CN116942111A

CN116942111A - Age detection method and wearable device

Info

Publication number: CN116942111A
Application number: CN202210469077.8A
Authority: CN
Inventors: 许德省; 李靖; 许培达; 叶际隆; 陈文娟
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2022-04-16
Filing date: 2022-04-29
Publication date: 2023-10-27

Abstract

The application provides an age detection method and wearable equipment, and relates to the technical field of terminals. The accuracy of the obtained age can be improved, and the accuracy of the user in health detection by using the age can be improved. The method may be applied to a wearable device, the method comprising: physiological parameters and sound data of the user are acquired, and then an age group of the user is determined according to the physiological parameters and the sound data. Further, different athletic health courses may be recommended to the user based on the detected age bracket of the user.

Description

Age detection method and wearable device

The present application claims priority from the chinese patent application filed 16 months 04 in 2022, filed national intellectual property agency, application number 202210400462.7, entitled "a physiological age detection device and method", the entire contents of which are incorporated herein by reference.

Technical Field

The application relates to the technical field of terminals, in particular to an age detection method and wearable equipment.

Background

Respiratory diseases are common diseases and frequently-occurring diseases, and have complex disease types and high mortality rate. It is common in children and the elderly. Arteriosclerosis is a non-inflammatory lesion of arteries, usually occurring in adolescence to exacerbation in middle-aged and elderly people. It can be seen that the occurrence of diseases such as these is greatly related to the age of the user, so that by combining the age of the user, the health (such as possible diseases) of the user is detected, and early warning is timely performed, which is of great significance to ensuring the life health of the user.

However, in the current scheme of performing health detection on a user according to the age of the user, the user is required to manually input the age and then perform health detection according to the age input by the user. The method for acquiring the age of the user cannot determine whether the age input by the user is correct or not, and accuracy is low.

Disclosure of Invention

The application provides an age detection method and wearable equipment, which can improve the accuracy of the obtained age.

In order to achieve the above purpose, the application adopts the following technical scheme:

in a first aspect, the present application provides an age detection method, applied to a wearable device, the method comprising: acquiring physiological parameters and sound data of a user; the age group of the user is determined from the physiological parameters and the sound data.

It will be appreciated that physiological parameters are different for users of different ages and that sound data are also different. Therefore, based on the technical scheme, the physiological parameters and the sound data of the user are comprehensively considered, the age bracket of the user is determined according to the physiological parameters and the sound data, the age bracket of the user can be intelligently predicted, manual input of the user is not needed, and the accuracy of the obtained age can be improved. Also, for users of different ages, the kinds of diseases that the user may have, the probability of a certain disease that may have, etc. may be different. Therefore, the user is subjected to health detection by the determined age bracket, and the accuracy of health detection can be improved.

In one possible design, acquiring sound data of a user includes: displaying a target interface; receiving a target operation of a user; in response to the target operation, a microphone is activated to record sound data of the user. Based on the design, the wearable device reminds a user of inputting sound data through the target interface, and after detecting the operation of inputting sound data by the user, a sensor such as a microphone is activated to record the sound data of the user, so that the sensor for recording the sound data of the user can be prevented from being always started, and the power consumption of the wearable device is reduced.

In one possible design, the time period for acquiring the physiological parameter of the user is overnight. It will be appreciated that as the user is in different states, such as: the physiological parameters of the users may be different in the static state, the active state, the motion state and the like, and the possibility that the users are in the static state is high at night, so that the accuracy of the obtained physiological parameters is high. In addition, even if the user is in the same state, due to errors, physiological parameters measured at different moments may fluctuate, so that the physiological parameters acquired overnight can be higher in referenceability, and further accuracy of the determined age group can be further improved when the age group of the user is determined by using the physiological parameters.

In one possible design, the physiological parameter includes body temperature and the sound data includes a cough sound; after acquiring the physiological parameters and sound data of the user, the method further comprises: under the condition that the body temperature of the user is abnormal, outputting target information, wherein the target information is used for reminding the user that the age bracket obtained by current measurement is inaccurate. As the user may not accurately measure the body temperature of the user when suffering from a disease such as cold fever, etc., which affects the body temperature of the user, the measured age group of the user may also be inaccurate when determining the age group of the user based on the inaccurate body temperature. Therefore, the user is reminded that the age measurement is inaccurate under the condition, the user knows details, the user can selectively detect the age at normal temperature, age detection errors can be avoided, and the accuracy of the measured age is improved.

In one possible design, the age group includes a first age group and a second age group; determining the age group of the user from the physiological parameters and the sound data comprises: determining a first probability of the first age group and a second probability of the second age group according to the physiological parameter; determining a third probability of the first age group and a fourth probability of the second age group according to the sound data; determining an age group of the user according to the first probability and the third probability; or determining the age group of the user according to the second probability and the fourth probability.

In one possible design, determining the age group of the user based on the first probability and the third probability includes: if the first probability is greater than or equal to a first preset threshold value, or the third probability is greater than or equal to a second preset threshold value, or the first probability is greater than or equal to a third preset threshold value and smaller than the first preset threshold value, and the third probability is greater than or equal to a fourth preset threshold value and smaller than the second preset threshold value, determining that the age group of the user is a first age group; and if the first probability is greater than or equal to a third preset threshold value and is smaller than the first preset threshold value and the third probability is smaller than a fourth preset threshold value, or the first probability is smaller than the third preset threshold value and the third probability is smaller than the second preset threshold value, determining that the age group of the user is the second age group.

In one possible design, determining the age group of the user based on the second probability and the fourth probability includes: if the second probability is greater than or equal to the first preset threshold value, or the fourth probability is greater than or equal to the second preset threshold value, or the second probability is greater than or equal to the third preset threshold value and smaller than the first preset threshold value, and the fourth probability is greater than or equal to the fourth preset threshold value and smaller than the second preset threshold value, determining that the age group of the user is a second age group; and if the second probability is greater than or equal to the third preset threshold value and is smaller than the first preset threshold value and the fourth probability is smaller than the fourth preset threshold value, or the second probability is smaller than the third preset threshold value and the fourth probability is smaller than the second preset threshold value, determining that the age group of the user is the first age group.

In one possible design, after determining the age group of the user from the physiological parameters and the sound data, the method further comprises: acquiring the age input by a user; and prompting the user to confirm whether the input age is correct or not under the condition that the age is not matched with the determined age range of the user. Based on the design, whether the predicted age bracket of the user is matched with the age input by the user or not is judged, and under the condition of inconsistency, the user can be reminded of confirmation, the correct age bracket can be finally obtained, and the accuracy of the obtained age can be further improved. Subsequently, when the age bracket is used for health detection of the user, the problem of health detection errors caused by inconsistent age input by the user and age of a wearer of the wearable device can be solved.

In one possible design, the method further comprises, prior to prompting the user to confirm whether the age of the input is correct: and determining whether the last time for reminding the user of confirming whether the input age is correct or not meets the preset condition. Because the frequency of the wearable device outputting the reminding message may influence the user experience, based on the design, before the reminding message is output, whether the time for reminding the user last time meets the preset condition is determined, and the user is reminded again under the condition that the preset condition is met, so that the situation that the user experience is poor due to the fact that the wearable device frequently outputs the reminding message can be avoided.

In one possible design, the method further comprises: if the age bracket of the user is determined to be the first age bracket, adopting a first preset model to carry out health detection on the user; and if the age group of the user is determined to be the second age group, adopting a second preset model to carry out health detection on the user. Based on the design, because physiological parameters, sound data and the like of users of different age groups are different, the health detection of the users of different age groups is carried out by adopting different health detection models, and the accuracy of the health detection can be improved.

In one possible design, the physiological parameters include at least one or more of the following: heart rate, heart rate variability HRV, respiration rate, blood oxygen, and body temperature.

In one possible design, the sound data includes at least one or more of the following: cough, "a" sound, blow, voice.

In a second aspect, the present application provides a wearable device having the functionality to implement the age detection method as described in the first aspect and any one of the designs above. The functions may be implemented by hardware, or by corresponding software executed by hardware. The hardware or software includes one or more modules corresponding to the functions described above. In one possible example, the wearable device includes an acquisition unit (or acquisition module) and a processing unit (or processing module); an acquisition unit for acquiring physiological parameters and sound data of a user; and the processing unit is used for determining the age bracket of the user according to the physiological parameters and the sound data.

In one possible design, the display device further comprises a display unit (or display module); the display unit is used for displaying a target interface; the processing unit is also used for receiving target operation of a user; and the processing unit is also used for responding to the target operation and activating the microphone to record the voice data of the user.

In one possible design, the time period for acquiring the physiological parameter of the user is overnight.

In one possible design, the device further comprises a reminding unit; physiological parameters include body temperature, sound data including cough sounds; after the acquisition unit is used for acquiring the physiological parameters and the sound data of the user, the reminding unit (or called a reminding module) is used for outputting target information under the condition that the body temperature of the user is abnormal, and the target information is used for reminding the user that the age bracket obtained by current measurement is inaccurate.

In one possible design, the age group includes a first age group and a second age group; the processing unit is further used for determining a first probability of the first age group and a second probability of the second age group according to the physiological parameters; the processing unit is further used for determining a third probability of the first age group and a fourth probability of the second age group according to the sound data; the processing unit is further used for determining the age bracket of the user according to the first probability and the third probability; or the processing unit is further used for determining the age bracket of the user according to the second probability and the fourth probability.

In one possible design, the processing unit is further configured to determine that the age group of the user is the first age group if the first probability is greater than or equal to a first preset threshold, or the third probability is greater than or equal to a second preset threshold, or the first probability is greater than or equal to a third preset threshold and less than the first preset threshold, and the third probability is greater than or equal to a fourth preset threshold and less than the second preset threshold. The processing unit is further configured to determine that the age group of the user is the second age group if the first probability is greater than or equal to a third preset threshold and less than the first preset threshold and the third probability is less than a fourth preset threshold, or the first probability is less than the third preset threshold and the third probability is less than the second preset threshold.

In one possible design, the processing unit is further configured to determine that the age group of the user is the second age group if the second probability is greater than or equal to the first preset threshold, or the fourth probability is greater than or equal to the second preset threshold, or the second probability is greater than or equal to the third preset threshold and less than the first preset threshold, and the fourth probability is greater than or equal to the fourth preset threshold and less than the second preset threshold; the processing unit is further configured to determine that the age group of the user is the first age group if the second probability is greater than or equal to the third preset threshold and less than the first preset threshold and the fourth probability is less than the fourth preset threshold, or if the second probability is less than the third preset threshold and the fourth probability is less than the second preset threshold.

In a possible design, the obtaining unit is further configured to obtain an age input by the user; and the reminding unit is also used for reminding the user of confirming whether the input age is correct or not under the condition that the age is not matched with the determined age bracket of the user.

In a possible design, the processing unit is further configured to determine whether the last time the user was reminded to confirm whether the input age is correct meets the preset condition.

In one possible design, the processing unit is further configured to perform health detection on the user by using a first preset model if it is determined that the age group of the user is the first age group; and the processing unit is further used for carrying out health detection on the user by adopting a second preset model if the age bracket of the user is determined to be the second age bracket.

In one possible design, the physiological parameters include at least one or more of the following: heart rate, heart rate variability HRV, respiration rate, blood oxygen, and temperature.

In a third aspect, the present application provides a wearable device comprising: comprising a processor, a memory, a sensor, and a display screen, the memory, the sensor, the display screen being coupled to the processor, the memory being for storing computer program code, the computer program code comprising computer instructions, the processor reading the computer instructions from the memory to cause the wearable device to perform the method as described in the first aspect and any one of the designs described above.

In one possible design, the wearable device further includes a communication interface that may be used for the wearable device to communicate with other devices (e.g., electronic devices). By way of example, the communication interface may be a transceiver, an input/output interface, an interface circuit, an output circuit, an input circuit, a pin or related circuit, or the like.

In a fourth aspect, the present application provides a wearable device comprising: at least one processor; the processor is configured to execute a computer program or instructions stored in the memory to cause the wearable device to perform the method of the first aspect and any one of the designs described above. The memory may be coupled to the processor or may be separate from the processor.

In one possible design, the wearable device further comprises a sensor coupled to the processor, the sensor operable to acquire physiological parameters and/or sound data of the user from the wearable device. The sensor may be, for example, a photoplethysmographic sensor, an acceleration sensor, a temperature sensor, a sound sensor (e.g., a microphone), etc.

In one possible design, the wearable device further includes a display screen coupled to the processor, the display screen being operable to enable display operations by the wearable device. For example: display of a target interface, display of target information, and the like.

In a fifth aspect, the present application provides a computer readable storage medium comprising a computer program or instructions which, when run on a wearable device, cause the wearable device to perform the method as described in the first aspect and any one of the designs described above.

In a sixth aspect, the application provides a computer program product enabling a computer to carry out the method of the first aspect and any one of the designs described above when the computer program product is run on the computer.

In a seventh aspect, the present application provides circuitry comprising processing circuitry configured to perform the method of the first aspect and any one of the designs.

In an eighth aspect, the present application provides a chip system comprising at least one processor and at least one interface circuit, the at least one interface circuit being configured to perform a transceiving function and to send instructions to the at least one processor, when executing the instructions, performing the method as described in the first aspect and any one of the designs above.

It should be noted that the technical effects caused by any one of the second to eighth aspects may be referred to the technical effects caused by the corresponding design in the first aspect, and will not be described herein.

Drawings

Fig. 1 is a schematic diagram of a communication system to which an age detection method according to an embodiment of the present application is applied;

fig. 2 is a schematic structural diagram of a wearable device according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of still another wearable device according to an embodiment of the present application;

fig. 4 is a schematic flow chart of an age detection method according to an embodiment of the present application;

FIG. 5 is a first schematic interface diagram according to an embodiment of the present application;

FIG. 6 is a second schematic interface diagram according to an embodiment of the present application;

FIG. 7 is a third schematic interface diagram according to an embodiment of the present application;

FIG. 8 is a fourth schematic interface diagram according to an embodiment of the present application;

FIG. 9 is a fifth interface diagram according to an embodiment of the present application;

FIG. 10 is a diagram illustrating an interface according to an embodiment of the present application;

FIG. 11 is a diagram of an interface according to an embodiment of the present application;

FIG. 12 is a schematic diagram of an interface eighth embodiment of the present application;

FIG. 13 is a diagram illustrating an interface according to an embodiment of the present application;

FIG. 14 is a schematic view of an interface provided by an embodiment of the present application;

fig. 15 is a schematic structural diagram of still another wearable device according to an embodiment of the present application;

fig. 16 is a schematic structural diagram of a chip system according to an embodiment of the present application.

Detailed Description

The age detection method and the wearable device provided by the embodiment of the application are described in detail below with reference to the accompanying drawings.

The terms "comprising" and "having" and any variations thereof, as referred to in the description of the application, are intended to cover non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the description of the present application, unless otherwise indicated, the meaning of "a plurality" means two or more. "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone.

Respiratory diseases are common diseases and frequently-occurring diseases, and have complex disease types and high mortality rate. It is common in children and the elderly. Arteriosclerosis is a non-inflammatory lesion of arteries, usually occurring in adolescence to exacerbation in middle-aged and elderly people. It can be seen that the occurrence of diseases such as these is greatly related to the age of the user, so that by detecting the health of the user (such as possible diseases) in combination with the age of the user, early warning is performed in time, so that the user can be prevented or treated early according to the early warning message, which is of great importance to the life health of the user. However, in the current scheme of performing health detection on a user according to the age of the user, the user is required to manually input the age and then perform health detection according to the age input by the user. The method for acquiring the age of the user cannot determine whether the age input by the user is correct or not, and accuracy is low.

In order to solve the technical problems, the application provides an age detection method capable of improving accuracy of an obtained age.

Fig. 1 is a schematic diagram of a communication system to which an age detection method according to an embodiment of the present application is applied. As shown in fig. 1, the communication system includes a wearable device 100 and an electronic device 200.

The wearable device 100 may establish a communication connection with the electronic device 200 through wired communication technology and/or wireless communication technology. Among other wireless communication technologies, at least one of the following is included but not limited to: near field wireless communication (near field communication, NFC), bluetooth (BT) (e.g., conventional bluetooth or low energy (bluetooth low energy, BLE) bluetooth), wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), zigbee (Zigbee), frequency modulation (frequency modulation, FM), infrared (IR), and the like.

In some embodiments, both the wearable device 100 and the electronic device 200 support proximity discovery functionality. Illustratively, after the wearable device 100 approaches the electronic device 200, the wearable device 100 and the electronic device 200 can discover each other, and then establish a wireless communication connection such as a Wi-Fi end-to-end (P2P) connection, a bluetooth connection, or the like. After establishing the wireless communication connection, the wearable device 100 and the electronic device 200 may implement signal interaction through the wireless communication connection.

In some embodiments, the wearable device 100 establishes a wireless communication connection with the electronic device 200 over a local area network. For example, the wearable device 100 and the electronic device 200 are both connected to the same router.

In some embodiments, the wearable device 100 establishes a wireless communication connection with the electronic device 200 through a cellular network, the internet, or the like. For example, the electronic device 200 accesses the internet through a router, and the wearable device 100 accesses the internet through a cellular network; further, the wearable device 100 establishes a wireless communication connection with the electronic device 200.

Alternatively, the wearable device 100 may be, for example, a terminal device with an age detection function, such as a smart watch, a smart bracelet, a smart foot ring, a wireless earphone, smart glasses, a smart helmet, or the like. Operating systems installed by the wearable device 100 include, but are not limited toOr other operating system. In some embodiments, the wearable device 100 may be a fixed device or a portable device. The application is not limited to the specific type of wearable device 100, the operating system installed.

Alternatively, the electronic device 200 may be a terminal device such as a mobile phone (mobile phone), a personal computer (personal computer, PC), a tablet (Pad), a notebook, a desktop, a notebook, a computer with a transceiver function, a wearable device, a vehicle-mounted device, an artificial intelligence (artificial intelligence, AI) device, etc. Operating systems installed on electronic device 200 include, but are not limited to Or other operating system. In some embodiments, the electronic device 200 may be a stationary device or a portable device. The application is not limited by the particular type of electronic device 200, the operating system installed.

Wherein the wearable device 100 may be used to obtain a physiological parameter of a user and/or sound data of the user, for which reference is made to the following. Wearable device 100 may then determine the age bracket of the user based on the physiological parameters of the user and/or the sound data of the user. Optionally, the wearable device 100 may also send the physiological parameter of the user and/or the sound data of the user, etc. to the electronic device 200, and the age group of the user is determined by the electronic device 200.

Further, different athletic health courses may be recommended to the user based on the detected age bracket of the user.

In some embodiments, an application for a user to input an age is installed in the electronic device 200, through which the user can input his own age.

In one possible example, the wearable device 100 may obtain the age entered by the user from the electronic device 200 connected in wireless communication, and then determine whether the age entered by the user is consistent with the determined age range, that is, whether the age entered by the user is between the determined age ranges. Optionally, the determined age group may be determined by the wearable device 100 itself, or may be determined by the electronic device 200 and then sent to the wearable device 100. If not, the wearable device 100 may output a prompt (e.g., display a prompt via a display screen, speaker voice broadcast a prompt, etc.), prompting the user to confirm whether the age entered in the electronic device 200 is correct. Of course, the wearable device 100 may also send a prompt message to the electronic device 200, and the electronic device 200 outputs the corresponding message to prompt the user to confirm whether the age input in the electronic device 200 is correct.

In another possible example, the electronic device 200 may determine whether the age entered by the user is consistent with the determined age bracket. Optionally, the determined age group may be determined by the wearable device 100 and then sent to the electronic device 200, or may be determined by the electronic device 200 after the wearable device 100 sends the physiological parameter of the user and/or the sound data of the user to the electronic device 200. If not, the electronic device 200 may output a prompt message prompting the user to confirm whether the age entered in the electronic device 200 is correct. Of course, the electronic device 200 may also send a prompt message to the wearable device 100, and the wearable device 100 outputs a corresponding message to prompt the user to confirm whether the age input in the electronic device 200 is correct.

In other embodiments, the communication system may not include the electronic device 200. Alternatively, an application for inputting age by the user is installed in the wearable device 100, and the user can directly input own age through the application. Then, the wearable device 100 may determine whether the age input by the user is consistent with the age group determined by the wearable device, and if not, the wearable device 100 may output a prompt message prompting the user to confirm whether the age input in the wearable device 100 is correct.

Of course, the wearable device 100 and the electronic device 200 may also each be installed with an application for user input of age, to which the present application is not limited.

Fig. 2 shows a schematic structural diagram of the wearable device 100.

The wearable device 100 may include a processor 110, a memory 120, a universal serial bus (universal serial bus, USB) interface 130, a charge management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a sensor module 180, keys 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and the like. The sensor module 180 may include a photo volume pulse wave sensor 180A, an Acceleration (ACC) sensor 180B, a temperature sensor 180C, a touch sensor 180D, and the like.

The processor 110 may include one or more processing units, such as: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.

The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Repeated accesses are avoided and the latency of the processor 110 is reduced, thereby improving the efficiency of the system.

In some embodiments, processor 110 may include one or more interfaces, such as USB interface 130, and the like. The USB interface 130 may be an interface conforming to USB standard, specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the wearable device 100, and may also be used to transfer data between the wearable device 100 and a peripheral device. And can also be used for connecting with a headset, and playing audio through the headset. The interface may also be used to connect other devices, such as AR devices, etc.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger.

The power management module 141 is used for connecting the battery 142, and the charge management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140 and provides power to the processor 110, the memory 120, the display 194, the camera 193, the wireless communication module 160, and the like.

The wireless communication function of the wearable device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, and the like.

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the wearable device 100 may be used to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed into a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution for wireless communication including 2G/3G/4G/5G or the like for use on the wearable device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field wireless communication technology (near field communication, NFC), infrared technology (IR), etc., for use on the wearable device 100.

In some embodiments, antenna 1 and mobile communication module 150 of wearable device 100 are coupled, and antenna 2 and wireless communication module 160 are coupled, such that wearable device 100 may communicate with a network and other devices through wireless communication techniques.

The wearable device 100 implements display functions through a GPU, a display screen 194, an application processor, and the like. The GPU is a microprocessor for image processing, and is connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display 194 includes a display panel. The display panel may be manufactured using a liquid crystal display (liquid crystal display, LCD), for example, using an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED) or an active-matrix organic light-emitting diode (matrix organic light emitting diode), a flexible light-emitting diode (FLED), a Mini-led, a Micro-OLED, a quantum dot light-emitting diode (quantum dot light emitting diodes, QLED), or the like. In some embodiments, the wearable device 100 may include 1 or N display screens 194, N being a positive integer greater than 1.

The camera 193 is used to capture still images or video. In some embodiments, wearable device 100 may include 1 or N cameras 193, N being a positive integer greater than 1.

Memory 120 may be used to store computer-executable program code that includes instructions. The memory 120 may include a stored program area and a stored data area. The storage program area may store an application program (such as a sound playing function, an image playing function, etc.) required for at least one function of the operating system, etc. The storage data area may store data created during use of the wearable device 100 (e.g., audio data, phonebook, etc.), and so on. In addition, the memory 120 may include a high-speed random access memory, and may also include a nonvolatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (universal flash storage, UFS), and the like. The processor 110 performs various functional applications and data processing of the wearable device 100 by executing instructions stored in the memory 120 and/or instructions stored in a memory provided in the processor. In some embodiments of the present application, the memory may store an age detection model (e.g., a first age detection model, a second age detection model, a third age detection model, etc.), which may be used to determine an age range of the user, for which reference is made to the description below.

The wearable device 100 may implement audio functions through an audio module 170, an application processor, and the like. Such as music playing, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal output and also to convert an analog audio input into a digital audio signal. The audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be disposed in the processor 110, or a portion of the functional modules of the audio module 170 may be disposed in the processor 110. The wearable device 100 may be played through an audio module 170, such as a music play, a recording, etc. The audio module 170 may include a speaker, a receiver, a microphone, an application processor, etc. to implement audio functions. In some embodiments of the present application, the audio module 170 may also be used to obtain sound data of the user, and reference is made to the description of the sound data.

The photoplethysmography sensor 180A may obtain a PPG signal by measuring the attenuated light reflected and absorbed by the blood vessels and tissues of the human body based on an LED light source and a detector through photoplethysmography (PPG). In some embodiments of the present application, the wearable device 100 analyzes the PPG signal obtained by the photo-capacitive product pulse wave sensor 180A, and may obtain physiological parameters of the user, such as: heart rate, respiration rate, blood oxygen, etc. Optionally, the wearable device may further determine the heart rate variability (heart rate variability, HRV) of the user from the obtained heart rate. The HRV may refer to the time of each heartbeat cycle and the change rule of the heartbeat, which can reflect different physiological conditions or disease conditions of the user.

The acceleration sensor 180B may detect the magnitude of acceleration of the wearable device 100 in various directions (typically three axes). The magnitude and direction of gravity can be detected when the wearable device 100 is stationary. The method can also be used for identifying the gesture of the wearable equipment, and is applied to applications such as switching of horizontal and vertical screens, pedometers and the like. In some embodiments of the present application, the acceleration sensor 180B measures an acceleration signal, wherein the acceleration signal may be used to determine a state of a user, such as: stationary state, moving state, etc. Physiological parameters (e.g., respiration rate, heart rate, blood oxygen, etc.) may differ due to the user being in different states. Therefore, to improve the accuracy of the obtained physiological parameters of the user, the wearable device 100 may further assist in determining the physiological parameters of the user by determining the state of the user from the acceleration signal acquired by the acceleration sensor 180B.

The temperature sensor 180C is used to detect temperature. In some embodiments, wearable device 100 performs a temperature processing strategy using the temperature detected by temperature sensor 180C. For example, when the temperature reported by temperature sensor 180C exceeds a threshold, wearable device 100 performs a reduction in performance of a processor located in proximity to temperature sensor 180C in order to reduce power consumption to implement thermal protection. In other embodiments, when the temperature is below another threshold, the wearable device 100 heats the battery 142 to avoid low temperatures causing the wearable device 100 to shut down abnormally. In other embodiments, when the temperature is below yet another threshold, wearable device 100 performs boosting of the output voltage of battery 142 to avoid abnormal shutdown caused by low temperatures. In some embodiments of the present application, the wearable device 100 may be equipped with one or more temperature sensors 180C for detecting the body temperature of the user.

The touch sensor 180D, also referred to as a "touch device". The touch sensor 180D may be disposed on the display 194, and the touch sensor 180D and the display 194 form a touch screen, which is also referred to as a "touch screen". The touch sensor 180D is used to detect a touch operation acting thereon or thereabout. The touch sensor may communicate the detected touch operation to the application processor to determine the touch event type. Visual output related to touch operations may be provided through the display 194. In other embodiments, touch sensor 180D may also be disposed on the surface of wearable device 100 in a different location than display 194.

Optionally, the sensor module 180 may also include a pressure sensor, a barometric sensor, a magnetic sensor, a distance sensor, a proximity sensor, a gyroscopic sensor, a fingerprint sensor, an ambient light sensor, a bone conduction sensor, etc.

The keys 190 include a power-on key, a volume key, etc. The keys 190 may be mechanical keys. Or may be a touch key. The wearable device 100 may receive key inputs, generating key signal inputs related to user settings and function control of the wearable device 100.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration alerting as well as for touch vibration feedback.

The indicator 192 may be an indicator light, may be used to indicate a state of charge, a change in charge, a message indicating a missed call, a notification, etc.

It will be appreciated that the foregoing is merely illustrative of the structure of the wearable device in the embodiments of the present application, and is not to be construed as limiting the structure or form of the wearable device. The embodiment of the application does not limit the structure and the form of the wearable equipment. By way of example, fig. 3 shows another exemplary structure of a wearable device. As shown in fig. 3, the wearable device includes: a processor 301, a memory 302, a transceiver 303. The implementation of the processor 301, memory 302 may be seen in the implementation of the wearable device processor, memory. A transceiver 303 for the wearable device to interact with other devices, such as electronic devices. The transceiver 303 may be a device based on a communication protocol such as Wi-Fi, bluetooth, or other.

In other embodiments of the application, the wearable device may include more or fewer components than shown in fig. 2, 3, or combine certain components, or split certain components, or replace certain components, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Alternatively, regarding the structure of the electronic device 200, reference may be made to the structure of the wearable device 100, and the electronic device 200 may have more or less structures than the wearable device 100, which is not particularly limited by the present application.

The technical solutions involved in the following embodiments may be implemented in devices having structures as shown in fig. 2 and 3.

The embodiment of the application provides an age detection method which is applied to wearable equipment. The wearable device may acquire the physiological parameter of the user and/or the sound data of the user, and then determine the age bracket of the user based on the physiological parameter of the user and/or the sound data of the user. Optionally, the determined age group may be used for health detection of the user later.

Wherein the physiological parameters are different for users of different ages. The physiological parameters of the user may include physiological parameters that differ at different ages, which may include, but are not limited to: heart rate, HRV, respiration rate, body temperature, blood oxygen, blood pressure, pulse rate, and the like, may be used to determine parameters of the age group of the user. Illustratively, the heart rate of the young person is higher than the heart rate of the elderly person, e.g., the heart rate of the young person is 60-100 times per minute and the heart rate of the elderly person is 55-90 times per minute. The HRV of young people is higher than that of old people, for example, the HRV in the age range of 20 years to 25 years is between 55-105, and the HRV in the age range of 60 years to 65 years is between 25-45. Physiological parameters such as respiration rate, body temperature, blood oxygen, blood pressure, pulse rate and the like are also different for different age groups, and are not exemplified.

For users of different ages, the characteristics of their sound data (e.g., tone, timbre, etc.) are also different due to the difference in the sound production structure (e.g., vocal cords) of the users. By way of example, the sound data includes, but is not limited to, various types of sound data such as a user's cough sound, "a" sound, "o" sound, expiration sound, blowing sound, speaking voice, speech, and the like, and the present application is not limited thereto.

The age groups can be divided into different modes.

In one possible division, the age group includes young people, middle aged and elderly people, for example: the age at and before 40 years is called young, and the age at and after 40 is called middle aged and elderly.

In another possible division, the age group includes teenagers, young, middle-aged, elderly, for example: the ages 17 and before 17 are called teenagers, the ages 18 to 40 are called young, the ages 41 to 65 are called middle-aged, and the ages 65 are called elderly.

In yet another possible division, the age group includes teenagers, middle aged and elderly people, for example: the ages 40 and before 40 are called teenagers, the ages between 41 and 65 are called middle aged and the ages 65 are called elderly.

In yet another possible division, the age group includes young, middle aged, young, elderly with longevity, for example: the ages 44 and before 44 are called young, the ages 45 to 59 are called middle aged, the ages 60 to 74 are called young aged, the ages 75 to 89 are called elderly, and the ages above 90 are called long-life aged.

It is to be understood that the above-mentioned dividing manner is only an exemplary illustration, and the age range to which each age group belongs is also only an exemplary illustration, and does not limit the present application, and in practical application, the division of the age groups and the age range to which each age group belongs may be set by a developer according to actual requirements.

In some embodiments, the wearable device may only acquire the physiological parameters of the user and then determine the age bracket of the user from the acquired physiological parameters of the user. The wearable device may input the acquired physiological parameter of the user into a preset first age detection model, and output the probability of each age group through the first age detection model, where optionally, the sum of the probabilities of each age group is 1.

Alternatively, the first age detection model may be a machine learning model, which may be obtained by model training, such as: and taking the physiological parameters of the user as input, and taking the age bracket of the user as output training to obtain a first age detection model. The specific algorithm employed by the first age detection model is not limited by the present application.

In one possible example, the wearable device may first determine whether there is a first threshold or more in the probabilities of the age groups output by the first age detection model, and if so, determine the age groups with probabilities of the first threshold or more as the age groups of the user. For example, in the embodiment of the present application, the first threshold may be set to a value close to 1, for example: a value between 0.85 and 0.9, etc. It will be appreciated that the closer the probability of an age group is to 1, the greater the likelihood that the user is in that age group, and thus the wearable device may directly determine the age group with the probability equal to or greater than the first threshold as the age group of the user. For example: taking the age group including young people and middle-aged and elderly people as an example, the first threshold value is 0.9, assuming that the probability of the young people output by the first age detection model is 0.08 and the probability of the middle-aged and elderly people is 0.92, the wearable device determines that the probability of the middle-aged and elderly people is greater than the first threshold value of 0.9, so that the age group of the user is determined to be the middle-aged and elderly people.

Optionally, if the probability of the age group output by the first age detection model is not greater than or equal to the first threshold, the wearable device may further determine whether the probability of the age group output by the first age detection model is greater than or equal to the second threshold and less than the first threshold, and if so, determine the age group with the probability of greater than or equal to the second threshold and less than the first threshold as the age group of the user. Wherein the second threshold is smaller than the first threshold, the second threshold may be set to a value smaller than the first threshold but also close to 1, such as: a value between 0.6 and 0.85, etc.

Optionally, if the probability of the age group output by the first age detection model is not greater than or equal to the second threshold, that is, the probability of each age group is smaller than the second threshold, the wearable device may further determine the age group with the highest probability in each age group as the age group of the user.

In another possible example, the wearable device need not determine whether there is a first threshold or more in the probabilities of the age groups output by the first age detection model and/or whether there is a second threshold or more and less in the probabilities of the age groups output by the first age detection model. The wearable device may directly determine the age group with the highest probability as the age group of the user. For example: taking the age group including the young people and the middle-aged and elderly people as an example, assuming that the probability of the young people output by the wearable device through the first age detection model is 0.58 and the probability of the middle-aged and elderly people is 0.42, the wearable device determines that the probability of the young people is 0.58 to be greater than the probability of the middle-aged and elderly people, so that the age group of the user is determined to be the young people.

In other embodiments, the wearable device may only acquire sound data of the user. And then determining the age group of the user according to the acquired voice data of the user. Optionally, the wearable device may input the acquired sound data of the user into a preset second age detection model, and output the probability of each age group through the second age detection model, where the sum of the probabilities of each age group is 1.

Alternatively, the second age detection model may also be a machine learning model, which may also be obtained by model training, such as: and obtaining a second age detection model by taking the voice data of the user as input and taking the age bracket of the user as output training. The specific algorithm employed by the second age detection model is not limited by the present application.

Similar to the embodiment of obtaining only the physiological parameter of the user, the wearable device may also determine whether the probability of the age group output in the second age detection model is greater than or equal to the first threshold, and if so, determine the age group with the probability greater than or equal to the first threshold as the age group of the user. Optionally, if the probability of the age group output by the second age detection model is not present, the wearable device may further determine whether the probability of the age group output by the second age detection model is greater than or equal to the second threshold and less than the first threshold, and if the probability of the age group output by the second age detection model is greater than or equal to the second threshold and less than the first threshold, determining the age group of the user as the age group of the user. Optionally, if not, the wearable device may further determine an age group with the highest probability among the age groups as the age group of the user. Of course, the wearable device may directly determine the age group with the highest probability among the age groups as the age group of the user without executing the foregoing step of determining according to the first threshold and/or the second threshold.

In still other embodiments, the wearable device may also simultaneously acquire the physiological parameter of the user and the sound data of the user, and then determine the age bracket of the user according to the acquired physiological parameter of the user and the sound data of the user. Optionally, the wearable device may input the acquired physiological parameter of the user into a preset first age detection model, and input the acquired sound data of the user into a preset second age detection model. The probability of each age group is output through the first age detection model, and optionally, the sum of the probabilities of each age group is 1. And outputting the probability of each age group through the second age detection model, wherein optionally, the sum of the probabilities of each age group is also 1. It will be appreciated that the probability of the age group output by the first age detection model is determined based on the physiological parameters of the user and the probability of the age group output by the second age detection model is determined based on the sound data of the user.

In one possible example, the wearable device may determine whether there is a third threshold or more in the probabilities of the age groups output by the first age detection model, and if so, determine the age groups of the user for which the probabilities of the first age detection model output are greater than or equal to the third threshold. Or determining whether the probability of the age group output by the second age detection model is greater than or equal to a fourth threshold value, and if so, determining the age group of the user as the age group of the second age model, wherein the probability of the age group output by the second age model is greater than or equal to the fourth threshold value.

In this embodiment of the present application, the third threshold and the fourth threshold may be set to a value close to 1, for example: is set to a value between 0.8 and 0.9. Optionally, the third threshold value and the fourth threshold value may be the same or different, and the third threshold value and the fourth threshold value may be the same or different from the first threshold value, and may be the same or different from the second threshold value when they are different from the first threshold value.

Taking age groups including young people and middle aged and elderly people as examples, the third threshold value is 0.9, and the fourth threshold value is 0.8.

Such as: the probability of the young people output by the first age detection model is 0.95, the probability of the middle-aged and the elderly people is 0.05, the probability of the young people output by the second age detection model is 0.7, the probability of the middle-aged and the elderly people is 0.3, and the wearable equipment determines that the probability of the young people output by the first age detection model is 0.95 and is larger than a third threshold value of 0.9, and then determines that the age group of the user is the young people.

For another example: the probability of the young people output by the first age detection model is 0.6, the probability of the middle-aged and the elderly people is 0.4, the probability of the young people output by the second age detection model is 0.95, the probability of the middle-aged and the elderly people is 0.05, and the wearable equipment determines that the probability of the young people output by the second age detection model is 0.95 which is larger than the fourth threshold value 0.8, and then determines that the age group of the user is the young people.

For another example: the probability of young people output by the first age detection model is 0.95, the probability of middle-aged and elderly people is 0.05, the probability of young people output by the second age detection model is 0.85, and the probability of middle-aged and elderly people is 0.15. The wearable device determines that the probability 0.95 of the young person output by the first age detection model is greater than the third threshold value 0.9, and determines that the age group of the user is the young person. Or, if the wearable device determines that the probability 0.85 of the young person output by the second age detection model is greater than the fourth threshold value 0.8, determining that the age group of the user is the young person.

Optionally, when the wearable device determines that the probabilities of the age groups output by the first age detection module are smaller than the third threshold, and the probabilities of the age groups output by the second age detection module are smaller than the fourth threshold, the wearable device may further determine whether there is an age group greater than or equal to the fifth threshold and smaller than the third threshold in the probabilities of the age groups output by the first age detection module, and if there is an age group greater than or equal to the fifth threshold and smaller than the third threshold, determine the age group of the user as the age group of the user.

Or, the wearable device may further determine whether there is an age group greater than or equal to the sixth threshold and less than the fourth threshold in the probabilities of the age groups output by the second age detection model, and if so, determine the age group greater than or equal to the sixth threshold and less than the fourth threshold as the age group of the user.

Alternatively, the wearable device may further determine whether there is an age group in which the probability of the first age detection model output is equal to or greater than the fifth threshold value and less than the third threshold value, and the probability of the second age detection model output is equal to or greater than the sixth threshold value and less than the fourth threshold value (i.e., an age group in which the probability of the first age detection model output is equal to or greater than the fifth threshold value and less than the third threshold value, the same as an age group in which the probability of the second age detection model output is equal to or greater than the sixth threshold value and less than the fourth threshold value), and if there is, determine that the probability of the first age detection model output is equal to or greater than the fifth threshold value and less than the third threshold value, and the probability of the second age detection model output is equal to or greater than the sixth threshold value and less than the fourth threshold value as the age group of the user.

Optionally, in the embodiment of the present application, the fifth threshold and the sixth threshold may also be set to a value close to 1, for example: a value between 0.7 and 0.8. Optionally, the fifth threshold value and the sixth threshold value may be the same or different, and the fifth threshold value and the sixth threshold value may be the same or different from the first threshold value, the second threshold value, and the like.

Optionally, when the probabilities of the age groups output by the first age detection model are smaller than the fifth threshold and the probabilities of the age groups output by the second age detection model are smaller than the sixth threshold, the wearable device may further determine the age group of the user according to the probabilities of the age groups output by the two age detection models. It may be appreciated that, for the same age group, the probability of the age group output by the first age detection model may be the same as or different from the probability of the age group output by the second age detection model, and the wearable device may determine the age group with the highest probability as the age group of the user.

Taking age groups including young and middle aged and elderly people as examples, the fifth threshold is 0.7 and the sixth threshold is 0.75.

If the probability of the young person output by the first age detection model is 0.65, the probability of the middle-aged and the elderly person is 0.35, the probability of the middle-aged and the elderly person output by the second age detection model is 0.7, and the probability of the young person is 0.3, the wearable device can determine that the probability of the second age detection model output is the largest of all probabilities, so that the wearable device determines that the age group of the user is the middle-aged and the elderly person.

Optionally, the wearable device may directly determine the age group with the highest probability among the age groups as the age group of the user without performing the foregoing determination operations according to the third threshold and the fourth threshold and/or without performing the foregoing determination operations according to the fifth threshold and the sixth threshold.

Alternatively, in this embodiment, the probability of the age group with the highest probability may be output by the first age detection model, or may be output by the second age detection model, or may be an average probability, where the average probability of an age group is an average of the probability of the age group output by the first age detection model and the probability of the age group output by the second age detection model.

In the embodiment of the present application, the values of the thresholds (such as the first threshold, the second threshold, the third threshold, the fourth threshold, the fifth threshold, the sixth threshold, etc.) are only exemplary, and do not limit the present application, and in practical application, a developer may set the values according to actual requirements. The thresholds may take the form of a specific value or a range of values, which is not particularly limited by the present application and is collectively described herein.

In a specific embodiment, taking an example that the age group includes two age groups, namely, a young person (i.e., a first age group) and an elderly person (i.e., a second age group), the wearable device may determine the age group of the user according to the probability (i.e., the first probability) of the young person output by the first age detection model and the probability (i.e., the third probability) of the young person output by the second age detection model.

If the wearable device determines that the probability of the young person output by the first age detection model is greater than or equal to a third threshold (i.e., a first preset threshold); or, the probability of the young person output by the second age detection model is greater than or equal to a fourth threshold (i.e., a second preset threshold); or, the probability of the young person output by the first age detection model is greater than or equal to a fifth threshold (i.e., a third preset threshold) and less than the third threshold, and the probability of the young person output by the second age detection model is greater than or equal to a sixth threshold (i.e., a fourth preset threshold) and less than the fourth threshold, then the wearable device determines that the age group of the user is young person. If the wearable device determines that the probability of the young person output by the first age detection model is greater than or equal to a fifth threshold and less than a third threshold, and the probability of the young person input by the second age detection model is less than the sixth threshold; or, if the probability of the young person output by the first age detection model is smaller than the fifth threshold and the probability of the young person output by the second age detection model is smaller than the fourth threshold, the wearable device determines that the age group of the user is middle-aged and elderly people.

It will be appreciated that the probability of the young person output by the first age detection model being less than the fifth threshold and the young person output by the second age detection model being less than the fourth threshold includes two cases: one is that the probability of the young person output by the first age detection model is smaller than the fifth threshold value and the probability of the young person output by the second age detection model is greater than or equal to the sixth threshold value and smaller than the fourth threshold value. The other is that the probability of the young person output by the first age detection model is smaller than the fifth threshold value and the probability of the young person output by the second age detection model is smaller than the sixth threshold value.

Similarly, the wearable device may also determine the age group of the user according to the probability (i.e., the second probability) of the middle-aged and the elderly people output by the first age detection model and the probability (i.e., the fourth probability) of the middle-aged and the elderly people output by the second age detection model. If the wearable device determines that the probability of the middle-aged and the elderly people output by the first age detection model is greater than or equal to a third threshold value; or the probability of the middle-aged and elderly people output by the second age detection model is larger than or equal to a fourth threshold value; or, the probability of the middle-aged and the elderly people output by the first age detection model is greater than or equal to a fifth threshold value and less than a third threshold value, and the probability of the middle-aged and the elderly people output by the second age detection model is greater than or equal to a sixth threshold value and less than a fourth threshold value, and the wearable device determines that the age group of the user is the middle-aged and the elderly people. If the wearable device determines that the probability of the middle-aged and the elderly people output by the first age detection model is greater than or equal to a fifth threshold value and smaller than a third threshold value, and the probability of the middle-aged and the elderly people input by the second age detection model is smaller than the sixth threshold value; or, if the probability of the middle-aged and the elderly people output by the first age detection model is smaller than the fifth threshold value and the probability of the middle-aged and the elderly people output by the second age detection model is smaller than the fourth threshold value, the wearable device determines that the age group of the user is young.

It can be understood that the probability of the middle aged and the elderly people output by the first age detection model is smaller than the fifth threshold value and the probability of the middle aged and the elderly people output by the second age detection model is smaller than the fourth threshold value also comprises two cases: one is that the probability of the middle aged and elderly people output by the first age detection model is smaller than the fifth threshold value and the probability of the middle aged and elderly people output by the second age detection model is larger than or equal to the sixth threshold value and smaller than the fourth threshold value. The other is that the probability of the middle aged and the elderly people output by the first age detection model is smaller than a fifth threshold value and the probability of the middle aged and the elderly people output by the second age detection model is smaller than a sixth threshold value.

Optionally, in this embodiment, in other possible implementations, the wearable device may acquire the sound data of the user again if the acquired physiological parameter of the user meets a certain condition. Such as: after the wearable device inputs the acquired physiological parameters of the user into the first age detection model, the probability of determining the age group output by the first age detection model is smaller than a third threshold value. Or, determining that the maximum probability of the probabilities of the age groups output by the first age detection model is greater than or equal to a fifth threshold value and less than a third threshold value. Alternatively, the probabilities of determining the age groups output by the first age detection model are all less than a fifth threshold. The wearable device determines that the sound data of the user needs to be acquired, i.e. starts to perform the process of acquiring the sound data of the user.

Or, the wearable device may acquire the physiological parameter of the user again when the acquired sound data of the user satisfies a certain condition. Such as: the wearable device inputs the acquired sound data of the user into the second age detection model, and determines that probabilities of age groups output by the second age detection model are smaller than a fourth threshold. Or, determining that the largest probability of the probabilities of the age groups output by the second age detection model is greater than or equal to the sixth threshold and less than the fourth threshold. Alternatively, the probabilities of determining the age groups of the users output by the second age detection model are all less than the sixth threshold. The wearable device may determine that the physiological parameter of the user needs to be acquired, i.e. start performing the process of acquiring the physiological parameter of the user.

Taking age groups including young people and middle-aged and elderly people as examples, fig. 4 shows an age detection method provided by an embodiment of the present application, which is applied to a wearable device, the method includes the following steps:

s401, acquiring physiological parameters of a user.

S402, determining whether the probability of middle-aged and elderly people is greater than or equal to a third threshold according to the physiological parameters of the user.

Optionally, in this step, the probability of the middle-aged and elderly people may refer to the probability of the middle-aged and elderly people output by the first age detection model, and the wearable device may input the physiological parameter of the user into the first age detection model to obtain the probability of the middle-aged and elderly people.

If yes, step S403 is executed, and if no, step S404 is executed.

S403, determining the age group of the user as middle-aged and elderly people

S404, determining whether the probability of the middle-aged and the elderly is greater than or equal to a fifth threshold value and less than a third threshold value according to the physiological parameters of the user.

Optionally, in this step, the probability of the middle-aged and elderly people may also refer to the probability of the middle-aged and elderly people output by the first age detection model.

If not, step S405 is executed, and if yes, step S406 is executed.

S405, determining that the age group of the user is young.

S406, acquiring voice data of the user.

In the embodiment of the application, the sound data can be acquired by the wearable equipment, or can be acquired by other equipment (such as electronic equipment), and the wearable equipment can acquire the sound data from the other equipment.

S407, determining whether the probability of middle-aged and elderly people is greater than or equal to a fourth threshold according to the voice data of the user.

Optionally, in this step, the probability of the middle-aged and elderly people may refer to the probability of the middle-aged and elderly people output by the second age detection model, and the wearable device may input the sound data of the user into the second age detection model to obtain the probability of the middle-aged and elderly people.

If yes, step S408 is executed, and if no, step S409 is executed.

S408, determining the age group of the user as middle-aged and elderly people.

S409, determining whether the probability of the middle-aged and elderly people is greater than or equal to a sixth threshold value and less than a fourth threshold value according to the voice data of the user.

Optionally, in this step, the probability of the middle-aged and elderly people may also refer to the probability of the middle-aged and elderly people output by the second age detection model.

If yes, step S410 is executed, and if no, step S411 is executed.

S410, determining the age group of the user as middle-aged and elderly people.

S411, determining that the age group of the user is young.

Optionally, in the embodiment of the present application, the first age detection model and the second age detection model may be further implemented by an age detection model (e.g., a third age detection model), and the wearable device may input the acquired physiological parameter of the user and the sound data of the user into the third age detection model to determine the age bracket of the user.

Optionally, for the physiological parameter of the user, the third age detection model may output probabilities of each age group, for the sound data of the user, the third age detection model may also output probabilities of each age group, i.e. for one age group, two probabilities may be corresponding, where one probability is determined according to the physiological parameter of the user and the other probability is determined according to the sound data of the user. Optionally, for the physiological parameter of the user and the sound data of the user, the third age detection model may also output probabilities of the respective age groups, i.e. for one age group, one probability is corresponding, which is determined jointly according to the physiological parameter of the user and the sound data of the user.

Alternatively, the third age detection model may be a machine learning model, or may be obtained through model training, such as: and taking the physiological parameters of the user and the voice data of the user as inputs, and obtaining a third age detection model by taking the age bracket of the user as output training. The specific algorithm employed by the third age detection model is not limited by the present application.

It will be appreciated that the above embodiments are described with respect to the probability that the first age detection model, the second age detection model, and the third age detection model output the respective age groups. Alternatively, one or more of the first age detection model, the second age detection model, the third age detection model, and the like may also directly output the finally determined age group, which is not limited in the present application.

Optionally, in the embodiment of the present application, the first age detection model, the second age detection model, the third age detection model and the like may be preset in the wearable device, or may be preset in other devices (such as electronic devices), which is not limited in particular. In the case that the age detection model is preset in other devices, the wearable device may send the acquired physiological parameters of the user and/or the sound data of the user to the other devices, and the other devices determine the age group of the user. Optionally, the wearable device may also obtain the determined age bracket of the user from the other device.

In the above embodiments, the wearable device (or other device) determines the age bracket of the user through a machine learning model. In other embodiments, the wearable device (or other device) may also determine the age bracket of the user through preset rules. Such as: the range of physiological parameters corresponding to different age groups is different, and the characteristics of the corresponding sound data are also different. Taking the wearable device as an example, the wearable device can determine the age bracket of the user according to the acquired physiological parameters of the user and the preset range of the physiological parameters corresponding to different age brackets. Or the wearable device can determine the age of the user according to the acquired sound data of the user and the characteristics of the sound data corresponding to the preset different ages. Or the wearable device can determine the age of the user according to the acquired physiological parameters of the user, the voice data of the user, the range of the physiological parameters corresponding to the preset different age groups, and the characteristics of the voice data corresponding to the preset different age groups.

Based on the technical scheme, in the embodiment of the application, the age bracket of the user is determined by identifying the acquired physiological parameters of the user and/or the voice data of the user, so that the age bracket of the user can be intelligently predicted, manual input of the user is not needed, and the accuracy of the acquired age can be improved.

In the following, taking the wearable device 100 as a smart watch and the electronic device 200 as a mobile phone as an example, the age detection method provided by the embodiment of the present application is described in detail with reference to specific scenarios.

In one possible scenario, the smart watch may automatically turn on the age detection function. Such as: in the case that the smart watch is in a power-on state, the smart watch may perform a process of acquiring a physiological parameter of a user and/or sound data of the user, and determining an age group of the user according to the physiological parameter of the user and/or the sound data of the user. Optionally, the smart watch may further perform the foregoing process of determining if it is determined that it is currently in a worn state. Whereas the operation of the smart watch to determine whether to be in a worn state is known in the art, the specific implementation may refer to the description in the related art, and will not be repeated herein.

In another possible scenario, the user is required to actively turn on the age detection function of the smart watch. For example: an instruction of a user for starting an age detection function of the smart watch is detected, and in response to the instruction, the smart watch performs a process of acquiring a physiological parameter of the user and/or sound data of the user and determining an age group of the user according to the physiological parameter of the user and/or the sound data of the user. Alternatively, the user may wake up the age detection function of the smart watch in various ways, such as by voice, gestures, keys, shortcut buttons, etc.

In some embodiments, the user may turn on the age detection function of the smart watch through the smart watch itself. Illustratively, as shown in fig. 5 (1), the smart watch displays a main interface 500, and various applications are included in the main interface 500, and different applications may be used to implement different functions. The health application 501 is included, and the health application 501 may be used to perform health detection on a user, for example, to detect whether the user is in a sub-health state, has some diseases, is likely to have some diseases, and the like. The smart watch detects a user's start operation of the health application 501, such as: a click operation such as a user's icon of the health application 501 is detected, and in response to the operation, the smart watch launches the health application 501. For example, as shown in fig. 5 (2), the smart watch may display the running interface 510 of the health application 501. A button 511 for activating the age detection function of the smart watch is included in the operation interface 510. The smart watch detects a click operation such as a user on the button 511, and in response to the operation, the smart watch starts to execute a process of determining the age group of the user. Optionally, the result of the last health test and/or the test time may also be included in the running interface 510.

In other embodiments, the user may also turn on the age detection function of the smart watch through other devices (e.g., a cell phone). As shown in fig. 6 (1), the mobile phone displays a main interface 600, wherein one or more application programs are included in the main interface 600, and the health application 601 is included, and the description of the health application 601 can refer to the related description of the health application 501 shown in fig. 5 (1). The handset detects an operation by the user to launch the health application 601, for example: a click operation such as a user's icon of the health application 601 is detected, and in response to the operation, the mobile phone starts the health application 601. In some embodiments, as shown in fig. 6 (2), the handset may display the running interface 610 of the health application 601. It is to be appreciated that the running interface 610 can be a main interface of the health application 601, a sub-interface, etc., and the application is not limited in this regard. A button 611 for activating an age detection function of the smart watch is included in the operation interface 610. The mobile phone detects an operation for starting the age detection function of the smart watch, such as a click of the button 611 by the user, and in response to the operation, the mobile phone transmits an instruction message to the smart watch having the communication connection established, the instruction message being for instructing the smart watch to start the age detection function, and the smart watch starts the process of determining the age group of the user after receiving the instruction message transmitted by the mobile phone.

In some embodiments, after the smart watch turns on the age detection function, the smart watch may detect physiological parameters of the user and/or sound data of the user in real-time or periodically. In other embodiments, after the smart watch turns on the age detection function, the smart watch begins to detect the physiological parameters of the user and/or the sound data of the user in real-time or periodically only at a specific time or under a specific scene. Such as: considering that the state of the user (such as a static state, a motion state, an active state and the like) may have a certain influence on the physiological parameter, the smart watch can detect the physiological parameter of the user and/or the sound data of the user at night (such as overnight), and the accuracy of the detected physiological parameter of the user is higher because the user is in the static state at night. Or, the smart watch detects physiological parameters of the user and/or sound data of the user again under the condition that the user is in a static state in daytime.

Optionally, the smart watch may further determine the age group of the user according to the physiological parameters of the user and/or the sound data of the user when the obtained physiological parameters of the user and/or the sound data of the user satisfy preset conditions (for example, preset duration, preset number, etc.). It can be understood that, because the physiological parameters and the sound data of the user may have fluctuation, the physiological parameters and/or the sound data of the preset conditions are obtained, so that the reliability of the physiological parameters and the sound data is higher, and the accuracy of the measured age bracket can be improved when the age bracket of the user is determined according to the data.

Optionally, after the smart watch turns on (including automatically turning on and/or the user turns on actively) the age detection function once, the process of determining the age group of the user may be performed periodically or aperiodically multiple times, i.e., the smart watch may continuously perform the process of determining the age group of the user. Alternatively, to reduce the power consumption of the smart watch, the smart watch may perform the process of determining the age group of the user only a limited number of times (e.g., 1 time, 2 times, etc.) after turning on the age detection function once, and the specific number of times may be set by the developer according to the actual requirement.

In some possible scenarios, after the smart watch turns on the age detection function, the smart watch may start to automatically acquire the sound data of the user, but in the process that the smart watch detects the sound data of the user, the user may not make any sound, and in this case, the user may be prompted to input the sound data by outputting a reminder message (such as a display screen displaying a reminder message, a speaker broadcasting a reminder message, etc.). Optionally, the reminding message may be output by the smart watch itself, may be output by the mobile phone, or may be output by both the smart watch and the mobile phone, which is not limited in this aspect of the application.

In other possible scenarios, after the smart watch turns on the age detection function, the user is required to actively input own sound data, and in response to the user inputting the sound data, the smart watch may acquire the sound data of the user, for example: the smart watch records the user's voice data by activating a microphone. Optionally, the smart watch and/or the mobile phone may also output a reminder message to remind the user to actively input own voice data. Therefore, after detecting the operation of inputting sound data by the user, the smart watch activates the sensor such as the microphone to record the sound data of the user, and the sensor for recording the sound data of the user can be prevented from being always turned on, so that the power consumption of the wearable device is reduced.

Optionally, the recording of the voice data of the user may be completed by the smart watch, or the recording of the voice data of the user may be completed by the mobile phone, which is not limited in the present application.

Taking sound data as a cough sound, as shown in fig. 7 (1) (i.e., a target interface), the smart watch may display "record of cough sound by long pressing down button-! And the like, so that the user can record the cough through the intelligent watch. As shown in (2) of fig. 7, the smart watch detects an operation (i.e., a target operation) such as a long press of the cough tone recording button 701 by the user, and in response to the operation, the smart watch starts acquiring the cough tone input by the user, for example: activating the microphone begins recording the user's cough tone, etc. Optionally, the smart watch may also display a progress bar 702, and a "recording" text prompt 703 and other various warm prompts to remind the user of the progress of the cough recording. Optionally, the smart watch may also alert the user to a successful or failed recording message. For example, as shown in FIG. 7 (3), the smart watch may display "cough sound recording was successful, age detection is ongoing-! "and the like, alerts the user to the successful recording. Optionally, the smart watch may also automatically hide the record button 701 after the cough sound is recorded successfully. Alternatively, as shown in fig. 7 (4), the smart watch may display "cough recording failed, please re-record-! "and the like, alerts the user to recording failure.

For example, taking sound data as a cough sound as an example, as shown in (1) of fig. 8, the mobile phone may display "the smart watch does not detect your cough sound currently, cannot complete age detection, please go to the smart watch side to complete recording of the cough sound-! And the like, and the user can be reminded of completing the recording of the cough sound through the intelligent watch according to the message. Alternatively, as shown in fig. 8 (2), the mobile phone may display "recording of your cough sound is not currently detected, age detection cannot be completed, please press down the button for a long time-! And the like, and reminds the user of completing the cough sound recording through the mobile phone. Alternatively, the handset detects a long press of, for example, the user cough sound recording button 801, and the handset acquires the cough sound input by the user. Optionally, the mobile phone may also display a progress bar 702, such as that shown in fig. 7 (2), a text prompt 703 that "recording" is taking place, to alert the user to the progress of the cough note recording, etc. Alternatively, the mobile phone may also use a form such as that shown in (3) and (4) in fig. 7 to remind the user that the cough sound recording is successful or that the cough sound recording is failed. Subsequently, the mobile phone can send the successfully recorded cough sound to the intelligent watch, so that the intelligent watch can conveniently complete detection of the age group.

Optionally, after the detection of the age group of the user is completed, the age group may also be output (e.g., displayed and/or voice broadcast) by the smart watch or the mobile phone to inform the user of the age group.

In a specific embodiment, the physiological parameter includes body temperature and the sound data includes a cough sound. Taking the operation of determining the age group of the user performed by the smart watch as an example, the smart watch can also judge whether the body temperature is normal or not after acquiring the body temperature of the user, and under the condition that the body temperature is abnormal, the smart watch can also output target information (such as displaying the target information and/or voice broadcasting target information and the like) so as to remind the user that the age group obtained by current measurement is inaccurate. Optionally, the user can be reminded of the reasons for inaccurate age groups obtained by measurement. Such as: alerting the user that the measured age group is likely to be inaccurate due to the occurrence of the target type of disease. Wherein the target type of disease refers to a disease with symptoms such as fever, cough, etc., such as: cold, fever, etc. Of course, the smart watch may also send the target information to the mobile phone, and the mobile phone outputs the target information, or the smart watch and the mobile phone may also output the target information simultaneously.

Optionally, the smart watch and/or the mobile phone may output the target information when outputting the age bracket of the user, or may output the target information at other occasions, which is not limited in the present application.

Similarly, if the mobile phone performs the operation of determining the age group of the user, the mobile phone may also perform the operation of determining whether the body temperature of the user is normal, and outputting the target information in case of abnormality. Of course, the mobile phone may also send the target information to the smart watch, and the smart watch outputs the target information, or the smart watch and the mobile phone may also output the target information at the same time, which is not limited in the present application.

The measured body temperature of the user may be inaccurate when the user suffers from a disease such as cold, fever, etc., which affects the body temperature of the user, and thus the measured age group of the user may be inaccurate when the age group of the user is determined according to the inaccurate body temperature. Therefore, the user is reminded that the age measurement is inaccurate under the condition, the user knows details, the age detection can be selectively carried out when the body temperature is normal, the age detection error can be avoided, and the accuracy of the measured age is improved.

In some embodiments, the user may also enter age through the smartwatch and/or cell phone.

By way of example, a schematic diagram of a user entering age through a smart watch is shown in fig. 9 (1). For example: the user may obtain the age of the user input through an application installed in the smart watch, such as the health application 501 shown in fig. 5 (1). Illustratively, as shown in (1) of fig. 9, the wearable device may display a personal information interface 900 in the health application 901, include an age option 901 in the personal information interface 900, and the user may input the age through the age option 901. Optionally, other information may also be included in personal information interface 900, such as, but not limited to: sex, height, etc.

By way of example, fig. 9 (2) shows a schematic diagram of a user inputting age through a mobile phone, for example: the user can acquire the age of the user input through an application installed in the cellular phone, such as the health application 601 shown in fig. 6 (1), and the like. Illustratively, as shown in (2) of fig. 9, the handset may display a personal information interface 910 of the health application 901, including an age option 911 in the personal information interface 910, through which the user may input the age. Optionally, other information may be included in the personal information interface 911, such as: including but not limited to gender, height, weight, date of birth, etc.

In some embodiments, the smart watch and/or the mobile phone may further determine whether the ages entered by the user are consistent according to the determined age group, such as: it is determined whether the age entered by the user is between the determined age groups. It will be appreciated that the determined age range may be determined by the smart watch itself or by the mobile phone, and the specific description may be referred to above. The age input by the user can be input by the user through the smart watch or input by the user through the mobile phone. If the input age is inconsistent, the intelligent watch and/or the mobile phone can display a reminding message to prompt the user to confirm whether the input age is correct. Therefore, by judging whether the predicted age bracket of the user is matched with the age input by the user or not, and under the condition of inconsistent, the user can be reminded to confirm, the correct age bracket is finally obtained, and the accuracy of the obtained age can be further improved. Subsequently, when the age bracket is used for health detection of the user, the problem of health detection errors caused by inconsistent age input by the user and age of a wearer of the wearable device can be solved.

For example, in a scenario where the user inputs an age through the smartwatch, as shown in (1) of fig. 10, the smartwatch may display a reminder interface 1000, where "your current input age is 48 years old, please confirm whether it is correct? "or the like for a user to confirm whether the age entered in the smart watch is correct. Optionally, the alert interface 1000 may further include a correct button 1001 and/or an incorrect button 1002, etc. to facilitate the user performing the confirmation operation. Optionally, the smart watch detects a click operation such as the user on the correct button 1001, and in response to this operation, the smart watch determines that the age of the user input is correct. Alternatively, the smart watch detects a click operation such as the user on the incorrect button 1002, in response to which the smart watch determines that the age entered by the user is incorrect, and illustratively, as shown in fig. 10 (2), the smart watch displays an age input interface 1010 in which an age input option 1011 is included in the input interface 1010, which is available for the user to re-enter the correct age. The age input interface 1010 may be, for example, the personal information interface 900 shown in fig. 9 (1), or may be another new interface, which is not limited by the present application. Optionally, a reminder message 1012 may also be included in the age input interface 1010 for the user to input the correct age based on the reminder message 1012.

As shown in fig. 11 (1), the mobile phone may display a reminder interface 1100, where "your current age input on the smart watch side is 48 years old, please confirm whether it is correct? "or the like for a user to confirm whether the age entered in the smart watch is correct. Optionally, a correct button 1101 and/or an incorrect button 1102 may also be included in the alert interface 1100 to facilitate user confirmation operations. For example, the mobile phone detects a click operation such as the user on the incorrect button 1102, and in response to the operation, the mobile phone confirms that the age input by the user on the smart watch side is incorrect. Optionally, as shown in fig. 11 (2), the mobile phone may further display a message such as "please go to enter the correct age to the smart band side", and the user may re-enter the correct age through the smart band according to the prompt, for example: the user may reenter the correct age through a health application 501 such as that shown in fig. 5 (1).

In a scenario where the user inputs the age through the mobile phone, as shown in (1) of fig. 12, the smart watch may display a reminder interface 1200, where "you input the age 48 years at the mobile phone side, please confirm whether it is correct? "or the like for a user to confirm whether or not the age entered at the handset side is correct. Optionally, alert interface 1200 may also include correct buttons 1201 and/or incorrect buttons 1202, etc. Such as: the smart watch detects a click operation such as the user on the correct button 1201, and in response to the operation, the smart watch determines that the age of the user input on the handset side is correct. Alternatively, the smart watch detects a click operation such as the user's click on the incorrect button 1202, and in response to the operation, the smart watch determines that the age input by the user on the mobile phone side is incorrect, and optionally, as shown in (2) in fig. 12, the smart watch may further display a message such as "please go to the mobile phone side to input the correct age", and the user may re-input the correct age on the mobile phone side according to a prompt, for example: the user may reenter the correct age through a health application 601 such as that shown in fig. 6 (1).

As shown in fig. 13, the mobile phone may alert interface 1300, where in alert interface 13 may include, for example, "your current input is 48 years old, please confirm whether it is correct? "or the like for a user to confirm whether or not the age entered at the handset side is correct. Optionally, a correct button 1301 and/or an incorrect button 1302 may also be included in the alert interface 1300 to facilitate user confirmation operations. For example: the handset detects a click operation such as the user on the incorrect button 1302, and in response to the operation, the handset confirms that the age of the user input is incorrect. Optionally, the phone may also display an age input interface to facilitate the user to re-enter the correct age. The age input interface may be in the form of a personal information interface 910 such as that shown in fig. 9 (2), or in the form of a new interface such as that shown in fig. 10 (2), to which the present application is not limited.

It can be understood that the reminding message in the embodiment of the application can be presented in a form of a floating window or a brand new interface, and the application is not particularly limited to this.

In some embodiments, it is contemplated that the frequency with which the wearable device and/or the handset display alert messages may affect the user experience. Therefore, in the case that the determined age group is inconsistent with the age input by the user, in order to avoid frequent reminding of the smart watch and/or the mobile phone, bad experience is brought to the user, before reminding the user to confirm whether the input age is correct, the smart watch and/or the mobile phone can determine whether the user is to be reminded to confirm whether the input age is correct or not according to the time of last reminding the user. Such as: if the smart watch and/or the mobile phone determines that the difference between the current time and the time of last reminding the user meets a preset condition (such as 3 days, 1 week and the like), or the time of last reminding the user meets a preset condition (such as 3 days before, 1 week and the like), the smart watch and/or the mobile phone determines that the user is not reminded currently. Otherwise, the smart watch and/or the mobile phone can remind the user to confirm whether the input age is correct.

It can be appreciated that in the embodiment of the present application, the holders of the smart watch and the mobile phone may be the same user, or may be different users, for example: in the scene of monitoring the old and child, the holder of the smart watch can be the old and child, the holder of the mobile phone can be the guardian, and the like, and in the scene, the guardian can input the ages of the old and child, and the like, through the mobile phone.

An application scenario of the determined age group of the user, or the age input by the user is given below.

It will be appreciated that the type of disease, risk of disease, etc. that may occur may be different for users of different ages. Therefore, the smart watch can also utilize the determined age group, or the age input by the user, to perform health detection on the user.

In some embodiments, the smart watch may input the determined age group or the age input by the user, the physiological parameter of the user, the sound data of the user, etc. into a preset health detection model, and output the health condition of the user (for example, a possible disease, a probability of occurrence of a certain disease, etc.) through the health detection model.

Optionally, in the process of performing health detection, the physiological parameters of the user acquired by the smart watch may be the same or different from the physiological parameters of the user acquired by the smart watch in the process of determining the age group of the user, and the sound data of the user acquired by the smart watch may be the same or different from the sound data of the user acquired by the smart watch in the process of determining the age group of the user.

Alternatively, for different age groups, it may correspond to different health detection models, such as: taking age groups including young people and middle-aged and elderly people as examples, the health detection model corresponding to the young people is a young people model (namely a first preset model), and the health detection model corresponding to the middle-aged and elderly people is a middle-aged and elderly people model (namely a second preset model). If the wearable device determines that the age group of the user is young, or determines that the age group of the user is young according to the age input by the user, the wearable device may perform health detection on the user by using a young person model. If the wearable device determines that the age group of the user is middle-aged and elderly people or the age group of the user is terminal-aged according to the age input by the user, the wearable device can utilize the elderly model to perform health detection on the user. Because physiological parameters, sound data and the like of users in different age groups are different, the health detection of the users in different age groups is performed by adopting different health detection models, and the accuracy of the health detection can be improved.

Alternatively, the health detection model may be a machine learning model, which may be obtained by taking age, physiological parameters, voice data of a user, and the like as input, and possibly occurring diseases, probability of occurrence of diseases, and the like as output training, and the application is not limited to the algorithm adopted by the health detection model.

Alternatively, the health detection model may be preset in the smart watch or other devices (such as a mobile phone).

Optionally, after determining the health condition of the user, the wearable device and/or the mobile phone may also output a prompt message (such as a display screen, a speaker, etc.) to remind the user. For example, as shown in the alert interface 1400 of fig. 14, the smart watch may display an alert message such as "lung infection is at high risk, suspected pneumonia" for informing a possible illness and a risk of the illness, etc., so that the user can know his or her health condition. Optionally, the alert interface 1400 may further include various types of messages such as a detection time 1401, a warmth prompt 1402, where the detection time 1401 may be the last time the health detection was performed, and the warmth prompt 1402 may be used to alert the user to make a timely visit, view health details, and so on. Optionally, a start measurement button 1403 may be included in the alert interface 1400, and the smart band monitors for a click operation on the start measurement button 1403 by the user, for example, and in response to this operation, the smart band starts executing a process of health detection on the user.

Similar to the smart watch, the handset may also display a reminder interface 1400, such as that shown in fig. 14, to remind the user of his own physical health condition, or to remind the guardian of the physical health condition of the guardian (e.g., parent, child), etc.

It will be appreciated that the age range determined by the embodiments of the present application, or the age entered by the user, may also be used for other purposes, and the present application is not limited thereto.

It should be noted that, in the embodiment of the present application, each interface is only a schematic diagram, and does not constitute a limitation of the present application, and in practical application, each interface may include more or less contents, and may include more or less interfaces.

The foregoing description of the solution provided by the embodiments of the present application has been mainly presented in terms of a method. It will be appreciated that the wearable device, in order to implement the above-described functions, includes corresponding hardware structures and/or software modules that perform the respective functions. The various illustrative units and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or a combination of hardware and computer software. Whether a function is implemented as hardware or computer-driven hardware depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not to be considered as beyond the scope of the embodiments of the present application.

The embodiment of the application can divide the functional modules of the wearable device according to the method example, for example, each functional module can be divided corresponding to each function, and two or more functions can be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.

As shown in fig. 15, a schematic structural diagram of a wearable device 1500 provided in an embodiment of the present application is shown, where the wearable device 1500 may be used to implement the methods described in the above method embodiments. By way of example, the wearable device 1500 may specifically include: a processing unit 1501, an acquisition unit 1502.

Wherein, the acquisition unit 1502 is configured to acquire physiological parameters and sound data of a user. A processing unit 1501 for determining the age bracket of the user based on the physiological parameters and the sound data.

In one possible design, the display unit 1503 is also included; a display unit 1503 for displaying a target interface; a processing unit 1501 for receiving a target operation of a user; the processing unit 1501 is further configured to activate the microphone to record sound data of the user in response to the target operation.

In one possible design, the device further comprises a reminding unit 1504; physiological parameters include body temperature, sound data including cough sounds; after the acquiring unit 1502 is configured to acquire the physiological parameter and the sound data of the user, the reminding unit 1504 is configured to output target information (e.g. display target information and/or voice broadcast target information, etc.) when the body temperature of the user is abnormal, where the target information is used to remind the user that the age group obtained by the current measurement is inaccurate.

In one possible design, the age group includes a first age group and a second age group; the processing unit 1501 is further configured to determine a first probability of the first age group and a second probability of the second age group according to the physiological parameter; the processing unit 1501 is further configured to determine a third probability of the first age group and a fourth probability of the second age group according to the sound data; the processing unit 1501 is further configured to determine an age group of the user according to the first probability and the third probability; or the processing unit 1501 is further configured to determine an age bracket of the user according to the second probability and the fourth probability.

In one possible design, the processing unit 1501 is further configured to determine that the age group of the user is the first age group if the first probability is greater than or equal to the first preset threshold, or the third probability is greater than or equal to the second preset threshold, or the first probability is greater than or equal to the third preset threshold and less than the first preset threshold, and the third probability is greater than or equal to the fourth preset threshold and less than the second preset threshold. The processing unit 1501 is further configured to determine that the age group of the user is the second age group if the first probability is greater than or equal to a third preset threshold and less than the first preset threshold and the third probability is less than a fourth preset threshold, or the first probability is less than the third preset threshold and the third probability is less than the second preset threshold.

In a possible design, the processing unit 1501 is further configured to determine that the age group of the user is the second age group if the second probability is greater than or equal to the first preset threshold, or the fourth probability is greater than or equal to the second preset threshold, or the second probability is greater than or equal to the third preset threshold and less than the first preset threshold, and the fourth probability is greater than or equal to the fourth preset threshold and less than the second preset threshold; the processing unit 1501 is further configured to determine that the age group of the user is the first age group if the second probability is greater than or equal to the third preset threshold and less than the first preset threshold and the fourth probability is less than the fourth preset threshold, or if the second probability is less than the third preset threshold and the fourth probability is less than the second preset threshold.

In one possible design, the obtaining unit 1502 is further configured to obtain an age input by a user; the reminding unit 1504 is further configured to remind the user to confirm whether the input age is correct if the age does not match the determined age group of the user.

In a possible design, the processing unit 1501 is further configured to determine whether the last time the user was reminded to confirm that the input age is correct meets the preset condition.

In one possible design, the processing unit 1501 is further configured to perform health detection on the user by using a first preset model if it is determined that the age group of the user is the first age group; the processing unit 1501 is further configured to perform health detection on the user by using a second preset model if it is determined that the age bracket of the user is the second age bracket.

Optionally, the processing unit 1501, the obtaining unit 1502, the display unit 1503, and the reminding unit 1504 are further configured to support the wearable device 1500 to perform other steps executed by the wearable device in the embodiment of the present application.

Optionally, the wearable device 1500 shown in fig. 15 may further include a storage unit (not shown in fig. 15) storing a program or instructions. When executed by the processing unit 1501, causes the wearable device 1500 shown in fig. 15 to perform the method shown in the embodiment of the present application.

Optionally, the wearable device 1500 shown in fig. 15 may further include a communication unit (not shown in fig. 15) for supporting the wearable device 1500 to perform the step of communicating between the wearable device and other devices in the embodiment of the present application.

Technical effects of the wearable device 1500 shown in fig. 15 may be the technical effects of the above-described method embodiments, which are not described herein. The processing unit 1501 involved in the wearable device 1500 shown in fig. 15 may be implemented by a processor or processor-related circuit components, which may be a processor or a processing module. The communication unit may be implemented by a transceiver or transceiver-related circuit component, and may be a transceiver or transceiver module. The acquisition unit 1502 may be implemented by a sensor or sensor related circuit component and/or by a transceiver or transceiver related circuit component. The display unit 1503 may be implemented by a display screen-related component. The reminder unit 1504 may be implemented by a display-related component, a microphone-related component, or the like.

Embodiments of the present application also provide a chip system, as shown in fig. 16, which includes at least one processor 1601 and at least one interface circuit 1602. The processor 1601 and the interface circuit 1602 may be interconnected by wires. For example, interface circuit 1602 may be used to receive signals from other devices. For another example, interface circuit 1602 may be used to send signals to other devices (e.g., processor 1601). For example, the interface circuit 1602 may read instructions stored in a memory and send the instructions to the processor 1601. The instructions, when executed by the processor 1601, may cause the wearable device to perform the various steps performed by the wearable device in the embodiments described above. Of course, the system-on-chip may also include other discrete devices, which are not particularly limited in accordance with embodiments of the present application.

Alternatively, the processor in the system-on-chip may be one or more. The processor may be implemented in hardware or in software. When implemented in hardware, the processor may be a logic circuit, an integrated circuit, or the like. When implemented in software, the processor may be a general purpose processor, implemented by reading software code stored in a memory.

Alternatively, the memory in the system-on-chip may be one or more. The memory may be integral with the processor or separate from the processor, and the application is not limited. The memory may be a non-transitory processor, such as a ROM, which may be integrated on the same chip as the processor, or may be separately provided on different chips, and the type of memory and the manner of providing the memory and the processor are not particularly limited in the present application.

The system-on-chip may be, for example, a field programmable gate array (field programmable gate array, FPGA), an application specific integrated chip (application specific integrated circuit, ASIC), a system on chip (SoC), a central processing unit (central processor unit, CPU), a network processor (network processor, NP), a digital signal processing circuit (digital signal processor, DSP), a microcontroller (micro controller unit, MCU), a programmable controller (programmable logic device, PLD) or other integrated chip.

It should be understood that the steps in the above-described method embodiments may be accomplished by integrated logic circuitry in hardware in a processor or instructions in the form of software. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution.

The embodiment of the application also provides a computer storage medium, in which computer instructions are stored, which when run on a computer, cause the computer to perform the method described in the above method embodiment.

An embodiment of the present application provides a computer program product comprising: computer program or instructions which, when run on a computer, cause the computer to perform the method described in the method embodiments described above.

An embodiment of the present application provides a circuit system, where the circuit system includes a processing circuit configured to perform the method described in the above method embodiment.

In addition, the embodiment of the application also provides a device, which can be a chip, a component or a module, and can comprise a processor and a memory which are connected; the memory is configured to store computer-executable instructions, and when the apparatus is running, the processor may execute the computer-executable instructions stored in the memory, so that the apparatus performs the methods in the method embodiments described above.

The wearable device, the computer storage medium, the computer program product, the circuit system, the chip or the apparatus provided in this embodiment are used to execute the corresponding method provided above, so that the beneficial effects achieved by the wearable device, the computer storage medium, the computer program product, the circuit system, the chip or the apparatus can refer to the beneficial effects in the corresponding method provided above, and are not repeated herein.

It will be appreciated by those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The embodiments may be combined or referenced to each other without conflict. The above-described apparatus embodiments are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another apparatus, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. An age detection method, applied to a wearable device, the method comprising:

acquiring physiological parameters and sound data of a user;

and determining the age bracket of the user according to the physiological parameter and the sound data.

2. The method of claim 1, wherein obtaining sound data of the user comprises:

displaying a target interface;

receiving a target operation of the user;

and in response to the target operation, activating a microphone to record sound data of the user.

3. The method according to claim 1 or 2, wherein the period of time for obtaining the physiological parameter of the user is an overnight time.

4. A method according to any one of claims 1-3, wherein the physiological parameter comprises body temperature and the sound data comprises a cough sound;

After the acquiring the physiological parameters and the sound data of the user, the method further comprises:

and under the condition that the body temperature of the user is abnormal, outputting target information, wherein the target information is used for reminding the user that the age bracket obtained by current measurement is inaccurate.

5. The method of any one of claims 1-4, wherein the age group comprises a first age group and a second age group;

the determining of the age bracket of the user from the physiological parameter and the sound data comprises:

determining a first probability of the first age group and a second probability of the second age group according to the physiological parameter;

determining a third probability of the first age group and a fourth probability of the second age group according to the sound data;

determining an age group of the user according to the first probability and the third probability; or determining the age group of the user according to the second probability and the fourth probability.

6. The method of claim 5, wherein determining the age group of the user based on the first probability and the third probability comprises:

if the first probability is greater than or equal to a first preset threshold, or the third probability is greater than or equal to a second preset threshold, or the first probability is greater than or equal to a third preset threshold and less than the first preset threshold and the third probability is greater than or equal to a fourth preset threshold and less than the second preset threshold, determining that the age group of the user is the first age group;

And if the first probability is greater than or equal to the third preset threshold and less than the first preset threshold and the third probability is less than the fourth preset threshold, or the first probability is less than the third preset threshold and the third probability is less than the second preset threshold, determining that the age group of the user is the second age group.

7. The method according to claim 5 or 6, wherein determining the age group of the user based on the second probability and the fourth probability comprises:

if the second probability is greater than or equal to a first preset threshold, or the fourth probability is greater than or equal to a second preset threshold, or the second probability is greater than or equal to a third preset threshold and less than the first preset threshold and the fourth probability is greater than or equal to a fourth preset threshold and less than the second preset threshold, determining that the age group of the user is the second age group;

and if the second probability is greater than or equal to the third preset threshold and less than the first preset threshold and the fourth probability is less than the fourth preset threshold, or the second probability is less than the third preset threshold and the fourth probability is less than the second preset threshold, determining that the age group of the user is the first age group.

8. The method according to any one of claims 1-7, wherein after said determining an age group of the user from the physiological parameter and the sound data, the method further comprises:

acquiring the age input by the user;

and prompting the user to confirm whether the input age is correct or not under the condition that the age is not matched with the determined age range of the user.

9. The method of claim 8, wherein prior to said prompting the user to confirm whether the entered age is correct, the method further comprises:

and determining whether the last time reminding the user of confirming whether the input age is correct or not meets the preset condition.

10. The method according to any one of claims 5-9, further comprising:

if the age bracket of the user is determined to be the first age bracket, adopting a first preset model to carry out health detection on the user;

and if the age group of the user is determined to be the second age group, adopting a second preset model to carry out health detection on the user.

11. A method according to any one of claims 1-3, wherein the physiological parameter comprises at least one or more of: heart rate, heart rate variability HRV, respiration rate, blood oxygen, and body temperature.

12. A method according to any one of claims 1-3, wherein the sound data comprises at least one or more of: cough, "a" sound, blow, voice.

13. A wearable device, comprising an acquisition unit and a processing unit;

the acquisition unit is used for acquiring physiological parameters and sound data of a user;

the processing unit is used for determining the age bracket of the user according to the physiological parameter and the sound data.

14. The wearable device of claim 13, further comprising a display unit;

the display unit is used for displaying a target interface;

the processing unit is further used for receiving target operation of the user;

the processing unit is further used for responding to the target operation and activating a microphone to record the voice data of the user.

15. The wearable device of claim 13 or 14, wherein the period of time to obtain the physiological parameter of the user is an overnight time.

16. The wearable device of any of claims 13-15, further comprising a reminder unit; the physiological parameter includes body temperature, and the sound data includes a cough sound;

After the acquisition unit is used for acquiring the physiological parameters and the sound data of the user, the reminding unit is used for outputting target information under the condition that the body temperature of the user is abnormal, and the target information is used for reminding the user that the age bracket obtained by current measurement is inaccurate.

17. The wearable device of any of claims 13-16, wherein the age brackets comprise a first age bracket and a second age bracket;

the processing unit is further used for determining a first probability of the first age group and a second probability of the second age group according to the physiological parameter;

the processing unit is further used for determining a third probability of the first age group and a fourth probability of the second age group according to the sound data;

the processing unit is further configured to determine an age group of the user according to the first probability and the third probability; or the processing unit is further configured to determine an age group of the user according to the second probability and the fourth probability.

18. The wearable device according to claim 17, wherein,

the processing unit is further configured to determine that the age group of the user is the first age group if the first probability is greater than or equal to a first preset threshold, or the third probability is greater than or equal to a second preset threshold, or the first probability is greater than or equal to a third preset threshold and less than the first preset threshold and the third probability is greater than or equal to a fourth preset threshold and less than the second preset threshold;

The processing unit is further configured to determine that the age group of the user is the second age group if the first probability is greater than or equal to the third preset threshold and less than the first preset threshold and the third probability is less than the fourth preset threshold, or the first probability is less than the third preset threshold and the third probability is less than the second preset threshold.

19. The wearable device according to claim 17 or 18, characterized in that,

the processing unit is further configured to determine that the age group of the user is the second age group if the second probability is greater than or equal to a first preset threshold, or the fourth probability is greater than or equal to a second preset threshold, or the second probability is greater than or equal to a third preset threshold and less than the first preset threshold, and the fourth probability is greater than or equal to a fourth preset threshold and less than the second preset threshold;

the processing unit is further configured to determine that the age group of the user is the first age group if the second probability is greater than or equal to the third preset threshold and less than the first preset threshold and the fourth probability is less than the fourth preset threshold, or if the second probability is less than the third preset threshold and the fourth probability is less than the second preset threshold.

20. The wearable device according to any of the claims 13-19, characterized in that,

the acquisition unit is also used for acquiring the age input by the user;

the reminding unit is further used for reminding the user of confirming whether the input age is correct or not under the condition that the age is not matched with the determined age bracket of the user.

21. The wearable device according to claim 20, wherein,

the processing unit is further configured to determine that a time for reminding the user of whether the input age is correct last time meets a preset condition.

22. The wearable device according to any of the claims 17-21, characterized in that,

the processing unit is further configured to perform health detection on the user by using a first preset model if it is determined that the age bracket of the user is the first age bracket;

and the processing unit is further configured to perform health detection on the user by adopting a second preset model if the age bracket of the user is determined to be the second age bracket.

23. The wearable device according to any of claims 13-15, wherein the physiological parameter comprises at least one or more of: heart rate, heart rate variability HRV, respiration rate, blood oxygen, and temperature.

24. The wearable device of any of claims 13-15, wherein the sound data includes at least one or more of: cough, "a" sound, blow, voice.

25. A wearable device, comprising: comprising a processor, a memory, a sensor, and a display screen, the memory, the sensor, the display screen being coupled to the processor, the memory for storing computer program code, the computer program code comprising computer instructions, the processor reading the computer instructions from the memory to cause the wearable device to perform the method of any of claims 1-12.

26. A computer readable storage medium, characterized in that the computer readable storage medium comprises a computer program or instructions which, when run on a wearable device, cause the wearable device to perform the method of any of claims 1-12.

27. A computer program product, characterized in that the computer program product, when run on a computer, is enabled to carry out the method according to any of claims 1-12.