CN113854973A - Body temperature measuring method and device, wearable device and storage medium - Google Patents
Body temperature measuring method and device, wearable device and storage medium Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Abstract
The embodiment of the application discloses a body temperature measuring method, a body temperature measuring device, wearable equipment and a storage medium, which are applied to the technical field of wearable equipment and can solve the problem that the accuracy of a method for measuring the body temperature through an infrared sensor is reduced when a lens of the wearable equipment is dirty. The method comprises the following steps: under the condition that a lens of the wearable device is not dirty, measuring body temperature through a first algorithm, wherein the first algorithm is a temperature measurement algorithm based on an infrared sensor; in case the lens of the wearable device is dirty, the body temperature is measured by a second algorithm, which is a temperature measurement algorithm based on a PPG sensor.
Description
Technical Field
The embodiment of the application relates to the technical field of wearable equipment, in particular to a body temperature measuring method and device, wearable equipment and a storage medium.
Background
With the rapid development of terminal technology, wearable devices are more and more widely applied. For example, the user's body temperature is measured by a wearable device.
Currently, the body temperature of a user is generally measured by an infrared sensor in a wearable device, but when a lens of the wearable device is dirty, the accuracy of a method for measuring the body temperature by the infrared sensor is reduced.
Disclosure of Invention
The embodiment of the application provides a body temperature measuring method, a body temperature measuring device, wearable equipment and a storage medium, and aims to solve the problem that the accuracy of a method for measuring the body temperature through an infrared sensor is reduced when a lens of the wearable equipment is dirty.
In order to solve the above technical problem, the embodiment of the present application is implemented as follows:
in a first aspect, a body temperature measurement method is provided, which is applied to a wearable device including an infrared sensor and a photoplethysmography (PPG) sensor, and includes: under the condition that a lens of the wearable device is not dirty, measuring the body temperature through a first algorithm, wherein the first algorithm is a temperature measurement algorithm based on the infrared sensor; in case the lens of the wearable device is dirty, the body temperature is measured by a second algorithm, which is a thermometry algorithm based on the PPG sensor.
As an optional implementation manner, in the first aspect of the embodiments of the present application, the method further includes: determining that a lens of the wearable device is not soiled if a drop amplitude of a Direct Current (DC) signal in a PPG signal measured by a PPG sensor is smaller than an amplitude threshold; determining that a lens of the wearable device is soiled in the event that a magnitude of a drop in a direct current DC signal in a PPG signal measured by the PPG sensor is greater than or equal to a magnitude threshold.
As an optional implementation manner, in the first aspect of the embodiments of the present application, the second algorithm is an algorithm that measures the body temperature according to a heart rate parameter of the user, the heart rate parameter being obtained according to a PPG signal measured by a PPG sensor; alternatively, the second algorithm is an algorithm that measures body temperature from a heart rate parameter and a heart rate variability parameter of the user, the heart rate parameter and the heart rate variability parameter being obtained from a PPG signal measured by a PPG sensor.
As an optional implementation manner, in the first aspect of the embodiments of the present application, before measuring the body temperature by the second algorithm in case that the lens of the wearable device is dirty, the method further includes: calibrating a second algorithm by the first body temperature data; the first body temperature data is body temperature data measured through a first algorithm under the condition that a lens of the wearable device is not dirty.
As an optional implementation manner, in the first aspect of the embodiments of the present application, before calibrating the second algorithm by using the first body temperature data, the method further includes: in the case where it is determined that the user is in a resting state, the first body temperature data is measured by a first algorithm.
As an optional implementation manner, in the first aspect of the embodiment of the present application, before the measuring the first body temperature data by the first algorithm in the case that it is determined that the user is in the resting state, the method further includes: determining that the user is in a resting state under the condition that the acceleration signal of the wearable device is less than or equal to the acceleration threshold value within the first duration; or determining that the user is in a resting state under the condition that the heart rate variation of the user is smaller than or equal to the heart rate variation threshold value within the second duration; wherein the heart rate variation is obtained from a PPG signal measured by a PPG sensor.
As an optional implementation manner, in the first aspect of the embodiments of the present application, before calibrating the second algorithm by using the first body temperature data, the method further includes: first body temperature data is measured by the first algorithm upon receiving a user instruction to calibrate the second algorithm.
In a second aspect, there is provided a body temperature measurement device comprising an infrared sensor and a photoplethysmography, PPG, sensor, the body temperature measurement device comprising: the temperature measuring device comprises a first temperature measuring module and a second temperature measuring module; the first temperature measurement module is used for measuring the body temperature through a first algorithm under the condition that a lens of the wearable device is not dirty, and the first algorithm is a temperature measurement algorithm based on an infrared sensor; and the second temperature measurement module is used for measuring the body temperature through a second algorithm under the condition that the lens of the wearable device is dirty, and the second algorithm is a temperature measurement algorithm based on a PPG sensor.
As an alternative implementation, in a second aspect of the embodiments of the present application, the body temperature measurement device further includes: a determination module; the determining module is used for determining that the lens of the wearable device is not dirty when the drop amplitude of a direct current DC signal in the PPG signal measured by the PPG sensor is smaller than an amplitude threshold value; determining that a lens of the wearable device is soiled in the event that a magnitude of a drop in a direct current DC signal in a PPG signal measured by the PPG sensor is greater than or equal to a magnitude threshold.
As an optional implementation manner, in a second aspect of the embodiments of the present application, the second algorithm is an algorithm for measuring body temperature according to a heart rate parameter of the user, the heart rate parameter being obtained according to a PPG signal measured by a PPG sensor; alternatively, the second algorithm is an algorithm that measures body temperature from a heart rate parameter and a heart rate variability parameter of the user, the heart rate parameter and the heart rate variability parameter being obtained from a PPG signal measured by a PPG sensor.
As an alternative implementation, in a second aspect of the embodiments of the present application, the body temperature measurement device further includes: a calibration module; the calibration module is used for calibrating the second algorithm through the first body temperature data before the second temperature measurement module measures the body temperature through the second algorithm under the condition that the lens of the wearable device is dirty; the first body temperature data is body temperature data measured through a first algorithm under the condition that a lens of the wearable device is not dirty.
As an optional implementation manner, in a second aspect of the embodiment of the present application, the first thermometry module is further configured to measure the first body temperature data through the first algorithm under the condition that it is determined that the user is in a resting state before the calibration module calibrates the second algorithm through the first body temperature data.
As an alternative implementation, in a second aspect of the embodiments of the present application, the body temperature measurement device further includes: a determination module; the determining module is used for determining that the user is in the rest state under the condition that the acceleration signal of the wearable device is smaller than or equal to the acceleration threshold value within the first duration before the first temperature measuring module measures the first body temperature data through a first algorithm under the condition that the user is in the rest state; or determining that the user is in a resting state under the condition that the heart rate variation of the user is smaller than or equal to the heart rate variation threshold value within the second duration; wherein the heart rate variation is obtained from a PPG signal measured by a PPG sensor.
As an optional implementation manner, in a second aspect of the embodiment of the present application, the first thermometry module is further configured to measure the first body temperature data through the first algorithm when receiving a user instruction to calibrate the second algorithm before the calibration module calibrates the second algorithm through the first body temperature data.
In a third aspect, a wearable device is provided, comprising: a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing part or all of the steps of the method of body temperature measurement according to the first aspect.
In a fourth aspect, a computer-readable storage medium is characterized in that the computer-readable storage medium stores thereon a computer program, which when executed by a processor implements part or all of the steps of the body temperature measurement method according to the first aspect.
In a fifth aspect, a computer program product is provided, which when run on a computer causes the computer to perform some or all of the steps of the body temperature measurement method of the first aspect.
In a sixth aspect, an application distribution platform is provided for distributing a computer program product, wherein the computer program product, when run on a computer, causes the computer to perform some or all of the steps of the body temperature measurement method of the first aspect.
Compared with the prior art, the embodiment of the application has the following beneficial effects:
in the embodiment of the application, the body temperature can be measured through the first algorithm under the condition that the lens of the wearable device is not dirty; measuring body temperature by a second algorithm in the event that a lens of the wearable device is soiled; the first algorithm is a temperature measurement algorithm based on the infrared sensor, and the second algorithm is a temperature measurement algorithm based on the PPG sensor. In this scheme, under the not dirty condition of lens of wearable equipment, through first algorithm measurement body temperature, under the dirty condition of lens of wearable equipment, through second algorithm measurement body temperature to can avoid under the dirty condition of lens of wearable equipment, through the inaccurate problem of body temperature that first algorithm measurement body temperature leads to measuring.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a body temperature measurement method provided in an embodiment of the present application;
fig. 2 is a second schematic flowchart of a body temperature measurement method according to an embodiment of the present application;
FIG. 3 is a third schematic flowchart of a body temperature measurement method according to an embodiment of the present application;
FIG. 4 is a fourth schematic flowchart of a body temperature measurement method provided in the embodiments of the present application;
FIG. 5 is a schematic structural diagram of a body temperature measuring device according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a wearable device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first" and "second," and the like, in the description and in the claims of the present application, are used for distinguishing between different objects and not for describing a particular order of the objects. For example, the first resolution and the second resolution, etc. are for distinguishing different resolutions, not for describing a specific order of resolutions.
The terms "comprises," "comprising," and "having," and any variations thereof, of the embodiments of the present application, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the embodiment of the application, the body temperature can be measured through the first algorithm under the condition that the lens of the wearable device is not dirty; measuring body temperature by a second algorithm in the event that a lens of the wearable device is soiled; the first algorithm is a temperature measurement algorithm based on an infrared sensor, and the second algorithm is a temperature measurement algorithm based on a PPG sensor. In this scheme, under the not dirty condition of lens of wearable equipment, through first algorithm measurement body temperature, under the dirty condition of lens of wearable equipment, through second algorithm measurement body temperature to can avoid under the dirty condition of lens of wearable equipment, through the inaccurate problem of body temperature that first algorithm measurement body temperature leads to measuring.
In the embodiment of the application, the lens of the wearable device is arranged on the surface of the wearable device, and when the user wears the wearable device, the lens of the wearable device is in contact with the body surface of the user; moreover, the lens of the wearable device is used for protecting the infrared sensor and the PPG sensor which are arranged inside the wearable device, so that the functions of preventing water and dust and preventing the damage of a user are achieved for the infrared sensor and the PPG sensor. Sometimes the lens of the wearable device is a fresnel lens for the purpose of appearance of the wearable device (e.g., to keep the user from seeing the internal sensors).
In this embodiment of the application, the lens of the wearable device may be made of a natural material (such as crystal, etc.), a glass material, a plastic (such as a plastic material), and the like, which is not limited in this embodiment of the application. Moreover, the lens of the wearable device needs to satisfy the following conditions: high transmittance of visible light and infrared light, and high abrasion resistance.
In the embodiment of the application, the temperature measuring principle of the infrared sensor is as follows: the infrared sensor is an infrared signal receiver, infrared signals (the infrared signals can be actively emitted by skin or any object with temperature) emitted by the skin penetrate through the lens and reach the infrared sensor, the higher the skin temperature is, the stronger the signals received by the infrared sensor are, and the higher the measured user temperature is. When the lens of wearable equipment is dirty, the infrared signal that user's skin sent is partly absorbed by the dirt when the lens of wearable equipment is dirty to the infrared signal who reaches infrared sensor weakens, and then leads to the measuring body temperature to be than actual body temperature on the low side.
In an embodiment of the present application, the PPG sensor comprises a plurality of light emitting diodes (LEDs, light emitting elements) and a plurality of photodiodes (PDs, receivers). The principle of the PPG sensor is as follows: the LED is used for emitting light, enters skin (including blood vessels) through the lens, is absorbed by the lens, the skin, bones, the blood vessels and the like, and then is reflected by the lens and received by the PD. Wherein, the signal received by the PD includes: direct Current (DC) signals, which are obtained due to a fixed (substantially constant) absorption of light by lenses, skin, bones, etc., and Alternating Current (AC) signals, which are obtained due to a change in the absorption of light by arteries (blood vessels are beating due to the heart, in which the volume of blood flow is changing, and the absorption of light is also changing). The temperature measurement principle of the PPG sensor is as follows: the AC signal received by the PD is extracted and the body temperature is calculated from the measured AC signal (the change in blood flow caused by the beating of the heart). When the lens of the wearable device is dirty, the dirt is basically unchanged, and the AC signal measured by the PPG sensor is not influenced, so that the body temperature measured by the method for measuring the temperature of the PPG sensor is high in accuracy when the lens of the wearable device is dirty.
The wearable equipment that this application embodiment relates to can be for intelligent wrist-watch, intelligent bracelet, phone wrist-watch, intelligent foot ring, intelligent earring, intelligent necklace, intelligent earphone etc. and this application embodiment does not limit.
An execution main body of the body temperature measurement method provided in the embodiment of the present application may be the wearable device, or may also be a functional module and/or a functional entity capable of implementing the body temperature measurement method in the wearable device, which may be specifically determined according to actual use requirements, and the embodiment of the present application is not limited. The following takes a wearable device as an example to exemplarily describe the body temperature measurement method provided in the embodiments of the present application.
The following describes an exemplary body temperature measurement method according to an embodiment of the present invention with reference to the drawings.
As shown in fig. 1, an embodiment of the present application provides a body temperature measurement method applied to a wearable device including an infrared sensor and a photoplethysmography (PPG) sensor, and the method may include the following steps 101 to 102.
101. The wearable device measures the body temperature through a first algorithm without soiling a lens of the wearable device.
The first algorithm is a temperature measurement algorithm based on an infrared sensor.
For example, the infrared sensor may be an infrared thermopile sensor, and may also be other infrared sensors that can measure temperature, which is not limited in this embodiment of the application.
Optionally, the first algorithm may be any existing temperature measurement algorithm based on an infrared sensor, and the embodiment of the present application is not limited.
Optionally, the first algorithm may be to input the body surface temperature of the user measured by the infrared sensor and the ambient temperature of the user into the body temperature model 1 to obtain the body temperature of the user. The body temperature model 1 is: t ═ a × To + b (Ta-Ta _ s) + c, where T is the measured body temperature of the user, To is the body surface temperature variable measured by the infrared sensor, Ta is the ambient temperature variable of the sensor measured by the infrared sensor, Ta _ s is the ambient temperature constant (usually, Ta _ s is 25 ℃) where the black body calibration is located when the infrared sensor leaves the factory, a is the body surface temperature coefficient corresponding To the body surface temperature variable, b is the ambient temperature coefficient corresponding To the ambient temperature variable, and c is the body temperature model 1 constant.
The body surface temperature variable is a variable corresponding to the body surface temperature in the body temperature model 1, and the body surface temperature measured by the infrared sensor is used as the value of the body surface temperature variable in the process of measuring the body temperature through the body temperature model 1.
The body surface temperature coefficient is a constant value corresponding to a body surface temperature variable, and is used for measuring the influence of the body surface temperature on the body temperature finally calculated by the body temperature model 1, that is, the larger the body surface temperature coefficient is, the larger the influence of the body temperature finally calculated by the body temperature model 1 on the body surface temperature is, the smaller the body surface temperature coefficient is, and the smaller the influence of the body temperature finally calculated by the body temperature model 1 on the body surface temperature is.
The environment temperature variable is a variable corresponding to the environment temperature in the body temperature model 1, and the environment temperature measured by the infrared sensor is used as a value of the environment temperature variable in the process of actually calculating the body temperature by the body temperature model 1.
The environment temperature coefficient is a coefficient corresponding to an environment temperature variable and is a constant value, and the environment temperature coefficient is used for measuring the influence of the environment temperature on the body temperature finally calculated by the body temperature model 1, that is, the larger the environment temperature coefficient is, the larger the influence of the environment temperature on the body temperature finally calculated by the body temperature model 1 is, the smaller the environment temperature coefficient is, and the smaller the influence of the environment temperature on the body temperature finally calculated by the body temperature model 1 is.
The constant of the body temperature model 1 is a constant value in the body temperature model 1.
Wherein, a, b and c in the body temperature model 1 may be constant values, and may be constant values related to the age, sex and standard body temperature of the user. Or a, b and c can be obtained by calculation through a linear regression algorithm according to a preset body temperature deviation p (such as 0.1 ℃) and a large number of body temperature sample data (each body temperature sample comprises body surface temperature and environment temperature); the method can be determined according to actual use requirements, and the embodiment of the application is not limited.
Optionally, the first algorithm may be to input the body surface temperature of the user measured by the infrared sensor and the ambient temperature of the user into the body temperature model 2 to obtain the body temperature of the user. The body temperature model 2 is: t ═ a × To + b (Ta-Ta _ s) + c + T1, where T is the measured body temperature of the user, To is the body surface temperature variable measured by the infrared sensor, Ta is the ambient temperature variable of the sensor measured by the infrared sensor, Ta _ s is the ambient temperature constant of the black body calibration when the infrared sensor leaves the factory (usually, Ta _ s is 25 ℃), a is the body surface temperature coefficient corresponding To the body surface temperature variable, b is the ambient temperature coefficient corresponding To the ambient temperature variable, c is the body temperature model 2 constant, and T1 is the temperature constant related To the gender and age of the user. The T1 may be obtained by looking up a table according to the gender and age of the user, or may update the T1 by self-learning (for example, self-learning according to the age, gender, a large amount of user body temperature sample data, a linear regression algorithm, etc.), which may refer to the related art, and the embodiment of the present application is not limited.
Wherein, a, b and c in the body temperature model 2 may be constant values, and may be constant values related to the age, sex and standard body temperature of the user. Or a, b and c can be obtained by calculation through a linear regression algorithm according to a preset body temperature deviation p (such as 0.1 ℃) and a large number of body temperature sample data (each body temperature sample comprises body surface temperature and environment temperature); the method can be determined according to actual use requirements, and the embodiment of the application is not limited.
102. In the event that the lens of the wearable device is soiled, the wearable device measures the body temperature by a second algorithm.
And the second algorithm is a temperature measurement algorithm based on the PPG sensor.
PPG sensors are capable of optically obtaining organ plethysmograms, typically by illuminating the skin with LEDs and measuring the amount of change in light absorption to achieve a pulse wave measurement.
Optionally, the second algorithm may be any existing temperature measurement algorithm based on a PPG sensor, and the embodiment of the present application is not limited.
Optionally, the second algorithm is an algorithm that measures body temperature from a heart rate parameter of the user, the heart rate parameter being obtained from a PPG signal measured by the PPG sensor.
For example, the second algorithm may be to process the PPG signal measured by the PPG sensor to obtain a heart rate parameter of the user, and then input the heart rate parameter as a heart rate variable into the body temperature model 3 to obtain the body temperature of the user. The body temperature model 3 is: t0+ a (HR-HR0) + c, where T is the measured user body temperature, T0 is the basal body temperature, HR is the heart rate variable, HR0 is the basal heart rate constant, a is the heart rate coefficient corresponding to the heart rate variable, and c is the body temperature model 3 constant.
Wherein, T0 may be a default value for the system, and the system may automatically match the basal body temperature most relevant to the user by inputting the user's gender and age information; if the user gender and age information is not entered, the system automatically matches the intermediate values to reduce the error.
Alternatively, HR0 may be a system default, and the system may automatically match the basal heart rate most relevant to the user by entering user gender and age information; if the user gender and age information is not entered, the system automatically matches the intermediate values to reduce the error.
Alternatively, the HR0 may be configured to detect the heart rate of the user for a preset time period (e.g., several days), and take the heart rate mode of the user at rest as the basic heart rate. Thus, the method is matched with the actual situation of the user best.
Wherein, the mode value: the sign value with the most occurrence times in the population mainly reflects the general level of a certain phenomenon, and is usually applied to the analysis of single-term indexes and corresponds to the variable value with the maximum frequency.
Wherein, the resting state refers to the state of a person without activity, without emotional excitement and resting quietly.
Wherein, a and c in the body temperature model 3 may be constant values, and may be constant values related to the age and sex of the user. The a and c can also be obtained by calculation according to a preset body temperature deviation p (such as 0.1 ℃) and a large amount of body temperature sample data through a linear regression algorithm and the like; the method can be determined according to actual use requirements, and the embodiment of the application is not limited.
In the embodiment of the application, a second algorithm for measuring the body temperature of the user according to the heart rate parameters is provided, when the lens of the wearable device is dirty, the body temperature of the user is measured through the second algorithm based on the heart rate parameters, and compared with the body temperature of the user measured through the first algorithm based on the infrared sensor, the accuracy of the measured body temperature data is higher.
Optionally, the second algorithm is an algorithm that measures body temperature from a heart rate parameter and a heart rate variability parameter of the user, the heart rate parameter and the heart rate variability parameter being obtained from a PPG signal measured by the PPG sensor.
For example, the second algorithm may be to process the PPG signal measured by the PPG sensor to obtain a heart rate parameter and a heart rate variability parameter of the user, then to input the heart rate parameter as a heart rate variable and the heart rate variability parameter as a heart rate variability variable into the body temperature model 4 to obtain the body temperature of the user. The body temperature model 4 is: t0+ a (HR-HR0) + b HRV + c, where T is the measured user body temperature, T0 is the basal body temperature, HR is the heart rate variable, HR0 is the basal heart rate constant, HRV is the heart rate variability variable, a is the heart rate coefficient corresponding to the heart rate variable, b is the heart rate variability coefficient corresponding to the heart rate variability variable, and c is the body temperature model 4 constant.
For the description of T0 and HR0, reference may be made to the above description of T0 and HR0 in the body temperature model 3, and details are not repeated here.
Wherein, a, b and c in the body temperature model 4 may be constant values, and may be constant values related to the age and sex of the user. Or a, b and c can be calculated according to a preset body temperature deviation p (such as 0.1 ℃) and a large amount of body temperature sample data through a linear regression algorithm and the like; the method can be determined according to actual use requirements, and the embodiment of the application is not limited.
In the embodiment of the application, a second algorithm for measuring the body temperature of the user according to the heart rate parameter and the heart rate variability parameter is provided, and the accuracy of measuring the body temperature of the user based on the PPG sensor can be improved by adding the heart rate variability parameter.
It should be noted that, in the embodiment of the present application, the execution order of the step 101 and the step 102 is not limited, for example, the step 101 may be executed first, and then the step 102 may be executed; the step 102 may be executed first, and then the step 101 may be executed.
In the embodiment of the application, the body temperature is measured by switching different algorithms according to whether the lens of the wearable device is dirty or not, and then the accuracy of body temperature measurement is improved when the lens of the wearable device is used.
It can be understood that, since the lens of the wearable device is in contact with the body surface of the user, most of the cases of the soiling of the lens are caused by sweating of the user, but it may also be caused by splashing of external liquid onto the lens of the wearable device or by attachment of external greasy gas onto the lens of the wearable device, which may be determined according to actual use conditions, and the embodiment of the present application is not limited.
Optionally, in this embodiment of the application, whether a lens of the wearable device is dirty may be detected by an electrochemical sensor in the wearable device, whether the lens of the wearable device is dirty may also be detected by a PPG sensor, whether the lens of the wearable device is dirty may also be determined according to a user input, and whether the lens of the wearable device is dirty may also be detected by another method, which is not limited in this embodiment of the application.
Exemplarily, with reference to fig. 1, as shown in fig. 2, before the step 101, the body temperature measurement method provided by the embodiment of the present application may further include the following step 103; before the step 102, the body temperature measurement method provided by the embodiment of the present application may further include the step 104 described below.
103. In the event that the magnitude of the drop in the DC signal in the PPG signal measured by the PPG sensor is less than an amplitude threshold, the wearable device determines that the lens of the wearable device is not soiled.
104. In the event that the magnitude of the drop in the DC signal in the PPG signal measured by the PPG sensor is greater than or equal to a magnitude threshold, the wearable device determines that the lens of the wearable device is soiled.
The amplitude threshold may be determined according to an actual situation, and the embodiment of the present application is not limited.
Illustratively, the amplitude threshold may be a value in the range of 3% to 10%, for example, the amplitude threshold may be 5%.
In the embodiment of the present application, the principle of determining whether the lens of the wearable device is dirty through the PPG sensor is briefly described below. When the lens of the wearable device is soiled, the soiling may absorb green light resulting in a weaker measured signal strength, and since the lens of the wearable device may be considered to be soiled to a constant extent over time (e.g., a constant amount of perspiration from the user), the soiling may result in a reduced amplitude of the DC signal portion of the PPG signal. For example, in general, the amplitude of the DC signal part is reduced by more than 5%, and it can be determined that the lens of the wearable device is dirty. While when the lens of the wearable device is dirty, the AC signal part of the PPG signal is not affected, so the user's body temperature can be measured from the AC signal, and the second algorithm.
In the embodiment of the application, whether the lens of the wearable device is dirty or not is determined through the PPG signal measured by the PPG sensor, the user does not need to participate, and the accuracy of detecting the body temperature of the user can be improved.
It is understood that basal body temperatures of different individuals are due to individual variability. The basic heart rate is different, and before the body temperature is actually measured through the second algorithm, the body temperature model corresponding to the second algorithm needs to be calibrated through the body temperature data of the corresponding user (the existing related technology can be referred to in the specific calibration process, and no limitation is made here), so as to improve the accuracy of the body temperature measurement for the user. Taking the body temperature model 3 as follows: for example, T0+ a (HR-HR0) + c, the specific procedure for calibrating the second algorithm may be: and (3) calculating the a and the c by taking the body temperature data of the user as a basic body temperature T0, combining a preset body temperature deviation p (such as 0.1 ℃) and a linear regression algorithm and the like, substituting the calculated a and c into the body temperature model 3 to obtain a second algorithm matched with the user, namely obtaining a calibrated second algorithm.
Alternatively, the body temperature data of the user for calibrating the second algorithm may be calibration body temperature data (body temperature data obtained by measuring forehead temperature, underarm temperature, ear temperature, mouth temperature, etc.) which is manually input; the body temperature data matched with the user can be searched by inputting the information of the user gender, the user age and the like (the body temperature data matched with the information of the user gender, the user age and the like can be found by searching a mapping table (a mapping relation table of the information of the gender, the age and the like and the body temperature data); but also body temperature data of the user measured by the first algorithm in case the lens of the wearable device is not soiled; the user body temperature data may also be obtained by other methods, which is not limited in the embodiment of the present application.
Illustratively, in conjunction with fig. 2, as shown in fig. 3 (which is illustrated in fig. 3 before step 104), before step 102 or step 104, the body temperature measurement method provided in the embodiment of the present application may further include step 105 described below.
105. The wearable device calibrates the second algorithm with the first body temperature data.
Wherein the first body temperature data is body temperature data measured by a first algorithm when a lens of the wearable device is not soiled.
The first body temperature data may be one set of data or multiple sets of data, and the embodiment of the application is not limited.
In the embodiment of the present application, calibrating the second algorithm through the first body temperature data may be understood as: and taking the first body temperature data as the basic body temperature (T0) of the user in the body temperature model corresponding to the second algorithm, combining preset body temperature deviation p (such as 0.1 ℃), calculating coefficients (taking the body temperature model 3 as an example, the coefficients are a and c; taking the question model 4 as an example, the coefficients are a, b and c) in the body temperature model through a linear regression algorithm and the like, and thus obtaining the calibrated second algorithm matched with the user.
In the embodiment of the application, under the condition that the lens of the wearable device is not dirty, the body temperature data measured by the first algorithm is used as the first body temperature data, the second algorithm is calibrated, the user does not need to manually input data, the operation is convenient, and the calibration efficiency can be quickly improved.
Optionally, with reference to fig. 3, as shown in fig. 4, before the step 105, the body temperature measurement method provided by the embodiment of the present application may further include a step 106 described below.
106. In an instance in which it is determined that the user is at rest, the wearable device measures first body temperature data via a first algorithm.
The embodiment of the present application does not limit a method how to determine whether a user is in a resting state.
In the embodiment of the application, when the user is in a resting state and the lens of the wearable device is not dirty, the wearable device can automatically measure the first body temperature data through the first algorithm, automatically calibrate the second algorithm according to the first body temperature data, quickly realize automatic calibration, do not need the user to participate in, and improve the calibration efficiency.
Alternatively, the wearable device may select a fixed sleep period for the user (e.g., 1-5 am), determining that the user is at rest.
Optionally, the wearable device may determine that the user is currently in a resting state from user input (the user selects an option that is currently in a resting state); the wearable device can also acquire an acceleration signal of the wearable device within a certain time (which can reflect the acceleration signal of the user within the certain time) through an acceleration sensor in the wearable device, and then determine whether the user is currently in a resting state or not according to the acceleration signal; the wearable device can also acquire the heart rate of the user within a certain time through the PPG sensor, and then determine whether the user is currently in a resting state or not according to the change of the heart rate.
Illustratively, before the step 106, the body temperature measurement method provided by the embodiment of the present application may further include the following step 107; alternatively, before step 106, the body temperature measurement method provided by the embodiment of the present application may further include step 108 described below.
107. The wearable device determines that the user is in a resting state if the acceleration signal of the wearable device is less than or equal to the acceleration threshold for the first duration.
The first time period may be determined according to an actual use condition, and the embodiment of the present application is not limited.
The acceleration threshold may be determined according to actual use requirements, and the embodiment of the present application is not limited. For example, the acceleration threshold may be g, where g is the gravitational acceleration value of the current geographic location.
For example, the acceleration signal may be a composite acceleration measured by a three-axis acceleration sensor, and the composite acceleration may be calculated by:
and the acc _ x, acc _ y and acc _ z are data of the acceleration sensors of the x axis, the y axis and the z axis at the same moment. and the acc _ f is less than g, the user can be considered to be in a resting state, and the heart rate of the user is the resting heart rate at the moment.
In the three-dimensional rectangular coordinate system, the three-axis acceleration sensor detects the acceleration of the x-axis, the y-axis and the z-axis, respectively, to obtain three acceleration components.
In the embodiment of the application, the wearable device can monitor the acceleration signal of the user through the acceleration sensor of the wearable device; and under the condition that the acceleration signal of the wearable device is monitored to be less than or equal to the acceleration threshold value in the first time period, the wearable device determines that the user is in a resting state. Therefore, the wearable device can automatically determine that the user is in a resting state, then sequentially execute the step 106 and the step 105, and finally automatically calibrate the second algorithm to obtain the second algorithm related to the user, so that the body temperature of the user can be measured more accurately through the second algorithm when the lens of the wearable device is dirty.
108. And under the condition that the heart rate variation of the user is less than or equal to the heart rate variation threshold value within the second time length, the wearable device determines that the user is in a resting state.
Wherein the heart rate variation is obtained from a PPG signal measured by the PPG sensor.
The second duration may be determined according to an actual use condition, and the embodiment of the present application is not limited. The second time period may be the same as or different from the first time period, and the embodiment of the present application is not limited.
The heart rate change threshold value can be determined according to actual use requirements, and the embodiment of the application is not limited. Typically, the ratio of the heart rate variation threshold to the user's basic heart rate is between 15% and 25%, for example, the ratio of the heart rate variation threshold to the user's basic heart rate is 20%.
In the embodiment of the application, the wearable device can monitor the heart rate of the user through the PPG sensor of the wearable device; in the event that it is monitored that the change in the heart rate of the user is less than or equal to the heart rate change threshold for the second length of time, the wearable device determines that the user is in a resting state. Therefore, the wearable device can automatically determine that the user is in a resting state, then sequentially execute the step 106 and the step 105, and finally automatically calibrate the second algorithm to obtain the second algorithm related to the user, so that the body temperature of the user can be measured more accurately through the second algorithm when the lens of the wearable device is dirty.
Optionally, before the step 105, the body temperature measurement method provided by the embodiment of the present application may further include the step 109 described below.
109. Upon receiving a user instruction to calibrate the second algorithm, the wearable device measures the first body temperature data with the first algorithm.
Wherein the user instruction may select a touch input to calibrate the second algorithm on a screen of the wearable device for the user.
In the embodiment of the application, the wearable device responds to the user instruction, and the first body temperature data is measured through the first algorithm, so that the more accurate first data can be obtained, the second algorithm is better calibrated, and when the lens of the wearable device is dirty, the body temperature of the user is more accurately measured through the second algorithm.
In the embodiment of the application, the wearable device can automatically determine the time for measuring the first body temperature data through the first algorithm, and also can determine the time for measuring the first body temperature data through the first algorithm according to a user instruction, so that the proper time for measuring the first body temperature data through the first algorithm can be determined according to the actual use condition.
As shown in fig. 5, an embodiment of the present application provides a body temperature measurement device including an infrared sensor and a photoplethysmography (PPG) sensor, the body temperature measurement device including: a first temperature measurement module 501 and a second temperature measurement module 502; the first temperature measurement module 501 is used for measuring the body temperature through a first algorithm under the condition that a lens of the wearable device is not dirty, wherein the first algorithm is a temperature measurement algorithm based on an infrared sensor; the second temperature measurement module 502 is configured to measure the body temperature through a second algorithm when the lens of the wearable device is dirty, where the second algorithm is a temperature measurement algorithm based on a PPG sensor.
As an optional implementation manner of the embodiment of the present application, the body temperature measurement device further includes: a determination module; the determining module is used for determining that the lens of the wearable device is not dirty when the drop amplitude of a direct current DC signal in the PPG signal measured by the PPG sensor is smaller than an amplitude threshold value; determining that a lens of the wearable device is soiled in the event that a magnitude of a drop in a direct current DC signal in a PPG signal measured by the PPG sensor is greater than or equal to a magnitude threshold.
As an optional implementation manner of the embodiment of the present application, the second algorithm is an algorithm for measuring the body temperature according to a heart rate parameter of the user, where the heart rate parameter is obtained according to a PPG signal measured by a PPG sensor; alternatively, the second algorithm is an algorithm that measures body temperature from a heart rate parameter and a heart rate variability parameter of the user, the heart rate parameter and the heart rate variability parameter being obtained from a PPG signal measured by a PPG sensor.
As an optional implementation manner of the embodiment of the present application, the body temperature measurement device further includes: a calibration module; the calibration module is used for calibrating the second algorithm according to the first body temperature data before the second temperature measurement module 501 measures the body temperature through the second algorithm under the condition that the lens of the wearable device is dirty; the first body temperature data is body temperature data measured through a first algorithm under the condition that a lens of the wearable device is not dirty.
As an optional implementation manner of this embodiment of the present application, the first temperature measurement module 501 is further configured to measure the first body temperature data through the first algorithm under the condition that it is determined that the user is in a resting state before the calibration module calibrates the second algorithm through the first body temperature data.
As an optional implementation manner of the embodiment of the present application, the body temperature measurement device further includes: a determination module; the determining module is configured to determine that the user is in the resting state under the condition that the acceleration signal of the wearable device is less than or equal to the acceleration threshold within the first duration before the first temperature measuring module 501 measures the first body temperature data through the first algorithm under the condition that the user is in the resting state; or determining that the user is in a resting state under the condition that the heart rate variation of the user is smaller than or equal to the heart rate variation threshold value within the second duration; wherein the heart rate variation is obtained from a PPG signal measured by a PPG sensor.
As an optional implementation manner of this embodiment of the present application, the first temperature measurement module 501 is further configured to measure the first body temperature data through the first algorithm when a user instruction for calibrating the second algorithm is received before the calibration module calibrates the second algorithm through the first body temperature data.
It should be noted that: in this embodiment of the application, the body temperature measuring device may be a wearable device having the above function, or may also be a functional module or a functional entity having the above function in the wearable device, such as a component, an integrated circuit, or a chip in the wearable device, and this embodiment of the application is not limited.
In the embodiment of the present application, each module can implement the body temperature measurement method provided in the above method embodiment, and can achieve the same technical effect, and is not described here again to avoid repetition.
As shown in fig. 6, which is a schematic diagram of a hardware structure of a wearable device, the wearable device may include: radio Frequency (RF) circuitry 610, memory 620, input unit 630, display unit 640, sensor 650, audio circuitry 660, wireless fidelity (WiFi) module 670, processor 680, and power supply 690. Therein, the radio frequency circuit 610 includes a receiver 611 and a transmitter 612. Those skilled in the art will appreciate that the wearable device structure shown in fig. 6 does not constitute a limitation of the wearable device, and may include more or fewer components than shown, or combine certain components, or a different arrangement of components.
The RF circuit 610 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, receives downlink information of a base station and then processes the received downlink information to the processor 680; in addition, the data for designing uplink is transmitted to the base station. In general, the RF circuit 610 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 610 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), etc.
The memory 620 may be used to store software programs and modules, and the processor 680 may execute various functional applications and data processing of the wearable device by operating the software programs and modules stored in the memory 620. The memory 620 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phone book, etc.) created according to the use of the wearable device, and the like. Further, the memory 620 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The input unit 630 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the wearable device. Specifically, the input unit 630 may include a touch panel 631 and other input devices 632. The touch panel 631, also referred to as a touch screen, may collect touch operations of a user (e.g., operations of the user on the touch panel 631 or near the touch panel 631 by using any suitable object or accessory such as a finger or a stylus) thereon or nearby, and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 631 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 680, and can receive and execute commands sent by the processor 680. In addition, the touch panel 631 may be implemented using various types, such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 630 may include other input devices 632 in addition to the touch panel 631. In particular, other input devices 632 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 640 may be used to display information input by or provided to the user and various menus of the wearable device. The display unit 640 may include a display panel 641, and optionally, the display panel 641 may be configured in the form of a Liquid Crystal Display (LCD), an organic light-Emitting diode (OLED), or the like. Further, the touch panel 631 can cover the display panel 641, and when the touch panel 631 detects a touch operation thereon or nearby, the touch panel is transmitted to the processor 680 to determine the type of the touch event, and then the processor 680 provides a corresponding visual output on the display panel 641 according to the type of the touch event. Although in fig. 6, the touch panel 631 and the display panel 641 are two separate components to implement the input and output functions of the wearable device, in some embodiments, the touch panel 631 and the display panel 641 may be integrated to implement the input and output functions of the wearable device.
The wearable device may also include sensors 650, which sensors 650 may include an infrared sensor 651 and a PPG sensor 652. The sensor 650 may further include a light sensor, a motion sensor, other sensors, and the like. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 641 according to the brightness of ambient light, and a proximity sensor that may turn off the display panel 641 and/or the backlight when the wearable device is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration) for recognizing wearable device attitude, and related functions (such as pedometer and tapping) for vibration recognition; as for other sensors such as a gyroscope, a pressure gauge, a hygrometer, a thermometer, and an infrared sensor, which can be further configured on the wearable device, detailed description is omitted here.
WiFi belongs to short-distance wireless transmission technology, and the wearable device can help a user to send and receive e-mails, browse webpages, access streaming media and the like through the WiFi module 670, and provides wireless broadband Internet access for the user. Although fig. 6 shows the WiFi module 670, it is understood that it does not belong to the essential constitution of the wearable device, and can be omitted entirely as needed within the scope not changing the essence of the invention.
The processor 680 is a control center of the wearable device, and connects various parts of the entire wearable device through various interfaces and lines, and performs various functions of the wearable device and processes data by running or executing software programs and/or modules stored in the memory 620 and calling up data stored in the memory 620, thereby performing overall monitoring of the wearable device. Optionally, processor 680 may include one or more processing units; preferably, the processor 680 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 680.
The wearable device also includes a power supply 690 (e.g., a battery) for powering the various components, which may preferably be logically connected to the processor 680 via a power management system, such that functions of managing charging, discharging, and power consumption are performed via the power management system. Although not shown, the wearable device may further include a camera, a bluetooth module, etc., which are not described herein.
In this embodiment, the processor 680 is configured to measure the body temperature through a first algorithm when a lens of the wearable device is not dirty, where the first algorithm is a temperature measurement algorithm based on an infrared sensor; in case the lens of the wearable device is dirty, the body temperature is measured by a second algorithm, which is a temperature measurement algorithm based on a PPG sensor.
Optionally, the processor 680 may also be configured to implement other processes implemented by the wearable device in the foregoing method embodiment, and may achieve the same technical effect, and for avoiding repetition, details are not described here again.
An embodiment of the present application further provides a wearable device, and the wearable device may include: the processor, the memory and the computer program stored in the memory and capable of running on the processor, when being executed by the processor, the computer program can implement each process of the body temperature measurement method provided by the above method embodiments, and can achieve the same technical effect, and in order to avoid repetition, the details are not repeated here.
The embodiments of the present application provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the body temperature measurement method provided in the foregoing method embodiments, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
An embodiment of the present application further provides a computer program product, where the computer program product includes a computer instruction, and when the computer program product runs on a processor, the processor executes the computer instruction, so as to implement each process of the body temperature measurement method provided in the foregoing method embodiment, and achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
An embodiment of the present application further provides an application publishing platform, where the application publishing platform is configured to publish a computer program product, where when the computer program product runs on a computer, the computer is enabled to execute each process of the method in the above method embodiments, and the same technical effect can be achieved, and in order to avoid repetition, details are not repeated here.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art should also appreciate that the embodiments described in this specification are all alternative embodiments and that the acts and modules involved are not necessarily required for this application.
The wearable device provided by the embodiment of the application can realize each process shown in the method embodiments, and is not repeated here for avoiding repetition.
In various embodiments of the present application, it should be understood that the size of the serial number of each process described above does not mean that the execution sequence is necessarily sequential, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present application, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, may be embodied in the form of a software product, stored in a memory, including several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of the embodiments of the present application.
It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by hardware instructions of a program, and the program may be stored in a computer-readable storage medium, where the storage medium includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM), or other disk memories, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
Claims (10)
1. A body temperature measurement method, applied to a wearable device comprising an infrared sensor and a photoplethysmography (PPG) sensor, the method comprising:
measuring body temperature through a first algorithm under the condition that a lens of the wearable device is not dirty, wherein the first algorithm is a temperature measurement algorithm based on the infrared sensor;
measuring body temperature by a second algorithm, the second algorithm being a thermometry algorithm based on the PPG sensor, in case the lens of the wearable device is dirty.
2. The method of claim 1, further comprising:
determining that a lens of the wearable device is not soiled if a magnitude of a drop of a Direct Current (DC) signal in a PPG signal measured by the PPG sensor is less than a magnitude threshold;
determining that a lens of the wearable device is soiled if a magnitude of a drop in a Direct Current (DC) signal in a PPG signal measured by the PPG sensor is greater than or equal to a magnitude threshold.
3. The method according to claim 1, characterized in that the second algorithm is an algorithm for measuring body temperature from a heart rate parameter of the user, the heart rate parameter being obtained from a PPG signal measured by the PPG sensor;
alternatively, the first and second electrodes may be,
the second algorithm is an algorithm that measures body temperature from a heart rate parameter and a heart rate variability parameter of the user, the heart rate parameter and the heart rate variability parameter being obtained from a PPG signal measured by the PPG sensor.
4. The method of any of claims 1-3, wherein prior to measuring body temperature by the second algorithm with the lens of the wearable device soiled, the method further comprises:
calibrating the second algorithm with the first body temperature data;
wherein the first body temperature data is body temperature data measured by the first algorithm when a lens of the wearable device is not soiled.
5. The method of claim 4, wherein prior to calibrating the second algorithm with the first body temperature data, the method further comprises:
and under the condition that the user is determined to be in a resting state, measuring the first body temperature data through the first algorithm.
6. The method of claim 5, wherein prior to measuring the first body temperature data via the first algorithm in the event that the user is determined to be at rest, the method further comprises:
determining that the user is in a resting state under the condition that the acceleration signal of the wearable device is less than or equal to the acceleration threshold value within the first duration;
alternatively, the first and second electrodes may be,
determining that the user is in a resting state under the condition that the heart rate variation of the user is smaller than or equal to the heart rate variation threshold value within the second duration;
wherein the heart rate variation is obtained from a PPG signal measured by the PPG sensor.
7. The method of claim 4, wherein prior to calibrating the second algorithm with the first body temperature data, the method further comprises:
measuring, by the first algorithm, the first body temperature data upon receiving a user instruction to calibrate the second algorithm.
8. A body temperature measurement device, characterized in that the device comprises an infrared sensor and a photoplethysmography (PPG) sensor, the device comprising: the temperature measuring device comprises a first temperature measuring module and a second temperature measuring module;
the first temperature measurement module is used for measuring the body temperature through a first algorithm under the condition that a lens of the wearable device is not dirty, wherein the first algorithm is a temperature measurement algorithm based on the infrared sensor;
the second temperature measurement module is used for measuring the body temperature through a second algorithm under the condition that the lens of the wearable device is dirty, and the second algorithm is a temperature measurement algorithm based on the PPG sensor.
9. A wearable device, comprising: a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method of body temperature measurement according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of body temperature measurement according to any one of claims 1 to 7.
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| CN116107432A (en) * | 2023-01-13 | 2023-05-12 | 深圳曼瑞德科技有限公司 | Body temperature monitoring method for wearable temperature measuring equipment |
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| CN116107432B (en) * | 2023-01-13 | 2024-02-13 | 深圳曼瑞德科技有限公司 | Body temperature monitoring method for wearable temperature measuring equipment |
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