Core body temperature detection method and device, electronic equipment and medium

Abstract

The invention relates to a core body temperature detection method and device, electronic equipment and a medium. The method comprises the steps that absorbance of multiple light waves penetrating through a target object is obtained; and determining the core body temperature of the target object based on the plurality of absorbance and the relationship between the absorbance and the human tissue temperature. The core body temperature is estimated by using the multispectral technology, and the multispectral technology has non-invasiveness and non-contact performance and is high in anti-interference capability, so that the accuracy of acquiring the absorbance penetrating through the target object by the electronic equipment can be ensured, the core body temperature of the target object can be accurately reflected, a user can conveniently monitor the physical condition, and the user experience is improved. And the use experience of the user is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electronic devices, and particularly to a core body temperature detection method, apparatus, electronic device, and medium. Background Art

[0002] Body temperature is one of the important vital signs for judging the health status of the human body. Normal body temperature can reflect the stable functions of various systems of the human body, while abnormal body temperature often indicates that the human body has diseases or health problems. In the prior art, skin temperature is usually measured using a temperature sensor, and then a prediction model is established to estimate the core body temperature of the user. Since the temperature sensor is easily affected by the environment, and sweating and poor contact of the skin will cause large measurement errors, it is impossible to accurately obtain the core body temperature of the user. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a core body temperature detection method, apparatus, electronic device, and medium.

[0004] According to a first aspect of an embodiment of the present disclosure, there is provided a core body temperature detection method, the core body temperature detection method including:

[0005] Obtaining the absorbance of multiple light waves after passing through a target object, where the wavelength of each of the light waves is different, and the absorbance of human tissues for light waves of different wavelengths is different;

[0006] Based on the multiple absorbances and the relationship between the absorbance and the temperature of human tissues, determining the core body temperature of the target object.

[0007] In some exemplary embodiments of the present disclosure, the determining the core body temperature of the target object based on the multiple absorbances and the relationship between the absorbance and the temperature of human tissues includes:

[0008] Inputting the multiple absorbances into a pre-stored core body temperature prediction model to obtain multiple temperature values, where the core body temperature prediction model is used to represent the relationship between the absorbance of different light waves passing through human tissues and the temperature of human tissues;

[0009] Taking the highest temperature among the multiple temperature values as the core body temperature.

[0010] In some exemplary embodiments of the present disclosure, the core body temperature prediction model is obtained by the following method:

[0011] Establishing a first correlation relationship between the depth of human tissues and the absorbance of the multiple light waves;

[0012] Establishing a second correlation relationship between the depth of human tissues and the temperature of human tissues;

[0013] Based on the first correlation relationship and the second correlation relationship, obtain the core body temperature prediction model.

[0014] In some exemplary embodiments of the present disclosure, the establishing of the first correlation relationship between the human tissue depth and the absorbance of the multiple light waves includes:

[0015] Based on the Lambert-Beer law, establish a first functional relationship between the human tissue depth and the absorbance of the multiple light waves, where the independent variable of the first functional relationship is the human tissue depth, the dependent variable of the first functional relationship is the absorbance of different light waves, and the reference coefficients of the first functional relationship include tissue concentration and absorption coefficient, wherein the absorption coefficient is used to characterize the absorption degree of the human tissue for light waves of different wavelengths;

[0016] Based on the first functional relationship, determine the first correlation relationship.

[0017] In some exemplary embodiments of the present disclosure, the establishing of the second correlation relationship between the human tissue depth and the human tissue temperature includes:

[0018] Based on the bioheat conduction equation, determine the human tissue temperature distribution function, where the human tissue temperature distribution function includes a first parameter, a second parameter, and a heat transfer coefficient, and the heat transfer coefficient is a constant;

[0019] Based on the temperature sample parameters and the human tissue temperature distribution function, determine the first parameter and the second parameter, where the temperature sample parameters include multiple groups of sample tissue depths and the temperature values corresponding to the sample tissue depths;

[0020] Based on the first parameter, the second parameter, and the human tissue temperature distribution function, obtain a second functional relationship;

[0021] Based on the second functional relationship, obtain the second correlation relationship.

[0022] In some exemplary embodiments of the present disclosure, the obtaining of the second correlation relationship based on the second functional relationship includes:

[0023] Based on the second functional relationship, determine a third functional relationship between the human tissue depth difference and the human tissue temperature difference, and use the third functional relationship as the second correlation relationship, where the human tissue depth difference is used to characterize the depth between different tissues of the human body, and the human tissue temperature difference is used to characterize the temperature difference between different tissues of the human body.

[0024] In some exemplary embodiments of the present disclosure, the obtaining of the core body temperature prediction model based on the first correlation relationship and the second correlation relationship includes:

[0025] For multiple light waves of different wavelengths, fitting is performed based on the second correlation relationship and the first correlation relationship to obtain the core body temperature prediction model.

[0026] In some exemplary embodiments of the present disclosure, the core body temperature detection method further includes:

[0027] Obtaining body temperature influencing factors, where the body temperature influencing factors include one or more of skin temperature, exercise parameters, environmental temperature, and heart rate;

[0028] Based on the body temperature influencing factors, correcting the core body temperature of the target object.

[0029] According to a second aspect of the embodiments of the present disclosure, there is provided a core body temperature detection device, where the core body temperature detection device includes:

[0030] An acquisition module, configured to acquire the absorbance of multiple light waves passing through a target object, where the wavelength of each light wave is different, and the absorbance of human tissues for light waves of different wavelengths is different;

[0031] An execution module, configured to determine the core body temperature of the target object based on the multiple absorbances and the relationship between the absorbance and the human tissue temperature.

[0032] According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, where the electronic device includes:

[0033] A processor;

[0034] A memory for storing executable instructions executable by the processor;

[0035] Wherein, the processor is configured to execute the executable instructions in the memory to implement the core body temperature detection method provided in the first aspect of the present disclosure.

[0036] According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, on which executable instructions are stored, and when the executable instructions are executed by a processor, the core body temperature detection method provided in the first aspect of the present disclosure is implemented.

[0037] Adopting the above method of the present disclosure has the following beneficial effects: The present disclosure uses multi-spectral technology to estimate the core body temperature. Since multi-spectral technology is non-invasive, non-contact, and has strong anti-interference ability, it can ensure the accuracy of the electronic device in obtaining the absorbance of light waves passing through the target object, and thus accurately reflect the core body temperature of the target object, facilitating the user to monitor the physical condition and improving the user experience.

[0038] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Brief Description of the Drawings

[0039] The drawings herein are incorporated into and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0040] Figure 1 is a flowchart of a core body temperature detection method shown according to an exemplary embodiment.

[0041] Figure 2 is a flowchart of a core body temperature detection method shown according to an exemplary embodiment.

[0042] Figure 3 is a flowchart of a core body temperature detection method shown according to an exemplary embodiment.

[0043] Figure 4 is a flowchart of a core body temperature detection method shown according to an exemplary embodiment.

[0044] Figure 5 is a schematic diagram of a sensor involved in core body temperature detection shown according to an exemplary embodiment.

[0045] Figure 6 is a block diagram of a core body temperature detection device shown according to an exemplary embodiment.

[0046] Figure 7 is a block diagram of an electronic device shown according to an exemplary embodiment. Detailed Description of the Embodiments

[0047] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0048] In the field of human body temperature detection, there are two types of temperatures, namely core temperature (Tc) and skin temperature (Ts). Skin temperature refers to the temperature of the peripheral tissues of the human body, and core temperature refers to the temperature of internal organs in the human body such as the brain, heart, and lungs. The normal body temperature mentioned by people in daily life is the core temperature, which generally remains around 37°C, and at this time, the tissues and organs of the human body are in the best functional state. Since it is difficult to measure the core temperature of the human body, existing body temperature measurement technologies usually use temperature sensors to measure the skin temperature and use a modeling prediction algorithm to estimate the core body temperature.

[0049] Temperature sensors are divided into two types: contact type and non-contact type. Contact type temperature sensors include, for example, thermistors and thermopiles, etc. Non-contact type temperature sensors include, for example, infrared radiation sensors and microwave sensors, etc. Most of the existing body temperature measurement technologies use contact type temperature sensors to measure skin temperature. However, temperature sensors are easily affected by environmental disturbances such as air flow, as well as factors such as sweat secretion at the skin contact point and poor sensor contact. For example, when the user wears more clothes, the skin temperature will be relatively high; another example is that when there is water on the skin surface and there is wind, the skin surface temperature will be carried away, resulting in a lower skin temperature. Therefore, there is an error between the skin temperature detected by the temperature sensor and the user's true skin temperature, and thus the user's true core body temperature information cannot be obtained.

[0050] To solve the above technical problems, the present disclosure provides a core body temperature measurement method, which is applied to an electronic device. The photoelectric sensor emits multiple light waves with different wavelengths, and obtains the absorbance of the multiple light waves after passing through the target object. Based on the multiple absorbances and the relationship between the absorbance and the human tissue temperature, the core body temperature of the target object is determined. Since the multispectral technology is non-invasive and non-contact, using the multispectral technology to estimate the core body temperature of the human body will not cause discomfort to the user; and the multispectral technology has strong anti-interference ability, which can ensure the accuracy of the electronic device to obtain the absorbance of the light wave passing through the target object, and thus accurately reflect the core body temperature of the target object, facilitating the user to monitor the physical condition and improving the user experience.

[0051] An exemplary embodiment of the present disclosure provides a core body temperature detection method, which is applied to an electronic device. The electronic device can specifically be intelligent devices such as smart watches, smart bracelets, smart rings, etc., or professional body temperature detection devices. By using the core body temperature detection method in this embodiment, the core body temperature of the target object wearing the electronic device can be detected, reflecting the physical condition of the user and helping the user or medical staff to understand the body temperature of the target object.

[0052] As Figure 1 shown, the core body temperature detection method shown in the present disclosure includes:

[0053] S101. Obtain the absorbance of multiple light waves after passing through the target object;

[0054] S102. Based on the multiple absorbances and the relationship between the absorbance and the human tissue temperature, determine the core body temperature of the target object.

[0055] In step S101, the electronic device is built-in with a photoelectric sensor, and the photoelectric sensor includes a multi-spectral sensor, a single-spectral sensor, etc. When the electronic device activates the core body temperature detection function, the photoelectric sensor emits light waves of different wavelengths at fixed intervals to irradiate the target object, and captures the light waves reflected back from the target object. By performing time-frequency analysis on the reflected light wave signals, the absorbances of multiple light waves passing through the target object are obtained. As Figure 5 shown, the human tissue includes multiple layers from the outside to the inside, including skin, fat, muscle, blood vessels, and blood. Since the penetration capabilities of light waves of different wavelengths are different, for example, some light waves can only penetrate the skin and irradiate the fat of the human tissue, and some light waves can penetrate the skin, fat, and muscle and irradiate the blood vessels and blood of the human tissue. And the light waves of different wavelengths are emitted at fixed intervals. Therefore, the electronic device can obtain the light waves reflected back from multiple different human tissue layers, and by comparing the intensity of the emitted light waves with the intensity of the reflected light waves, the absorbances corresponding to different human tissue depths are obtained.

[0056] If the single-spectral sensor is built-in in the electronic device, it is necessary to ensure that the wavelength of the light wave emitted by the single-spectral sensor can penetrate deep into the human tissue, so that the electronic device can obtain the absorbance used to estimate the core body temperature. If the multi-spectral sensor is built-in in the electronic device, the multi-spectral sensor will emit light waves of multiple wavelengths, and the electronic device will obtain the absorbances of the light waves of different wavelengths passing through the human tissue. The electronic device can perform calculations such as averaging and weighted averaging on multiple absorbances to obtain a comprehensively evaluated value as the absorbance of the target object. The electronic device can also select the maximum value or the median value among multiple absorbances as the absorbance of the target object.

[0057] In step S102, the relationship between the absorbance and the human tissue temperature is pre-stored in the electronic device. In one example, the relationship between the absorbance and the human tissue temperature is stored in the electronic device in the form of a function. When multiple absorbances of the target object are obtained, the corresponding multiple human tissue temperatures are calculated through the function. In another example, the relationship between the absorbance and the human tissue temperature is stored in the electronic device in the form of a data table. When multiple absorbances of the target object are obtained, by determining the wavelength of each light wave and the range to which the absorbance belongs, the human tissue temperature corresponding to the range is found in the data table. In yet another example, the relationship between the absorbance and the human tissue temperature is stored in the electronic device in the form of a neural network model. When multiple absorbances of the target object are input, the neural network model outputs multiple human tissue temperatures through analysis.

[0058] Due to the heat conduction of human tissues, blood circulation will carry the heat in the body to the body surface. Therefore, different levels (different depths) of human tissues correspond to different temperature values. Among them, the blood vessels and blood in human tissues are the same as deep organs such as the brain, heart, and lungs, and all contain abundant hemoglobin. Therefore, the temperature corresponding to the deepest part of human tissues, that is, the blood vessels and blood, is approximately used as the core body temperature of the target object. Since the penetration ability of light waves is proportional to the wavelength, that is, the longer the wavelength of the light wave, the stronger the penetration ability of the light wave, and the more levels (depths) of human tissues it can penetrate. Therefore, in order to ensure that the core body temperature of the target object can be obtained, it is necessary to ensure that the optoelectronic sensor can emit light waves with a longer wavelength that are sufficient to penetrate to the blood vessels and blood.

[0059] After the electronic device obtains multiple absorbances, it can select the value obtained by comprehensive evaluation of multiple absorbances as the absorbance of the target object, and determine the core temperature of the human body based on the absorbance after comprehensive evaluation and the relationship between the absorbance and the temperature of human tissues; the electronic device can also select the maximum value or median value of multiple absorbances as the absorbance of the target object, and determine the core temperature of the human body based on the maximum value or median value of the absorbances and the relationship between the absorbance and the temperature of human tissues; the electronic device can also determine the temperature value of human tissues corresponding to each absorbance based on the relationship between the absorbance and the temperature of human tissues, and use the maximum value of the human tissue temperature as the core body temperature.

[0060] In the present disclosure, the estimation of the core body temperature is completed by using multi-spectral technology. Since multi-spectral technology is non-invasive, non-contact, and has strong anti-interference ability, it can ensure the accuracy of the electronic device in obtaining the absorbance passing through the target object, and then accurately reflect the core body temperature of the target object, which is convenient for users to monitor their physical conditions and improve the user experience.

[0061] According to an exemplary embodiment, as Figure 2 shown, the core body temperature detection method shown in the present disclosure includes:

[0062] S201. Obtain the absorbances after multiple light waves pass through the target object;

[0063] S202. Input the multiple absorbances into a pre-stored core body temperature prediction model to obtain multiple temperature values;

[0064] S203. Use the highest temperature among the multiple temperature values as the core body temperature.

[0065] Among them, the implementation manner of step S201 is the same as that in the above embodiment, and will not be elaborated here.

[0066] In step S202, the core body temperature prediction model is used to characterize the relationship between the absorbance of different light waves passing through human tissues and the temperature of human tissues. The core body temperature prediction model has been established and stored in the electronic device before the electronic device leaves the factory. The model basis of the core body temperature prediction model can be a linear model, a non-linear model, a machine learning model, a deep learning model, etc. The core body temperature prediction model is calculated or trained using temperature sample parameters. Since the temperature sample parameters include multiple sets of sample tissue depths and the temperature values corresponding to the sample tissue depths, the finally pre-stored core body temperature prediction model can output the temperature value corresponding to the input absorbance according to the input absorbance.

[0067] In step S203, since human tissues include multiple layers such as skin, fat, muscle, blood vessels and blood, and heat conduction occurs from the inside to the outside of the human body, the temperature values corresponding to each layer decrease sequentially from the inside to the outside. As the deepest part of human tissues, the temperature value at the blood vessels and blood is the highest. Since the temperature value at the blood vessels and blood is close to the actual core body temperature of human organs such as the heart and lungs, after the electronic device obtains multiple temperature values, the electronic device can use the highest temperature value among the multiple temperature values as the core body temperature. The core body temperature can be displayed on the screen of the electronic device in real time, which is convenient for users to monitor their physical conditions. When the core body temperature exceeds the normal value, the electronic device can give a prompt to the user in the form of flashing, vibration, sound, etc., reminding the user to take measures in time to prevent danger from occurring. In addition, in order to reduce the power consumption of the electronic device, the optoelectronic sensor can be made to emit only one light wave with a relatively long wavelength that is sufficient to penetrate to the blood vessels and blood, and the electronic device directly determines the core body temperature of the target object based on the absorbance of the light wave.

[0068] In the present disclosure, the electronic device uses the core body temperature prediction model to obtain the core body temperature. Since the core body temperature prediction model estimates using the absorbance obtained by the multispectral technology, and the multispectral technology is non-contact and not easily affected by environmental factors, the estimation result of the electronic device using the core body temperature prediction model is more accurate, closer to the true core body temperature of the target object, and effectively improves the detection accuracy of the core body temperature.

[0069] According to an exemplary embodiment, as Figure 3 shown, the core body temperature detection method in this embodiment includes:

[0070] S301. Establish a first correlation relationship between the human tissue depth and the absorbance of multiple light waves;

[0071] S302. Establish a second correlation relationship between the human tissue depth and the human tissue temperature;

[0072] S303. Based on the first correlation relationship and the second correlation relationship, obtain a core body temperature prediction model;

[0073] S304. Obtain the absorbance of multiple light waves after passing through the target object;

[0074] S305. Input the multiple absorbances into a pre-stored core body temperature prediction model to obtain multiple temperature values;

[0075] S306. Take the highest temperature among the multiple temperature values as the core body temperature.

[0076] Among them, steps S304 - S306 are the same as the implementation manners in the above embodiments and will not be elaborated here.

[0077] In step S301, the light wave penetration abilities of different wavelengths are different. The longer the wavelength, the greater the light wave intensity, the stronger the light wave penetration ability, and the greater the depth of the human tissue that the light wave can penetrate. The absorbance is the logarithm of the ratio of the incident light intensity before the light wave passes through the substance to the transmitted light intensity after the light wave passes through the substance. Therefore, there is a linear relationship between the depth of human tissue and the absorbance of the light wave, and a first correlation relationship can be established between the depth of human tissue and the absorbances of multiple light waves. The first correlation relationship between the depth of human tissue d and the absorbance of the light wave λ is d = f(λ1, λ2, …, λ n ), that is, there is a functional relationship f between the depth of human tissue d and the absorbance of the light wave λ, and λ1, λ2, …, λ n represent light waves with different wavelengths. In the function f of the first correlation relationship, the absorbance of one light wave λ corresponds to one depth of human tissue d. For example, the absorbance of light wave λ1 corresponds to the depth of human tissue d1, the absorbance of light wave λ2 corresponds to the depth of human tissue d2, and the absorbance of light wave λ n corresponds to the depth of human tissue d n . When the electronic device obtains the absorbance from the photoelectric sensor, it can determine the depth of human tissue based on the first correlation relationship.

[0078] In step S302, there is heat conduction in the human tissue from the inside to the outside, that is, the greater the depth of human tissue, the higher the human tissue temperature. There is a linear relationship between the depth of human tissue and the human tissue temperature. Therefore, a second correlation relationship can be established between the depth of human tissue and the human tissue temperature. The second correlation relationship between the human tissue temperature t and the depth of human tissue d is the function t = T(d), that is, there is a functional relationship T between the human tissue temperature t and the depth of human tissue d. In the function T of the second correlation relationship, one depth of human tissue d corresponds to one human tissue temperature t. For example, the depth of human tissue d1 corresponds to the human tissue temperature t1, the depth of human tissue d2 corresponds to the human tissue temperature t2, and the depth of human tissue d n corresponds to the human tissue temperature t n . When the electronic device determines the depth of human tissue, it can determine the human tissue temperature based on the second correlation relationship.

[0079] In step S303, in the function f of the first association relationship, the absorbance of the light wave λ is the independent variable, and the human tissue depth d is the dependent variable; in the function T of the second association relationship, the human tissue depth d is the independent variable, and the human tissue temperature t is the dependent variable. The function f of the first association relationship and the function T of the second association relationship have the same parameter of the human tissue depth d. Therefore, by combining the function f of the first association relationship and the function T of the second association relationship, the functional relationship between the human tissue temperature t and the absorbance of the light wave λ can be obtained: t=T(f(λ1,λ2,…,λ n )), the functional relationship is the core temperature prediction model of the electronic device, where λ1,λ2,…,λ n Represents light waves with different wavelengths. The absorbance of a light wave λ corresponds to a human tissue temperature t. For example, the absorbance of light wave λ1 corresponds to human tissue temperature t1, the absorbance of light wave λ2 corresponds to human tissue temperature t2, and the absorbance of light wave λ n The absorbance corresponds to the temperature of human tissue t n .

[0080] In some embodiments, step S301 establishes a first correlation between the depth of human tissue and the absorbance of multiple light waves, including:

[0081] S3011. Based on the Beer-Lambert law, a first functional relationship between the depth of human tissue and the absorbance of multiple light waves is established, wherein the independent variable of the first functional relationship is the depth of human tissue, the dependent variable of the first functional relationship is the absorbance of different light waves, and the reference coefficient of the first functional relationship includes tissue concentration and absorption coefficient, wherein the absorption coefficient is used to characterize the absorption degree of human tissue to light waves of different wavelengths;

[0082] S3012. Determine a first association relationship based on the first functional relationship.

[0083] Among them, Lambert-Beer's law describes the relationship between the strength of a substance's absorption of a certain wavelength of light and the concentration of the absorbing substance and the thickness of its liquid layer. Therefore, based on Lambert-Beer's law, the first functional relationship between the depth of human tissue and the absorbance of multiple light waves can be determined. That is, the expression of the first functional relationship is:

[0084] A λ =α λ ×c×d

[0085] λ represents light wave, A λ Indicates the absorbance of different light waves λ, α λ represents the absorption coefficient of different light waves λ, c is the tissue concentration, and d is the depth of human tissue. In the first functional relationship, the depth of human tissue d is the independent variable, and the absorbance of different light waves A λ is the dependent variable, the absorption coefficient α λCharacterize the absorption degree of human tissue to light waves λ of different wavelengths, and the absorption coefficient α λ The specific value of can be obtained by using empirical values or by calibration methods. The concentration c of different layers of human tissue can be regarded as the same value.

[0086] Since the absorbance A λ can be obtained by using a photoelectric sensor, and the absorbance A λ is a known quantity. In order to solve for the depth d of human tissue, the function f of the first correlation relationship can be determined by using the first functional relationship:

[0087]

[0088] In the function f of the first correlation relationship, A λi represents the absorbance of different light waves λ i , and d i represents the depth of human tissue corresponding to the absorbance of different light waves λ i . Since the depth d of human tissue is the dependent variable, and the absorbance A of different light waves λ is the independent variable, and the absorption coefficient α λ and the concentration c are both known values, the absorbance A of different light waves λi and the depth d of human tissue i show a linear relationship. After the electronic device obtains the absorbance A λi through the photoelectric sensor, the depth d of human tissue i can be estimated.

[0089] In some embodiments, step S302 establishes a second correlation relationship between the depth of human tissue and the temperature of human tissue, including:

[0090] S3021. Based on the bioheat conduction equation, determine the human tissue temperature distribution function. The human tissue temperature distribution function includes a first parameter, a second parameter, and a heat transfer coefficient, where the heat transfer coefficient is a constant;

[0091] S3022. Based on the temperature sample parameters and the human tissue temperature distribution function, determine the first parameter and the second parameter, where the temperature sample parameters include multiple groups of sample tissue depths and the temperature values corresponding to the sample tissue depths;

[0092] S3023. Based on the first parameter, the second parameter, and the human tissue temperature distribution function, obtain the second functional relationship;

[0093] S3024. Based on the second functional relationship, obtain the second correlation relationship.

[0094] The bioheat conduction equation is a mathematical model that describes heat conduction in living organisms. The bioheat conduction equation is:

[0095]

[0096] where ρ t c t represents the volumetric specific heat of human tissue, q met represents the heat generated by metabolism, ρ b c b represents the volumetric specific heat of blood, k t represents the thermal conductivity of human tissue, ω represents the blood perfusion rate, T a represents the arterial blood temperature entering the human tissue, T v represents the venous blood temperature flowing out of the tissue.

[0097] When considering one-dimensional steady-state heat transfer (i.e., ), neglecting metabolic heat (i.e., q met = 0) and blood flow effects (i.e., ω = 0), and the human tissue being a homogeneous material with the heat transfer coefficient k being a constant, the bioheat conduction equation can be simplified:

[0098]

[0099] Integrating both sides gives:

[0100]

[0101] where the first parameter C1 is a constant. Integrating this equation again gives the human tissue temperature distribution function:

[0102]

[0103] where the second parameter C2 is a constant, T represents the human tissue temperature, x represents the human tissue depth. T(x) represents the human tissue temperature corresponding to different human tissue depths. For example, when the human tissue depth is x1, the corresponding human tissue temperature T(x1) can be determined through the human tissue temperature distribution function; when the human tissue depth is x2, the corresponding human tissue temperature T(x2) can be determined through the human tissue temperature distribution function; when the human tissue depth is x n at this time, the corresponding human tissue temperature T(x n ) can be determined through the human tissue temperature distribution function.

[0104] The temperature sample parameters include multiple groups of sample tissue depths and the temperature values corresponding to the sample tissue depths. The experimental data can be used as the temperature sample parameters, or the empirical values can be directly used as the temperature sample parameters. Substituting the temperature sample parameters into the human tissue temperature distribution function can determine the specific values of the first parameter C1 and the second parameter C2, and further determine the second functional relationship. For example, the skin temperature and the core body temperature in the temperature sample parameters can be substituted into the human tissue temperature distribution function. The skin temperature T s is the temperature corresponding to the human tissue depth of 0, which can be obtained based on the skin temperature sensor. Substituting the skin temperature into the human tissue temperature distribution function can obtain the value of C2, that is, T(0) = T s = C2; The core body temperature T c can be the empirical values of the intestinal temperature, rectal temperature, etc. The human tissue depth dc is set according to the measurement site, that is, T(dc) = T c , in the case of solving C2, substituting the empirical value of the core body temperature can solve the value of C1. Thus, substituting the specific values of C1 and C2 into the human tissue temperature distribution function can obtain the second functional relationship.

[0105] In some embodiments, based on the second functional relationship, a second correlation relationship is obtained, including:

[0106] Based on the second functional relationship, a third functional relationship between the human tissue depth difference and the human tissue temperature difference is determined, and the third functional relationship is used as the second correlation relationship, where the human tissue depth difference is used to characterize the depth between different tissues of the human body, and the human tissue temperature difference is used to characterize the temperature difference between different tissues of the human body.

[0107] Since the core body temperature is an unknown quantity in the actual measurement process, the electronic device needs to determine the core body temperature based on the comparison of the temperature values determined by multiple absorbances. The different depths and the temperature values corresponding to different depths of the human tissue can be obtained by sampling and are known quantities. Therefore, multiple light waves with different wavelengths can be emitted by the optoelectronic sensor to determine the human tissue temperature difference corresponding to the human tissue depth difference, and the third functional relationship between the human tissue depth difference and the human tissue temperature difference is used as the second correlation relationship.

[0108] The second functional relationship is: The human tissue depth d1 corresponds to the human tissue temperature T(d1), that is The human tissue depth d2 corresponds to the human tissue temperature T(d2), that is Subtracting the two equations can obtain the third functional relationship between the human tissue depth difference Δd and the human tissue temperature difference ΔT, where the human tissue depth difference Δd = d2 - d1, and the heat transfer coefficient k is a constant:

[0109]

[0110] In some embodiments, step S303 obtains a core body temperature prediction model based on a first correlation relationship and a second correlation relationship, including:

[0111] For multiple light waves of different wavelengths, perform fitting based on the second correlation relationship and the first correlation relationship to obtain a core body temperature prediction model.

[0112] The first correlation relationship is:

[0113] The second correlation relationship is:

[0114] The first correlation relationship and the second correlation relationship have the same parameter of human tissue depth d. Therefore, substituting the first correlation relationship into the second correlation relationship, we can obtain:

[0115]

[0116] where C1 is a known value, the heat transfer coefficient k is a known value, and α λ1 , α λ2 is the absorption coefficient of light wave λ of different wavelengths, which can be determined based on empirical values, and A λ1 , A λ2 is the sampled value of the absorbance of light wave λ of different wavelengths. Based on the above formula, the optoelectronic sensor emits light waves with longer wavelengths to obtain the absorbances of multiple longer wavelengths. The electronic device uses the rich sampled values for fitting to fit a curve reflecting the relationship between the absorbance A λi and the human tissue temperature T i . This curve is the function g corresponding to the core body temperature prediction model:

[0117] T i = g(A λi )

[0118] After obtaining the absorbance a λi collected by the optoelectronic sensor, using the function g corresponding to the core body temperature prediction model, the human tissue temperature T i can be obtained. For example, when the absorbance is A λ1 , the corresponding human tissue temperature T1 can be determined through the function g corresponding to the core body temperature prediction model; when the absorbance is A λ2 , the corresponding human tissue temperature T2 can be determined through the function g corresponding to the core body temperature prediction model; when the absorbance is A λn , the corresponding human tissue temperature T n can be determined through the function g corresponding to the core body temperature prediction model.

[0119] In the present disclosure, the electronic device obtains absorbance using non-contact multispectral technology according to the law of absorption characteristics of human tissues for light waves of each band, and combines the heat transfer law of human tissues to establish a core body temperature prediction model, which can directly perform model operations on the core body temperature of the target object, simplifies the detection process of the core body temperature, avoids interference from environmental factors, and improves the accuracy of the core body temperature.

[0120] According to an exemplary embodiment, as Figure 4 shown, the core body temperature detection method in this embodiment includes:

[0121] S401. Obtain the absorbance after multiple light waves pass through the target object;

[0122] S402. Determine the core body temperature of the target object based on the multiple absorbances and the relationship between the absorbance and the temperature of human tissues;

[0123] S403. Obtain body temperature influencing factors, where the body temperature influencing factors include one or more of skin temperature, exercise parameters, environmental temperature, and heart rate;

[0124] S404. Correct the core body temperature of the target object based on the body temperature influencing factors.

[0125] Among them, steps S401 and S402 are the same as the implementation manners in the above embodiment, and will not be elaborated here.

[0126] The core body temperature refers to the temperature of deep organs such as the brain, heart, and lungs. In step S402, the temperature corresponding to the blood vessels and blood of the target object is approximately used as the core body temperature. Since the environmental temperature, the motion state of the target object, etc. will all affect the core body temperature of the human body, in order to improve the accuracy of the core body temperature, it is necessary to correct the core body temperature of the target object based on the body temperature influencing factors.

[0127] In step S403, the electronic device is built-in with an environmental temperature sensor, a skin temperature sensor, an acceleration sensor, a heart rate sensor, etc. When the electronic device turns on the temperature measurement function, all sensors are turned on together to detect the environmental conditions and the state of the target object. Among them, the environmental temperature sensor is usually set on the outer side of the electronic device in contact with the air to detect the environmental temperature; the heart rate sensor and the skin temperature sensor are usually set on the inner side of the electronic device in contact with the user's skin to detect the heart rate and skin temperature of the target object; the acceleration sensor can detect the magnitude of the acceleration of the electronic device in each direction, and determine the motion parameters of the target object based on the acceleration data.

[0128] In step S404, the electronic device adjusts the value or weight value of the core body temperature based on the body temperature influencing factors to correct the core body temperature. If there is one abnormality among the body temperature influencing factors, the core body temperature is adjusted only based on the one abnormal body temperature influencing factor; if there are multiple abnormalities among the body temperature influencing factors, the core body temperature is adjusted based on the multiple abnormal body temperature influencing factors. To improve the accuracy of the core body temperature, a neural network layer can be used to learn the correction process of the core body temperature, and the trained neural network layer for core body temperature correction is added to the pre-stored core body temperature prediction model to improve the core body temperature prediction model determined based on the linear relationship.

[0129] In one example, when not considering the body temperature influencing factors, the weight value of the core body temperature determined based on the absorbance is 1. In the same environment, the core body temperatures of the same target object in the moving state and the stationary state are different, and the moving state will cause the core body temperature to rise. Therefore, the weight value of the core body temperature can be increased, and the weight value of the core body temperature is adjusted to 1.02. Therefore, the corrected core body temperature can reflect the true body temperature of the target object in the moving state. When the target object is in the stationary state, the weight value of the core body temperature is not adjusted and remains 1.

[0130] In another example, when not considering the body temperature influencing factors, the weight value of the core body temperature determined based on the absorbance is 1. Since the heart rate of the human body is related to the core body temperature, when the core body temperature rises, the heart rate of the human body increases. Therefore, the core body temperature of the target object can be corrected based on the collected heart rate data. When the heart rate of the target object is high, the weight value of the core body temperature can be increased, and the weight value of the core body temperature is adjusted to 1.03; when the heart rate of the target object reflects that the target object is in a normal state, the weight value of the core body temperature is not adjusted and remains 1.

[0131] In the present disclosure, considering multiple body temperature influencing factors, for example, when the target object is in the moving state or sick, using the human tissue temperature obtained based on the absorbance as the core body temperature may result in deviations, reducing the detection accuracy of the core body temperature. Correcting the core body temperature based on the body temperature influencing factors can achieve a better detection effect and improve the detection accuracy of the core body temperature.

[0132] An exemplary embodiment of the present disclosure provides a core body temperature detection device applied to an electronic device. As Figure 6 shown, a block diagram of a core body temperature detection device shown in the present disclosure.

[0133] The block diagram includes: an acquisition module 61 and an execution module 62. The acquisition module 61 is configured to acquire the absorbance of multiple light waves after passing through a target object, where the wavelengths of each light wave are different, and the absorbance of human tissues for light waves of different wavelengths is different; the execution module 62 is configured to determine the core body temperature of the target object based on the multiple absorbances and the relationship between the absorbance and the human tissue temperature.

[0134] In an exemplary embodiment of the present disclosure, the execution module 62 is further configured to: input the multiple absorbances into a pre-stored core body temperature prediction model to obtain multiple temperature values, where the core body temperature prediction model is used to characterize the relationship between the absorbance of different light waves passing through human tissues and the human tissue temperature; and take the highest temperature among the multiple temperature values as the core body temperature.

[0135] In an exemplary embodiment of the present disclosure, the execution module 62 is further configured to: establish a first correlation relationship between the human tissue depth and the absorbance of multiple light waves; establish a second correlation relationship between the human tissue depth and the human tissue temperature; and obtain the core body temperature prediction model based on the first correlation relationship and the second correlation relationship.

[0136] In an exemplary embodiment of the present disclosure, the execution module 62 is further configured to: based on the Lambert-Beer law, establish a first functional relationship between the human tissue depth and the absorbance of multiple light waves, where the independent variable of the first functional relationship is the human tissue depth, the dependent variable of the first functional relationship is the absorbance of different light waves, and the reference coefficients of the first functional relationship include tissue concentration and absorption coefficient, where the absorption coefficient is used to characterize the absorption degree of human tissues for light waves of different wavelengths; and determine the first correlation relationship based on the first functional relationship.

[0137] In an exemplary embodiment of the present disclosure, the execution module 62 is further configured to: based on the bioheat conduction equation, determine a human tissue temperature distribution function, where the human tissue temperature distribution function includes a first parameter, a second parameter, and a heat transfer coefficient, and the heat transfer coefficient is a constant; determine the first parameter and the second parameter based on the temperature sample parameters and the human tissue temperature distribution function, where the temperature sample parameters include multiple sets of sample tissue depths and the temperature values corresponding to the sample tissue depths; obtain a second functional relationship based on the first parameter, the second parameter, and the human tissue temperature distribution function; and obtain the second correlation relationship based on the second functional relationship.

[0138] In an exemplary embodiment of the present disclosure, the execution module 62 is further configured to: based on the second functional relationship, determine a third functional relationship between the human tissue depth difference and the human tissue temperature difference, and take the third functional relationship as the second correlation relationship, where the human tissue depth difference is used to characterize the depth between different tissues of the human body, and the human tissue temperature difference is used to characterize the temperature difference between different tissues of the human body.

[0139] In an exemplary embodiment of the present disclosure, the execution module 62 is further configured to: fit multiple light waves of different wavelengths based on the second correlation relationship and the first correlation relationship to obtain a core body temperature prediction model.

[0140] In an exemplary embodiment of the present disclosure, the execution module 62 is further configured to: obtain factors affecting body temperature, where the factors affecting body temperature include one or more of skin temperature, exercise parameters, environmental temperature, and heart rate; and correct the core body temperature of the target object based on the factors affecting body temperature.

[0141] Regarding the core body temperature detection device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0142] Figure 7 FIG. is a block diagram of an electronic device 700 shown according to an exemplary embodiment. For example, the electronic device 700 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0143] Referring to Figure 7 , the electronic device 700 may include one or more of the following components: a processing component 702, a memory 704, a power component 706, a multimedia component 708, an audio component 710, an input / output (I / O) interface 712, a sensor component 714, and a communication component 716.

[0144] The processing component 702 generally controls the overall operation of the electronic device 700, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 702 may include one or more modules to facilitate the interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate the interaction between the multimedia component 708 and the processing component 702.

[0145] The memory 704 is configured to store various types of data to support the operation of the electronic device 700. Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phone book data, messages, pictures, videos, and the like. The memory 704 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.

[0146] The power supply component 706 provides power to various components of the electronic device 700. The power supply component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 700.

[0147] The multimedia component 708 includes a screen that provides an output interface between the electronic device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 708 includes a front camera and / or a rear camera. When the electronic device 700 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

[0148] The audio component 710 is configured to output and / or input audio signals. For example, the audio component 710 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 700 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 704 or transmitted via the communication component 716. In some embodiments, the audio component 710 further includes a speaker for outputting audio signals.

[0149] The I / O interface 712 provides an interface between the processing component 702 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power-on button, and a lock button.

[0150] The sensor assembly 714 includes one or more sensors for providing a status assessment of various aspects of the electronic device 700. For example, the sensor assembly 714 can detect the on / off state of the electronic device 700, the relative positioning of components, such as the display and keypad of the electronic device 700. The sensor assembly 714 can also detect a change in the position of the electronic device 700 or a component of the electronic device 700, the presence or absence of user contact with the electronic device 700, the orientation or acceleration / deceleration of the electronic device 700, and a change in the temperature of the electronic device 700. The sensor assembly 714 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 714 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0151] The communication component 716 is configured to facilitate communication between the electronic device 700 and other devices in a wired or wireless manner. The electronic device 700 can access a wireless network based on communication standards, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 716 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 716 further 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.

[0152] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described methods.

[0153] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as the memory 704 including instructions, and the above instructions can be executed by the processor 720 of the electronic device 700 to complete the above core body temperature detection method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0154] A non - transitory computer - readable storage medium, when the instructions in the storage medium are executed by a processor of an electronic device, enables the processing device of the electronic device to execute the core body temperature detection method provided by the exemplary embodiments of the present disclosure.

[0155] A computer program product, the computer program product includes computer instructions, the computer instructions are stored in a computer - readable storage medium; a processor of an electronic device reads the computer instructions from the computer - readable storage medium, and the processor executes the computer instructions, enabling the electronic device to execute the core body temperature detection method provided by the exemplary embodiments of the present disclosure.

[0156] After considering the specification and practicing the content disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0157] It should be understood that the present disclosure is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A core body temperature detection method, characterized in that, The core body temperature detection method includes: Obtaining the absorbance of multiple light waves after passing through a target object, where the wavelengths of each of the light waves are different, and the absorbance of human tissues for light waves of different wavelengths is different; Determining the core body temperature of the target object based on the multiple absorbances and the relationship between the absorbance and the human tissue temperature.

2. The core body temperature detection method according to claim 1, wherein The determining the core body temperature of the target object based on the multiple absorbances and the relationship between the absorbance and the human tissue temperature includes: Inputting the multiple absorbances into a pre-stored core body temperature prediction model to obtain multiple temperature values, where the core body temperature prediction model is used to characterize the relationship between the absorbance of different light waves passing through human tissues and the human tissue temperature; Taking the highest temperature among the multiple temperature values as the core body temperature.

3. The core body temperature detection method according to claim 2, wherein The core body temperature prediction model is obtained by the following method: Establishing a first correlation relationship between the depth of human tissues and the absorbance of the multiple light waves; Establishing a second correlation relationship between the depth of the human tissues and the human tissue temperature; Obtaining the core body temperature prediction model based on the first correlation relationship and the second correlation relationship.

4. The core body temperature detection method according to claim 3, wherein The establishing the first correlation relationship between the depth of human tissues and the absorbance of the multiple light waves includes: Based on the Lambert-Beer law, establishing a first functional relationship between the depth of the human tissues and the absorbance of the multiple light waves, where the independent variable of the first functional relationship is the depth of the human tissues, the dependent variable of the first functional relationship is the absorbance of different light waves, and the reference coefficients of the first functional relationship include tissue concentration and absorption coefficient, where the absorption coefficient is used to characterize the absorption degree of human tissues for light waves of different wavelengths; Determining the first correlation relationship based on the first functional relationship.

5. The core body temperature detection method according to claim 3, wherein The establishing the second correlation relationship between the depth of the human tissues and the human tissue temperature includes: Determining a human tissue temperature distribution function based on the bioheat conduction equation, where the human tissue temperature distribution function includes a first parameter, a second parameter, and a heat transfer coefficient, and the heat transfer coefficient is a constant; Determining the first parameter and the second parameter based on temperature sample parameters and the human tissue temperature distribution function, where the temperature sample parameters include multiple sets of sample tissue depths and temperature values corresponding to the sample tissue depths; Obtaining a second functional relationship based on the first parameter, the second parameter, and the human tissue temperature distribution function; Obtaining the second correlation relationship based on the second functional relationship.

6. The core body temperature detection method according to claim 5, wherein The obtaining the second correlation relationship based on the second functional relationship includes: Based on the second functional relationship, determining a third functional relationship between the difference in human tissue depth and the difference in human tissue temperature, and taking the third functional relationship as the second correlation relationship, where the difference in human tissue depth is used to characterize the depth between different tissues of the human body, and the difference in human tissue temperature is used to characterize the temperature difference between different tissues of the human body.

7. The core body temperature detection method according to claim 6, wherein The obtaining the core body temperature prediction model based on the first correlation relationship and the second correlation relationship includes: For multiple light waves of different wavelengths, fitting is performed based on the second correlation relationship and the first correlation relationship to obtain the core body temperature prediction model.

8. The core body temperature detection method according to any one of claims 1 to 7, characterized in that, The core body temperature detection method further includes: obtaining factors affecting body temperature, where the factors affecting body temperature include one or more of skin temperature, exercise parameters, environmental temperature, and heart rate; correcting the core body temperature of the target object based on the factors affecting body temperature.

9. A core body temperature detection device, characterized in that, The core body temperature detection device includes: an acquisition module configured to acquire the absorbance of multiple light waves after passing through a target object, where the wavelength of each light wave is different, and the absorbance of human tissues for light waves of different wavelengths is different; an execution module configured to determine the core body temperature of the target object based on the multiple absorbances and the relationship between the absorbance and the temperature of human tissues.

10. An electronic device, characterized in that, The electronic device includes: a processor; a memory for storing executable instructions executable by the processor; wherein the processor is configured to execute the executable instructions in the memory to implement the core body temperature detection method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium, characterized in that, Stored thereon are executable instructions which, when executed by the processor of the electronic device, implement the core body temperature detection method according to any one of claims 1 to 8.

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