Body temperature detection method of wearable device and related device thereof

Abstract

The present application relates to a body temperature detection method of a wearable device and a related device thereof. The body temperature detection method comprises: separately acquiring a skin temperature of a wearer, a device temperature of the wearable device, and an environment temperature outside the wearable device; acquiring an initial body temperature of the wearer according to the skin temperature, the device temperature, and the environment temperature; and acquiring a body temperature influence factor of the wearer, and detecting whether the wearer is in a body temperature normal state or not according to the initial body temperature and the body temperature influence factor. Therefore, according to the present application, factors such as device temperature and environment temperature which interfere with the initial body temperature can be eliminated, such that the accurate initial body temperature can be obtained. In another aspect, the initial body temperature and the body temperature influence factor of the wearer are combined and analyzed to judge whether the wearer is in a body temperature normal state or not, and the body temperature result of the wearer can be further detected, thereby preventing various diseases caused by abnormal body temperature of the wearer.

Description

Body temperature detection method for wearable device and related equipment Technical Field

[0001] The present application relates to the technical field of wearable devices, and in particular to a body temperature detection method for a wearable device and related equipment. Background Art

[0002] With the advancement of technology, wearable devices have evolved from simple functions like checking the time to comprehensive functions such as making calls, playing games, and learning. Currently, as people pay more attention to their health, wearable devices have added the ability to monitor the wearer's physical condition. Specifically, wearable devices can track the wearer's activity or physiological data. These data can be analyzed to provide information to the user, such as measuring the wearer's body temperature and issuing alerts if the temperature is abnormal. However, the temperature detected by wearable devices is significantly affected by other factors and may not accurately reflect the wearer's actual body temperature.

[0003] Summary of the Invention

[0004] Based on this, it is necessary to provide a body temperature detection method for a wearable device and related equipment to address the above technical problems, which can eliminate factors that interfere with body temperature and thus obtain accurate body temperature.

[0005] In a first aspect, the present application provides a body temperature detection method for a wearable device, the body temperature detection method comprising: respectively obtaining the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device; obtaining the initial body temperature of the wearer based on the skin temperature, the device temperature, and the ambient temperature; obtaining the body temperature influencing factor of the wearer, and determining the final body temperature of the wearer based on the initial body temperature and the body temperature influencing factor.

[0006] In one embodiment, the step of respectively obtaining the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device includes:

[0007] Acquiring the skin temperature by means of at least two spaced-apart skin temperature sensors, wherein the skin temperature sensors are arranged close to the skin surface of the wearer;

[0008] obtaining the device temperature by using a device temperature sensor, wherein the device temperature sensor is disposed near a processor of the wearable device;

[0009] The ambient temperature is obtained by an ambient temperature sensor, wherein the ambient temperature sensor is arranged away from the processor and the skin surface of the wearer.

[0010] In one embodiment, the step of obtaining the wearer's initial body temperature based on the skin temperature, the device temperature, and the ambient temperature further includes:

[0011] The skin temperature, the device temperature, and the ambient temperature are analyzed by a first neural network model to obtain the wearer's initial body temperature.

[0012] In one embodiment, the step of obtaining the wearer's initial body temperature based on the skin temperature, the device temperature, and the ambient temperature further includes:

[0013] The relationship between the skin temperature, the device temperature, the ambient temperature and the initial body temperature is as follows: T = T1 - aT2 - bT3;

[0014] Wherein, T is the initial body temperature, T1 is the skin temperature, T2 is the device temperature, T3 is the ambient temperature, a and b correspond to coefficients of the device temperature and the ambient temperature, respectively, and a and b change with changes in the device temperature and the ambient temperature, respectively.

[0015] In one embodiment, the step of obtaining the wearer's body temperature influencing factor includes:

[0016] The wearer's age, gender, blood pressure, and blood oxygen are obtained.

[0017] In one embodiment, the step of determining the wearer's final body temperature based on the initial body temperature and the body temperature influencing factor further includes:

[0018] The final body temperature of the wearer is determined according to the initial body temperature and the body temperature influencing factor by a second neural network model.

[0019] In one embodiment, the step of determining the wearer's final body temperature based on the initial body temperature and the body temperature influencing factor further includes:

[0020] The functional relationship between the final body temperature, the initial body temperature and the body temperature influencing factor is as follows: f =F(xA,yS,zO,iP,jT)

[0021] Among them, the T f The final body temperature, the F is a function, the A, S, O, P and T are respectively the age, gender, blood pressure, blood oxygen and initial body temperature, the x, y, z, i and j are respectively the coefficients of the age, gender, blood pressure, blood oxygen and initial body temperature, which change according to the changes in the age, gender, blood pressure, blood oxygen and initial body temperature.

[0022] In a second aspect, the present application further provides a body temperature detection system for a wearable device, the body temperature detection system comprising:

[0023] a first acquisition module, configured to respectively acquire the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device;

[0024] a second acquisition module, configured to acquire the initial body temperature of the wearer according to the skin temperature, the device temperature, and the ambient temperature;

[0025] The first acquisition module is further used to obtain the wearer's body temperature influencing factor;

[0026] A detection module is used to determine the final body temperature of the wearer based on the initial body temperature and the body temperature influencing factor.

[0027] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores computer-readable instructions, and the processor is configured to implement the steps of the method described above when executing the computer-readable instructions.

[0028] In a fourth aspect, the present application further provides a computer program product, which includes a computer program, and the computer program is used to implement the steps of the method described above when executed by a computer.

[0029] The above describes a body temperature detection method for a wearable device and related equipment. The body temperature detection method includes: obtaining the wearer's skin temperature, the device temperature of the wearable device, and the ambient temperature outside the wearable device; obtaining the wearer's initial body temperature based on the skin temperature, the device temperature, and the ambient temperature; obtaining the wearer's body temperature influencing factor, and determining the wearer's final body temperature based on the initial body temperature and the body temperature influencing factor. Therefore, the present application can eliminate factors such as device temperature and ambient temperature that interfere with the initial body temperature, thereby obtaining an accurate initial body temperature. On the other hand, combining the initial body temperature with the wearer's body temperature influencing factor for analysis can determine an accurate final body temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] FIG1 is a flow chart of a method for detecting body temperature of a wearable device according to an embodiment of the present application;

[0031] FIG2 is a schematic structural diagram of a body temperature detection system for a wearable device provided in an embodiment of the present application;

[0032] FIG3 is a basic structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0034] Please refer to FIG1 , which is a flow chart of a method for detecting body temperature of a wearable device according to an embodiment of the present application. As shown in FIG1 , the method for detecting body temperature includes the following steps:

[0035] Step S1: respectively obtaining the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device.

[0036] A wearable device is a portable device that is worn directly on the body or integrated into the wearer's clothing or accessories. Wearable devices are not just hardware devices; they also achieve powerful functions through software support, data interaction, and cloud interaction. Wearable devices mostly exist as portable accessories with some computing functions that can be connected to mobile phones and various terminals. Mainstream product forms include wrist-supported watches, such as health rings (finger rings), health watches, and wristbands; foot-supported shoes, such as shoes, socks, and other leg-worn products in the future; and head-supported glasses, such as glasses, helmets, and headbands.

[0037] Skin temperature refers to the temperature of the wearer's skin in contact with a wearable device. Device temperature refers to the temperature generated by the wearable device when in operation. Ambient temperature refers to the temperature of the space surrounding the wearable device, such as indoor ambient temperature or outdoor ambient temperature under sunlight.

[0038] The wearable device of the present application not only obtains skin temperature, but also further obtains device temperature and ambient temperature. That is, the wearer's body temperature detection of the present application needs to consider interference factors such as device temperature and ambient temperature.

[0039] Step S2: Obtaining the wearer's initial body temperature based on the skin temperature, the device temperature, and the ambient temperature.

[0040] Although the human body is a warm-blooded animal, the temperature of its skin can be affected by the surrounding environment. For example, if a wearer has been exposed to the sun, their skin temperature will be higher than their initial body temperature due to the sun's radiation. Alternatively, if a wearer has been in a cold storage, their exposed skin will be cooled, causing the skin temperature to be lower than their actual body temperature. Furthermore, if a wearable device is constantly heating, the skin temperature near the wearable device will also be higher than their actual body temperature. Therefore, it is necessary to eliminate interfering factors such as device temperature and ambient temperature to ensure that the skin temperature is closer to the wearer's body temperature.

[0041] Therefore, when obtaining the initial body temperature in this step, three factors, skin temperature, device temperature, and ambient temperature, are comprehensively considered to make the initial body temperature more accurate.

[0042] Step S3: Obtain the wearer's body temperature influencing factor, and determine the wearer's final body temperature based on the initial body temperature and the body temperature influencing factor.

[0043] Because the initial temperature obtained in step S2 is based on skin temperature, which often differs from the actual body temperature in the body, the difference lies in the fluctuation of the actual body temperature when it is transmitted to the skin. Therefore, it is necessary to further obtain the final body temperature based on the initial temperature. This final body temperature is related to the wearer's body temperature influencing factor. Therefore, this step combines the body temperature influencing factor and the initial temperature to further obtain the final body temperature.

[0044] The body temperature influencing factor in this step refers to the wearer's own body temperature influencing factor, excluding factors such as device temperature and ambient temperature mentioned above. Specifically, the body temperature influencing factor may include factors such as the wearer's physiological parameters, age, and gender, where physiological parameters include blood pressure and blood oxygen.

[0045] It should be understood that the normal body temperature range varies at different ages. Generally speaking, adolescents have the most active metabolism and the highest body temperature. The body temperatures at different ages are shown in Table 1 below.

[0046] Table 1: Normal body temperature ranges at different ages

[0047] Body temperature also varies between genders, with women generally having higher body temperatures than men. Similarly, different blood pressure levels can lead to different body temperatures, and different blood oxygen levels can also affect body temperature.

[0048] Body temperature is generated by the breakdown of glucose in the blood. The breakdown of glucose requires oxygen, which means that the oxygen in the blood provides energy to break down glucose. Therefore, there is a certain correlation between body temperature and blood oxygen.

[0049] This step further determines the wearer's final body temperature based on the wearer's body temperature influencing factor and the initial body temperature, combining factors that affect body temperature to avoid inaccurate judgment results caused by only using the initial body temperature as a single factor.

[0050] Therefore, the present embodiment can eliminate factors such as device temperature and ambient temperature that interfere with the initial body temperature, thereby obtaining an accurate initial body temperature. Furthermore, combining this initial body temperature with the wearer's body temperature influencing factors can determine the wearer's final body temperature. Taking into account the wearer's different body temperature influencing factors, a more accurate final body temperature can be obtained.

[0051] Specifically, step S1 can obtain different temperatures using different temperature sensors. Specifically, the skin temperature is obtained using at least two spaced-apart skin temperature sensors, wherein the skin temperature sensors are located close to the wearer's skin surface. Furthermore, different skin temperature sensors can be located at different areas of the wearer's skin. For example, if the wearable device is a health ring, some of the skin temperature sensors can be located on the back of the hand, while others can be located on the palm.

[0052] Furthermore, the device temperature can be obtained via a device temperature sensor, wherein the device temperature sensor is disposed near the processor of the wearable device. Since the processor is an important heat-generating component in the wearable device when in operation, the temperature of the processor can be approximately equal to the device temperature of the wearable device. Placing the device temperature sensor near the processor allows accurate acquisition of the device temperature. It is understood that when multiple device temperature sensors are included, they can also be disposed near other heat-generating components, such as on a circuit board.

[0053] The ambient temperature is obtained by an ambient temperature sensor, wherein the ambient temperature sensor is positioned away from the processor and the wearer's skin surface. Positioning the ambient temperature sensor away from the skin surface prevents the skin temperature from being obtained. Similarly, positioning the ambient temperature sensor away from the processor prevents the device temperature from being obtained. Positioning the ambient temperature sensor away from the skin surface ensures that the ambient temperature sensor is positioned near the outside of the wearable device, facilitating the acquisition of the ambient temperature near the outside of the wearable device.

[0054] The ambient temperature sensor may include multiple ones, which are spaced apart around the wearable device to facilitate the collection of multiple ambient temperatures at different locations and directions outside the wearable device, and then the multiple ambient temperatures obtained are calculated to obtain the final ambient temperature. The average of the multiple ambient temperatures can be taken as the final ambient temperature, or the lowest and highest ambient temperatures can be removed and the average value can be taken as the final ambient temperature, or the ambient temperatures at different locations and directions can be multiplied by corresponding weight coefficients and the average value can be taken as the final ambient temperature. The final ambient temperature obtained by calculating multiple ambient temperatures makes the final ambient temperature more accurate.

[0055] In particular, step S2 can specifically analyze the skin temperature, the device temperature and the ambient temperature through a first neural network model to obtain the initial body temperature of the wearer.

[0056] The first neural network model can be pre-trained. Specifically, different temperature samples can be obtained when multiple wearers wear the wearable device. For example, for each wearer, ambient temperature samples, corresponding skin temperature samples, and device temperature samples are obtained in different environments. Further, the corresponding actual initial body temperature can be obtained. For example, after obtaining different temperature samples, the wearer can be placed in an environment with an appropriate ambient temperature, such as a room with a suitable temperature, and then a professional thermometer can be used to obtain the body's axillary temperature or rectal temperature, which is closer to the body temperature, as the actual initial body temperature.

[0057] The different temperature samples and actual initial body temperature are further input into the first neural network model, the first neural network model is trained, the initial body temperature result output by the training is compared with the actual initial body temperature, and the comparison result is fed back to the first neural network model to adjust and correct the first neural network model. Training and correction are continuously performed until the difference between the initial body temperature result output by the first neural network model and the actual initial body temperature is within a preset range. In subsequent applications, the first neural network model thus trained can directly input the acquired skin temperature, device temperature, and ambient temperature to obtain the wearer's initial body temperature.

[0058] The relationship between the skin temperature, the device temperature, the ambient temperature and the initial body temperature is as follows: T=T1-aT2-bT3;

[0059] Wherein, T is the initial body temperature, T1 is the skin temperature, T2 is the device temperature, and T3 is the ambient temperature. a and b correspond to coefficients of the device temperature and the ambient temperature, respectively. a and b change with changes in the device temperature and the ambient temperature, respectively. For example, when the device temperature is in the range of 35-40 degrees, a takes one value, while when the device temperature is in the range of 41-45 degrees, a takes another value. Similarly, when the ambient temperature is in the range of 20-25 degrees, b takes one value, while when the ambient temperature is in the range of 26-30 degrees, b takes another value. a and b can also have positive and negative values. For example, in extremely cold weather, when T2 and T3 are both very low, a and b can be negative values. In normal weather or relatively warm weather, a and b can be positive values.

[0060] This relationship can be used to directly construct the network logic of the above-mentioned first neural network model, and can also be used alone. That is, in other applications, the first neural network model algorithm can be omitted, and the above-mentioned relationship can be directly used to obtain the initial body temperature.

[0061] Among them, step S3 specifically determines the wearer's final body temperature according to the initial body temperature and the body temperature influencing factor through a second neural network model.

[0062] Similar to the first neural network model, the second neural network model can be pre-trained. Specifically, different initial temperature samples and body temperature influencing factors can be obtained for multiple wearers wearing the wearable device. For example, different initial temperature samples, different blood pressure samples, and different blood oxygen samples can be obtained for each wearer. Data can also be obtained for wearers of different age groups and genders. The corresponding actual final body temperature can also be obtained, for example, by using professional equipment in a hospital or other medical institution.

[0063] The different initial temperature samples and body temperature influencing factors obtained are further input into the second neural network model, and the second neural network model is trained. The final body temperature result output by the training is compared with the actual final body temperature, and the comparison result is fed back to the second neural network model to adjust and correct the second neural network model. Training and correction are continuously performed until the difference between the final body temperature result output by the second neural network model and the actual final body temperature is within a preset range. In subsequent applications, the second neural network model thus trained can directly input the obtained initial temperature and body temperature influencing factors to obtain the wearer's final body temperature.

[0064] In step S3, the final body temperature is obtained by the following functional relationship: The functional relationship between the final body temperature, the initial body temperature and the body temperature influencing factor is as follows: f=F(xA,yS,zO,iP,jT)

[0065] Among them, the T f The final body temperature, the F is a function, the A, S, O, P and T are respectively the age, gender, blood pressure, blood oxygen and initial body temperature, the x, y, z, i and j are respectively the coefficients of the age, gender, blood pressure, blood oxygen and initial body temperature, which change according to the changes in the age, gender, blood pressure, blood oxygen and initial body temperature.

[0066] This relationship can be used to directly construct the network logic of the above-mentioned second neural network model, and can also be used alone. That is, in other applications, the second neural network model algorithm can be omitted, and the above-mentioned relationship can be directly used to obtain the final body temperature.

[0067] In other embodiments, only one neural network model may be used to obtain the final body temperature. That is, samples of the skin temperature, device temperature, ambient temperature, and body temperature influencing factors are obtained to train the neural network model. The logical relationships for training the neural network model may include the two relationships described above.

[0068] Therefore, the embodiment of the present application considers the factors affecting the wearer's body temperature, such as skin temperature, device temperature, ambient temperature, and the wearer's own body temperature, and obtains the final body temperature after analyzing all these influencing factors, so as to obtain an accurate final body temperature.

[0069] The present application also provides a wearable device temperature detection system, which is used in the temperature detection method described above. Referring to FIG2 , the temperature detection system 20 of the present application embodiment includes:

[0070] The first acquisition module 21 is configured to respectively acquire the wearer's skin temperature, the wearable device's device temperature, and the ambient temperature outside the wearable device. Skin temperature refers to the temperature of the wearer's skin in contact with the wearable device. Device temperature refers to the temperature generated by the wearable device when in operation. Ambient temperature refers to the temperature of the surrounding space surrounding the wearable device, such as indoor ambient temperature or outdoor ambient temperature under sunlight.

[0071] The wearable device of the present application not only obtains skin temperature, but also further obtains device temperature and ambient temperature. That is, the wearer's body temperature detection of the present application needs to consider interference factors such as device temperature and ambient temperature.

[0072] The second acquisition module 22 is configured to acquire the wearer's initial body temperature based on the skin temperature, the device temperature, and the ambient temperature. Although the human body is a warm-blooded animal, the temperature of its skin can be affected by the surrounding environment. For example, if the wearer has been exposed to the sun, their skin will be exposed to the sun and will be higher than the initial body temperature. Alternatively, if the wearer has been in a cold storage, their exposed skin will be cold, causing the skin temperature to be lower than the initial body temperature. Alternatively, if the wearable device worn on the hand is in a heating state, the skin temperature near the wearable device will also be higher than the initial body temperature. Therefore, it is necessary to eliminate interfering factors such as device temperature and ambient temperature that affect skin temperature in order to make the skin temperature closer to the wearer's body temperature.

[0073] Therefore, when acquiring the initial body temperature, the second acquisition module 22 comprehensively considers the three factors of skin temperature, device temperature and ambient temperature, so that the initial body temperature is more accurate.

[0074] The first acquisition module 21 is further configured to acquire a body temperature influencing factor of the wearer.

[0075] The detection module 23 is configured to determine the wearer's final body temperature based on the initial body temperature and the body temperature influencing factor.

[0076] Because the initial temperature acquired by the second acquisition module 22 is based on the skin temperature, which often differs from the actual body temperature in the body, the difference lies in the fluctuation of the actual body temperature when it is transmitted to the skin. Therefore, it is necessary to further acquire the final body temperature based on the initial temperature. This final body temperature is related to the wearer's body temperature influencing factor. Therefore, the detection module 23 combines the body temperature influencing factor and the initial temperature to further acquire the final body temperature.

[0077] Body temperature influencing factors refer to the wearer's own body temperature, excluding factors such as device temperature and ambient temperature mentioned above. Specifically, body temperature influencing factors may include factors such as the wearer's physiological parameters, age, and gender. Physiological parameters include blood pressure and blood oxygen levels.

[0078] It's important to understand that normal body temperature ranges vary across different age groups. Generally speaking, adolescents have the most active metabolism and the highest body temperatures. Body temperature also varies by gender, with women generally having higher temperatures than men. Similarly, varying blood pressure can lead to varying body temperatures, and varying blood oxygen levels can also affect body temperature.

[0079] Body temperature is generated by the breakdown of glucose in the blood. The breakdown of glucose requires oxygen, which means that the oxygen in the blood provides energy to break down glucose. Therefore, there is a certain correlation between body temperature and blood oxygen.

[0080] The detection module 23 further detects whether the wearer is in a normal body temperature state based on the wearer's body temperature influencing factor and the initial body temperature, combining factors that affect body temperature to avoid inaccurate judgment results caused by only using body temperature as a single factor for judgment.

[0081] Specifically, the first acquisition module 21 can acquire different temperatures using different temperature sensors. Specifically, the skin temperature is acquired using at least two spaced-apart skin temperature sensors, wherein the skin temperature sensors are located close to the wearer's skin surface. Furthermore, different skin temperature sensors can be located at different areas of the wearer's skin. For example, if the wearable device is a health ring, some skin temperature sensors can be located on the back of the hand, while others can be located on the palm.

[0082] Furthermore, the device temperature can be obtained via a device temperature sensor, wherein the device temperature sensor is disposed near the processor of the wearable device. Since the processor is an important heat-generating component in the wearable device when in operation, the temperature of the processor can be approximately equal to the device temperature of the wearable device. Placing the device temperature sensor near the processor allows accurate acquisition of the device temperature. It is understood that when multiple device temperature sensors are included, they can also be disposed near other heat-generating components, such as on a circuit board.

[0083] The ambient temperature is obtained through an ambient temperature sensor, wherein the ambient temperature sensor is set away from the processor and the skin surface of the wearer. Setting it away from the skin surface can avoid obtaining the skin temperature. Setting it away from the skin surface can ensure that the ambient temperature sensor is set near the outside of the wearable device, which is convenient for obtaining the ambient temperature near the outside of the wearable device.

[0084] The ambient temperature sensor may include multiple ones, which are spaced apart around the wearable device to facilitate the collection of multiple ambient temperatures at different locations and directions outside the wearable device, and then the obtained multiple ambient temperatures are calculated to obtain the final ambient temperature. The average of the multiple ambient temperatures can be taken as the final ambient temperature, or the average of the lowest and highest ambient temperatures can be taken as the final ambient temperature, or the ambient temperatures at different locations and directions are multiplied by corresponding weight coefficients and then the average is taken as the final ambient temperature. The final ambient temperature obtained by calculating multiple ambient temperatures makes the final ambient temperature more accurate.

[0085] The second acquisition module 22 may specifically analyze the skin temperature, the device temperature, and the ambient temperature through a first neural network model to obtain the initial body temperature of the wearer.

[0086] The first neural network model can be pre-trained. Specifically, different temperature samples can be obtained when multiple wearers wear the wearable device. For example, for each wearer, ambient temperature samples, corresponding skin temperature samples, and device temperature samples are obtained in different environments. Further, the corresponding actual initial body temperature can be obtained. For example, after obtaining different temperature samples, the wearer can be placed in an environment with an appropriate ambient temperature, such as a room with a suitable temperature, and then a professional thermometer can be used to obtain the body's axillary temperature or rectal temperature, which is closer to the body temperature, as the actual initial body temperature.

[0087] The different temperature samples and actual initial body temperature are further input into the first neural network model, the first neural network model is trained, the initial body temperature result output by the training is compared with the actual initial body temperature, and the comparison result is fed back to the first neural network model to adjust and correct the first neural network model. Training and correction are continuously performed until the difference between the initial body temperature result output by the first neural network model and the actual initial body temperature is within a preset range. In subsequent applications, the first neural network model thus trained can directly input the acquired skin temperature, device temperature, and ambient temperature to obtain the wearer's initial body temperature.

[0088] The relationship between the skin temperature, the device temperature, the ambient temperature and the initial body temperature is as follows: T=T1-aT2-bT3;

[0089] Wherein, T is the initial body temperature, T1 is the skin temperature, T2 is the device temperature, and T3 is the ambient temperature. a and b correspond to coefficients of the device temperature and the ambient temperature, respectively. a and b change with changes in the device temperature and the ambient temperature, respectively. For example, when the device temperature is in the range of 35-40 degrees, a takes one value, while when the device temperature is in the range of 41-45 degrees, a takes another value. Similarly, when the ambient temperature is in the range of 20-25 degrees, b takes one value, while when the ambient temperature is in the range of 26-30 degrees, b takes another value. a and b can also have positive and negative values. For example, in extremely cold weather, when T2 and T3 are both very low, a and b can be negative values. In normal weather or relatively warm weather, a and b can be positive values.

[0090] This relationship can be used to directly construct the network logic of the above-mentioned first neural network model, and can also be used alone. That is, in other applications, the first neural network model algorithm can be omitted, and the above-mentioned relationship can be directly used to obtain the initial body temperature.

[0091] Among them, the detection module 23 specifically determines the wearer's final body temperature according to the initial body temperature and the body temperature influencing factor through a second neural network model.

[0092] Similar to the first neural network model, the second neural network model can be pre-trained. Specifically, different initial temperature samples and body temperature influencing factors can be obtained for multiple wearers wearing the wearable device. For example, different initial temperature samples, different blood pressure samples, and different blood oxygen samples can be obtained for each wearer. Data can also be obtained for wearers of different age groups and genders. The corresponding actual final body temperature can also be obtained, for example, by using professional equipment in a hospital or other medical institution.

[0093] The different initial temperature samples and body temperature influencing factors obtained are further input into the second neural network model, and the second neural network model is trained. The final body temperature result output by the training is compared with the actual final body temperature, and the comparison result is fed back to the second neural network model to adjust and correct the second neural network model. Training and correction are continuously performed until the difference between the final body temperature result output by the second neural network model and the actual final body temperature is within a preset range. In subsequent applications, the second neural network model thus trained can directly input the obtained initial temperature and body temperature influencing factors to obtain the wearer's final body temperature.

[0094] The detection module 23 specifically obtains the final body temperature through the following functional relationship. The functional relationship between the final body temperature, the initial body temperature and the body temperature influencing factor is as follows: f =F(xA,yS,zO,iP,jT)

[0095] Among them, the T f The final body temperature, the F is a function, the A, S, O, P and T are respectively the age, gender, blood pressure, blood oxygen and initial body temperature, the x, y, z, i and j are respectively the coefficients of the age, gender, blood pressure, blood oxygen and initial body temperature, which change according to the changes in the age, gender, blood pressure, blood oxygen and initial body temperature.

[0096] This relationship can be used to directly construct the network logic of the above-mentioned second neural network model, and can also be used alone. That is, in other applications, the second neural network model algorithm can be omitted, and the above-mentioned relationship can be directly used to obtain the final body temperature.

[0097] Therefore, the embodiment of the present application considers the factors affecting the wearer's body temperature, such as skin temperature, device temperature, ambient temperature, and the wearer's own body temperature, and obtains the final body temperature after analyzing all these influencing factors, so as to obtain an accurate final body temperature.

[0098] To solve the above technical problems, the present application also provides a computer device. Specific reference is made to FIG3 , which is a basic structural block diagram of the computer device of the present embodiment.

[0099] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 6 with components 61-63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0100] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0101] The memory 61 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the computer device 6. Of course, the memory 61 may also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system installed on the computer device 6 and various information management operating systems, such as computer-readable instructions for the temperature detection method of a wearable device. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0102] In some embodiments, the processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to execute computer-readable instructions or process data stored in the memory 61, such as computer-readable instructions for executing the body temperature detection method of the wearable device.

[0103] The network interface 63 may include a wireless network interface or a wired network interface. The network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0104] The present application also provides another embodiment, namely, providing a computer program product, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the body temperature detection method of the wearable device as described above.

[0105] The embodiment of the present application provides a body temperature detection method for a wearable device and related equipment. The body temperature detection method includes: respectively obtaining the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device; obtaining the initial body temperature of the wearer based on the skin temperature, the device temperature, and the ambient temperature; obtaining the body temperature influencing factor of the wearer, and detecting whether the wearer is in a normal body temperature state based on the initial body temperature and the body temperature influencing factor. Therefore, the present application can eliminate factors such as device temperature and ambient temperature that interfere with the initial body temperature, thereby obtaining an accurate initial body temperature. On the other hand, combining the initial body temperature and the wearer's body temperature influencing factor for analysis can determine whether the wearer is in a normal body temperature state, and the wearer's body temperature result can be further detected to prevent various diseases caused by abnormal body temperature of the wearer.

[0106] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0107] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A body temperature detection method for a wearable device, characterized in that: The body temperature detection method comprises: respectively acquiring the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device; obtaining the initial body temperature of the wearer according to the skin temperature, the device temperature, and the ambient temperature; A body temperature influencing factor of the wearer is obtained, and a final body temperature of the wearer is determined according to the initial body temperature and the body temperature influencing factor.

2. The body temperature detection method according to claim 1, characterized in that: The steps of respectively obtaining the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device include: Acquiring the skin temperature by means of at least two spaced-apart skin temperature sensors, wherein the skin temperature sensors are arranged close to the skin surface of the wearer; obtaining the device temperature by using a device temperature sensor, wherein the device temperature sensor is disposed near a processor of the wearable device; The ambient temperature is obtained by an ambient temperature sensor, wherein the ambient temperature sensor is arranged away from the processor and the skin surface of the wearer.

3. The body temperature detection method according to claim 1, characterized in that: The step of obtaining the initial body temperature of the wearer according to the skin temperature, the device temperature and the ambient temperature further includes: The skin temperature, the device temperature, and the ambient temperature are analyzed by a first neural network model to obtain the initial body temperature of the wearer.

4. The body temperature detection method according to any one of claims 1 to 3, characterized in that: The step of obtaining the initial body temperature of the wearer according to the skin temperature, the device temperature and the ambient temperature further includes: The relationship between the skin temperature, the device temperature, the ambient temperature and the initial body temperature The system is as follows: T = T1-aT2-bT3; Wherein, T is the initial body temperature, T1 is the skin temperature, T2 is the device temperature, T3 is the ambient temperature, a and b correspond to coefficients of the device temperature and the ambient temperature, respectively, and a and b change with changes in the device temperature and the ambient temperature, respectively.

5. The body temperature detection method according to claim 1, characterized in that: The step of obtaining the wearer's body temperature influencing factor includes: The wearer's age, gender, blood pressure, and blood oxygen are obtained.

6. The body temperature detection method according to claim 5, characterized in that: The step of determining the wearer's final body temperature based on the initial body temperature and the body temperature influencing factor further includes: The final body temperature of the wearer is determined according to the initial body temperature and the body temperature influencing factor by a second neural network model.

7. The body temperature detection method according to claim 5 or 6, characterized in that: The step of determining the wearer's final body temperature based on the initial body temperature and the body temperature influencing factor further includes: The functional relationship between the final body temperature, the initial body temperature, and the body temperature influencing factor is as follows: T f =F(xA,yS,zO,iP,jT) Among them, the T f The final body temperature, the F is a function, the A, S, O, P and T are respectively the age, gender, blood pressure, blood oxygen and initial body temperature, the x, y, z, i and j are respectively the coefficients of the age, gender, blood pressure, blood oxygen and initial body temperature, which change according to the changes in the age, gender, blood pressure, blood oxygen and initial body temperature.

8. A body temperature detection system for a wearable device, characterized in that: The body temperature detection system comprises: a first acquisition module, configured to respectively acquire the skin temperature of the wearer, the device temperature of the wearable device, and the ambient temperature outside the wearable device; a second acquisition module, configured to acquire the initial body temperature of the wearer according to the skin temperature, the device temperature, and the ambient temperature; The first acquisition module is further used to obtain the wearer's body temperature influencing factor; A detection module is used to determine the final body temperature of the wearer based on the initial body temperature and the body temperature influencing factor.

9. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor is configured to implement the steps of the method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer program product, characterized in that The computer program product comprises a computer program for implementing the method according to any one of claims 1 to 7 when the computer program is executed by a computer.

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