CN118050094A - Body temperature measuring method, electronic device, and storage medium - Google Patents
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Abstract
The present disclosure provides a body temperature measurement method, an electronic device, and a storage medium. The method comprises the following steps: acquiring the skin temperature of a user and acquiring the ambient temperature; obtaining an initial predicted body temperature of the user by using the skin temperature and the ambient temperature; determining a target correction model of the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature; and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user. Therefore, at least one of skin temperature, ambient temperature and predicted body temperature can be comprehensively considered to determine the target correction model, and the initial predicted body temperature is corrected based on the target correction model, so that errors of the initial predicted body temperature are eliminated, and the accuracy of body temperature measurement is improved.
Description
Technical Field
The disclosure relates to the technical field of body temperature measurement, and in particular relates to a body temperature measurement method, electronic equipment and a storage medium.
Background
At present, with the development of artificial intelligence technology, body temperature measurement is widely applied to electronic equipment such as smart watches, smart phones and the like, has the advantages of portability, convenient use, real-time monitoring and the like, and facilitates the daily life of people. However, the body temperature measurement method in the related art has a problem of low accuracy.
Disclosure of Invention
The present disclosure aims to solve, at least to some extent, one of the technical problems in the art described above.
In a first aspect, an embodiment of the present disclosure provides a method for measuring body temperature, including: acquiring the skin temperature of a user and acquiring the ambient temperature; obtaining an initial predicted body temperature of the user by using the skin temperature and the ambient temperature; determining a target correction model of the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature; and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user.
In some embodiments, the determining the target correction model for the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature comprises: determining at least one target interval in which at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is located in a plurality of preset intervals; and determining the target correction model from a plurality of preset correction models based on the at least one target interval.
In some embodiments, before the determining the target correction model for the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature, further comprising: at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is pre-treated.
In some embodiments, the determining the target correction model for the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature comprises: acquiring a difference between the skin temperature and the ambient temperature; and determining the target correction model based on the interval where the difference value is located.
In some embodiments, the correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user includes: correcting the skin temperature, the ambient temperature and the initial predicted body temperature by using the target correction model to obtain a corrected predicted body temperature; and filtering the corrected predicted body temperature to obtain the target predicted body temperature.
In some embodiments, the correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user includes: determining a correction parameter of the initial predicted body temperature based on the target correction model; and correcting the initial predicted body temperature by using the correction parameters to obtain the target predicted body temperature.
In some embodiments, said deriving an initial predicted body temperature of said user using said skin temperature and said ambient temperature comprises: inputting the skin temperature and the ambient temperature into a body temperature prediction model for processing to obtain the initial predicted body temperature.
In some embodiments, the obtaining the skin temperature of the user comprises: collecting first temperature data through a first temperature sensing module of a wearable device, wherein at least a part of the first temperature sensing module is in contact with the skin of the user; and processing the first temperature data to obtain the skin temperature.
In some embodiments, the acquiring the ambient temperature comprises: acquiring second temperature data through a second temperature sensing module of the wearable device, wherein the second temperature sensing module is arranged inside the wearable device; and processing the second temperature data to obtain the ambient temperature.
In a second aspect, an embodiment of the disclosure further provides an electronic device, including a memory and a processor; wherein the processor is configured to implement the method for measuring body temperature according to any possible embodiment of the first aspect of the present disclosure by reading the executable program code stored in the memory.
In a third aspect, the embodiments of the present disclosure further provide a computer readable storage medium storing a computer program, which when executed by a computer device implements the body temperature measurement method according to any embodiment of the first aspect of the present disclosure.
In a fourth aspect, embodiments of the present disclosure also propose a computer program product comprising computer readable instructions which, when executed by a computer device, implement the method of body temperature measurement according to any embodiment of the first aspect of the present disclosure.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
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The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a block schematic diagram of a system according to some embodiments;
Fig. 2 is a flow chart of a method of body temperature measurement according to some embodiments;
Fig. 3 is a flow chart of a method of body temperature measurement according to some embodiments;
fig. 4 is a schematic diagram of a temperature space in a body temperature measurement method according to some embodiments;
fig. 5 is a flow chart of a method of body temperature measurement according to some embodiments;
fig. 6 is a schematic diagram of a method of body temperature measurement according to some embodiments;
Fig. 7 is a block schematic diagram of a body temperature measurement device according to some embodiments;
fig. 8 is a block schematic diagram of an electronic device according to some embodiments.
Detailed Description
Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.
Wearable devices are increasingly used to monitor physiological information of users, such as heart rate, blood oxygen level, etc. Many wearable devices record physiological measurements in response to user input, such as a user clicking a button or other interface element of the wearable device to cause the measurement. The wearable device may be a wrist-worn device, a head-worn device, a foot-worn device, or other wearable device, to which embodiments of the present disclosure are not limited.
At present, with the development of artificial intelligence technology, body temperature measurement is widely applied to electronic equipment such as smart watches, smart phones and the like, has the advantages of portability, convenient use, real-time monitoring and the like, and facilitates the daily life of people. However, the body temperature measurement method in the related art has a problem of low accuracy.
The embodiment of the disclosure provides a body temperature measuring method, which comprises the steps of obtaining skin temperature of a user and obtaining environmental temperature; obtaining an initial predicted body temperature of a user by using the skin temperature and the environmental temperature; determining a target correction model of the initial predicted body temperature based on at least one of skin temperature, ambient temperature, and the initial predicted body temperature; and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user. Therefore, at least one of skin temperature, ambient temperature and predicted body temperature can be comprehensively considered to determine the target correction model, and the initial predicted body temperature is corrected based on the target correction model, so that errors of the initial predicted body temperature are eliminated, and the accuracy of body temperature measurement is improved.
For a more detailed description of some implementations, reference is first made to examples of hardware and software structures for a body temperature measurement method. Fig. 1 is a block diagram illustrating an example of a system 100 for detecting one or more of a health condition, a sports condition, a sleep condition, or a combination thereof. The system 100 includes a wearable device 102, a server device 104, and an intermediary device 106, the intermediary device 106 being an intermediary device of the wearable device 102 and the server device 104.
Wearable device 102 is a computing device configured to be worn by a human user during operation. The wearable device 102 may be implemented as a wristwatch, wristband, bracelet, brace, wristband, armband, leg band, ring, headband, necklace or earphone, or in the form of another wearable device. The wearable device 102 includes one or more sensors 108 for detecting a physiological parameter indicative of a user of the wearable device 102. The sensor 108 may include one or more of a photoplethysmogram (Photo Plethysmo Graphy, PPG) sensor, an Electrocardiogram (ECG) sensor, an electrode, a pulse pressure sensor, a vascular property sensor, another sensor, or a combination thereof. The physiological parameter refers to one or more physiological parameters of a user of the wearable device. The physiological parameter represents a measurable physiological parameter related to one or more important systems of the body of the user of the wearable device 102 (e.g., the cardiovascular system, the respiratory system, the autonomic nervous system, or another system). For example, the physiological parameter may be one or more of a heart rate, heart rate variability, blood oxygen level, blood pressure, or another physiological parameter of the user of the wearable device 102.
A program 110 is run on the wearable device 102 for processing physiological signal data generated based on the physiological parameters acquired by the sensor 108. Program 110 may be an application program.
A server program 112 runs on the server device 104 to process the computing device of the physiological signal data. The server device 104 may be or include a hardware server (e.g., a server device), a software server (e.g., a web server and/or a virtual server), or both. For example, where the server device 104 is or includes a hardware server, the server device 104 may be a server device located in a rack, such as a rack of a data center.
The server program 112 is software for detecting one or more of a health condition, a movement condition, a sleep condition, or a combination thereof of the user of the wearable device 102 to detect one or more of a health condition, a movement condition, a sleep condition, or a combination thereof of the user of the wearable device 102 using the physiological signal data. For example, the server program 112 may receive physiological signal data from the intermediate device 106, and may then use the received physiological signal data to detect one or more of a health condition, a movement condition, a sleep condition, or a combination thereof, of the user of the wearable device 102. For example, the server program 112 may use the physiological signal data to determine a change in the physiological state of the user and then detect one or more of a health condition, a movement condition, a sleep condition, or a combination thereof, of the user of the wearable device 102 based on the determined change.
The server program 112 may access a database 114 on the server device 104 to perform at least some functions of the server program 112. Database 114 is a database or other data store for storing, managing, or otherwise providing data for delivering the functionality of server program 112. For example, the database 114 may store physiological signal data received by the server device 104, information generated or otherwise determined from the physiological signal data. For example, database 114 may be a relational database management system, an object database, an XML database, a configuration management database, a management information database, one or more flat files, other suitable non-transitory storage mechanisms, or a combination thereof.
The intermediary device 106 is a device for facilitating communication between the wearable device 102 and the server device 104. Specifically, the intermediary device 106 receives data from the wearable device 102 and transmits the received data to the server device 104, e.g., for use by the server program 112. The intermediary device 106 may be a computing device, such as a mobile device (e.g., a smart phone, tablet, notebook, or other mobile device) or other computer (e.g., a desktop computer or other non-mobile computer). Or the intermediate device 106 may be or include network hardware such as a router, a switch, a load balancer, another network device, or a combination thereof. As another alternative, the intermediate device 106 may be another network connection device. For example, the intermediate device 106 may be a networked power charger of the wearable device 102.
For example, depending on the particular implementation of the intermediary 106, the intermediary 106 may run the application 118 and the application 118 may be one or more applications installed on the intermediary 106. In some implementations, the application software may be installed on the intermediate device 106 after purchasing the intermediate device 106 by a user of the intermediate device 106 (typically the same person as the user of the wearable device 102, but in some cases may not be the same person as the user of the wearable device 102), or may be preloaded on the intermediate device 106 by a manufacturer of the intermediate device 106 before the intermediate device 106 is shipped. The application 118 configures the intermediate device 106 to send data to the wearable device 102 or receive data from the wearable device 102 and/or to send data to the server device 104 or receive data from the server device 104. The application may receive commands from a user of the intermediate device 106. The application 118 may receive commands from its user through a user interface of the application 118. For example, where the intermediary device 106 is a computing device having a touch screen display, the user of the intermediary device 106 may receive the command by touching a portion of the display corresponding to the user interface element in the application.
For example, the command received by the application 118 from the user of the intermediate device 106 may be a command to transfer physiological signal data received at the intermediate device 106 (e.g., received from the wearable device 102) to the server device 104. The intermediate device 106 transmits physiological signal data to the server device 104 in response to such commands. In another example, the command received by the application 118 from the user of the intermediate device 106 may be a command to review information received from the server device 104, such as information related to one or more of a detected health condition, a movement condition, a sleep condition, or a combination thereof, of the user of the wearable device 102.
In some implementations, the client device is given access to the server program 112. For example, the client device may be a mobile device, such as a smart phone, tablet, notebook, or the like. In another example, the client device may be a desktop computer or another non-mobile computer. The client device may run a client application to communicate with the server program 112. For example, the client application may be a mobile application capable of accessing some or all of the functionality and/or data of the server program 112. For example, a client device may communicate with the server device 104 over the network 116. In some such implementations, the client device may be an intermediary device 106.
In some implementations, the server device 104 may be a virtual server. For example, a virtual server may be implemented using a virtual machine (e.g., a Java virtual machine). The implementation of the virtual machine may use one or more virtual software systems, such as an HTTP server, java servlet container, hypervisor, or other software system. In some such implementations, one or more virtual software systems for implementing the virtual server may instead be implemented in hardware.
In some implementations, the intermediate device 106 receives data from the wearable device 102 using a short-range communication protocol. For example, the short-range communication protocol may be bluetoothLow energy, infrared, Z wave, zigBee, other protocols, or combinations thereof. The intermediary device 106 transmits the data received from the wearable device 102 to the server device 104 over the network 116. For example, the network 116 may be a local area network, a wide area network, a machine-to-machine network, a virtual private network, or another public or private network. The network 116 may use a telecommunications protocol. For example, the remote communication protocol may be Ethernet, transmission control protocol (Transmission Control Protocol, TCP), internet protocol (Internet Protocol, IP), power line communication, wireless fidelity (WIRELESS FIDELITY, wi-Fi), general packet radio service (GENERAL PACKET radio service, GPRS), global System for Mobile communications (Global System for Mobile Communications, GSM), code division multiple Access (Code Division Multiple Access, CDMA), other protocols, or a combination thereof.
The system 100 is for continuously transmitting physiological signal data from a wearable device 102 to a server device 104. The sensor 108 may continuously or otherwise periodically acquire physiological signal data of the user of the wearable device 102 on a frequent basis.
The implementation of the system 100 may differ from that shown and described with respect to fig. 1. In some implementations, the intermediate device 106 may be omitted. For example, wearable device 102 may be configured to communicate directly with server device 104 over network 116. For example, direct communication between wearable device 102 and server device 104 over network 116 may include using a remote, low power system, or another communication mechanism. In some implementations, both the intermediary device 106 and the server device 104 may be omitted. For example, wearable device 102 may be configured to perform the functions described above with respect to server device 104. In such implementations, wearable device 102 may process and store data independent of other computing devices.
Body temperature measurement methods, apparatus, electronic devices, computer-readable storage media, and computer program products of embodiments of the present disclosure are described below with reference to the accompanying drawings.
Fig. 2 is a flow chart of a method of body temperature measurement according to some embodiments. The method may be performed by a body temperature measurement device. In particular, the method may be performed by a wearable device, or by an intermediate device, or by a server device, or a part of the procedure and/or step may be performed by a wearable device, and another part of the procedure and/or step may be performed by an intermediate device or a server device, without limitation.
S201, acquiring skin temperature of a user and acquiring ambient temperature.
It should be noted that the skin temperature and the ambient temperature are not limited, for example, the skin temperature may be skin surface temperature, including but not limited to skin temperature of a wrist, face, neck, arm, leg, etc., and the ambient temperature may be ambient temperature of an environment where the wearable device is currently located, including but not limited to ambient temperature inside the device, ambient temperature outside the device, etc.
In some optional implementations, the first temperature data may be collected by a first temperature sensing module of the wearable device, where at least a portion of the first temperature sensing module is in contact with skin of the user, and the first temperature data is processed to obtain the skin temperature.
For example, taking a wearable device as a wrist-worn device such as a watch and a bracelet, in a state that the wearable device is worn by a user, a part of the first temperature sensing module is in contact with skin of a wrist of a human body, for example, the first temperature sensing module comprises a first temperature sensor and at least one first sensing terminal, one end of the first sensing terminal is connected with the first temperature sensor, and the other end of the first sensing terminal is used for performing temperature sensing. For example, the other end of the first sensing terminal protrudes out of the surface of the casing of the wearable device, and for another example, a first part of the casing of the wearable device is made of a heat conducting material, the sensor of the first temperature sensing module is directly connected with the inner surface of the first part, and the outer surface of the first part is in contact with the skin of the user in the wearing state. At this time, the first temperature data can be collected through the first temperature sensing module of the wrist-worn device, and the first temperature data is processed to obtain the wrist skin temperature of the user.
In some alternative implementations, the environmental information may be received from outside and the environmental temperature may be derived based on the received environmental information, e.g., from a connected terminal device or network-side device. In other alternative implementations, the second temperature data may be collected by a second temperature sensing module of the wearable device, where the second temperature sensing module is disposed inside the wearable device, and the second temperature data is processed to obtain the ambient temperature.
For example, a second temperature sensing module is arranged in the casing of the wearable device, the second temperature sensing module comprises a second temperature sensor and at least one second sensing terminal, the second sensing terminal is arranged inside the casing, one end of the second sensing terminal is connected with the second temperature sensor, the other end of the second sensing terminal is used for temperature sensing, for example, the other end of the second sensing terminal is connected with an electronic component in the casing of the wearable device, or the other end of the second sensing terminal is connected with the inner surface of the casing of the wearable device, or the other end of the second sensing terminal is suspended and is not contacted with other objects. At this time, the second temperature data can be collected through the second temperature sensing module of the wrist-worn device, and the second temperature data is processed to obtain the ambient temperature inside the wrist-worn device.
It should be noted that the structures of the first temperature sensing module and the second temperature sensing module are not limited too much, for example, the first temperature sensing module includes at least one contact temperature sensor such as a capacitor, and the second temperature sensing module includes at least one non-contact temperature sensor such as an infrared sensor.
In some alternative implementations, the skin temperature and the ambient temperature of the user may be acquired simultaneously. For example, the skin temperature and the ambient temperature of the user are collected at set periods. For another example, in response to receiving a user instruction or other trigger condition being met, a skin temperature and an ambient temperature of the user are collected. At this time, before the skin temperature of the user is acquired and the ambient temperature is acquired, it may also be recognized whether the setting condition is currently satisfied. For example, the set conditions include, but are not limited to, receiving a body temperature measurement instruction, the current time reaching a set time, detecting that the user is sleeping while getting in bed, detecting that the user is in motion, initiating a physiological cycle tracking of the user, and so forth.
In other alternative implementations, the skin temperature and the ambient temperature of the user may be acquired in an asynchronous manner. For example, the skin temperature of the user may be acquired at smaller time intervals, while the ambient temperature is acquired at larger time intervals. As another example, the acquisition of the skin temperature of the user and the acquisition of the ambient temperature may have different trigger conditions, wherein the trigger conditions of the acquisition of the skin temperature of the user are more easily satisfied. For another example, after the environmental temperature is obtained at a certain time, the environmental temperature may be stored in the device, after the skin temperature of the user is obtained at the next time, it may be determined whether the skin temperature of the user meets a trigger condition, for example, the skin temperature changes by a certain threshold value or the like, the environmental temperature is obtained only if it is determined that the trigger condition is met, and if it is determined that the trigger condition is not met, the environmental temperature is not obtained any more, and the stored environmental temperature is used for body temperature prediction.
S202, obtaining the initial predicted body temperature of the user by using the skin temperature and the ambient temperature.
In some optional implementations, a mapping relation or mapping table between the skin temperature, the ambient temperature and the initial predicted body temperature may be pre-established, and after the skin temperature and the ambient temperature are acquired, the mapping relation or mapping table is queried to determine the initial predicted body temperature corresponding to the skin temperature and the ambient temperature as the initial predicted body temperature of the user.
In some alternative implementations, the skin temperature and the ambient temperature are processed using a body temperature prediction model to obtain an initial predicted body temperature for the user. For example, the skin temperature and the ambient temperature are input into a body temperature prediction model for processing to obtain an initial predicted body temperature. It should be noted that the body temperature prediction model is not limited too much, for example, the body temperature prediction model may be a machine learning algorithm or model obtained through pre-training, or may be generated or determined in real time. Thus, the method can automatically obtain the initial predicted body temperature of the user by using the body temperature measurement model.
In some examples, multiple pieces of sample data are obtained, each piece of sample data includes a sample skin temperature and a sample environment temperature, the sample body temperature is used as a label of the sample data, and the multiple pieces of sample data can be used for training the body temperature prediction model to obtain the body temperature prediction model. For example, a sample skin temperature and a sample ambient temperature included in the sample data are input into a body temperature prediction model for processing to obtain a sample predicted body temperature, a loss function is obtained based on a difference between the sample predicted body temperature and a label of the sample data (i.e., the sample body temperature), and at least one model parameter of the body temperature prediction model is updated based on the loss function.
In other alternative implementations, the ambient temperature data and the skin temperature data are processed using a body temperature prediction model to obtain an initial predicted body temperature for the user. For example, the ambient temperature data and the skin temperature data are input into a body temperature prediction model for processing, so as to obtain an initial predicted body temperature of the user. For another example, feature data is extracted from the ambient temperature data and the skin temperature data, and the extracted feature data is input into a body temperature prediction model for processing to obtain an initial predicted body temperature of the user.
S203, determining a target correction model of the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature and the initial predicted body temperature.
The target correction model is not limited too much. For example, the target modification model may be a machine learning algorithm or model obtained by training, or may be generated or determined in real time.
In some alternative implementations, the target correction model is determined from a plurality of candidate correction models based on at least one of skin temperature, ambient temperature, and initial predicted body temperature.
In some examples, at least one of skin temperature, ambient temperature, and initial predicted body temperature is processed to obtain a target parameter, and a target correction model is determined from a plurality of candidate correction models based on the target parameter.
In some examples, a target region is determined from a plurality of preset regions based on at least one of skin temperature, ambient temperature, and initial predicted body temperature, and a correction model corresponding to the target region is determined as a target correction model. For example, a plurality of skin temperature areas are preset, and a corresponding correction model is set for each skin temperature of the plurality of skin temperature areas, and in response to determining that the skin temperature is within a first preset area, the correction model corresponding to the first preset area is determined as the target correction model. For another example, a plurality of ambient temperature regions are preset, a corresponding correction model is set for each of the plurality of ambient temperature regions, and in response to determining that the ambient temperature is within the first preset region, the correction model corresponding to the second preset region is determined as the target correction model. For another example, a plurality of predicted body temperature regions are preset, a corresponding correction model is set for each of the plurality of predicted body temperature regions, and in response to determining that the initial predicted body temperature is located in a third preset region, the correction model corresponding to the third preset region is determined as the target correction model.
For another example, a plurality of temperature regions are preset, and a corresponding correction model is set for each of the plurality of temperature regions. A target region of the plurality of preset temperature regions is determined based on at least two of the skin temperature, the ambient temperature, and the initial predicted temperature, for example, the target region is determined based on a difference between the skin temperature and the ambient temperature, and a correction model corresponding to the target region is determined as a target correction model.
For another example, a plurality of two-dimensional or three-dimensional temperature regions are preset, and a corresponding correction model is set for each of the plurality of two-dimensional or three-dimensional temperature regions. A target region of the plurality of temperature regions is determined in response to at least two of the skin temperature, the ambient temperature, and the initial predicted temperature, and a correction model corresponding to the target region is determined as a target correction model. As one example, the target temperature region is determined from a plurality of two-dimensional temperature regions in response to the skin temperature being in a first preset region and the ambient temperature being in a second preset region. As another example, the target temperature region is determined from the plurality of three-dimensional temperature regions in response to the skin temperature being in the first preset region, the ambient temperature being in the second preset region, the initial predicted temperature being in the third predicted region.
In some embodiments, the determination of the target correction model may be made directly based on skin temperature, ambient temperature, and initial predicted body temperature. In other embodiments, at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature may also be pre-processed prior to determining the target correction model for the initial predicted body temperature based on the at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature. It should be noted that the preprocessing is not limited too much, for example, preprocessing includes, but is not limited to, washing, moving average, normalization, extremum removal, missing value filling, nonlinear transformation, and the like. Therefore, at least one of skin temperature, ambient temperature and initial predicted body temperature can be preprocessed in the method, and the accuracy of data is improved.
S204, correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user.
In some alternative implementations, the initial predicted body temperature is corrected using a target correction model to obtain a target predicted body temperature. For example, the initial predicted body temperature is input into the target correction model for processing directly or after one or more preprocessing, and the target predicted body temperature is output. For another example, the initial predicted body temperature and at least one of the skin temperature and the ambient temperature are directly or after one or more pre-treatments, input into the target correction model for treatment, and output the target predicted body temperature.
In some alternative implementations, the correction parameters of the initial predicted body temperature are determined based on the target correction model, and the initial predicted body temperature is corrected by using the correction parameters to obtain the target predicted body temperature. For example, at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is input directly or after one or more pre-treatments into the target correction model for processing, and correction parameters are output.
The correction parameters are not limited too much, and include, for example, a correction direction, a correction value, a correction coefficient, and the like. Wherein the correction direction includes increasing, decreasing, etc.
In some examples, in response to the correction parameter comprising an elevated, corrected value, a sum of the initial predicted body temperature and the corrected value may be determined as the target predicted body temperature. Or in response to the correction parameter comprising a reduced, corrected value, the difference between the initial predicted body temperature and the corrected value may be determined as the target predicted body temperature.
In some examples, the correction parameters include a correction coefficient, and the product of the initial predicted body temperature and the correction coefficient may be determined as the target predicted body temperature. In other examples, the correction parameter includes a correction value, and the sum or difference between the initial predicted body temperature and the correction value may be determined as the target predicted body temperature. In other examples, the correction parameters include a correction factor, the correction temperature may be derived based on the correction factor, the skin temperature, and the ambient temperature, and the target predicted body temperature may be derived based on the correction temperature and the initial predicted body temperature. For example, the sum or difference between the corrected temperature and the initial predicted body temperature is taken as the target predicted body temperature. Wherein a difference between the skin temperature and the ambient temperature may be determined and the corrected temperature may be obtained based on the difference and the correction coefficient, for example, a product between the difference and the correction coefficient as the corrected temperature, but the embodiment of the present application is not limited thereto.
In some alternative implementations, the initial predicted body temperature is modified based on the target modification model to obtain a plurality of modified predicted body temperatures, and the target predicted body temperature is determined based on the plurality of modified predicted body temperatures. In some examples, the target predicted body temperature is selected from a plurality of corrected predicted body temperatures. For example, the maximum corrected predicted body temperature may be determined as the target predicted body temperature. For example, a mode of the plurality of corrected predicted body temperatures may be determined as the target predicted body temperature. In some examples, an average of the plurality of corrected predicted body temperatures is determined as the target predicted body temperature.
In summary, according to the body temperature measurement method of the embodiment of the present disclosure, the skin temperature of the user is obtained, and the ambient temperature is obtained; obtaining an initial predicted body temperature of a user by using the skin temperature and the environmental temperature; determining a target correction model of the initial predicted body temperature based on at least one of skin temperature, ambient temperature, and the initial predicted body temperature; and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user. Therefore, at least one of skin temperature, ambient temperature and predicted body temperature can be comprehensively considered to determine the target correction model, and the initial predicted body temperature is corrected based on the target correction model, so that errors of the initial predicted body temperature are eliminated, and the accuracy of body temperature measurement is improved.
Fig. 3 is a flow chart of a method of body temperature measurement according to some embodiments.
S301, acquiring the skin temperature of a user and acquiring the environment temperature.
S302, obtaining the initial predicted body temperature of the user by using the skin temperature and the ambient temperature.
For the relevant content of steps S301 to S302, refer to the above embodiment, and are not repeated here.
S303, determining at least one target interval in which at least one of skin temperature, ambient temperature and initial predicted body temperature is located in a plurality of preset intervals.
The skin temperature may be divided into a plurality of first sections in advance, the ambient temperature may be divided into a plurality of second sections in advance, and the initial predicted body temperature may be divided into a plurality of third sections in advance. It should be noted that, the dividing modes of the first section, the second section and the third section are not limited too much, and the dividing modes of the first section, the second section and the third section may be different.
In an alternative embodiment, determining at least one target interval in which at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is located in a plurality of preset intervals includes determining a first target interval in which the skin temperature is located in a plurality of first intervals, the first target interval being any one of the first intervals, and/or determining a second target interval in which the ambient temperature is located in a plurality of second intervals, the second target interval being any one of the second intervals, and/or determining a third target interval in which the initial predicted body temperature is located in a plurality of third intervals, the third target interval being any one of the third intervals.
S304, determining a target correction model from a plurality of preset correction models based on at least one target interval.
In some optional implementations, a mapping relation or a mapping table between at least one of the first interval, the second interval and the third interval and the preset correction model may be pre-established, and after at least one of the first target interval, the second target interval and the third target interval is acquired, the mapping relation or the mapping table is queried, and the preset correction model mapped by at least one of the first target interval, the second target interval and the third target interval is determined as the target correction model.
In some examples, determining the preset correction model mapped by at least one of the first target section, the second target section, and the third target section as the target correction model includes determining the preset correction model mapped by the first target section as the target correction model, further includes determining the preset correction model mapped by the first target section, the second target section, and the third target section as the target correction model.
In some alternative implementations, determining the target correction model from the plurality of preset correction models based on the at least one target interval includes determining the target region from a plurality of preset regions in a temperature space based on the at least one target interval, the temperature space including at least one dimension, and determining the target correction model corresponding to the target region from the plurality of preset correction models based on a mapping relationship between the plurality of preset regions and the plurality of preset correction models. It should be noted that the above mapping relationship may be preset, and the dimensions of the temperature space include skin temperature, ambient temperature, and initial predicted body temperature.
It will be appreciated that the temperature space is divided into a plurality of predicted regions, and the target region is any predicted region.
In some alternative implementations, as shown in fig. 4, the temperature space includes 3 dimensions, namely, a skin temperature, an ambient temperature, and an initial predicted body temperature, the entire cuboid in fig. 4 is used to characterize the temperature space, the entire cuboid is divided into a plurality of sub-cuboids, each sub-cuboid is used to characterize a predicted area, and the length, width, and height of the sub-cuboids are respectively used to characterize a second interval of the ambient temperature, a third interval of the initial predicted body temperature, and a first interval of the skin temperature.
S305, correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user.
For the relevant content of step S305, refer to the above embodiment, and will not be described herein.
In summary, according to the body temperature measurement method of the embodiment of the present disclosure, at least one target interval in which at least one of a skin temperature, an ambient temperature, and an initial predicted body temperature is located is determined, and a target correction model is determined from a plurality of preset correction models based on the at least one target interval. Therefore, at least one target interval where at least one of skin temperature, ambient temperature and initial predicted body temperature is located can be comprehensively considered to determine the target correction model, and further correction processing is carried out on the initial predicted body temperature based on the target correction model, so that errors of the initial predicted body temperature are eliminated, and accuracy of body temperature measurement is improved.
Fig. 5 is a flow chart of a method of body temperature measurement according to some embodiments.
S501, acquiring the skin temperature of a user and acquiring the ambient temperature.
S502, obtaining the initial predicted body temperature of the user by using the skin temperature and the environment temperature.
For the relevant content of steps S501-S502, refer to the above embodiment, and are not repeated here.
S503, obtaining a difference value between the skin temperature and the ambient temperature.
In some alternative implementations, obtaining the difference between the skin temperature and the ambient temperature includes obtaining the difference between the skin temperature minus the ambient temperature, or obtaining the difference between the ambient temperature minus the skin temperature, or obtaining an absolute difference between the skin temperature and the ambient temperature.
S504, determining a target correction model based on the interval where the difference value is located.
In some optional implementations, a mapping relationship or a mapping table between the interval where the difference value is located and the preset correction model may be pre-established, and after the interval where the difference value is located is obtained, the mapping relationship or the mapping table is queried, and the preset correction model mapped by the interval where the difference value is located is determined as the target correction model.
In some alternative implementations, the method further includes non-linearly transforming the difference values prior to determining the target correction model based on the interval in which the difference values lie.
In some examples, the skin temperature is TW and the ambient temperature is TE, the difference TW-TE of the skin temperature TW minus the ambient temperature TE may be obtained, and the difference TW-TE may be subjected to nonlinear transformation to obtain TT, and if TT is less than a set threshold, the target correction model may be determined to be model A.
S505, correcting the skin temperature, the environment temperature and the initial predicted body temperature by using the target correction model to obtain a corrected predicted body temperature.
In some alternative implementations, the skin temperature, the ambient temperature, and the initial predicted body temperature are corrected using a target correction model to obtain a corrected predicted body temperature, including inputting the skin temperature, the ambient temperature, and the initial predicted body temperature into the target correction model for processing to obtain the corrected predicted body temperature.
In some alternative implementations, the skin temperature, the ambient temperature, and the initial predicted body temperature are corrected using the target correction model to obtain a corrected predicted body temperature, including inputting the skin temperature, the ambient temperature, and the initial predicted body temperature into the target correction model for processing to obtain correction parameters, and correcting the initial predicted body temperature using the correction parameters to obtain the corrected predicted body temperature.
S506, filtering the corrected predicted body temperature to obtain a target predicted body temperature.
It should be noted that, the specific mode of the filtering process is not limited too much, for example, any filtering algorithm may be used to perform the filtering process on the corrected predicted body temperature to obtain the target predicted body temperature, where the filtering algorithm includes an LMS (LEAST MEAN Square) algorithm.
In summary, according to the body temperature measurement method of the embodiment of the disclosure, the interval where the difference between the skin temperature and the ambient temperature is located can be considered to determine the target correction model, the skin temperature, the ambient temperature and the initial predicted body temperature are corrected by using the target correction model to obtain the corrected predicted body temperature, the corrected predicted body temperature is subjected to filtering processing to obtain the target predicted body temperature, and the filtering processing is beneficial to improving the accuracy and stability of the target predicted body temperature and improving the accuracy and stability of body temperature measurement.
On the basis of any of the above embodiments, the user wears the wearable device, and takes the wearable device as a watch, as shown in fig. 6, the wrist skin temperature and the in-table environmental temperature of the user may be collected, the wrist skin temperature and the in-table environmental temperature may be input into the body temperature prediction model for processing, so as to obtain an initial predicted body temperature, a target correction model is determined based on the wrist skin temperature, the in-table environmental temperature and the initial predicted body temperature, and correction processing is performed on the initial predicted body temperature based on the target correction model, so as to obtain the target predicted body temperature of the user.
Fig. 7 is a block schematic diagram of a body temperature measurement device according to some embodiments of the present disclosure.
As shown in fig. 7, a body temperature measurement device 700 of an embodiment of the present disclosure includes: an acquisition module 701, a prediction module 702, a determination module 703 and a correction module 704.
The acquiring module 701 is configured to acquire a skin temperature of a user and acquire an ambient temperature;
The prediction module 702 is configured to obtain an initial predicted body temperature of the user by using the skin temperature and the ambient temperature;
A determination module 703 for determining a target correction model of the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature;
The correction module 704 is configured to perform correction processing on the initial predicted body temperature based on the target correction model, so as to obtain a target predicted body temperature of the user.
In some embodiments, the determining module 703 is further configured to: determining at least one target interval in which at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is located in a plurality of preset intervals; and determining the target correction model from a plurality of preset correction models based on the at least one target interval.
In some embodiments, before said determining a target correction model for said initial predicted body temperature based on at least one of said skin temperature, said ambient temperature and said initial predicted body temperature, said determining module 703 is further configured to: at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is pre-treated.
In some embodiments, the determining module 703 is further configured to: acquiring a difference between the skin temperature and the ambient temperature; and determining the target correction model based on the interval where the difference value is located.
In some embodiments, the correction module 704 is further configured to: correcting the skin temperature, the ambient temperature and the initial predicted body temperature by using the target correction model to obtain a corrected predicted body temperature; and filtering the corrected predicted body temperature to obtain the target predicted body temperature.
In some embodiments, the correction module 704 is further configured to: determining a correction parameter of the initial predicted body temperature based on the target correction model; and correcting the initial predicted body temperature by using the correction parameters to obtain the target predicted body temperature.
In some embodiments, the prediction module 702 is further configured to: inputting the skin temperature and the ambient temperature into a body temperature prediction model for processing to obtain the initial predicted body temperature.
In some embodiments, the obtaining module 701 is further configured to: collecting first temperature data through a first temperature sensing module of a wearable device, wherein at least a part of the first temperature sensing module is in contact with the skin of the user; and processing the first temperature data to obtain the skin temperature.
In some embodiments, the obtaining module 701 is further configured to: acquiring second temperature data through a second temperature sensing module of the wearable device, wherein the second temperature sensing module is arranged inside the wearable device; and processing the second temperature data to obtain the ambient temperature.
It should be noted that, for details not disclosed in the body temperature measurement device in the embodiment of the disclosure, please refer to details disclosed in the body temperature measurement method in the above embodiment of the disclosure, and details are not described here again.
In summary, the body temperature measuring device according to the embodiment of the present disclosure obtains a skin temperature of a user and obtains an ambient temperature; obtaining an initial predicted body temperature of a user by using the skin temperature and the environmental temperature; determining a target correction model of the initial predicted body temperature based on at least one of skin temperature, ambient temperature, and the initial predicted body temperature; and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user. Therefore, at least one of skin temperature, ambient temperature and predicted body temperature can be comprehensively considered to determine the target correction model, and the initial predicted body temperature is corrected based on the target correction model, so that errors of the initial predicted body temperature are eliminated, and the accuracy of body temperature measurement is improved.
To implement the above embodiment, the disclosure further proposes an electronic device 800, as shown in fig. 8, the electronic device 800 including a memory 801, a processor 802. Wherein the processor 802 is configured to implement the method of body temperature measurement according to any of the possible embodiments of the present disclosure by reading executable program code stored in the memory 801.
The electronic device of the embodiment of the disclosure executes a computer program stored on a memory through a processor, acquires skin temperature of a user, and acquires ambient temperature; obtaining an initial predicted body temperature of a user by using the skin temperature and the environmental temperature; determining a target correction model of the initial predicted body temperature based on at least one of skin temperature, ambient temperature, and the initial predicted body temperature; and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user. Therefore, at least one of skin temperature, ambient temperature and predicted body temperature can be comprehensively considered to determine the target correction model, and the initial predicted body temperature is corrected based on the target correction model, so that errors of the initial predicted body temperature are eliminated, and the accuracy of body temperature measurement is improved.
To achieve the above embodiments, the present disclosure further proposes a computer-readable storage medium storing a computer program which, when executed by a computer device, implements the body temperature measurement method according to any of the possible embodiments of the present disclosure.
The computer-readable storage medium of the embodiments of the present disclosure acquires a skin temperature of a user and acquires an ambient temperature by storing a computer program and being executed by a computer device; obtaining an initial predicted body temperature of a user by using the skin temperature and the environmental temperature; determining a target correction model of the initial predicted body temperature based on at least one of skin temperature, ambient temperature, and the initial predicted body temperature; and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user. Therefore, at least one of skin temperature, ambient temperature and predicted body temperature can be comprehensively considered to determine the target correction model, and the initial predicted body temperature is corrected based on the target correction model, so that errors of the initial predicted body temperature are eliminated, and the accuracy of body temperature measurement is improved.
To achieve the above embodiments, the present disclosure also proposes a computer program product comprising computer readable instructions which, when executed by a computer device, implement the body temperature measurement method according to any of the possible embodiments of the present disclosure.
In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present disclosure and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present disclosure.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present disclosure, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present disclosure, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the terms in this disclosure will be understood by those of ordinary skill in the art as the case may be.
In this disclosure, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact through an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.
Claims (10)
1. A method of measuring body temperature, comprising:
acquiring the skin temperature of a user and acquiring the ambient temperature;
obtaining an initial predicted body temperature of the user by using the skin temperature and the ambient temperature;
Determining a target correction model of the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature;
and correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user.
2. The method of claim 1, wherein the determining a target correction model for the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature comprises:
Determining at least one target interval in which at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is located in a plurality of preset intervals;
and determining the target correction model from a plurality of preset correction models based on the at least one target interval.
3. The method of claim 1 or 2, further comprising, prior to the determining the target correction model for the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature:
at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature is pre-treated.
4. The method of any one of claims 1 to 3, wherein the determining a target correction model for the initial predicted body temperature based on at least one of the skin temperature, the ambient temperature, and the initial predicted body temperature comprises:
acquiring a difference between the skin temperature and the ambient temperature;
And determining the target correction model based on the interval where the difference value is located.
5. The method according to any one of claims 1 to 4, wherein said correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user comprises:
Correcting the skin temperature, the ambient temperature and the initial predicted body temperature by using the target correction model to obtain a corrected predicted body temperature;
and filtering the corrected predicted body temperature to obtain the target predicted body temperature.
6. The method according to any one of claims 1 to 5, wherein said correcting the initial predicted body temperature based on the target correction model to obtain the target predicted body temperature of the user comprises:
Determining a correction parameter of the initial predicted body temperature based on the target correction model;
and correcting the initial predicted body temperature by using the correction parameters to obtain the target predicted body temperature.
7. The method of any one of claims 1 to 6, wherein said deriving an initial predicted body temperature of said user using said skin temperature and said ambient temperature comprises:
Inputting the skin temperature and the ambient temperature into a body temperature prediction model for processing to obtain the initial predicted body temperature.
8. The method according to any one of claims 1 to 7, wherein the obtaining a skin temperature of the user comprises: collecting first temperature data through a first temperature sensing module of the wearable device, wherein at least one part of the first temperature sensing module is in contact with the skin of the user, and processing the first temperature data to obtain the skin temperature; and/or
The obtaining the ambient temperature includes: the method comprises the steps of collecting second temperature data through a second temperature sensing module of the wearable device, wherein the second temperature sensing module is arranged inside the wearable device, and processing the second temperature data to obtain the environmental temperature.
9. An electronic device, comprising:
A memory, a processor;
wherein the processor implements the body temperature measurement method of any one of claims 1-8 by reading executable program code stored in the memory.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a computer device, implements the body temperature measurement method according to any one of claims 1-8.
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