this time, the body is performing some basic maintenance and recovery functions, such as metabolism and tissue repair.

[0089] Movement and rest are two opposing physical states that we switch between in our daily lives. The normal alternation of movement and rest is one of the important conditions for maintaining good health and normal functioning of the body.

[0090] In the implementation of this disclosure, sports physiological parameters refer to the sports environment parameters and physiological parameters of the user in the sports state. Sports physiological parameters include but are not limited to: skin temperature, skin humidity, ambient temperature, ambient humidity and heart rate. Resting physiological parameters include but are not limited to: skin temperature, skin humidity, ambient temperature, ambient humidity, heart rate and human body temperature.

[0091] In one application scenario, when the user is in a resting state, the smart wearable device collects the user's resting physiological parameters; when the user switches from a resting state to a moving state, the smart wearable device collects the user's motion physiological parameters. The smart wearable device determines the user's heat generation in the motion state based on the motion physiological parameters; and determines the user's heat dissipation in the motion state based on the motion physiological parameters and the resting physiological parameters; based on the heat generation and heat dissipation, the human body temperature is corrected to obtain a more accurate motion body temperature of the user in the motion state.

[0092] In some embodiments of the present disclosure, the smart wearable device collects the user's resting physiological parameters and exercise physiological parameters, which can be obtained by using corresponding types of sensors set on the smart wearable device. For example, a temperature sensor close to the skin side can collect the user's skin temperature, a humidity sensor close to the skin side can collect the user's skin humidity, a temperature sensor close to the external environment side can collect the ambient temperature, a humidity sensor close to the external environment side can collect the ambient humidity, and a heart rate sensor can collect the user's heart rate.

[0093] In some embodiments of the present disclosure, the calorific value of the user in the exercise state is determined based on the sports physiological parameters. One achievable method is to determine the basal metabolic rate based on the user's height, weight, age and gender, and determine the total calorific value based on the user's gender, weight and heart rate; determine the calorific value of the user in the exercise state based on the total calorific value and the basal metabolic rate. It should be noted that the calorific value can be an absolute calorific value or a relative calorific value. The following embodiments are explained with relative calorific value.

[0094] It should be noted that the calculation of heat output can also be performed through a neural network model, which will not be elaborated in the embodiments of the present disclosure.

[0095] In the above embodiment, the basal metabolic rate is determined according to the height, weight, age and gender of the user, and the basal metabolic rate BMR calculation formula is as follows:

[0096] Males:E=66.4730+13.7516*W+5.0033*H-6.7750*A

[0097] Females:E=665.0955+9.5634*W+1.8496*H-4.6756*A

[0098] Among them, Males: E is the basal metabolic rate of men, Females: E is the basal metabolic rate of women, W is weight, H is height, and A is age.

[0099] In the above embodiment, the total calorific value is determined according to the user's gender, weight and heart rate. The calculation formula is as follows: EE = gender*(-55.0969+0.6309*HR+0.1988*weight+0.2017*age)+(1

[0100] -gender)*(-20.4022+0.4472*HR-0.1263*weight+0.074*age)

[0101] Among them, EE is the total calorific value, gender is the gender coefficient, HR is the heart rate, weight is the weight, and age is the age.

[0102] In the above embodiment, the calorific value of the user in the exercise state is determined based on the total calorific value and the basal metabolic rate. In this embodiment, the calorific value in the exercise state is the relative calorific value, and the calculation formula of the calorific value ΔEE is as follows:

[0103] ΔEE=EE-BMR

[0104] Among them, ΔEE is the user's calorific value during exercise, EE is the total calorific value, and BMR is the basal metabolic rate.

[0105] In another embodiment of the present disclosure, the basal metabolic rate is determined based on the user's weight and gender. The calculation formula is as follows:

[0106] Males: E = 815 + 36.6 * weight

[0107] Females: E = 580 + 31.1 * weight

[0108] Among them, Males:E is the basal metabolic rate of men, Females:E is the basal metabolic rate of women, and weight is body weight.

[0109] In some embodiments of the present disclosure, the motion physiological parameters include at least one of the following parameters: motion skin temperature, motion environment temperature and motion skin humidity, and the resting physiological parameters include at least one of the following parameters: resting skin temperature, resting environment temperature and resting skin humidity. The heat dissipation of the user in motion is determined based on the motion physiological parameters and the resting physiological parameters. One achievable method is to determine the skin temperature change based on the motion skin temperature and the resting skin temperature; determine the ambient temperature change based on the motion environment temperature and the resting ambient temperature; determine the skin humidity change based on the motion skin humidity and the resting skin humidity; and determine the heat dissipation of the user in motion based on at least one of the skin temperature change, ambient temperature change and skin humidity change. It should be noted that the heat dissipation can be an absolute heat dissipation or a relative heat dissipation. The following embodiments are described with reference to relative heat dissipation.

[0110] In the above embodiment, sweating is the main heat dissipation mode in the motion state, and the skin temperature, ambient temperature, and skin humidity during the motion and resting process can reflect the sweating state. The heat dissipation of the user in the motion state is determined based on the skin temperature change, the ambient temperature change, and the skin humidity change. One achievable method is to input the skin temperature change, the ambient temperature change, and the skin humidity change into an existing heat dissipation estimation model to obtain the heat dissipation of the user in the motion state. The embodiment of the present disclosure can estimate the heat dissipation of the user in the motion state more accurately through the heat dissipation estimation model, thereby improving the accuracy of the heat dissipation.

[0111] In some embodiments of the present disclosure, before using the heat dissipation estimation model, it is necessary to first perform model training to obtain the heat dissipation estimation model.

[0112] Collect a large number of sample users' sample exercise skin temperature, sample resting skin temperature, sample exercise skin humidity, sample resting skin humidity, sample exercise environment temperature and sample resting environment temperature and the sample heat dissipation corresponding to the plurality of sample users. Among them, the sample exercise skin temperature, sample resting skin temperature, sample exercise skin humidity, sample resting skin humidity, sample exercise environment temperature and sample resting environment temperature can be obtained by sensors of corresponding types. The sample heat dissipation needs to be measured by a respiratory metabolic instrument.

[0113] Next, the sample skin temperature change of each sample user is determined according to the sample exercise skin temperature and the sample resting skin temperature of each sample user; the sample skin humidity change of each sample user is determined according to the sample exercise skin humidity and the sample resting skin humidity of each sample user; the sample environment temperature change of each sample user is determined according to the sample exercise environment temperature and the sample resting environment temperature of each sample user. The sample heat dissipation of each sample user is labeled to obtain a training sample.

[0114] Input the labeled training samples into a neural network model. Inside the neural network model, the training samples can be subjected to feature extraction and calculation operations according to the model parameters, and the heat dissipation is output by the output layer of the neural network model. Next, the loss function layer of the neural network model can calculate the loss function according to the difference between the heat dissipation output by the output layer and the sample heat dissipation of the training sample. If the loss function does not meet the set requirements, the model parameters can be adjusted and the iterative training can be continued. When the loss function of the neural network model meets the set requirements, a trained heat dissipation estimation model can be obtained.

[0115] In some embodiments of the present disclosure, the human body temperature is corrected according to the calorific value and the heat dissipation to obtain the user's exercise body temperature. The body temperature correction coefficient is determined according to the calorific value, the heat dissipation and the basal metabolic rate; the exercise body temperature is determined according to the body temperature correction coefficient and the human body temperature.

[0116] In the above embodiment, the body temperature correction coefficient is determined according to the calorific value, the heat dissipation and the basal metabolic rate. One possible way is to determine the heat change, i.e. Qc-Qd, according to the calorific value and the heat dissipation; and determine the body temperature correction coefficient according to the heat change and the basal metabolic rate. The calculation formula is as follows:

[0117] a=(Qc–Qd) / Qb

[0118] Among them, a is the body temperature correction coefficient, Qc is the calorific value, Qd is the heat dissipation, and Qb is the basal metabolic rate.

[0119] In another embodiment of the present disclosure, the entire exercise process is divided into three parts: heating, stabilization, and cooling. During the heating process, the heat generation is continuously greater than the heat dissipation; during the stabilization process, the heat generation and the heat dissipation are maintained in a relatively stable state; during the cooling process, the heat generation is continuously less than the heat dissipation. Under the three states, correction coefficient mapping functions are established to describe the body temperature change process. In addition, the body temperature correction coefficient can also be calculated through other models, including but not limited to: linear models, nonlinear models, machine learning models, deep learning models, etc.

[0120] In the above embodiment, the exercise body temperature is determined based on the body temperature correction coefficient and the human body temperature. The calculation formula is as follows:

[0121] Ta=(1+a)*Tb

[0122] Where Ta is exercise body temperature, Tb is human body temperature, and a is the body temperature correction coefficient.

[0123] It should be noted that human body temperature can be obtained by measuring the user's body temperature in a resting state through a body temperature sensor, and can also be calculated through other models. Other models include but are not limited to: linear models, nonlinear models, machine learning models, deep learning models, etc.

[0124] Combined with the description of the above embodiments, Figure 2 The following is a flow chart of a method for measuring body temperature during exercise provided by an embodiment of the present disclosure. Figure 2 As shown, the method includes:

[0125] S201: Collecting the user's sports physiological parameters in a sports state and the user's resting physiological parameters in a resting state, wherein the resting physiological parameters include: human body temperature;

[0126] S202: Determine the heat generated by the user in the exercise state according to the exercise physiological parameters; and

[0127] S203: Determine the heat dissipation of the user in a motion state according to the motion physiological parameters and the resting physiological parameters;

[0128] S204: determining a body temperature correction coefficient according to heat output, heat dissipation and basal metabolic rate;

[0129] S205: Determine the exercise body temperature according to the body temperature correction coefficient and the human body temperature.

[0130] In this embodiment, the execution subject of the above method is a smart wearable device. The disclosed embodiment does not limit the type of smart wearable devices, and smart wearable devices include but are not limited to the following: smart watches, smart bracelets, smart earplugs, smart glasses, smart clothing, wearable fitness equipment, smart wristbands, smart anklets, and smart head-mounted devices.

[0131] In this embodiment, the specific implementation of each step of the above method can refer to the description of the above embodiments, which will not be repeated here.

[0132] In the above method embodiment of the present disclosure, the user's sports physiological parameters in the motion state and the resting physiological parameters in the resting state are collected; the heat generation of the user in the motion state is determined based on the sports physiological parameters; and the heat dissipation of the user in the motion state is determined based on the sports physiological parameters and the resting physiological parameters; the heat generation and heat dissipation of the user in the motion state are determined by combining the resting physiological parameters and the sports physiological parameters, and the human body temperature is corrected based on the heat generation and heat dissipation to obtain the exercise body temperature of the user in the exercise state; and the exercise body temperature with higher accuracy is obtained through the quantified heat generation and heat dissipation.

[0133] Figure 3 FIG. 3 is a schematic diagram of a sports body temperature measurement device 30 provided by an exemplary embodiment of the present disclosure. Figure 3 As shown, the exercise body temperature measurement device 30 includes: a collection module 31, a first determination module 32, a second determination module 33 and a third determination module 34.

[0134] The acquisition module 31 is used to collect the user's sports physiological parameters in a sports state and the resting physiological parameters in a resting state, wherein the resting physiological parameters include: human body temperature;

[0135] A first determination module 32 is used to determine the heat generation of the user in a sports state according to the sports physiological parameters; and

[0136] A second determination module 33 determines the heat dissipation of the user in a motion state according to the motion physiological parameters and the resting physiological parameters;

[0137] The third determination module 34 corrects the human body temperature according to the heat generation and heat dissipation to obtain the user's exercise body temperature in the exercise state.

[0138] Optionally, the exercise physiological parameter includes: heart rate. When determining the heat generation of the user in the exercise state according to the exercise physiological parameter, the first determination module 32 is used to:

[0139] Determine the basal metabolic rate based on the user's height, weight, age and gender, and

[0140] Determine total caloric output based on the user's gender, weight, and heart rate;

[0141] Determine the user's calorific value during exercise based on total calorific value and basal metabolic rate.

[0142] Optionally, the sports physiological parameters include at least one of the following parameters: sports skin temperature, sports environment temperature and sports skin humidity, and the resting physiological parameters include at least one of the following parameters: resting skin temperature, resting environment temperature and resting skin humidity. When determining the heat dissipation of the user in the sports state according to the sports physiological parameters and the resting physiological parameters, the second determination module 33 is used to:

[0143] Determine the amount of skin temperature change based on the exercise skin temperature and the resting skin temperature;

[0144] Determine the change in ambient temperature based on the ambient temperature of the exercise environment and the ambient temperature of the resting environment;

[0145] Determine the change in skin humidity based on the skin humidity during exercise and the skin humidity at rest;

[0146] The heat dissipation of the user in motion is determined according to at least one of the skin temperature change, the ambient temperature change and the skin humidity change.

[0147] Optionally, when determining the heat dissipation of the user in motion according to the skin temperature change, the ambient temperature change and the skin humidity change, the second determination module 33 is configured to:

[0148] The skin temperature change, the ambient temperature change and the skin humidity change are input into an existing heat dissipation estimation model to obtain the heat dissipation of the user in motion.

[0149] Optionally, before using the heat dissipation estimation model, the second determination module 33 may also be used to:

[0150] Collecting sample exercise skin temperature, sample resting skin temperature, sample exercise skin humidity, sample resting skin humidity, sample exercise environment temperature and sample resting environment temperature of multiple sample users and sample heat dissipation corresponding to the multiple sample users;

[0151] Determine a change amount of the sample skin temperature of each sample user according to the sample exercise skin temperature and the sample resting skin temperature of each sample user;

[0152] Determine a change amount of sample skin humidity of each sample user according to the sample exercise skin humidity and the sample resting skin humidity of each sample user;

[0153] Determine a change in the sample ambient temperature of each sample user according to the sample motion ambient temperature and the sample resting ambient temperature of each sample user;

[0154] The initial model is trained according to the sample skin temperature change, the sample skin humidity change, the sample ambient temperature change and the sample heat dissipation of multiple sample users to obtain a heat dissipation estimation model.

[0155] Optionally, when the third determination module 34 corrects the human body temperature according to the heat generation and heat dissipation to obtain the exercise body temperature of the user in the exercise state, it is used to:

[0156] Determine the body temperature correction factor based on heat output, heat dissipation and basal metabolic rate;

[0157] Determine exercise body temperature based on the body temperature correction coefficient and human body temperature.

[0158] Optionally, when determining the body temperature correction coefficient according to the calorific value, the heat dissipation and the basal metabolic rate, the third determination module 34 is used to:

[0159] Determine the amount of heat change based on the amount of heat generated and the amount of heat dissipated;

[0160] Determine the body temperature correction coefficient based on the heat change and basal metabolic rate.

[0161] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0162] Figure 4 FIG. 1 is a schematic diagram of a structure of an electronic device provided by an exemplary embodiment of the present disclosure. Figure 4 As shown, the electronic device includes: a memory 41 and a processor 42. In addition, the electronic device also includes a power supply component 43 and a communication component 44.

[0163] The memory 41 is used to store computer programs and can be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application program or method operating on the electronic device.

[0164] The memory 41 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0165] The communication component 44 is used for data transmission with other devices.

[0166] The processor 42 can execute the computer instructions stored in the memory 41 to:

[0167] Collect the user's sports physiological parameters in the sports state and the resting physiological parameters in the resting state, wherein the resting physiological parameters include: human body temperature;

[0168] Determine the heat generated by the user during exercise based on exercise physiological parameters; and

[0169] Determine the heat dissipation of the user in motion based on the motion physiological parameters and resting physiological parameters;

[0170] The human body temperature is corrected according to the heat generation and heat dissipation to obtain the user's exercise body temperature.

[0171] Optionally, the exercise physiological parameter includes: heart rate. When the processor 42 determines the heat generation of the user in the exercise state according to the exercise physiological parameter, it is used to:

[0172] Determines the basal metabolic rate based on the user's height, weight, age and gender, and

[0173] Determine total caloric output based on the user's gender, weight, and heart rate;

[0174] Determine the user's calorific value during exercise based on total calorific value and basal metabolic rate.

[0175] Optionally, the exercise physiological parameter includes at least one of the following parameters: exercise skin temperature, exercise environment temperature and exercise skin humidity, and the resting physiological parameter includes at least one of the following parameters: resting skin temperature, resting environment temperature and resting skin humidity. When the processor 42 determines the heat dissipation of the user in the exercise state according to the exercise physiological parameter and the resting physiological parameter, it is used to:

[0176] Determine the amount of skin temperature change based on the exercise skin temperature and the resting skin temperature;

[0177] Determine the change in ambient temperature based on the ambient temperature of the exercise environment and the ambient temperature of the resting environment;

[0178] Determine the change in skin humidity based on the skin humidity during exercise and the skin humidity at rest;

[0179] The heat dissipation of the user in motion is determined according to at least one of the skin temperature change, the ambient temperature change and the skin humidity change.

[0180] Optionally, when determining the heat dissipation of the user in motion according to the skin temperature change, the ambient temperature change and the skin humidity change, the processor 42 is configured to:

[0181] The skin temperature change, the ambient temperature change and the skin humidity change are input into an existing heat dissipation estimation model to obtain the heat dissipation of the user in motion.

[0182] Optionally, before the processor 42 uses the heat dissipation estimation model, the method further includes:

[0183] Collecting sample exercise skin temperature, sample resting skin temperature, sample exercise skin humidity, sample resting skin humidity, sample exercise environment temperature and sample resting environment temperature of multiple sample users and sample heat dissipation corresponding to the multiple sample users;

[0184] Determine a change amount of the sample skin temperature of each sample user according to the sample exercise skin temperature and the sample resting skin temperature of each sample user;

[0185] Determine a change amount of sample skin humidity of each sample user according to the sample exercise skin humidity and the sample resting skin humidity of each sample user;

[0186] Determine a change in the sample ambient temperature of each sample user according to the sample motion ambient temperature and the sample resting ambient temperature of each sample user;

[0187] The initial model is trained according to the sample skin temperature change, the sample skin humidity change, the sample ambient temperature change and the sample heat dissipation of multiple sample users to obtain a heat dissipation estimation model.

[0188] Optionally, when the processor 42 corrects the human body temperature according to the heat generation and heat dissipation to obtain the user's exercise body temperature in the exercise state, it is used to:

[0189] Determine the body temperature correction factor based on heat output, heat dissipation and basal metabolic rate;

[0190] Determine exercise body temperature based on the body temperature correction coefficient and human body temperature.

[0191] Optionally, when determining the body temperature correction coefficient according to the heat output, heat dissipation and basal metabolic rate, the processor 42 is used to:

[0192] Determine the amount of heat change based on the amount of heat generated and the amount of heat dissipated;

[0193] Determine the body temperature correction coefficient based on the heat change and basal metabolic rate.

[0194] Accordingly, the present disclosure also provides a computer-readable storage medium storing a computer program. When the computer-readable storage medium stores the computer program and the computer program is executed by one or more processors, the one or more processors execute Figure 1 Each step in the method embodiment.

[0195] Accordingly, the present disclosure also provides a computer program product, the computer program product includes a computer program / instruction, the computer program / instruction is executed by a processor Figure 1 Each step in the method embodiment.

[0196] Above Figure 4 The communication component in is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0197] Above Figure 4 The power supply component in the device provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0198] The electronic device also includes a display screen and an audio component.

[0199] The display screen includes a screen, and the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0200] The audio component may be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0201] In the above-mentioned device, equipment, storage medium and computer program product embodiments of the present disclosure, the user's exercise physiological parameters in the exercise state and the resting physiological parameters in the resting state are collected; the heat generation of the user in the exercise state is determined based on the exercise physiological parameters; and the heat dissipation of the user in the exercise state is determined based on the exercise physiological parameters and the resting physiological parameters; the heat generation and heat dissipation of the user in the exercise state are determined in combination with the resting physiological parameters and the exercise physiological parameters, and the human body temperature is corrected based on the heat generation and heat dissipation to obtain the exercise body temperature of the user in the exercise state; the exercise body temperature with higher accuracy is obtained through the quantified heat generation and heat dissipation.

[0202] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0203] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0205] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0206] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0207] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0208] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0209] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0210] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring body temperature during exercise, characterized in that: include: Collecting the user's sports physiological parameters in a sports state and the resting physiological parameters in a resting state, wherein the resting physiological parameters include: human body temperature; Determining the heat generation of the user in a state of exercise according to the exercise physiological parameters; and Determining the heat dissipation of the user in a motion state according to the motion physiological parameter and the resting physiological parameter; The human body temperature is corrected according to the heat generation and the heat dissipation to obtain the exercise body temperature of the user in the exercise state.

2. The method according to claim 1, characterized in that The exercise physiological parameter includes: heart rate, and determining the heat generation of the user in the exercise state according to the exercise physiological parameter includes: Determine a basal metabolic rate according to the height, weight, age and gender of the user, and determine a total calorie output according to the gender, weight and heart rate of the user; The calorific value of the user in the exercise state is determined according to the total calorific value and the basal metabolic rate.

3. The method according to claim 1, characterized in that: The sports physiological parameter includes at least one of the following parameters: sports skin temperature, sports environment temperature and sports skin humidity; the resting physiological parameter includes at least one of the following parameters: resting skin temperature, resting environment temperature and resting skin humidity; and determining the heat dissipation of the user in a sports state according to the sports physiological parameter and the resting physiological parameter includes: determining a skin temperature change amount according to the exercise skin temperature and the resting skin temperature; Determining an amount of change in ambient temperature according to the exercise ambient temperature and the resting ambient temperature; Determining a skin humidity change amount according to the exercise skin humidity and the resting skin humidity; The heat dissipation of the user in motion is determined according to at least one of the skin temperature change, the ambient temperature change and the skin humidity change.

4. The method according to claim 3, characterized in that The step of determining the heat dissipation of the user in motion according to the skin temperature change, the ambient temperature change, and the skin humidity change includes: The skin temperature change, the ambient temperature change and the skin humidity change are input into a trained heat dissipation estimation model to obtain the heat dissipation of the user in motion.

5. The method according to claim 4, characterized in that Before using the heat dissipation estimation model, the method further includes: Collecting sample exercise skin temperature, sample resting skin temperature, sample exercise skin humidity, sample resting skin humidity, sample exercise environment temperature and sample resting environment temperature of multiple sample users and sample heat dissipation corresponding to the multiple sample users; Determine a change in the sample skin temperature of each sample user according to the sample exercise skin temperature and the sample resting skin temperature of each sample user; Determine a change in sample skin humidity of each sample user according to the sample exercise skin humidity and the sample resting skin humidity of each sample user; Determining a change in the sample environment temperature of each sample user according to the sample exercise environment temperature and the sample resting environment temperature of each sample user; The initial model is trained according to the sample skin temperature changes, the sample skin humidity changes, the sample environment temperature changes and the sample heat dissipation of the plurality of sample users to obtain a heat dissipation estimation model.

6. The method according to claim 1, characterized in that The correcting the human body temperature according to the heat generation and the heat dissipation to obtain the exercise body temperature of the user in the exercise state includes: Determining a body temperature correction coefficient according to the calorific value, the heat dissipation and the basal metabolic rate; The exercise body temperature is determined according to the body temperature correction coefficient and the human body temperature.

7. The method according to claim 6, characterized in that Determining the body temperature correction coefficient according to the calorific value, the heat dissipation and the basal metabolic rate includes: Determining a heat change amount according to the heat generation amount and the heat dissipation amount; A body temperature correction coefficient is determined according to the heat change and the basal metabolic rate.

8. A sports body temperature measurement device, characterized in that: include: A collection module, used to collect the user's sports physiological parameters in a sports state and the resting physiological parameters in a resting state, wherein the resting physiological parameters include: human body temperature; A first determination module, configured to determine the heat generation of the user in a state of exercise according to the exercise physiological parameters; and A second determination module determines the heat dissipation of the user in a motion state according to the motion physiological parameters and the resting physiological parameters; The third determination module corrects the human body temperature according to the heat generation and the heat dissipation to obtain the exercise body temperature of the user in the exercise state.

9. A smart wearable device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement each step in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the method according to any one of claims 1 to 7 is implemented.

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