Body temperature detection method, wearable device, electronic device, and storage medium
By acquiring skin heat flux, skin temperature, and photoplethysmography (PPG) signals from wearable devices, calculating the real-time perfusion index, and adjusting the equivalent thermal resistance, the problem of deviation in body surface temperature detection was solved, and stable and accurate core temperature measurement was achieved.
Patent Information
- Application Number
- CN202610599719.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-30
- Publication Date
- 2026-06-23
AI Technical Summary
Most existing non-invasive skin surface detection solutions are based on infrared thermometry and skin surface temperature acquisition. Skin temperature is easily affected by the environment, resulting in large deviations and poor stability in skin surface temperature detection results.
By acquiring skin heat flux, skin temperature, and photoplethysmography (PPG) signals from wearable devices, the real-time perfusion index is calculated, the baseline equivalent thermal resistance is adjusted, and the core temperature is determined by combining skin heat flux and skin temperature.
It improves the accuracy of body temperature detection, reduces the impact of changes in the external environment and human physiological state on the detection results, and achieves stable core temperature measurement.
Smart Images
Figure CN122250944A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wearable device technology, and in particular to a body temperature detection method, wearable device, electronic device and storage medium. Background Technology
[0002] Core body temperature, also known as core temperature, is an important physiological indicator for evaluating human health. Core body temperature refers to the temperature of the deep internal environment of the human body, which is different from the skin surface temperature, which is easily affected by the external environment, heat dissipation from the body surface, and peripheral circulation. The normal metabolism and physiological functions of human organs depend on the maintenance of a stable core body temperature.
[0003] Currently, most non-invasive body temperature detection methods are based on infrared thermometry and surface temperature acquisition, which only directly detect the temperature of the human skin surface. Skin temperature is easily affected by ambient temperature, air flow, clothing status, limb movement, and changes in the contraction and dilation of peripheral blood vessels. The thermal impedance of subcutaneous tissue changes dynamically with the microcirculation state, resulting in large deviations and poor stability in body temperature detection results. Summary of the Invention
[0004] The main objective of this application is to provide a body temperature detection method, wearable device, electronic device, and storage medium to improve the accuracy of body temperature detection.
[0005] To achieve the above objectives, this application proposes a body temperature detection method, comprising: Acquire the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device; Real-time perfusion index calculated based on photoplethysmography pulse wave signal; The calibrated equivalent thermal resistance is obtained by adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time injection index. The core temperature of the wearer is determined based on the calibrated equivalent thermal resistance, skin heat flux, and skin temperature.
[0006] Optionally, in one embodiment, adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time injection index to obtain the calibrated equivalent thermal resistance includes: Obtain the preset baseline equivalent thermal resistance and the preset mapping relationship model. The mapping relationship model indicates the correspondence between the infusion index and the thermal resistance correction coefficient. The real-time thermal resistance correction coefficient is determined based on the mapping relationship model and the real-time infusion index. The calibrated equivalent thermal resistance is obtained by scaling the reference equivalent thermal resistance based on the real-time thermal resistance correction coefficient.
[0007] Optionally, in one embodiment, before acquiring the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device, the method further includes: Obtain the registration command from the wearer, which includes the measured core temperature of the wearer; The initial skin heat flux and initial skin temperature of the wearer are collected based on the registration instructions; The baseline equivalent thermal resistance is determined based on the measured core temperature, initial skin heat flux, and initial skin temperature.
[0008] Optionally, in one implementation, collecting the initial skin heat flux and initial skin temperature of the wearer based on the registration instruction includes: Data is continuously collected from the wearer based on the registration instructions; When the data fluctuation during the data acquisition process is less than the preset threshold, the initial skin heat flux and initial skin temperature of the wearer are obtained.
[0009] Optionally, in one embodiment, before acquiring the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device, the method further includes: Obtain the sample dataset, which includes sample core temperature, sample skin heat flux, and sample skin temperature; The baseline equivalent thermal resistance is determined based on the sample core temperature, sample skin heat flux, and sample skin temperature.
[0010] Alternatively, in one implementation, the sample dataset covers multiple sets of human detection data with different human body differential attributes.
[0011] Optionally, in one embodiment, before adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time injection index, the method further includes: Obtain the equivalent thermal resistance set, which includes general equivalent thermal resistances corresponding to multiple different scenarios; The reference equivalent thermal resistance is determined from multiple general equivalent thermal resistances based on the current scenario.
[0012] Another aspect of this application provides a wearable device, including a heat flux sensor, a digital temperature sensor, a photoplethysmography analog front end, a secure storage module, and a processing module; The processing module connects to a heat flux sensor, a digital temperature sensor, a photoplethysmography (PPG) analog front-end, and a secure storage module to execute the body temperature detection method described above.
[0013] Another aspect of this application provides an electronic device, comprising: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is used to execute programs in memory, including methods for performing the aspects mentioned above; Bus systems are used to connect memory and processor to enable communication between them.
[0014] Another aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the methods described above.
[0015] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: The perfusion index, which characterizes the peripheral microcirculation state, is obtained by solving the photoplethysmography pulse wave signal. Based on the preset benchmark equivalent thermal resistance, the benchmark equivalent thermal resistance is dynamically adjusted and calibrated by utilizing the real-time subcutaneous blood perfusion change pattern reflected by the perfusion index. Then, combined with skin heat flux and skin temperature, the core temperature is calculated in a collaborative manner according to the steady-state heat conduction relationship of the human body. Through the dynamic calibration of thermal resistance driven by the perfusion index, the heat transfer deviation caused by peripheral circulation fluctuations, individual tissue differences, and environmental factors is compensated, thereby improving the accuracy of body temperature detection. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the architecture of the body temperature detection method provided in the embodiments of this application; Figure 2 This is a schematic flowchart of the body temperature detection method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the wearable device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0018] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0019] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.
[0020] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.
[0021] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.
[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0023] Core body temperature, also known as core temperature, is an important measure of human health. It is the temperature of the body's deep internal environment and a key parameter for maintaining the normal functioning of internal organs' metabolism and physiological regulation.
[0024] Currently, mainstream body temperature detection solutions are mainly divided into two categories: invasive detection and non-invasive surface detection. Invasive detection solutions include rectal temperature measurement, esophageal temperature measurement, and tympanic membrane temperature measurement. These methods offer high measurement accuracy, but the operation is cumbersome and uncomfortable, failing to meet users' needs for long-term, continuous daily monitoring, thus limiting their applicability. Conventional non-invasive detection often relies on infrared thermometry and surface temperature acquisition, directly detecting only the temperature of the human skin surface. Skin temperature is highly susceptible to interference from ambient temperature, airflow, clothing status, limb movement, and changes in the contraction and dilation of peripheral blood vessels. The thermal impedance of subcutaneous tissue dynamically changes with microcirculation, leading to significant deviations and poor stability in the final body temperature detection results.
[0025] Based on this, this application provides a body temperature detection method that can solve the above-mentioned technical problems.
[0026] System architecture and scenario description used in the embodiments of this application: Figure 1 This is a system architecture diagram of a body temperature detection method according to an embodiment of this application. It includes a terminal 140, an Internet connection 130, a gateway 120, a server 110, etc.
[0027] Terminal 140 includes various forms of devices with display screens, such as desktop computers, laptops, PDAs (personal digital assistants), mobile phones, in-vehicle terminals, home theater terminals, dedicated terminals, intelligent voice interaction devices, smart home appliances, or aircraft. Furthermore, it can be a single device or a collection of multiple devices. Terminal 140 can communicate with the Internet 130 via wired or wireless means to exchange data.
[0028] Server 110 refers to a computer system that can provide certain services to terminal 140. Compared to ordinary terminal 140, server 110 has higher requirements in terms of stability, security, and performance. Server 110 can be a single high-performance computer in a network platform, a cluster of multiple high-performance computers, a portion of a single high-performance computer (e.g., a virtual machine), or a combination of portions of multiple high-performance computers (e.g., virtual machines).
[0029] Gateway 120, also known as an internetwork connector or protocol converter, is a computer system or device that acts as a translator, enabling network interconnection at the transport layer. It bridges the gap between two systems using different communication protocols, data formats, languages, or even completely different architectures. Gateways can also provide filtering and security functions. Messages sent from terminal 140 to server 110 are forwarded to the corresponding server 110 via gateway 120. Messages sent from server 110 to terminal 140 are also forwarded to the corresponding terminal 140 via gateway 120.
[0030] The body temperature detection method provided in this application embodiment can be implemented separately in terminal 140, separately in server 110, or partially in terminal 140 and partially in server 110.
[0031] When the body temperature detection method provided in this application embodiment is implemented alone in terminal 140, terminal 140 acquires the skin heat flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device; terminal 140 calculates the real-time perfusion index based on the PPG signal; terminal 140 adjusts the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time perfusion index to obtain the calibrated equivalent thermal resistance; terminal 140 determines the core temperature of the wearer based on the calibrated equivalent thermal resistance, skin heat flux, and skin temperature.
[0032] The body temperature detection method provided in the embodiments of this application will be described below with reference to the accompanying drawings. The execution subject of the body temperature detection method described below is a terminal device, specifically implemented by the terminal device running the various computer programs described above. Of course, based on the understanding of the following text, it is not difficult to see that the body temperature detection method provided in the embodiments of this application can also be implemented collaboratively by the terminal device and the server. The body temperature detection of a smart ring is taken as an example in this embodiment.
[0033] Please see Figure 2 ,like Figure 2 The diagram shown is a flowchart of a body temperature detection method provided in this embodiment. The method includes: Step 201: Acquire the skin heat flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device.
[0034] Skin heat flux can be the value of heat transferred through a unit area of skin per unit time, representing the rate of heat transfer. Here, it can be the real-time heat flow data of the human body core of the smart ring wearer, which is conducted to the skin surface and dissipated outward through the skin.
[0035] Skin temperature can be the surface temperature of the outermost layer of human skin tissue; here, it refers to the real-time temperature of the body surface directly collected by the sensor.
[0036] Photoplethysmography (PPG) signals can be obtained by capturing the fluctuating electrical signals formed by the periodic changes in subcutaneous blood vessel volume with the heartbeat using photoelectric detection. They can be obtained by real-time acquisition of the original physiological signals of subcutaneous blood flow fluctuations using wearable photoelectric sensors.
[0037] In one embodiment, before acquiring the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device, the method further includes: Obtain the registration command from the wearer, which includes the measured core temperature of the wearer; The initial skin heat flux and initial skin temperature of the wearer are collected based on the registration instructions; The baseline equivalent thermal resistance of the wearer is determined based on the measured core temperature, initial skin heat flux, and initial skin temperature.
[0038] In this embodiment, the registration instruction can be a trigger operation instruction for device binding and identity entry, or a personalized parameter calibration start instruction actively triggered by the wearer.
[0039] The measured core temperature can be the actual temperature of the deep organs inside the body of the wearer at the time of registration, or it can be a standard core temperature value actually measured by a medical precision temperature measuring device.
[0040] Initial skin heat flux can be the baseline skin heat flux collected from the wearer in a fixed state and stable environment during the calibration period. Initial skin temperature can be the baseline body surface temperature collected during the calibration phase.
[0041] Then, the measured core temperature can be used as the true value. Combined with the initial skin heat flux and initial skin temperature collected during registration, the baseline equivalent thermal resistance can be calculated in reverse according to the human body heat conduction law to obtain the specific physiological characteristics of the user.
[0042] By adding a personalized calibration step before the routine testing process, using the real and reliable measured core temperature as the calibration benchmark, and combining it with the initial heat transfer and temperature data collected under steady-state conditions, an exclusive benchmark equivalent thermal resistance adapted to the physiological structure of a single individual is independently calculated, reducing the inherent calculation errors caused by differences in human physique.
[0043] In one implementation, the initial skin heat flux and initial skin temperature of the wearer are collected based on the registration instruction, including: Data is continuously collected from the wearer based on the registration instructions; When the data fluctuation during the data acquisition process is less than the preset threshold, the initial skin heat flux and initial skin temperature of the wearer are obtained.
[0044] In this embodiment, after the user triggers the registration command for calibration, the device automatically enters the steady-state acquisition mode and continuously collects skin heat flux and skin temperature data. During the calibration process, skin heat flux and skin temperature are affected by external interference, limb activity, and short-term physiological fluctuations, resulting in dynamic deviations in real-time values. The device detects the change range of the collected data in real time and compares the data fluctuation range with a preset threshold. Only when the heat flux and temperature values tend to be stable and the fluctuation range is controlled within the allowable range are the stable data selected and locked as the initial parameters for calibration, which can avoid parameter deviations caused by instantaneous data fluctuations.
[0045] In one embodiment, before acquiring the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device, the method further includes: Obtain the sample dataset, which includes sample core temperature, sample skin heat flux, and sample skin temperature; The baseline equivalent thermal resistance is determined based on the sample core temperature, sample skin heat flux, and sample skin temperature.
[0046] In this implementation, the sample dataset can be a standardized set of test data collected from a batch of healthy individuals under a uniform steady-state environment.
[0047] The sample core temperature can be the true core temperature measured under uniform detection conditions. The sample skin heat flux can be a standardized skin heat transfer parameter uniformly collected under steady-state conditions. The sample skin temperature can be a simultaneously collected steady-state body surface temperature.
[0048] By collecting synchronous detection data from a large number of subjects in advance and integrating it into a standardized sample dataset containing core temperature, skin heat flux, and skin temperature, a universal benchmark equivalent thermal resistance suitable for the general population can be derived based on the stable heat conduction law of the human body and the correspondence between core temperature, skin heat flux, and skin temperature in the sample data through batch data statistical fitting and calculation.
[0049] By using a large-sample standardized dataset and a unified fitting analysis of population physiological heat transfer characteristics to determine the universal benchmark equivalent thermal resistance, stable and reliable parameters can be quickly provided for body temperature detection.
[0050] In one implementation, the sample dataset covers multiple sets of human detection data with different human body differential attributes.
[0051] In this implementation, a unified calculation and fitting is performed using batches of differentiated sample data to obtain the average value of multiple sets of heat transfer impedance. This is used to summarize the common subcutaneous heat transfer characteristics of the population and finally generate a universal reference equivalent thermal resistance that can be directly reused. This weakens the parameter differences caused by individual physiological structure and metabolic state, extracts the common subcutaneous heat transfer characteristics of the population and generates a universal reference equivalent thermal resistance, thereby improving the universality of the universal reference equivalent thermal resistance.
[0052] Step 202: Calculate the real-time perfusion index based on the photoplethysmography pulse wave signal.
[0053] The perfusion index is a physiological parameter that reflects the strength of pulsatile blood flow in the peripheral blood vessels of the human body, indicating the level of blood filling and flow in local microvessels. Here, it is used to directly characterize the current state of subcutaneous vasoconstriction or vasodilation, and can be called the real-time perfusion index. Specifically, the raw photoplethysmography (PPG) signal can be analyzed to separate the DC base component and the AC fluctuation component brought by the heartbeat. The real-time perfusion index is obtained by calculating the component ratio, transforming the vasoconstriction and vasodilation and microcirculatory changes that cannot be directly quantified into calculable quantitative values.
[0054] Step 203: Adjust the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time injection index to obtain the calibrated equivalent thermal resistance.
[0055] The baseline equivalent thermal resistance can be the fixed thermal impedance of the heat transfer path from the human body core to the skin surface under standard resting conditions. A pre-calibrated baseline equivalent thermal resistance is retrieved, and based on the variation in blood perfusion intensity, the baseline thermal resistance is proportionally corrected to simulate the changes in heat transfer capacity caused by vasodilation and vasoconstriction. This dynamic correction of the baseline equivalent thermal resistance ultimately generates a calibrated equivalent thermal resistance that adapts to physiological changes.
[0056] In one embodiment, the calibrated equivalent thermal resistance is obtained by adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time injection index, including: Obtain the preset baseline equivalent thermal resistance and the preset mapping relationship model. The mapping relationship model indicates the correspondence between the infusion index and the thermal resistance correction coefficient. The real-time thermal resistance correction coefficient is determined based on the mapping relationship model and the real-time infusion index. The calibrated equivalent thermal resistance is obtained by scaling the reference equivalent thermal resistance based on the real-time thermal resistance correction coefficient.
[0057] In this implementation, the mapping relationship model can be a rule model that corresponds between two types of parameters, which in this case is a pre-calibrated mapping relationship between the infusion index and the thermal resistance correction coefficient.
[0058] The thermal resistance correction factor can be a scaling parameter used to adjust the base impedance value. Using the real-time calculated infusion index as input, a matching search is performed using this mapping model to dynamically output a thermal resistance correction factor that adapts to the current microcirculation state. Finally, the baseline equivalent thermal resistance is scaled.
[0059] By using a pre-defined mapping model, the perfusion index change is transformed into a quantifiable coefficient adjustment behavior. The correction coefficient is dynamically matched and adjusted according to the real-time perfusion index, and the scaling of the baseline equivalent thermal resistance is completed. This allows the thermal resistance adjustment to avoid deviations caused by subjective parameter adjustments, enabling the calibrated equivalent thermal resistance to adapt to changes in the human body's vasoconstriction and vasodilation states, objectively restoring the subcutaneous heat transfer patterns under different physiological states, and improving calibration accuracy.
[0060] In one embodiment, before adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time injection index, the method further includes: Obtain the equivalent thermal resistance set, which includes general equivalent thermal resistances corresponding to multiple different scenarios; The reference equivalent thermal resistance is determined from multiple general equivalent thermal resistances based on the current scenario.
[0061] In this implementation, different scenarios can be based on varying external environmental conditions and human body states, such as different ambient temperatures, seasonal conditions, and actual wearing and usage scenarios involving rest or slight activity. For a single specific scenario, a pre-calibrated standard thermal resistance value, i.e., a universal equivalent thermal resistance, can be pre-fitted.
[0062] The smart ring can identify the actual usage scenario corresponding to the detection time, match a universal equivalent thermal resistance that is suitable for the current environment and human body state from the set of equivalent thermal resistances, and select it as the benchmark equivalent thermal resistance used in this body temperature detection process.
[0063] By classifying multiple application scenarios and configuring corresponding general equivalent thermal resistances, the benchmark equivalent thermal resistance can be flexibly matched and adapted according to the real-time environment, adapting to the heat transfer variation between the human body surface and core under different external conditions, and reducing the adaptation deviation of basic parameters caused by differences in environmental scenarios.
[0064] Step 204: Determine the core temperature of the wearer based on the calibrated equivalent thermal resistance, skin heat flux, and skin temperature.
[0065] Core temperature can be the central temperature of the deep organs inside the human chest cavity, abdominal cavity, and cranial cavity.
[0066] Based on the fundamental laws of steady-state heat conduction in the human body, the system uses dynamically corrected calibrated equivalent thermal resistance coupled with synchronously collected skin heat flux and skin temperature for calculation. By using the heat transfer difference on the body surface to infer the temperature difference between the body and the skin, the core body temperature is finally calculated, thus completing the final solution for non-invasive body temperature detection.
[0067] This application embodiment uses the perfusion index to dynamically calibrate and correct the equivalent thermal resistance. It can adaptively adjust the heat transfer impedance parameters according to the real-time changes in the human subcutaneous microcirculation, making up for the shortcomings of fixed thermal resistance in adapting to physiological fluctuations. It adapts to the real heat transfer pattern from the human body surface to the core, avoids the calculation deviation caused by the skin temperature being easily affected by the external environment and vasomotor activity, improves the accuracy of core temperature detection, and can detect core temperature at any time, thus improving convenience.
[0068] In one example, the core temperature detection process can be as follows: First, measure the skin-environment heat flux HF and skin temperature T_skin in real time, acquire PPG signals, and calculate the perfusion index PI; second, based on the mapping adjustment relationship between PI and equivalent thermal resistance R_thermal, calculate the calibrated equivalent thermal resistance R_eff: R_eff = R_base × f(PI), where f(PI) is a monotonically decreasing function (the larger the PI, the more dilated the blood vessels and the smaller the thermal resistance); finally, dynamically calculate the core temperature CBT: CBT = T_skin + HF × R_eff(PI).
[0069] The above describes the body temperature detection method; the following describes the wearable device that performs this method. See Figure 3 ,like Figure 3 The diagram shown is a structural schematic of a wearable device provided in this application, including: Heat flux sensor 301, digital temperature sensor 302, photoplethysmography pulse wave analog front end 303, secure storage module 304 and processing module 305; The heat flux sensor 301 is used to collect the heat flux of the skin; The digital temperature sensor 302 is used to collect skin temperature. The photoplethysmography (PPG) simulation front-end 303 is used to acquire PPG waves. The secure storage module 304 is used to store the reference equivalent thermal resistance; The processing module 305 is connected to the heat flux sensor 301, the digital temperature sensor 302, the photoplethysmography analog front-end 303, and the secure storage module 304, and executes the aforementioned body temperature detection method.
[0070] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described body temperature detection method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.
[0071] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 402 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and called and executed by the processor 401 using the body temperature detection method of the embodiments of this application. Input / output interface 403 is used to implement information input and output; The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.
[0072] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described body temperature detection method.
[0073] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0074] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.
[0075] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.
[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0077] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0078] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0079] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0080] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0081] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0082] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0083] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0084] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.
Claims
1. A method for detecting body temperature, characterized in that, include: Acquire the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device; The real-time perfusion index is calculated based on the aforementioned photoplethysmography signal. The calibrated equivalent thermal resistance is obtained by adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time infusion index. The core temperature of the wearer is determined based on the calibrated equivalent thermal resistance, the skin heat flux, and the skin temperature.
2. The method according to claim 1, characterized in that, The process of adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time injection index to obtain the calibrated equivalent thermal resistance includes: Obtain a preset baseline equivalent thermal resistance and a preset mapping relationship model, wherein the mapping relationship model indicates the correspondence between the infusion index and the thermal resistance correction coefficient. The real-time thermal resistance correction coefficient is determined based on the mapping relationship model and the real-time infusion index. The reference equivalent thermal resistance is scaled based on the real-time thermal resistance correction coefficient to obtain the calibrated equivalent thermal resistance.
3. The method according to claim 1, characterized in that, Before acquiring the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device, the method further includes: Obtain the registration instruction of the wearer, the registration instruction including the measured core temperature of the wearer; Based on the registration instruction, the initial skin heat flux and initial skin temperature of the wearer are collected; The baseline equivalent thermal resistance is determined based on the measured core temperature, the initial skin heat flux, and the initial skin temperature.
4. The method according to claim 3, characterized in that, The step of collecting the initial skin heat flux and initial skin temperature of the wearer based on the registration instruction includes: Data is continuously collected from the wearer based on the registration instructions; When the data fluctuation during the data acquisition process is less than a preset threshold, the initial skin heat flux and initial skin temperature of the wearer are obtained.
5. The method according to claim 1, characterized in that, Before acquiring the skin thermal flux, skin temperature, and photoplethysmography (PPG) signal of the wearer of the wearable device, the method further includes: Obtain a sample dataset, which includes sample core temperature, sample skin heat flux, and sample skin temperature; The baseline equivalent thermal resistance is determined based on the sample core temperature, the sample skin heat flux, and the sample skin temperature.
6. The method according to claim 5, characterized in that, The sample dataset covers multiple sets of human detection data with different human body characteristics.
7. The method according to claim 1, characterized in that, Before adjusting the value of the reference equivalent thermal resistance based on the reference equivalent thermal resistance and the real-time infusion index, the method further includes: Obtain an equivalent thermal resistance set, which includes multiple general equivalent thermal resistances corresponding to different scenarios; The reference equivalent thermal resistance is determined from among the multiple general equivalent thermal resistances described in the current scenario.
8. A wearable device, characterized in that, It includes a heat flux sensor, a digital temperature sensor, a photoplethysmography (PPG) analog front-end, a secure storage module, and a processing module; The processing module is connected to the heat flux sensor, the digital temperature sensor, the photoplethysmography analog front end, and the secure storage module, and executes the method as described in any one of claims 1-7.
9. An electronic device, characterized in that, include: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is configured to execute a program in the memory, including performing the method as described in any one of claims 1 to 7; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.
10. A computer-readable storage medium, characterized in that, Includes instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.