Sleep state detection method and electronic device

Abstract

A sleep state detection method and an electronic device are used to improve the accuracy of detecting the sleep state of a user. The method is applied to a wearable device. The wearable device collects behavior data generated by a target user during sleep. The behavior data includes motion signals and / or sound signals. The motion signals are used to determine a first detection result for representing the sleep state of the target user. The sound signals are used to determine a second detection result for representing the sleep state of the target user. A final detection result of the sleep state of the target user is determined according to the first detection result and / or the second detection result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of terminal, and in particular, to a sleep state detection method and an electronic device. BACKGROUND

[0002] Sleep is an important physiological activity of human body, which can help human body recover physical strength, mental strength and spirit, and can alleviate stress and enhance learning ability, so as to maintain physical health. If people lack sleep or have sleep disorders (such as insomnia, narcolepsy, sleepwalking, etc.), it will likely lead to some sequelae, such as emotional instability, melancholy, anxiety, etc. Therefore, sleep state detection is very necessary, which can help people understand their sleep quality and improve sleep.

[0003] At present, how to accurately detect the sleep state is a problem that is being explored. SUMMARY

[0004] The present application aims to provide a sleep state detection method and an electronic device, which can improve the accuracy of detecting the sleep state.

[0005] In a first aspect, a sleep state detection method is provided, which is applied to a wearable device, the wearable device is currently in a non-wearing mode, and the method comprises: collecting behavior data generated by a target user when sleeping, the behavior data comprising a motion signal and / or a sound signal; wherein the motion signal is used to determine a first detection result for representing the sleep state of the target user, and the sound signal is used to determine a second detection result for representing the sleep state of the target user; and determining a final detection result of the sleep state of the target user according to the first detection result and / or the second detection result.

[0006] In the embodiments of the present application, the sleep state is detected from two different angles of the motion signal and the sound signal, and the final detection result is determined according to the two detection results, which helps to improve the accuracy of detecting the sleep state of the user.

[0007] In some embodiments, after the wearable device collects the behavior data generated by the target user when sleeping, the behavior data can be sent to an electronic device connected with the wearable device. The electronic device determines the first detection result based on the motion signal in the behavior data, determines the second detection result based on the sound signal in the behavior data, and then obtains the final detection result according to the first detection result and / or the second detection result. That is, all or part of the execution steps of the wearable device can be executed by the electronic device connected with the wearable device. The electronic device can send the final detection result to the wearable device for display or display locally. The electronic device connected with the wearable device can be a mobile phone, a tablet computer or other portable electronic device.

[0008] In a possible design, the final detection result is an average or weighted average of the first detection result and the second detection result. For example, the first detection result is 70 points and the second detection result is 80 points, and then the final detection result is 75 points.

[0009] It should be understood that, in addition to the score value, the first detection result and the second detection result can also have other forms of representation, for example, a grade. For example, the first detection result is a first grade and the second detection result is a third grade, and then the final detection result is a middle grade between the first grade and the third grade.

[0010] In a possible design, before the behavior data generated when the target user is sleeping is collected, the method further includes: when it is determined that the environment includes multiple people, outputting first prompt information, where the first prompt information is used to prompt input of a specific sound signal of the target user; and extracting, according to the input specific sound signal of the target user, a sound signal matching the specific sound signal of the target user from the sound signal; and wherein the extracted sound signal is used to determine the second detection result used to represent the sleep state of the target user.

[0011] In the embodiments of this application, in a multiple-person environment, if the sleep state of a target user is to be detected, a sound signal of the target user is input, and then the sound signal of the target user extracted from the collected sound signal can be used to determine the second detection result of the target user. That is, even in a multiple-person environment, the sleep state of a specific user can be detected by using the embodiments of this application, and the use is convenient.

[0012] Optionally, when it is determined that the environment includes multiple people, the first prompt information is output, including: an electronic device connected to the wearable device determines that the environment includes multiple people, and then outputs the first prompt information; and after the electronic device detects that the specific sound signal of the target user is input, the electronic device sends an instruction to the wearable device, where the instruction is used to instruct the wearable device to start collecting the behavior data. Of course, the electronic device can also not need to send the instruction, for example, the wearable device can always collect the behavior data by default. After the wearable device collects the behavior data, the wearable device sends the behavior data to the electronic device, the electronic device extracts, according to the input specific sound signal of the target user, a sound signal matching the specific sound signal of the target user from the sound signal in the behavior data, and determines the second detection result according to the extracted sound signal.

[0013] In a possible design, before the behavior data generated when the target user is sleeping is collected, it can be further determined that multiple people are included in the environment, and second prompt information is output, the second prompt information being used to prompt input of the orientation of the target user relative to the wearable device; and the motion signal matching the orientation of the target user relative to the wearable device is extracted from the motion signal according to the input orientation of the target user relative to the wearable device; and the extracted motion signal is used to determine the first detection result used to represent the sleep state of the target user.

[0014] In the embodiments of the present application, in a multiple-person environment, if the sleep state of a target user is to be detected, the orientation of the target user relative to the wearable device is input, so that the motion signal matching the orientation is extracted from the collected motion signal, and the motion signal can be used to determine the first detection result of the target user. That is, even in a multiple-person environment, the sleep state of a specific user can be detected, and the sleep state can be detected from both the motion signal and the sound signal, and the accuracy is high.

[0015] Optionally, when it is determined that multiple people are included in the environment, the second prompt information is output, including: when the electronic device connected to the wearable device determines that multiple people are included in the environment, the second prompt information is output; and after the electronic device detects that the orientation of the target user relative to the wearable device is input, the electronic device sends an instruction to the wearable device, the instruction being used to instruct the wearable device to collect behavior data. Of course, the electronic device can also not send the instruction, for example, the wearable device can always collect behavior data by default. After the wearable device collects the behavior data, the wearable device sends the behavior data to the electronic device, the electronic device extracts the corresponding motion signal from the motion signal in the behavior data according to the input orientation of the target user relative to the wearable device, and determines the first detection result according to the extracted motion signal.

[0016] In a possible design, when it is determined that multiple people are included in the environment, the third prompt information is output, the third prompt information being used to prompt whether multiple people are included in the environment; and according to a confirmation instruction input by a user, it is determined that multiple people are included in the environment. That is, the wearable device can determine whether the environment is a multiple-person environment by outputting prompt information. In a multiple-person environment, the sleep state of a specific user can be detected, and the sleep state can be detected from both the motion signal and the sound signal, and the accuracy is high.

[0017] Optionally, determining that multiple people are present in the environment includes: outputting a third prompt message via an electronic device connected to the wearable device, wherein the electronic device determines that multiple people are present in the environment based on a confirmation instruction input by a user. When the electronic device determines that multiple people are present, it may output the first prompt message or the second prompt message, as described above.

[0018] In a possible design, before collecting the behavioral data generated by the target user while sleeping, it also includes: setting the current mode to a non-wearing mode in response to a user operation; and outputting a fourth prompt information, wherein the fourth prompt information is used to prompt the user to fix the wearable device.

[0019] In an embodiment of the present application, the wearable device has a wearing mode and a non-wearing mode. In the wearing mode, the sleep detection process in the wearing mode can be used, and in the non-wearing mode, the sleep detection process in the non-wearing mode can be used. In the non-wearing mode, the user can be prompted to fix the device, such as fixing it on a mattress, pillow, etc., and the user experience is higher.

[0020] Optionally, in response to user operation, setting the current mode to non-wearing mode includes: the electronic device connected to the wearable device sets the current mode of the wearable device to non-wearing mode in response to the user operation, outputting a fourth prompt message to prompt the user to fix the wearable device.

[0021] That is, in the embodiment of the present application, the wearable device can be connected to an electronic device (such as a mobile phone), and the above steps of the wearable device can be performed in whole or in part by the electronic device. For example, at least one of the first prompt information, the second prompt information, the third prompt information, and the fourth prompt information can be displayed by the electronic device connected to the wearable device.

[0022] In one possible design, when the target user includes multiple people, the final detection result can be used to characterize the overall sleep quality of the multiple people. In other words, the embodiment of the present application can detect the overall sleep status of multiple people, so that the sleep status of multiple people can be detected at one time, which is more efficient.

[0023] In a possible design, the motion signal generated by the target user during sleep is collected by a motion sensor, and the motion signal includes at least one of displacement, acceleration, speed, angular speed, and angular acceleration, and the like. The method further includes: identifying a body movement feature according to the motion signal, the body movement feature including at least one of turning over, getting up, and shaking, and the like; and determining the first detection result according to at least one of a frequency, a strength, and a number of occurrences of the body movement feature within a set time length. That is, in the embodiment of the application, the wearable device determines the first detection result according to the frequency, the strength, and the number of occurrences of the body movement feature of the target user. The body movement feature (such as turning over, getting up, and the like) can more accurately reflect the sleep state of the user, and therefore the detection manner is more accurate.

[0024] In a possible design, the collected sound signal generated by the target user during sleep includes at least one sound feature of snoring, breathing, and the like of the target user. The method further includes: determining the second detection result according to at least one of a strength, a frequency, and a number of occurrences of at least one of the snoring or the breathing sound of the target user within a set time length. That is, in the embodiment of the application, the wearable device determines the second detection result according to the strength and the frequency of the snoring or the breathing sound of the target user. The snoring or the breathing sound can more accurately reflect the sleep state of the user, and therefore the detection manner is more accurate.

[0025] In a second aspect, an electronic device is provided, including a processor, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions, which when executed by the processor, cause the electronic device to perform the method steps provided in the first aspect.

[0026] In a third aspect, a computer readable storage medium is provided, which is configured to store a computer program, and when the computer program is run on a computer, the computer program causes the computer to perform the method provided in the first aspect.

[0027] In a fourth aspect, a computer program product is provided, which includes a computer program, and when the computer program is run on a computer, the computer program causes the computer to perform the method provided in the first aspect.

[0028] In a fifth aspect, a graphical user interface on an electronic device is further provided, the electronic device having a display screen, a memory, and a processor configured to execute one or more computer programs stored in the memory. The graphical user interface includes a graphical user interface displayed when the electronic device performs the method provided in the first aspect.

[0029] In a sixth aspect, an embodiment of the present application further provides a chip coupled with a memory in an electronic device, for calling a computer program stored in the memory and executing the technical solution of the first aspect of the embodiment of the present application. In the embodiment of the present application, the "coupled" means that two components are directly or indirectly combined with each other.

[0030] The beneficial effects of the second aspect to the sixth aspect are described in the beneficial effects of the first aspect, and are not repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 A schematic diagram of a hardware structure of a wearable device provided by an embodiment of the present application;

[0032] Figure 2 A flowchart of a sleep state detection method provided by an embodiment of the present application;

[0033] Figure 3 A schematic diagram of display information on a display screen of a wearable device provided by an embodiment of the present application;

[0034] Figure 4 A schematic diagram of display information on a display screen of a wearable device provided by an embodiment of the present application;

[0035] Figure 5A A schematic diagram of a prompt for fixing a device on a mobile phone provided by an embodiment of the present application;

[0036] Figure 5B And Figure 5C A schematic diagram of a sleep state detection result displayed on a mobile phone provided by an embodiment of the present application;

[0037] Figure 6 A schematic diagram of a sleep state detection flowchart of a user in a non-wearing mode provided by an embodiment of the present application;

[0038] Figure 7 Another flowchart of a sleep state detection method provided by an embodiment of the present application;

[0039] Figures 8A-8B A schematic diagram of a prompt for inputting sound information of a target user on a mobile phone provided by an embodiment of the present application;

[0040] Figure 9 Another schematic diagram of a prompt for inputting sound information of a target user on a mobile phone provided by an embodiment of the present application;

[0041] Figures 10-12 A schematic diagram of a prompt for multiple detection modes on a mobile phone provided by an embodiment of the present application;

[0042] Figure 13A structural schematic diagram of a wearable device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0043] Common sleep state detection methods mainly include the following:

[0044] The most traditional sleep state detection method is to use a medical instrument (such as a polysomnography) to scan the user's brain waves, breathing, and the like when the user is sleeping, which can provide a scientific and accurate clinical diagnosis for the user. However, the medical instrument is complex and large in size, and cannot realize daily sleep detection.

[0045] In order to meet the daily sleep state detection, sports bands, watches and the like are integrated with sleep detection functions, so that the functions of the sports bands and watches are more abundant, and not only can the sports state be detected when the user is daily exercising (such as running), but also the sleep state can be detected when the user is sleeping. Generally, an electrode sheet is arranged on the sports band or watch, and when the electrode sheet contacts the user's body, the body signal (such as heart rate) can be sensed, and the sleep state is determined through the body signal. That is, the user needs to wear the sports band or watch when sleeping, so as to detect the sleep state. However, most users do not like to wear things on the wrist when sleeping, which will affect sleep, so the user experience is low.

[0046] At present, there are some non-wearable sleep detection devices on the market, such as sleep trackers, which can be fixed on the bed by the user, and the sleep tracker can collect the changes of the user's breathing and heart rate during sleep; for example, a pillow with a recess capable of fixing a sports band or watch, when the sports band or watch is fixed in the recess, the head movement signal of the user during sleep can be detected to determine the sleep quality of the user. However, this way, the user needs to additionally purchase a non-wearable sleep detection device (such as a pillow with a recess), and the non-wearable sleep detection device has a single function, which can only detect the sleep state, and cannot meet the detection of the exercise state when the user is daily exercising (such as running).

[0047] In view of this, the embodiment of the present application provides a sleep state detection method, which is suitable for a wearable device. The wearable device has two states, a wearing state and a non-wearing state. In the wearing state, the user can wear the wearable device to realize daily motion detection. In the non-wearing state, the user can set the wearable device near the user's sleeping position, without wearing it on the wrist, to detect the sleep state. Moreover, the wearable device can collect motion signals (such as turning over) and / or sound signals (such as breathing sound) during the user's sleep. The motion signals can be used to determine a first detection result of the user's sleep state, and the sound signals can be used to determine a second detection result of the user's sleep state. According to the first detection result and / or the second detection result, a final detection result of the target user's sleep state is determined, so that the sleep state detection result is more accurate.

[0048] The sleep state detection method provided by the embodiment of the present application can be applied to a wearable device. The wearable device can be a smart watch, a smart bracelet, a smart necklace, smart clothing, smart shoes, a smart earring, etc. The embodiment of the present application does not limit the form of the wearable device.

[0049] Figure 1 The structure of the wearable device is shown. As shown in Figure 1 The wearable device can include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charge management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a key 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 can include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0050] The processor 110 can include one or more processing units, such as: an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units can be independent devices or integrated in one or more processors. The controller can be the nerve center and command center of the wearable device. The controller can generate operation control signals according to instruction operation codes and timing signals, and complete the control of fetching and executing instructions. A memory can also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory can save instructions or data that the processor 110 has just used or repeatedly uses. If the processor 110 needs to use the instructions or data again, it can directly call from the memory. This avoids repeated access and reduces the waiting time of the processor 110, thereby improving the efficiency of the system.

[0051] The USB interface 130 is an interface that conforms to the USB standard specification, and can 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, or to transmit data between the wearable device and a peripheral device. The charging management module 140 is used to receive charging input from the 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 to power the processor 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160, etc.

[0052] The wireless communication function of the wearable device can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor, etc. The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the wearable device can be used to cover a single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization rate of the antennas. For example, the antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0053] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to wearable devices. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the same device as at least some of the modules of the processor 110.

[0054] The wireless communication module 160 can provide wireless communication solutions for wearable devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc. The wireless communication module 160 can be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110. The wireless communication module 160 can also receive the signal to be sent from the processor 110, frequency modulate it, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0055] In some embodiments, the antennas 1 and the mobile communication module 150 of the wearable device are coupled, and the antennas 2 and the wireless communication module 160 are coupled, so that the wearable device can communicate with a network and other devices through wireless communication technology. The wireless communication technology can include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology, etc. The GNSS can include a global positioning system (GPS), a global navigation satellite system (GLONASS), a beidu navigation satellite system (BDS), a quasi-zenith satellite system (QZSS), and / or a satellite based augmentation systems (SBAS).

[0056] The display screen 194 is configured to display a display interface of an application, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flex light-emitting diode (FLED), a Miniled, a MicroLed, a Micro-oLed, a quantum dot light emitting diode (QLED), etc. In some embodiments, the wearable device can include one or N display screens 194, where N is a positive integer greater than 1.

[0057] The wearable device 100 can implement a photographing function through an ISP, the camera 193, a video codec, a GPU, the display screen 194, and an application processor, etc.

[0058] The ISP is configured to process data fed back by the camera 193. For example, when taking a photo, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing to convert it into a visible image. The ISP can also optimize the noise, brightness, and skin color of the image. The ISP can also optimize the exposure and color temperature of the shooting scene. In some embodiments, the ISP can be disposed in the camera 193.

[0059] The camera 193 is configured to capture a still image or a video. An object generates an optical image through a lens and projects it onto a photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then transmitted to the ISP to convert it into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into a standard RGB, YUV, etc. format image signal. In some embodiments, the wearable device can include one or N cameras 193, where N is a positive integer greater than 1.

[0060] The internal memory 121 can be used to store computer executable program codes including instructions. The processor 110 performs various functional applications of the wearable device and data processing by running the instructions stored in the internal memory 121. The internal memory 121 can include a program storage area and a data storage area. The program storage area can store an operating system and software codes of at least one application program (e.g., an iQiyi application, a WeChat application, etc.). The data storage area can store data (e.g., images, videos, etc.) generated during use of the wearable device. In addition, the internal memory 121 can include a high-speed random access memory, and can further include a non-volatile memory such as at least one of a magnetic disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0061] The external memory interface 120 can be used to connect an external memory card such as a Micro SD card, to expand the storage capacity of the wearable device. The external memory card communicates with the processor 110 through the external memory interface 120 to perform a data storage function. For example, files such as pictures and videos are saved in the external memory card.

[0062] The wearable device can implement an audio function through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, and an application processor, etc. For example, music playing, recording, etc.

[0063] The pressure sensor 180A is used to sense a pressure signal and can convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A can be disposed on the display screen 194. The gyroscope sensor 180B can be used to determine a motion posture of the wearable device. In some embodiments, the angular velocity of the wearable device around three axes (i.e., x, y, and z axes) can be determined through the gyroscope sensor 180B.

[0064] The gyro sensor 180B can be used for taking a photo to prevent shaking. The barometric sensor 180C is used to measure air pressure. In some embodiments, the wearable device calculates altitude, assists in positioning and navigation, using the air pressure value measured by the barometric sensor 180C. The magnetic sensor 180D includes a Hall sensor. The wearable device can detect the opening and closing of a flip cover case using the magnetic sensor 180D. In some embodiments, when the wearable device is a flip phone, the wearable device can detect the opening and closing of the flip cover according to the magnetic sensor 180D. In turn, according to the detected opening and closing state of the case or the opening and closing state of the flip cover, a feature such as automatic unlocking of the flip cover is set. The acceleration sensor 180E can detect the magnitude of acceleration of the wearable device in various directions (typically three axes). When the wearable device is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of the wearable device and applied to landscape / portrait switching, pedometer, etc.

[0065] The distance sensor 180F is used to measure distance. The wearable device can measure distance using infrared or laser. In some embodiments, when taking a photo, the wearable device can use the distance sensor 180F to measure distance to achieve fast focusing. The proximity light sensor 180G can include, for example, a light emitting diode (LED) and a light detector such as a photodiode. The light emitting diode can be an infrared light emitting diode. The wearable device emits infrared light outwardly through the light emitting diode. The wearable device detects infrared reflected light from nearby objects using the photodiode. When sufficient reflected light is detected, it can be determined that there is an object near the wearable device. When insufficient reflected light is detected, the wearable device can determine that there is no object near the wearable device. The wearable device can use the proximity light sensor 180G to detect that the user is holding the wearable device close to the ear to talk, so as to automatically turn off the screen to achieve power saving. The proximity light sensor 180G can also be used for automatic unlocking and locking of the case mode and pocket mode.

[0066] The ambient light sensor 180L is used to sense ambient light brightness. The wearable device can adaptively adjust the brightness of the display screen 194 according to the sensed ambient light brightness. The ambient light sensor 180L can also be used to automatically adjust white balance when taking a photo. The ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the wearable device is in the pocket to prevent accidental touch. The fingerprint sensor 180H is used to collect fingerprints. The wearable device can use the collected fingerprint characteristics to achieve fingerprint unlocking, access application lock, fingerprint photo, fingerprint answer incoming call, etc.

[0067] The temperature sensor 180J is configured to detect temperature. In some embodiments, the wearable device utilizes the temperature detected by the temperature sensor 180J to perform temperature handling strategies. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the wearable device performs a performance reduction of a processor located in proximity to the temperature sensor 180J in order to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is below another threshold, the wearable device heats the battery 142 to avoid abnormal shutdown of the wearable device caused by low temperature. In yet other embodiments, when the temperature is below yet another threshold, the wearable device performs a boost of the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.

[0068] The touch sensor 180K, also referred to as a "touch panel". The touch sensor 180K can be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 together form a touch screen, also referred to as a "touch panel". The touch sensor 180K is configured to detect a touch operation acting on or in proximity to the touch sensor 180K. The touch sensor 180K can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through the display screen 194. In other embodiments, the touch sensor 180K can also be disposed on the surface of the wearable device, which is different from the location where the display screen 194 is disposed.

[0069] The bone conduction sensor 180M can obtain a vibration signal. In some embodiments, the bone conduction sensor 180M can obtain a vibration signal of a human body sound part vibration bone block. The bone conduction sensor 180M can also contact the human body pulse to receive a blood pressure pulsation signal.

[0070] The keys 190 include a power on key, a volume key, and the like. The keys 190 can be mechanical keys. They can also be touch keys. The wearable device can receive key input and generate key signal input related to user settings and function control of the wearable device. The motor 191 can generate a vibration prompt. The motor 191 can be used for incoming call vibration prompts and also for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, playing audio, and the like) can correspond to different vibration feedback effects. The indicator 192 can be an indicator light, which can be used to indicate charging status, power changes, and also to indicate messages, missed calls, notifications, and the like. The SIM card interface 195 is configured to connect a SIM card. The SIM card can be inserted into or removed from the SIM card interface 195 to achieve contact and separation with the wearable device.

[0071] It can be understood that, Figure 1 The components shown do not constitute a specific limitation on the wearable device. The wearable device in the embodiments of the present application can include more or fewer components than those shown. In addition, Figure 1 In the embodiments of the present application, the wearable device can include more or fewer components than those shown. In addition, Figure 1The combination / connection relationship between the components in the above-mentioned embodiments can also be adjusted and modified.

[0072] For the convenience of description, the following embodiments take the wearable device as a smart bracelet (referred to as bracelet) as an example for introduction.

[0073] Embodiment One

[0074] Please refer to Figure 2 The flowchart of the sleep state detection method provided by the embodiments of the present application is shown. The method can be executed by a wearable device or an electronic device (such as a mobile phone) connected to the wearable device. The following mainly takes the wearable device as an example for introduction. As shown in Figure 2 The flow of the method includes:

[0075] S201, the wearable device judges whether the wearable device is currently in a sleep mode or a non-sleep mode.

[0076] Exemplarily, the sleep mode refers to a mode for detecting a sleep state, and the non-sleep mode refers to a mode for detecting a daily motion state (such as running). Wherein, the calculation process of the wearable device in the background is different in the sleep mode and the non-sleep mode, and the specific process will be introduced later.

[0077] It should be understood that before S201, a step of setting the wearable device in the sleep mode or the non-sleep mode can also be included. The specific implementation manner includes but is not limited to at least one of the following:

[0078] One implementation manner is that the wearable device can automatically enter the sleep mode or the non-sleep mode. For example, when the wearable device detects that the current time is a sleep time (such as 12 o'clock to 1 o'clock in the morning), it automatically enters the sleep mode; when the wearable device detects that the current time is a getting-up time (such as 8 o'clock to 9 o'clock in the morning), it automatically enters the non-sleep mode. Wherein, the specific value of the sleep time and / or the getting-up time can be the value of the system default setting of the wearable device, or it can also be manually inputted by the user to set the wearable device, and the embodiments of the present application are not limited.

[0079] Another implementation manner is that the user can manually set the wearable device to enter the sleep mode or the non-sleep mode. Exemplarily, please refer to Figure 3 (a), the mode setting icon is displayed on the display screen of the bracelet, when detecting the operation instruction for the icon, the display as Figure 3The identification of the sleep mode and the identification of the non-sleep mode of (b) can be selected by the user to set a certain mode. Alternatively, the wearable device can be connected with a mobile phone, and the mobile phone can control the wearable device (for example, an app is set on the mobile phone to control the wearable device), so that the user can set the wearable device to enter the sleep mode or the non-sleep mode on the mobile phone. The following embodiments are described by taking the example that the user controls the wearable device to set the sleep mode or the non-sleep mode on the mobile phone.

[0080] When the wearable device is in the sleep mode, a sleep flag in the wearable device is set as a first flag (for example, 1), and when the wearable device is in the non-sleep mode, the sleep flag in the wearable device is set as a second flag (for example, 0). Therefore, the wearable device can read the sleep flag in S201, and if the sleep flag is the first flag, it is determined that the current is in the sleep mode, and if the sleep flag is the second flag, it is determined that the current is in the non-sleep mode. The sleep flag can be stored in the memory and not displayed on the display screen of the wearable device, or can also be displayed on the display screen of the wearable device, and the display position can be arbitrary, for example, displayed in the status bar (used to display the power, operator information, wireless signal flag, etc.).

[0081] It should be noted that S201 is an optional step, which can be executed or not executed, and the embodiments of the present application are not limited.

[0082] In S202, if it is the sleep mode, the wearable device determines whether it is in the non-wearing mode in the sleep mode.

[0083] In the embodiments of the present application, the sleep mode includes the wearing mode and the non-wearing mode. If the user selects the wearing mode, the sleep detection process in the wearing mode can be used, and if the user selects the non-wearing mode, the sleep detection process in the non-wearing mode can be used.

[0084] It should be understood that if S201 is not executed, S202 is replaced by the wearable device determining whether it is in the non-wearing mode.

[0085] One implementation manner is that when the wearable device determines that the current is in the sleep mode, it can automatically enter the non-wearing mode. For example, the user or the system sets in advance that when the current is in the sleep mode, the wearable device automatically enters the non-wearing mode.

[0086] Another implementation manner is that the user can set the wearing mode or the non-wearing mode. For example, please refer to Figure 4 (a), when the bracelet detects the click operation for the sleep mode identification, it is determined to enter the sleep mode, and the display is as shown in Figure 4(b) the identification of the wearing mode and the identification of the non-wearing mode, and the user can set a certain mode by selecting a certain identification.

[0087] In another implementation, the wearable device determines whether it is currently in the sleep mode, and detects whether it is currently worn on the wrist of the user. If yes, it enters the wearing mode, otherwise, it enters the non-wearing mode. For example, the wearable device can determine whether it is worn on the wrist of the user by using the electrode patch arranged on the wearable device. For example, when the electrode patch can sense the body signal of the user, it is determined that the wearable device is worn on the wrist of the user, otherwise, it is determined that the wearable device is not worn on the wrist of the user. Alternatively, the motion sensor on the wearable device can detect whether the wearable device is in a motion state. If yes, it is determined that the wearable device is worn on the wrist of the user, otherwise, it is determined that the wearable device is not worn on the wrist of the user.

[0088] If it is the wearing mode, the wearable device executes the sleep state detection process in the wearing mode. For example, the electrode patch on the wearable device senses the body signal (such as heart rate, pulse, etc.) of the user, and determines the sleep state by the body signal. For example, the higher the heart rate, the worse the sleep state. If it is the non-wearing mode, the following steps can be executed.

[0089] S203, if it is the non-wearing mode, the wearable device prompts the user whether the device is fixed.

[0090] For example, please refer to Figure 5A (a), when the mobile phone detects the click operation for the identification of the non-wearing mode, it is determined to enter the non-wearing mode, and displays the prompt information as shown in Figure 5A (b): whether the device is fixed, and also displays two buttons. When the mobile phone detects that the user selects the "Yes" button, it is determined that the wearable device is fixed. Of course, the mobile phone can also prompt the user to fix the device on the pillow, mattress, bedclothes, or user's sleepwear, etc.

[0091] For example, when the wearable device is a bracelet, the bracelet has a detachable module, and the module has a holding device, which can be used to hold on the pillow, sleepwear, mattress, or bedclothes. Alternatively, the user can purchase or the merchant can give the device for binding the bracelet when purchasing the bracelet, and use the binding device to fix the bracelet on the pillow, sleepwear, mattress, or bedclothes.

[0092] S204, the wearable device executes the non-wearing mode detection process to detect the sleep state of the user. The specific implementation process of S204 will be described in detail later.

[0093] S205, the wearable device outputs the detection result.

[0094] Optionally, the wearable device can directly output the detection result, or send the detection result to a mobile phone to display the detection result through the mobile phone.

[0095] Optionally, before S205, a step of stopping sleep state detection can be further included. One implementation manner is that the wearable device can automatically stop sleep state detection, such as automatically stopping detection (e.g., exiting sleep mode) at a specific time (e.g., 8:00 am) and then executing S205 to output the detection result. The specific time can be set by the user or set by default by the wearable device system. Another implementation manner is that the user manually stops sleep state detection on the wearable device or a mobile phone connected to the wearable device. For example, please refer to Figure 5B (a), the mobile phone displays a prompt message: Hello, do you want to stop sleep detection, and displays two buttons. When the mobile phone detects an operation of selecting the "Yes" button, it displays an interface as shown in Figure 5B (b) after executing S205, which includes the detection result.

[0096] Figure 5B In the above example, the detection result is a score value used to represent the sleep state, and the higher the score value, the better the sleep quality. It should be understood that the detection result can also be in other forms, such as quality levels of "excellent", "good", "poor", sleep curves, etc. The embodiments of the present application are not limited. For example, please refer to Figure 5C As an example of the detection result, the detection result is a sleep quality report, which includes a sleep quality curve used to represent the sleep quality of the user at different times, and historical records such as sleep-in time, sleep duration, deep sleep duration, and light sleep duration.

[0097] Please refer to Figure 6 , for the detailed process of S204 in the above Figure 2 , specifically, the wearable device executes a detection process of the non-wearing mode to detect the sleep state of the user, which includes at least one of two detection branches, wherein the first branch is to determine a first detection result of the sleep state of the user through a motion signal, and the second branch is to determine a second detection result of the sleep state of the user according to a sound signal, and then obtain a final detection result according to the first detection result and / or the second detection result. As shown in Figure 6 , specifically including the following steps:

[0098] Step 1, the wearable device collects a motion signal.

[0099] Suppose the wearable device is fixed on a mattress, if the user has behaviors such as turning over and getting up when sleeping, which will drive the mattress to move, and in turn drive the wearable device to move, so that the wearable device can collect the motion signal.

[0100] Exemplarily, the motion signal includes, but is not limited to, displacement, acceleration, speed, angular speed, angular acceleration, and the like. For example, the motion sensor (accelerometer, gyroscope, etc.) in the wearable device can collect the motion signal.

[0101] Step 2. The wearable device identifies the body movement feature according to the motion signal.

[0102] Exemplarily, the body movement feature includes, but is not limited to, turning over, getting up, shaking, and the like. For example, if the motion speed collected by the wearable device is greater than a first threshold, it is determined that the user gets up, or if the motion speed is less than a second threshold, it is determined that the user turns over.

[0103] Step 3. The wearable device determines the first detection result according to the body movement feature.

[0104] One implementation manner is that the wearable device detects the body movement feature in real time, determines the number and / or frequency of the body movement feature (such as turning over) in a period of time, and determines the first detection result according to the number and / or frequency. For example, the higher the number and / or the higher the frequency, the lower the first detection result.

[0105] Another implementation manner is that the wearable device detects the body movement feature in real time, determines the intensity of the body movement feature, and determines the first detection result according to the intensity. For example, the body movement feature is turning over, the intensity of turning over can be determined through the motion signal, which includes, but is not limited to, turning over angle (angle change detected by the motion sensor), turning over speed (angular speed detected by the motion sensor), and the like. If the intensity is greater, the first detection result is lower, which means the sleep quality is worse.

[0106] The above two manners can be used alone or in combination, and the embodiments of the present application are not limited.

[0107] Exemplarily, the first detection result can be a score value, and the higher the number and / or the higher the frequency, the lower the score. For example, the wearable device stores a corresponding relationship between the number and / or frequency of the body movement feature and the score value, and the wearable device can determine the score value according to the corresponding relationship.

[0108] Step 4. The wearable device collects the sound signal.

[0109] Step 5. The wearable device extracts the sound feature of the user's snoring sound, breathing sound, and the like from the collected sound signal.

[0110] It can be understood that the collected sound signal includes the breathing sound, snoring sound, and the like generated by the user during sleep. Generally, the room is a quiet environment during sleep, so the breathing sound or snoring sound is easy to identify.

[0111] Step 6. The wearable device determines the second detection result according to the extracted sound signal.

[0112] In an implementation, the wearable device collects sound signals in real time, and extracts continuous breathing sounds or snoring sounds. The second detection result can be determined according to the number or frequency of the breathing sounds or snoring sounds. For example, the higher the number and / or the higher the frequency, the lower the second detection result. For example, the second detection result can be a score value, and the higher the number and / or the higher the frequency, the lower the score value. For example, the wearable device can store a corresponding relationship between the number and / or frequency of the breathing sounds or snoring sounds and the score value, and determine the score value according to the corresponding relationship.

[0113] In another implementation, the wearable device can determine the second detection result according to the sound intensity of the breathing sounds or snoring sounds. For example, the greater the sound intensity, the lower the score value.

[0114] The above two methods can be used alone or in combination, and the embodiments of the present application are not limited.

[0115] It should be noted that the execution order between the above steps 1 to 3 and steps 4 to 6 is not limited. The first detection result can be determined by the motion signal first, and then the second detection result is determined by the sound signal. Alternatively, the second detection result is determined by the sound signal first, and then the first detection result is determined by the motion signal. Alternatively, the two can be performed simultaneously, and the embodiments of the present application are not limited.

[0116] In step 7, the wearable device obtains a final detection result according to the first detection result and / or the second detection result.

[0117] The above first branch and second branch can have only one or both. If only the first branch, the final detection result in step 7 is equal to the first detection result. If only the second branch, the final detection result in step 7 is equal to the second detection result. If both the first branch and the second branch, step 7 is to fuse the first detection result and the second detection result to obtain the final detection result. The process of fusing the first detection result and the second detection result to obtain the final detection result is described below.

[0118] In a first implementation, the first detection result is a first score, the second detection result is a second score, and the final detection result can be an average or weighted average of the first score and the second score. Taking the weighted average as an example, if the weight of the first branch is higher than that of the second branch, the first weight corresponding to the first score can be higher than the second weight corresponding to the second score. For example, the final detection result = P1*X1+P2*X2, wherein X1 is the first score, X2 is the second score, P1 is the first weight, and P2 is the second weight. P1 is higher than P2. The weight relationship between the first branch and the second branch can be a system default setting or a user setting.

[0119] In the above manner, the first detection result and the second detection result can be the total score of the night. In other embodiments, the night can be divided into multiple time periods, and the first detection result and the second detection result in each time period can be counted once, and then the final detection result can be obtained according to the counting results of the multiple time periods. See the second implementation manner below.

[0120] In the second implementation manner, the first branch (e.g., step 3) periodically detects to obtain the first detection result, and the second branch (e.g., step 6) periodically detects to obtain the second detection result. The third detection result in each period can be obtained according to the first detection result and the second detection result in the period, and then the third detection result in multiple periods can be obtained, and the final detection result can be obtained according to the third detection results in the multiple periods. For example, the final detection result can be the average or weighted average of the multiple third detection results. See Table 1 below for an example:

[0121] Table 1

[0122]

[0123] Taking Table 1 above as an example, in period 1, the first branch obtains the first detection result of 80 points, and the second branch obtains the second detection result of 90 points, so the third detection result is 85 points (taking the average of the first detection result and the second detection result as an example). In period 2, the first branch obtains the first detection result of 70 points, and the second branch obtains the second detection result of 80 points, so the third detection result is 75 points (taking the average of the first detection result and the second detection result as an example). The average of the third detection result in period 1 (i.e., 85 points) and the third detection result in period 2 (i.e., 75 points) is the final detection result. It can be understood that Table 1 above takes two periods as an example, and in fact, it can include more periods. The more periods, the more accurate the calculation.

[0124] Embodiment Two

[0125] The difference between this embodiment two and the above embodiment one is that this embodiment two considers the case where other people sleep with the user. In this case, the wearable device can only detect the sleep state of the user. In short, the application scenario of this embodiment two is to detect the sleep state of a specific user (or target user) in a multi-person sleep environment.

[0126] See Figure 7 for a flowchart of a sleep state detection method provided by this embodiment. Figure 7 The difference between this embodiment two and the above embodiment one is that this embodiment two considers the case where other people sleep with the user. In this case, the wearable device can only detect the sleep state of the user. In short, the application scenario of this embodiment two is to detect the sleep state of a specific user (or target user) in a multi-person sleep environment. Figure 2The difference is that steps S202-1 and S202-3 are added between S202 and S203. Figure 7 S202-1 to S202-3 in Figure 7 For other steps in Figure 2 Introduction.

[0127] S202-1: If the mode is non-wearing mode, the wearable device prompts the user whether there are other people around.

[0128] S202-2: If it is determined that there are other people around, a prompt is given to record a voice of the target user.

[0129] It should be noted that this second embodiment takes into account that in a multi-person environment, the sound signals collected by the wearable device include the sound signals of multiple people. In order to detect the sleep state of a specific user, it is necessary to determine the sound signal of the target user from the collected sound signals including multiple people. The target user refers to the user whose sleep state is to be detected in the case of multiple people. If there are multiple people, the sound of the person whose sleep state is to be detected is recorded.

[0130] One way to do this is to see Figure 8A (a) When the phone detects an operation for a non-wearing mode indicator, it displays the following Figure 8A (b) The prompt information shows whether there are other people around, and two buttons are displayed. When the operation of the "Yes" button is detected, it is determined that there are other people around the user, and the following is displayed: Figure 8A (c) shows the prompt message: Hello, record the breathing sound for 10 seconds, and the prompt message "Press and hold the button to record" can also be displayed. When the recording is completed, the following is displayed Figure 8A (d) shown in the interface. Or, above Figure 8A The order of the interface display in can be adjusted, for example, see Figure 8A (a) When the bracelet detects an operation for a non-wearing mode indicator, it will first display Figure 8A (d) When you select “Yes”, the interface will be displayed. Figure 8A (b) The interface shown.

[0131] Another possible implementation is to see Figure 8B (a) When the phone detects an operation for a non-wearing mode indicator, it displays the following Figure 8B (b) shows the prompt message: Hello, record the breathing sound for 10 seconds, and can also display the prompt message "Press and hold the button to record" and the prompt message "Skip", indicating that the user can choose to skip this setting process if there is no one around. When the recording is completed or the skip button is clicked, the following display can be displayed: Figure 8B (c) shows the interface.

[0132] After adding S202-1 and S202-2 in the second embodiment, the implementation principle of S204 is the same as Figure 2 The implementation principle of S204 (i.e. Figure 6 Specifically, in Figure 6 Step 5 can be refined as follows: extracting breathing sounds that match the pre-recorded breathing sounds from the collected sound signal based on the pre-recorded breathing sounds. This is because when there are other people around, the sound signal collected by the wearable device includes the breathing sounds of all of them. Generally speaking, each person's breathing sound has specific characteristics (such as intensity, frequency, duration, etc.). Therefore, based on the pre-recorded breathing sounds, the breathing sound of the target user can be identified from the collected sound signal. The target user refers to the user whose sleeping state is to be detected in the case of multiple people. For example, the breathing sound of the user whose breathing sound was recorded will be the extracted user.

[0133] S202-3, prompting the user to place the device near the target user.

[0134] It should be noted that this embodiment 2 takes into account that if it is a multi-person environment, each person may turn over, get up, etc. when sleeping, which may cause the mattress to move (assuming that the wearable device is fixed on the mattress), and then drive the wearable device to move. Therefore, the motion signal collected by the wearable device includes the motion signals corresponding to multiple people. In order to more accurately detect the sleeping state of a specific user, the user can be prompted to place the wearable device near the specific user.

[0135] One possible way to do this is to see Figure 9 (a) When the phone detects that the user has selected the non-wearing mode, it will display the following Figure 9 (b) shows the interface, which is used to prompt whether there are other people around. If yes, it will display Figure 9 (c) shows the interface, which prompts the user to record the breathing sound. After the recording is completed, the following Figure 9 (d) shows an interface, which displays a prompt message: whether to fix the device properly, and whether to fix the device between you and other people and close to your side.

[0136] For example, in Figure 9 In (d), after the mobile phone detects that the user selects "yes", it can also display the following Figure 9(e) the interface shown, which prompts information that the device is on the left side or right side of you, assuming that the bracelet detects that the user chooses the left side, if the specific user who needs to detect the sleep state is on the right side of the device, then when the bracelet detects the motion signal, the motion signal from the left side is filtered, and the motion signal from the right side (i.e. the motion signal corresponding to the target user) is reserved, and then the first detection result of the target user is determined according to the reserved motion signal.

[0137] Assuming that the mode of embodiment one is called single-person individual detection mode, and the mode of embodiment two is called non-single-person individual detection mode (or multi-person individual detection mode), i.e. detecting the sleep state of a specific user (i.e. the target user whose respiratory sound is recorded in advance) in a non-single-person environment, the user can choose to use the single-person individual detection mode or the non-single-person individual detection mode.

[0138] For example, please refer to Figure 10 (a), when the phone detects that the user clicks the icon of the non-wearing mode, it displays the prompt information shown in Figure 10 (b), which prompts you to select the following mode, and also displays the icon of the single-person individual detection mode and the icon of the non-single-person individual detection mode. When the phone detects that the user selects the single-person individual detection mode, it is determined that it is a single-person environment (i.e. there is no other person in the environment), and the mode of embodiment one (such as the flow shown in Figure 2 ) can be used for processing, for example, the interface shown in Figure 10 (c) is displayed.

[0139] For example, please refer to Figure 11 (a), when the phone detects that the user clicks the icon of the non-wearing mode, it displays the prompt information shown in Figure 11 (b), which prompts you to select the following mode, and also displays the icon of the single-person individual detection mode and the icon of the non-single-person individual detection mode. When the phone detects that the user selects the non-single-person individual detection mode, it is determined that it is a non-single-person environment (i.e. there is another person in the environment), and the mode of embodiment two (such as the flow shown in Figure 7 ) can be used for processing. For example, the interface shown in Figure 11 (c) is displayed, which displays prompt information for prompting the user to record the respiratory sound. When the recording is completed or the skip button is clicked, the interface shown in Figure 11 (d) is displayed.

[0140] Embodiment three

[0141] The previous embodiment one is a single-person individual detection mode, and the embodiment two is a non-single-person individual detection mode. The non-single-person overall detection mode of this embodiment three is different from the embodiment two in that the embodiment two detects the sleep state of a specific user in a multi-person environment, and the embodiment three detects the overall sleep state of all people in a multi-person environment.

[0142] The implementation principle of this embodiment three is the same as that of the foregoing embodiment one. Specifically, in a multi-person environment, each person can generate a motion signal and a sound signal, so the motion signal collected by the wearable device includes the motion signals of multiple persons, which can be understood as a total motion signal, and the sound signal collected by the wearable device includes the sound signals of multiple persons (such as the breathing sounds of multiple persons), which can be understood as a total sound signal. Then, the first detection result for representing the overall sleep state of multiple persons is determined according to the total motion signal; then, the second detection result for representing the overall sleep state of multiple persons is determined according to the total sound signal, and the final detection result for representing the sleep state of multiple persons is determined according to the first detection result and / or the second detection result.

[0143] For example, referring to Figure 12 (a), when the phone detects that the user selects the non-wearing mode, an interface as shown in Figure 12 (b) is displayed, which includes a single-person individual detection mode, a non-single-person individual detection mode, a non-single-person overall detection mode, and when the phone detects that the user selects the non-single-person overall detection mode, an interface as shown in Figure 12 (c) is displayed, which is used to prompt whether the device is fixed.

[0144] The implementation principle of this embodiment three is the same as that of the foregoing embodiment one, that is, Figure 2 the flowchart as shown in (d) is used to determine the final detection result for representing the sleep state of multiple persons. Specifically, Figure 2 the detailed process in S204 in (d) (i.e. Figure 6 ) is used to determine the final detection result for representing the sleep state of multiple persons. Specifically, the motion signal collected in the first branch reflects the sum of the motion of the bedclothes caused by each person turning over or getting up during the sleep of multiple persons, and the sound signal collected in the second branch includes the sound signal generated by each person during the sleep of multiple persons.

[0145] Based on the same concept, Figure 13 the electronic device 1300 provided by the present application is shown in (e). The electronic device 1300 can be the phone or the wearable device in the foregoing. As shown in Figure 13 , the electronic device 1300 can include one or more processors 1301, one or more memories 1302, a communication interface 1303, and one or more computer programs 1304, and the above devices can be connected through one or more communication buses 1305. Among them, the one or more computer programs 1304 are stored in the above memory 1302 and are configured to be executed by the one or more processors 1301, the one or more computer programs 1304 include instructions, and the above instructions can be used to execute the related steps of the phone in the above corresponding embodiments. The communication interface 1303 is used to realize the communication with other devices, such as the communication interface can be a transceiver.

[0146] In the embodiments of the present application provided above, the method provided by the embodiments of the present application is introduced from the perspective of an electronic device (e.g., a mobile phone) as an execution subject. To implement each function in the method provided by the embodiments of the present application, the electronic device can include a hardware structure and / or a software module, and implement the above functions in the form of the hardware structure, the software module, or the hardware structure plus the software module. Whether a certain function in the above functions is implemented in the form of the hardware structure, the software module, or the hardware structure plus the software module depends on the specific application of the technical solution and the design constraint conditions.

[0147] In the above embodiments, according to the context, the term "when" or "after" can be interpreted as meaning "if" or "after" or "in response to determining" or "in response to detecting". Similarly, according to the context, the phrase "upon determining" or "if detecting (a stated condition or event)" can be interpreted as meaning "if determining" or "in response to determining" or "upon detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)". In addition, in the above embodiments, relational terms such as first, second and the like are used to distinguish one entity from another entity, and do not limit any actual relationship and order between the entities.

[0148] In this specification, the phrase "one embodiment" or "some embodiments" etc. means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrase "in one embodiment", "in some embodiments", "in other embodiments", "in additional embodiments" etc. in various places in the specification are not necessarily all referring to the same embodiment, but can refer to one or more but not all embodiments, unless otherwise specifically noted. The terms "including", "containing", "having" and variations thereof mean "including but not limited to", unless otherwise specifically noted.

[0149] In the embodiments described above, the entire or part can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, the entire or part can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the entire or part generates the processes or functions described in the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable apparatus. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. integrated with one or more available media sets. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)) and the like. The solutions of the above embodiments can be combined without conflict.

[0150] It should be noted that a part of this patent application file contains content protected by copyright. The copyright owner has no objection to the facsimile reproduction of the patent document or the patent document content of the patent office's patent file or record, but otherwise reserves all copyrights.

Claims

1. A sleep state detection method characterized by comprising: The method is applied to a wearable device including a detachable module having a holding device, and the method includes: In a case where it is determined that the current is in a sleep mode, the wearable device determines whether the current is in a wearing mode or a non-wearing mode; If it is determined that the current is in the wearing mode, a sleep detection process in the wearing mode is adopted to detect a sleep state of a user; If it is determined that the current is in the non-wearing mode, fourth prompt information is output, the fourth prompt information is used to prompt the user to fix the wearable device, and the wearable device is used to execute a sleep detection process in the non-wearing mode, the sleep detection process in the non-wearing mode includes: When it is determined that the environment includes multiple people, second prompt information is output, the second prompt information is used to prompt an entry of an orientation of a target user relative to the detection device; Behavior data is collected, the behavior data includes motion signals and / or sound signals; According to the entry of the orientation of the target user relative to the detection device, motion signals and / or sound signals matched with the orientation are extracted from the motion signals and / or the sound signals, the extracted motion signals are used to determine a first detection result used to represent a sleep state of the target user, and the extracted sound signals are used to determine a second detection result used to represent the sleep state of the target user; According to the first detection result and / or the second detection result, a final detection result of the sleep state of the target user is determined.

2. The method of claim 1, wherein, The final detection result is an average value or a weighted average value of the first detection result and the second detection result.

3. The method according to claim 1 or 2, characterized in that, Before the behavior data is collected, the method further includes: When it is determined that the environment includes multiple people, first prompt information is output, the first prompt information is used to prompt an entry of specific sound signals of the target user; According to the entry of the specific sound signals of the target user, sound signals matched with the specific sound signals of the target user are extracted from the sound signals; The extracted sound signals are used to determine the second detection result used to represent the sleep state of the target user.

4. The method of claim 1, wherein, The determination that the environment includes multiple people includes: Third prompt information is output, the third prompt information is used to prompt whether multiple people are in the environment; According to a confirmation instruction input by the user, it is determined that multiple people are in the environment.

5. The method of claim 1, wherein, Before the behavior data is collected, the method further includes: In response to a user operation, a current mode is set to the non-wearing mode.

6. The method of claim 1, wherein, The behavior data includes: Motion signals caused by behaviors such as turning over, getting up or shaking during sleep of the user are collected by a motion sensor, the motion signals include at least one of displacement, acceleration, speed, angular velocity and angular acceleration; The method further includes: According to the collected motion signals, body movement features are recognized, the body movement features include getting up, turning over and shaking; According to at least one of a frequency, a number of times and an intensity of occurrence of the body movement features within a set time length, the first detection result is determined.

7. The method of claim 1, wherein, At least one sound feature of snoring sound and breathing sound of the target user is included in the sound signals; The method further includes: The second detection result is determined according to at least one of intensity, frequency and number of at least one of snoring sound or breathing sound of the target user within a set time length.

8. An electronic device, comprising: Comprise: A processor, a memory, and one or more programs; Wherein the one or more programs are stored in the memory, and the one or more programs include instructions which, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium is used to store a computer program, when the computer program runs on a computer, so that the computer executes the method of any one of claims 1 to 7.

10. A computer program product, characterised in that, Comprise a computer program, when the computer program runs on a computer, so that the computer executes the method of any one of claims 1-7.

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