WO2023197957A1 - 年龄检测方法及可穿戴设备 - Google Patents

年龄检测方法及可穿戴设备 Download PDF

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Publication number
WO2023197957A1
WO2023197957A1 PCT/CN2023/086997 CN2023086997W WO2023197957A1 WO 2023197957 A1 WO2023197957 A1 WO 2023197957A1 CN 2023086997 W CN2023086997 W CN 2023086997W WO 2023197957 A1 WO2023197957 A1 WO 2023197957A1
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WO
WIPO (PCT)
Prior art keywords
user
probability
age
age group
preset threshold
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Application number
PCT/CN2023/086997
Other languages
English (en)
French (fr)
Inventor
许德省
李靖
许培达
叶际隆
陈文娟
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from CN202210469077.8A external-priority patent/CN116942111A/zh
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Publication of WO2023197957A1 publication Critical patent/WO2023197957A1/zh

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition

Definitions

  • the present application relates to the field of terminal technology, and in particular to an age detection method and a wearable device.
  • Respiratory disease is a common and frequently-occurring disease with complex types and high fatality rate. It occurs more often in children and the elderly.
  • Arteriosclerosis is a non-inflammatory disease of the arteries that usually occurs in adolescence and worsens in middle-aged and elderly people. It can be seen that the occurrence of such diseases is closely related to the user's age. Therefore, by combining the user's age, the user's health (such as possible diseases, etc.) is detected and timely warning is played to ensure the user's life and health. of great significance.
  • This application provides an age detection method and wearable device, which can improve the accuracy of the obtained age.
  • this application provides an age detection method applied to wearable devices.
  • the method includes: obtaining the user's physiological parameters and voice data; and determining the user's age group based on the physiological parameters and voice data.
  • the physiological parameters are different, and the sound data are also different. Therefore, based on the above technical solution, the user's physiological parameters and voice data are comprehensively considered, and the user's age group is determined based on the physiological parameters and voice data.
  • the user's age group can be intelligently predicted without manual input by the user, which can improve the obtained age information. accuracy.
  • the types of diseases that may occur to users, the probability of certain diseases that may occur, etc. may be different. Therefore, subsequent use of the determined age group to conduct health testing on users can improve the accuracy of health testing.
  • obtaining the user's voice data includes: displaying the target interface; receiving the user's target operation; and in response to the target operation, activating the microphone to record the user's voice data.
  • the wearable device reminds the user to input voice data through the target interface. After detecting the user's operation of inputting voice data, the wearable device will activate the microphone and other sensors to record the user's voice data, which can avoid being used to record the user's voice data.
  • the sensors are always on, thereby reducing the power consumption of wearable devices.
  • the time period for obtaining the user's physiological parameters is all night. It can be understood that since the user is in different states, such as resting state, active state, sports state, etc., the user's physiological parameters may be different, and at night, the user is more likely to be in a still state, so the physiological parameters obtained by such measurement The accuracy of parameters is higher. Moreover, even if the user is in the same state, the physiological parameters measured at different times may fluctuate due to errors. In this way, obtaining physiological parameters throughout the night can make the obtained physiological parameters more reliable. The reference is high, and when using this physiological parameter to determine the user's age group, the accuracy of the determined age group can be further improved.
  • the physiological parameters include body temperature
  • the sound data includes cough sounds
  • the method also includes: when the user's body temperature is abnormal, output target information, and the target information is It reminds the user that the age group currently measured is inaccurate. Because when a user suffers from a cold, fever or other diseases that affect the user's body temperature, the measured user's body temperature may be inaccurate. This may also lead to the measurement being inaccurate when determining the user's age based on the inaccurate body temperature. The user's age group is incorrect. Therefore, by reminding the user that this situation may lead to inaccurate age measurement, so that the user can understand the details, the user can selectively perform age detection when the body temperature is normal, which can avoid age detection errors and improve the accuracy of the measured age. .
  • the age group of the user is determined based on the first probability and the third probability, including: 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 If the 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, then it is determined that the user's age group is the first 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, then the user's The age group is the second age group.
  • the age group of the user is determined based on the second probability and the fourth probability, including: 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 If the 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, it is determined that the user's age group is the second 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 the second probability is less than the third preset threshold and the fourth probability is less than the second preset threshold, then the user's The age group is the first age group.
  • the method after determining the user's age group based on physiological parameters and voice data, the method also includes: obtaining the age input by the user; and, if the age does not match the determined user's age group, reminding the user to confirm the input. Is the age correct? Based on this design, by judging whether the predicted age group of the user matches the age input by the user, and in case of inconsistency, the user can be reminded to confirm, and finally the correct age group can be obtained, which can further improve the accuracy of the obtained age. . Subsequently, when using this age group to perform health detection on users, it can also solve the problem of health detection errors caused by the inconsistency between the age entered by the user and the age of the wearer of the wearable device.
  • the method before reminding the user to confirm whether the entered age is correct, the method further includes: determining that the last time the user was reminded to confirm whether the entered age is correct satisfies a preset condition. Since the frequency of reminder messages output by wearable devices may affect user experience, based on this design, before outputting reminder messages, it is first determined whether the last time the user was reminded meets the preset conditions, and then the user is reminded when the preset conditions are met. In this way, it can be avoided that the wearable device frequently outputs reminder messages, resulting in poor user experience.
  • the method also includes: if it is determined that the user's age group is the first age group, then using the first preset model to perform health detection on the user; if it is determined that the user's age group is the second age group, then using The second preset model performs health detection on the user. Based on this design, since the physiological parameters, voice data, etc. of users of different age groups are different, using different health detection models for users of different age groups to perform health detection can improve the accuracy of health detection.
  • the physiological parameters include at least one or more of the following: heart rate, heart rate variability HRV, respiratory rate, blood oxygen, and body temperature.
  • the sound data includes at least one or more of the following: cough sounds, "a" sounds, blowing sounds, and speech.
  • the present application provides a wearable device that has the function of implementing the age detection method described in the above first aspect and any of the designs.
  • This function can be implemented by hardware, or can be implemented by hardware and corresponding software.
  • the hardware or software includes one or more modules corresponding to the above functions.
  • the wearable device includes an acquisition unit (or acquisition module) and a processing unit (processing module); the acquisition unit is used to acquire the user's physiological parameters and voice data; the processing unit is used to obtain the user's physiological parameters and voice data according to Physiological parameters and vocal data determine the user's age group.
  • One possible design also includes a display unit (or display module); the display unit is used to display the target interface; the processing unit is also used to receive the user's target operation; the processing unit is also used to respond to the target operation. Activate the microphone to record the user's voice data.
  • the time period for obtaining the user's physiological parameters is all night.
  • One possible design also includes a reminder unit; the physiological parameters include body temperature, and the sound data includes cough sounds; after the acquisition unit is used to obtain the user's physiological parameters and sound data, the reminder unit (or reminder module) is used to When the user's body temperature is abnormal, target information is output, and the target information is used to remind the user that the age group currently measured is inaccurate.
  • the reminder unit or reminder module
  • the age group includes a first age group and a second age group; the processing unit is also used to determine the first probability of the first age group and the second probability of the second age group based on the physiological parameters; the processing unit , is also used to determine the third probability of the first age group and the fourth probability of the second age group based on the sound data; the processing unit is also used to determine the age group of the user based on the first probability and the third probability; or the processing unit, It is also used to determine the age group of the user based on the second probability and the fourth probability.
  • the processing unit is also configured to: 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 If the first preset threshold and the third probability are greater than or equal to the fourth preset threshold and less than the second preset threshold, it is determined that the user's age group is the first age group.
  • the processing unit is also configured to: 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 determines that the user's age group is the second age group.
  • the processing unit is also configured to: 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 are greater than or equal to the fourth preset threshold and less than the second preset threshold, then determine the user's age group to be the second age group; the processing unit is also configured to if the second probability is greater than or equal to the third The preset threshold is 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, the user's age group is determined to be the first age group.
  • the acquisition unit is also used to obtain the age input by the user; the reminder unit is also used to remind the user to confirm whether the entered age is correct when the age does not match the determined age group of the user.
  • the processing unit is also used to determine that the last time the user was reminded to confirm whether the entered age is correct satisfies the preset condition.
  • the processing unit is also used to use the first preset model to perform health detection on the user if it is determined that the user's age group is the first age group; the processing unit is also used to determine the user's age group.
  • the second preset model is used to perform health testing on the user.
  • the physiological parameters include at least one or more of the following: heart rate, heart rate variability (HRV), respiratory rate, blood oxygen, and temperature.
  • HRV heart rate variability
  • the sound data includes at least one or more of the following: cough sounds, "a" sounds, blowing sounds, and speech.
  • this application provides a wearable device, including: a processor, a memory, a sensor, and a display screen.
  • the memory, sensor, and display screen are coupled to the processor.
  • the memory is used to store computer program code.
  • the computer program code includes a computer program. Instructions: the processor reads computer instructions from the memory to cause the wearable device to execute the method described in the above first aspect and any of the designs.
  • the wearable device further includes a communication interface, which can be used for the wearable device to communicate with other devices (such as electronic devices).
  • the communication interface may be a transceiver, an input/output interface, an interface circuit, an output circuit, an input circuit, a pin or a related circuit, etc.
  • the present application provides a wearable device, including: at least one processor; the processor is configured to execute a computer program or instructions stored in a memory, so that the wearable device executes the above first aspect and any design therein the method described.
  • the memory may be coupled to the processor, or may be independent of the processor.
  • the wearable device further includes a sensor, which is coupled to the processor.
  • the sensor can be used by the wearable device to obtain the user's physiological parameters and/or voice data.
  • the sensor may be a photoplethysmographic sensor, an acceleration sensor, a temperature sensor, a sound sensor (such as a microphone), etc.
  • the wearable device further includes a display screen, the display screen is coupled to the processor, and the display screen can be used by the wearable device to implement display operations. For example: display the target interface, display target information, etc.
  • the wearable device further includes a communication interface, which can be used for the wearable device to communicate with other devices (such as electronic devices).
  • the communication interface may be a transceiver, an input/output interface, an interface circuit, an output circuit, an input circuit, a pin or a related circuit, etc.
  • the present application provides a computer-readable storage medium.
  • the computer-readable storage medium includes a computer program or instructions.
  • the wearable device causes the wearable device to perform the above-mentioned first step.
  • the present application provides a computer program product.
  • the computer program product When the computer program product is run on a computer, the computer can execute the method described in the above-mentioned first aspect and any one of the designs.
  • the present application provides a circuit system.
  • the circuit system includes a processing circuit configured to perform the method described in the first aspect and any one of the designs.
  • the present application provides a chip system, including at least one processor and at least one interface circuit.
  • the at least one interface circuit is used to perform transceiver functions and send instructions to at least one processor.
  • the processor executes the instruction, at least one processor executes the method described in the above first aspect and any of the designs.
  • Figure 1 is a schematic diagram of a communication system in which an age detection method is applied according to an embodiment of the present application
  • Figure 2 is a schematic structural diagram of a wearable device provided by an embodiment of the present application.
  • Figure 3 is a schematic structural diagram of another wearable device provided by an embodiment of the present application.
  • Figure 4 is a schematic flow chart of an age detection method provided by an embodiment of the present application.
  • FIG. 5 is a schematic diagram of the interface provided by the embodiment of the present application.
  • Figure 6 is a second schematic diagram of the interface provided by the embodiment of the present application.
  • Figure 7 is a schematic diagram three of the interface provided by the embodiment of the present application.
  • Figure 8 is a schematic diagram 4 of the interface provided by the embodiment of the present application.
  • Figure 9 is a schematic diagram 5 of the interface provided by the embodiment of the present application.
  • Figure 10 is a schematic diagram 6 of the interface provided by the embodiment of this application.
  • FIG 11 is a schematic diagram 7 of the interface provided by the embodiment of this application.
  • Figure 12 is a schematic diagram 8 of the interface provided by the embodiment of the present application.
  • Figure 13 is a schematic diagram 9 of the interface provided by the embodiment of the present application.
  • Figure 14 is a schematic diagram 10 of the interface provided by the embodiment of the present application.
  • Figure 15 is a schematic structural diagram of another wearable device provided by an embodiment of the present application.
  • Figure 16 is a schematic structural diagram of a chip system provided by an embodiment of the present application.
  • Respiratory disease is a common and frequently-occurring disease with complex types and high fatality rate. It occurs more often in children and the elderly.
  • Arteriosclerosis is a non-inflammatory disease of the arteries that usually occurs in adolescence and worsens in middle-aged and elderly people. It can be seen that the occurrence of such diseases is closely related to the user's age. Therefore, by combining the user's age, the user's health (such as possible diseases, etc.) can be detected in a timely manner. Early warning allows users to prevent or treat early based on the early warning message, which is of great significance to ensuring the life and health of users.
  • this application provides an age detection method that can improve the accuracy of the obtained age.
  • FIG. 1 shows a schematic diagram of a communication system in which an age detection method provided in the embodiment of the present application is applied.
  • 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.
  • wireless communication technology includes but is not limited to at least one of the following: near field communication (NFC), Bluetooth (bluetooth, BT) (for example, traditional Bluetooth or low power (bluetooth low energy, BLE) Bluetooth) ), wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) network), Zigbee, frequency modulation (FM), infrared (IR), etc.
  • both the wearable device 100 and the electronic device 200 support the proximity discovery function.
  • the wearable device 100 and the electronic device 200 can discover each other, and then establish wireless communications such as Wi-Fi peer to peer (P2P) connection, Bluetooth connection, etc. connect.
  • P2P Wi-Fi peer to peer
  • Bluetooth connection etc. connect.
  • the wearable device 100 and the electronic device 200 can implement signal interaction through the wireless communication connection.
  • the wearable device 100 and the electronic device 200 establish a wireless communication connection through a local area network.
  • the wearable device 100 and the electronic device 200 are both connected to the same router.
  • the wearable device 100 and the electronic device 200 establish a wireless communication connection through a cellular network, the Internet, etc.
  • 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 .
  • the wearable device 100 may be, for example, a smart watch, a smart bracelet, a smart anklet, a wireless headset, a smart glasses, a smart helmet, or other terminal device with an age detection function.
  • the operating systems installed on the wearable device 100 include but are not limited to or other operating systems.
  • the wearable device 100 may be a fixed device or a portable device. This application does not limit the specific type of the wearable device 100 or the operating system installed.
  • the electronic device 200 may be, for example, a mobile phone (mobile phone), a personal computer (PC), a tablet computer (Pad), a notebook computer, a desktop computer, a notebook computer, a computer with transceiver functions, a wearable device, Vehicle-mounted equipment, artificial intelligence (AI) equipment and other terminal equipment.
  • Operating systems installed on the electronic device 200 include but are not limited to or other operating systems.
  • the electronic device 200 may be a fixed device or a portable device. This application does not limit the specific type of the electronic device 200 or the operating system installed.
  • the wearable device 100 may be used to obtain the user's physiological parameters and/or the user's voice data. For introduction to the physiological parameters and voice data, please refer to the following.
  • the wearable device 100 may then determine the user's age group based on the user's physiological parameters and/or the user's voice data.
  • wearable device 100 also The user's physiological parameters and/or the user's voice data, etc. may be sent to the electronic device 200, and the electronic device 200 determines the age group of the user.
  • different sports and health courses can be recommended to the user based on the detected age group of the user.
  • an application for the user to input age is installed in the electronic device 200, and the user can input his/her age through the application.
  • the wearable device 100 can obtain the age input by the user from the electronic device 200 connected through wireless communication, and then determine whether the age input by the user is consistent with the determined age group, that is, whether the age input by the user is in between identified age groups.
  • 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 they are inconsistent, the wearable device 100 can output prompt information (for example, display the prompt information through the display screen, broadcast the prompt information via a speaker, etc.) to prompt the user to confirm whether the age entered in the electronic device 200 is correct.
  • the wearable device 100 can also send prompt information to the electronic device 200, and the electronic device 200 outputs corresponding information to prompt the user to confirm whether the age entered in the electronic device 200 is correct.
  • the electronic device 200 may determine whether the age input by the user is consistent with the determined age group.
  • the determined age group may be determined by the wearable device 100 and then sent to the electronic device 200 , or the wearable device 100 may send the user's physiological parameters and/or the user's voice data to the electronic device 200 . After the device 200, it is determined by the electronic device 200 itself. If they are inconsistent, the electronic device 200 can output prompt information to prompt the user to confirm whether the age entered in the electronic device 200 is correct.
  • the electronic device 200 can also send prompt information to the wearable device 100, and the wearable device 100 outputs corresponding information to prompt the user to confirm whether the age entered in the electronic device 200 is correct.
  • the above communication system may not include the electronic device 200.
  • an application for the user to input age is installed in the wearable device 100, and the user can directly input his or her age through the application. Then, the wearable device 100 can determine whether the age input by the user is consistent with the age group determined by the wearable device. If not, the wearable device 100 can output prompt information to prompt the user to confirm whether the age entered in the wearable device 100 is correct. .
  • the wearable device 100 and the electronic device 200 may both be installed with an application for the user to input age, and the present application is not limited to this.
  • 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 (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, and a mobile communication module 150 , wireless communication module 160, audio module 170, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen 194, etc.
  • the sensor module 180 may include a photoplethysmographic 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.
  • the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural network processing unit (NPU), etc. where different processing units can be independent
  • the device can also be integrated into one or more processors.
  • the controller can generate operation control signals based on the instruction operation code and timing signals to complete the control of fetching and executing instructions.
  • the processor 110 may also be provided with a memory for storing instructions and data.
  • the memory in processor 110 is cache memory. This memory may hold instructions or data that have been recently used or recycled by processor 110 . If the processor 110 needs to use the instructions or data again, it can be called directly from the memory. Repeated access is avoided and the waiting time of the processor 110 is reduced, thus improving the efficiency of the system.
  • the processor 110 may include one or more interfaces, such as a USB interface 130 or the like.
  • the USB interface 130 may be an interface that complies with USB standard specifications, and specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc.
  • the USB interface 130 can be used to connect a charger to charge the wearable device 100, and can also be used to transmit data between the wearable device 100 and peripheral devices. It can also be used to connect headphones to play audio through them. This interface can also be used to connect other devices, such as AR devices, etc.
  • the charging management module 140 is used to receive charging input from the charger.
  • the charger can be a wireless charger or a wired charger.
  • the power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110.
  • the power management module 141 receives input from the battery 142 and/or the charging management module 140, and supplies power to the processor 110, the memory 120, the display screen 194, the camera 193, the wireless communication module 160, and the like.
  • the wireless communication function of the wearable device 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, and so on.
  • Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals.
  • Each antenna in wearable device 100 may be used to cover a single or multiple communication bands. Different antennas can also be reused to improve antenna utilization.
  • Antenna 1 can be reused as a diversity antenna for a wireless LAN. In other embodiments, antennas may be used in conjunction with tuning switches.
  • the mobile communication module 150 can provide solutions for wireless communication including 2G/3G/4G/5G applied on the wearable device 100 .
  • the mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc.
  • the wireless communication module 160 can provide applications on the wearable device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) network), Bluetooth (bluetooth, BT), and global navigation.
  • WLAN wireless local area networks
  • WiFi wireless fidelity
  • Bluetooth bluetooth, BT
  • global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions.
  • the antenna 1 of the wearable device 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the wearable device 100 can communicate with the network and other devices through wireless communication technology.
  • the wearable device 100 implements display functions through a GPU, a display screen 194, an application processor, and the like.
  • the GPU is an image processing microprocessor and is connected to the display screen 194 and the application processor. GPUs are 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 alter display information.
  • the display screen 194 is used to display images, videos, etc.
  • Display 194 includes a display panel.
  • the display panel can be Use a liquid crystal display (LCD), such as an organic light-emitting diode (OLED), an active matrix organic light emitting diode or an active matrix organic light emitting diode (active-matrix organic light emitting diode). diodes, AMOLED), flexible light-emitting diodes (FLED), Mini-led, Micro-led, Micro-oled, quantum dot light emitting diodes (QLED), etc.
  • the wearable device 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.
  • Camera 193 is used to capture still images or video.
  • the wearable device 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.
  • Memory 120 may be used to store computer executable program code, which includes instructions.
  • the memory 120 may include a program storage area and a data storage area.
  • the stored program area can store an operating system, at least one application program required for a function (such as a sound playback function, an image playback function, etc.).
  • the storage data area may store data created during use of the wearable device 100 (such as audio data, phone book, etc.).
  • the memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.
  • UFS universal flash storage
  • the processor 110 executes 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.
  • an age detection model (such as a first age detection model, a second age detection model, a third age detection model, etc.) may be stored in the memory, and the age detection model may be used to determine the age group of the user. , please refer to the following for the introduction of this age detection model.
  • the wearable device 100 can implement audio functions through the audio module 170 and an application processor. Such as music playback, recording, etc.
  • the audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. Audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110 , or some functional modules of the audio module 170 may be provided in the processor 110 . The wearable device 100 can perform music playback, recording, etc. through the audio module 170 .
  • 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 can also be used to obtain the user's voice data. For an introduction to the voice data, please refer to what is described later.
  • Photoplethysmography sensor 180A can obtain PPG signals through photoplethysmography (PPG), based on LED light sources and detectors, by measuring the attenuated light reflected and absorbed by human blood vessels and tissues.
  • the wearable device 100 analyzes the PPG signal obtained by the photoplethysm sensor 180A to obtain the user's physiological parameters, such as heart rate, respiratory rate, blood oxygen, etc.
  • the wearable device can further determine the user's heart rate variability (heart rate variability, HRV) based on the obtained heart rate.
  • HRV can refer to the time of each heartbeat cycle and the changing patterns of heartbeats. These changing patterns can reflect the user's different physiological conditions or diseases.
  • the acceleration sensor 180B can detect the acceleration of the wearable device 100 in various directions (generally three axes). When the wearable device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of wearable devices and be used in horizontal and vertical screen switching, pedometer and other applications. In some embodiments of the present application, the acceleration transmitter The sensor 180B measures an acceleration signal, where the acceleration signal can be used to determine the user's state, such as a stationary state, a moving state, etc. Because users are in different states, physiological parameters (such as breathing rate, heart rate, blood oxygen, etc.) may be different. Therefore, in order to improve the accuracy of the obtained user's physiological parameters, the wearable device 100 can also determine the user's status through the acceleration signal collected by the acceleration sensor 180B, and further assist in determining the user's physiological parameters.
  • physiological parameters such as breathing rate, heart rate, blood oxygen, etc.
  • Temperature sensor 180C is used to detect temperature.
  • the wearable device 100 utilizes the temperature detected by the temperature sensor 180C to execute the temperature processing strategy. For example, when the temperature reported by the temperature sensor 180C exceeds a threshold, the wearable device 100 reduces the performance of a processor located near the temperature sensor 180C to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the wearable device 100 heats the battery 142 to avoid the low temperature causing the wearable device 100 to shut down abnormally. In some other embodiments, when the temperature is lower than another threshold, the wearable device 100 performs boosting on the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature. In some embodiments of the present application, the wearable device 100 may be equipped with one or more temperature sensors 180C for detecting the user's body temperature.
  • Touch sensor 180D is also called a "touch device”.
  • the touch sensor 180D can be disposed on the display screen 194.
  • the touch sensor 180D and the display screen 194 form a touch screen, which is also called a "touch screen”.
  • the touch sensor 180D is used to detect a touch operation acting on or near the touch sensor 180D.
  • the touch sensor can pass the detected touch operation to the application processor to determine the touch event type.
  • Visual output related to the touch operation may be provided through display screen 194 .
  • the touch sensor 180D may also be disposed on the surface of the wearable device 100 at a location different from that of the display screen 194 .
  • the sensor module 180 may also include a pressure sensor, an air pressure sensor, a magnetic sensor, a distance sensor, a proximity light sensor, a gyroscope sensor, a fingerprint sensor, an ambient light sensor, a bone conduction sensor, etc.
  • the buttons 190 include a power button, a volume button, etc.
  • Key 190 may be a mechanical key. It can also be a touch button.
  • the wearable device 100 may receive key inputs and generate key signal inputs related to user settings and function control of the wearable device 100 .
  • the motor 191 can generate vibration prompts.
  • the motor 191 can be used for vibration prompts for incoming calls and can also be used for touch vibration feedback.
  • the indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, or may be used to indicate messages, missed calls, notifications, etc.
  • FIG. 3 shows another exemplary structure of a wearable device.
  • the wearable device includes: a processor 301, a memory 302, and a transceiver 303.
  • the processor 301 and the memory 302 please refer to the implementation of the wearable device processor and memory.
  • Transceiver 303 is used for the wearable device to interact with other devices (such as electronic devices).
  • Transceiver 303 may be a device based on a communication protocol such as Wi-Fi, Bluetooth, or other communication protocols.
  • the wearable device may include more or fewer components than those shown in Figures 2 and 3, or some components may be combined, some components may be separated, or some components may be replaced, Or a different component arrangement.
  • the components illustrated may be implemented in hardware, software, or a combination of software and hardware.
  • the structure of the electronic device 200 reference may be made to the structure of the wearable device 100.
  • the electronic device 200 It may have more or less structures than the wearable device 100, and this application does not impose specific limitations on this.
  • the embodiment of the present application provides an age detection method, which is applied to wearable devices.
  • the wearable device may obtain the user's physiological parameters and/or the user's voice data, and then determine the user's age group based on the user's physiological parameters and/or the user's voice data.
  • the determined age group can also be used to perform health testing on the user.
  • the physiological parameters of the user may include physiological parameters that differ in different age groups.
  • the physiological parameters may include but are not limited to: heart rate, HRV, respiratory rate, body temperature, blood oxygen, blood pressure, pulse rate, and the like.
  • Parameters used to determine the user's age group For example, the heart rate of young people is higher than that of old people. For example, the heart rate of young people is 60-100 beats per minute, and the heart rate of old people is 55-90 beats per minute.
  • the HRV of young people is higher than that of older people. For example, the HRV of people aged 20 to 25 is between 55-105, and the HRV of people aged 60 to 65 is between 25-45.
  • physiological parameters such as respiratory rate, body temperature, blood oxygen, blood pressure, pulse rate, etc. are also different. I will not give examples one by one.
  • the characteristics of their voice data are also different.
  • the sound data includes but is not limited to the user's cough sound, "a" sound, “ah” sound, “ah” sound, exhalation sound, blowing sound, speaking sound, voice, etc. sound data, this application is not limited to this.
  • age groups can be divided in different ways.
  • the age group includes young people and middle-aged and old people. For example, those aged 40 and before are called young people, and those aged after 40 are called middle-aged and old people.
  • age groups include teenagers, young people, middle-aged people, and old people. For example: those who are 17 years old and before are called teenagers, those who are between 18 and 40 years old are called youth, and age Those between the ages of 41 and 65 are called middle age, and those after 65 are called old age.
  • the age group includes teenagers, middle-aged and old people. For example: those aged 40 and before are called teenagers, those aged between 41 and 65 are called middle-aged, and those aged between 40 and 65 are called middle-aged. After the age of 65, it is called old age.
  • the age groups include young people, middle-aged people, young old people, old people, and long-lived old people.
  • those aged 44 years old and those before 44 years old are called young people, and those aged 45 years old or younger.
  • those between the ages of 59 and 59 are called middle-aged people
  • those between 60 and 74 are called young old people
  • those between 75 and 89 are called old people
  • those over 90 are called long-lived people.
  • each age group belongs is also an illustrative explanation, and does not constitute a limitation of this application.
  • the division of age groups and the age range of each age group are only illustrative.
  • the age range can be set by developers according to actual needs.
  • the wearable device may only obtain the user's physiological parameters, and then determine the user's age group based on the obtained user's physiological parameters.
  • the wearable device can input the acquired physiological parameters of the user into a preset first age detection model, and output the probability of each age group through the first age detection model.
  • the sum of the probabilities of each age group is 1. .
  • the first age detection model can be a machine learning model, which can be obtained through model training.
  • the first age detection model can be obtained by using the user's physiological parameters as input and the user's age group as output. This application does not limit the specific algorithm used by the first age detection model.
  • the wearable device may first determine whether the probabilities of the age groups output by the first age detection model are greater than or equal to the first threshold, and if so, determine the age groups whose probabilities are greater than or equal to the first threshold. is the age group of the user.
  • the first threshold may be set to a value close to 1, such as a value between 0.85 and 0.9. It can be understood that the closer the probability of an age group is to 1, the greater the possibility that the user is in that age group. Therefore, the wearable device can directly determine the age group with a probability greater than or equal to the first threshold as the user's age group. For example: Take the age group including young people and middle-aged and elderly people, and the first threshold is 0.9.
  • the wearable device determines the middle age.
  • the probability 0.92 of the elderly is greater than the first threshold 0.9, so the user's age group is determined to be middle-aged and elderly.
  • the wearable device can also determine whether any of the probabilities of the age groups output by the first age detection model are greater than or equal to the second threshold.
  • threshold and less than the first threshold if it exists, then the age group whose probability is greater than or equal to the second threshold and less than the first threshold is determined as the age group of the user.
  • the second threshold is smaller than the first threshold, and the second threshold can be set to a value that is smaller than the first threshold but close to 1, such as a value between 0.6 and 0.85.
  • the wearable device can also determine the probability of each age group.
  • the largest age group is determined as the user's age group.
  • the wearable device does not need to determine whether the probability of the age group output by the first age detection model is greater than or equal to the first threshold, and/or, it does not need to determine the age group output by the first age detection model. Whether there is a probability greater than or equal to the second threshold and less than the first threshold.
  • the wearable device can directly determine the age group with the highest probability as the user's age group. For example: Taking the age group including young people and middle-aged and elderly people as an example, assuming that the probability of young people output by the wearable device through the first age detection model is 0.58, and the probability of middle-aged and elderly people is 0.42, then the wearable device determines the probability of young people. The probability of 0.58 is greater than the probability of 0.42 for middle-aged and elderly people
  • the age group of the user is determined to be young people.
  • the wearable device may only obtain the user's voice data.
  • the user's age group is then determined based on the obtained user's voice data.
  • the wearable device can input the acquired user's voice data into a preset second age detection model, and output the probability of each age group through the second age detection model.
  • the probability of each age group is The sum is 1.
  • the second age detection model can also be a machine learning model, which can also be obtained through model training.
  • the second age detection model can be obtained by using the user's voice data as input and the user's age group as output. This application does not limit the specific algorithm used by the second age detection model.
  • the wearable device can also first determine whether the probability of the age group output in the second age detection model is greater than or equal to the first threshold. If there is, then the probability is greater than or equal to the first threshold. A threshold age group is determined as the user's age group. Optionally, if it does not exist, the wearable device can also 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. If it exists, then the probability is greater than or equal to the second threshold. Threshold and the age group smaller than the first threshold is determined as a user age group.
  • the wearable device can also determine the age group with the highest probability among the various age groups as the user's age group.
  • the wearable device may not perform the aforementioned determination steps based on the first threshold and/or the second threshold, and directly determine the age group with the highest probability among the various age groups as the user's age group.
  • the wearable device can also acquire the user's physiological parameters and the user's voice data at the same time, and then determine the user's age group based on the acquired user's physiological parameters and the user's voice data.
  • the wearable device can input the acquired physiological parameters of the user into the preset first age detection model, and input the acquired user's voice data into the preset second age detection model.
  • the first age detection model outputs the probability of each age group.
  • the sum of the probabilities of each age group is 1.
  • the sum of the probabilities of each age group is also 1. It can be understood that the probability of the age group output by the first age detection model is determined based on the user's physiological parameters, and the probability of the age group output by the second age detection model is determined based on the user's voice data.
  • the wearable device may determine whether the probability of the age group output by the first age detection model is greater than or equal to the third threshold, and if so, the probability of the age detection model output by the first age detection model is greater than or equal to the third threshold.
  • the age group of the three thresholds is determined as the age group of the user. Or, determine whether the age group outputted by the second age detection model has a probability greater than or equal to the fourth threshold, and if so, determine the age group whose probability output by the second age model is greater than or equal to the fourth threshold as the user's age group. .
  • the third threshold and the fourth threshold can be set to a value close to 1, for example, set to a value between 0.8 and 0.9.
  • the third threshold and the fourth threshold may be the same or different.
  • the third threshold and the fourth threshold may be the same as or different from the first threshold. When they are different from the first threshold, they may be the same as or different from the second threshold. .
  • the third threshold is 0.9
  • the fourth threshold is 0.8 as an 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 0.7
  • the probability of middle-aged and elderly people is 0.3.
  • the wearable device determines If the probability 0.95 of young people output by the first age detection model is greater than the third threshold 0.9, then the user's age group is determined to be young.
  • the first age detection model outputs a probability of 0.6 for young people and a probability of 0.4 for middle-aged and elderly people.
  • the second age detection model outputs a probability of 0.95 for young people and 0.05 for middle-aged and elderly people. Wearable devices It is determined that the probability 0.95 of the young person output by the second age detection model is greater than the fourth threshold 0.8, then it is determined that the user's age group is young.
  • the first age detection model outputs a probability of 0.95 for young people and a probability of 0.05 for middle-aged and elderly people.
  • the second age detection model outputs a probability of 0.85 for young people and 0.15 for middle-aged and elderly people. If 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 0.9, it determines that the user's age group is young. Alternatively, the wearable device determines that the probability 0.85 of young people output by the second age detection model is greater than the fourth threshold 0.8, and then determines that the user's age group is young.
  • the wearable device determines that the probabilities of the age groups output by the first age detection module are all less than the third threshold, and the probabilities of the age groups output by the second age detection module are less than the fourth threshold, the wearable device It may also be determined whether there is an age group that is greater than or equal to the fifth threshold and less than the third threshold among the probabilities of the age groups output by the first age detection model. If there is, then the probability of the age group output by the first age detection model is greater than or equal to the fifth threshold and The age group smaller than the third threshold is determined as the age group of the user.
  • the wearable device may also determine whether there is an age group that is greater than or equal to the sixth threshold and less than the fourth threshold among the probabilities of the age groups output by the second age detection model. If there is, then the probability of the age group output by the second age detection model is greater than or equal to the sixth threshold. The age group equal to the sixth threshold and smaller than the fourth threshold is determined as the age group of the user.
  • the wearable device may also determine whether there is an age for which the probability of the first age detection model output is greater than or equal to the fifth threshold and less than the third threshold, and the probability of the second age detection model output is greater than or equal to the sixth threshold and less than the fourth threshold.
  • Segment (that is, the age group where the probability of the output of the first age detection model is greater than or equal to the fifth threshold and less than the third threshold is the same as the age group where the probability of the output of the second age detection model is greater than or equal to the sixth threshold and less than the fourth threshold), If it exists, then the age group in which the probability output by the first age detection model is greater than or equal to the fifth threshold and less than the third threshold, and the probability output by the second age detection model is greater than or equal to the sixth threshold and less than the fourth threshold is determined as the user's age group generation.
  • the fifth threshold and the sixth threshold can also be set to a value close to 1, such as a value between 0.7 and 0.8.
  • the fifth threshold and the sixth threshold may be the same or different, and the fifth threshold and the sixth threshold may be the same or different from the first threshold and the second threshold.
  • the wearable device can also be based on the aforementioned The probabilities of each age group output by the two age detection models determine the user's age group. It can be understood that for the same age group, 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 may be the same or different. The wearable device can determine the age group with the highest probability. Identifies the age group of the user.
  • the age group includes young people and middle-aged and elderly people
  • the fifth threshold is 0.7
  • the sixth threshold is 0.75.
  • the wearable device can It is determined that the probability 0.7 output by the second age detection model is the largest among all probabilities, so the wearable device determines that the user's age group is middle-aged and elderly.
  • the wearable device may not perform the aforementioned judgment operation based on the third threshold and the fourth threshold, and/or not perform the aforementioned judgment operation based on the fifth threshold and the sixth threshold, and directly determine the age group with the highest probability.
  • the age group of is determined as the user's age group.
  • 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 it may be the average probability, the average of an age group
  • the probability is the 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.
  • each threshold (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 are not It does not constitute a limitation of this application. In actual applications, developers can set it according to actual needs. Each threshold can be in the form of a specific numerical value or a numerical range. This application does not impose specific restrictions on this and will be described uniformly here.
  • the wearable device can be based on the first age group.
  • the probability of young people output by the detection model ie, the first probability
  • the probability of young people output by the second age detection model ie, the third probability
  • the wearable device determines that the probability of young people output by the first age detection model is greater than or equal to the third threshold (i.e., the first preset threshold); or, the probability of young people output by the second age detection model is greater than or equal to the fourth threshold (i.e., second preset threshold); or, the probability of young people output by the first age detection model is greater than or equal to the fifth threshold (ie, the third preset threshold) and less than the third threshold, and the probability of young people output by the second age detection model If the probability is greater than or equal to the sixth threshold (ie, the fourth preset threshold) and less than the fourth threshold, the wearable device determines that the user's age group is young.
  • the third threshold i.e., the first preset threshold
  • the probability of young people output by the second age detection model is greater than or equal to the fourth threshold (i.e., second preset threshold); or, the probability of young people output by the first age detection model is greater than or equal to the fifth threshold (ie, the third preset threshold
  • the wearable device determines that the probability of young people output by the first age detection model is greater than or equal to the fifth threshold and less than the third threshold, and the probability of young people input by the second age detection model is less than the sixth threshold; or, the first age detection If the probability of young people output by the model is less than the fifth threshold and the probability of young people output by the second age detection model is less than the fourth threshold, then the wearable device determines that the age group of the user is middle-aged and elderly.
  • the probability that the young person output by the first age detection model is less than the fifth threshold and the probability that the young person output by the second age detection model is less than the fourth threshold includes two situations: one is that the young person output by the first age detection model is The probability of a person is less than the fifth threshold and the probability of a young person output by the second age detection model is greater than or equal to the sixth threshold and less than the fourth threshold. The other is that the probability of young people output by the first age detection model is less than the fifth threshold and the probability of young people output by the second age detection model is less than the sixth threshold.
  • the wearable device can also determine the age of the user based on the probability of middle-aged and elderly people output by the first age detection model (i.e., the second probability) and the probability of middle-aged and elderly people output by the second age detection model (i.e., the fourth probability). part.
  • the wearable device determines that the probability of middle-aged and elderly people output by the first age detection model is greater than or equal to the third threshold; or, the probability of middle-aged and elderly people output by the second age detection model is greater than or equal to the fourth threshold; or, the first age detection model If the output probability of middle-aged and elderly people is greater than or equal to the fifth threshold and less than the third threshold, and the probability of middle-aged and elderly people output by the second age detection model is greater than or equal to the sixth threshold and less than the fourth threshold, then the wearable device determines the age of the user Duan is middle-aged and elderly.
  • the wearable device determines that the probability of middle-aged and elderly people output by the first age detection model is greater than or equal to the fifth threshold and less than the third threshold, and the probability of middle-aged and elderly people input by the second age detection model is less than the sixth threshold; or, first If the probability of middle-aged and elderly people output by the age detection model is less than the fifth threshold and the probability of middle-aged and elderly people output by the second age detection model is less than the fourth threshold, then the wearable device determines that the age group of the user is young.
  • the probability of middle-aged and elderly people output by the first age detection model is less than the fifth threshold and the probability of middle-aged and elderly people output by the second age detection model is less than the fourth threshold, which also includes two situations: one is the first age detection.
  • the probability of middle-aged and elderly people output by the model is less than the fifth threshold and the probability of middle-aged and elderly people output by the second age detection model is greater than or equal to the sixth threshold and less than the fourth threshold.
  • the other is that the probability of middle-aged and elderly people output by the first age detection model is less than the fifth threshold and the probability of middle-aged and elderly people output by the second age detection model is less than the sixth threshold.
  • the wearable device can acquire the user's voice data when the acquired physiological parameters of the user meet certain conditions. For example: after the wearable device inputs the acquired user physiological parameters into the first age detection model, it determines that the probability that the age groups output by the first age detection model are all less than the third threshold. Alternatively, it is determined that the largest probability among the probabilities of the age groups output by the first age detection model is greater than or equal to the fifth threshold and less than the third threshold. Alternatively, it is determined that the probabilities of the age groups output by the first age detection model are all less than the fifth threshold. Then the wearable device determines that it needs to obtain the user's voice data, and then starts the process of obtaining the user's voice data.
  • the wearable device can obtain the user's physiological parameters when the acquired user's voice data meets certain conditions. For example: the wearable device will obtain the user's voice data and input it into the second age detection model, It is determined that the probabilities of the age groups output by the second age detection model are all less than the fourth threshold. Alternatively, it is determined that the largest probability among 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, it is determined that the probabilities of the age groups of the users output by the second age detection model are all less than a sixth threshold. Then the wearable device can determine that it needs to obtain the user's physiological parameters, that is, start the process of obtaining the user's physiological parameters.
  • Figure 4 shows an age detection method provided by an embodiment of the present application, applied to wearable devices, and the method includes the following steps:
  • S402. Determine whether the probability of middle-aged and elderly people is greater than or equal to the third threshold based on the user's physiological parameters.
  • the probability of middle-aged and elderly people can refer to the probability of middle-aged and elderly people output by the first age detection model.
  • the wearable device can input the user's physiological parameters into the first age detection model to obtain the probability of middle-aged and elderly people. Probability.
  • step S403 If yes, execute step S403; if not, execute step S404.
  • S403. Determine the user's age group to be middle-aged and elderly.
  • S404 Determine whether the probability of middle-aged and elderly people is greater than or equal to the fifth threshold and less than the third threshold according to the user's physiological parameters.
  • the probability of middle-aged and elderly people may also refer to the probability of middle-aged and elderly people output by the first age detection model.
  • step S405 If not, execute step S405; if yes, execute step S406.
  • S405. Determine the user's age group to be young people.
  • the sound data can be collected by the wearable device itself, or it can be collected by other devices (such as electronic devices), and the wearable device can then obtain it from other devices.
  • S407. Determine whether the probability of middle-aged and elderly people is greater than or equal to the fourth threshold based on the user's voice data.
  • the probability of middle-aged and elderly people can refer to the probability of middle-aged and elderly people output by the second age detection model.
  • the wearable device can input the user's voice data into the second age detection model to obtain the middle-aged and elderly people. The probability.
  • S408. Determine the user's age group to be middle-aged and elderly.
  • S409 Determine whether the probability of middle-aged and elderly people is greater than or equal to the sixth threshold and less than the fourth threshold based on the user's voice data.
  • the probability of middle-aged and elderly people may also refer to the probability of middle-aged and elderly people output by the second age detection model.
  • S410 Determine the user's age group to be middle-aged and elderly.
  • S411. Determine the user's age group to be young people.
  • the first age detection model and the second age detection model can also be implemented through an age detection model (such as a third age detection model), and the wearable device can obtain the user's physiological parameters and The user's voice data is input into the third age detection model to determine the user's age group.
  • an age detection model such as a third age detection model
  • the third age detection model can output the probability of each age group.
  • the third age detection model can also output the probability of each age group, that is, for one age group, it can correspond to two probabilities, one of which is determined based on the user's physiological parameters, and the other is based on the user's physiological parameters.
  • the sound data is determined.
  • the third age detection model can also output the probability of each age group, that is, for an age group, it corresponds to a probability, which probability is based on the user's physiological parameters and the user's voice data. sound data are jointly determined.
  • the third age detection model can also be a machine learning model, or can be obtained through model training.
  • the user's physiological parameters and the user's voice data are used as input, and the user's age group is used as the output for training to obtain the third age detection.
  • Model This application does not limit the specific algorithm used by the third age detection model.
  • the first age detection model, the second age detection model, and the third age detection model output the probabilities of each age group for explanation.
  • one or more of the first age detection model, the second age detection model, the third age detection model, etc. can also directly output the final age group, which is not limited in this application.
  • the first age detection model, the second age detection model, the third age detection model, etc. can be preset in the wearable device, or can also be preset in other devices (such as electronic devices). , this application does not specifically limit this.
  • the wearable device can send the acquired physiological parameters of the user and/or the user's voice data to other devices, and the other devices determine the age group of the user.
  • the wearable device can also obtain the determined user's age group from other devices.
  • the wearable device determines the user's age group through a machine learning model. In other embodiments, the wearable device (or other device) can also determine the user's age group through preset rules. For example: for different age groups, the corresponding ranges of physiological parameters are different, and the corresponding characteristics of the sound data are also different. Taking wearable devices as an example, the wearable device can determine the age group of the user based on the acquired physiological parameters of the user and the preset range of physiological parameters corresponding to different age groups. Alternatively, the wearable device may determine the age group of the user based on the acquired voice data of the user and preset characteristics of the voice data corresponding to different age groups.
  • the wearable device can determine the age group of the user based on the acquired physiological parameters of the user, the user's voice data, the preset range of physiological parameters corresponding to different age groups, and the characteristics of the preset voice data corresponding to different age groups. .
  • the age group of the user is determined by identifying the acquired physiological parameters of the user and/or the user's voice data, and the user's age group can be intelligently predicted without manual input by the user, which can improve the obtained Age accuracy.
  • the smartwatch can automatically turn on the age detection function. For example: when the smart watch is turned on, the smart watch can perform the process of obtaining the user's physiological parameters and/or the user's voice data, and determine the user's age group based on the user's physiological parameters and/or the user's voice data.
  • the smart watch can also perform the aforementioned process of determining after determining that it is currently in the wearing state. Since the operation of determining whether a smart watch is in a wearing state is an existing technology, please refer to the introduction in related technologies for specific implementation, and will not be described in detail here.
  • the user needs to actively turn on the age detection function of the smart watch. For example: check After detecting the user's instruction to turn on the age detection function of the smart watch, in response to the instruction, the smart watch will execute the acquisition of the user's physiological parameters and/or the user's voice data, and based on the user's physiological parameters and/or the user's voice The process by which data determines a user's age group.
  • users can wake up the age detection function of the smart watch through various methods such as voice, gestures, keys, shortcut buttons, etc.
  • the user can enable the age detection function of the smart watch through the smart watch itself.
  • the smart watch displays a main interface 500.
  • the main interface 500 includes various application programs, and different application programs can be used to implement different functions.
  • health application 501 is included.
  • Health application 501 can be used to perform health testing on users and detect their physical health conditions, such as whether they are in a sub-healthy state, whether they already suffer from certain diseases, whether they are likely to suffer from certain diseases, etc. .
  • the smart watch detects the user's startup operation on the health application 501, such as detecting the user's click operation on the icon of the health application 501, and in response to the operation, the smart watch starts the health application 501.
  • the smart watch can display the running interface 510 of the health application 501.
  • the running interface 510 includes a button 511 for activating the age detection function of the smart watch.
  • the smart watch detects an operation such as a click of button 511 by the user, and in response to the operation, the smart watch starts performing a process of determining the age group of the user.
  • the running interface 510 may also include the results and/or detection time of the last health test.
  • users can also turn on the age detection function of the smart watch through other devices (such as mobile phones).
  • the mobile phone displays a main interface 600, which includes one or more applications, including a health application 601.
  • a health application 601. For an introduction to the health application 601, please refer to Figure 5 (1) Relevant introduction of the health application 501 shown.
  • the mobile phone detects the user's operation for starting the health application 601, for example: detecting the user's click operation on the icon of the health application 601, and in response to the operation, the mobile phone starts the health application 601.
  • the mobile phone can display the running interface 610 of the health application 601.
  • the running interface 610 can be the main interface of the health application 601, or it can be a sub-interface, etc., which is not limited in this application.
  • the running interface 610 includes a button 611 for activating the age detection function of the smart watch.
  • the mobile phone detects an operation for activating the age detection function of the smart watch, such as the user's click on button 611.
  • the mobile phone sends an instruction message to the smart watch that has established a communication connection.
  • the instruction message is used to instruct the smart watch to turn on.
  • Age detection function After receiving the instruction message sent by the mobile phone, the smart watch turns on the age detection function and begins the process of determining the user's age group.
  • the smart watch can detect the user's physiological parameters and/or the user's voice data in real time or periodically. In other embodiments, after the smart watch turns on the age detection function, the smart watch only starts to detect the user's physiological parameters and/or the user's voice data in real time or periodically at a specific time or in a specific scene. For example: considering that the user's state (such as resting state, exercise state, active state, etc.) may have a certain impact on physiological parameters, smart watches can detect the user's physiological parameters at night (such as all night) and/or User's voice data, because the user is in a still state at night, the user's physiological parameters detected in this way are more accurate. Alternatively, when the smart watch determines that the user is in a stationary state during the day, the smart watch then detects the user's physiological parameters and/or the user's voice data.
  • the smart watch determines that the user is in a stationary state during the day, the smart watch then detects the user's physiological parameters and/or the user's voice
  • the smart watch can also obtain the user's physiological parameters and/or the user's voice data when they meet preset conditions (such as: preset duration, preset quantity, etc.), and then based on these users' physiological parameters and /or the user's voice data determines the user's age group.
  • preset conditions such as: preset duration, preset quantity, etc.
  • the smart watch can periodically or non-periodically perform the process of determining the user's age group multiple times, that is, the smart watch can continuously Performs the process of determining the user's age group.
  • the smart watch can only perform the process of determining the user's age group a limited number of times (such as once, twice, etc.). The specific number of times can be determined by the developer. Personnel are set according to actual needs.
  • the smart watch can start to automatically obtain the user's voice data.
  • the user may not make any sound.
  • the user can also be prompted to input sound data by outputting a reminder message (such as a reminder message displayed on the display screen, a reminder message broadcast by a speaker, etc.).
  • the reminder message can be output by the smart watch itself, or can be output by the mobile phone, or can be output by the smart watch and the mobile phone at the same time. This application does not limit this.
  • the smart watch After the smart watch turns on the age detection function, the user needs to actively input his or her voice data.
  • the smart watch will obtain the user's voice data. For example: the smart watch passes Activate the microphone to record the user's voice data.
  • the smart watch and/or mobile phone can also output a reminder message to remind the user to actively input their own voice data.
  • the smart watch will activate the microphone and other sensors to record the user's voice data only after detecting the user's operation for inputting voice data. This can prevent the sensor used to record the user's voice data from being turned on all the time, thereby reducing the wearable device's power consumption.
  • the user's voice data can be recorded by a smart watch, or the user's voice data can be recorded by a mobile phone, which is not limited in this application.
  • the smart watch can display "Your cough sound is not currently detected, and the age detection cannot be completed. Please wait.” Press the button below to complete the recording of cough sounds! and other reminder messages to facilitate users to complete the recording of cough sounds through smart watches.
  • the smart watch detects an operation such as the user's long press on the cough sound recording button 701 (ie, the target operation). In response to the operation, the smart watch starts to obtain the cough sound input by the user, for example: Activate the microphone to start recording the user's cough, etc.
  • the smart watch can also display a progress bar 702, a "recording" text prompt 703 and other various forms of warm reminders to remind the user of the progress of cough sound recording.
  • the smart watch can also remind the user of successful or failed recording.
  • the smart watch can display a message such as "Cough sound recorded successfully, age detection is in progress!” to remind the user that the recording is successful.
  • the smart watch can also automatically hide the cough sound recording button 701.
  • the smart watch can display a message such as "Cough sound recording failed, please try again! to remind the user that the recording failed.
  • the mobile phone can display "The smart watch has not detected your cough sound currently and cannot complete the age detection. Please go to the smart watch side.” Complete the recording of cough sounds! and other similar messages to remind the user to go to the smart watch side to complete the recording of cough sounds, use Users can complete the recording of cough sounds through smart watches based on this message. Or, as shown in (2) in Figure 8, the mobile phone can also display "Your cough sound has not been detected currently, and the age detection cannot be completed. Please press and hold the button below to complete the recording of the cough sound! and the like to remind the user. Complete cough sound recording message via mobile phone.
  • the mobile phone detects a long press of the user's cough sound recording button 801, and obtains the cough sound input by the user.
  • the mobile phone can also display a progress bar 702 as shown in (2) in Figure 7 and a text prompt 703 of "Recording" to remind the user of the progress of cough sound recording.
  • mobile phones can also use methods such as those in Figure 7
  • the forms shown in (3) and (4) remind the user that the cough sound recording is successful or the cough sound recording fails. Subsequently, the mobile phone can send the successfully recorded cough sound to the smart watch so that the smart watch can complete the age detection.
  • the age group can also be output (for example, displayed and/or voice broadcasted) through the smart watch or mobile phone to inform the user.
  • physiological parameters include body temperature and sound data include cough sounds as an example.
  • the smart watch performing the operation of determining the user's age as an example. After obtaining the user's body temperature, the smart watch can also determine whether the body temperature is normal. If the body temperature is abnormal, the smart watch can also output target information (such as displaying the target information and/or voice broadcasting the target information, etc.) to remind the user that the age group currently measured is inaccurate.
  • target information Such as displaying the target information and/or voice broadcasting the target information, etc.
  • the user can also be reminded of the reasons why the age group obtained by measurement is inaccurate. For example: remind users that the measured age group may be inaccurate due to the occurrence of the target type of disease.
  • the target type of disease refers to diseases with symptoms such as fever, cough, etc., such as: cold, fever, etc.
  • the smart watch can also send the target information to the mobile phone, and the mobile phone outputs the target information.
  • the smart watch and the mobile phone can also output the target information at the same time.
  • the smart watch and/or mobile phone can output the target information when outputting the user's age group, or can output the target information at other times.
  • This application is not limited to this.
  • the mobile phone can also perform the operation of determining whether the user's body temperature is normal, and if it is abnormal, output the target information.
  • the mobile phone can also send the target information to the smart watch, and the smart watch can output the target information.
  • the smart watch and the mobile phone can also output the target information at the same time. This application does not limit this.
  • the measured body temperature of the user may be inaccurate. This may also result in the measurement being inaccurate when determining the user's age based on the inaccurate body temperature.
  • the user's age group is incorrect. Therefore, by reminding the user that this situation may lead to inaccurate age measurement, the user can understand the details and selectively perform age detection when the body temperature is normal. This can avoid age detection errors and improve the accuracy of the measured age.
  • the user can also enter age via a smart watch and/or mobile phone.
  • (1) in Figure 9 shows a schematic diagram of a user inputting age through a smart watch.
  • the user can obtain the age input by the user through a similar application installed in the smart watch, such as the health application 501 shown in (1) in FIG. 5 .
  • the wearable device can display the personal information interface 900 in the health application 901 .
  • the personal information interface 900 includes an age option 901 , and the user can input the age through the age option 901 .
  • the personal information interface 900 may also include other information, including but not limited to: gender, height, etc.
  • (2) in Figure 9 shows a schematic diagram of a user inputting age through a mobile phone.
  • the user can obtain the user input through a similar application installed in the mobile phone, such as the health application 601 shown in (1) in Figure 6. Entering age.
  • the mobile phone can display the personal information interface 910 of the health application 901.
  • the personal information interface 910 includes an age option 911, and the user can input the age through the age option 911.
  • 911 can also include other information in the personal information interface, such as but not limited to gender, height, weight, date of birth, etc.
  • the smart watch and/or mobile phone can also determine whether the age input by the user is consistent based on the determined age group, for example: determine whether the age input by the user is between the determined age groups. It can be understood that the determined age group can be determined by the smart watch itself, or it can be determined by the mobile phone. For specific introduction, please refer to the above.
  • the age entered by the user can be entered by the user through a smart watch, or it can be entered by the user through a mobile phone. If there is any inconsistency, the smart watch and/or mobile phone can also display a reminder message to prompt the user to confirm whether the age they entered is correct.
  • the smart watch can display a reminder interface 1000, where the reminder interface 1000 can include "The age you currently input is 48.” Years old, please confirm whether it is correct?" and other such reminder messages are used by users to confirm whether the age entered in the smart watch is correct.
  • the reminder interface 1000 may also include a correct button 1001 and/or an incorrect button 1002 to facilitate the user to perform a confirmation operation.
  • the smart watch detects an operation such as a click of the correct button 1001 by the user, and in response to the operation, the smart watch determines that the age entered by the user is correct.
  • the smart watch detects an operation such as the user's click on the incorrect button 1002.
  • the smart watch determines that the age input by the user is incorrect. For example, as shown in (2) in Figure 10, the smart watch displays the age.
  • the input interface 1010 includes an age input option 1011 in the input interface 1010, which can be used by the user to re-enter the correct age.
  • the age input interface 1010 may be the personal information interface 900 shown in (1) in FIG. 9 , or may be other new interfaces, and the present application is not limited thereto.
  • the age input interface 1010 may also include a reminder message 1012 for the user to input the correct age based on the reminder message 1012 .
  • the mobile phone can display a reminder interface 1100.
  • the reminder interface 1100 can include reminders such as "The age you currently entered on the smart watch side is 48 years old. Please confirm whether it is correct?" Message for the user to confirm that the age entered into the smartwatch is correct.
  • the reminder interface 1100 may also include a correct button 1101 and/or an incorrect button 1102 to facilitate the user to perform a confirmation operation.
  • the mobile phone detects an operation such as the user's click on the incorrect button 1102, and in response to the operation, the mobile phone confirms that the age entered by the user on the smart watch side is incorrect.
  • the mobile phone can also display a message such as "Please go to the smart bracelet side to enter the correct age", and the user can re-enter the correct age through the smart bracelet according to the prompts, for example : The user can re-enter the correct age through the health application 501 such as shown in (1) in Figure 5.
  • the smart watch can display a reminder interface 1200, in which the reminder interface 1200 can include "The age you entered on the mobile phone is 48 years old, please Is it correct?" and other reminder messages are used for users to confirm that the age entered on the mobile phone is correct.
  • the reminder interface 1200 may also include a correct button 1201 and/or an incorrect button 1202
  • the smart watch detects the user's click operation on the correct button 1201, and in response to the operation, the smart watch determines that the age entered by the user on the mobile phone side is correct.
  • the smart watch detects an operation such as the user's click on the incorrect button 1202.
  • the smart watch determines that the age entered by the user on the mobile phone side is incorrect.
  • the interface shown in (2) in Figure 12 1210 the smart watch can also display messages such as "Please go to the mobile phone to enter the correct age".
  • the user can re-enter the correct age on the mobile phone according to the prompts.
  • the user can enter the correct age via the mobile phone as shown in (1) in Figure 6. Health App 601 Re-enter the correct age.
  • the mobile phone can remind the interface 1300, in which the reminder interface 1300 can include reminder messages such as "The age you are currently entering is 48 years old, please confirm whether it is correct?" for the user to confirm that the age is 48 years old. Is the age entered on the mobile phone correct?
  • the reminder interface 1300 may also include a correct button 1301 and/or an incorrect button 1302 to facilitate the user to perform a confirmation operation.
  • the mobile phone detects an operation such as the user's click on the incorrect button 1302, and in response to the operation, the mobile phone confirms that the age entered by the user is incorrect.
  • the mobile phone can 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 the personal information interface 910 shown in (2) in Figure 9, or in the form of a new interface such as (2) in Figure 10. This application also Not limited to this.
  • reminder message in the embodiment of the present application can be presented in the form of a floating window or in the form of a completely new interface, which is not specifically limited in this application.
  • consideration may be given to how frequently the wearable device and/or mobile phone displays reminder messages that may impact the user experience. Therefore, when the determined age group is inconsistent with the age entered by the user, in order to avoid frequent reminders from the smart watch and/or mobile phone and bring a bad experience to the user, the smart watch and/or mobile phone remind the user to confirm the entered age. Before confirming whether the age is correct or not, you can determine whether the user should be reminded to confirm whether the entered age is correct based on the last time the user was reminded.
  • the smart watch and/or mobile phone determines that the difference between the current time and the last time the user was reminded satisfies the preset conditions (such as: 3 days, 1 week, etc.), or whether the last time the user was reminded meets the preset conditions. If the preset conditions are set (for example: 3 days ago, 1 week ago, etc.), the smart watch and/or mobile phone will determine not to remind the user currently. Instead, the smartwatch and/or phone can prompt the user to confirm that the entered age is correct.
  • the preset conditions such as: 3 days, 1 week, etc.
  • the holders of the smart watch and the mobile phone can be the same user, or they can be different users.
  • the holders of the smart watch can be the elderly and children.
  • the holder of the mobile phone can be a guardian, etc.
  • the guardian can input the age of the elderly, children, etc. through the mobile phone.
  • the following is an application scenario in which the age group of the user is determined, or the age input by the user is determined.
  • smart watches can also use the determined age group or the age input by the user to perform health testing on the user.
  • the smart watch can input the determined age group or the age input by the user, the user's physiological parameters, the user's voice data, etc. into a preset health detection model, and output the user's health status through the health detection model ( For example: possible diseases, probability of a certain disease, etc.).
  • the user's physiological parameters obtained by the smart watch are consistent with
  • the user's physiological parameters obtained in the process of determining the user's age group may be the same or different.
  • the user's voice data obtained by the smart watch and the user's voice data obtained in the process of determining the user's age group may be the same or different. There are no restrictions on this application.
  • the health detection model corresponding to young people is the young person model (i.e., the first default model)
  • the health detection model corresponding to the middle-aged and elderly people is the middle-aged and elderly people model (i.e., the second preset model). If the wearable device determines that the user's age group is a young person, or determines that the user's age group is a young person based on the age input by the user, the wearable device can use the young person model to perform health testing on the user.
  • the wearable device determines that the user's age group is middle-aged and elderly, or determines that the user's age group is late-elderly based on the age input by the user, the wearable device can use the elderly model to perform health testing on the user. Since the physiological parameters, voice data, etc. of users of different age groups are different, using different health detection models for users of different age groups to perform health detection can improve the accuracy of health detection.
  • the above-mentioned health detection model can be a machine learning model, which can be obtained by taking age group, physiological parameters, user's voice data, etc. as input, and possible diseases, probability of disease, etc. as output training.
  • This application does not The algorithm used by the health detection model is not limited.
  • the above health detection model can be preset in a smart watch or other devices (such as mobile phones).
  • the wearable device and/or mobile phone can also output prompt messages (such as display screen, speaker voice broadcast, etc.) to remind the user.
  • prompt messages such as display screen, speaker voice broadcast, etc.
  • the smart watch can display reminder messages such as "High risk of lung infection, suspected pneumonia" to notify of possible diseases and the risk of the disease, and the like. , so that users can understand their physical health.
  • the reminder interface 1400 can also include various types of messages such as detection time 1401 and warm reminder 1402.
  • the detection time 1401 can be the time of the last health test, and the warm reminder 1402 can be used to remind the user to seek medical treatment in time and check health. Details etc.
  • the reminder interface 1400 may also include a start measurement button 1403.
  • the smart bracelet detects, for example, the user's click operation on the start measurement button 1403. In response to this operation, the smart bracelet begins to perform a health detection process for the user. .
  • a mobile phone can also display a reminder interface 1400 such as shown in Figure 14 to remind the user to learn about their own health status, or to remind the guardian to learn about the health status of the ward (such as parents, children), etc.
  • a reminder interface 1400 such as shown in Figure 14 to remind the user to learn about their own health status, or to remind the guardian to learn about the health status of the ward (such as parents, children), etc.
  • age group determined in the embodiment of the present application or the age input by the user can also be used for other purposes, and the present application is not limited thereto.
  • each interface is only a schematic diagram and does not constitute a limitation of the present application. In practical applications, each interface may include more or less content, or may include more or more content. Less interface.
  • the wearable device includes corresponding hardware structures and/or software modules to perform each function.
  • the embodiments of this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is performed in hardware or computer-driven hardware depends on the specific application and design constraints of the technical solution. Technology in this field Personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solutions of the embodiments of the present application.
  • This application is an embodiment that can divide the wearable device into functional modules according to the above method examples.
  • each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing unit.
  • the above-mentioned integrated units can be implemented in the form of hardware or software function modules. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. In actual implementation, there may be other division methods.
  • FIG. 15 it is a schematic structural diagram of a wearable device 1500 provided by an embodiment of the present application.
  • the wearable device 1500 can be used to implement the methods described in each of the above method embodiments.
  • the wearable device 1500 may specifically include: a processing unit 1501 and an acquisition unit 1502.
  • the acquisition unit 1502 is used to acquire the user's physiological parameters and voice data.
  • the processing unit 1501 is used to determine the age group of the user based on physiological parameters and voice data.
  • a possible design also includes a display unit 1503; the display unit 1503 is used to display the target interface; the processing unit 1501 is also used to receive the user's target operation; the processing unit 1501 is also used to activate the microphone in response to the target operation Record the user's voice data.
  • One possible design also includes a reminder unit 1504; the physiological parameters include body temperature, and the sound data includes cough sounds; after the acquisition unit 1502 is used to obtain the user's physiological parameters and sound data, the reminder unit 1504 is used to obtain the user's physiological parameters and sound data.
  • the body temperature is abnormal, target information is output (such as displaying target information and/or voice broadcasting target information, etc.). The target information is used to remind the user that the age group currently measured is inaccurate.
  • the age group includes a first age group and a second age group; the processing unit 1501 is also used to determine the first probability of the first age group and the second probability of the second age group based on physiological parameters; processing Unit 1501 is further configured to determine the third probability of the first age group and the fourth probability of the second age group based on the sound data; the processing unit 1501 is also configured to determine the age group of the user based on the first probability and the third probability; or The processing unit 1501 is also configured to determine the age group of the user based on the second probability and the fourth probability.
  • the processing unit 1501 is also configured to: 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 is 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, then it is determined that the user's age group is the first age group.
  • the processing unit 1501 is also configured to: 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, the user's age group is determined to be the second age group.
  • the processing unit 1501 is also configured to: 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 is 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, then the user's age group is determined to be the second age group; the processing unit 1501 is also configured to if the second probability is greater than or equal to The third preset threshold is 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, then the age group of the user is determined For the first age group.
  • the acquisition unit 1502 is also used to obtain the age input by the user; the reminder unit 1504 is also used to remind the user to confirm whether the input age is correct when the age does not match the determined age group of the user. .
  • the processing unit 1501 is also used to determine that the last time the user was reminded to confirm whether the entered age is correct satisfies the preset condition.
  • the processing unit 1501 is also configured to use the first preset model to perform health detection on the user if it is determined that the user's age group is the first age group; the processing unit 1501 is also configured to perform health detection on the user if it is determined that the user's age group is the first age group. If the age group is the second age group, the second preset model is used to perform health detection on the user.
  • processing unit 1501, acquisition unit 1502, display unit 1503, and reminder unit 1504 are also used to support the wearable device 1500 to perform other steps performed by the wearable device in the embodiment of the present application.
  • the wearable device 1500 shown in Figure 15 may also include a storage unit (not shown in Figure 15), which stores programs or instructions.
  • a storage unit not shown in Figure 15
  • the processing unit 1501 executes the program or instruction
  • the wearable device 1500 shown in FIG. 15 can perform the method shown in the embodiment of the present application.
  • the wearable device 1500 shown in Figure 15 may also include a communication unit (not shown in Figure 15), which is used to support the wearable device 1500 to perform the functions of the wearable device and other devices in the embodiments of the present application. communication steps.
  • the processing unit 1501 involved in the wearable device 1500 shown in Figure 15 can be implemented by a processor or a processor-related circuit component, and can be a processor or a processing module.
  • the communication unit can be implemented by a transceiver or a transceiver-related circuit component, and can be a transceiver or a transceiver module.
  • the acquisition unit 1502 may be implemented by a sensor or a sensor-related circuit component, and/or by a transceiver or a transceiver-related circuit component.
  • the display unit 1503 may be implemented by display screen related components.
  • the reminder unit 1504 can be implemented by display screen related components, microphone related components, etc.
  • the chip system 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.
  • interface circuitry 1602 may be used to receive signals from other devices.
  • interface circuit 1602 may be used to send signals to other devices (eg, processor 1601).
  • the interface circuit 1602 can read instructions stored in the memory and send the instructions to the processor 1601.
  • the wearable device may be caused to perform various steps performed by the wearable device in the above embodiments.
  • the chip system may also include other discrete devices, which are not specifically limited in the embodiments of this application.
  • processors in the chip system there may be one or more processors in the chip system.
  • the processor can be implemented in hardware or software.
  • the processor may be a logic circuit, an integrated circuit, or the like.
  • the processor may be a general-purpose processor implemented by reading software code stored in memory.
  • the memory may be integrated with the processor or may be provided separately from the processor, which is not limited by this application.
  • the memory can be a non-transient processor, such as a read-only memory ROM, which can be integrated on the same chip as the processor, or can be separately provided on different chips.
  • This application describes the type of memory, and the relationship between the memory and the processor. There is no specific limitation on how the processor is configured.
  • the chip system can be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a system on chip (SoC). It can also be a central processing unit (central processing unit) processor unit (CPU), it can also be a network processor (NP), it can also be a digital signal processor (DSP), it can also be a microcontroller (micro controller unit (MCU)), it can also It is a programmable logic device (PLD) or other integrated chip.
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • SoC system on chip
  • CPU central processing unit
  • NP network processor
  • DSP digital signal processor
  • MCU microcontroller
  • PLD programmable logic device
  • each step in the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software.
  • the method steps disclosed in conjunction with the embodiments of this application can be directly implemented by a hardware processor, or executed by a combination of hardware and software modules in the processor.
  • Embodiments of the present application also provide a computer storage medium.
  • Computer instructions are stored in the computer storage medium. When the computer instructions are run on a computer, they cause the computer to execute the method described in the above method embodiment.
  • Embodiments of the present application provide a computer program product.
  • the computer program product includes: a computer program or instructions. When the computer program or instructions are run on a computer, the computer is caused to execute the method described in the above method embodiment.
  • Embodiments of the present application provide a circuit system.
  • the circuit system includes a processing circuit, and the processing circuit is configured to execute the method described in the above method embodiment.
  • the embodiment of the present application also provides a device.
  • This device may be a chip, a component or a module.
  • the device may include a connected processor and a memory.
  • the memory is used to store computer execution instructions. When the device is running, the processing The device can execute computer execution instructions stored in the memory, so that the device executes the methods in each of the above method embodiments.
  • the wearable devices, computer storage media, computer program products, circuit systems, chips or devices provided in this embodiment are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the above. The beneficial effects of the corresponding methods provided in this article will not be repeated here.
  • a unit described as a separate component may or may not be physically separate.
  • a component shown as a unit may be one physical unit or multiple physical units, that is, it may be located in one place, or it may be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit.
  • the above integration The unit can be implemented in the form of hardware or software functional units.
  • Integrated units may be stored in a readable storage medium if they are implemented in the form of software functional units and sold or used as independent products.
  • the technical solutions of the embodiments of the present application are essentially or contribute to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the software product is stored in a storage medium , including several instructions to cause a device (which can be a microcontroller, a chip, etc.) or a processor to execute all or part of the steps of the methods of various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program code.

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Abstract

本申请提供一种年龄检测方法及可穿戴设备，涉及终端技术领域。能够提升获得的年龄的准确性，进而可以提升利用年龄对用户进行健康检测时的精度。方法可以应用于可穿戴设备，该方法包括：获取用户的生理参数和声音数据，然后根据该生理参数和声音数据确定用户的年龄段。进一步的，可以根据检测到的用户的年龄段向用户推荐不同的运动健康课程。

Description

年龄检测方法及可穿戴设备
本申请要求于2022年04月16日提交国家知识产权局、申请号为202210400462.7、发明名称为“一种生理年龄检测设备和方法”以及要求于2022年04月29日提交国家知识产权局、申请号为202210469077.8、发明名称为“年龄检测方法及可穿戴设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及终端技术领域，尤其涉及一种年龄检测方法及可穿戴设备。
背景技术
呼吸系统疾病是一种常见病、多发病，疾病种类复杂，致死率高。其多发于儿童和老年人。动脉硬化是动脉的一种非炎性病变，通常是在青少年时期发生，至中老年时期加重、发病。可见，诸如此类的疾病的发生与用户的年龄是有很大关系的，因此，通过结合用户的年龄，对用户进行健康(比如：可能发生的疾病等)检测，及时预警，对于保证用户的生命健康具有重要意义。
但是，目前根据用户的年龄对用户进行健康检测的方案中，需要用户手动输入年龄，然后再根据用户输入的年龄进行健康检测。该获取用户的年龄的方式无法确定用户输入的年龄是否正确，准确性较低。
发明内容
本申请提供一种年龄检测方法及可穿戴设备，可以提升获得的年龄的准确性。
为达到上述目的，本申请采用如下技术方案：
第一方面，本申请提供一种年龄检测方法，应用于可穿戴设备，方法包括：获取用户的生理参数和声音数据；根据生理参数和声音数据确定用户的年龄段。
可以理解，对于不同年龄段的用户，生理参数是有所差别的，声音数据也是有所差别的。因此，基于上述技术方案，综合考虑用户的生理参数和声音数据，根据该生理参数和声音数据确定用户的年龄段，可以智能的预测用户的年龄段，无需用户手动输入，能够提升获得的年龄的准确性。并且，对于不同年龄段的用户，用户可能发生的疾病的种类，可能发生的某疾病的概率等可能是不同的。因此，后续利用该确定出的年龄段对用户进行健康检测，可以提升健康检测的精度。
一种可能的设计中，获取用户的声音数据，包括：显示目标界面；接收用户的目标操作；响应于目标操作，激活麦克风录制用户的声音数据。基于该设计，可穿戴设备通过目标界面提醒用户输入声音数据，在检测到用户输入声音数据的操作之后，才会激活麦克风等诸如此类的传感器录制用户的声音数据，可以避免用于录制用户的声音数据的传感器一直开启，从而降低可穿戴设备的功耗。
一种可能的设计中，获取用户的生理参数的时间段为整夜时间。可以理解，由于用户在不同状态下，比如：静止状态、活动状态、运动状态等，用户的生理参数可能有所差别，而在夜间，用户处于静止状态的可能性较大，这样测量获得的生理参数的准确性较高。并且，即使用户处于同一状态下，由于误差的原因，不同时刻测得的生理参数也可能有所波动，这样，获取整夜的生理参数，可以使得获得的生理参数的可 参考性较高，进而在利用该生理参数确定用户的年龄段时，可以进一步提升确定的年龄段的准确性。
一种可能的设计中，生理参数包括体温，声音数据包括咳嗽音；在获取用户的生理参数和声音数据之后，方法还包括：在用户的体温不正常的情况下，输出目标信息，目标信息用于提醒用户当前测量获得的年龄段不准确。由于，用户在患有感冒发烧等诸如此类的会影响用户的体温的疾病时，测量得到的用户的体温可能不准确，这样在根据该不准确的体温确定用户的年龄段时，也可能导致测量得到的用户的年龄段不准确。由此，通过提醒用户该情况下可能会导致年龄测量不准确，使得用户了解详情，用户能够选择性的在体温正常的时候进行年龄检测，可以避免年龄检测失误，提升测量得到的年龄的准确性。
一种可能的设计中，年龄段包括第一年龄段和第二年龄段；根据生理参数和声音数据确定用户的年龄段包括：根据生理参数确定第一年龄段的第一概率和第二年龄段的第二概率；根据声音数据确定第一年龄段的第三概率和第二年龄段的第四概率；根据第一概率和第三概率确定用户的年龄段；或者根据第二概率和第四概率确定用户的年龄段。
一种可能的设计中，根据第一概率和第三概率确定用户的年龄段，包括：若第一概率大于等于第一预设阈值，或者第三概率大于等于第二预设阈值，或者第一概率大于等于第三预设阈值且小于第一预设阈值并且第三概率大于等于第四预设阈值且小于第二预设阈值，则确定用户的年龄段为第一年龄段；若第一概率大于等于第三预设阈值且小于第一预设阈值并且第三概率小于第四预设阈值，或者第一概率小于第三预设阈值且第三概率小于第二预设阈值，则确定用户的年龄段为第二年龄段。
一种可能的设计中，根据第二概率和第四概率确定用户的年龄段，包括：若第二概率大于等于第一预设阈值，或者第四概率大于等于第二预设阈值，或者第二概率大于等于第三预设阈值且小于第一预设阈值并且第四概率大于等于第四预设阈值且小于第二预设阈值，则确定用户的年龄段为第二年龄段；若第二概率大于等于第三预设阈值且小于第一预设阈值并且第四概率小于第四预设阈值，或者第二概率小于第三预设阈值且第四概率小于第二预设阈值，则确定用户的年龄段为第一年龄段。
一种可能的设计中，根据生理参数和声音数据确定用户的年龄段之后，方法还包括：获取用户输入的年龄；在年龄与确定的用户的年龄段不匹配的情况下，提醒用户确认输入的年龄是否正确。基于该设计，通过判断预测的用户的年龄段与用户输入的年龄是否匹配，并且在不一致的情况下，还可以提醒用户确认，最终可以获得正确的年龄段，可以进一步提升获得的年龄的准确性。后续，在利用该年龄段对用户进行健康检测时，还可以解决由于用户输入的年龄与可穿戴设备的佩戴者的年龄不一致而导致的健康检测出错的问题。
一种可能的设计中，在提醒用户确认输入的年龄是否正确之前，方法还包括：确定上一次提醒用户确认输入的年龄是否正确的时间满足预设条件。由于可穿戴设备输出提醒消息的频率可能影响用户体验，基于该设计，在输出提醒消息之前，先确定上一次提醒用户的时间是否满足预设条件，在满足预设条件的情况下再提醒用户，这样，可以避免可穿戴设备频繁输出提醒消息而导致用户体验不佳的情况。
一种可能的设计中，方法还包括：若确定用户的年龄段为第一年龄段，则采用第一预设模型对用户进行健康检测；若确定用户的年龄段为第二年龄段，则采用第二预设模型对用户进行健康检测。基于该设计，由于不同年龄段的用户的生理参数、声音数据等是有所差别的，这样，对于不同年龄段的用户采用不同的健康检测模型对用户进行健康检测，可以提升健康检测的精度。
一种可能的设计中，生理参数至少包括以下一种或多种：心率、心率变异性HRV、呼吸率、血氧、体温。
一种可能的设计中，声音数据至少包括以下一种或多种：咳嗽音、发“a”音、吹气音、语音。
第二方面，本申请提供一种可穿戴设备，该可穿戴设备具有实现如上述第一方面及其中任一设计中所述的年龄检测方法的功能。该功能可以通过硬件实现，也可以通过硬件执行相应地软件实现。该硬件或软件包括一个或多个与上述功能相对应的模块。在一种可能的示例中，可穿戴设备包括获取单元(或称获取模块)和处理单元(或称处理模块)；获取单元，用于获取用户的生理参数和声音数据；处理单元，用于根据生理参数和声音数据确定用户的年龄段。
一种可能的设计中，还包括显示单元(或称显示模块)；显示单元，用于显示目标界面；处理单元，还用于接收用户的目标操作；处理单元，还用于响应于目标操作，激活麦克风录制用户的声音数据。
一种可能的设计中，获取用户的生理参数的时间段为整夜时间。
一种可能的设计中，还包括提醒单元；生理参数包括体温，声音数据包括咳嗽音；在获取单元，用于获取用户的生理参数和声音数据之后，提醒单元(或称提醒模块)，用于在用户的体温不正常的情况下，输出目标信息，目标信息用于提醒用户当前测量获得的年龄段不准确。
一种可能的设计中，年龄段包括第一年龄段和第二年龄段；处理单元，还用于根据生理参数确定第一年龄段的第一概率和第二年龄段的第二概率；处理单元，还用于根据声音数据确定第一年龄段的第三概率和第二年龄段的第四概率；处理单元，还用于根据第一概率和第三概率确定用户的年龄段；或者处理单元，还用于根据第二概率和第四概率确定用户的年龄段。
一种可能的设计中，处理单元，还用于若第一概率大于等于第一预设阈值，或者第三概率大于等于第二预设阈值，或者第一概率大于等于第三预设阈值且小于第一预设阈值并且第三概率大于等于第四预设阈值且小于第二预设阈值，则确定用户的年龄段为第一年龄段。处理单元，还用于若第一概率大于等于第三预设阈值且小于第一预设阈值并且第三概率小于第四预设阈值，或者第一概率小于第三预设阈值且第三概率小于第二预设阈值，则确定用户的年龄段为第二年龄段。
一种可能的设计中，处理单元，还用于若第二概率大于等于第一预设阈值，或者第四概率大于等于第二预设阈值，或者第二概率大于等于第三预设阈值且小于第一预设阈值并且第四概率大于等于第四预设阈值且小于第二预设阈值，则确定用户的年龄段为第二年龄段；处理单元，还用于若第二概率大于等于第三预设阈值且小于第一预设阈值并且第四概率小于第四预设阈值，或者第二概率小于第三预设阈值且第四概率 小于第二预设阈值，则确定用户的年龄段为第一年龄段。
一种可能的设计中，获取单元，还用于获取用户输入的年龄；提醒单元，还用于在年龄与确定的用户的年龄段不匹配的情况下，提醒用户确认输入的年龄是否正确。
一种可能的设计中，处理单元，还用于确定上一次提醒用户确认输入的年龄是否正确的时间满足预设条件。
一种可能的设计中，处理单元，还用于若确定用户的年龄段为第一年龄段，则采用第一预设模型对用户进行健康检测；处理单元，还用于若确定用户的年龄段为第二年龄段，则采用第二预设模型对用户进行健康检测。
一种可能的设计中，生理参数至少包括以下一种或多种：心率、心率变异性HRV、呼吸率、血氧、温度。
一种可能的设计中，声音数据至少包括以下一种或多种：咳嗽音、发“a”音、吹气音、语音。
第三方面，本申请提供一种可穿戴设备，包括：包括处理器、存储器、传感器和显示屏，存储器、传感器、显示屏与处理器耦合，存储器用于存储计算机程序代码，计算机程序代码包括计算机指令，处理器从存储器中读取计算机指令，以使得可穿戴设备执行如上述第一方面及其中任一设计所述的方法。
一种可能的设计中，可穿戴设备还包括通信接口，该通信接口可用于可穿戴设备与其他装置(比如：电子设备)通信。示例性的，该通信接口可以为收发器、输入/输出接口、接口电路、输出电路、输入电路、管脚或相关电路等。
第四方面，本申请提供一种可穿戴设备，包括：至少一个处理器；所述处理器用于执行存储器中存储的计算机程序或指令，以使可穿戴设备执行上述第一方面及其中任一设计所述的方法。该存储器可以与处理器耦合，或者，也可以独立于该处理器。
一种可能的设计中，可穿戴设备还包括传感器，该传感器与处理器耦合，该传感器可用于可穿戴设备获取用户的生理参数和/或声音数据。示例性的，该传感器可以是光电容积脉搏波传感器，加速度传感器，温度传感器、声音传感器(如：麦克风)等。
一种可能的设计中，可穿戴设备还包括显示屏，该显示屏与处理器耦合，该显示屏可用于可穿戴设备实现显示操作。例如：显示目标界面、显示目标信息等。
一种可能的设计中，可穿戴设备还包括通信接口，该通信接口可用于可穿戴设备与其他装置(比如：电子设备)通信。示例性的，该通信接口可以为收发器、输入/输出接口、接口电路、输出电路、输入电路、管脚或相关电路等。
第五方面，本申请提供一种计算机可读存储介质，计算机可读存储介质包括计算机程序或指令，当计算机程序或指令在可穿戴设备上运行的情况下，使得可穿戴设备执行如上述第一方面及其中任一设计所述的方法。
第六方面，本申请提供一种计算机程序产品，当计算机程序产品在计算机上运行时，使得计算机可以执行如上述第一方面及其中任一设计所述的方法。
第七方面，本申请提供一种电路系统，电路系统包括处理电路，处理电路被配置为执行第一方面及其中任一设计所述的方法。
第八方面，本申请提供一种芯片系统，包括至少一个处理器和至少一个接口电路，至少一个接口电路用于执行收发功能，并将指令发送给至少一个处理器，当至少一个 处理器执行指令时，至少一个处理器执行如上述第一方面及其中任一设计所述的方法。
需要说明的是，上述第二方面至第八方面中任一设计所带来的技术效果可以参见第一方面中对应设计所带来的技术效果，此处不再赘述。
附图说明
图1为本申请实施例提供的一种年龄检测方法应用的通信系统的示意图；
图2为本申请实施例提供的一种可穿戴设备的结构示意图；
图3为本申请实施例提供的又一种可穿戴设备的结构示意图；
图4为本申请实施例提供的一种年龄检测方法的流程示意图；
图5为本申请实施例提供的界面示意图一；
图6为本申请实施例提供的界面示意图二；
图7为本申请实施例提供的界面示意图三；
图8为本申请实施例提供的界面示意图四；
图9为本申请实施例提供的界面示意图五；
图10为本申请实施例提供的界面示意图六；
图11为本申请实施例提供的界面示意图七；
图12为本申请实施例提供的界面示意图八；
图13为本申请实施例提供的界面示意图九；
图14为本申请实施例提供的界面示意图十；
图15为本申请实施例提供的又一种可穿戴设备的结构示意图；
图16为本申请实施例提供的一种芯片系统的结构示意图。
具体实施方式
下面结合附图对本申请实施例提供的年龄检测方法及可穿戴设备进行详细的描述。
本申请的描述中所提到的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括其他没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。
需要说明的是，本申请实施例中，“示例性的”或者“例如”等词用于表示作例子、例证或说明。本申请实施例中被描述为“示例性的”或者“例如”的任何实施例或设计方案不应被解释为比其它实施例或设计方案更优选或更具优势。确切而言，使用“示例性的”或者“例如”等词旨在以具体方式呈现相关概念。
在本申请的描述中，除非另有说明，“多个”的含义是指两个或两个以上。本文中的“和/或”仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。
呼吸系统疾病是一种常见病、多发病，疾病种类复杂，致死率高。其多发于儿童和老年人。动脉硬化是动脉的一种非炎性病变，通常是在青少年时期发生，至中老年时期加重、发病。可见，诸如此类的疾病的发生与用户的年龄是有很大关系的，因此，通过结合用户的年龄，对用户进行健康(比如：可能发生的疾病等)检测，及时 预警，使得用户可以根据预警消息及早预防或者及早治疗，这对于保证用户的生命健康具有重要意义。但是，目前根据用户的年龄对用户进行健康检测的方案中，需要用户手动输入年龄，然后再根据用户输入的年龄进行健康检测。该获取用户的年龄的方式无法确定用户输入的年龄是否正确，准确性较低。
为了解决上述技术问题，本申请提供一种年龄检测方法，能够提升获得的年龄的准确性。
示例性的，图1示出了本申请实施例中提供的一种年龄检测方法应用的通信系统的示意图。如图1所示，该通信系统包括可穿戴设备100和电子设备200。
可穿戴设备100可以通过有线通信技术和/或无线通信技术与电子设备200建立通信连接。其中，无线通信技术包括但不限于以下的至少一种：近距离无线通信(near field communication，NFC)，蓝牙(bluetooth，BT)(例如，传统蓝牙或者低功耗(bluetooth low energy，BLE)蓝牙)，无线局域网(wireless local area networks，WLAN)(如无线保真(wireless fidelity，Wi-Fi)网络)，紫蜂(Zigbee)，调频(frequency modulation，FM)，红外(infrared，IR)等。
在一些实施例中，可穿戴设备100与电子设备200都支持靠近发现功能。示例性地，可穿戴设备100靠近电子设备200后，可穿戴设备100和电子设备200能够互相发现对方，之后建立诸如Wi-Fi端到端(peer to peer，P2P)连接、蓝牙连接等无线通信连接。在建立无线通信连接后，可穿戴设备100与电子设备200可通过该无线通信连接实现信号交互。
在一些实施例中，可穿戴设备100与电子设备200通过局域网，建立无线通信连接。比如，可穿戴设备100与电子设备200都连接至同一路由器。
在一些实施例中，可穿戴设备100与电子设备200通过蜂窝网络、因特网等，建立无线通信连接。比如，电子设备200通过路由器接入因特网，可穿戴设备100通过蜂窝网络接入因特网；进而，可穿戴设备100与电子设备200建立无线通信连接。
可选的，可穿戴设备100例如可以是智能手表、智能手环、智能脚环、无线耳机、智能眼镜、智能头盔等具有年龄检测功能的终端设备。可穿戴设备100安装的操作系统包括但不限于或者其它操作系统。在一些实施例中，可穿戴设备100可以为固定式设备，也可以为便携式设备。本申请对可穿戴设备100的具体类型、所安装的操作系统均不作限制。
可选的，电子设备200例如可以是手机(mobile phone)、个人计算机(personal computer，PC)、平板电脑(Pad)、笔记本电脑、台式电脑、笔记本电脑、带收发功能的电脑、可穿戴设备、车载设备、人工智能(artificial intelligence，AI)设备等终端设备。电子设备200安装的操作系统包括但不限于 或者其它操作系统。在一些实施例中，电子设备200可以为固定式设备，也可以为便携式设备。本申请对电子设备200的具体类型、所安装的操作系统也不作限制。
其中，可穿戴设备100可以用于获取用户的生理参数和/或用户的声音数据，关于该生理参数和声音数据的介绍可参考后文所述。然后，可穿戴设备100可以根据用户的生理参数和/或用户的声音数据确定用户的年龄段。可选的，可穿戴设备100还 可以将用户的生理参数和/或用户的声音数据等发送给电子设备200，由电子设备200确定用户的年龄段。
进一步的，可以根据检测到的用户的年龄段向用户推荐不同的运动健康课程。
在一些实施例中，在电子设备200中安装有用于用户输入年龄的应用，用户可以通过该应用输入自己的年龄。
一种可能的示例中，可穿戴设备100可以从无线通信连接的电子设备200中获取用户输入的年龄，然后判断用户输入的年龄与确定出的年龄段是否一致，也即用户输入的年龄是否处于确定出的年龄段之间。可选的，该确定出的年龄段可以是由可穿戴设备100自己确定的，也可以是由电子设备200确定后发送给可穿戴设备100的。若不一致，可穿戴设备100可以输出提示信息(比如：通过显示屏显示提示信息，扬声器语音播报提示信息等)，提示用户确认在电子设备200中输入的年龄是否正确。当然，可穿戴设备100还可以将提示信息发送给电子设备200，由电子设备200输出对应的信息提示用户确认在电子设备200中输入的年龄是否正确。
另一种可能的示例中，电子设备200可以判断用户输入的年龄与确定出的年龄段是否一致。可选的，该确定出的年龄段可以是由可穿戴设备100确定后发送给电子设备200的，也可以是由可穿戴设备100将用户的生理参数和/或用户的声音数据等发送给电子设备200后，由电子设备200自己确定出的。若不一致，电子设备200可以输出提示信息，提示用户确认在电子设备200中输入的年龄是否正确。当然，电子设备200还可以将提示信息发送给可穿戴设备100，由可穿戴设备100输出对应的信息提示用户确认在电子设备200中输入的年龄是否正确。
在另一些实施例中，上述通信系统也可以不包括电子设备200。可选的，在可穿戴设备100中安装有用于用户输入年龄的应用，用户可以直接通过该应用输入自己的年龄。然后，可穿戴设备100可以判断用户输入的年龄与可穿戴设备确定出的年龄段是否一致，若不一致，可穿戴设备100可以输出提示信息，提示用户确认在可穿戴设备100中输入的年龄是否正确。
当然，可穿戴设备100以及电子设备200也可以均安装有用于用户输入年龄的应用，本申请不局限此。
图2示出了可穿戴设备100的结构示意图。
可穿戴设备100可以包括处理器110，存储器120，通用串行总线(universal serial bus，USB)接口130，充电管理模块140，电源管理模块141，电池142，天线1，天线2，移动通信模块150，无线通信模块160，音频模块170，传感器模块180，按键190，马达191，指示器192，摄像头193，显示屏194等。其中传感器模块180可以包括光电容积脉搏波传感器180A，加速度(acceleration，ACC)传感器180B，温度传感器180C，触摸传感器180D等。
处理器110可以包括一个或多个处理单元，例如：处理器110可以包括应用处理器(application processor，AP)，调制解调处理器，图形处理器(graphics processing unit，GPU)，图像信号处理器(image signal processor，ISP)，控制器，视频编解码器，数字信号处理器(digital signal processor，DSP)，基带处理器，和/或神经网络处理器(neural-network processing unit，NPU)等。其中，不同的处理单元可以是独立的 器件，也可以集成在一个或多个处理器中。
控制器可以根据指令操作码和时序信号，产生操作控制信号，完成取指令和执行指令的控制。
处理器110中还可以设置存储器，用于存储指令和数据。在一些实施例中，处理器110中的存储器为高速缓冲存储器。该存储器可以保存处理器110刚用过或循环使用的指令或数据。如果处理器110需要再次使用该指令或数据，可从所述存储器中直接调用。避免了重复存取，减少了处理器110的等待时间，因而提高了系统的效率。
在一些实施例中，处理器110可以包括一个或多个接口，如USB接口130等。其中USB接口130可以是符合USB标准规范的接口，具体可以是Mini USB接口，Micro USB接口，USB Type C接口等。USB接口130可以用于连接充电器为可穿戴设备100充电，也可以用于可穿戴设备100与外围设备之间传输数据。也可以用于连接耳机，通过耳机播放音频。该接口还可以用于连接其他设备，例如AR设备等。
充电管理模块140用于从充电器接收充电输入。其中，充电器可以是无线充电器，也可以是有线充电器。
电源管理模块141用于连接电池142，充电管理模块140与处理器110。电源管理模块141接收电池142和/或充电管理模块140的输入，为处理器110，存储器120，显示屏194，摄像头193，和无线通信模块160等供电。
可穿戴设备100的无线通信功能可以通过天线1，天线2，移动通信模块150，无线通信模块160等实现。
天线1和天线2用于发射和接收电磁波信号。可穿戴设备100中的每个天线可用于覆盖单个或多个通信频带。不同的天线还可以复用，以提高天线的利用率。例如：可以将天线1复用为无线局域网的分集天线。在另外一些实施例中，天线可以和调谐开关结合使用。
移动通信模块150可以提供应用在可穿戴设备100上的包括2G/3G/4G/5G等无线通信的解决方案。移动通信模块150可以包括至少一个滤波器，开关，功率放大器，低噪声放大器(low noise amplifier，LNA)等。
无线通信模块160可以提供应用在可穿戴设备100上的包括无线局域网(wireless local area networks，WLAN)(如无线保真(wireless fidelity，Wi-Fi)网络)，蓝牙(bluetooth，BT)，全球导航卫星系统(global navigation satellite system，GNSS)，调频(frequency modulation，FM)，近距离无线通信技术(near field communication，NFC)，红外技术(infrared，IR)等无线通信的解决方案。
在一些实施例中，可穿戴设备100的天线1和移动通信模块150耦合，天线2和无线通信模块160耦合，使得可穿戴设备100可以通过无线通信技术与网络以及其他设备通信。
可穿戴设备100通过GPU，显示屏194，以及应用处理器等实现显示功能。GPU为图像处理的微处理器，连接显示屏194和应用处理器。GPU用于执行数学和几何计算，用于图形渲染。处理器110可包括一个或多个GPU，其执行程序指令以生成或改变显示信息。
显示屏194用于显示图像，视频等。显示屏194包括显示面板。显示面板可以采 用液晶显示屏(liquid crystal display，LCD)，例如采用有机发光二极管(organic light-emitting diode，OLED)，有源矩阵有机发光二极体或主动矩阵有机发光二极体(active-matrix organic light emitting diode的，AMOLED)，柔性发光二极管(flex light-emitting diode，FLED)，Mini-led，Micro-led，Micro-oled，量子点发光二极管(quantum dot light emitting diodes，QLED)等生产制造。在一些实施例中，可穿戴设备100可以包括1个或N个显示屏194，N为大于1的正整数。
摄像头193用于捕获静态图像或视频。在一些实施例中，可穿戴设备100可以包括1个或N个摄像头193，N为大于1的正整数。
存储器120可以用于存储计算机可执行程序代码，所述可执行程序代码包括指令。存储器120可以包括存储程序区和存储数据区。其中，存储程序区可存储操作系统，至少一个功能所需的应用程序(比如声音播放功能，图像播放功能等)等。存储数据区可存储可穿戴设备100使用过程中所创建的数据(比如音频数据，电话本等)等。此外，存储器120可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件，闪存器件，通用闪存存储器(universal flash storage，UFS)等。处理器110通过运行存储在存储器120的指令，和/或存储在设置于处理器中的存储器的指令，执行可穿戴设备100的各种功能应用以及数据处理。在本申请的一些实施例中，存储器中可以存储有年龄检测模型(如第一年龄检测模型、第二年龄检测模型、第三年龄检测模型等)，该年龄检测模型可用于确定用户的年龄段，关于该年龄检测模型的介绍请参考后文所述。
可穿戴设备100可以通过音频模块170以及应用处理器等实现音频功能。例如音乐播放，录音等。
音频模块170用于将数字音频信息转换成模拟音频信号输出，也用于将模拟音频输入转换为数字音频信号。音频模块170还可以用于对音频信号编码和解码。在一些实施例中，音频模块170可以设置于处理器110中，或将音频模块170的部分功能模块设置于处理器110中。可穿戴设备100可以通过音频模块170，例如音乐播放，录音等。音频模块170可以包括扬声器，受话器，麦克风，以及应用处理器等实现音频功能。在本申请的一些实施例中，音频模块170还可用于获取用户的声音数据，关于该声音数据的介绍可参考后文所述。
光电容积脉搏波传感器180A，可以通过光电容积脉搏波描记法(photoplethysmography，PPG)，以LED光源和探测器为基础，通过测量经过人体血管和组织反射、吸收后的衰减光，获得PPG信号。在本申请的一些实施例中，可穿戴设备100对光电容积脉搏波传感器180A得到的PPG信号进行分析，可以获得用户的生理参数，例如：心率、呼吸率、血氧等。可选的，可穿戴设备还可以根据获得的心率进一步确定用户的心率变异性(heart rate variability，HRV)。其中，HRV可以指每个心跳周期的时间以及心跳的变化规律，这些变化规律能够反映出用户的不同生理状况或疾病情况。
加速度传感器180B可检测可穿戴设备100在各个方向上(一般为三轴)加速度的大小。当可穿戴设备100静止时可检测出重力的大小及方向。还可以用于识别可穿戴设备姿态，应用于横竖屏切换，计步器等应用。在本申请的一些实施例中，加速度传 感器180B测量加速度信号，其中，加速度信号可用于确定用户的状态，比如：静止状态、运动状态等。由于用户在不同状态下，生理参数(比如：呼吸率、心率、血氧等)可能有所差别。因此，为提高获得的用户的生理参数的准确性，可穿戴设备100还可以通过加速度传感器180B采集的加速度信号确定用户的状态，进一步辅助确定用户的生理参数。
温度传感器180C用于检测温度。在一些实施例中，可穿戴设备100利用温度传感器180C检测的温度，执行温度处理策略。例如，当温度传感器180C上报的温度超过阈值，可穿戴设备100执行降低位于温度传感器180C附近的处理器的性能，以便降低功耗实施热保护。在另一些实施例中，当温度低于另一阈值时，可穿戴设备100对电池142加热，以避免低温导致可穿戴设备100异常关机。在其他一些实施例中，当温度低于又一阈值时，可穿戴设备100对电池142的输出电压执行升压，以避免低温导致的异常关机。在本申请的一些实施例中，可穿戴设备100可以安装一个或多个温度传感器180C，用于检测用户的体温。
触摸传感器180D，也称“触控器件”。触摸传感器180D可以设置于显示屏194，由触摸传感器180D与显示屏194组成触摸屏，也称“触控屏”。触摸传感器180D用于检测作用于其上或附近的触摸操作。触摸传感器可以将检测到的触摸操作传递给应用处理器，以确定触摸事件类型。可以通过显示屏194提供与触摸操作相关的视觉输出。在另一些实施例中，触摸传感器180D也可以设置于可穿戴设备100的表面，与显示屏194所处的位置不同。
可选的，传感器模块180还可以包括压力传感器，气压传感器，磁传感器，距离传感器，接近光传感器，陀螺仪传感器、指纹传感器，环境光传感器，骨传导传感器等。
按键190包括开机键，音量键等。按键190可以是机械按键。也可以是触摸式按键。可穿戴设备100可以接收按键输入，产生与可穿戴设备100的用户设置以及功能控制有关的键信号输入。
马达191可以产生振动提示。马达191可以用于来电振动提示，也可以用于触摸振动反馈。
指示器192可以是指示灯，可以用于指示充电状态，电量变化，也可以用于指示消息，未接来电，通知等。
可以理解，上述仅是举例说明本申请实施例中可穿戴设备的结构的，并不构成对可穿戴设备结构、形态的限制。本申请实施例对可穿戴设备的结构、形态不做限制。示例性的，图3示出了可穿戴设备的另一种示例性结构。如图3所示，可穿戴设备包括：处理器301、存储器302、收发器303。处理器301、存储器302的实现可参见可穿戴设备处理器、存储器的实现。收发器303，用于可穿戴设备与其他设备(比如电子设备)交互。收发器303可以是基于诸如Wi-Fi、蓝牙或其他通信协议的器件。
在本申请另一些实施例中，可穿戴设备可以包括比图2、图3所示的更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者替换某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
可选的，关于电子设备200的结构可参考可穿戴设备100的结构，电子设备200 可以具有比可穿戴设备100更多或者更少的结构，本申请对此不作具体限制。
以下实施例中所涉及的技术方案均可以在具有如图2、图3所示结构的装置中实现。
本申请实施例提供一种年龄检测方法，应用于可穿戴设备。可穿戴设备可以获取用户的生理参数和/或用户的声音数据，然后根据用户的生理参数和/或用户的声音数据确定用户的年龄段。可选的，后续，该确定出的年龄段还可用于对用户进行健康检测。
其中，对于不同年龄段的用户，其生理参数是所差别的。用户的生理参数可以包括在不同的年龄段存在差异的生理参数，示例性的，该生理参数可以包括但不限于：心率、HRV、呼吸率、体温、血氧、血压、脉搏率等诸如此类的可用于确定用户的年龄段的参数。示例性的，年轻人的心率要高于老年人的心率，如年轻人的心率为每分钟60-100次，老年人的心率为每分钟55-90次。年轻人的HRV要高于老年人的HRV，如20岁至25岁年龄段的HRV位于55-105之间，60岁至65岁年龄段的HRV位于25-45之间。对于不同年龄段，呼吸率、体温、血氧、血压、脉搏率等诸如此类的生理参数也是有所差别的，不再一一举例。
对于不同年龄段的用户，由于用户的发声结构(比如：声带)有所差别，其声音数据的特征(比如：音调、音色等)也是有所差别的。示例性的，该声音数据包括但不限于用户的咳嗽音、发“a”音、发“啊”音、发“呀”音、呼气音、吹气音、说话音、语音等各种类型的声音数据，本申请不局限此。
其中，年龄段可以不同的划分方式。
在一种可能的划分方式中，年龄段包括年轻人、中老年人，例如：年龄位于40岁以及40岁之前称为年轻人，年龄位于40之后称为中老年人。
在另一种可能的划分方式中，年龄段包括少年、青年、中年、老年，例如：年龄位于17岁以及17岁之前称为少年，年龄位于18岁至40岁之间称为青年，年龄位于41岁至65岁之间称为中年，年龄位于65岁之后称为老年。
在又一种可能的划分方式中，年龄段包括青少年，中年、老年，例如：年龄位于40岁以及40岁之前称为青少年，年龄位于41岁至65岁之间称为中年，年龄位于65岁之后称为老年。
在又一种可能的划分方式中，年龄段包括青年人、中年人、年轻老年人、老年人、长寿老年人，例如：年龄位于44岁以及44岁之前称为青年人，年龄位于45岁至59岁之间称为中年人，年龄位于60岁至74岁之间称为年轻老年人，75岁至89岁之间称为老年人，90岁以上称为长寿老年人。
可以理解，上述划分方式仅为示例性说明，且每个年龄段所属的年龄范围也仅为示例性说明，并不构成对本申请的限定，在实际应用中，年龄段的划分、每个年龄段所属的年龄范围均可以由开发人员根据实际需求设定。
在一些实施例中，可穿戴设备可以仅获取用户的生理参数，然后根据获取的用户的生理参数确定用户的年龄段。可穿戴设备可以将获取的用户的生理参数输入预置的第一年龄检测模型，通过该第一年龄检测模型输出每个年龄段的概率，可选的，每个年龄段的概率之和为1。
可选的，第一年龄检测模型可以是机器学习模型，其可以通过模型训练获得，比如：以用户的生理参数为输入，用户的年龄段为输出训练获得第一年龄检测模型。本申请并不限定第一年龄检测模型所采用的具体算法。
在一种可能的示例中，可穿戴设备可以先确定第一年龄检测模型输出的年龄段的概率中是否存在大于等于第一阈值的，若存在，则将概率大于等于第一阈值的年龄段确定为用户的年龄段。示例性的，本申请实施例中，第一阈值可以设定为一个接近于1的数值，比如：0.85至0.9之间的数值等。可以理解，一个年龄段的概率越接近于1，则说明用户处于该年龄段的可能性越大，因此，可穿戴设备可以直接将概率大于等于第一阈值的年龄段确定为用户的年龄段。例如：以年龄段包括年轻人和中老年人，第一阈值为0.9为例，假设第一年龄检测模型输出的年轻人的概率为0.08，中老年人的概率为0.92，则可穿戴设备确定中老年人的概率0.92是大于第一阈值0.9的，因此确定用户的年龄段为中老年人。
可选的，若第一年龄检测模型输出的年龄段的概率中不存在大于等于第一阈值的，可穿戴设备还可以确定第一年龄检测模型输出的年龄段的概率中是否存在大于等于第二阈值且小于第一阈值的，若存在，则将概率大于等于第二阈值且小于第一阈值的年龄段确定为用户的年龄段。其中，第二阈值小于第一阈值，第二阈值可以设定为一个小于第一阈值但是也接近1的数值，比如：0.6至0.85之间的数值等。
可选的，若第一年龄检测模型输出的年龄段的概率中不存在大于等于第二阈值的，即各年龄段的概率均小于第二阈值，则可穿戴设备还可以将各年龄段中概率最大的年龄段确定为用户的年龄段。
在另一种可能的示例中，可穿戴设备无需确定第一年龄检测模型输出的年龄段的概率中是否存在大于等于第一阈值的，和/或，无需确定第一年龄检测模型输出的年龄段的概率中是否存在大于等于第二阈值且小于第一阈值的。可穿戴设备可以直接将概率最大的年龄段确定为用户的年龄段。例如：还是以年龄段包括年轻人和中老年人为例，假设可穿戴设备通过第一年龄检测模型输出的年轻人的概率为0.58，中老年人的概率为0.42，则可穿戴设备确定年轻人的概率0.58是大于中老年人的概率0.42
的，因此确定用户的年龄段为年轻人。
在另一些实施例中，可穿戴设备可以仅获取用户的声音数据。然后根据获取的用户的声音数据确定用户的年龄段。可选的，可穿戴设备可以将获取的用户的声音数据输入预置的第二年龄检测模型，通过第二年龄检测模型输出每个年龄段的概率，可选的，每个年龄段的概率之和为1。
可选的，第二年龄检测模型也可以是机器学习模型，其也可以通过模型训练获得，比如：以用户的声音数据为输入，用户的年龄段为输出训练获得第二年龄检测模型。本申请并不限定第二年龄检测模型所采用的具体算法。
与仅获取用户的生理参数的实施方式类似，可穿戴设备也可以先确定第二年龄检测模型中输出的年龄段的概率中是否存在大于等于第一阈值的，若存在，则将概率大于等于第一阈值的年龄段确定为用户的年龄段。可选的，若不存在，可穿戴设备还可以确定第二年龄检测模型输出的年龄段的概率中是否存在大于等于第二阈值且小于第一阈值的，若存在，则将概率大于等于第二阈值且小于第一阈值的年龄段确定为用户 的年龄段。可选的，若不存在，可穿戴设备还可以将各年龄段中概率最大的年龄段确定为用户的年龄段。当然，可穿戴设备可以不执行前述根据第一阈值和/或第二阈值的判断步骤，直接将各年龄段中概率最大的年龄段确定为用户的年龄段。
在又一些实施例中，可穿戴设备还可以同时获取用户的生理参数以及用户的声音数据，然后根据获取的用户的生理参数和用户的声音数据确定用户的年龄段。可选的，可穿戴设备可以将获取的用户的生理参数输入预置的第一年龄检测模型，将获取的用户的声音数据输入预置的第二年龄检测模型。通过第一年龄检测模型输出每个年龄段的概率，可选的，每个年龄段的概率之和为1。以及通过第二年龄检测模型输出每个年龄段的概率，可选的，该每个年龄段的概率之和也为1。可以理解，第一年龄检测模型输出的年龄段的概率是根据用户的生理参数确定的，第二年龄检测模型输出的年龄段的概率是根据用户的声音数据确定的。
在一种可能的示例中，可穿戴设备可以确定第一年龄检测模型输出的年龄段的概率中是否存在大于等于第三阈值的，若存在，则将第一年龄检测模型输出的概率大于等于第三阈值的年龄段确定为用户的年龄段。或者，确定第二年龄检测模型输出的年龄段的概率中是否存在大于等于第四阈值的，若存在，则将第二年龄模型输出的概率大于等于第四阈值的年龄段确定为用户的年龄段。
其中，本申请实施例中，第三阈值、第四阈值可以设定为接近1的数值，比如：设定为0.8至0.9之间的数值。可选的，第三阈值和第四阈值可以相同也可以不同，第三阈值、第四阈值与第一阈值可以相同也可以不同，与第一阈值不同时，与第二阈值可以相同也可以不同。
以年龄段包括年轻人和中老年人，第三阈值为0.9，第四阈值为0.8为例。
比如：第一年龄检测模型输出的年轻人的概率为0.95，中老年人的概率为0.05，第二年龄检测模型输出的年轻人的概率为0.7，中老年人的概率为0.3，可穿戴设备确定第一年龄检测模型输出的年轻人的概率0.95是大于第三阈值0.9的，则确定用户的年龄段为年轻人。
再比如：第一年龄检测模型输出的年轻人的概率为0.6，中老年人的概率为0.4，第二年龄检测模型输出的年轻人的概率为0.95，中老年人的概率为0.05，可穿戴设备确定第二年龄检测模型输出的年轻人的概率0.95是大于第四阈值0.8的，则确定用户的年龄段为年轻人。
再比如：第一年龄检测模型输出的年轻人的概率为0.95，中老年人的概率为0.05，第二年龄检测模型输出的年轻人的概率为0.85，中老年人的概率为0.15。可穿戴设备确定第一年龄检测模型输出的年轻人的概率0.95是大于第三阈值0.9的，则确定用户的年龄段为年轻人。或者，可穿戴设备确定第二年龄检测模型输出的年轻人的概率0.85是大于第四阈值0.8的，则确定用户的年龄段为年轻人。
可选的，在可穿戴设备确定第一年龄检测模块输出的年龄段的概率均小于第三阈值，且第二年龄检测模块输出的年龄段的概率均小于第四阈值的情况下，可穿戴设备还可以确定第一年龄检测模型输出的年龄段的概率中是否存在大于等于第五阈值且小于第三阈值的年龄段，若存在，则将第一年龄检测模型输出的概率大于等于第五阈值且小于第三阈值的年龄段确定为用户的年龄段。
或者，可穿戴设备还可以确定第二年龄检测模型输出的年龄段的概率中是否存在大于等于第六阈值且小于第四阈值的年龄段，若存在，则将第二年龄检测模型输出的概率大于等于第六阈值且小于第四阈值的年龄段确定为用户的年龄段。
或者，可穿戴设备还可以确定是否存在第一年龄检测模型输出的概率大于等于第五阈值且小于第三阈值，并且第二年龄检测模型输出的概率大于等于第六阈值且小于第四阈值的年龄段(即第一年龄检测模型输出的概率大于等于第五阈值且小于第三阈值的年龄段，与第二年龄检测模型输出的概率大于等于第六阈值且小于第四阈值的年龄段相同)，若存在，则将第一年龄检测模型输出的概率大于等于第五阈值且小于第三阈值，并且第二年龄检测模型输出的概率大于等于第六阈值且小于第四阈值的年龄段确定为用户的年龄段。
可选的，本申请实施例中，第五阈值、第六阈值也可以设定为接近于1的数值，比如：0.7至0.8之间的数值。可选的，第五阈值、第六阈值可以相同也可以不同，第五阈值、第六阈值与第一阈值、第二阈值等可以相同也可以不同。
可选的，在第一年龄检测模型输出的年龄段的概率均小于第五阈值，且第二年龄检测模型输出的年龄段的概率均小于第六阈值的情况下，可穿戴设备还可以根据前述两个年龄检测模型输出的各年龄段的概率确定用户的年龄段。可以理解，对于同一个年龄段，第一年龄检测模型输出的该年龄段的概率与第二年龄检测模型输出的该年龄段的概率可能相同也可能不同，可穿戴设备可以将概率最大的年龄段确定为用户的年龄段。
以年龄段包括年轻人和中老年人，第五阈值为0.7，第六阈值为0.75为例。
若第一年龄检测模型输出的年轻人的概率为0.65，中老年人的概率为0.35，第二年龄检测模型输出的中老年人的概率为0.7，年轻人的概率为0.3，则可穿戴设备可以确定第二年龄检测模型输出的概率0.7是所有概率中最大的，因此可穿戴设备确定用户的年龄段为中老年人。
可选的，可穿戴设备也可以不执行前述根据第三阈值、第四阈值的判断操作，和/或不执行前述根据第五阈值、第六阈值的判断操作，直接将各年龄段中概率最大的年龄段确定为用户的年龄段。
可选的，在该实施例中，该概率最大的年龄段的概率可能是第一年龄检测模型输出的，也可能是第二年龄检测模型输出的，还可以是平均概率，一个年龄段的平均概率为第一年龄检测模型输出的该年龄段的概率，与第二年龄检测模型输出的该年龄段的概率的平均值，本申请对此不作具体限制。
需要说明的是，本申请实施例中，各阈值(如第一阈值、第二阈值、第三阈值、第四阈值、第五阈值、第六阈值等)的取值仅为示例性说明，并不构成本申请的限定，实际应用中，开发人员可以根据实际需求设定。各阈值可以采用某具体数值的形式，也可以采用数值范围的形式，本申请对此不作具体限制，在此统一说明。
在一个具体的实施例中，以年龄段包括两个年龄段，分别为年轻人(即第一年龄段)和中老年人(即第二年龄段)为例，可穿戴设备可以根据第一年龄检测模型输出的年轻人的概率(即第一概率)和第二年龄检测模型输出的年轻人的概率(即第三概率)确定用户的年龄段。
若可穿戴设备确定第一年龄检测模型输出的年轻人的概率大于等于第三阈值(即第一预设阈值)；或者，第二年龄检测模型输出的年轻人的概率大于等于第四阈值(即第二预设阈值)；或者，第一年龄检测模型输出的年轻人的概率大于等于第五阈值(即第三预设阈值)且小于第三阈值，并且第二年龄检测模型输出的年轻人的概率大于等于第六阈值(即第四预设阈值)且小于第四阈值，则可穿戴设备确定用户的年龄段为年轻人。若可穿戴设备确定第一年龄检测模型输出的年轻人的概率大于等于第五阈值且小于第三阈值，并且第二年龄检测模型输入的年轻人的概率小于第六阈值；或者，第一年龄检测模型输出的年轻人的概率小于第五阈值且第二年龄检测模型输出的年轻人的概率小于第四阈值，则可穿戴设备确定用户的年龄段为中老年人。
可以理解，第一年龄检测模型输出的年轻人的概率小于第五阈值且第二年龄检测模型输出的年轻人的小于第四阈值包括了两种情况：一种是第一年龄检测模型输出的年轻人的概率小于第五阈值并且第二年龄检测模型输出的年轻人的概率大于等于第六阈值且小于第四阈值。另一种是第一年龄检测模型输出的年轻人的概率小于第五阈值且第二年龄检测模型输出的年轻人的概率小于第六阈值。
同样的，可穿戴设备也可以根据第一年龄检测模型输出的中老年人的概率(即第二概率)和第二年龄检测模型输出的中老年人的概率(即第四概率)确定用户的年龄段。若可穿戴设备确定第一年龄检测模型输出的中老年人的概率大于等于第三阈值；或者，第二年龄检测模型输出的中老年人的概率大于等于第四阈值；或者，第一年龄检测模型输出的中老年人的概率大于等于第五阈值且小于第三阈值，并且第二年龄检测模型输出的中老年人的概率大于等于第六阈值且小于第四阈值，则可穿戴设备确定用户的年龄段为中老年人。若可穿戴设备确定第一年龄检测模型输出的中老年人的概率大于等于第五阈值且小于第三阈值，并且第二年龄检测模型输入的中老年人的概率小于第六阈值；或者，第一年龄检测模型输出的中老年人的概率小于第五阈值且第二年龄检测模型输出的中老年人的概率小于第四阈值，则可穿戴设备确定用户的年龄段为年轻人。
可以理解，第一年龄检测模型输出的中老年人的概率小于第五阈值且第二年龄检测模型输出的中老年人的概率小于第四阈值也包含了两种情况：一种是第一年龄检测模型输出的中老年人的概率小于第五阈值并且第二年龄检测模型输出的中老年人的概率大于等于第六阈值且小于第四阈值。另一种是第一年龄检测模型输出的中老年人的概率小于第五阈值且第二年龄检测模型输出的中老年人的概率小于第六阈值。
可选的，该实施例中，在其他可能的实现方式中，可穿戴设备可以在获取的用户的生理参数满足一定条件的情况下，再获取用户的声音数据。比如：可穿戴设备将获取的用户生理参数输入第一年龄检测模型之后，确定第一年龄检测模型输出的年龄段的概率均小于第三阈值。或者，确定第一年龄检测模型输出的年龄段的概率中最大的概率大于等于第五阈值且小于第三阈值。或者，确定第一年龄检测模型输出的年龄段的概率均小于第五阈值。则可穿戴设备确定需要获取用户的声音数据，即开始执行获取用户的声音数据的过程。
或者，可穿戴设备可以在获取的用户的声音数据满足一定条件的情况下，再获取用户的生理参数。比如：可穿戴设备将获取用户的声音数据输入第二年龄检测模型， 确定第二年龄检测模型输出的年龄段的概率均小于第四阈值。或者，确定第二年龄检测模型输出的年龄段的概率中最大的概率大于等于第六阈值且小于第四阈值。或者，确定第二年龄检测模型输出的用户的年龄段的概率均小于第六阈值。则可穿戴设备可以确定需要获取用户的生理参数，即开始执行获取用户的生理参数的过程。
下面以年龄段包括年轻人和中老年人为例，图4示出了本申请实施例提供的一种年龄检测方法，应用于可穿戴设备，该方法包括以下步骤：
S401、获取用户的生理参数。
S402、根据用户的生理参数确定中老年人的概率是否大于等于第三阈值。
可选的，该步骤中，中老年人的概率可以指第一年龄检测模型输出的中老年人的概率，可穿戴设备可以将用户的生理参数输入第一年龄检测模型，以获得中老年人的概率。
若是，则执行步骤S403，若否，则执行步骤S404。
S403、确定用户的年龄段为中老年人。
S404、根据用户的生理参数确定中老年人的概率是否大于等于第五阈值且小于第三阈值。
可选的，该步骤中，中老年人的概率也可以指第一年龄检测模型输出的中老年人的概率。
若否，则执行步骤S405，若是，则执行步骤S406。
S405、确定用户的年龄段为年轻人。
S406、获取用户的声音数据。
本申请实施例中，该声音数据可以由可穿戴设备自身采集，也可以由其他设备(如电子设备)采集，可穿戴设备再从其他设备获取。
S407、根据用户的声音数据确定中老年人的概率是否大于等于第四阈值。
可选的，该步骤中，中老年人的概率可以指第二年龄检测模型输出的中老年人的概率，可穿戴设备可以将用户的声音数据输入第二年龄检测模型，以获得该中老年人的概率。
若是，则执行步骤S408，若否，则执行步骤S409。
S408、确定用户的年龄段为中老年人。
S409、根据用户的声音数据确定中老年人的概率是否大于等于第六阈值且小于第四阈值。
可选的，该步骤中，中老年人的概率也可以指第二年龄检测模型输出的中老年人的概率。
若是，则执行步骤S410，若否，则执行步骤S411。
S410、确定用户的年龄段为中老年人。
S411、确定用户的年龄段为年轻人。
可选的，本申请实施例中，第一年龄检测模型以及第二年龄检测模型还可以通过一个年龄检测模型(如第三年龄检测模型)实现，可穿戴设备可以将获取的用户的生理参数和用户的声音数据均输入第三年龄检测模型以确定用户的年龄段。
可选的，对于用户的生理参数，第三年龄检测模型可以输出各个年龄段的概率， 对于用户的声音数据，第三年龄检测模型也可以输出各个年龄段的概率，即对于一个年龄段，可以对应两个概率，其中一个概率是根据用户的生理参数确定的，另一个概率是根据用户的声音数据确定的。可选的，对于用户的生理参数和用户的声音数据，第三年龄检测模型也可以输出各个年龄段的概率，即对于一个年龄段，其对应一个概率，该概率是根据用户的生理参数和用户的声音数据共同确定的。
可选的，第三年龄检测模型也可以是机器学习模型，也可以通过模型训练获得，比如：以用户的生理参数以及用户的声音数据为输入，用户的年龄段为输出训练获得第三年龄检测模型。本申请并不限定第三年龄检测模型所采用的具体算法。
可以理解，上述实施例中均是以第一年龄检测模型、第二年龄检测模型、第三年龄检测模型输出的是各年龄段的概率进行说明的。可选的，第一年龄检测模型、第二年龄检测模型和第三年龄检测模型等中的一个或多个也可以直接输出最终确定的年龄段，本申请对此不作限定。
可选的，本申请实施例中，第一年龄检测模型、第二年龄检测模型和第三年龄检测模型等可以预置于可穿戴设备中，也可以预置于其他设备(如电子设备)中，本申请对此也不作具体限定。在前述年龄检测模型预置于其他设备的情况下，可穿戴设备可以将获取的用户的生理参数和/或用户的声音数据发送给其他设备，由其他设备确定出用户的年龄段。可选的，可穿戴设备还可以从其他设备获取确定出的用户的年龄段。
上述实施例中，可穿戴设备(或者其他设备)是通过机器学习模型确定用户的年龄段的。在其他的实施例中，可穿戴设备(或者其他设备)还可以通过预设规则确定用户的年龄段。比如：对于不同的年龄段，其对应的生理参数的范围是不同的，对应的声音数据的特征也是不同的。以可穿戴设备为例，可穿戴设备可以根据获取的用户的生理参数以及预设的不同年龄段对应的生理参数的范围确定用户的年龄段。或者，可穿戴设备可以根据获取的用户的声音数据以及预设的不同年龄段对应的声音数据的特征确定用户的年龄段。或者，可穿戴设备可以根据获取的用户的生理参数、用户的声音数据、预设的不同的年龄段对应的生理参数的范围、预设的不同年龄段对应的声音数据的特征确定用户的年龄段。
基于上述技术方案，本申请实施例中，通过识别获取的用户的生理参数和/或用户的声音数据确定用户的年龄段，可以智能的预测用户的年龄段，无需用户手动输入，可以提升获得的年龄的准确性。
下面结合附图，以可穿戴设备100为智能手表，电子设备200为手机为例，结合具体的场景，对本申请实施例提供的年龄检测方法进行详细介绍。
在一种可能的场景中，智能手表可以自动开启年龄检测功能。比如：在智能手表处于开机状态的情况下，智能手表可以执行获取用户的生理参数和/或用户的声音数据，并根据用户的生理参数和/或用户的声音数据确定用户的年龄段的过程。可选的，智能手表还可以在确定当前处于佩带状态的情况下，再执行确定前述过程。鉴于智能手表确定是否处于佩带状态的操作为现有技术，具体实现可参考相关技术中的介绍，本文不再赘述。
在另一种可能的场景中，需要用户主动开启智能手表的年龄检测功能。例如：检 测到用户用于开启智能手表的年龄检测功能的指令，响应于该指令，智能手表才会执行获取用户的生理参数和/或用户的声音数据，并根据用户的生理参数和/或用户的声音数据确定用户的年龄段的过程。可选的，用户可以通过诸如语音、手势、按键、快捷按钮等各种方式唤醒智能手表的年龄检测功能。
在一些实施例中，用户可以通过智能手表自身开启智能手表的年龄检测功能。示例性的，如图5中(1)所示，智能手表显示主界面500，在主界面500中包括有各种应用程序，不同的应用程序可用于实现不同的功能。其中，包括有健康应用501，健康应用501可用于对用户进行健康检测，检测用户的身体健康情况，比如:是否处于亚健康状态、是否已患有某些疾病、是否将要可能患某些疾病等。智能手表检测到用户对健康应用501的启动操作，如：检测到诸如用户对健康应用501的图标的点击操作，响应于该操作，智能手表启动健康应用501。示例性的，如图5中的(2)所示，智能手表可以显示健康应用501的运行界面510。其中，在运行界面510中包括有启动智能手表的年龄检测功能的按钮511。智能手表检测到诸如用户对按钮511的点击操作，响应于该操作，智能手表开始执行确定用户的年龄段的过程。可选的，在运行界面510中还可以包括上次进行健康检测的结果和/或检测时间等。
在另一些实施例中，用户也可以通过其他设备(如手机)开启智能手表的年龄检测功能。如图6中(1)所示，手机显示主界面600，其中在主界面600中包括有一个或多个应用程序，其中，包括健康应用601，关于该健康应用601的介绍可参考图5中(1)所示的健康应用501的相关介绍。手机检测到用户用于启动健康应用601的操作，例如：检测到诸如用户对健康应用601的图标的点击操作，响应于该操作，手机启动健康应用601。在一些实施例中，如图6中的(2)所示，手机可以显示健康应用601的运行界面610。可以理解，运行界面610可以是健康应用601的主界面，也可以是子界面等，本申请对此不作限定。其中，在运行界面610中包括有用于启动智能手表的年龄检测功能的按钮611。手机检测到诸如用户对按钮611的点击等用于启动智能手表的年龄检测功能的操作，响应于该操作，手机向建立有通信连接的智能手表发送指令消息，该指令消息用于指示智能手表开启年龄检测功能，智能手表在接收到手机发送的该指令消息之后，开启年龄检测功能，开始执行确定用户的年龄段的过程。
在一些实施例中，智能手表开启年龄检测功能之后，智能手表可以实时的或者周期性的检测用户的生理参数和/或用户的声音数据。在另一些实施例中，智能手表开启年龄检测功能之后，智能手表仅在特定时间或者特定场景下开始实时的或者周期性的检测用户的生理参数和/或用户的声音数据。比如：考虑到用户的状态(比如：静止状态、运动状态、活动状态等)可能会对生理参数造成一定影响，因此，智能手表可以在夜间(如整夜时间)检测用户的生理参数和/或用户的声音数据，由于夜间用户处于静止状态，这样检测的用户的生理参数的准确性较高。或者，智能手表在白天确定用户处于静止状态的情况下，再检测用户的生理参数和/或用户的声音数据。
可选的，智能手表还可以在获取的用户的生理参数和/或用户的声音数据满足预设条件(比如：预设时长、预设数量等)的情况下，再根据这些用户的生理参数和/或用户的声音数据确定用户的年龄段。可以理解，由于，用户的生理参数、声音数据 可能存在波动情况，因此，获取预设条件的生理参数和/或声音数据，可以使得这些生理参数、声音数据的可靠性较高，进而再根据这些数据确定用户的年龄段时，可以提升测量得到的年龄段的准确性。
可选的，智能手表在开启(包括自动开启和/或用户主动开启)一次年龄检测功能之后，可以周期性的或者非周期性的执行多次确定用户的年龄段的过程，即智能手表可以持续执行确定用户的年龄段的过程。或者，为降低智能手表的功耗，智能手表在开启一次年龄检测功能之后，也可以仅执行有限次(比如：1次、2次等)的确定用户的年龄段的过程，具体的次数可由开发人员根据实际需求设定。
在一些可能的场景中，智能手表在开启年龄检测功能之后，智能手表可以开始自动获取用户的声音数据，但是在智能手表检测用户的声音数据的过程中，用户可能未发出任何声音，这种情况下，还可以通过输出提醒消息(比如显示屏显示提醒消息、扬声器语音播报提醒消息等)提示用户输入声音数据。可选的，该提醒消息可以由智能手表自身输出，也可以由手机输出，还可以由智能手表和手机同时输出，本申请对此不作限制。
在另一些可能的场景中，在智能手表开启年龄检测功能之后，需要用户主动输入自己的声音数据，响应于用户输入声音数据的操作，智能手表才会获取用户的声音数据，比如：智能手表通过激活麦克风录制用户的声音数据。可选的，智能手表和/或手机也可以输出提醒消息，提醒用户主动输入自己的声音数据。这样，智能手表在检测用户用于输入声音数据的操作之后，才会激活麦克风等诸如此类的传感器录制用户的声音数据，可以避免用于录制用户的声音数据的传感器一直开启，从而降低可穿戴设备的功耗。
可选的，可以由智能手表完成用户的声音数据的录制，也可以由手机完成用户的声音数据的录制，本申请对此不作限定。
示例性的，以声音数据为咳嗽音为例，如图7中的(1)所示(即目标界面)，智能手表可以显示“当前未检测到您的咳嗽音，无法完成年龄检测，请长按下方按钮完成咳嗽音的录制！”等诸如此类的提醒消息，以便于用户通过智能手表完成咳嗽音的录制。如图7中(2)所示，智能手表检测到诸如用户对咳嗽音录制按钮701的长按等操作(即目标操作)，响应于该操作，智能手表开始获取用户输入的咳嗽音，例如：激活麦克风开始录制用户的咳嗽音等。可选的，智能手表还可以显示进度条702，“正在录制”的文字提示703等各种形式的温馨提示，以提醒用户咳嗽音录制的进度等。可选的，智能手表还可以提醒用户录制成功或者录制失败的消息。示例性的，如图7中(3)所示，智能手表可以显示“咳嗽音录制成功，正在进行年龄检测！”等诸如此类的消息提醒用户录制成功。可选的，在咳嗽音录制成功之后，智能手表还可以自动隐藏咳嗽音录制按钮701。或者，如图7中(4)所示，智能手表可以显示“咳嗽音录制失败，请您重新进行录制！”等诸如此类的消息提醒用户录制失败。
示例性的，还是以声音数据为咳嗽音为例，如图8中的(1)所示，手机可以显示“智能手表当前未检测到您的咳嗽音，无法完成年龄检测，请前往智能手表侧完成咳嗽音的录制！”等诸如此类的，提醒用户到智能手表侧完成咳嗽音录制的消息，用 户可以根据该消息通过智能手表完成咳嗽音的录制。或者，如图8中的(2)所示，手机也可以显示“当前未检测到您的咳嗽音，无法完成年龄检测，请长按下方按钮完成咳嗽音的录制！”等诸如此类的，提醒用户通过手机完成咳嗽音录制的消息。可选的，手机检测到诸如用户咳嗽音录制按钮801的长按，手机获取用户输入的咳嗽音。可选的，手机也可以显示诸如图7中(2)所示的进度条702、“正在录制”的文字提示703，以提醒用户咳嗽音录制的进度等。可选的，手机也可以采用诸如图7中
(3)、(4)所示的形式提醒用户咳嗽音录制成功或者咳嗽音录制失败等。后续，手机可以将成功录制的咳嗽音发送给智能手表，以便于智能手表完成年龄段的检测。
可选的，在完成用户年龄段的检测之后，还可以通过智能手表或者手机输出(例如：显示和/或语音播报)该年龄段以告知用户。
在一个具体的实施例中，以生理参数包括体温，声音数据包括咳嗽音为例。以智能手表执行确定用户的年龄段的操作为例，智能手表在获取到用户的体温之后，还可以判断该体温是否是正常体温，在体温不正常的情况下，智能手表还可以输出目标信息(如显示该目标信息和/或语音播报目标信息等)，以提醒用户当前测量获得的年龄段不准确。可选的，同时还可以提醒用户导致测量获得的年龄段不准确的原因。比如：提醒用户由于发生目标类型的疾病可能导致测量的年龄段不准确。其中，目标类型的疾病指的是具有发烧、咳嗽等症状的疾病，如：感冒、发烧等。当然，智能手表也可以将目标信息发送给手机，由手机输出目标信息，或者，智能手表和手机也可以同时输出目标信息。
可选的，智能手表和/或手机可以在输出用户的年龄段的时候输出该目标信息，也可以在其他的时机输出目标信息，本申请不局限此。
同样的，若由手机执行确定用户的年龄段的操作，手机也可以执行判断用户的体温是否正常，在不正常的情况下，输出目标信息的操作。当然，手机也可以将目标信息发送给智能手表，由智能手表输出目标信息，或者，智能手表和手机也可以同时输出目标信息，本申请对此不作限定。
由于用户在患有感冒、发烧等诸如此类的会影响用户的体温的疾病时，测量得到的用户的体温可能不准确，这样在根据该不准确的体温确定用户的年龄段时，也可能导致测量得到的用户的年龄段不准确。由此，通过提醒用户该情况下可能会导致年龄测量不准确，使得用户了解详情，能够选择性的在体温正常的时候进行年龄检测，可以避免年龄检测失误，提升测量得到的年龄的准确性。
在一些实施例中，用户还可以通过智能手表和/或手机输入年龄。
示例性的，图9中(1)示出了用户通过智能手表输入年龄的示意图。例如：用户可以通过智能手表中安装的诸如图5中(1)所示的健康应用501等类似的应用获取用户输入的年龄。示例性的，如图9的中(1)所示，可穿戴设备可以显示健康应用901中的个人信息界面900，在个人信息界面900中包括年龄选项901，用户可以通过年龄选项901输入年龄。可选的，在个人信息界面900中还可以包括其他的信息，比如包括但不限于：性别、身高等等。
示例性的，图9中(2)示出了用户通过手机输入年龄的示意图，例如：用户可以通过手机中安装的诸如图6中(1)所示的健康应用601等类似的应用获取用户输 入的年龄。示例性的，如图9中的(2)所示，手机可以显示健康应用901的个人信息界面910，在个人信息界面910中包括年龄选项911，用户可以通过年龄选项911输入年龄。可选的，在个人信息界面中911中还可以包括其他的信息，比如：包括但不限于性别、身高、体重、出生日期等。
在一些实施例中，智能手表和/或手机还可以根据确定出的年龄段确定用户输入的年龄是否一致，比如：确定用户输入的年龄是否位于确定出的年龄段之间。可以理解，该确定出的年龄段可以是智能手表自己确定的，也可以是由手机确定的，具体介绍可参考上文所述。用户输入的年龄可以是用户通过智能手表输入的，也可以是用户通过手机输入的。若不一致，智能手表和/或手机还可以显示提醒消息，提示用户确认自己输入的年龄是否正确。这样，通过判断预测的用户的年龄段与用户输入的年龄是否匹配，并且在不一致的情况下，还可以提醒用户确认，最终获得正确的年龄段，可以进一步提升获得的年龄的准确性。后续，在利用该年龄段对用户进行健康检测时，还可以解决由于用户输入的年龄与可穿戴设备的佩戴者的年龄不一致而导致的健康检测出错的问题。
示例性的，在用户通过智能手表输入年龄的场景下，如图10中(1)所示，智能手表可以显示提醒界面1000，其中，在提醒界面1000中可以包括“您当前输入的年龄为48岁，请确认是否正确？”等诸如此类的提醒消息，以用于用户确认在智能手表中输入的年龄是否正确。可选的，提醒界面1000还可以包括正确按钮1001和/或不正确按钮1002等，以便于用户执行确认操作。可选的，智能手表检测到诸如用户对正确按钮1001的点击操作，响应于该操作，智能手表确定用户输入的年龄正确。或者，智能手表检测到诸如用户对不正确按钮1002的点击操作，响应于该操作，智能手表确定用户输入的年龄不正确，示例性的，如图10中(2)所示，智能手表显示年龄输入界面1010，其中在输入界面1010中包括年龄输入选项1011，其可用于用户重新输入正确的年龄。示例性的，该年龄输入界面1010可以为图9中(1)所示的个人信息界面900，也可以为其他新的界面，本申请不局限此。可选的，在年龄输入界面1010中还可以包括提醒消息1012，以用于用户据提醒消息1012输入正确的年龄。
如图11中(1)所示，手机可以显示提醒界面1100，其中，在提醒界面1100中可以包括“您当前在智能手表侧输入的年龄为48岁，请确认是否正确？”等诸如此类的提醒消息，以用于用户确认在智能手表中输入的年龄是否正确。可选的，提醒界面1100中也可以包括正确按钮1101和/或不正确按钮1102等，以便于用户执行确认操作。例如，手机检测到诸如用户对不正确按钮1102的点击操作，响应于该操作，手机确认用户在智能手表侧输入的年龄不正确。可选的，如图11中(2)所示，手机还可以显示“请前往智能手环侧输入正确的年龄”等诸如此类的消息，用户可以根据提示通过智能手环重新输入正确的年龄，例如：用户可以通过诸如图5中(1)所示的健康应用501重新输入正确的年龄。
在用户通过手机输入年龄的场景下，如图12中(1)所示，智能手表可以显示提醒界面1200，其中，在提醒界面1200中可以包括“您在手机侧输入的年龄为48岁，请确认是否正确？”等诸如此类的提醒消息，以用于用户确认在手机侧输入的年龄是 否正确。可选的，提醒界面1200也可以包括正确按钮1201和/或不正确按钮1202
等。比如：智能手表检测诸如用户对正确按钮1201的点击操作，响应于该操作，智能手表确定用户在手机侧输入的年龄正确。或者，智能手表检测到诸如用户对不正确按钮1202的点击操作，响应于该操作，智能手表确定用户在手机侧输入的年龄不正确，可选的，如图12中(2)所示的界面1210，智能手表还可以显示“请前往手机侧输入正确的年龄”等诸如此类的消息，用户可以根据提示在手机侧重新输入正确的年龄，比如：用户可以通过诸如图6中(1)所示的健康应用601重新输入正确的年龄。
如图13所示，手机可以提醒界面1300，其中，在提醒界面1300中可以包括诸如“您当前输入的年龄为48岁，请确认是否正确？”等诸如此类的提醒消息，以用于用户确认在手机侧输入的年龄是否正确。可选的，在提醒界面1300也可以包括正确按钮1301和/或不正确按钮1302，以便于用户执行确认操作。例如：手机检测到诸如用户对不正确按钮1302的点击操作，响应于该操作，手机确认用户输入的年龄不正确。可选的，手机还可以显示年龄输入界面，以便于用户重新输入正确的年龄。示例性的，该年龄输入界面可以呈现为诸如图9中(2)所示的个人信息界面910的形式，也可以呈现为诸如图10中(2)所示的新界面的形式，本申请也不局限于此。
可以理解，本申请实施例中的提醒消息可以以悬浮窗的形式呈现，也可以以全新的界面的形式呈现，本申请对此不作具体限制。
在一些实施例中，考虑到可穿戴设备和/或手机显示提醒消息的频率可能会影响用户体验。因此，在确定的年龄段与用户输入的年龄不一致的情况下，为避免智能手表和/或手机频繁提醒，给用户带来不好的体验，智能手表和/或手机在提醒用户确认输入的年龄是否正确之前，可以根据上一次提醒用户的时间确定当前是否要提醒用户确认输入的年龄是否正确。比如：若智能手表和/或手机确定当前时间与上一次提醒用户的时间之间的差值满足预设条件(比如：3天、1周等)，或称，上一次提醒用户的时间是否满足预设条件(比如：3天前，1周前等)，则智能手表和/或手机确定当前不提醒用户。反之，智能手表和/或手机可以提醒用户确认输入的年龄是否正确。
可以理解，本申请实施例中，智能手表和手机的持有者可以为同一用户，也可以为不同用户，比如：在监护老人、子女的场景下，智能手表的持有者可以为老人、子女，手机的持有者可以为监护人等，该场景下，监护人可以通过手机输入老人、子女等的年龄。
下面给出一种确定出的用户的年龄段，或者用户输入的年龄的应用场景。
可以理解，对于不同年龄段的用户，其可能发生的疾病的种类、疾病的风险等可能是不同的。因此，智能手表还可以利用确定出的年龄段，或者用户输入的年龄对用户进行健康检测。
在一些实施例中，智能手表可以将确定出的年龄段或者用户输入的年龄、用户的生理参数、用户的声音数据等输入预置的健康检测模型中，通过健康检测模型输出用户的健康情况(比如：可能发生的疾病、发生某疾病的概率等)。
可选的，智能手表在执行健康检测的过程中，智能手表获取的用户的生理参数与 确定用户的年龄段的过程中获取的用户的生理参数可以相同也可以不同，智能手表获取的用户的声音数据与确定用户的年龄段的过程中获取的用户的声音数据可以相同也可以不同，本申请对此不作限定。
可选的，对于不同的年龄段，其可以对应不同的健康检测模型，比如：以年龄段包括年轻人、中老年人为例，年轻人对应的健康检测模型为年轻人模型(即第一预设模型)，中老年人对应的健康检测模型为中老年人模型(即第二预设模型)。若可穿戴设备确定用户的年龄段为年轻人，或者根据用户输入的年龄确定用户的年龄段为年轻人，则可穿戴设备可以利用年轻人模型对用户进行健康检测。若可穿戴设备确定用户的年龄段为中老年人，或者根据用户输入的年龄确定用户的年龄段为终老年人，则可穿戴设备可以利用老年人模型对用户进行健康检测。由于不同年龄段的用户的生理参数、声音数据等是有所差别的，这样，对不同年龄段的用户采用不同的健康检测模型对用户进行健康检测，可以提升健康检测的精度。
可选的，上述健康检测模型可以是机器学习模型，其可以通过以年龄段、生理参数、用户的声音数据等为输入，可能发生的疾病、发生疾病的概率等为输出训练获得，本申请并不限定健康检测模型所采用的算法。
可选的，上述健康检测模型可以预置于智能手表中，也可以预置于其他设备(比如：手机)中。
可选的，在确定出用户的健康状况之后，可穿戴设备和/或手机还可以输出提示消息(比如：显示屏显示、扬声器语音播报等)提醒用户。示例性的，如图14所示的提醒界面1400，智能手表可以显示诸如“肺部感染高风险，疑似肺炎”等提醒消息，用于通知可能发生的疾病以及发生该疾病的风险等诸如此类的消息，以便于用户了解自己的身体健康情况。可选的，提醒界面1400还可以包括检测时间1401、温馨提示1402等各种类型的消息，其中检测时间1401可以为上一次进行健康检测的时间，温馨提示1402可用于提醒用户及时就诊，查看健康详情等。可选的，在提醒界面1400中还可以包括开始测量按钮1403，智能手环监测到诸如用户对开始测量按钮1403的点击操作，响应于该操作，智能手环开始执行对用户进行健康检测的过程。
与智能手表类似，手机也可以显示诸如图14所示的提醒界面1400，以提醒用户获知自己的身体健康情况，或者，以提醒监护人获知被监护人(比如：父母、子女)等的身体健康情况。
可以理解，本申请实施例确定出的年龄段，或者用户输入的年龄还可用于其他用途，本申请不局限于此。
需要说明的是，本申请实施例中，各界面仅仅是示意图，并不构成本申请的限定，在实际应用中，各界面中可以包括更多或者更少的内容，也可以包括更多或者更少的界面。
上述主要是从方法的角度对本申请实施例提供的方案进行了介绍。可以理解的是，可穿戴设备为了实现上述功能，其包含了执行各个功能相应的硬件结构和/或软件模块。结合本申请中所公开的实施例描述的各示例的单元及算法步骤，本申请实施例能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。本领域技术 人员可以对每个特定的应用来使用不同的方法来实现所描述的功能，但是这种实现不应认为超过本申请实施例的技术方案的范围。
本申请是实施例可以根据上述方法示例对可穿戴设备进行功能模块的划分，例如，可以对应各个功能划分各个功能模块，也可以将两个或两个以上的功能集成在一个处理单元中。上述集成的单元既可以采用硬件的形式，也可以采用软件功能模块的形式实现。需要说明的是，本申请实施例中对单元的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
如图15所示，为本申请实施例提供的一种可穿戴设备1500的结构示意图，该可穿戴设备1500可用于实现以上各个方法实施例中记载的方法。示例性的，该可穿戴设备1500具体可以包括：处理单元1501、获取单元1502。
其中，获取单元1502，用于获取用户的生理参数和声音数据。处理单元1501，用于根据生理参数和声音数据确定用户的年龄段。
一种可能的设计中，还包括显示单元1503；显示单元1503，用于显示目标界面；处理单元1501，还用于接收用户的目标操作；处理单元1501，还用于响应于目标操作，激活麦克风录制用户的声音数据。
一种可能的设计中，还包括提醒单元1504；生理参数包括体温，声音数据包括咳嗽音；在获取单元1502，用于获取用户的生理参数和声音数据之后，提醒单元1504，用于在用户的体温不正常的情况下，输出目标信息(比如：显示目标信息和/或语音播报目标信息等)，目标信息用于提醒用户当前测量获得的年龄段不准确。
一种可能的设计中，年龄段包括第一年龄段和第二年龄段；处理单元1501，还用于根据生理参数确定第一年龄段的第一概率和第二年龄段的第二概率；处理单元1501，还用于根据声音数据确定第一年龄段的第三概率和第二年龄段的第四概率；处理单元1501，还用于根据第一概率和第三概率确定用户的年龄段；或者处理单元1501，还用于根据第二概率和第四概率确定用户的年龄段。
一种可能的设计中，处理单元1501，还用于若第一概率大于等于第一预设阈值，或者第三概率大于等于第二预设阈值，或者第一概率大于等于第三预设阈值且小于第一预设阈值并且第三概率大于等于第四预设阈值且小于第二预设阈值，则确定用户的年龄段为第一年龄段。处理单元1501，还用于若第一概率大于等于第三预设阈值且小于第一预设阈值并且第三概率小于第四预设阈值，或者第一概率小于第三预设阈值且第三概率小于第二预设阈值，则确定用户的年龄段为第二年龄段。
一种可能的设计中，处理单元1501，还用于若第二概率大于等于第一预设阈值，或者第四概率大于等于第二预设阈值，或者第二概率大于等于第三预设阈值且小于第一预设阈值并且第四概率大于等于第四预设阈值且小于第二预设阈值，则确定用户的年龄段为第二年龄段；处理单元1501，还用于若第二概率大于等于第三预设阈值且小于第一预设阈值并且第四概率小于第四预设阈值，或者第二概率小于第三预设阈值且第四概率小于第二预设阈值，则确定用户的年龄段为第一年龄段。
一种可能的设计中，获取单元1502，还用于获取用户输入的年龄；提醒单元1504，还用于在年龄与确定的用户的年龄段不匹配的情况下，提醒用户确认输入的年龄是否正确。
一种可能的设计中，处理单元1501，还用于确定上一次提醒用户确认输入的年龄是否正确的时间满足预设条件。
一种可能的设计中，处理单元1501，还用于若确定用户的年龄段为第一年龄段，则采用第一预设模型对用户进行健康检测；处理单元1501，还用于若确定用户的年龄段为第二年龄段，则采用第二预设模型对用户进行健康检测。
可选的，上述处理单元1501、获取单元1502、显示单元1503、提醒单元1504还用于支持可穿戴设备1500执行本申请实施例中可穿戴设备执行的其他步骤。
可选的，图15所示的可穿戴设备1500还可以包括存储单元(图15中未示出)，该存储单元存储有程序或指令。当处理单元1501执行该程序或指令时，使得图15所示的可穿戴设备1500可以执行本申请实施例所示的方法。
可选的，图15所示的可穿戴设备1500还可以包括通信单元(图15中未示出)，该通信单元，用于支持可穿戴设备1500执行本申请实施例中可穿戴设备与其他设备之间通信的步骤。
图15所示的可穿戴设备1500的技术效果可以上述方法实施例的技术效果，此处不再赘述。图15所示的可穿戴设备1500中涉及的处理单元1501可以由处理器或处理器相关电路组件实现，可以为处理器或处理模块。通信单元可以由收发器或收发器相关电路组件实现，可以为收发器或收发模块。获取单元1502可以由传感器或传感器相关电路组件实现，和/或由收发器或收发器相关电路组件实现。显示单元1503可以由显示屏相关组件实现。提醒单元1504可以由显示屏相关组件、麦克风相关组件等实现。
本申请实施例还提供一种芯片系统，如图16所示，该芯片系统包括至少一个处理器1601和至少一个接口电路1602。处理器1601和接口电路1602可通过线路互联。例如，接口电路1602可用于从其它装置接收信号。又例如，接口电路1602可用于向其它装置(例如处理器1601)发送信号。示例性的，接口电路1602可读取存储器中存储的指令，并将该指令发送给处理器1601。当所述指令被处理器1601执行时，可使得可穿戴设备执行上述实施例中的可穿戴设备执行的各个步骤。当然，该芯片系统还可以包含其他分立器件，本申请实施例对此不作具体限定。
可选地，该芯片系统中的处理器可以为一个或多个。该处理器可以通过硬件实现也可以通过软件实现。当通过硬件实现时，该处理器可以是逻辑电路、集成电路等。当通过软件实现时，该处理器可以是一个通用处理器，通过读取存储器中存储的软件代码来实现。
可选地，该芯片系统中的存储器也可以为一个或多个。该存储器可以与处理器集成在一起，也可以和处理器分离设置，本申请并不限定。示例性的，存储器可以是非瞬时性处理器，例如只读存储器ROM，其可以与处理器集成在同一块芯片上，也可以分别设置在不同的芯片上，本申请对存储器的类型，以及存储器与处理器的设置方式不作具体限定。
示例性的，该芯片系统可以是现场可编程门阵列(field programmable gate array，FPGA)，可以是专用集成芯片(application specific integrated circuit，ASIC)，还可以是系统芯片(system on chip，SoC)，还可以是中央处理器(central  processor unit，CPU)，还可以是网络处理器(network processor，NP)，还可以是数字信号处理电路(digital signal processor，DSP)，还可以是微控制器(micro controller unit，MCU)，还可以是可编程控制器(programmable logic device，PLD)或其他集成芯片。
应理解，上述方法实施例中的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。结合本申请实施例所公开的方法步骤可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。
本申请实施例还提供一种计算机存储介质，该计算机存储介质中存储有计算机指令，当该计算机指令在计算机上运行时，使得计算机执行上述方法实施例所述的方法。
本申请实施例提供一种计算机程序产品，该计算机程序产品包括：计算机程序或指令，当计算机程序或指令在计算机上运行时，使得该计算机执行上述方法实施例所述的方法。
本申请实施例提供一种电路系统，电路系统包括处理电路，处理电路被配置为执行上述方法实施例所述的方法。
另外，本申请实施例还提供一种装置，这个装置具体可以是芯片，组件或模块，该装置可包括相连的处理器和存储器；其中，存储器用于存储计算机执行指令，当装置运行时，处理器可执行存储器存储的计算机执行指令，以使装置执行上述各方法实施例中的方法。
其中，本实施例提供的可穿戴设备、计算机存储介质、计算机程序产品、电路系统、芯片或装置均用于执行上文所提供的对应的方法，因此，其所能达到的有益效果可参考上文所提供的对应的方法中的有益效果，此处不再赘述。
通过以上实施方式的描述，所属领域的技术人员可以了解到，为描述的方便和简洁，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即将装置的内部结构划分成不同的功能模块，以完成以上描述的全部或者部分功能。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。各实施例在不冲突的情况下可以相互结合或相互参考。以上所描述的装置实施例仅仅是示意性的，例如，模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个装置，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是一个物理单元或多个物理单元，即可以位于一个地方，或者也可以分布到多个不同地方。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成 的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个可读取存储介质中。基于这样的理解，本申请实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该软件产品存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上内容，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以权利要求的保护范围为准。

Claims (27)

  1. 一种年龄检测方法，其特征在于，应用于可穿戴设备，所述方法包括：
    获取用户的生理参数和声音数据；
    根据所述生理参数和所述声音数据确定所述用户的年龄段。
  2. 根据权利要求1所述方法，其特征在于，获取所述用户的声音数据，包括：
    显示目标界面；
    接收所述用户的目标操作；
    响应于所述目标操作，激活麦克风录制所述用户的声音数据。
  3. 根据权利要求1或2所述的方法，其特征在于，所述获取用户的生理参数的时间段为整夜时间。
  4. 根据权利要求1-3任一项所述的方法，其特征在于，所述生理参数包括体温，所述声音数据包括咳嗽音；
    在所述获取用户的生理参数和声音数据之后，所述方法还包括：
    在所述用户的体温不正常的情况下，输出目标信息，所述目标信息用于提醒所述用户当前测量获得的年龄段不准确。
  5. 根据权利要求1-4任一项所述的方法，其特征在于，所述年龄段包括第一年龄段和第二年龄段；
    所述根据所述生理参数和所述声音数据确定所述用户的年龄段包括：
    根据所述生理参数确定所述第一年龄段的第一概率和所述第二年龄段的第二概率；
    根据所述声音数据确定所述第一年龄段的第三概率和所述第二年龄段的第四概率；
    根据所述第一概率和所述第三概率确定所述用户的年龄段；或者根据所述第二概率和所述第四概率确定所述用户的年龄段。
  6. 根据权利要求5所述的方法，其特征在于，根据所述第一概率和所述第三概率确定所述用户的年龄段，包括：
    若所述第一概率大于等于第一预设阈值，或者所述第三概率大于等于第二预设阈值，或者所述第一概率大于等于第三预设阈值且小于所述第一预设阈值并且所述第三概率大于等于第四预设阈值且小于所述第二预设阈值，则确定所述用户的年龄段为所述第一年龄段；
    若所述第一概率大于等于所述第三预设阈值且小于所述第一预设阈值并且所述第三概率小于所述第四预设阈值，或者所述第一概率小于所述第三预设阈值且所述第三概率小于所述第二预设阈值，则确定所述用户的年龄段为所述第二年龄段。
  7. 根据权利要求5或6所述的方法，其特征在于，根据所述第二概率和所述第四概率确定所述用户的年龄段，包括：
    若所述第二概率大于等于第一预设阈值，或者所述第四概率大于等于第二预设阈值，或者所述第二概率大于等于第三预设阈值且小于所述第一预设阈值并且所述第四概率大于等于第四预设阈值且小于所述第二预设阈值，则确定所述用户的年龄段为所述第二年龄段；
    若所述第二概率大于等于所述第三预设阈值且小于所述第一预设阈值并且所述第四概率小于所述第四预设阈值，或者所述第二概率小于所述第三预设阈值且所述第四概率小于所述第二预设阈值，则确定所述用户的年龄段为所述第一年龄段。
  8. 根据权利要求1-7任一项所述的方法，其特征在于，在所述根据所述生理参数和所述声音数据确定所述用户的年龄段之后，所述方法还包括：
    获取所述用户输入的年龄；
    在所述年龄与确定的所述用户的年龄段不匹配的情况下，提醒所述用户确认输入的年龄是否正确。
  9. 根据权利要求8所述的方法，其特征在于，在所述提醒所述用户确认输入的年龄是否正确之前，所述方法还包括：
    确定上一次提醒所述用户确认输入的年龄是否正确的时间满足预设条件。
  10. 根据权利要求5-9任一项所述的方法，其特征在于，所述方法还包括：
    若确定所述用户的年龄段为所述第一年龄段，则采用第一预设模型对所述用户进行健康检测；
    若确定所述用户的年龄段为所述第二年龄段，则采用第二预设模型对所述用户进行健康检测。
  11. 根据权利要求1-3任一项所述的方法，其特征在于，所述生理参数至少包括以下一种或多种：心率、心率变异性HRV、呼吸率、血氧、体温。
  12. 根据权利要求1-3任一项所述的方法，其特征在于，所述声音数据至少包括以下一种或多种：咳嗽音、发“a”音、吹气音、语音。
  13. 一种可穿戴设备，其特征在于，包括获取单元和处理单元；
    所述获取单元，用于获取用户的生理参数和声音数据；
    所述处理单元，用于根据所述生理参数和所述声音数据确定所述用户的年龄段。
  14. 根据权利要求13所述的可穿戴设备，其特征在于，还包括显示单元；
    所述显示单元，用于显示目标界面；
    所述处理单元，还用于接收所述用户的目标操作；
    所述处理单元，还用于响应于所述目标操作，激活麦克风录制所述用户的声音数据。
  15. 根据权利要求13或14所述的可穿戴设备，其特征在于，所述获取用户的生理参数的时间段为整夜时间。
  16. 根据权利要求13-15任一项所述的可穿戴设备，其特征在于，还包括提醒单元；所述生理参数包括体温，所述声音数据包括咳嗽音；
    在所述获取单元，用于获取用户的生理参数和声音数据之后，所述提醒单元，用于在所述用户的体温不正常的情况下，输出目标信息，所述目标信息用于提醒所述用户当前测量获得的年龄段不准确。
  17. 根据权利要求13-16任一项所述的可穿戴设备，其特征在于，所述年龄段包括第一年龄段和第二年龄段；
    所述处理单元，还用于根据所述生理参数确定所述第一年龄段的第一概率和所述第二年龄段的第二概率；
    所述处理单元，还用于根据所述声音数据确定所述第一年龄段的第三概率和所述第二年龄段的第四概率；
    所述处理单元，还用于根据所述第一概率和所述第三概率确定所述用户的年龄段；或者所述处理单元，还用于根据所述第二概率和所述第四概率确定所述用户的年龄段。
  18. 根据权利要求17所述的可穿戴设备，其特征在于，
    所述处理单元，还用于若所述第一概率大于等于第一预设阈值，或者所述第三概率大于等于第二预设阈值，或者所述第一概率大于等于第三预设阈值且小于所述第一预设阈值并且所述第三概率大于等于第四预设阈值且小于所述第二预设阈值，则确定所述用户的年龄段为所述第一年龄段；
    所述处理单元，还用于若所述第一概率大于等于所述第三预设阈值且小于所述第一预设阈值并且所述第三概率小于所述第四预设阈值，或者所述第一概率小于所述第三预设阈值且所述第三概率小于所述第二预设阈值，则确定所述用户的年龄段为所述第二年龄段。
  19. 根据权利要求17或18所述的可穿戴设备，其特征在于，
    所述处理单元，还用于若所述第二概率大于等于第一预设阈值，或者所述第四概率大于等于第二预设阈值，或者所述第二概率大于等于第三预设阈值且小于所述第一预设阈值并且所述第四概率大于等于第四预设阈值且小于所述第二预设阈值，则确定所述用户的年龄段为所述第二年龄段；
    所述处理单元，还用于若所述第二概率大于等于所述第三预设阈值且小于所述第一预设阈值并且所述第四概率小于所述第四预设阈值，或者所述第二概率小于所述第三预设阈值且所述第四概率小于所述第二预设阈值，则确定所述用户的年龄段为所述第一年龄段。
  20. 根据权利要求13-19任一项所述的可穿戴设备，其特征在于，
    所述获取单元，还用于获取所述用户输入的年龄；
    所述提醒单元，还用于在所述年龄与确定的所述用户的年龄段不匹配的情况下，提醒所述用户确认输入的年龄是否正确。
  21. 根据权利要求20所述的可穿戴设备，其特征在于，
    所述处理单元，还用于确定上一次提醒所述用户确认输入的年龄是否正确的时间满足预设条件。
  22. 根据权利要求17-21任一项所述的可穿戴设备，其特征在于，
    所述处理单元，还用于若确定所述用户的年龄段为所述第一年龄段，则采用第一预设模型对所述用户进行健康检测；
    所述处理单元，还用于若确定所述用户的年龄段为所述第二年龄段，则采用第二预设模型对所述用户进行健康检测。
  23. 根据权利要求13-15任一项所述的可穿戴设备，其特征在于，所述生理参数至少包括以下一种或多种：心率、心率变异性HRV、呼吸率、血氧、温度。
  24. 根据权利要求13-15任一项所述的可穿戴设备，其特征在于，所述声音数据至少包括以下一种或多种：咳嗽音、发“a”音、吹气音、语音。
  25. 一种可穿戴设备，其特征在于，包括：包括处理器、存储器、传感器和显示屏，所述存储器、所述传感器、所述显示屏与所述处理器耦合，所述存储器用于存储计算机程序代码，所述计算机程序代码包括计算机指令，所述处理器从所述存储器中读取所述计算机指令，以使得所述可穿戴设备执行如权利要求1-12中任一项所述的方法。
  26. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质包括计算机程序或指令，当所述计算机程序或指令在可穿戴设备上运行的情况下，使得所述可穿戴设备执行如权利要求1-12中任一项所述的方法。
  27. 一种计算机程序产品，其特征在于，当所述计算机程序产品在计算机上运行时，使得所述计算机可以执行如权利要求1-12中任一项所述的方法。
PCT/CN2023/086997 2022-04-16 2023-04-07 年龄检测方法及可穿戴设备 WO2023197957A1 (zh)

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