WO2022088938A1

WO2022088938A1 - Sleep monitoring method and apparatus, and electronic device and computer-readable storage medium

Info

Publication number: WO2022088938A1
Application number: PCT/CN2021/115753
Authority: WO
Inventors: 许德省; 李靖
Original assignee: 华为技术有限公司
Priority date: 2020-10-29
Filing date: 2021-08-31
Publication date: 2022-05-05
Also published as: CN114424927A

Abstract

The present application is suitable for the field of data processing. Provided are a sleep monitoring method and apparatus, and an electronic device and a computer-readable storage medium. The sleep monitoring method comprises: an electronic device determining whether a user is located in a vehicle; if the user is located in the vehicle, the electronic device acquiring first monitoring data of the user in the vehicle, wherein the first monitoring data comprises information related to the vehicle; the electronic device obtaining target data according to the first monitoring data, wherein the target data is data obtained after the information related to the vehicle is filtered from the first monitoring data; and the electronic device monitoring the sleep of the user according to the target data. By means of the method, the sleep of a user in a vehicle can be accurately recorded.

Description

Sleep monitoring method, apparatus, electronic device, and computer-readable storage medium

This application claims the priority of a Chinese patent application filed with the State Intellectual Property Office on October 29, 2020, the application number is 202011185682.X, and the application name is "sleep monitoring method, device, electronic device and computer-readable storage medium", The entire contents of which are incorporated herein by reference.

technical field

The present application relates to the field of data processing, and in particular, to a sleep monitoring method, apparatus, electronic device, and computer-readable storage medium.

Background technique

At present, sleep monitoring is a common function of electronic devices (such as mobile phones, wristbands, smart watches, etc.). Through this function, users can understand their own sleep data such as the time of going out/sleep, sleep duration, etc., and then master their own sleep status.

In the sleep monitoring method, the user's sleep-onset point and sleep-off point should be determined. At present, the electronic device usually determines the sleeping point and the sleeping point of the user according to the acceleration data monitored by the electronic device, so as to monitor the sleep of the user. However, at present, when the user is on the vehicle, the electronic device may not be able to accurately determine the sleeping point and the sleeping point of the user, so that the electronic device cannot accurately record the user's sleep situation on the vehicle.

SUMMARY OF THE INVENTION

The present application provides a sleep monitoring method, device, electronic device, and computer-readable storage medium, which can accurately record the sleep condition of a user on a vehicle.

To achieve the above object, the application adopts the following technical solutions:

In a first aspect, a sleep monitoring method is provided, applied to an electronic device, the method comprising:

The electronic device determines whether the user is on the vehicle; if the user is on the vehicle, the electronic device obtains the first monitoring data of the user on the vehicle, and the first monitoring data includes information related to the vehicle; Target data is obtained from the monitoring data, and the target data is data obtained after filtering out information related to vehicles in the first monitoring data; the sleep of the user is monitored according to the target data.

In the above sleep monitoring method, since the user is on the vehicle, the first monitoring data includes information related to the vehicle. However, since the introduction of vehicle-related information may cause the electronic device to monitor the user's sleep according to the first monitoring data, it may not be able to accurately calculate the user's sleep-onset point and/or sleep-off point on the vehicle. Therefore, there is usually a situation in which the user is on the vehicle and the electronic device does not record the user's sleep or lacks the user's sleep record. However, in the embodiment of the present application, the electronic device filters out the information related to the vehicle in the first monitoring data, thus filtering out the data affecting the user action data in the first monitoring data. Therefore, the target data obtained after filtering can more accurately reflect the user's own action state, so that the electronic device can accurately calculate the user's sleep-onset point and/or sleep-off point on the vehicle, thereby solving the problem of the user's sleep in the traffic. The problem of not recording or less recording sleep on the tool has improved the user experience.

With reference to the first aspect, in some embodiments, the method provided by the embodiments of the present application further includes: if the user is not on the vehicle, the electronic device monitors the user's sleep according to the acquired second monitoring data, and the second monitoring The data includes the user's status information.

In this embodiment of the present application, the electronic device may monitor the sleep of the user in different states. That is to say, the above electronic device can not only monitor the sleep of the user when the user is on the vehicle, but also can be used to monitor the sleep of the user when the user is not on the vehicle, which improves the user experience. In addition, the first monitoring mode is employed when the user is on the vehicle, i.e. the influence of vehicle-related information needs to be filtered out. When the user is not on the vehicle, the second monitoring mode is adopted. At this time, since the monitoring data collected by the electronic device usually does not include information related to the vehicle, there is no need to filter the data and use the collected monitoring data for sleep monitoring. Through the above two monitoring modes, the sleep monitoring is more targeted, and the sleep monitoring results are more accurate.

With reference to the first aspect, in some embodiments, before the electronic device acquires the first monitoring data of the user on the vehicle, the method provided in this embodiment of the present application further includes: the electronic device determines to use the first monitoring mode to monitor the user on the vehicle The first monitoring mode refers to the process of filtering out the information related to the vehicle on the monitoring data obtained by the electronic device.

With reference to the first aspect, in some embodiments, before the electronic device monitors the sleep of the user according to the acquired second monitoring data, the method provided in this embodiment of the present application further includes: the electronic device determines to use the second monitoring mode to monitor the sleep of the user. The user's sleep is monitored, and the second monitoring mode means that information related to the vehicle in the monitoring data obtained by the electronic device does not need to be filtered out.

In this embodiment of the present application, the electronic device may determine whether the user is on a vehicle, and determine, according to the determination result, to use the first monitoring mode or the second monitoring mode to monitor the user's sleep. Through the above method, the electronic device can automatically switch the monitoring mode, the degree of intelligence is high, and the user experience is improved.

With reference to the first aspect, in some embodiments, the electronic device obtains the target data according to the first monitoring data, including: the electronic device obtains the driving data of the vehicle; the electronic device determines the data component of the first monitoring data that matches the driving data as Vehicle-related information; the electronic device filters out the vehicle-related information from the first monitoring data to obtain target data.

Compared with performing denoising processing on the first monitoring data, by using the above processing method, information related to vehicles can be filtered out in a targeted manner, and more accurate user status information can be obtained.

With reference to the first aspect, in some embodiments, the driving data includes first acceleration data when the vehicle is in a stationary state and second acceleration data when the vehicle is in a driving state; the electronic device combines the first monitoring data with the driving data The matched data components are determined to be vehicle-related information, including: the electronic device constructs a noise data matrix according to the first acceleration data and the second acceleration data; the electronic device inputs the noise data matrix and the first monitoring data into a preset decomposition model, Obtaining data components in the first monitoring data that match the driving data; the electronic device determines the data components in the first monitoring data that match the driving data as vehicle-related information.

The noise data matrix includes the first acceleration data and the second acceleration data, which can ensure that in the subsequent data filtering process, both the data interference caused by the driving process of the vehicle and the data existing when the vehicle is stationary are considered. Interference makes data filtering more thorough. In addition, using the trained preset decomposition model for decomposition processing can effectively improve the monitoring efficiency and monitoring accuracy.

With reference to the first aspect, in some embodiments, the electronic device monitors the user's sleep according to the target data, including: when the amount of data in the target data reaches a preset amount, the electronic device monitors the user's sleep according to the target data; When the amount of data in the target data does not reach the preset amount, the electronic device continues to acquire the first monitoring data.

Performing sleep monitoring according to a preset amount of target data can reduce the amount of data processing and avoid the contingency of monitoring results obtained from data at a single sampling moment.

With reference to the first aspect, in some embodiments, the electronic device monitors the user's sleep according to the target data, including: the electronic device obtains multiple sets of data according to the target data; the electronic device obtains multiple sets of data according to the respective statistical characteristics of each set of data in the multiple sets of data The value determines a first state label corresponding to each group of data, wherein the first state label includes a sleep state and an awake state; the electronic device determines the sleep monitoring result according to the first state label corresponding to each group of data.

With reference to the first aspect, in some embodiments, the electronic device determines the sleep monitoring result according to the first state label corresponding to each group of data, including: the electronic device acquires the user's pulse wave data; the electronic device determines the sleep monitoring result according to the pulse wave data and each group of data. The respective corresponding statistical feature values determine the second state label corresponding to each group of data, wherein the second state label includes a sleep state and an awake state; the electronic device determines the corresponding data of each group of data according to the first state label and the second state label. Final state label; the electronic device determines the sleep monitoring result according to the final state label corresponding to each set of data.

By using the above method, the monitoring results of the sensor data and the monitoring results of the PPG data are comprehensively considered, so that the accuracy of the sleep monitoring results can be further improved.

With reference to the first aspect, in some embodiments, determining whether the user is on a vehicle by the electronic device includes: the electronic device obtains travel information and/or exercise information of the user, and determines whether the user is located in the vehicle according to the travel information and/or exercise information. or, the electronic device displays prompt information on the display screen, and the prompt information is used to prompt the user to select whether the user is on the vehicle; the electronic device monitors the first operation instruction input by the user; the electronic device determines the user according to the first operation instruction whether on a vehicle.

By judging whether the user is on a vehicle through travel information/motion information, the electronic device can automatically switch the sleep monitoring mode, making the sleep monitoring function of the electronic device more intelligent and improving the user experience. In addition, a user-selected method is also provided, and the user determines whether the vehicle is on a vehicle. This method can more accurately determine the user's state.

In a second aspect, an embodiment of the present application provides a sleep monitoring device, the device comprising:

a judging unit, used for the electronic device to judge whether the user is on the vehicle;

The first monitoring unit is used for obtaining the first monitoring data of the user on the vehicle if the user is on the vehicle, and the first monitoring data includes information related to the vehicle; the electronic device obtains the target data according to the first monitoring data , the target data is the data obtained after filtering out the vehicle-related information in the first monitoring data; the electronic device monitors the user's sleep according to the target data.

In a third aspect, an embodiment of the present application provides an electronic device, the electronic device includes a processor, and the processor is configured to run a computer program stored in a memory to implement the method provided by any possible implementation manner of the first aspect.

In a fourth aspect, the embodiments of the present application provide a computer-readable storage medium, including computer instructions, when the computer instructions are executed on a computer or a processor, the computer or the processor is made to perform any possible implementation of the first aspect. method provided.

In a fifth aspect, the embodiments of the present application provide a computer program product, which when the computer program product runs on a computer or a processor, causes the computer or processor to execute the method provided by any possible implementation manner of the first aspect.

Understandably, the sleep monitoring device described in the second aspect provided above corresponds to the method provided in the first aspect, the electronic device described in the third aspect, the computer storage medium described in the fourth aspect, or the fifth aspect. The computer program products described are all used to execute the method provided by the first aspect. Therefore, for the beneficial effects that can be achieved, reference may be made to the beneficial effects in the corresponding method, which will not be repeated here.

Description of drawings

FIG. 1 is a schematic diagram of a sleep phase heart rate variation trend provided by an embodiment of the present application;

2 is a schematic diagram of a change trend of acceleration data amplitude provided by an embodiment of the present application;

FIG. 3 is a schematic structural diagram of an electronic device 100 provided by an embodiment of the present application;

FIG. 4 is a software structural block diagram of the electronic device 100 provided by an embodiment of the present application;

5 is a schematic diagram of an application interface of sleep monitoring provided by an embodiment of the present application;

6 is a schematic diagram of a main screen interface provided by an embodiment of the present application;

7 is a schematic diagram of an application interface for monitoring mode selection provided by an embodiment of the present application;

8 is a schematic flowchart of a sleep monitoring method provided by an embodiment of the present application;

9 is a schematic flowchart of a sleep monitoring method in a first monitoring mode provided by an embodiment of the present application;

10 is a schematic diagram of a decomposition result provided by an embodiment of the present application;

FIG. 11 is a structural block diagram of a sleep monitoring apparatus provided by an embodiment of the present application.

Detailed ways

In order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with basically the same function and effect. For example, the first message and the second message are only for distinguishing different messages, and the sequence of the first message is not limited. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like are not necessarily different.

It should be noted that, in this application, words such as "exemplary" or "for example" are used to represent examples, illustrations or illustrations. Any embodiment or design described in this application as "exemplary" or "such as" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present the related concepts in a specific manner.

In this application, "at least one" means one or more, and "plurality" means two or more. "And/or", which describes the association relationship of the associated objects, indicates that there can be three kinds of relationships, for example, A and/or B, which can indicate: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A, B can be singular or plural. The character "/" generally indicates that the associated objects are an "or" relationship. "At least one item(s) below" or similar expressions thereof refer to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (a) of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c may be single or multiple .

The sleep monitoring technology usually monitors the user's going out/sleeping time based on acceleration data, gyroscope data, and/or PPG (Photopleth aysmography, photocapacitive pulse wave marking) data, etc.

Since the accelerometer and the gyroscope themselves have high sensitivity and are easily affected by external factors, the specificity of the monitoring result of the user's action is high, and the magnitude of the user's action cannot be accurately monitored. For heart rate monitoring based on PPG data, due to individual differences, the user's heart rate monitoring results are highly specific. Exemplarily, referring to FIG. 1 , it is a schematic diagram of a change trend of heart rate in a sleep phase provided by an embodiment of the present application. As shown in (a) of Figure 1, the heart rate of user A changes. The time corresponding to the vertical solid line in the figure is the time of falling asleep. It can be seen from the figure that the heart rate of user A decreases as a whole after falling asleep. As shown in (b) of Figure 1, the heart rate of user B changes. The time corresponding to the vertical solid line in the figure is the time of falling asleep. It can be seen from the figure that the heart rate of user B increases as a whole after falling asleep. As can be seen from the example in Figure 1, the trend of heart rate changes may be different in different sleep stages of users. To sum up, if sleep monitoring is performed solely on the basis of acceleration data, gyroscope data or PPG data, the accuracy of sleep monitoring results obtained is low. Therefore, in the prior art, the user's action is usually monitored according to acceleration data and gyroscope data, and the user's heart rate is monitored according to PPG data, and then the user's sleep/sleep time is determined in combination with the user's action and the user's heart rate.

However, existing sleep monitoring techniques do not take into account the fact that the user is on a vehicle. When the user is on a vehicle such as a subway, high-speed rail, bus or plane, the monitored acceleration data or gyroscope data may not be able to accurately reflect the user's actions due to the shaking of the vehicle or the influence of the acceleration of the vehicle itself. size, so it is impossible to accurately determine the user's going out/sleeping time. For example, referring to FIG. 2 , it is a schematic diagram of the variation trend of the acceleration data amplitude provided by the embodiment of the present application. Fig. 2 shows the trend of amplitude variation of the monitored acceleration data when the user is on the vehicle. It can be seen from FIG. 2 that the user's falling asleep time determined according to the acceleration data is not the real falling asleep time of the user. Therefore, when the user is on the vehicle, the existing sleep monitoring technology cannot accurately monitor the user's going-out/going-to-sleep time, resulting in the user's sleep on the vehicle being not recorded or less recorded, thereby affecting the user experience.

Based on the above problems, embodiments of the present application provide a sleep monitoring method. In this method, when the user is on the vehicle, a process of filtering out information related to the vehicle needs to be performed on the monitoring data obtained by the electronic device, and then the user's sleep is monitored according to the filtered data. Since the influence of the shaking or acceleration of the vehicle on the user's actions is filtered out, the sleep monitoring method provided by the embodiments of the present application can accurately monitor the time points of the user's going out/falling asleep on the vehicle, and finally realize the solution to the problem of the user's sleep on the vehicle. Sleep on is not recorded or is less recorded for the purpose.

In order to illustrate the technical solutions described in the present application, the following specific embodiments are used for description.

First, the electronic device involved in the embodiments of the present application is introduced. The electronic device may be a mobile phone, a wearable device, a tablet computer, a netbook, a notebook computer, an ultra-mobile personal computer (UMPC), or a personal digital assistant (personal digital assistant). Assistant, PDA) and other devices with the function of monitoring user sleep, the embodiments of the present application do not impose any restrictions on the specific types of electronic devices. For example, the wearable device may be a smart bracelet, a smart watch, or the like.

Please refer to FIG. 3 , which is a schematic structural diagram of an electronic device 100 provided by an embodiment of the present application.

The electronic device 100 may 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 , mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber identification module (subscriber identification module, SIM) card interface 195 and so on. The sensor module 180 may 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, and ambient light. Sensor 180L, bone conduction sensor 180M, etc.

It can be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the electronic device 100 . In other embodiments of the present application, the electronic device 100 may include more or less components than shown, or combine some components, or separate some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, for example, the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), controller, memory, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU) Wait. Wherein, different processing units may be independent devices, or may be integrated in one or more processors. Exemplarily, the processor is configured to execute the sleep monitoring method provided by the embodiment of the present application, for example, the processor executes the following steps S801-S803 or steps S901-S903.

The controller may be the nerve center and command center of the electronic device 100 . The controller can generate an operation control signal according to the instruction operation code and timing signal, and complete the control of fetching and executing instructions.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in processor 110 is cache memory. This memory may hold instructions or data that have just been used or recycled by the processor 110 . If the processor 110 needs to use the instruction or data again, it can be called directly from memory. Repeated accesses are avoided and the waiting time of the processor 110 is reduced, thereby increasing the efficiency of the system.

In some embodiments, the processor 110 may include one or more interfaces. The interface may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous transceiver (universal asynchronous transmitter) receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and / or universal serial bus (universal serial bus, USB) interface, etc.

The I2C interface is a bidirectional synchronous serial bus that includes a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may contain multiple sets of I2C buses. The processor 110 can be respectively coupled to the touch sensor 180K, the charger, the flash, the camera 193 and the like through different I2C bus interfaces. For example, the processor 110 may couple the touch sensor 180K through the I2C interface, so that the processor 110 and the touch sensor 180K communicate with each other through the I2C bus interface, so as to realize the touch function of the electronic device 100 .

The I2S interface can be used for audio communication. In some embodiments, the processor 110 may contain multiple sets of I2S buses. The processor 110 may be coupled with the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170 . In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 through the I2S interface, so as to realize the function of answering calls through a Bluetooth headset.

The PCM interface can also be used for audio communications, sampling, quantizing and encoding analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 can also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communication. The bus may be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is typically used to connect the processor 110 with the wireless communication module 160 . For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to implement the Bluetooth function. In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 through the UART interface, so as to realize the function of playing music through the Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 . MIPI interfaces include camera serial interface (CSI), display serial interface (DSI), etc. In some embodiments, the processor 110 communicates with the camera 193 through a CSI interface, so as to realize the photographing function of the electronic device 100 . The processor 110 communicates with the display screen 194 through the DSI interface to implement the display function of the electronic device 100 .

The GPIO interface can be configured by software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface may be used to connect the processor 110 with the camera 193, the display screen 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like. The GPIO interface can also be configured as I2C interface, I2S interface, UART interface, MIPI interface, etc.

The USB interface 130 is an interface that conforms to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, and the like. The USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones to play audio through the headphones. The interface can also be used to connect other electronic devices, such as AR devices.

It can be understood that the interface connection relationship between the modules illustrated in the embodiments of the present application is only a schematic illustration, and does not constitute a structural limitation of the electronic device 100 . In other embodiments of the present application, the electronic device 100 may also adopt different interface connection manners in the foregoing embodiments, or a combination of multiple interface connection manners.

The charging management module 140 is used to receive charging input from the charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from the wired charger through the USB interface 130 . In some wireless charging embodiments, the charging management module 140 may receive wireless charging input through a wireless charging coil of the electronic device 100 . While the charging management module 140 charges the battery 142 , it can also supply power to the electronic device through the power management module 141 .

The power management module 141 is used for connecting 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 internal memory 121 , the external memory, the display screen 194 , the camera 193 , and the wireless communication module 160 . The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, battery health status (leakage, impedance). In some other embodiments, the power management module 141 may also be provided in the processor 110 . In other embodiments, the power management module 141 and the charging management module 140 may also be provided in the same device.

The wireless communication function of the electronic device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modulation and demodulation processor, the baseband processor, and the like.

Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 may be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, the antenna 1 can be multiplexed as a diversity antenna of the wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide wireless communication solutions including 2G/3G/4G/5G etc. applied on the electronic device 100 . The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA) and the like. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, filter and amplify 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 then turn it into an electromagnetic wave for radiation through the antenna 1 . In some embodiments, at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110 . In some embodiments, at least part of the functional modules of the mobile communication module 150 may be provided in the same device as at least part of the modules of the processor 110 .

The modem processor may include a modulator and a demodulator. Wherein, the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low frequency baseband signal. Then the demodulator transmits the demodulated low-frequency baseband signal to the baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and passed to the application processor. The application processor outputs sound signals through audio devices (not limited to the speaker 170A, the receiver 170B, etc.), or displays images or videos through the display screen 194 . In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be independent of the processor 110, and may be provided in the same device as the mobile communication module 150 or other functional modules.

The wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellites Wireless communication solutions such as global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared technology (IR). The wireless communication module 160 may be one or more devices integrating 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 , perform frequency modulation on it, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2 .

In some embodiments, the antenna 1 of the electronic device 100 is coupled with the mobile communication module 150, and the antenna 2 is coupled with the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology. Wireless communication technologies may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), broadband code division Multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), BT, GNSS, WLAN, NFC, FM , and/or IR technology, etc. GNSS may include global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (quasi-zenith) satellite system, QZSS) and/or satellite based augmentation systems (SBAS).

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

Display screen 194 is used to display images, videos, and the like. 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 or an active-matrix organic light-emitting diode (active-matrix organic light). emitting diode, AMOLED), flexible light-emitting diode (flex light-emitting diode, FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (quantum dot light emitting diodes, QLED) and so on. In some embodiments, the electronic device 100 may include one or N display screens 194 , where N is a positive integer greater than one.

The electronic device 100 may implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The ISP is used to process the data fed back by the camera 193 . For example, when taking a photo, the shutter is opened, the 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, converting it into an image visible to the naked eye. ISP can also perform algorithm optimization on image noise, brightness, and skin tone. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193 .

Camera 193 is used to capture still images or video. The object is projected through the lens to generate an optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then transmits the electrical signal to the ISP to convert it into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. DSP converts digital image signals into standard RGB, YUV and other formats of image signals. In some embodiments, the electronic device 100 may include 1 or N cameras 193 , where N is a positive integer greater than 1.

A digital signal processor is used to process digital signals, in addition to processing digital image signals, it can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy and so on.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos of various encoding formats, such as: Moving Picture Experts Group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4 and so on.

The NPU is a neural-network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transfer mode between neurons in the human brain, it can quickly process the input information and can continuously learn by itself. Applications such as intelligent cognition of the electronic device 100 can be implemented through the NPU, such as image recognition, face recognition, speech recognition, text understanding, and the like.

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 electronic device 100 . The external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example, information such as the first monitoring data obtained in the sleep monitoring method provided in the embodiment of the present application, the filtered data obtained after processing, and the like are stored in an external memory card.

Internal memory 121 may be used to store computer executable program code, which includes instructions. The processor 110 executes various functional applications and data processing of the electronic device 100 by executing the instructions stored in the internal memory 121 . The internal memory 121 may include a storage program area and a storage data area. The storage program area may store an operating system, an application program required for at least one function (such as a sleep monitoring function, etc.), and the like. The storage data area may store data created during the use of the electronic device 100 (such as monitoring data obtained by the sleep monitoring function, etc.) and the like. In addition, the internal memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, universal flash storage (UFS), and the like.

The electronic device 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. For example, music playback, recording, prompt signal playback, etc.

The audio module 170 is used for converting digital audio information into analog audio signal output, and also for converting analog audio input into digital audio signal. 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 .

Speaker 170A, also referred to as a "speaker", is used to convert audio electrical signals into sound signals. The electronic device 100 can listen to music, listen to a hands-free call, or listen to a prompt signal through the speaker 170A.

The receiver 170B, also referred to as "earpiece", is used to convert audio electrical signals into sound signals. When the electronic device 100 answers a call or a voice message, the voice can be answered by placing the receiver 170B close to the human ear.

The microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can make a sound by approaching the microphone 170C through a human mouth, and input the sound signal into the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, which can implement a noise reduction function in addition to collecting sound signals. In other embodiments, the electronic device 100 may further be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and implement directional recording functions.

The earphone jack 170D is used to connect wired earphones. The earphone interface 170D may be the USB interface 130, or may be a 3.5mm open mobile terminal platform (OMTP) standard interface, a cellular telecommunications industry association of the USA (CTIA) standard interface.

The pressure sensor 180A is used to sense pressure signals, and can convert the pressure signals into electrical signals. In some embodiments, the pressure sensor 180A may be provided on the display screen 194 . There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors, and the like. The capacitive pressure sensor may be comprised of at least two parallel plates of conductive material. When a force is applied to the pressure sensor 180A, the capacitance between the electrodes changes. The electronic device 100 determines the intensity of the pressure according to the change in capacitance. When a touch operation acts on the display screen 194, the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic device 100 may also calculate the touched position according to the detection signal of the pressure sensor 180A. In some embodiments, touch operations acting on the same touch position but with different touch operation intensities may correspond to different operation instructions. For example, when a touch operation whose intensity is less than the first pressure threshold acts on the short message application icon, the instruction for viewing the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, the instruction to create a new short message is executed.

The gyro sensor 180B may be used to determine the motion attitude of the electronic device 100 . In some embodiments, the angular velocity of electronic device 100 about three axes (ie, x, y, and z axes) may be determined by gyro sensor 180B. The gyroscope sensor 180B can be used for anti-shake shooting, and can also be used for navigation and somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates the altitude through the air pressure value measured by the air pressure sensor 180C to assist in positioning and navigation.

The magnetic sensor 180D includes a Hall sensor. The electronic device 100 can detect the opening and closing of the flip holster using the magnetic sensor 180D. In some embodiments, when the electronic device 100 is a flip machine, the electronic device 100 can detect the opening and closing of the flip according to the magnetic sensor 180D. Further, according to the detected opening and closing state of the leather case or the opening and closing state of the flip cover, characteristics such as automatic unlocking of the flip cover are set.

The acceleration sensor 180E can monitor the magnitude of the acceleration of the electronic device 100 in various directions (generally three axes, ie, x, y and z axes). The magnitude and direction of gravity can be monitored when the electronic device 100 is stationary. It can also be used to identify the posture of electronic devices, and can be used in applications such as horizontal and vertical screen switching, pedometers, etc.

Distance sensor 180F for measuring distance. The electronic device 100 can measure the distance through infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 can use the distance sensor 180F to measure the distance to achieve fast focusing.

Proximity light sensor 180G may include, for example, light emitting diodes (LEDs) and light detectors, such as photodiodes. The light emitting diodes may be infrared light emitting diodes. The electronic device 100 emits infrared light to the outside through the light emitting diode. Electronic device 100 uses photodiodes to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100 . When insufficient reflected light is detected, the electronic device 100 may determine that there is no object near the electronic device 100 . The electronic device 100 can use the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear to talk, so as to automatically turn off the screen to save power. Proximity light sensor 180G can also be used in holster mode, pocket mode automatically unlocks and locks the screen.

The ambient light sensor 180L is used to sense ambient light brightness. The electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived ambient light brightness. The ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in a pocket, so as to prevent accidental touch.

The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can use the collected fingerprint characteristics to realize fingerprint unlocking, accessing application locks, taking pictures with fingerprints, answering incoming calls with fingerprints, and the like.

The temperature sensor 180J is used to detect the temperature. In some embodiments, the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold value, the electronic device 100 reduces the performance of the processor located near the temperature sensor 180J in order to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to avoid abnormal shutdown of the electronic device 100 caused by the low temperature. In some other embodiments, when the temperature is lower than another threshold, the electronic device 100 boosts the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.

Touch sensor 180K, also called "touch panel". The touch sensor 180K may be disposed on the display screen 194 , and the touch sensor 180K and the display screen 194 form a touch screen, also called a “touch screen”. The touch sensor 180K is used to detect a touch operation on or near it. The touch sensor can pass the detected touch operation to the application processor to determine the type of touch event. Visual output related to touch operations may be provided through display screen 194 . In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100 , which is different from the location where the display screen 194 is located.

The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire the vibration signal of the vibrating bone mass of the human voice. The bone conduction sensor 180M can also contact the pulse of the human body and receive the blood pressure beating signal. In some embodiments, the application processor may analyze the heart rate information based on the blood pressure beat signal obtained by the bone conduction sensor 180M, so as to realize the function of heart rate monitoring.

The keys 190 include a power-on key, a volume key, and the like. Keys 190 may be mechanical keys. It can also be a touch key. The electronic device 100 may receive key inputs and generate key signal inputs related to user settings and function control of the electronic device 100 .

Motor 191 can generate vibrating cues. The motor 191 can be used for vibrating alerts for incoming calls, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, playing audio, etc.) can correspond to different vibration feedback effects. The motor 191 can also correspond to different vibration feedback effects for touch operations on different areas of the display screen 194 . Different application scenarios (for example: time reminder, receiving information, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

The indicator 192 can be an indicator light, which can be used to indicate the charging state, the change of the power, and can also be used to indicate a message, a missed call, a notification, and the like.

The SIM card interface 195 is used to connect a SIM card. The SIM card can be contacted and separated from the electronic device 100 by inserting into the SIM card interface 195 or pulling out from the SIM card interface 195 . The electronic device 100 may support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM card, Micro SIM card, SIM card and so on. Multiple cards can be inserted into the same SIM card interface 195 at the same time. Multiple cards can be of the same type or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 is also compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as call and data communication. In some embodiments, the electronic device 100 employs an eSIM, ie: an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100 .

The software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. The embodiment of the present invention takes an Android system with a layered architecture as an example to illustrate the software structure of the electronic device 100 as an example.

FIG. 4 is a block diagram of the software structure of the electronic device 100 provided by the embodiment of the present application.

The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers, which are, from top to bottom, an application layer, an application framework layer, a system library and Android runtime (Android runtime), and a kernel layer.

The application layer can include a series of application packages.

As shown in Figure 4, the application package can include applications such as camera, gallery, calendar, call, map, navigation, sports health, bluetooth, music, video, short message and so on. For example, a sports health application can include sleep monitoring capabilities. When a sports health application including a sleep monitoring function is installed on the electronic device, the electronic device can obtain monitoring data through the sleep monitoring function, and analyze and process the monitoring data through the sleep monitoring function, so as to monitor the user's sleep. The application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

As shown in Figure 4, the application framework layer may include window managers, content providers, view systems, telephony managers, resource managers, notification managers, and the like.

A window manager is used to manage window programs. The window manager can get the size of the display screen, determine whether there is a status bar, lock the screen, take screenshots, etc.

Content providers are used to store and retrieve data and make these data accessible to applications. The data may include monitoring data obtained by the sensor module 180 (eg, acceleration data obtained by the acceleration sensor 180E, etc.), videos, images, audios, calls made and received, browsing history and bookmarks, phonebooks, and the like.

The view system includes visual controls, such as controls for displaying text, controls for displaying pictures, and so on. View systems can be used to build applications. A display interface can consist of one or more views. For example, the display interface including the short message notification icon may include a view for displaying text and a view for displaying pictures.

The phone manager is used to provide the communication function of the electronic device 100 . For example, the management of call status (including connecting, hanging up, etc.).

The resource manager provides various resources for the application, such as localization strings, icons, pictures, layout files, video files and so on.

The notification manager enables applications to display notification information in the status bar, which can be used to convey notification-type messages, and can automatically disappear after a brief pause without user interaction. For example, the notification manager is used to notify the download completion, message reminders (such as travel messages, etc.) and so on. The notification manager can also display notifications in the status bar at the top of the system in the form of graphs or scroll bar text, such as notifications of applications running in the background, and notifications that appear on the screen in the form of dialog windows. For example, text information is prompted in the status bar, a prompt sound is issued, the electronic device vibrates, and the indicator light flashes.

Android Runtime includes core libraries and a virtual machine. Android runtime is responsible for scheduling and management of the Android system.

The core library consists of two parts: one is the function functions that the java language needs to call, and the other is the core library of Android.

The application layer and the application framework layer run in virtual machines. The virtual machine executes the java files of the application layer and the application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, safety and exception management, and garbage collection.

The system layer can include multiple functional modules. For example: surface manager (surface manager), media library (Media Libraries), 3D graphics processing library (eg: OpenGL ES), 2D graphics engine (eg: SGL), etc.

The Surface Manager is used to manage the display subsystem and provides a fusion of 2D and 3D layers for multiple applications.

The media library supports playback and recording of a variety of commonly used audio and video formats, as well as still image files. The media library can support a variety of audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is the layer between hardware and software. The kernel layer contains at least display drivers, camera drivers, audio drivers, and sensor drivers.

The following embodiments of the sleep monitoring method may be implemented on an electronic device having the above-mentioned hardware structure/software structure.

The sleep detection function involved in the embodiments of the present application will be introduced below.

The sleep monitoring function on the electronic device may be set to be enabled by default. For example, as long as the electronic device is powered on, the sleep monitoring function is enabled. The sleep monitoring function can also be turned on/off by a user-defined time period. For example, if the user-defined time period is 22:00-07:00, the sleep monitoring function will be turned on at 22:00 and turned off at 07:00 the next day.

When the sleep monitoring function in the electronic device is turned on, the function can run in the foreground, the electronic device displays the interactive interface of the sleep monitoring application, and the interactive interface displays software function controls and real-time display of sleep monitoring related information. Referring to FIG. 5 , FIG. 5 is a schematic diagram of an application interface of sleep monitoring provided by an embodiment of the present application. As shown in FIG. 5 , it is an application interface 50 of sleep monitoring of the electronic device 100 . Interface 50 may include monitoring indicators 501 , data display area 502 and function controls 503 . in:

The monitoring identifier 501 is used to indicate that the sleep monitoring function is in progress. The monitoring identification may be a static image or a dynamic image, which is not specifically limited here.

The data display area 502 is used to display monitoring data. The display form of monitoring data can be in digital form, as shown in (a) in FIG. 5 ; or in the form of a statistical graph, as shown in (b) in FIG. 5 . The display form of the monitoring data is not specifically limited in the embodiment of the present application.

The function control 503 is used to implement corresponding software functions when the user clicks/touches. As shown in FIG. 5 , the function control 503 may be an “end” control, and when the user clicks the control, the sleep monitoring function is turned off.

The foreground operation mode is convenient for users to operate, but it will occupy more system resources. Also, when the sleep monitoring function is in foreground mode, users cannot operate other applications.

When the sleep monitoring function in the electronic device is turned on, the function can also run in the background. Through this function, the electronic device can acquire the monitoring data of the sensor in real time, and can notify the user through the display device after generating a message in the background. Running in the background mode saves system resources. Referring to FIG. 6, FIG. 6 is a schematic diagram of a home screen interface provided by an embodiment of the present application. As shown in FIG. 6 , it is the main screen interface 60 of the electronic device 100 . Home screen interface 60 may include a status bar 601 and a notification bar 602 . in:

The status bar 601 may include information such as operator name, time, signal strength, and current remaining power.

The notification bar 602 is used for displaying notification information of the application program, as shown in FIG. 6 , displaying the notification information of the sleep monitoring application program. The position, display mode and display duration of the notification bar on the main screen interface can be set by the user, which is not specifically limited here. The user can enter the sleep monitoring application through the notification bar, such as by clicking/long pressing the area occupied by the notification bar to enter the sleep monitoring application.

It should be noted that the interfaces described in the above embodiments of FIG. 5 and FIG. 6 are only examples, and are not used to limit the application interface of sleep monitoring and the main screen interface of the electronic device.

The sleep monitoring method provided by the embodiments of the present application is described below.

In practical applications, the user's travel state can be divided into two types: a travel state on a vehicle and a travel state not on a vehicle. When the user is on the vehicle, the monitoring data obtained by the electronic device includes both the user's state information and the vehicle-related information. In this case, when the user's sleep is monitored, the information related to the vehicle will interfere with the user's state information, thereby affecting the accuracy of the sleep monitoring result. When the user is not on the vehicle, the monitoring data acquired by the electronic device includes the state information of the user, but does not include information related to the vehicle. In this case, the monitoring data can be used to monitor the user's sleep. Correspondingly, in the sleep monitoring method provided by the embodiments of the present application, two sleep monitoring modes may be provided. When the user is on the vehicle, the electronic device determines to use the first monitoring mode to monitor the user's sleep on the vehicle. The first monitoring mode refers to a process of filtering out information related to vehicles on monitoring data acquired by the electronic device. When the user is not on the vehicle, the electronic device determines to use the second monitoring mode to monitor the sleep of the user. The second monitoring mode refers to the information related to the vehicle in the monitoring data obtained by the electronic device without filtering.

In an application scenario, the user can choose the monitoring mode by himself. The electronic device determines to use the first monitoring mode or the second monitoring mode to perform sleep monitoring according to the operation instruction input by the user. For example, referring to FIG. 7 , it is a schematic diagram of an application interface for monitoring mode selection provided by an embodiment of the present application. As shown in FIG. 7 , it is an application interface 70 of sleep detection of the electronic device 100 . Interface 70 may include selection information area 701 and selection controls 702 . in:

Select the information area 701 for displaying the name of the monitoring mode. As shown in FIG. 7( a ), the selection information area 701 displays a mode of “taking a vehicle” and a mode of “not taking a vehicle”. Wherein, the mode of "taking a vehicle" corresponds to the first monitoring mode, and the mode of "not taking a vehicle" corresponds to the second monitoring mode. It should be noted that the names of the monitoring modes shown in FIG. 7 are only examples and are not used for specific limitations. In practical applications, the names of the monitoring modes can be defined as other content, as long as it is convenient for users to distinguish.

The selection control 702 is used to implement corresponding software functions when the user clicks/touches, and the selection control corresponds to the above selection information. As shown in (a) of FIG. 7 , each of the selection information “taking a vehicle” and “not taking a vehicle” corresponds to a selection control. For example: when the user clicks/touches the corresponding selection control behind "take a vehicle", the electronic device 100 displays the interface shown in (b) in FIG. 7 , at this time, the electronic device determines that the user is on the vehicle, and It is determined to use the first monitoring mode in the sleep monitoring function to detect the sleep of the user. When the user clicks/touches "not on the vehicle", the electronic device 100 displays the interface shown in (c) in FIG. 7 , at this time, the electronic device determines that the user is not on the vehicle, and determines to use the sleep monitoring function The second monitoring mode in the user's sleep is detected.

In another application scenario, the electronic device determines whether the user is on a vehicle, and determines to use the first monitoring mode or the second monitoring mode to monitor the user's sleep according to the determination result.

Referring to FIG. 8 , it is a schematic flowchart of a sleep monitoring method provided by an embodiment of the present application. As shown in Figure 8, the sleep monitoring method may include the following steps:

S801, the electronic device determines whether the user is on a vehicle.

S802, if the user is on the vehicle, the electronic device determines to use the first monitoring mode to monitor the sleep of the user on the vehicle.

S803, if the user is not on the vehicle, the electronic device determines to use the second monitoring mode to monitor the sleep of the user on the vehicle.

Exemplarily, the second monitoring mode may be set as the default monitoring mode, and when the electronic device determines that the user is on the vehicle, the electronic device switches the monitoring mode to the first monitoring mode. Optionally, when the electronic device determines to use the first monitoring mode, it may also send prompt information to the user to prompt the user to use the first monitoring mode for sleep monitoring in the future. The prompt information can be one or more of voice information, negative one-screen information, or vibration signals, etc. In this application scenario, sleep monitoring is more intelligent and user experience is better.

In an embodiment of the present application, when the electronic device determines that the user is on a vehicle, the electronic device may prompt the user whether to adopt the first monitoring mode. Then, it is determined whether to adopt the first monitoring mode according to the operation instruction input by the user. In this way, the wrong selection of the monitoring mode caused by the misjudgment of the electronic device can be effectively avoided.

For example, when the electronic device determines that the user is on the vehicle, the electronic device prompts the user whether to use the first monitoring mode. If the user agrees to use the first monitoring mode, the electronic device will follow the user's instruction, that is, the first monitoring mode will be used subsequently. The monitoring mode monitors the user's sleep.

Optionally, after the electronic device prompts the user whether to adopt the first monitoring mode, if the user does not give feedback within a specified time (for example, 1 minute), the electronic device determines to use the first monitoring mode to monitor the user's sleep.

For example, when the electronic device determines that the user is on the vehicle, the electronic device prompts the user whether to adopt the first monitoring mode. If the user does not agree to adopt the first monitoring mode, the electronic device will follow the user's instruction, that is, the first monitoring mode will be adopted subsequently. The second monitoring mode monitors the user's sleep.

The method for determining whether the user is on the vehicle by the electronic device in S801 will be described below. Two methods can be included:

Method 1: The electronic device obtains travel information of the user, and the electronic device determines whether the user is on a vehicle according to the travel information.

The travel information may include the departure time, running time, arrival time, etc. of the vehicle that the user takes, such as the takeoff/landing time of an airplane, and the arrival/departure time of a train. Travel information can be obtained from other applications on the electronic device, such as travel reminder text messages, booking emails, booking applications and negative one-screen information (as shown in Figure 6, the event reminder information set by the user, "flight number CN2222" , "Departure time 2020/09/10 10:00") and so on.

The electronic device can determine the time range in which the user is on the vehicle according to the travel information. For example, assuming that the obtained travel information is that the departure time of the plane is 10:00 and the landing time is 14:00, it can be determined that the user is on the plane from 10:00 to 14:00. Another example: Assuming that the obtained travel information is "Beijing-Shanghai, G2222", the electronic device can query the timetable of the G2222 train on the Internet according to the travel information, and determine the timetable from Beijing to Shanghai from the obtained timetable. The time period is 10:00-17:00, then it can be determined that the user is on the high-speed rail from 10:00-17:00.

Correspondingly, the electronic device may preset the working time of the monitoring mode according to the determined time range. For example, assuming that the electronic device has determined that the time range for the user to take the vehicle is 10:00-14:00, the electronic device can switch the monitoring mode from the second monitoring mode to the first monitoring mode at 10:00, and at 14:00: 00 switches the monitoring mode from the first monitoring mode to the second monitoring mode.

In an embodiment of the present application, when the user leaves the vehicle, the electronic device can switch from the first monitoring mode to the second monitoring mode. That is, the first monitoring mode is exited and the second monitoring mode is enabled. In one embodiment of the present application, the electronic device can automatically leave the first monitoring mode and automatically enable the second monitoring mode.

In another embodiment of the present application, the electronic device can automatically leave the first monitoring mode and enable the second monitoring mode under the user's instruction.

For example, before the electronic device exits the first monitoring mode, the user may be prompted to enter the second monitoring mode after n time. If the user agrees to enter the second monitoring mode, the electronic device will enter the second monitoring mode after n times. If the user does not agree to enter the second monitoring mode, the electronic device will continue to use the first monitoring mode to monitor the user's sleep after n time. Alternatively, the electronic device automatically enters the second monitoring mode after the electronic device prompts the user for a preset time. Or after the electronic device prompts the user, if the user does not give feedback within the specified time, the electronic device enters the second monitoring mode after n time.

Since the user usually takes the transportation before the departure of the transportation, for example, the departure time of the plane is 10:00, and the user boards the plane at 09:45. Therefore, the time range in which the user is actually on the vehicle is usually larger than the time range determined according to the travel information above. In order to ensure the monitoring accuracy, the determined time range of the user on the vehicle (ie, the working time of the first monitoring mode) may be appropriately extended. For example, assuming that the departure time of the plane is determined to be 10:00 according to the travel information, the electronic device can set the monitoring mode to the first monitoring mode at 09:30.

When the travel information of the user cannot be obtained, the second method can be used for judgment.

Method 2: The electronic device acquires the user's motion information, and the electronic device determines whether the user is on a vehicle according to the motion information.

The exercise information may include exercise data and health data.

For example, the processor in the electronic device may acquire the user's movement data (such as movement of speed, exercise time, exercise route, etc.), the processor in the electronic device can obtain the user's health data (such as heart rate value, etc.) through the bone conduction sensor (180M shown in Figure 3) installed on the electronic device.

The electronic device may determine the speed threshold of the user's movement speed according to the obtained user's movement data. The electronic device can determine the normal heart rate range of the user according to the acquired health data of the user (the normal heart rate range in this embodiment of the present application refers to the range of the user's heart rate value in a non-exercise state).

For example, the electronic device can monitor the user's exercise data and health data through the sports health application, and then determine the speed threshold of the user's exercise speed and the user's normal heart rate range according to the user's exercise data and health data, and then according to the determined speed threshold and the normal heart rate range to determine whether the user is on a vehicle.

In one application scenario, the user can set the time range for allowing the electronic device to obtain the user's exercise data and health data through the sleep monitoring function. For example, a setting page of a sleep monitoring function in an electronic device gives options "Always allow", "Allow only during use". When the user selects the "Always Allow" option, even if the sleep monitoring function is turned off in the foreground, the electronic device can obtain the user's health data in real time in the background through the sleep monitoring function. After the user selects the "allowed during use only" option, the electronic device can obtain the user's health data through the sleep monitoring function only when the sleep monitoring function is turned on in the foreground.

Usually, when the user is in a state of exercise (such as running), the movement speed of the user monitored by the electronic device is relatively large, and the heart rate value of the user exceeds the normal heart rate range. However, when the user is on the vehicle, the movement speed of the user detected by the electronic device is relatively large, but the user's heart rate value is within the normal heart rate range.

In an embodiment of the present application, one way for the electronic device to determine whether the user is on a vehicle according to the motion information may be: when the user's motion speed is greater than the speed threshold and the user's heart rate exceeds the normal heart rate range, the electronic device determines The user is in a vehicle.

When the vehicle is stationary or driving at a low speed, since the movement speed of the user obtained by the electronic device is small and the heart rate value of the user is within the normal heart rate range, the electronic device may not be able to determine whether the user is in the On the vehicle, the electronic device cannot accurately determine the time range when the user is on the vehicle.

In order to improve the accuracy of monitoring, a caching mechanism can be set to ensure that data can be cached for a period of time. For example, assuming that the user is on the vehicle at 10:00 according to the motion information, and the cache duration is 0.5h, then 09:30 can be recorded as the time node when the user is on the vehicle. It is equivalent to extending the working time of the first monitoring mode forward by 0.5h.

Of course, in order to ensure the accuracy of the identified time when the user is on the vehicle, the first method and the second method can be combined, and the identification results of the two methods can be comprehensively considered.

The first monitoring mode in S802 will be introduced below. Referring to FIG. 9 , it is a schematic flowchart of a sleep monitoring method in a first monitoring mode provided by an embodiment of the present application. As shown in FIG. 9 , by way of example and not limitation, when the user is on the vehicle, the electronic device determines to use the first monitoring mode to detect the sleep of the user. In the first monitoring mode, the following steps may be specifically included:

S901, the electronic device acquires first monitoring data of the user on the vehicle.

The processor in the electronic device can acquire various sensor data in real time through the sensor module installed on the electronic device, such as acceleration data, speed data, heart rate value, and the like. Electronic devices can acquire sensor data in real time, whether or not the user is in a vehicle. However, in the first monitoring mode, the data used for sleep monitoring is actually sensor data acquired when the user is on the vehicle. Therefore, the first monitoring data in the embodiment of the present application is the data monitored when the user is on the vehicle.

For example, the working time of the sleep monitoring function in the electronic device is 06:00-22:00, and the electronic device has been collecting sensor data during the time period of 06:00-22:00. Assuming that the electronic device determines that the user is on the vehicle during the time period of 09:00-10:00, the electronic device records the monitoring data obtained by the electronic device during the time period of 09:00-10:00 as the first monitoring The monitoring data obtained by the electronic device in the two time periods of 06:00-09:00 and 10:00-22:00 are recorded as the second monitoring data.

The first monitoring data includes not only the user's state information, but also the vehicle-related information, and may also include measurement error data caused by the hardware configuration of the sensor itself. Wherein, the information related to the vehicle may include acceleration data generated due to the shaking of the vehicle, and may also include acceleration data of the vehicle itself during the running of the vehicle, and so on.

Since the user is on the vehicle, the first monitoring data is not only affected by the user's actions, but also by the information related to the vehicle. Therefore, it is necessary to filter out the vehicle-related information from the first monitoring data by using the method in the following S902.

S902, the electronic device obtains target data according to the first monitoring data.

The target data is the data obtained after filtering out the vehicle-related information in the first monitoring data.

Monitoring the user's sleep requires the use of the user's own state information. Therefore, the information related to the vehicle can be regarded as a kind of noise data, and the first monitoring data can be regarded as the mixed data of the user's state information and the noise data. Then, the process of filtering out the state information of the vehicle in the first monitoring data can be regarded as a process of denoising the first monitoring data.

In one embodiment, a way for the electronic device to obtain target data according to the first monitoring data in S902 may include the following steps:

I. The electronic device obtains the driving data of the vehicle.

Among them, the driving data of the vehicle can be used to represent the driving state of the vehicle, and is the data that interferes the most with the user's state information. The travel data of the vehicle may include speed data and/or acceleration data of the traffic, and the like.

The data attribute of the driving data of the vehicle is consistent with the data attribute of the first monitoring data. For example, assuming that the first monitoring data includes acceleration data, the corresponding vehicle driving data also includes acceleration data; assuming that the first monitoring data includes speed data, the corresponding vehicle driving data also includes speed data. In other words, if the speed data in the first monitoring data needs to be denoised, the electronic device needs to acquire the speed data of the vehicle. If the acceleration data in the first monitoring data needs to be denoised, the electronic device needs to acquire the acceleration data of the vehicle.

Velocity is used to characterize the change in displacement per unit time, and acceleration is used to characterize the change in velocity per unit time. In contrast, acceleration data can more sensitively reflect the user's action state. Therefore, in this embodiment of the present application, the driving data of the vehicle may be acceleration data of the vehicle.

Usually, the acceleration sensor obtains three-axis acceleration, that is, the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration. Therefore, the three-axis acceleration may be included in the driving data of the vehicle. Correspondingly, the first monitoring data species may also include triaxial acceleration.

The driving data of the vehicle can represent the driving state of the vehicle, and the driving state of the vehicle may include two states: stationary and driving. When the vehicle is running, the user's state information will be greatly affected due to the large acceleration of the vehicle itself. Therefore, the running data of the vehicle generally refers to the acceleration data when the vehicle is running. However, in practical applications, when the vehicle is stationary, the vehicle itself usually has a certain acceleration due to slight shaking of the vehicle itself or sensor measurement errors, which in turn affects the user's status information.

In order to obtain accurate user status information and improve monitoring accuracy, in this embodiment of the present application, the driving data of the vehicle may include both first acceleration data used to indicate that the vehicle is in a stationary state, and may also include first acceleration data used to indicate that the vehicle is in a stationary state. The second acceleration data of the vehicle in a driving state. In this way, in the subsequent denoising process, not only the data interference caused by the driving process of the vehicle, but also the data interference existing when the vehicle is stationary is filtered out, so that the target data obtained after filtering can more accurately reflect the user's own Action status.

The following describes the acquisition methods of the first acceleration data and the second acceleration data respectively.

The acquisition methods of the first acceleration data may include but are not limited to the following two methods:

Mode 1: The electronic device confirms the departure time of the vehicle according to the travel information of the user; within a preset time before the departure time of the vehicle, the electronic device acquires acceleration data in real time, and records the acceleration data as candidate data; The candidate data is subjected to noise reduction filtering processing; the electronic device determines whether the user is relatively stationary according to the candidate data processed by the noise reduction filtering; if the user is relatively stationary, the electronic device records the candidate data corresponding to when the user is relatively stationary as the first acceleration data.

Usually, the user is not in a state of absolute stillness. Due to the user's breathing, the small movements of the limbs, and the shaking of the vehicle, the user's body will produce slight movements. But these slight movements will not cause the electronic device to obtain relatively large acceleration data. Therefore, after the electronic device performs noise reduction filtering processing on the acquired acceleration data, it can effectively filter out the acceleration data generated by the slight actions of the user.

If the vehicle is in a driving state, the value of the acceleration data obtained by the electronic device worn by the user is relatively large. If the vehicle is in a stationary state, the value of the acceleration data obtained by the electronic device worn by the user is relatively small.

In an embodiment of the present application, the electronic device determines whether the user is relatively stationary according to the candidate data processed by noise reduction filtering, which may include: if the variation of any two-axis acceleration in the candidate data is less than the first preset value, the electronic device determines whether the user is relatively stationary. The user is relatively stationary. The change of the single-axis acceleration is small, indicating that the user's motion range is small in a certain direction. The variation of the acceleration of any two axes is small, indicating that the user is in a relatively stationary state, and the vehicle is in a stationary state at this time. The first preset value may be preset according to the needs of monitoring accuracy.

If the travel information of the user cannot be obtained, the first acceleration data may be obtained according to the second method.

Mode 2: The electronic device acquires acceleration data in real time, records the acceleration data as candidate data, and caches the candidate data; the electronic device performs noise reduction filtering processing on the candidate data; the electronic device determines whether the vehicle is a vehicle according to the candidate data after noise reduction filtering processing. is in a driving state; if it is determined that the vehicle is in a driving state, the electronic device determines the departure time of the vehicle, and obtains the cached data before the departure time; the electronic device determines whether the user is relatively stationary according to the cached data; if the user is relatively stationary, the electronic device The corresponding candidate data when the user is relatively stationary is recorded as the first acceleration data.

In an embodiment of the present application, the electronic device judging whether the vehicle is in a driving state according to the candidate data processed by noise reduction filtering may include: if the variation of the acceleration of a certain axis in the candidate data is greater than the second preset value, then the electronic device The device determines that the vehicle is in a running state. The second preset value may be preset with reference to the traveling speed of the vehicle and the movement speed of the user.

Optionally, the electronic device may also determine whether the vehicle is in a driving state by using the acquired data of the gyro sensor. Since the gyro sensor can detect the shift of the center of gravity of the human body, when the vehicle is started, the center of gravity of the user on the vehicle is shifted. The electronic device can judge whether the vehicle is in a driving state through the offset of the center of gravity monitored by the gyro sensor. For example, if the electronic device detects a continuous and regular angular offset through the gyro sensor, and the angular offset is greater than the preset offset, the electronic device determines that the vehicle is in a driving state. Among them, the angular offset of the gyro sensor generated when the vehicle is started is usually greater than the angular offset of the gyro sensor generated when the user is walking. Therefore, the preset offset can refer to the angular offset when the user is walking. Make a preset.

For the manner in which the electronic device determines whether the user is relatively stationary according to the cached data, reference may be made to the above-mentioned manner in which the electronic device determines whether the user is relatively stationary according to the candidate data processed by noise reduction filtering, which will not be repeated here.

In practical applications, during the period from the time the user takes the vehicle to the departure time of the vehicle, the user may be moving all the time, that is, there is no situation where the user is relatively stationary. In this case, the first acceleration data may be set as the first preset acceleration. The first preset acceleration may be 0, or a small constant value, or a random value, or the like.

The acquisition methods of the second acceleration data may include but are not limited to the following two methods:

Mode 1: After determining that the vehicle is in a driving state, the electronic device acquires acceleration data in real time, and records the acceleration data as the data to be monitored; the electronic device determines the second acceleration according to the single-axis acceleration with the largest change in the data to be monitored.

Since the acceleration of the vehicle varies greatly during acceleration and deceleration, this method is more suitable for the acceleration and deceleration phases of the vehicle. However, when the vehicle moves at a constant speed, the acceleration of the vehicle often changes little, and the second acceleration obtained in the first method may be inaccurate.

Method 2: After determining that the vehicle is in a driving state, the electronic device obtains Global Positioning System (GPS, Global Positioning System) data; the electronic device calculates the driving distance according to the GPS data, and calculates the second acceleration data according to the driving distance.

For the method of determining whether the vehicle is in a driving state, reference may be made to the description in the method for acquiring the first acceleration data, which will not be repeated here.

In practical applications, during the period from the moment the user takes the vehicle to the departure time of the vehicle, the vehicle is in a stationary state, that is, the second acceleration data is not generated. In this case, the second acceleration data may be set as the second preset acceleration. The second preset acceleration may be 0, or a smaller constant value, or a random value, or the like.

In addition, in practical applications, in order to improve the accuracy of the acquired second acceleration data, the first and second modes, as well as other possible implementation modes, may be comprehensively considered.

II. The electronic device determines the data components in the first monitoring data that match the driving data as vehicle-related information.

III. The electronic device filters out the vehicle-related information from the first monitoring data to obtain target data.

The sensor module in the embodiment shown in FIG. 3 usually acquires sensor data at a certain sampling frequency. Therefore, the first monitoring data may include one or more sensor data, and each sensor data in the first monitoring data corresponds to a sampling moment. Correspondingly, the data in the driving data is in one-to-one correspondence with the data in the first monitoring data. Exemplarily, as described in S902, both the first monitoring data and the driving data may include acceleration data. Assuming that the first monitoring data includes acceleration data corresponding to the sampling times t1 and t2, correspondingly, the driving data also includes acceleration data corresponding to the sampling times t1 and t2.

It should be noted that the data components matched with the driving data include the data components corresponding to the driving data which are equal, and also include the data components corresponding to the driving data proportional to the driving data.

Correspondingly equal means that the values of the data corresponding to the two at the same sampling time are equal. For example, it is assumed that the values of the acceleration data corresponding to the sampling times t1 and t2 in the driving data are p, q, and the values of the acceleration data corresponding to the sampling times t1 and t2 in the data components matching the driving data are also p, q .

Corresponding proportional means that the values of the data corresponding to the two at the same sampling time are proportional. For example, it is assumed that the values of the acceleration data corresponding to the sampling times t1 and t2 in the driving data are respectively p and q, and the values of the acceleration data corresponding to the sampling times t1 and t2 in the data components matched with the driving data are respectively 5p and 5q.

Optionally, an implementation manner for the electronic device to obtain the target data from the first monitoring data is:

The electronic device determines the data components of the first monitoring data that match the driving data as vehicle-related information, and then subtracts the data components that match the driving data from the first monitoring data. The difference data obtained by subtracting the data components matched with the driving data from the first monitoring data is the target data.

When the electronic device subtracts the data components matching the driving data from the first monitoring data, the three-axis acceleration can be processed separately. Specifically, the electronic device subtracts the x-axis acceleration in the corresponding data components matching the driving data from the x-axis acceleration at each sampling moment in the first monitoring data, and subtracts the y-axis acceleration at each sampling moment in the first monitoring data The acceleration subtracts the y-axis acceleration in the corresponding data components matching the driving data, and subtracts the z-axis acceleration in the corresponding data components matching the driving data from the z-axis acceleration at each sampling time in the first monitoring data. .

When the electronic device subtracts the data component matched with the driving data from the first monitoring data, the three-axis acceleration can also be fused first, and then the fused acceleration can be processed. Specifically, the electronic device fuses the x-axis, y-axis, and z-axis accelerations corresponding to each sampling moment in the first monitoring data into one acceleration to obtain the first fusion acceleration; and fuses each sampling moment in the data components matched with the driving data into one acceleration The corresponding x-axis, y-axis, and z-axis accelerations are fused into one acceleration to obtain the second fusion acceleration; then the second fusion acceleration is subtracted from the first fusion acceleration corresponding to each sampling time.

The above-mentioned method for the electronic device to obtain the target data from the first monitoring data has low algorithm complexity and is easy to implement. However, the user's status information and the vehicle-related information are usually not simply superimposed together, but mixed together. If the first monitoring data is directly used to subtract the data component matching the driving data, the obtained target data may not accurately reflect the user's action state.

In order to improve the monitoring accuracy, optionally, another implementation manner for the electronic device to obtain the target data from the first monitoring data is as follows: it may include:

The electronic device constructs a noise data matrix according to the first acceleration data and the second acceleration data; the electronic device inputs the noise data matrix and the first monitoring data into a preset decomposition model, and obtains a data component of the first monitoring data that matches the driving data; The electronic device determines data components in the first monitoring data that match the driving data as vehicle-related information, and the electronic device determines data components that do not match the driving data in the first monitoring data as target data.

As described in I, the travel data includes the first acceleration data and the second acceleration data. Each of the first acceleration data and the second acceleration data may include a plurality of data.

Optionally, a way for the electronic device to construct the noise data matrix according to the first acceleration data and the second acceleration data may be: using the data in the first acceleration data and the second acceleration data as elements in the noise data matrix.

Exemplarily, it is assumed that the first acceleration data includes two sets of three-axis accelerations (x1, y1, z1), (x2, y2, z2), and the second acceleration data includes (x3, y3, z3), (x4, y4) , z4), (x5, y5, z5) three sets of three-axis acceleration. An example of a noisy data matrix is given below:

The above noise data matrix is only an example, and the form of the noise data matrix is not specifically limited.

Optionally, another way for the electronic device to construct a noise data matrix according to the first acceleration data and the second acceleration data may be: the electronic device first performs data statistics on the first acceleration data and the second acceleration data respectively, and then according to the statistics The resulting data constructs a noisy data matrix.

Exemplarily, it is assumed that the first acceleration data includes two sets of three-axis accelerations (x1, y1, z1), (x2, y2, z2), and the second acceleration data includes (x3, y3, z3), (x4, y4) , z4), (x5, y5, z5) three sets of three-axis acceleration. The electronic device can first fuse each group of three-axis accelerations in the first acceleration data into one acceleration value to obtain a1 and a2; and fuse each group of three-axis accelerations in the second acceleration data into one acceleration value to obtain a3, a4 and a5, then a1, a2, a3, a4 and a5 are built into a noise data matrix. Since the amount of data in the first acceleration is not equal to the amount of data in the second acceleration, a preset value may be used to make up for it. An example of a noisy data matrix is given below:

The preset value in the above noise data matrix is 0.

The above method of fusing each group of three-axis accelerations into one acceleration value may be: determining the statistical characteristic value of each group of three-axis accelerations as the acceleration value after each group of three-axis accelerations is fused. Among them, the statistical eigenvalues can be mean, variance, median, root mean square, etc.

The above noise data matrix is only an example, and is not used to specifically limit the preset value and the form of the noise data matrix.

Since the preset decomposition model needs to process the noise data matrix and the first monitoring data, in order to ensure the consistency of data dimensions, when setting the form of the noise data matrix, the noise data matrix can be set according to the needs of the preset decomposition model for data processing. The dimensions of the data matrix are set to match the dimensions of the first monitoring data. For example, it is assumed that the cross-noise data matrix needs to be multiplied by the first monitoring data in the preset decomposition model, and the first monitoring data is (x0, y0, z0). The first monitoring data can be formed into a 3×1 vector, correspondingly, the dimension of the noise data matrix needs to satisfy n×3 (n is a positive integer); if the first monitoring data is formed into a 1×3 vector, correspondingly, the noise The dimension of the data matrix needs to satisfy 3×n.

The preset decomposition model may be an algorithm model capable of realizing a data denoising function, that is, taking the data in the noise data matrix as noise data, and performing denoising processing on the first monitoring data according to the noise data. The preset decomposition model may also be an algorithm model capable of realizing a signal decomposition function, that is, decomposing the first monitoring data into a noise part (ie, driving data) and a non-noise part (ie, target data), such as neural network, variational modal decomposition model , the classical modal decomposition model, etc.

Taking the preset decomposition model as an algorithm model capable of realizing a signal decomposition function as an example, see FIG. 10 , which is a schematic diagram of a decomposition result provided by an embodiment of the present application. As shown in FIG. 10 , (a) in FIG. 10 is a data curve fitted by the first monitoring data, (b) in FIG. 10 is a data curve fitted by driving data, and (c) in FIG. 10 is A data curve fitted with data components that are decomposed from the first monitoring data and matched with the driving data, (d) in FIG. 10 is a data curve fitted by the target data. It can be seen from FIG. 10 that the preset decomposition model decomposes the first monitoring data into data components that match the driving data and data components that do not match the driving data (ie, target data). The process of decomposing out the noise part and the non-noise part. Decomposing the data components matching the driving data from the first monitoring data is equivalent to filtering out information related to vehicles in the first monitoring data. The data components (ie target data) obtained after decomposition that do not match the driving data are equivalent to the status information of the user obtained after filtering out the information related to the vehicle.

In order to improve the monitoring efficiency and monitoring accuracy, the decomposition model can be pre-trained. For example, the sensor data when the user is not on the vehicle can be obtained as the sample data of the user's status information; the sensor data of the vehicle can be obtained as the sample data of the driving data; the sample data of the driving data can be fused into the user's status as the noise data In the sample data of the information, the fused data is used as the sample data of the first monitoring data; then the decomposition model is trained according to the sample data of the user's state information, the sample data of the driving data and the sample data of the monitoring data.

The target data obtained according to the above method can more accurately reflect the user's own action state. Monitoring the user's sleep according to the target data can avoid the influence of the acceleration of the vehicle itself on the user's actions, and effectively improve the accuracy of sleep monitoring.

S903, the electronic device monitors the user's sleep according to the target data.

Since the acceleration data can reflect the user's action state more sensitively, when the user performs some small movements (such as breathing, slight shaking, etc.), the acceleration data can still be more clearly represented, which will affect sleep. Monitoring interferes. In order to improve the monitoring accuracy, noise reduction filtering can be performed on the target data to filter out the influence of small amplitude movements on sleep monitoring. Further, since the data values of the acceleration data are complex and the data calculation is complicated, in order to simplify the data processing, the target data after the noise reduction filtering process can be normalized. For example, the target data less than a certain threshold is recorded as 0, and the target data greater than or equal to the threshold is recorded as 1.

In practical applications, the electronic device can perform sleep monitoring separately according to the target data at each sampling moment. However, such data processing is too frequent, the amount of calculation is large, and the monitoring results obtained from the data at a single sampling moment are contingent. Therefore, the electronic device can perform sleep monitoring according to the target data at multiple sampling times.

Specifically, S903 may include the following steps:

When the amount of data in the target data reaches the preset amount, the electronic device monitors the user's sleep according to the target data; when the amount of data in the target data does not reach the preset amount, the electronic device continues to acquire the first monitoring data.

Optionally, a caching mechanism can be set. Since the sampling frequency is fixed, only setting the cache duration is equivalent to limiting the amount of data.

Correspondingly, S903 may include: when the cache duration reaches the preset duration, the electronic device monitors the user's sleep according to the target data; when the cache duration does not reach the preset duration, the electronic device continues to obtain the first monitoring data.

Further, an implementation manner of monitoring the user's sleep according to the target data may be: inputting the target data into a preset monitoring model to obtain an output monitoring result. The preset monitoring model may be a neural network model, a clustering model, or the like, and the monitoring result may be used to indicate that the user is in an awake or sleeping state.

As described above, since the target data includes data at multiple sampling moments, the user may fall asleep or fall asleep at a certain sampling moment. However, by using the above method, only the sleep state of the user corresponding to the target data can be monitored, but the specific time of going to sleep and going to sleep of the user cannot be monitored.

In order to solve the above-mentioned problem, optionally, another implementation manner in which the electronic device monitors the sleep of the user according to the target data may be: the electronic device obtains multiple sets of data according to the target data; the electronic device obtains multiple sets of data according to the multiple sets of data; The respective corresponding statistical feature values determine the respective first state labels corresponding to each group of data; the electronic device determines the sleep monitoring result according to the respective first state labels corresponding to each group of data.

The electronic device obtains multiple sets of data according to the target data, and the electronic device may divide the target data into multiple sets of data.

Among them, the statistical eigenvalues can be median, root mean square, mean, dynamic frequency, spectral energy, etc. In addition, in order to make the statistical characteristics of the data more obvious, after acquiring the first monitoring data in S901 and the driving data of the vehicle in I, band-pass filtering can be performed on the acquired first monitoring data and driving data, and the polar Low and very high frequencies are filtered out.

Wherein, the first state label includes a sleep state and an awake state. The results of sleep monitoring include the time to fall asleep, the time to fall out of sleep, and the duration of sleep. The first state label corresponding to each group of data may be determined by using a neural network, a clustering algorithm, or a decision tree algorithm. In this way, it is possible to more accurately determine the time when the user falls asleep.

Exemplarily, it is assumed that the target data is divided into 10 groups, and the target data corresponds to the time period of 10:00-10:50. Among them, the first group includes the data of the time period of 10:00-10:05, the second group includes the data of the time period of 10:06-10:10, and so on, the tenth group includes the data of 10:46- 10:50 data in this time period (that is, the target data is divided according to time, and each group of data corresponds to 5 minutes). The electronic device calculates the corresponding statistical characteristic values of each group of data respectively, and obtains 10 statistical characteristic values. Determine the first state label corresponding to each statistical feature value, for example, determine that the first state label corresponding to the first feature value is the awake state, the first state label corresponding to the second feature value is the awake state, and the third The first state labels corresponding to the eigenvalues to the eighth eigenvalue are all sleep states, and the first state labels corresponding to the ninth eigenvalue and the tenth eigenvalue are all awake states. Then the electronic device can determine according to the first state label that the user's falling asleep time is the start time corresponding to the third set of data (ie 10:15), and the user's falling asleep time is the end time corresponding to the seventh set of data (ie 10:15). :35). Furthermore, the electronic device may determine that the sleep duration of the user is 20 minutes.

It should be noted that, the above is only an example in which the electronic device determines the sleep monitoring result according to the target data, and does not specifically limit the division rules and the like of the target data. Of course, the more groups the target data is divided into, the more accurate the monitored out/sleep time will be, but the larger the data processing volume will be.

In the above sleep monitoring method, acceleration data is used for monitoring. In order to improve the monitoring accuracy, further, PPG data, such as pulse wave time domain characteristic data, pulse wave frequency domain characteristic data, etc., can be comprehensively considered.

Specifically, monitoring methods may include:

The electronic device acquires the user's pulse wave data; the electronic device determines the second state label corresponding to each group of data according to the pulse wave data and the corresponding statistical characteristic value of each group of data; the electronic device determines according to the first state label and the second state label Each group of data has a corresponding final state label; the electronic device determines the sleep monitoring result according to the corresponding final state label of each group of data.

Wherein, the second state label includes the sleep state and the awake state.

A neural network, a decision tree algorithm, a machine learning algorithm, etc. can be used to classify the pulse wave data and the statistical feature values, and determine the second state label corresponding to the pulse wave data and the statistical feature values. In order to avoid the defects of a single algorithm, different algorithms can be used when obtaining the first state label and the second state label. For example, the decision tree algorithm is used to obtain the first state label, and the machine learning algorithm (such as the LightGBM model) is used to obtain the second state label.

After obtaining the first state label and the second state label, the first state label and the second state label may be fused into a final state label according to different weights.

Exemplarily, it is assumed that the awake state is quantized as 1, the sleep state is quantized as 0, the weight of the first state label is 0.8, and the weight of the second state label is 0.2. When the first state label is 1 and the second state label is 0, the quantization value of the final state label is calculated as 1×0.8+0×0.2=0.8. Assuming that the quantification threshold of the final state label is 0.5 (that is, when it is greater than or equal to 0.5, it is an awake state, and when it is less than 0.5, it is a sleep state), correspondingly, the final state label finally determined is the awake state.

It should be noted that the above is only an example of determining the final state label, and does not specifically limit the weight value, quantization threshold, and determination method.

The second monitoring mode in S803 is introduced below. When the user is not on the vehicle, the electronic device determines to use the second monitoring mode to detect the sleep of the user. In the second monitoring mode, it may specifically include: the electronic device monitors the sleep of the user according to the acquired second monitoring data.

The second monitoring data is the monitoring data obtained by the electronic device when the user is not on the vehicle.

When the user is not on the vehicle, since the second monitoring data includes the user's state information, the vehicle-related information is not included. Therefore, it is not necessary to perform denoising processing on the second monitoring data, and it is sufficient to perform sleep monitoring according to the second monitoring data.

Through the two sleep monitoring modes provided by the above embodiments, the electronic device can monitor the sleep of the user in different states. Specifically, the electronic device can not only monitor the sleep of the user when the user is on the vehicle, but also monitor the sleep of the user when the user is not on the vehicle. Thus, the problem of not recording or less recording of the user's sleep on the vehicle is solved, and the user experience is improved.

Based on the sleep monitoring method provided by the above embodiment, the application scenarios of the method are described below. Application scenarios may include offline monitoring scenarios, online monitoring scenarios, and fusion monitoring scenarios.

In an offline monitoring scenario, electronic devices acquire sensor data and cache the acquired sensor data. After the time node corresponding to when the user is on the vehicle is determined, the first monitoring data in the sensor data (that is, the data monitored by the electronic device when the user is on the vehicle) is determined according to the time node. Then, the vehicle-related information in the first monitoring data is filtered out to obtain target data, and the user's sleep is monitored according to the target data. Wherein, when filtering out the information related to the vehicle, the electronic device may acquire the driving data of the vehicle from the buffered sensor data.

In this application scenario, the electronic device needs to cache a large amount of sensor data, which has high requirements on the storage space of the electronic device. In order to relieve the storage pressure of electronic devices, in an application scenario, the electronic device can first upload the acquired sensor data to a third-party storage space such as a cloud server, and when the sleep monitoring method needs to be executed, the sensor data is stored in the storage space. Obtain relevant data from third-party storage space. In another application scenario, the electronic device that collects sensor data and the electronic device that executes the sleep monitoring method may be different electronic devices. Exemplarily, the electronic device for collecting sensor data may be a sports bracelet, and the electronic device for executing the sleep monitoring method may be a mobile phone. After the sports bracelet collects the sensor data, the sensor data is transmitted to the mobile phone, and the mobile phone executes the sleep monitoring method provided by the embodiment of the present application.

Offline monitoring needs to analyze and process the data after the electronic device has obtained all the monitoring data. The delay of the monitoring results is high, and the user cannot grasp the sleep situation in time. However, since the data obtained by offline monitoring is more comprehensive, the monitoring results of offline monitoring are more accurate. Therefore, offline monitoring is more suitable for situations where the timeliness of monitoring is low and the accuracy of monitoring results is high.

In an online monitoring scenario, electronic devices acquire sensor data in real time. When it is determined that the user is on the vehicle, the electronic device records the subsequently acquired sensor data as the first monitoring data, and enters the first monitoring mode. Then, the electronic device filters out the vehicle-related information in the first monitoring data, obtains target data, and monitors the user's sleep according to the target data.

Online monitoring requires real-time processing of monitoring data, and the delay of monitoring results is low. However, when filtering out information related to vehicles, since the acquired real-time monitoring data is not comprehensive, the acquired driving data of vehicles may be inaccurate, which may lead to errors in the final sleep monitoring results. Therefore, compared with offline monitoring, online monitoring is more suitable for situations where the timeliness of monitoring is higher and the monitoring results are allowed to have errors.

In order to avoid possible errors in online monitoring, in a fusion monitoring scenario, when it is determined that the user is on a vehicle, the electronic device can first cache part of the monitoring data to ensure that more accurate vehicle driving data can be obtained. In other words, the electronic device starts sleep monitoring after acquiring part of the monitoring data. In this way, although the monitoring result will be delayed for a short period of time at the initial stage of entering the first monitoring mode, the high accuracy of the monitoring result can be guaranteed. Compared with offline monitoring, fusion monitoring has lower latency; and compared with online monitoring, fusion monitoring has higher accuracy.

Corresponding to the sleep detection method described in the above embodiments, FIG. 11 is a structural block diagram of a sleep monitoring apparatus provided by an embodiment of the present application. For the convenience of description, only the parts related to the embodiments of the present application are shown. Referring to Figure 11, the device includes:

In the determination unit 111, the electronic device determines whether the user is on the vehicle.

In the first determining unit 112, if the user is on the vehicle, the electronic device determines to use the first monitoring mode to monitor the sleep of the user on the vehicle.

In the second determining unit 113, if the user is not on the vehicle, the electronic device determines to use the second monitoring mode to monitor the sleep of the user on the vehicle.

Optionally, the device further includes:

The first monitoring unit 114 is used for obtaining the first monitoring data of the user on the vehicle if the user is on the vehicle, and the first monitoring data includes information related to the vehicle; the electronic device obtains the target according to the first monitoring data The target data is the data obtained after filtering out the vehicle-related information in the first monitoring data; the electronic device monitors the user's sleep according to the target data.

The second monitoring unit 115 is configured to monitor the sleep of the user according to the second monitoring data obtained by the electronic device if the user is not on the vehicle, and the second monitoring data includes the state information of the user.

Optionally, the first monitoring unit 114 is also used for:

The electronic device obtains the driving data of the vehicle; the electronic device determines the data component in the first monitoring data that matches the driving data as the vehicle-related information; the electronic device filters out the vehicle-related information from the first monitoring data to obtain the target data.

Optionally, the driving data includes first acceleration data when the vehicle is in a stationary state and second acceleration data when the vehicle is in a driving state.

Further, the first monitoring unit 114 is also used for:

The electronic device constructs a noise data matrix according to the first acceleration data and the second acceleration data; the electronic device inputs the noise data matrix and the first monitoring data into a preset decomposition model, and obtains a data component of the first monitoring data that matches the driving data; The electronic device determines a data component of the first monitoring data that matches the driving data as vehicle-related information.

Optionally, the first monitoring unit 114 is also used for:

The electronic device obtains multiple sets of data according to the target data; the electronic device determines a first state label corresponding to each set of data according to the corresponding statistical characteristic value of each set of data in the multiple sets of data; the electronic device determines the corresponding first state according to each set of data Labels identify sleep monitoring results.

Wherein, the first state label includes a sleep state and an awake state.

Optionally, the first monitoring unit 114 is also used for:

Wherein, the second state label includes a sleep state and an awake state.

Optionally, the judging unit 111 is also used for:

The electronic device obtains the travel information and/or exercise information of the user, and determines whether the user is on a vehicle according to the travel information and/or exercise information;

Alternatively, the electronic device displays prompt information on the display screen, and the prompt information is used to prompt the user to select whether the user is on the vehicle; the electronic device monitors the first operation instruction input by the user; the electronic device determines whether the user is on the vehicle according to the first operation instruction superior.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

Embodiments of the present application further provide a computer-readable storage medium, including computer instructions, when the computer instructions are executed on a computer or a processor, the computer or processor can execute the steps in the above-mentioned embodiments of the sleep monitoring methods.

The embodiments of the present application provide a computer program product, when the computer program product runs on a computer or a processor, the computer or processor can implement the steps in the above-mentioned embodiments of the sleep monitoring methods.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part 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, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions can be sent from one website site, computer, server or data center to another website site, computer, server or data center for transmission. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state disks (SSDs)), and the like.

An embodiment of the present application further provides a chip system, which is characterized in that the chip system includes a processor, the processor is coupled to a memory, and the processor executes a computer program stored in the memory to implement the steps in each of the above sleep monitoring method embodiments . The chip system may be a single chip, or a chip module composed of multiple chips.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

Those of ordinary skill in the art can realize that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Finally, it should be noted that: the above are only the specific embodiments of the present application, but the protection scope of the present application is not limited to this, and any changes or replacements within the technical scope disclosed in the present application should be included in the present application. within the scope of protection of the application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

A sleep monitoring method, characterized in that, applied to an electronic device, the method comprising:

The electronic device determines whether the user is on a vehicle;

If the user is on a vehicle, the electronic device acquires first monitoring data of the user on the vehicle, where the first monitoring data includes information related to the vehicle;

The electronic device obtains target data according to the first monitoring data, and the target data is data obtained after filtering out the information related to the vehicle in the first monitoring data;

The electronic device monitors the user's sleep according to the target data.
The sleep monitoring method according to claim 1, wherein before the electronic device acquires the first monitoring data of the user on the vehicle, the method further comprises:

The electronic device determines to use a first monitoring mode to monitor the sleep of the user on the vehicle, where the first monitoring mode refers to the need to filter out information related to the vehicle on the monitoring data obtained by the electronic device the process of.
The sleep monitoring method according to claim 1 or 2, wherein the electronic device obtains target data according to the first monitoring data, comprising:

obtaining, by the electronic device, driving data of the vehicle;

The electronic device determines a data component of the first monitoring data that matches the driving data as information related to the vehicle;

The electronic device filters out the vehicle-related information from the first monitoring data to obtain the target data.
The sleep monitoring method according to claim 3, wherein the driving data includes first acceleration data when the vehicle is in a stationary state and second acceleration data when the vehicle is in a driving state;

The electronic device determines a data component of the first monitoring data that matches the driving data as information related to the vehicle, including:

The electronic device constructs a noise data matrix according to the first acceleration data and the second acceleration data;

The electronic device inputs the noise data matrix and the first monitoring data into a preset decomposition model to obtain a data component of the first monitoring data that matches the driving data;

The electronic device determines a data component of the first monitoring data that matches the driving data as information related to the vehicle.
The sleep monitoring method according to any one of claims 1 to 4, wherein the electronic device monitors the user's sleep according to the target data, comprising:

When the amount of data in the target data reaches a preset amount, the electronic device monitors the user's sleep according to the target data;

When the amount of data in the target data does not reach a preset amount, the electronic device continues to acquire the first monitoring data.
The sleep monitoring method according to claim 5, wherein the electronic device monitors the user's sleep according to the target data, comprising:

The electronic device obtains multiple sets of data according to the target data;

The electronic device determines a first state label corresponding to each group of data according to the corresponding statistical feature value of each group of data in the multiple groups of data, wherein the first state label includes a sleep state and an awake state;

The electronic device determines the sleep monitoring result according to the first state label corresponding to each set of data.
The sleep monitoring method according to claim 6, wherein the electronic device determines the sleep monitoring result according to the first state label corresponding to each set of data, comprising:

The electronic device acquires the user's pulse wave data;

The electronic device determines a second state label corresponding to each group of data according to the pulse wave data and the corresponding statistical characteristic value of each group of data, wherein the second state label includes the sleep state and the sleep state. the awake state;

The electronic device determines a final state label corresponding to each set of data according to the first state label and the second state label;

The electronic device determines the sleep monitoring result according to the final state label corresponding to each set of data.
The sleep monitoring method according to any one of claims 1 to 7, wherein the electronic device determines whether the user is on a vehicle, comprising:

The electronic device acquires travel information and/or movement information of the user, and determines whether the user is on the vehicle according to the travel information and/or the movement information;

or,

The electronic device displays prompt information on the display screen, the prompt information is used to prompt the user to select whether the user is on the vehicle;

The electronic device monitors the first operation instruction input by the user;

The electronic device determines whether the user is on the vehicle according to the first operation instruction.
The sleep monitoring method according to any one of claims 1 to 8, wherein the method further comprises:

If the user is not on the vehicle, the electronic device monitors the sleep of the user according to the acquired second monitoring data, where the second monitoring data includes the state information of the user.
The sleep monitoring method according to claim 9, wherein before the electronic device monitors the sleep of the user according to the acquired second monitoring data, the method further comprises:

The electronic device determines to use a second monitoring mode to monitor the sleep of the user, where the second monitoring mode means that information related to the vehicle in the monitoring data acquired by the electronic device does not need to be filtered out.
An electronic device, characterized in that, the electronic device comprises a processor, and the processor is configured to run a computer program stored in a memory to implement the method according to any one of claims 1 to 10.
A computer-readable storage medium comprising computer instructions which, when executed on a computer or processor, cause the computer or processor to perform the method of any one of claims 1 to 10.