WO2021213337A1

WO2021213337A1 - Usage monitoring method for wearable electronic device, medium, and electronic device

Info

Publication number: WO2021213337A1
Application number: PCT/CN2021/088174
Authority: WO
Inventors: 李靖; 张慧; 许德省; 周林峰
Original assignee: 华为技术有限公司
Priority date: 2020-04-23
Filing date: 2021-04-19
Publication date: 2021-10-28
Also published as: CN113545745A; CN113545745B

Abstract

A usage monitoring method for a wearable electronic device (100), a medium, and the wearable electronic device (100), relating to the technical field of information processing. The usage monitoring method for the wearable electronic device (100) comprises: acquiring sleep monitoring data of the wearable electronic device (100) in a user sleep period (702); if sleep monitoring data with abnormal monitoring quality exists in the sleep monitoring data, determining the reason why the sleep monitoring data with the abnormal monitoring quality exists (706); and on the basis of the determined reason, determining prompt information used for prompting a user to improve the monitoring quality of the wearable electronic device (100) in the user sleep period (708). By means of the usage monitoring method for the wearable electronic device (100), the reasons affecting the quality of the sleep monitoring data in different scenes are determined, and the user is targetedly prompted according to the reasons affecting the quality of the sleep monitoring data, thereby improving the sleep quality of the user and the monitoring quality of the wearable electronic device (100).

Description

Wearable electronic equipment usage monitoring method, medium and electronic equipment

This application claims the priority of a Chinese patent application filed on April 23, 2020 with the application number 202010330121.8 and the invention title "Wearable electronic device usage monitoring method, medium and electronic device", the entire content of which is incorporated by reference In this application.

Technical field

This application relates to the field of information processing technology, and in particular to a method, medium and electronic device for monitoring the use of wearable electronic devices.

Background technique

Sleep apnea is a common chronic sleep disorder, which can be seen everywhere in the world. The World Health Organization reports that 1%-10% of the global population is affected by sleep apnea. Among them, there are about 50 million sleep apnea patients in China. Sleep apnea is manifested as the user’s recurrent respiratory obstruction during sleep, resulting in hypoxia and repeated awakenings, which seriously affects people’s mental and health. Due to lack of effective sleep, people will become lethargic, sleepy during the day, and memory. Decline, which not only endangers health, but also indirectly increases the probability of traffic accidents, construction sites and other industrial accidents. If it is not treated for a long time, it will cause diabetes, high blood pressure, cardiovascular and cerebrovascular diseases, stroke, neurasthenia and other diseases, and even sudden death at night.

At present, there are fewer technologies for sleep apnea detection through wearable electronic devices, and for the poor signal quality (or the monitoring quality of wearable electronic devices), the methods used generally include: discarding the entire piece of data without using or filtering The sleep monitoring data with poor signal quality is dropped, and the sleep monitoring data with better signal quality is used for calculation.

Summary of the invention

The embodiments of the present application provide a method, medium and electronic device for monitoring the use of a wearable electronic device. The use monitoring method of the wearable electronic device of the present application determines the reasons that affect the quality of sleep monitoring data in different scenarios, and prompts the user according to the reasons that affect the quality of the sleep monitoring data, so as to improve the user's sleep quality and Monitoring quality of wearable devices. Below, the content of this application will be introduced in detail.

In the first aspect, an embodiment of the present application provides a method for monitoring the use of a wearable electronic device, including:

Obtain the sleep monitoring data of the wearable electronic device during the user’s sleep; in the case of sleep monitoring data with abnormal monitoring quality in the sleep monitoring data, determine the cause of the sleep monitoring data with abnormal monitoring quality; based on affecting the quality of sleep monitoring data The reason for determining the prompt information used to prompt the user to improve the monitoring quality of the wearable electronic device during the user’s sleep.

That is, the wearable electronic device will further determine the cause of the abnormal monitoring quality of the sleep monitoring data when the monitoring quality of the sleep monitoring data during the monitored user's sleep is abnormal. Then, according to the judged reasons that affect the quality of the sleep monitoring data, the prompt information for prompting the user is further determined, so as to improve the monitoring quality of the wearable electronic device and the user's sleep quality.

In a possible implementation of the foregoing first aspect, the reason for the presence of sleep monitoring data with abnormal monitoring quality in the foregoing method includes at least one of the following:

The tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirements; the movement amplitude or frequency of the user wearing the wearable electronic device during sleep affects the quality of monitoring; the wearable electronic device is held down during the user's sleep Condition.

That is, in some embodiments of the present application, the reasons that affect the quality of the sleep monitoring data of the wearable electronic device during the user's sleep are the above three conditions or a combination of the above three conditions.

In a possible implementation of the foregoing first aspect, the foregoing method determines that the prompt information used to prompt the user to improve the monitoring quality of the wearable electronic device during the user's sleep based on the determined reason includes at least one of the following:

When the reason is determined that the tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirements, the prompt information used to improve the monitoring quality is determined as the prompt information about wearing the wearable electronic device correctly; when the reason is determined to be the user In the case where the motion amplitude or frequency of the wearable electronic device during sleep affects the monitoring quality, the prompt information used to improve the monitoring quality is determined to be the prompt information about stable sleep; when the reason is determined to be the wearable electronic device while the user is sleeping In the case of being held down during the period, it is determined that the prompt information used to improve the monitoring quality is the suggested prompt information about the wearing part.

That is, the user prompts the user to improve the monitoring quality of the wearable electronic device is determined according to the reason that affects the monitoring quality of the sleep monitoring data of the wearable electronic device, if the reason that affects the monitoring quality of the sleep monitoring data of the wearable electronic device is different , Then the prompt information that prompts the user will be different. It is understandable that when the reason that affects the sleep monitoring data of the wearable electronic device is a combination of the above three reasons, then the prompt information prompting the user will also be a combination of prompt information corresponding to the above three reasons.

In a possible implementation of the above-mentioned first aspect, the reason for determining the existence of sleep monitoring data with abnormal monitoring quality in the above method includes:

When it is determined that there are both normal and abnormal conditions in the monitoring quality of the sleep monitoring data of the screen of the wearable electronic device worn by the user in different orientations, it is determined that the reason is that the tightness of the wearable electronic device worn by the user during sleep is loose In order to meet the tightness of the measurement requirements; when it is determined that the monitoring quality of the sleep monitoring data of the wearable electronic device worn by the user in different orientations is only abnormal, the reason is determined to be the user wearing the wearable electronic device during sleep The tightness of the equipment is tighter than the tightness that meets the measurement requirements.

That is, when the monitoring quality of the user's sleep monitoring data monitored by the wearable electronic device under different screen orientations is normal and abnormal, it is considered that the user wears the wearable electronic device relatively loosely; if the wearable electronic device The monitoring quality of the sleep monitoring data of the user monitored in different orientations of the screen is abnormal, and it is considered that the user wears the wearable electronic device tightly.

In a possible implementation of the foregoing first aspect, the foregoing wearable electronic device includes a pressure sensor and a display screen, the pressure sensor is located inside the display screen of the wearable electronic device; and the reason for determining that there is sleep monitoring data with abnormal monitoring quality includes :

According to the pressure monitoring data of the pressure sensor, it is determined whether the reason for the sleep monitoring data with abnormal monitoring quality is that the tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirements.

That is, judging the tightness of the wearable electronic device worn by the user can also be judged by setting a pressure sensor on the wearable electronic device. For example, the pressure sensor is set inside the display screen of the wearable electronic device, and the user can be judged by the monitoring data of the pressure sensor. The tightness of the wearable electronic device.

In a possible implementation of the foregoing first aspect, the foregoing wearable electronic device includes an acceleration sensor, and the reason for determining that there is sleep monitoring data with abnormal monitoring quality includes:

According to the fluctuation interval and the fluctuation amplitude of the measurement data of the acceleration sensor in the acquired sleep monitoring data, it is determined whether the cause is the motion amplitude or the motion frequency of the user wearing the wearable electronic device during sleep that affects the monitoring quality.

That is, the wearable electronic device obtains the acceleration sensor data fluctuation interval and the acceleration sensor data fluctuation amplitude in the sleep monitoring data through the acceleration sensor, and calculates the variance of the acceleration sensor data fluctuation interval and the variance of the acceleration sensor data fluctuation amplitude. The variance of the data fluctuation interval and the variance of the acceleration sensor data fluctuation amplitude are respectively compared with the acceleration sensor data fluctuation interval threshold and the acceleration sensor data fluctuation amplitude threshold to determine whether the reason affecting the monitoring quality of the sleep monitoring data of the wearable electronic device is due to The user's movement amplitude during sleep is too large or the movement frequency is too high. It is understandable that the acceleration sensor data fluctuation interval and the average value of the acceleration sensor data fluctuation amplitude or other values that can characterize the fluctuation of the measurement data of the acceleration sensor can also be calculated to determine the user's action amplitude or action frequency during sleep. .

When it is determined that the tightness of the wearable electronic device worn by the user during sleep meets the measurement requirements and the movement amplitude or frequency of the user wearing the wearable electronic device during sleep does not affect the monitoring quality, the cause is determined to be the wearable electronic device There is a case of being held down during the user's sleep.

That is, if the user wears the wearable electronic device in the correct manner and the user does not have a large amplitude or high frequency of action during sleep, then it is considered that the reason for the quality of the sleep monitoring data of the wearable electronic device is that the wearable electronic device is in the user Was crushed during sleep.

In a possible implementation of the foregoing first aspect, the foregoing method further includes:

According to the fluctuation of the photoplethysmography sensor data and/or the fluctuation of the measurement data of the acceleration sensor in the acquired sleep monitoring data, it is determined whether there is sleep monitoring data with abnormal monitoring quality in the sleep monitoring data.

That is, whether the sleep monitoring data of the wearable electronic device has abnormal monitoring quality is determined according to the fluctuation of the photoplethysmography sensor data obtained by the wearable electronic device and/or the measurement data of the acceleration sensor.

Specifically, calculating the average value of the heart rate peak interval and the heart rate peak amplitude in the acquired photoplethysmography sensor data and/or calculating the acceleration sensor data fluctuation interval and the acceleration sensor in the measurement data (or monitoring data) of the acceleration sensor The average value of the data fluctuation amplitude, and then the average value of the heart rate peak interval and the heart rate peak amplitude are compared with the preset heart rate peak interval threshold and the preset heart rate peak amplitude threshold respectively and/or the acceleration sensor data fluctuation interval and the acceleration sensor The average value of the data fluctuation amplitude is respectively compared with the preset acceleration sensor data fluctuation interval threshold and the acceleration sensor data fluctuation amplitude threshold to determine whether the monitoring quality of the sleep monitoring data of the wearable electronic device is abnormal.

Display determined prompt information for prompting the user to improve the monitoring quality of the wearable electronic device during the user's sleep. That is, prompt information for prompting the user is displayed on the wearable electronic device.

In a possible implementation of the foregoing first aspect, obtaining sleep monitoring data of the wearable electronic device during the user's sleep in the foregoing method includes:

Receive sleep monitoring data from the wearable electronic device. That is to say, the judgment process of the monitoring quality of the sleep monitoring data and the judgment process of the reasons that affect the monitoring quality of the sleep monitoring data can also be performed on other electronic devices (for example, mobile phones or servers, etc.), when performed on other electronic devices At the time, other electronic devices can receive sleep monitoring data of the user during sleep, which is monitored by the wearable electronic device, from the wearable electronic device.

Send out certain prompt information for prompting the user to improve the monitoring quality of the wearable electronic device during the user's sleep. That is, the prompt information used to prompt the user can also be displayed on other electronic devices (such as a mobile phone), and at this time, the wearable electronic device will send the prompt information used to prompt the user to another electronic device.

In the second aspect, an embodiment of the present application provides a sleep apnea monitoring method for an electronic device, including:

The wearable electronic device sends the sleep monitoring data of the wearable electronic device during the user’s sleep. Among them, there is sleep monitoring data with abnormal monitoring quality in the sleep monitoring data; the wearable electronic device receives to remind the user to improve the wearable electronic device’s sleep in the user Prompt information of the monitoring quality during the period.

That is, the above-mentioned sleep monitoring data monitoring quality is judged on other electronic devices and the reasons affecting the monitoring quality of the sleep monitoring data are determined, and then the determined reasons affecting the monitoring quality of the sleep monitoring data of the wearable electronic device are determined The prompt information for prompting the user is displayed on the wearable electronic device. That is, the analysis process of sleep monitoring data is transferred to other electronic devices to reduce the power consumption of wearable electronic devices and improve the endurance of wearable electronic devices.

In a possible implementation of the above second aspect, the above-mentioned wearable electronic device receives prompt information for prompting the user to improve the monitoring quality of the wearable electronic device during the user's sleep period, including at least one of the following:

Prompt information about wearing the wearable electronic device correctly; Prompt information about stable sleep; Prompt information about the recommended wearing position.

Specifically, when other electronic devices (such as mobile phones) determine that the reason is that the tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirements, the prompt information received by the wearable electronic device is about wearing the wearable electronic device correctly. Reminder information of the device; when other electronic devices determine that the reason is that the user's movement amplitude or frequency while wearing the wearable electronic device during sleep affects the monitoring quality, the reminder information received by the wearable electronic device is a reminder about a stable sleep Information; when other electronic devices determine that the cause is that the wearable electronic device is held down during the user's sleep, the prompt information received by the wearable electronic device is suggested prompt information about the wearing part. It is understandable that when other electronic devices determine that the reason that affects the sleep monitoring data of the wearable electronic device is a combination of the above three reasons, then the prompt information that prompts the user will also be the prompt information corresponding to the above three reasons. combination.

In a third aspect, an embodiment of the present application is a readable medium of an electronic device, and the readable medium stores an instruction, which when executed on the electronic device causes the electronic device to perform any of the above-mentioned methods.

In a fourth aspect, an embodiment of the present application provides an electronic device, including: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the processors of the electronic device, For performing the method of any one of the above-mentioned first aspect and second aspect.

In a fifth aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, a photoplethysmography sensor, and an acceleration sensor; the photoplethysmography sensor is used to obtain the electronic device's user's sleep during sleep Photoplethysmography sensor data; the acceleration sensor is used to obtain the acceleration sensor monitoring data of the user of the electronic device during sleep; the memory is used to store instructions executed by one or more processors of the electronic device; the processor is an electronic device One of the processors for executing the method of any one of the above-mentioned first aspect and second aspect.

In a sixth aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, a display screen, a photoplethysmography sensor, a pressure sensor, and an acceleration sensor; the photoplethysmography sensor is used to obtain information about the electronic device The photoplethysmography sensor data of the user during sleep; the acceleration sensor is used to obtain the acceleration sensor monitoring data of the user of the electronic device during sleep; the display screen is used to display and prompt the user to improve the wearable electronic device's The prompt information for monitoring quality; the pressure sensor is located on the inner side of the display screen to detect the tightness of the electronic device worn by the user; the memory is used to store instructions executed by one or more processors of the electronic device; the processor is the electronic device’s One of the processors is used in the method of any one of the above-mentioned first aspect and second aspect.

In a seventh aspect, an embodiment of the present application provides an electronic device that has the function of implementing the above search method. The function can be realized by hardware, or the corresponding software can be executed by hardware. The hardware or software includes one or more units corresponding to the above-mentioned functions.

Description of the drawings

Fig. 1 shows an application scenario diagram of a method for monitoring the use of a wearable electronic device according to some embodiments of the present application.

Fig. 2 shows a schematic diagram of the hardware structure of a wristband according to some embodiments of the present application.

Fig. 3a shows a schematic flowchart of a technical solution according to some embodiments of the present application.

Fig. 3b shows a schematic flowchart of a technical solution according to some embodiments of the present application.

Fig. 4a shows a schematic diagram of a human-computer interaction interface according to some embodiments of the present application.

Fig. 4b shows a schematic diagram of a human-computer interaction interface according to some embodiments of the present application.

Fig. 5a shows a schematic diagram of a human-computer interaction interface according to some embodiments of the present application.

Fig. 5b shows a schematic diagram of a human-computer interaction interface according to some embodiments of the present application.

Fig. 6a shows a schematic diagram of a human-computer interaction interface according to some embodiments of the present application.

Fig. 6b shows a schematic diagram of a human-computer interaction interface according to some embodiments of the present application.

FIG. 7 shows a schematic diagram of interaction between a mobile phone 200 and a wristband 100 according to some embodiments of the present application.

FIG. 8 shows a schematic structural diagram of an electronic device 800 capable of implementing the functions of the mobile phone 200 according to some embodiments of the present application.

FIG. 9 shows a software system of an electronic device 800 capable of implementing the functions of the mobile phone 200 according to some embodiments of the present application.

Detailed ways

The technical solutions of the embodiments of the present application will be further described in detail below through the accompanying drawings and embodiments.

Fig. 1 is an application scenario diagram of a method for monitoring the use of a wearable electronic device according to a specific embodiment of the present application. As shown in FIG. 1, the embodiment of the present application relates to a wearable electronic device 100. The wearable electronic device 100 can wirelessly communicate with other electronic devices through various wireless methods, for example, with the electronic device 200 or the server 300. Wireless communication. For another example, the wearable electronic device 100 may send a wireless signal to the server 300 through a wireless communication link through its own radio frequency circuit and antenna, requesting the server 300 to perform wireless network service processing. The wearable electronic device 100 may have specific service requirements, such as users Registration, data acquisition and monitoring, etc.; for another example, the wearable electronic device 100 can be matched with the electronic device 200 through its own Bluetooth, and after the matching is successful, data communication with the electronic device 200 through the Bluetooth communication link is carried out, of course, it can also be communicated with other wireless devices. The communication method performs data communication with the electronic device 200, such as radio frequency identification technology, short-range wireless communication technology and so on. In addition, the wearable electronic device 100 can also detect changes in the external environment through its own various sensors.

In a specific embodiment of the present application, the wearable electronic device 100 can monitor the sleep breathing condition of the user during sleep. For example, in a specific implementation, the wearable electronic device 100 obtains sleep monitoring data during sleep of the user, where the sleep monitoring data includes photo-plethysmo-graph (PPG) sensor data and electrocardiography (ECG) data. ) (For example, heart rate peak interval, heart rate peak amplitude, blood oxygen data, body temperature data), acceleration sensor (ACC) measurement data (or monitoring data) (for example, acceleration sensor data fluctuation interval, acceleration sensor data fluctuation Amplitude), gyroscope sensor data, etc. And in the case of sleep monitoring data with abnormal monitoring quality (or poor monitoring quality of sleep monitoring data) in the sleep monitoring data, determine the cause of the sleep monitoring data with abnormal monitoring quality, and then determine the reason for prompting the user according to the determined reason Prompt information to improve the monitoring quality of the wearable electronic device during the user's sleep, and push corresponding suggestions or services to the user. For example, in the case where it is determined that the sleep monitoring data with abnormal monitoring quality is because the tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirements, it is determined that the prompt information used to improve the monitoring quality is about wearing the wearable electronic device correctly. The prompt information of the wearable electronic device; in the case where it is determined that the cause of the sleep monitoring data with abnormal monitoring quality is that the user's motion amplitude or frequency while wearing the wearable electronic device during sleep affects the monitoring quality, determine the use The prompt information to improve the monitoring quality is the prompt information about stable sleep; when it is determined that the sleep monitoring data with abnormal monitoring quality is caused by the wearable electronic device being pressed during the user’s sleep, it is determined to be used to improve the monitoring The quality prompt information is the suggested prompt information about the wearing part, and then these specific reasons are sent to the user.

The technical solution of the present application can determine that the acquired sleep monitoring data of the wearable electronic device 100 has causes of abnormal monitoring quality, and provide users with wearing and sleep suggestions, thereby improving the monitoring quality of the sleep monitoring data of the smart wearable electronic device 100.

In the specific embodiment of the present application, the wearable electronic device 100 may be a variety of devices, including but not limited to smart watches, smart bracelets or glasses, helmets, headbands and other wearable electronic devices, medical testing instruments, etc. . In the following description, in order to simplify the description, the smart bracelet 100 is taken as an example to illustrate the technical solution of the present application.

The electronic device 200 can be a client that can communicate with the wearable electronic device 100, and can help the wearable electronic device 100 complete registration, control the firmware update of the wearable electronic device 100, receive sleep monitoring data of the wearable electronic device 100, and assist The wearable electronic device 100 analyzes the sleep monitoring data for monitoring and prompting the user's wearing style and sleep status. It can be understood that the electronic device 200 may include, but is not limited to, a laptop computer, a desktop computer, a tablet computer, a smart phone, a server, a wearable electronic device, a head-mounted display, a mobile email device, a portable game console, and a portable music player. Readers, reader devices, televisions with one or more processors embedded or coupled therein, or other electronic devices capable of accessing the Internet. In the following description, in order to simplify the description, the mobile phone 200 is taken as an example to illustrate the technical solution of the present application.

FIG. 2 shows a schematic diagram of the hardware structure of a smart bracelet 100 provided according to some embodiments of the present application. The smart bracelet may include a smart bracelet main body 100. In an embodiment of the present application, the main body of the smart bracelet 100 may include a touch screen 101 (also called a touch screen panel), a display screen 102, a housing (the housing includes a front case (not shown in FIG. 2) and Bottom case (not shown in Figure 2)), and processor 103, micro control unit (MCU) 104, memory 105, wireless communication module 106, pressure sensor 107, PPG sensor 108, acceleration sensor 109, power supply 111 , Power management system 112, etc.

The functional components of the smart bracelet 100 are introduced below:

The touch screen 101, which can also be called a touch panel, can collect the user's touch operations on the smart bracelet 100 (for example, the user uses any suitable objects or accessories such as fingers, stylus, etc.) on the touch panel or near the touch panel. ), and drive the responding connection device according to the preset program.

The display screen 102 may be used to display information input by the user or provide prompt information to the user and various menus on the bracelet. Further, the touch screen 101 can cover the display screen 102. When the touch screen 101 detects a touch operation on or near it, it transmits it to the processor 103 to determine the type of the touch event, and then the processor 103 displays it on the display screen according to the type of the touch event. Corresponding visual output is provided on 102. For example, in some embodiments of the present application, when the sleep monitoring data of the smart bracelet 100 has abnormal sleep monitoring data, it is determined that there is a reason for the abnormal monitoring quality of the sleep monitoring data, and the reason is determined to prompt the user to improve After the smart bracelet 100 monitors the quality of the user during sleep, the prompt information (such as a teaching video on how to properly wear the smart bracelet 100, etc.) can be displayed on the display screen 102.

The processor 103 is used to perform system scheduling, control the touch screen 101, the display screen 102, support Bluetooth 106, and so on.

The micro-control unit 104 is used to control the sensor, perform calculations on sensor data, communicate with the processor 103, and so on. Among them, the sensor may include a pressure sensor 107, a PPG sensor 108, an acceleration sensor 109, a motion sensor or other sensors. For example, in some embodiments of the present application, the micro-control unit 104 detects PPG data (for example, heart rate peak interval, heart rate peak amplitude) and ACC data (for example, ACC data fluctuation interval, ACC data fluctuation amplitude) analyze to determine the cause of abnormal monitoring quality of sleep monitoring data. In addition, it can be understood that, in other embodiments, the aforementioned processing of PPG data and ACC data may also be completed by the processor 103, which is not limited herein.

The memory 105 is used to store software programs and data. The processor 103 executes various functional applications and data processing of the smart bracelet 100 by running the software programs and data stored in the memory 105. For example, in some embodiments of the present application, the memory 105 may store PPG data collected by a PPG sensor or ACC data collected by an acceleration sensor. At the same time, the memory can also store user registration information, login information, and so on.

The wireless communication module 106 and the smart bracelet 100 can exchange information with other electronic devices (such as mobile phones, tablet computers, etc.) through the wireless communication module, and can connect to a network or a server through the above-mentioned electronic devices. Among them, the wireless communication module can provide applications on the smart bracelet 100 including wireless local area networks (WLAN), (such as wireless fidelity (Wi-Fi) networks), Bluetooth (bluetooth, BT) , Global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions.

It can be understood that the structure shown in FIG. 2 is only a specific structure for realizing the functions of the smart bracelet 100 in the technical solution of the present application, and the smart bracelet 100 or other types of wearable electronics with other structures and capable of achieving similar functions The equipment is also applicable to the technical solution of this application, which is not limited here.

In the following, in combination with specific scenarios, the technical solution of the present application will be introduced in detail.

In some embodiments of the present application, the smart bracelet 100 can independently complete the entire technical solution. For example, the smart bracelet 100 monitors the user's sleep status, and according to the sleep monitoring data ( Such as PPG data, ACC data) to determine the monitoring quality of the sleep monitoring data of the smart bracelet 100 during the user’s sleep, and in the case of abnormal monitoring quality of the sleep monitoring data, determine the cause of the abnormal monitoring quality of the sleep monitoring data, Then, based on specific reasons, targeted recommendations are pushed to the user through the mobile phone 200 that can make the monitoring quality of the sleep monitoring data of the smart bracelet 100 better.

In addition, in other embodiments of the present application, the mobile phone 200 may also determine the cause of abnormal monitoring quality of the sleep monitoring data. For example, when the user wants to reduce the power consumption of the smart bracelet 100, he can choose to judge the reason for the abnormal monitoring quality of the sleep monitoring data to the mobile phone 200, and only display on the side of the smart bracelet 100 for prompting. Prompt information. Specifically, at this time, the smart bracelet 100 can send the monitored sleep monitoring data during sleep of the user to the mobile phone 200 via Bluetooth or wireless network, and then the mobile phone 200 determines the monitoring of the sleep monitoring data according to the received sleep monitoring data. Quality, and determine the cause of the abnormal monitoring quality of the sleep monitoring data. Finally, determine the user’s prompt information according to the cause of the abnormal monitoring quality of the sleep monitoring data during the sleep period of the user monitored by the smart bracelet 100, and then the mobile phone 200 will The prompt information is sent to the side of the smart bracelet 100 worn by the user, and the prompt information is displayed on the display screen on the side of the smart bracelet 100.

In addition, in some other embodiments of the present application, the server 300 may also determine the monitoring quality of the sleep monitoring data. For example, the smart bracelet 100 sends to the server 300 the sleep monitoring data during the user’s sleep monitored by the smart bracelet 100 through wireless communication, and then the server 300 determines the sleep monitoring based on the received sleep monitoring data during the user’s sleep. Data monitoring quality, and determine the reason for the abnormal monitoring quality of the sleep monitoring data. Finally, according to the reason for the abnormal monitoring quality of the sleep monitoring data during the sleep period of the user monitored by the smart bracelet 100, targeted push to the user can improve the intelligence The monitoring quality prompt information of the sleep monitoring data of the wristband 100 during the user's sleep.

For the convenience of explanation, the following is an example of judging the monitoring quality of sleep monitoring data on the side of the smart bracelet 100 and determining the cause of abnormal monitoring quality of the sleep monitoring data, and pushing relevant prompt information on the side of the mobile phone 200 as an example. .

Fig. 3a shows a flow chart of the technical solution of the present application according to an embodiment of the present application. The details are shown in Figure 3a:

a301: Obtain sleep monitoring data during the user's sleep. For example, acquiring the PPG data during the user's sleep or the ACC data during the user's sleep, where the PPG data may include the user's heart rate fluctuation amplitude, heart rate fluctuation interval, pulse data, blood oxygen value, ECG data, etc., while the ACC data can It is the ACC data fluctuation interval, the ACC data fluctuation amplitude, the data monitored by the gyroscope, and so on.

a302: Determine the monitoring quality of the sleep monitoring data of the smart bracelet 100; if the monitoring quality of the sleep monitoring data of the smart bracelet 100 is normal, proceed to a303, and if the monitoring quality of the sleep monitoring data of the smart bracelet 100 is abnormal, proceed to a304.

a303: The corresponding sleep monitoring data is marked as "normal", and the user is not prompted.

a304: Further determine whether the monitoring quality of the sleep monitoring data of the smart bracelet 100 is abnormal due to the user's actions. If the monitoring quality of the sleep monitoring data of the smart bracelet 100 is abnormal due to the user's actions, proceed to a305 and a310 , If it is not because the user moves too much, go to a306.

a305: Mark the corresponding sleep monitoring data with "abnormality" and mark the cause of abnormal monitoring quality at the same time, for example, use the mark "U1" to indicate that the abnormal cause is "too many user actions", and then determine the prompt information based on the cause of the abnormal monitoring quality .

a306: Further determine whether the monitoring quality of the sleep monitoring data of the smart bracelet 100 is abnormal because the user wears the bracelet too loosely or too tightly. If the user wears the smart bracelet 100 too loosely or too tightly, go to a307 and a310, if it is not because the user wears the smart band 100 too loosely or too tightly, which leads to abnormal monitoring quality of the sleep monitoring data of the smart band 100, go to a308 .

a307: Mark the corresponding sleep monitoring data as "abnormal", and at the same time mark the reason for the abnormal monitoring quality. For example, use the mark "U2" to indicate that the reason for the abnormality is "the user wears the smart bracelet too loosely or too tightly", and then according to the cause Determine the prompt message for the reason of the abnormal quality monitoring.

a308: Further judge whether the smart bracelet is pressed. If the monitoring quality of the sleep monitoring data of the smartband 100 is abnormal because the smartband 100 is pressed to the point, perform a309 and a310.

a309: Mark the corresponding sleep monitoring data as "abnormal" and mark the reason for the abnormal monitoring quality at the same time. For example, use the mark "U3" to indicate that the reason for the abnormality is "smart bracelet 100 is pressed", and then according to the abnormal monitoring quality The reason is determined by the prompt message.

a310: Send the corresponding prompt information to the mobile phone.

As shown in Fig. 3a, in the above-mentioned technical solution process, the method of determining the reason for the abnormal monitoring quality of the sleep monitoring data is restricted in sequence. In some embodiments of the present application, the method of determining the reason for the abnormal monitoring quality of the sleep monitoring data can also be performed at the same time. For example, it is determined at the same time whether the cause of the abnormal sleep monitoring data is "too many user actions", "user wearing too loose or too tight", "smart bracelet being pressed", if it is determined that the sleep monitoring data of the smart bracelet 100 is caused Reasons for abnormal monitoring quality A combination of the above-mentioned reasons, for example, the abnormal monitoring of sleep monitoring data satisfies both "the user has a lot of actions" and "the smart bracelet is pressed". At this time, when the sleep monitoring data is marked as "abnormal", the sleep monitoring data with abnormal monitoring quality can be marked as "U1U3", where "U1" means that the reason for the abnormal monitoring quality is "too many user actions" and "U3" "Indicates that the reason for the abnormal monitoring quality is "smart bracelet is pressed", and at the same time, a variety of prompt messages are determined at the same time according to the reason for the abnormal monitoring quality of the sleep monitoring data.

In addition, it can be understood that in other embodiments of the present application, the method of determining the cause of abnormal monitoring quality of sleep monitoring data may not be determined in the order of a304, a306, and a308, and the order of determination may be arbitrary, for example, in specific In the implementation process, the above method can be:

First judge whether the reason for the abnormal monitoring quality of the sleep monitoring data of the smart bracelet 100 is due to "the user is wearing too loose or too tight" (a306), if it is not because the user wearing the smart bracelet is too loose or too tight, the smart bracelet If the monitoring quality of the sleep monitoring data of 100 is abnormal, then it is determined whether the monitoring quality of the sleep monitoring data of the smart bracelet 100 is abnormal (a304) because of "too many user actions". If the monitoring quality of the sleep monitoring data is abnormal, it is determined whether the "smart bracelet is pressed" causes the monitoring quality of the sleep monitoring data of the smart bracelet 100 to be abnormal (a308). In this judgment method, the other steps are consistent with the technical solution process described in FIG. 3a, and will not be repeated here.

FIG. 3b shows that after the reason for the abnormal monitoring quality of the sleep monitoring data of the smart bracelet 100 is determined through the above technical solution and the prompt message is determined according to the specific abnormal reason, the relevant prompt information is pushed to the user through the mobile phone 200 Specific circumstances. For example, when it is determined that "the user has no problems with wearing and operating b302", that is, the monitoring quality of the sleep monitoring data of the smart bracelet 100 is normal, the user is "not prompted (b304)"; when it is determined that "the user has problems with wearing (b305) )” means that when the monitoring quality of the sleep monitoring data of the smart bracelet 100 is abnormal, if the user is “wearing normally but doing more at night (b306)” means that the user wearing the smart bracelet is not too tight or too loose but the user has a large movement range or movement If the frequency is high, then “mark the abnormal data and confirm that the prompt information is “recommended push for stable sleep, recommended to wear multiple times” (b307)”; if the user “wears too loose or too tight (b308)”, then “mark” If the data is abnormal and the prompt message is determined to be'Play Wearing Guidance Video' (b309)"; if it is the user "the wristband is pressed to (b310) during sleep", then proceed to "Mark the data abnormally and confirm the prompt message as'recommended user's hand Wear the ring on the contralateral hand in the main side lying position'(b311)"; if it is "a combination of multiple reasons (b312)", the prompt message is determined to be "multiple suggested combinations (b313)".

Among them, the specific method of determining the monitoring quality of the sleep monitoring data of the smart bracelet 100 and the method of determining the cause of the abnormal monitoring quality of the sleep monitoring data of the smart bracelet 100 are as follows:

Obtain sleep monitoring data during user sleep

In the specific embodiment of the present application, the smart bracelet 100 monitors the user's sleep status, and obtains the user's sleep monitoring data during sleep (for example, the heart rate peak interval, the heart rate peak amplitude through the PPG sensor 108 and the acceleration sensor 109). , ACC data fluctuation interval, ACC data fluctuation amplitude, etc.).

In some embodiments of the present application, the smart bracelet 100 can obtain sleep monitoring data during sleep of the user in time periods, and can also obtain sleep monitoring data during sleep of the user in real time. For example, the user's sleep is generally divided into day and night. However, during the daytime, it may only take a half-hour or one hour nap, which is a short period of time, so the smart bracelet 100 can monitor the user in real time to obtain the user’s sleep monitoring data at this time; and at night, the user’s general sleep time is 7 -8 hours. At this time, real-time monitoring will increase the power consumption of the bracelet 100, so the user’s sleep monitoring data can be obtained in time periods (for example, to obtain sleep monitoring data from 9 pm to 3 am or early morning Sleep monitoring data from 1 o'clock to 5 o'clock in the morning). Or, because the possibility of entering deep sleep during a nap during the day is low, and the user is less likely to experience sleep apnea, the sleep monitoring data during the user’s sleep can be obtained by time periods, and at night, the user’s sleep With sufficient time, there are more cases of sleep apnea, and the user's sleep monitoring data at this time can be monitored and acquired in real time.

Hereinafter, a specific description will be given by taking the smart bracelet 100 real-time monitoring of the user from 1 pm to 5 am as an example.

Determine the monitoring quality of the acquired sleep monitoring data

The monitoring quality of the obtained sleep monitoring data is judged, where the sleep monitoring data includes the heart rate peak interval and the heart rate peak amplitude obtained by the PPG sensor 108. Of course, it can be understood that in other embodiments, the sleep monitoring data may also be other data signs that characterize the monitoring quality of the sleep monitoring data of the smart bracelet, such as related pulse data, blood pressure data, and so on.

In an embodiment of the present application, the smart bracelet 100 can determine the monitoring quality of the sleep monitoring data of the smart bracelet based on the heart rate peak interval and the heart rate peak amplitude obtained by the PPG sensor 108. For example, you can pre-set the heart rate peak interval threshold T1 and the heart rate peak amplitude threshold A1 through the historical heart rate peak interval and the historical heart rate peak amplitude monitored by the smart bracelet 100 in advance, when the heart rate peak obtained by the smart bracelet 100 If the interval is less than T1 and the peak amplitude of the heart rate is less than A1, the monitoring quality of the sleep monitoring data of the smart bracelet is normal at this time, otherwise the monitoring quality of the sleep monitoring data of the smart bracelet is considered abnormal.

For example, in a specific implementation process, the smart bracelet 100 detects the user's heart rate peak interval t1, t2, t3...tn and the heart rate peak amplitude a1, a2, a3...an in the time period T, and then The smart bracelet 100 calculates the user's average heart rate peak interval in the time period T according to formula (1)

And the average heart rate peak amplitude

(In other embodiments, it may also be the variance, range, etc. of the user's heart rate peak and heart rate interval within the time period T),

Among them, μ represents the average value of the data, h1, h2, h3···hn represents the data, and n represents the number of data.

At the same time, the smart bracelet 100 sets the heart rate peak interval threshold T1 and the heart rate peak amplitude threshold A1 according to the monitored historical heart rate peak interval and historical heart rate peak amplitude. The average value or variance of the heart rate peak amplitude is set)), and then the smart bracelet 100 sets the average heart rate peak interval of the user in the time period T

And the average heart rate peak amplitude

Compare with the preset heart rate peak interval threshold T1 and the preset heart rate peak amplitude threshold A1, if the user’s average heart rate peak interval in the time period T

Less than the preset heart rate peak interval threshold T1 and average heart rate peak amplitude

Less than the preset heart rate peak amplitude threshold A1, it is considered that the monitoring quality of the sleep monitoring data of the smart bracelet is normal, if the user’s average heart rate peak interval in the time period T

Greater than the preset heart rate peak interval threshold T1 or the average heart rate peak amplitude

If it is greater than the preset heart rate peak amplitude threshold A1, it is considered that the monitoring quality of the sleep monitoring data of the smart bracelet is abnormal.

In addition, in some embodiments of the present application, other methods may also be used to determine the quality of the physiological data monitored by the smart bracelet 100 is good or bad.

For example, in an embodiment of the present application, the smart bracelet 100 can also determine the monitoring quality of the sleep monitoring data of the smart bracelet 100 based on the ACC data. In a specific implementation, when the amplitude of the ACC data fluctuation is small and the fluctuation interval is short, it can be considered that the monitoring quality of the sleep monitoring data of the smart bracelet is normal at this time. Specifically, the smart bracelet 100 presets the ACC data fluctuation amplitude threshold A2 and the ACC data fluctuation interval threshold T2 according to the monitored user's historical ACC data fluctuation interval and historical ACC data fluctuation amplitude (the values of A2 and T2 are also specifically It can be set by obtaining the average or variance of historical ACC data fluctuation interval and historical ACC data fluctuation amplitude). At the same time, the smart bracelet 100 monitors the user's ACC data fluctuation amplitude b1, b2, b3· in the time period T. ··Bn and ACC data fluctuation interval c1, c2, c3···c4, and calculate the average fluctuation amplitude of the user's ACC data in the time period T according to formula (1)

And the average fluctuation interval

And the average fluctuation amplitude of the user's ACC data in the time period T

And the average fluctuation interval

Compared with the ACC data fluctuation amplitude threshold A2 and the ACC data fluctuation interval threshold T2, if the user has the average fluctuation amplitude of the ACC data in the time period T

Less than the ACC data fluctuation amplitude threshold A2 and the average fluctuation interval

If it is less than the ACC data fluctuation interval threshold T2, it is considered that the monitoring quality of the sleep monitoring data of the smart bracelet smart bracelet 100 is normal. If the user has the average fluctuation amplitude of the ACC data in the time period T

Greater than ACC data fluctuation amplitude threshold A2 or average fluctuation interval

If it is greater than the ACC data fluctuation interval threshold T2, it is considered that the monitoring quality of the sleep monitoring data of the smart bracelet 100 is abnormal.

In addition, in some embodiments of the present application, the smart bracelet 100 can also determine the monitoring quality of the sleep monitoring data by means of machine learning. For example, in a specific implementation, the smart bracelet 100 manually calibrates the historical sleep monitoring data based on the historical sleep monitoring data of the monitored user (where the quality of the historical sleep monitoring data is normal or abnormal), such as , You can mark data with normal quality as "normal" and data with abnormal quality as "abnormal". Then perform feature extraction on these historical sleep monitoring data to generate feature vectors, and then input the extracted feature vectors into the convolutional neural network model (Convolutional Neural Networks, CNN), and train the convolutional neural network model so that the model can Judge the quality of historical physiological data and historical physical data. For example, the difference between the input and output of the model can be used as a loss function. For example, randomly input historical sleep monitoring data, and compare the output of the model with the input historical sleep monitoring number. If the input historical sleep monitoring data is If the mark is "normal" and the output result of the model is also "normal", it is considered that the loss function is close to 0, and the model has been trained. If the mark of the input historical sleep data is "abnormal", and the output result is not " "Abnormal", it is considered that the loss function is not close to 0, then the relevant parameters of the model are adjusted, and the next round of training is performed until the loss function between the input and output is close to 0, then the model training is completed. Then input the user's sleep monitoring data currently acquired by the smart bracelet 100 into the trained convolutional neural network model, and the model can make a judgment on the quality of the input sleep monitoring data.

In other embodiments of the present application, the smart bracelet 100 may also judge the data quality of the smart bracelet based on other data, such as blood pressure data. Among them, the specific judgment method may be to replace the heart rate data in the PPG data described above with blood pressure data, and then the specific judgment is similar to the above judgment method, which will not be repeated here.

In some embodiments of the present application, the smart bracelet 100 may also simultaneously determine the monitoring quality of the sleep monitoring data of the smart bracelet based on the combination of the multiple sleep monitoring data. For example, weight assignment can be performed on each sleep monitoring data, and the specific assignment method can be performed according to each sleep monitoring data reflecting the health of the user. For example, the heart rate peak interval and the heart rate peak amplitude obviously reflect the user's physical condition better than the ACC data fluctuation interval and the ACC data fluctuation amplitude. Therefore, the weight of the heart rate peak interval and the heart rate peak amplitude is more important than the ACC data fluctuation interval and ACC. The weight of data fluctuation amplitude is high.

For example, in a specific implementation process, the weight of A heart rate peak interval and heart rate peak amplitude is 80%, and the weight of ACC data fluctuation interval and ACC data fluctuation amplitude is 20%. The smart bracelet 100 detects the user's heart rate peak interval t1, t2, t3...tn and the heart rate peak amplitude a1, a2, a3...an in the time period T, and the user's ACC in the time period T Movement data fluctuation amplitude b1, b2, b3···bn and ACC fluctuation interval c1, c2, c3···c4, and calculate the average heart rate peak interval of the user in the time period T

And the average heart rate peak amplitude

And the average fluctuation amplitude of ACC sports data

And the average fluctuation interval

At the same time, the smart bracelet 100 pre-sets the credibility threshold P of the user's smart bracelet according to the monitored historical sleep monitoring data, and then calculates the smart bracelet 100 based on the preset weight of each sleep monitoring data of the user according to formula (2) The credibility of the monitoring quality of sleep monitoring data,

p=α×(A+T)+β×(B+C) (2)

Among them, p represents credibility, α represents weight, β represents weight, A represents heart rate peak amplitude, T represents heart rate peak interval, B represents ACC data fluctuation amplitude, C represents ACC data fluctuation interval,

but

Then compare p with the credibility threshold P. When the credibility of the user's smart bracelet is greater than the credibility threshold P, the monitoring quality of the sleep monitoring data of the smart bracelet is considered normal, otherwise, the sleep monitoring data of the smart bracelet is considered to be normal. The monitoring quality of the monitoring data is abnormal.

Determine the reason for the abnormal monitoring quality of the sleep monitoring number of the smart bracelet

To determine the reason for the abnormal monitoring quality of the sleep monitoring data of the smart bracelet, the specific process is as follows:

A. Determine whether the reason for the abnormal quality of the sleep monitoring data monitoring is that the user has a large range of actions during sleep or a high frequency of actions

Generally, users often have unconscious actions during sleep, and these actions can be reflected by the data of the ACC sensor or the gyroscope sensor of the smart bracelet. For example, when the user's movement range is large, the fluctuation of the ACC data will become stronger, which is specifically reflected in the fluctuation interval and the fluctuation amplitude of the ACC data. When the user's movement amplitude is small, the fluctuation of the ACC data will become lessened, and the fluctuation interval and amplitude of the corresponding ACC data will also become smaller.

In some embodiments, when it is determined that the monitoring quality of the sleep monitoring data of the smart band 100 is abnormal, the smart band 100 may preset the ACC according to the historical ACC data fluctuation amplitude and the ACC data fluctuation interval of the monitored user. Data fluctuation amplitude threshold A3 and ACC data fluctuation interval threshold T3. At the same time, the smart bracelet 100 monitors the user's ACC data fluctuation amplitude b1, b2, b3...bn and ACC fluctuation interval c1, c2 during the time period T, c3···c4, and calculate the average fluctuation amplitude of ACC data according to formula (1)

And the average fluctuation interval

Calculate the fluctuation amplitude variance D and fluctuation interval variance E of the user's ACC data in the time period T according to formula (3),

Among them, δ ² represents the variance of the data to be calculated, n represents the number of data, μ is the average value of the data to be calculated, and X is the real-time data monitored by the smart bracelet. In the calculation, the ACC data fluctuation amplitude can be substituted as μ, and the obtained δ ^{2 is} the ACC data fluctuation amplitude variance D, and the ACC data fluctuation interval is substituted as μ, and the obtained δ ^{2 is} the ACC data fluctuation interval variance E.

At the same time, the smart bracelet 100 sets the ACC data fluctuation amplitude threshold A3 and the ACC motion data fluctuation interval threshold T3 according to the monitored user's historical ACC data fluctuation amplitude and the ACC data fluctuation interval. Compare the ACC data fluctuation amplitude variance D and the ACC data fluctuation interval variance E of the user in the time period T with the ACC data fluctuation amplitude threshold A3 and the ACC data fluctuation interval threshold T3 respectively. If the user is in the ACC movement in the time period T If the variance D of the fluctuation amplitude of the data is greater than A3 and the variance E of the fluctuation interval is greater than T3, the current time period is considered to have a large user action range.

Further, each time period T of the user's sleep phase is calculated in accordance with the above method, and the ratio of the time period during which the user's action amplitude is large to the user's total sleep time is calculated, if the ratio of the two is higher than the set Threshold V1, it is considered that the user's data action frequency that night is high. For example, the smart bracelet 100 can count the user's actions during the night sleep period (take 8 hours as an example).

Specifically, the smart bracelet 100 can count the user's ACC movement data fluctuation amplitude during 22:00-23:00, 00:00-01:00, 02:00-03:00, 04:00-05:00 ( d1,d2,d3···dn), (dn+1,dn+2,dn+3···d2n), (d2n+1,d2n+2,d2n+3···d3n), (d3n+ 1, d3n+2, d3n+3···d4n) and the interval between ACC motion data fluctuations (e1, e2, e3···en), (en+1, en+2, en+3···e2n) , (E2n+1, e2n+2, e2n+3···e3n), (e3n+1, e3n+2, e3n+3···e4n).

Then according to formulas (1) and (2), calculate the user's ACC data fluctuation amplitude variance (D1, D2, D3, D4) and fluctuation interval variance (E1, E2, E3, E4) during the above time period, assuming that the fluctuation is satisfied The time periods when the amplitude variance is greater than A3 and the fluctuation interval variance is greater than T3 are 00:00-01:00, 02:00-03:00, 04:00-05:00, that is, data (D2, D3, D4) and (E2 , E3, E4) Then it is considered that the user's action range is large in the time periods of 00:00-01:00, 02:00-03:00, and 04:00-05:00. In other words, the time period when the user has a large range of actions For 3 hours, the time period during which the user's action amplitude is large relative to the user's total sleep time is (3/8), and then the specific calculation result is compared with the threshold V1 to determine the user's action frequency during the sleep time . If the calculation result is greater than V1, it means that the user's action frequency is high; otherwise, it is considered that the user's action frequency is low.

In some embodiments of the present application, the smart bracelet 100 can also determine whether the user has a time period with a large continuous motion range through the above method of judging the user's motion range. If the user has a large motion range during a certain continuous time period, It can be judged that the user is awake for a long time at night. And because being awake at night for a long time will also affect the accuracy of sleep apnea detection, abnormal marks or prompts will be made on the sleep monitoring data of the corresponding time period.

B. Determine whether the abnormality of the monitoring data is caused by the improper tightening of the smart bracelet worn by the user

Generally, the monitoring quality of the sleep monitoring data of the smart bracelet is also affected by the way the user wears the smart bracelet. When the user wears the bracelet too loosely or too tightly, the monitoring quality of the sleep monitoring data of the smart bracelet will also be abnormal. The acceleration sensor can be used to determine whether the user wears the smart bracelet too loosely or too tightly. The ACC sensor can identify the screen orientation of the smart bracelet. When the screen orientation of the smart bracelet is in different directions, the monitoring quality of the sleep monitoring data of the smart bracelet is abnormal, and it can be considered that the user is wearing the bracelet too tightly; When the screen directions of the smart bracelet are in different directions, the monitoring quality of the sleep monitoring data of the smart bracelet is normal or abnormal, that is, when the monitoring quality is inconsistent, it can be considered that the user wears the bracelet too loosely.

In some embodiments of the present application, the smart bracelet 100 is provided with an acceleration sensor 109, and in general, the Z axis is perpendicular to the screen (touch screen) of the smart bracelet 100. The smart bracelet 100 can obtain the ACC data of the user's sleep time period through the acceleration sensor 109, and can identify the screen direction of the smart bracelet worn by the user by analyzing the fluctuation of the ACC data. In general, the screen of the smart bracelet can be: screen facing Top, screen down, and screen side situations.

For example, if the smart bracelet acceleration sensor 109 recognizes that the value of the Z axis is larger and greater than 0, and the value of the X/Y axis is close to 0, it can be considered that the screen of the smart bracelet is facing upwards; if the smart bracelet The acceleration sensor 109 recognizes that the value of the Z axis is less than 0, and the value of the X/Y axis is close to 0, it can be considered that the screen of the smart bracelet in this period of time is facing down; if the smart bracelet acceleration sensor 109 recognizes the value of the Y axis 0, and the value of the X/Z axis is close to 0, it can be considered that the smart bracelet screen is placed on the side of the time period.

In some embodiments of the present application, the direction of each axis of the acceleration sensor 109 in the smart bracelet and the position of the smart bracelet are not limited to the above situation. When the relative position changes, the above identification rules should also change accordingly. However, as long as the direction of each axis of the acceleration sensor 109 and the relative position of the smart bracelet screen are determined, the rules for identifying the screen direction are also determined, and will not be repeated here.

For example, if the monitoring quality of the sleep monitoring data obtained by the smart band 100 in different screen placement states is normal and the characteristics of the sleep monitoring data are consistent, the user is considered to be wearing it normally; if the smart band 100 is placed in a different screen placement state If the monitoring quality of the sleep monitoring data obtained under the following is abnormal, it can be considered that the user is wearing the smart bracelet too tightly; if the smart bracelet 100 is placed in a different screen state, the monitoring quality of the sleep monitoring data is normal or abnormal and the sleep monitoring According to the characteristics of the data, it can be considered that the user wears the smart bracelet too loosely. Among them, the consistent performance of the characteristics of the sleep monitoring data means that similar or the same characteristics of the sleep monitoring data can periodically appear in different states. For example, if the screen of the smart bracelet is facing up and the screen of the smart bracelet is facing down, the heart rate will have periodic peaks. Among them, the amplitude of the peaks in the two states is similar or the same, and the interval between the peaks is also similar or the same.

In some other embodiments of the present application, the smart bracelet 100 can also measure the position close to the human body in the smart bracelet screen, and add a pressure sensor 106 to determine the tightness of the smart bracelet 100 worn by the user. For example, the pressure sensor 106 is used to obtain the pressure value of the user's sleep time period on the smart bracelet. If the pressure value is always large, it can be considered that the user's smart bracelet is worn too tightly; if the pressure value is always moderate, It can be considered that the user's smart bracelet is worn to a moderate degree of tightness; if the pressure value is in a fluctuating state, it can be considered that the user's smart bracelet is worn too loosely. Specifically, the smart bracelet 100 can calculate the average value (or variance, etc.) of the data of the pressure sensor 106 during the normal wearing period of the user as the reference data, and then use the pressure sensor 106 obtained by the smart bracelet 100 during the sleep period of the user. The real-time pressure value of the user is compared with the reference data. If the ratio of the real-time pressure value of the pressure sensor 106 to the reference data during the user's sleep period is (0-0.4), it can be considered that the user's smart bracelet is worn too loosely; if the user is under pressure during the sleep period The ratio of the real-time pressure value of the sensor 106 to the reference data is (0.4-0.6), it can be considered that the user wears the smart bracelet moderately; if the user is sleeping, the ratio of the real-time pressure value of the pressure sensor 106 to the reference data is (0.6-1) It can be considered that the user wears the smart bracelet too tightly.

C. Determine whether the reason for the abnormal monitoring data is that the user's smart bracelet is pressed

In some embodiments, it may be further recognized whether the user's smart bracelet is pressed according to the wearing condition of the user's smart bracelet identified in step B. In a specific implementation, if the user's smart bracelet wears a moderate degree of tightness and the user's motion range is small, and the monitoring quality of the sleep monitoring data of the smart bracelet is still abnormal, it can be considered that the smart bracelet is pressed. For example, if the user wears the smart bracelet tightly during the sleep period, and the user's motion range is small, but the monitoring quality of the user's sleep monitoring data obtained during this period is still abnormal, then the user's smart hand is considered The ring was crushed. The specific method for judging the wearing degree of the smart bracelet and the monitoring quality of the sleep monitoring data of the smart bracelet is the same as the above-mentioned specific judging method, and will not be repeated here.

Through the judgment of the above steps, it is possible to detect the cause of abnormal monitoring quality of the sleep monitoring data of the user's smart bracelet, and then mark the abnormality of the sleep monitoring of the user in the corresponding time period.

Based on the determined cause of abnormal monitoring quality of sleep monitoring data, provide corresponding prompts to the user

As shown in Fig. 3b, after determining the reason for the abnormal monitoring quality of the sleep monitoring data, a prompt message for prompting the user to improve the monitoring quality of the sleep monitoring data of the smart bracelet is determined and prompted to the user. Wherein, the specific prompt may be performed through the smart bracelet 100 or through the mobile phone 200.

In an embodiment of the present application, the user is specifically prompted through the mobile phone 200, and the specific method is as follows:

The mobile phone 200 synchronizes data with the smart bracelet 100. When the mobile phone 200 recognizes that the reason for the abnormal monitoring quality of the sleep monitoring data of the smart bracelet is "the user has a large amplitude or high frequency", the mobile phone 200 uses the user interaction main interface 210 Recommend suggestions and services for stable sleep to the user. Specifically, as shown in Figure 4a, if the user is using a mobile phone, he can prompt the user to have a large amount of action at this time by directly popping up a dialog box, and push to the user "Sleep peacefully" Tips" 211, the user can operate by selecting "Exit" 212 or "Remind later" 213; if the user is still asleep or not using a mobile phone at this time, as shown in Figure 4b, you can The mobile phone lock screen interface 220 records and prompts, and displays "Sleep Tips" (specifically, some health exercise videos or sleeping posture science stickers, etc.) 211. The same user can also select "Display" 221 and "Delete" 222 to proceed. Related operations are convenient for the user to see relevant suggestions when using the mobile phone without disturbing the user's sleep or work. At the same time, the mobile phone 200 can also selectively set the time period for the smart bracelet 100 to obtain the user’s sleep monitoring data according to the user’s habit of wearing the smart bracelet 100. The way of instructions makes the smart bracelet 100 not obtain the user's sleep monitoring data during this time period, thereby improving the monitoring quality of the sleep monitoring data of the smart bracelet 100.

When it is identified that the reason for the abnormal monitoring quality of the sleep monitoring data of the smart bracelet is "the user's smart bracelet is worn too loosely or too tightly", the mobile phone 200 recommends suggestions and services for the user to sleep well through the user interaction interface 210. Specifically, Yes, as shown in Figure 5a, if the user is using a mobile phone, he can prompt the user that the smart bracelet is not worn properly in the form of a pop-up dialog box, and push the user a guide article or video to wear the device correctly, such as " “Guide to Wearing Smart Bracelet” (215), the user can select “Exit” (212) or “Remind later” (213) to operate; if the user is still sleeping or not using the phone at this time, As shown in Figure 5b, you can record and prompt on the lock screen of the mobile phone, and display "Guide to Wearing Smart Bracelet" (215) (for example, the manual for the smart bracelet or the video guide link for wearing the smart bracelet, etc.), the same Users can also choose "Display" (221) and "Delete" (222) to perform related operations, so that users can see relevant suggestions when using their mobile phones and will not disturb the user's sleep or work. At the same time, the mobile phone 200 can also selectively set the time period for the smart bracelet 100 to obtain the user’s sleep monitoring data according to the user’s habit of wearing the smart bracelet 100. By sending instructions, the smart bracelet 100 does not obtain the sleep monitoring data of the user during this time period, thereby improving the monitoring quality of the sleep monitoring data of the smart bracelet 100.

In the same way, when it is identified that the reason for the abnormal monitoring quality of the sleep monitoring data of the smart bracelet is "the smart bracelet is pressed", then the mobile phone 200 recommends suggestions and services for stable sleep to the user through the user interaction interface 210. The specific As shown in Figure 6a, if the user is using a mobile phone, the user can be prompted by a direct pop-up dialog box to remind the user that the smart bracelet is pressed at this time, and it is recommended that the user wear the device on the opposite hand of the main side lying position Or adjust the sleeping posture, such as displaying "Sleep Posture Guidance" (216) (for example, popular science stickers about healthy sleep, etc.), the user can select "Exit" (212) or "Remind later" (213) to operate ; If the user is still asleep or not using the phone at this time, as shown in Figure 6b, you can record on the lock screen interface of the phone to prompt and display "Sleep Posture Guidance" (216). The same user can also choose "Display" (221) and "Delete" (222) are used to perform related operations, so that the user can see related suggestions when using the mobile phone and will not disturb the user's sleep or work. At the same time, the mobile phone 200 can also selectively set the time period for the smart band 100 to obtain the user’s sleep monitoring data according to the user’s habit of wearing the smart band 100. For example, during the time period when the smart band is pressed, the mobile phone 200 can By sending instructions, the smart bracelet 100 does not obtain the sleep monitoring data of the user during this time period, thereby improving the monitoring quality of the sleep monitoring data of the smart bracelet 100.

In addition, as mentioned above, in some embodiments of the present application, the corresponding sleep monitoring data can also be monitored and analyzed on the side of the mobile phone 200, and then the mobile phone 200 directly causes the sleep monitoring data of the smart bracelet 100 to be determined. Monitor the causes of abnormal quality and push relevant suggestions to users, as shown in Figure 7:

700: The mobile phone 200 establishes a communication connection with the smart bracelet 100.

702: Send the user's historical sleep monitoring data and the sleep monitoring data acquired in the T time period. Among them, the historical sleep monitoring data is used to set related thresholds, and the T time period represents the current monitoring time period of the smart bracelet 100.

704: Determine the monitoring quality of the sleep monitoring data acquired in the T time period. The specific judgment method is the same as the judgment method on the side of the smart bracelet 100 described above, and will not be repeated here.

706: If it is determined that the monitoring quality of the sleep monitoring data of the smart bracelet is abnormal, further determine the cause of the abnormal monitoring quality of the sleep monitoring data of the smart bracelet. The specific judgment method is the same as the judgment method on the side of the smart bracelet 100 described above, and will not be repeated here.

708: According to the specific cause of the abnormal monitoring quality, push relevant prompt information to the user in a targeted manner. The specific push method is the same as the push method on the mobile phone 200 described above, and will not be repeated here.

FIG. 8 shows a structural block diagram of an electronic device 800 capable of realizing the functions of the electronic device 200 shown in FIG. 1 according to an embodiment of the present invention. Specifically, as shown in FIG. 8, the electronic device 800 may include a processor 810, an external memory interface 820, an internal memory 821, a universal serial bus (USB) interface 830, a charging management module 840, and a power management module 841 , Battery 842, antenna 1, antenna 2, mobile communication module 850, wireless communication module 860, audio module 870, speaker 870A, receiver 870B, microphone 870C, earphone jack 870D, sensor module 880, buttons 890, motor 898, indicator 892 , Camera 893, display screen 894, subscriber identification module (SIM) card interface 895, etc. The sensor module 880 can include pressure sensor 880A, gyroscope sensor 880B, air pressure sensor 880C, magnetic sensor 880D, acceleration sensor 880E, distance sensor 880F, proximity light sensor 880G, fingerprint sensor 880H, temperature sensor 880J, touch sensor 880K, ambient light Sensor 880L, bone conduction sensor 880M, etc.

It can be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 800. In other embodiments of the present application, the electronic device 800 may include more or fewer components than shown, or combine certain components, or split certain components, or arrange different components. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

The processor 810 may include one or more processing units. For example, the processor 810 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU), etc. Among them, the different processing units may be independent devices or integrated in one or more processors. For example, the processor 810 may calculate the credibility of the monitoring quality of the sleep monitoring data of the smart bracelet 100, and determine the monitoring quality of the sleep monitoring data of the smart bracelet 100, and determine the cause of the sleep monitoring data of the smart bracelet 100 Monitor the cause of abnormal quality.

The controller can generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching instructions and executing instructions.

A memory may also be provided in the processor 810 for storing instructions and data. In some embodiments, the memory in the processor 810 is a cache memory. The memory can store instructions or data that have just been used or recycled by the processor 810. If the processor 810 needs to use the instruction or data again, it can be directly called from the memory. Repeated accesses are avoided, the waiting time of the processor 810 is reduced, and the efficiency of the system is improved. At the same time, the processor 810 may also store the user's sleep monitoring data and historical sleep monitoring data sent by the wristband 100 received by the electronic device 200.

In some embodiments, the processor 810 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, and a universal asynchronous transmitter/receiver (universal asynchronous) interface. 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 (USB) interface, etc.

Micro USB interface, USB Type C interface, etc. The USB interface 830 can be used to connect a charger to charge the electronic device 800, and can also be used to transfer data between the electronic device 800 and peripheral devices. It can also be used to connect earphones and play audio through earphones. This 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 embodiment of the present invention is merely a schematic description, and does not constitute a structural limitation of the electronic device 800. In other embodiments of the present application, the electronic device 800 may also adopt different interface connection modes in the foregoing embodiments, or a combination of multiple interface connection modes.

The charging management module 840 is used to receive charging input from the charger. The power management module 848 is used to connect the battery 842, the charging management module 840 and the processor 880. The power management module 848 receives input from the battery 842 and/or the charge management module 840, and supplies power to the processor 880, the internal memory 821, the display screen 894, the camera 893, and the wireless communication module 860. The power management module 848 can also be used to monitor battery capacity, battery cycle times, battery health status (leakage, impedance) and other parameters. In some other embodiments, the power management module 841 may also be provided in the processor 880. In other embodiments, the power management module 841 and the charging management module 840 may also be provided in the same device.

The wireless communication function of the electronic device 800 can be implemented by the antenna 1, the antenna 2, the mobile communication module 850, the wireless communication module 860, the modem processor, and the baseband processor.

The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the electronic device 800 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example: Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna can be used in combination with a tuning switch.

The mobile communication module 850 may provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the electronic device 800. The wireless communication module 860 can provide applications on the electronic device 800 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), and global navigation satellites. System (global navigation satellite system, GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 860 may be one or more devices integrating at least one communication processing module. The wireless communication module 860 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 810. The wireless communication module 860 may also receive the signal to be sent from the processor 810, perform frequency modulation, amplify it, and convert it into electromagnetic wave radiation via the antenna 2.

In some embodiments, the electronic device 800 can communicate with the smart bracelet 100 through the mobile communication module 850 or the wireless communication module 860.

In some embodiments, the antenna 1 of the electronic device 800 is coupled with the mobile communication module 850, and the antenna 2 is coupled with the wireless communication module 860, so that the electronic device 800 can communicate with the network and other devices through wireless communication technology. The wireless communication technology 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 (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc. The 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 800 implements a display function through a GPU, a display screen 894, and an application processor. The GPU is a microprocessor for image processing, which connects the display 894 and the application processor. The GPU is used to perform mathematical and geometric calculations and is used for graphics rendering. The processor 810 may include one or more GPUs, which execute program instructions to generate or change display information.

The electronic device 800 can realize a shooting function through an ISP, a camera 893, a video codec, a GPU, a display screen 894, and an application processor. In some embodiments of the present application, the display screen 894 is used to realize human-computer interaction with the user.

The external memory interface 820 may be used to connect an external memory card, such as a Micro SD card, so as to expand the storage capacity of the electronic device 800. The external memory card communicates with the processor 810 through the external memory interface 820 to realize the data storage function. For example, save music, video and other files in an external memory card.

The internal memory 821 may be used to store computer executable program code, where the executable program code includes instructions. The internal memory 821 may include a storage program area and a storage data area. Among them, the storage program area can store an operating system, an application program (such as a sound playback function, an image playback function, etc.) required by at least one function, and the like. The data storage area can store data (such as audio data, phone book, etc.) created during the use of the electronic device 800. In addition, the internal memory 821 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), and the like. The processor 810 executes various functional applications and data processing of the electronic device 800 by running instructions stored in the internal memory 821 and/or instructions stored in a memory provided in the processor.

The electronic device 800 can implement audio functions through an audio module 870, a speaker 870A, a receiver 870B, a microphone 870C, a headphone interface 870D, and an application processor. For example, music playback, recording, etc.

The button 890 includes a power button, a volume button, and so on. The button 890 may be a mechanical button. It can also be a touch button. The electronic device 800 may receive key input, and generate key signal input related to user settings and function control of the electronic device 800.

The motor 891 can generate vibration prompts. The motor 891 can be used for incoming call vibration notification, and can also be used for touch vibration feedback. For example, touch operations applied to different applications (such as photographing, audio playback, etc.) can correspond to different vibration feedback effects. Acting on touch operations in different areas of the display screen 894, the motor 891 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminding, 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 892 can be an indicator light, which can be used to indicate the charging status, power change, and can also be used to indicate messages, missed calls, notifications, and so on.

The SIM card interface 895 is used to connect to the SIM card.

Referring now to FIG. 9, the software system of the electronic device 800 may adopt a layered architecture, an event-driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture. The embodiment of the present invention takes an Android system with a layered architecture as an example to exemplify the software structure of the terminal device. Fig. 9 is a software structure block diagram of a terminal device according to an embodiment of the present invention.

The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Communication between layers through software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.

The application layer can include a series of application packages.

As shown in Figure 9, the application package may include applications such as phone, camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.

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 9, the application framework layer can include a window manager, a content provider, a view system, a phone manager, a resource manager, and a notification manager.

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

The content provider is used to store and retrieve data and make these data accessible to applications. The data may include videos, images, audios, phone calls made and received, browsing history and bookmarks, phone book, etc.

The view system includes visual controls, such as controls that display text, controls that display pictures, and so on. The view system can be used to build applications. The display interface can be composed of one or more views. For example, a display interface that includes a short message notification icon may include a view that displays text and a view that displays pictures.

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

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

The notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and it can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify download completion, message reminders, and so on. The notification manager can also be a notification that appears in the status bar at the top of the system in the form of a chart or a scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, prompt text messages in the status bar, sound a prompt tone, terminal equipment vibration, flashing indicator lights, etc.

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

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

The application layer and application framework layer run in a virtual machine. 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 life cycle management, stack management, thread management, security and exception management, and garbage collection.

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

The surface manager is used to manage the display subsystem and provides a combination 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, synthesis, and layer processing.

The 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 driver, camera driver, audio driver, and sensor driver.

Reference to "one embodiment" or "an embodiment" in the specification means that a specific feature, structure, or characteristic described in conjunction with the embodiment is included in at least one exemplary embodiment or technique according to the present disclosure. The appearances of the phrase "in one embodiment" in various places in the specification do not necessarily all refer to the same embodiment.

The processes and displays presented herein do not inherently involve any specific computers or other devices. Various general-purpose systems may also be used with programs according to the teachings herein, or it may prove convenient to construct more specialized devices to perform one or more method steps. The structure used for a variety of these systems is discussed in the following description. In addition, any specific programming language sufficient to implement the techniques and embodiments of the present disclosure may be used. Various programming languages can be used to implement the present disclosure, as discussed herein.

In addition, the language used in this specification has been primarily selected for readability and instructional purposes and may not be selected to describe or limit the disclosed subject matter. Therefore, this disclosure is intended to illustrate rather than limit the scope of the concepts discussed herein.

Claims

A method for monitoring the use of wearable electronic equipment, which is characterized in that it comprises:

Acquiring sleep monitoring data of the wearable electronic device during the user's sleep;

In the case that there is sleep monitoring data with abnormal monitoring quality in the sleep monitoring data, determine the reason for the existence of the sleep monitoring data with abnormal monitoring quality;

Based on the reason, the prompt information used to prompt the user to improve the monitoring quality of the wearable electronic device during the user's sleep is determined.
The method of claim 1, wherein the reason for the sleep monitoring data with abnormal monitoring quality includes at least one of the following:

The tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirements;

The motion amplitude or frequency of the user wearing the wearable electronic device during sleep affects the monitoring quality;

The wearable electronic device may be pressed during the user's sleep.
3. The method according to claim 2, wherein, based on the reason, determining the prompt information used to prompt the user to improve the monitoring quality of the wearable electronic device during the user's sleep comprises at least one of the following:

In the case where it is determined that the reason is that the tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirements, it is determined that the prompt information for improving the monitoring quality is the prompt information about correctly wearing the wearable electronic device ；

In the case where it is determined that the reason is that the user's motion amplitude or motion frequency when wearing the wearable electronic device during sleep affects the monitoring quality, determining that the prompt information for improving the monitoring quality is the prompt information about stable sleep;

In the case where it is determined that the cause is that the wearable electronic device is held down during the user's sleep, it is determined that the prompt information used to improve the monitoring quality is the suggested prompt information about the wearing part.
The method according to claim 2 or 3, wherein the reason for determining that there is sleep monitoring data with abnormal monitoring quality comprises:

In the case where it is determined that there are both normal and abnormal conditions in the monitoring quality of sleep monitoring data of the screen of the wearable electronic device worn by the user in different orientations, it is determined that the reason is that the user wears the wearable electronic device during sleep. The tightness of the wearable electronic device is the tightness that meets the measurement requirements;

When it is determined that the monitoring quality of the sleep monitoring data on the screen of the wearable electronic device worn by the user in different orientations is only abnormal, it is determined that the reason is that the user wears the wearable electronic device during sleep. The tightness of the equipment is tighter than the tightness that meets the measurement requirements.
The method according to claim 2 or 3, wherein the wearable electronic device comprises a pressure sensor and a display screen, and the pressure sensor is located inside the display screen of the wearable electronic device; and

The reasons for determining that there is sleep monitoring data with abnormal monitoring quality include:

According to the pressure monitoring data of the pressure sensor, it is determined whether the cause of the sleep monitoring data with abnormal monitoring quality is that the tightness of the wearable electronic device worn by the user during sleep does not meet the measurement requirement.
The method of claim 2 or 3, wherein the wearable electronic device includes an acceleration sensor, and

The reasons for determining that there is sleep monitoring data with abnormal monitoring quality include:

According to the fluctuation interval and the fluctuation amplitude of the measurement data of the acceleration sensor in the acquired sleep monitoring data, it is determined whether the cause is the motion amplitude or the motion frequency when the user wears the wearable electronic device during sleep. Monitor the quality.
The method according to claim 2 or 3, wherein the reason for determining that there is sleep monitoring data with abnormal monitoring quality comprises:

When it is determined that the tightness of the wearable electronic device worn by the user during sleep meets the measurement requirements and the motion amplitude or frequency of the user wearing the wearable electronic device during sleep does not affect the monitoring quality, it is determined The reason is that the wearable electronic device is pressed during the user's sleep.
The method according to any one of claims 1 to 7, further comprising:

According to the fluctuation situation of the photoplethysmography sensor data and/or the fluctuation situation of the measurement data of the acceleration sensor in the acquired sleep monitoring data, it is determined whether there is sleep monitoring data with abnormal monitoring quality in the sleep monitoring data.
The method of claim 1, further comprising:

The determined prompt information for prompting the user to improve the monitoring quality of the wearable electronic device during the user's sleep is displayed.
8. The method of claim 1, wherein said acquiring sleep monitoring data of the wearable electronic device during the user's sleep comprises:

Receiving the sleep monitoring data from the wearable electronic device.
The method of claim 1, further comprising:

The determined prompt information used to prompt the user to improve the monitoring quality of the wearable electronic device during the user's sleep is sent out.
A method for monitoring the use of wearable electronic equipment, which is characterized in that it comprises:

Sending, by the wearable electronic device, sleep monitoring data of the wearable electronic device during the user's sleep, wherein there is sleep monitoring data with abnormal monitoring quality in the sleep monitoring data;

The wearable electronic device receives prompt information for prompting the user to improve the monitoring quality of the wearable electronic device during the user's sleep.
The method according to claim 12, wherein the wearable electronic device receives prompt information for prompting the user to improve the monitoring quality of the wearable electronic device during the user's sleep period, comprising at least one of the following:

Reminder information about wearing the wearable electronic device correctly;

Reminders about restful sleep;

Suggested information about the wearing part.
A readable medium for an electronic device, wherein an instruction is stored on the readable medium, and when the instruction is executed on an electronic device, the electronic device executes the method according to any one of claims 1 to 13 .
An electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the processors of the electronic device, for executing any one of claims 1 to 13 The method described in the item.
An electronic device, including: a memory, a processor, a photoplethysmography sensor, and an acceleration sensor;

The photoplethysmography sensor is used to obtain the photoplethysmography sensor data of the user of the electronic device during sleep;

The acceleration sensor is used to obtain the acceleration sensor monitoring data of the user of the electronic device during sleep;

The memory is used to store instructions executed by one or more of the processors of the electronic device;

The processor is one of the processors of the electronic device, and is configured to execute the method according to any one of claims 1 to 13.
An electronic device, including: a memory, a processor, a display screen, a photoplethysmography sensor, a pressure sensor, and an acceleration sensor;

The photoplethysmography sensor is used to obtain the photoplethysmography data of the user of the electronic device during sleep;

The acceleration sensor is used to obtain the acceleration sensor monitoring data of the user of the electronic device during sleep;

The display screen is used to display prompt information for prompting the user to improve the monitoring quality of the wearable electronic device during the user's sleep;

The pressure sensor is located on the inner side of the display screen, and is used to detect the tightness of the electronic device worn by the user;

The memory is used to store instructions executed by one or more of the processors of the electronic device;

The processor is one of the processors of the electronic device, and is configured to execute the method according to any one of claims 1 to 13.