Method for detecting sleep problems and electronic device

Abstract

The present application provides a method for detecting sleep problems and an electronic device. The electronic device can detect physiological data of a user, and determine sleep parameters of the user according to these physiological data. These sleep parameters can be used for determining an insomnia degree and an insomnia type of the user. The electronic device can also provide the insomnia degree, the insomnia type, and a sleep condition improvement suggestion for the insomnia type to the user, thereby assisting the user in solving the sleep problems. The method by which the electronic device determines the insomnia degree and / or the insomnia type can be dynamically adjusted according to the actual sleep condition of the user, so that the detection results of the sleep problems are more accurate and reliable, and the use experience of the user is better.

Description

Methods and electronic devices for detecting sleep problems

[0001] This application claims priority to Chinese Patent Application No. 202411204318.1, filed on August 29, 2024, entitled "An Evaluation Method and Apparatus", the entire contents of which are incorporated herein by reference.

[0002] This application claims priority to Chinese Patent Application No. 202411629453.0, filed on November 14, 2024, entitled "Method and Electronic Device for Detecting Sleep Problems", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of terminal device software, and more specifically, to a method and electronic device for detecting sleep problems. Background Technology

[0004] Sleep problems, especially insomnia, can lead to daytime fatigue and poor concentration. Long-term insomnia can affect mood stability, increase the risk of anxiety and depression, weaken the immune system, and raise the incidence of chronic diseases such as cardiovascular disease, severely impacting quality of life. Accurately identifying sleep problems as early as possible can significantly reduce the likelihood of these issues occurring, thus protecting health.

[0005] Improving the accuracy and reliability of electronic devices in detecting sleep problems is a question worth considering. Summary of the Invention

[0006] This application provides a method and electronic device for detecting sleep problems. The electronic device can determine the degree and / or type of insomnia of a user based on the user's sleep parameters. The sleep parameters can reflect the user's actual sleep situation. Therefore, the detection results of sleep problems output by the electronic device are more accurate and more reliable.

[0007] In a first aspect, a method for detecting sleep problems is provided, applied to an electronic device. The method includes: acquiring physiological data of a user; determining sleep parameters based on the physiological data; and displaying a first interface, the first interface including the degree of insomnia and / or the type of insomnia; wherein the degree of insomnia and the type of insomnia are determined based on the sleep parameters.

[0008] In one possible implementation, the electronic device can be a wearable device such as a watch, bracelet, or smart glasses, or it can be a portable device such as a mobile phone, tablet, or foldable electronic device.

[0009] In one possible implementation, the electronic device may include sensors for detecting the user's physiological data. Alternatively, the electronic device may receive the user's physiological data from other electronic devices with detection capabilities via network communication.

[0010] In one possible implementation, the user's physiological data may include physiological data during sleep and physiological data outside of sleep, and the electronic device may determine sleep parameters based on the user's physiological data during sleep.

[0011] In some contexts, the level of insomnia can be used to indicate whether a user is experiencing insomnia, and the severity of their sleep problems when they are experiencing insomnia. In other words, not having insomnia can also be understood as a form of insomnia.

[0012] In this technical solution, the electronic device can determine the user's degree and / or type of insomnia based on the user's sleep parameters. These sleep parameters are determined based on the user's physiological data and can reflect the user's actual sleep situation to a certain extent. The degree and / or type of insomnia determined by this method are more consistent with the user's actual sleep experience, and the detection results of sleep problems are highly accurate and reliable.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the degree and / or type of insomnia is determined based on the sleep parameters and the physiological data.

[0014] In one possible implementation, in determining the degree and / or type of insomnia, the sleep parameters are weighted by W1, and the physiological data are weighted by W2, with W1 being greater than W2.

[0015] In this technical solution, the electronic device can also combine the user's physiological data to determine the degree and type of insomnia. More input data helps to expand the evaluation dimensions of insomnia degree and type, and improves the accuracy and reliability of sleep problem detection results.

[0016] In conjunction with the first aspect, in some implementations of the first aspect, before displaying the first interface, the method further includes: determining the degree of insomnia if the physiological data belongs to a first range and / or the sleep parameter belongs to a second range; and / or determining the type of insomnia if the physiological data belongs to a third range and / or the sleep parameter belongs to a fourth range.

[0017] In one possible implementation, the first, second, third, and fourth ranges can be determined based on the user's actual sleep experience.

[0018] Here, the first range can be determined based on the upper limit and the lower limit of the first range. Similarly, the second range can be determined based on the upper limit and the lower limit of the second range, the third range can be determined based on the upper limit and the lower limit of the third range, and the fourth range can be determined based on the upper limit and the lower limit of the fourth range.

[0019] It should be noted that the values ​​for the first and second ranges may differ for different degrees of insomnia; and the values ​​for the third and fourth ranges may differ for different types of insomnia.

[0020] This technical solution specifically provides a method for determining the type and severity of insomnia based on the user's physiological data and / or sleep parameters. Utilizing the range of values ​​for physiological data and / or sleep parameters to differentiate between different degrees and types of insomnia facilitates a more refined classification of insomnia types and severity, and helps to more accurately determine the user's sleep problems.

[0021] In conjunction with the first aspect, in some implementations of the first aspect, the first range and the third range are determined based on historical data of the physiological data within a first preset time period; and / or, the second range and the fourth range are determined based on historical data of the sleep parameter within a second preset time period.

[0022] In one possible implementation, the first preset time period and the second preset time period can refer to the S days prior to determining the user's insomnia level and insomnia type, where S is a positive integer.

[0023] In this technical solution, the range of values ​​for physiological data and sleep parameters used to evaluate the degree and type of insomnia can be determined based on the user's historical sleep patterns. The range of parameter values ​​is more reasonable and more closely matches the user's individual sleep patterns, which helps to improve the reliability and accuracy of sleep problem detection results.

[0024] In conjunction with the first aspect, in some implementations of the first aspect, the physiological data includes a first parameter, or the sleep parameter includes a first parameter, and the method further includes: determining that the first parameter does not belong to a reference range, the reference range being determined based on historical data of the first parameter within a third preset time period; and, under preset conditions, adjusting one or more of the reference range, the first range, the second range, the third range, or the fourth range based on the first parameter.

[0025] In some scenarios, the reference range can be understood as the baseline of a user's physiological data, or as the baseline of a user's sleep parameters.

[0026] In this technical solution, the electronic device can dynamically adjust the baseline of physiological data, the baseline of sleep parameters, and the indicators used to evaluate the degree and type of insomnia based on whether the user's physiological data or sleep parameters deviate from the baseline. It can output relatively accurate detection results of sleep problems throughout the entire period of the user's use of the device, which is conducive to improving the user experience.

[0027] In conjunction with the first aspect, in some implementations of the first aspect, the preset condition includes: no planned event has occurred, and / or no sudden event has occurred, where both the planned event and the sudden event can cause a change in the first parameter.

[0028] In some scenarios, planned events can be understood as events that users have pre-arranged, while unexpected events can be understood as events that users have not pre-arranged.

[0029] In one possible implementation, if the first parameter is not within the reference range and a planned event and / or an unforeseen event occurs, the electronic device may ignore the first parameter, or in other words, the electronic device may not adjust any of the reference range, the first range, the second range, the third range, or the fourth range.

[0030] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: obtaining reference information used to determine the planned event.

[0031] In conjunction with the first aspect, in some implementations of the first aspect, the reference information includes one or more of the following: SMS information, calendar information, alarm clock information, memo information, to-do list information, note information, or subscription service information.

[0032] In one possible implementation, before obtaining the reference information, the method further includes: obtaining a first permission for reading the aforementioned reference information.

[0033] In this technical solution, electronic devices can determine a user's plans for future time through multiple methods. This helps eliminate interference from planned events in the evaluation of a user's sleep, thus improving the accuracy of sleep problem detection results. Furthermore, obtaining user authorization before accessing reference information enhances the security of personal data and improves the user experience.

[0034] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: displaying a second interface for requesting confirmation as to whether the planned event and / or the incident has occurred.

[0035] In this technical solution, the electronic device can determine planned events and / or unexpected events through interaction with the user, providing another way to determine the aforementioned events. This helps to eliminate the interference of planned events and unexpected events in the evaluation process of the user's sleep status and improves the accuracy of sleep problem detection results.

[0036] In some implementations of the first aspect, the method further includes: reading historical data of the physiological data, and / or reading historical data of the sleep parameter.

[0037] In one possible implementation, before reading historical data of physiological data or historical data of sleep parameters, the method further includes: obtaining a second permission for reading historical data of physiological data and / or historical data of sleep parameters within a preset time period.

[0038] Obtaining user authorization before accessing their personal historical data helps improve the security of user data and enhances the user experience.

[0039] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: receiving a first operation applied to a first interface; displaying a third interface, the third interface including statistical information about the sleep problem.

[0040] In one possible implementation, the aforementioned first interface can be a first-level interface, and the third interface can be a second-level interface of the first-level interface; or, the third interface and the first interface can be interfaces of the same level.

[0041] In one possible implementation, the statistical information on the aforementioned sleep problems may include the user's sleep problems over the past week, over the past month, etc.

[0042] In this technical solution, the electronic device can display statistical information on the user's long-term sleep problems, which helps the user to better understand the history and changes of their personal sleep problems, deepens the user's understanding of their personal sleep problems, reminds the user to seek early intervention and treatment, and improves the user experience.

[0043] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: receiving a second operation applied to the first interface; displaying a fourth interface including suggestions for improving the sleep problem, the suggestions corresponding to the type of insomnia.

[0044] In one possible implementation, the aforementioned first interface can be a first-level interface, and the fourth interface can be a second-level interface of the first-level interface; or, the fourth interface and the first interface can be interfaces of the same level.

[0045] In this technical solution, the electronic device can output suggestions for improving sleep problems based on the user's type of insomnia, which helps the user improve their sleep and enhances the user experience.

[0046] In conjunction with the first aspect, in some implementations of the first aspect, the degree of insomnia is used to indicate the severity of the sleep problem; and / or, the type of insomnia is used to indicate the symptoms corresponding to the type of insomnia and / or the cause of the type of insomnia.

[0047] In conjunction with the first aspect, in some implementations of the first aspect, the degree of insomnia includes any of the following: no insomnia, mild insomnia, moderate insomnia, or severe insomnia; and / or, the type of insomnia includes one or more of the following: psychological insomnia, behavioral insomnia, environmental insomnia, drug- or substance-induced insomnia, physiological insomnia, jet lag insomnia, difficulty falling asleep, difficulty maintaining sleep, early awakening insomnia, or poor sleep quality insomnia.

[0048] In conjunction with the first aspect, in some implementations of the first aspect, the degree and / or type of insomnia are further determined based on one or more of the following: noise in the sleep environment, temperature in the sleep environment, humidity in the sleep environment, light in the sleep environment, or the use of electronic devices in the sleep environment.

[0049] In this technical solution, the electronic device can also combine multiple sleep environment parameters to determine the user's degree and / or type of insomnia, which helps to more accurately determine the user's sleep problems.

[0050] For detailed explanations and descriptions of the beneficial effects of the following technical solutions, please refer to the relevant content in the first aspect; they will not be repeated hereafter.

[0051] Secondly, a device for detecting sleep problems is provided, including an acquisition module and a processing module. The acquisition module is used to: acquire physiological data of a user; the processing module is used to: determine sleep parameters based on the physiological data; and display a first interface, the first interface including the degree of insomnia and / or the type of insomnia; wherein the degree of insomnia and the type of insomnia are determined based on the sleep parameters.

[0052] In conjunction with the second aspect, in some implementations of the second aspect, the degree and / or type of insomnia are determined based on the sleep parameters and the physiological data.

[0053] In conjunction with the second aspect, in some implementations of the second aspect, before displaying the first interface, the processing module is specifically used to: determine the degree of insomnia if the physiological data belongs to a first range and / or the sleep parameter belongs to a second range; and / or determine the type of insomnia if the physiological data belongs to a third range and / or the sleep parameter belongs to a fourth range.

[0054] In conjunction with the second aspect, in some implementations of the second aspect, the first range and the third range are determined based on historical data of the physiological data within a first preset time period; and / or, the second range and the fourth range are determined based on historical data of the sleep parameter within a second preset time period.

[0055] In conjunction with the second aspect, in some implementations of the second aspect, the physiological data includes a first parameter, or the sleep parameter includes a first parameter, and the processing module is further configured to: determine that the first parameter does not belong to a reference range, the reference range being determined based on historical data of the first parameter within a third preset time period; and, under preset conditions, adjust one or more of the reference range, the first range, the second range, the third range, or the fourth range based on the first parameter.

[0056] In conjunction with the second aspect, in some implementations of the second aspect, the preset conditions include: no planned event has occurred, and / or no sudden event has occurred, where both the planned event and the sudden event can cause a change in the first parameter.

[0057] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is also used to: obtain reference information for determining the planned event.

[0058] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is also used to: display a second interface for requesting confirmation as to whether the planned event and / or the incident has occurred.

[0059] In some implementations of the second aspect, the acquisition module is also used to: read historical data of the physiological data, and / or read historical data of the sleep parameters.

[0060] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is also used to: receive a first operation applied to the first interface; and display a third interface, the third interface including statistical information about the sleep problem.

[0061] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is also configured to: receive a second operation applied to the first interface; and display a fourth interface including suggestions for improving the sleep problem, the suggestions corresponding to the type of insomnia.

[0062] In conjunction with the second aspect, in some implementations of the second aspect, the degree of insomnia is used to indicate the severity of the sleep problem; and / or, the type of insomnia is used to indicate the symptoms corresponding to the type of insomnia and / or the cause of the type of insomnia.

[0063] In conjunction with the second aspect, in some implementations of the second aspect, the degree of insomnia includes any of the following: no insomnia, mild insomnia, moderate insomnia, or severe insomnia; and / or, the type of insomnia includes one or more of the following: psychological insomnia, behavioral insomnia, environmental insomnia, drug- or substance-induced insomnia, physiological insomnia, jet lag insomnia, difficulty falling asleep, difficulty maintaining sleep, early awakening insomnia, or poor sleep quality insomnia.

[0064] In conjunction with the second aspect, in some implementations of the second aspect, the degree and / or type of insomnia are also determined based on one or more of the following: noise in the sleep environment, temperature in the sleep environment, humidity in the sleep environment, light in the sleep environment, or the use of electronic devices in the sleep environment.

[0065] Thirdly, an electronic device is provided, comprising a processor and a memory, wherein the memory stores computer program code, and the processor can be used to: acquire physiological data of a user; determine sleep parameters based on the physiological data; and display a first interface including a degree of insomnia and / or a type of insomnia; wherein the degree of insomnia and the type of insomnia are determined based on the sleep parameters.

[0066] In conjunction with the third aspect, in some implementations of the third aspect, the degree and / or type of insomnia are determined based on the sleep parameters and the physiological data.

[0067] In conjunction with the third aspect, in some implementations of the third aspect, before displaying the first interface, the processor is specifically configured to: determine the degree of insomnia if the physiological data falls within a first range and / or the sleep parameter falls within a second range; and / or determine the type of insomnia if the physiological data falls within a third range and / or the sleep parameter falls within a fourth range.

[0068] In conjunction with the third aspect, in some implementations of the third aspect, the first range and the third range are determined based on historical data of the physiological data within a first preset time period; and / or, the second range and the fourth range are determined based on historical data of the sleep parameter within a second preset time period.

[0069] In conjunction with the third aspect, in some implementations of the third aspect, the physiological data includes a first parameter, or the sleep parameter includes a first parameter, and the processor is further configured to: determine that the first parameter does not belong to a reference range, the reference range being determined based on historical data of the first parameter within a third preset time period; and, under preset conditions, adjust one or more of the reference range, the first range, the second range, the third range, or the fourth range based on the first parameter.

[0070] In conjunction with the third aspect, in some implementations of the third aspect, the preset conditions include: no planned event has occurred, and / or no sudden event has occurred, where both the planned event and the sudden event can cause a change in the first parameter.

[0071] In conjunction with the third aspect, in some implementations of the third aspect, the processor is also used to: acquire reference information for determining planned events.

[0072] In conjunction with the third aspect, in some implementations of the third aspect, the processor is also used to: display a second interface for requesting confirmation as to whether the planned event and / or the incident event have occurred.

[0073] In some implementations of the third aspect, the processor is also used to: read historical data of the physiological data, and / or read historical data of the sleep parameters.

[0074] In conjunction with the third aspect, in some implementations of the third aspect, the processor is also configured to: receive a first operation applied to the first interface; and display a third interface including statistical information about the sleep problem.

[0075] In conjunction with the third aspect, in some implementations of the third aspect, the processor is also configured to: receive a second operation acting on the first interface; and display a fourth interface including suggestions for improving the sleep problem, the suggestions corresponding to the type of insomnia.

[0076] In conjunction with the third aspect, in some implementations of the third aspect, the degree of insomnia is used to indicate the severity of the sleep problem; and / or, the type of insomnia is used to indicate the symptoms corresponding to the type of insomnia and / or the cause of the type of insomnia.

[0077] In conjunction with the third aspect, in some implementations of the third aspect, the degree of insomnia includes any of the following: no insomnia, mild insomnia, moderate insomnia, or severe insomnia; and / or, the type of insomnia includes one or more of the following: psychological insomnia, behavioral insomnia, environmental insomnia, drug- or substance-induced insomnia, physiological insomnia, jet lag insomnia, difficulty falling asleep, difficulty maintaining sleep, early awakening insomnia, or poor sleep quality insomnia.

[0078] In conjunction with the third aspect, in some implementations of the third aspect, the degree and / or type of insomnia are also determined based on one or more of the following: noise in the sleep environment, temperature in the sleep environment, humidity in the sleep environment, light in the sleep environment, or the use of electronic devices in the sleep environment.

[0079] Fourthly, a computer program product is provided, comprising computer program code that, when run on a computer, causes the methods in the first aspect and any possible implementation thereof to be executed.

[0080] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when run on a computer, causes the methods in the first aspect and any possible implementation thereof to be executed.

[0081] In a sixth aspect, a chip is provided, including a processor for reading instructions stored in a memory, wherein when the processor executes the instructions, the chip implements the methods of the first aspect and any possible implementation thereof. Attached Figure Description

[0082] Figure 1 is a schematic diagram of the hardware architecture of an electronic device provided in an embodiment of this application.

[0083] Figure 2 is a schematic diagram of the software architecture of an electronic device provided in an embodiment of this application.

[0084] Figures 3 to 6 are schematic diagrams of the detection results of a sleep problem provided in the embodiments of this application.

[0085] Figure 7 is a schematic diagram of the detection results of a sleep condition provided in an embodiment of this application.

[0086] Figures 8 to 11 are schematic diagrams of the detection results of different degrees of sleep problems provided in the embodiments of this application.

[0087] Figures 12 and 13 are schematic diagrams illustrating the details of the sleep problems provided in the embodiments of this application.

[0088] Figures 14 to 18 are schematic diagrams of the detection results of different types of sleep problems provided in the embodiments of this application.

[0089] Figure 19 is a schematic diagram of another detection result of sleep problems provided in an embodiment of this application.

[0090] Figures 20 to 23 are schematic diagrams of the user interface of the sleep problem detection method provided in the embodiments of this application.

[0091] Figure 24 is a schematic diagram of a method for detecting sleep problems provided in an embodiment of this application.

[0092] Figure 25 is a schematic diagram of another method for detecting sleep problems provided in an embodiment of this application.

[0093] Figure 26 is a schematic diagram of another method for detecting sleep problems provided in an embodiment of this application.

[0094] Figure 27 is a schematic diagram of another method for detecting sleep problems provided in an embodiment of this application.

[0095] Figure 28 is a schematic diagram of a sleep problem detection device provided in an embodiment of this application.

[0096] Figure 29 is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0097] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0098] The terminology used in the following embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one, two, or more than two. The term “and / or” is used to describe the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can indicate: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0099] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0100] The methods provided in this application can be applied to electronic devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This application does not impose any restrictions on the specific type of electronic device.

[0101] For example, Figure 1 shows a schematic diagram of the structure of an electronic device 100. The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an accelerometer sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0102] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0103] Processor 110 may include one or more processing units, such as: application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0104] The controller can be the nerve center and command center of the electronic device 100. The controller can generate operation control signals according to the instruction opcode and timing signals to complete the control of fetching and executing instructions.

[0105] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

[0106] In some embodiments, the processor 110 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0107] USB port 130 is a USB standard compliant interface, specifically a Mini USB port, Micro USB port, USB Type-C port, etc. USB port 130 can be used to connect a charger to charge electronic device 100, and can also be used for data transfer between electronic device 100 and peripheral devices. It can also be used to connect headphones for audio playback. This interface can also be used to connect other electronic devices, such as AR devices.

[0108] It is understood that the interface connection relationships between the modules illustrated in the embodiments of this application are merely illustrative and do not constitute a structural limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0109] The charging management module 140 receives charging input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 receives charging input from the wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 receives wireless charging input via the wireless charging coil of the electronic device 100. While charging the battery 142, the charging management module 140 can also supply power to the electronic device via the power management module 141.

[0110] The power management module 141 connects the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, providing power to the processor 110, internal memory 121, external memory, display screen 194, camera 193, and wireless communication module 160, etc. The power management module 141 can also monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some other embodiments, the power management module 141 may also be located within the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may be located in the same device.

[0111] The wireless communication function of electronic device 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor, etc.

[0112] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with tuning switches.

[0113] The mobile communication module 150 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc.

[0114] The wireless communication module 160 can provide solutions for wireless communication applications on the electronic device 100, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 110. The wireless communication module 160 can also receive signals to be transmitted from processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0115] Electronic device 100 implements display functions through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. Processor 110 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0116] Display screen 194 is used to display images, videos, etc. Display screen 194 includes a display panel. The display panel may be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a miniature LED, a microLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, electronic device 100 may include one or N displays 194, where N is a positive integer greater than 1.

[0117] Electronic device 100 can perform shooting functions through ISP, camera 193, video codec, GPU, display 194 and application processor.

[0118] The ISP (Image Signal Processor) is used to process data fed back from the camera 193. For example, when taking a picture, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, and the camera's photosensitive element transmits the electrical signal to the ISP for processing, transforming it into an image visible to the naked eye. The ISP can also perform algorithmic optimization of image noise, brightness, and skin tone. The ISP can also optimize parameters such as exposure and color temperature of the shooting scene. In some embodiments, the ISP can be set in the camera 193.

[0119] Camera 193 is used to capture still images or videos. An object is projected onto a photosensitive element by generating an optical image through the lens. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then passed to an ISP for conversion into a digital image signal. The ISP outputs the digital image signal to a DSP for processing. The DSP converts the digital image signal into image signals in standard RGB, YUV, or other formats. In some embodiments, the electronic device 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0120] Digital signal processors (DSPs) are used to process digital signals. Besides digital image signals, they can also process other digital signals. For example, when electronic device 100 selects a frequency, the DSP can perform Fourier transforms on the frequency energy.

[0121] Video codecs are used to compress or decompress digital video. Electronic device 100 may support one or more video codecs. Thus, electronic device 100 can play or record videos in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0122] An NPU (Neural Processing Unit) is a computational processor for neural networks (NNs). By borrowing the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, it can rapidly process input information and continuously learn on its own. NPUs enable intelligent cognitive applications in electronic devices, such as image recognition, facial recognition, speech recognition, and text understanding.

[0123] The external storage interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external storage interface 120 to perform data storage functions. For example, music, video, and other files can be saved on the external memory card.

[0124] Internal memory 121 can be used to store computer executable program code, which includes instructions. Processor 110 executes various functional applications and data processing of electronic device 100 by running the instructions stored in internal memory 121. Internal memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback, image playback, etc.), etc. The data storage area may store data created during the use of electronic device 100 (such as audio data, phonebook, etc.). Furthermore, internal memory 121 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0125] Electronic device 100 can implement audio functions, such as music playback and recording, through audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, and application processor.

[0126] The audio module 170 is used to convert digital audio information into analog audio signals for output, and also to convert analog audio input into digital audio signals. The audio module 170 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 170 may be located in the processor 110, or some functional modules of the audio module 170 may be located in the processor 110.

[0127] Buttons 190 include a power button, volume buttons, etc. Buttons 190 can be mechanical buttons or touch-sensitive buttons. Electronic device 100 can receive button input and generate key signal inputs related to user settings and function control of electronic device 100.

[0128] The software system of electronic device 100 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture. This application embodiment uses the layered architecture Android system as an example to exemplify the software structure of electronic device 100.

[0129] Figure 2 is a software structure block diagram of an electronic device 100 according to an embodiment of this application. The layered architecture divides the software into several layers, each with a clear role and function. Layers communicate with each other through software interfaces. 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 libraries, and the kernel layer. The application layer may include a series of application packages.

[0130] As shown in Figure 2, the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, and SMS.

[0131] The application framework layer provides application programming interfaces (APIs) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

[0132] As shown in Figure 2, the application framework layer may include a window manager, content provider, view system, phone manager, resource manager, notification manager, etc.

[0133] The window manager is used to manage windowed applications. It can retrieve screen size, determine the presence of a status bar, lock the screen, and capture screenshots, among other things.

[0134] Content providers store and retrieve data, making that data accessible to applications. This data may include videos, images, audio, made and received phone calls, browsing history and bookmarks, phone books, etc.

[0135] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text notification icon could include views for displaying text and views for displaying images.

[0136] The phone manager is used to provide communication functions for electronic device 100. For example, it manages call status (including connection and disconnection).

[0137] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, and more.

[0138] The notification manager allows applications to display notifications in the status bar. These notifications can be used to deliver informational messages and can disappear automatically after a short pause, requiring no user interaction. For example, the notification manager can be used to notify users of completed downloads or message alerts. The notification manager can also display notifications as icons or scrolling text in the top status bar, such as notifications from background applications, or as dialog boxes on the screen. Examples include displaying text messages in the status bar, emitting sounds, vibrating electronic devices, and flashing indicator lights.

[0139] The Android runtime consists of core libraries and a virtual machine. The Android runtime is responsible for scheduling and managing the Android system.

[0140] The core library consists of two parts: one part is the functionalities that need to be called by the Java language, and the other part is the Android core library.

[0141] The application layer and application framework layer run in a virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0142] System libraries can include multiple functional modules. For example: surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGL ES), 2D graphics engines (e.g., SGL), etc.

[0143] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0144] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0145] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0146] A 2D graphics engine is a graphics engine for 2D drawing.

[0147] The kernel layer is the layer between hardware and software. The kernel layer contains at least the display driver, camera driver, audio driver, and sensor driver.

[0148] It should be understood that the technical solutions in the embodiments of this application can be used in systems such as Android, iOS, and HarmonyOS.

[0149] To improve the accuracy and reliability of electronic devices in detecting sleep problems and help users improve their sleep, this application provides a method for detecting sleep problems, which will be described below in conjunction with the process of detecting sleep problems in users.

[0150] The sleep problem detection method provided in this application can be applied to wearable devices such as wristbands, watches, and smart glasses, as well as portable devices such as mobile phones and tablets. Alternatively, it can be applied to a device group or pair comprising multiple devices, including both portable and wearable devices. In other words, the relevant steps in the sleep problem detection method in the following examples can be executed individually by the aforementioned wearable or portable devices, or jointly by both. The following examples primarily use a watch 20 as the wearable device for illustration.

[0151] In some examples, watch 20 may be configured with a sleep detection function, which, when enabled, can detect potential sleep problems in the user. This sleep detection function may be enabled by default, or it may be activated in response to user input.

[0152] When the sleep detection function is enabled on the Watch 20, the Watch 20 can acquire the user's physiological data, environmental data, etc., and analyze some or all of this data to determine the user's possible sleep problems.

[0153] Figure 3 shows a schematic diagram of the interface 201 of the watch 20, which can be referred to as the interface for detecting sleep problems. In some examples, the interface 201 can be used to display the user's sleep profile (basic sleep information, hereinafter referred to as sleep profile information), and the interface 201 can also be used to display the user's possible sleep problems (hereinafter referred to as sleep problem information).

[0154] For example, interface 201 may include area 401, which may be used to display information about sleep profile.

[0155] As an example, this area 401 can display one or more of the user's "Nighttime Sleep Score," "Sleep Duration," or "Target Sleep Duration." For example, in Figure 3, the user's "Nighttime Sleep Score" could be 76 points, the user's "Sleep Duration" could be 6 hours and 10 minutes, and the user's "Target Sleep Duration" could be 8 hours.

[0156] For example, interface 201 may include area 402, which can be used to display information about sleep problems. As an example, the information about sleep problems may include one or more of the following: the type of sleep problem, the subtype of the sleep problem, or the severity of the sleep problem.

[0157] In some examples, area 402 may display the type of sleep problem the user may be experiencing, which may include one or more of the following: insomnia, sleep apnea, restless legs syndrome, bruxism, sleepwalking, REM sleep behavior disorder, or circadian rhythm disorder. For example, area 402 in interface 201 may display information indicating "insomnia," which may be text and / or graphics, etc., and this application is not limited in this regard.

[0158] In some examples, area 402 can display the severity of a user's potential sleep problems. Taking "insomnia" as an example, the severity of the aforementioned sleep problem can include any of the following: healthy (no insomnia), mild insomnia, moderate insomnia, or severe insomnia. For example, referring to the interface 202 shown in Figure 4, area 402 of interface 202 can display information indicating "mild insomnia," which can be text and / or graphics, etc., and this application is not limited in this regard. As another example, the aforementioned area 402 can also display any of the information such as "healthy," "moderate insomnia," or "severe insomnia."

[0159] In some examples, area 402 can display a subtype of a sleep problem that the user may have. Taking "insomnia" as an example, the aforementioned subtype of sleep problem can include one or more of the following: psychological insomnia, behavioral insomnia, environmental insomnia, physiological insomnia, jet lag insomnia, or insomnia caused by drugs or substances. For example, referring to interface 203 shown in Figure 5, area 402 of interface 203 can display information that the user may have "psychological insomnia," which can be text and / or graphics, etc., and this application does not limit this. As another example, area 402 can also display any one of the following: "behavioral insomnia," "physiological insomnia," or "jet lag insomnia."

[0160] In some examples, area 402 may display one or more of the aforementioned sleep problems; in other words, area 402 may display the type of sleep problem the user may have, the severity of the sleep problem the user may have, or multiple subtypes of the sleep problem the user may have. For example, referring to interface 204 shown in Figure 6, area 402 of interface 204 may display information that the user may have "mild insomnia" and "environmental insomnia".

[0161] As one possible implementation, the sleep overview information displayed in area 401 and the sleep problem information displayed in area 402 can be displayed on different interfaces. For example, the sleep overview information can serve as a primary interface for the detection results of the user's sleep problems, and the sleep problem information can serve as a secondary or tertiary interface, etc., of this primary interface; this application does not impose any limitations on this. In the following examples, the sleep overview information and sleep problem information are primarily displayed on the same interface in the multiple interfaces shown in Figures 3 to 6.

[0162] In some examples, in response to a user's operation A on any of the aforementioned interfaces 201 to 204, watch 20 may display detailed information about the user's sleep profile (hereinafter referred to as sub-information of the sleep profile), which may include: the duration of different sleep stages during sleep and / or the scores of different sub-score items that make up the sleep score.

[0163] For example, different sleep stages during sleep may include: deep sleep, light sleep, REM sleep, and wakefulness. For instance, referring to interface 205 shown in Figure 7, during nighttime sleep, the duration of the deep sleep stage is 1 hour and 46 minutes, the duration of the light sleep stage is 3 hours and 17 minutes, the duration of the REM sleep stage is 1 hour and 7 minutes, and the duration of the wakefulness stage is 0 minutes.

[0164] For example, the scores of the different sub-scores that make up the sleep score may include one or more of the following: deep sleep duration score, deep sleep continuity score, deep sleep percentage score, or number of nighttime awakenings score. For example, referring to interface 206 shown in Figure 8, the sleep duration score can be 44 points, and the deep sleep continuity score can be 32 points.

[0165] As an example, operation A above can be a swipe operation, a tap operation, a gesture operation, etc. A swipe operation can include swiping up, swiping down, swiping left, swiping right, etc. A tap operation can include a single tap, a double tap, or a long press, etc. A gesture operation can include pinching two fingers together, spreading two fingers apart, swiping with three fingers, or clenching and releasing a fist, etc. If the watch 20 includes a crown, buttons, etc., operation A above can also be rotating the crown, tapping a button, etc.

[0166] In some examples, in response to a user's action B on any of the aforementioned interfaces 201 to 204, watch 20 can display detailed information about the user's sleep problems, which can be used to explain or illustrate that the user may have sleep problems.

[0167] For example, in response to user action B on the aforementioned interface 201, watch 20 can display interface 207 as shown in Figure 9. This interface 207 can display detailed information about the sleep problem "insomnia" in interface 201. For example, interface 207 can display the degree of insomnia as "mild insomnia"; interface 207 can also display the type of insomnia as "environmental insomnia"; interface 207 can also display suggestions for improving sleep problems corresponding to the aforementioned type of insomnia, such as "eliminating noise interference before bed".

[0168] For example, in response to user operation B on the aforementioned interface 202, watch 20 can display interface 208 as shown in Figure 10. Interface 208 can display detailed information about "mild insomnia" from interface 202. For instance, interface 208 may include the duration and / or time period of insomnia experienced by the user during the day's sleep. As a possible approach, watch 20 can display the correspondence between different sleep stages and time periods during sleep, such as graph G1. Graph G1 may include the start and end times of the user's awake state, and / or the duration of awake state. Alternatively, graph G1 may include the start and end times of the user's insomnia state, and / or the duration of insomnia. Exemplarily, graph G1 may also include the user's fall asleep time, wake-up time, start and end times of deep sleep, start and end times of light sleep, or start and end times of REM sleep, etc.

[0169] Similarly, when the user's insomnia level is displayed as "healthy" in area 402 of interface 202, in response to the user's operation B on interface 202, watch 20 can display interface 209 as shown in Figure 11. This interface 209 can be used to show that the user's sleep status for the day is good and there is no insomnia. As a possible alternative, watch 20 can also display a graphic similar to graphic G1 mentioned above, which can be used to indicate the correspondence between different sleep stages and time during sleep.

[0170] Similarly, when the user's insomnia level is displayed as "moderate insomnia" in area 402 of interface 202, in response to the user's operation B on interface 202, watch 20 can display interface 210 as shown in Figure 12. This interface 210 is similar to the aforementioned interface 208, and can include the duration and / or time period of the user's insomnia during the day's sleep. As a possible approach, interface 210 can include a graphic G2 similar to graphic G1 above, which can be used to indicate the correspondence between different sleep stages and time during sleep. For example, graphic G2 can include the start and end times of the user's awake state, and / or the duration of the awake state. Graphic G2 can also include information such as the user's fall asleep time, wake-up time, start and end times of deep sleep, start and end times of light sleep, or start and end times of REM sleep.

[0171] Similarly, when the user's insomnia level is displayed as "severe insomnia" in area 402 of interface 202, in response to the user's operation B on interface 202, watch 20 can display interface 211 as shown in Figure 13. This interface 211 is similar to the aforementioned interface 208, and can include the length of time and / or time period during which the user was insomniad during the day's sleep. As a possible approach, interface 211 can include a graphic G3 similar to graphic G1 above, which can be used to indicate the correspondence between different sleep stages and time during sleep. For example, graphic G3 can include the start and end times of the user's awake state, and / or the duration of the awake state. Graphic G3 can also include information such as the user's fall asleep time, wake-up time, start and end times of deep sleep, start and end times of light sleep, or start and end times of REM sleep.

[0172] As an example, operation B above can be a swipe operation, a tap operation, or a gesture operation. Swipe operations can include swiping up, swiping down, swiping left, swiping right, etc. Tap operations can include single tap, double tap, or long press, etc. Gesture operations can include pinching two fingers together, spreading two fingers, swiping with three fingers, or clenching and releasing a fist, etc. If the watch 20 includes a crown or other button structure, operation B above can also be rotating the crown, tapping a button, etc.

[0173] As a possible approach, the above operations A and B can be different operations. Taking interface 201 as an example, operation A can be the operation of swiping interface 201 upwards, and operation B can be the operation of clicking area 402; taking interface 202 as an example, operation A can be the operation of swiping interface 202 to the right, and operation B can be the operation of clicking the crown.

[0174] If the watch detects that the user has sleep problems such as mild, moderate or severe insomnia, the watch 20 can display information about the type of insomnia the user has.

[0175] In some examples, the insomnia type information may include the insomnia type to which the user belongs. For instance, the insomnia type information may include the insomnia type corresponding to the user's degree of insomnia; in other words, the insomnia type information may correspond to the user's degree of insomnia.

[0176] As an example, referring to Figures 10 and 14, in response to an operation applied to interface 206, watch 20 can display interface 212 as shown in Figure 14. This interface 212 can display information about the type of insomnia corresponding to "mild insomnia" in interface 208. For example, the information about the type of insomnia may include the text information "environmental insomnia".

[0177] As an example, referring to Figures 12 and 15, in response to an operation applied to interface 210, watch 20 can display interface 213 as shown in Figure 15. This interface 213 can display information about the type of insomnia corresponding to "moderate insomnia" in interface 210. For example, the information about the type of insomnia may include the text information "psychogenic insomnia".

[0178] As an example, referring to Figures 13 and 16, in response to an operation applied to interface 211, watch 20 can display interface 214 as shown in Figure 16. This interface 214 can display information about the type of insomnia corresponding to "severe insomnia" in interface 211. For example, the information about the type of insomnia may include the text information "substance-induced insomnia".

[0179] In some examples, the information on the above-mentioned insomnia type may also include explanatory information on the insomnia type, which can be used to explain the meaning of the insomnia type, and / or, the explanatory information on the insomnia type can be used to explain the causes that may lead to the insomnia type.

[0180] Referring to Figure 14, for the case of insomnia type "environmental insomnia," the explanatory information for the above-mentioned insomnia type can be text information similar to "I experienced mild insomnia last night. There was environmental noise during the period of insomnia. It is recommended to eliminate noise interference before going to bed." Here, "environmental noise during the period of insomnia" corresponds to "environmental insomnia," or in other words, "environmental noise during the period of insomnia" can be used to explain the meaning and / or cause of "environmental insomnia."

[0181] As a result, the watch 20 can detect noise in the sleep environment during the user's sleep. If the ambient noise detected during sleep exceeds the loudness threshold, and if the user's sleep parameters are detected to be abnormal, such as waking up multiple times during a period when the ambient noise exceeds the loudness threshold, the watch 20 can determine that the user's insomnia may be related to the ambient noise during sleep and determine the user's insomnia type as "environmental insomnia".

[0182] Referring to Figure 15, for moderate insomnia, if the insomnia type is "psychogenic insomnia," the explanation for this type of insomnia could be a text message similar to, "You've experienced multiple bouts of insomnia this week, which is related to your psychological stress. We recommend seeking medical attention promptly to improve your sleep." Here, "This is related to your psychological stress" corresponds to "psychogenic insomnia," or in other words, "This is related to your psychological stress" can be used to explain the meaning and / or cause of "psychogenic insomnia."

[0183] As a means of implementation, the watch 20 can detect the user's emotional stress before falling asleep. If it is determined that the user experiences emotional stress exceeding the stress threshold before falling asleep on each day of insomnia, the watch 20 can determine that the user's insomnia may be related to psychological stress and identify the user's insomnia type as "psychological insomnia".

[0184] Referring to Figure 16, for severe insomnia, if the insomnia type is "substance-induced insomnia," the explanation for this type of insomnia could be a text message similar to "You have been suffering from insomnia for a long time, and the insomnia is severe. This may be related to your habit of drinking tea before bed. We recommend that you seek medical attention immediately." Here, "This may be related to your habit of drinking tea before bed" corresponds to "substance-induced insomnia," or in other words, "This may be related to your habit of drinking tea before bed" can be used to explain the meaning and / or cause of "substance-induced insomnia."

[0185] As a means of implementation, the watch 20 can obtain information about the user's dietary habits (e.g., by having the user fill out a questionnaire related to insomnia). If it is determined that the user has a habit of drinking tea before bed, the watch 20 can determine that the user's long-term insomnia may be related to the aforementioned dietary habits and determine that the user's insomnia type is "substance-induced insomnia".

[0186] Generally, users with moderate or severe insomnia may experience multiple episodes of insomnia over a period of time. To help users better understand their individual insomnia situation, the watch 20 can also display statistics on insomnia over consecutive time periods (e.g., 7 consecutive days, 30 consecutive days, etc.). In other words, in some examples, the sleep problem detection results can also include statistical information about sleep problems, which can be used to indicate the frequency of sleep problems experienced by the user over a period of time.

[0187] As an example, referring to Figures 12 and 17, in response to an operation applied to interface 210, watch 20 can display interface 215 as shown in Figure 17. This interface 215 can be used to display the user's insomnia over a consecutive 7 days. For example, the user experienced insomnia on 4 days—September 19, September 21, September 22, and September 24—during the 7 consecutive days from September 18 to September 24.

[0188] As an implementation, to help users understand their sleep patterns, the labels used to indicate dates with insomnia and dates without insomnia in interface 215 can be different. For example, in interface 215, if a user did not experience insomnia on September 18th but did on September 19th, the background of the cell for September 18th could be white, while the background of the cell for September 19th could be yellow. Similarly, to help users better understand each instance of insomnia, the labels used to indicate insomnia can differ based on the duration and severity of insomnia over the four days of insomnia. For example, if a user's insomnia lasted 1 hour on September 19th and 3 hours on September 22nd, the cell representing the calendar date on September 19th could be light yellow, and the cell representing the calendar date on September 24th could be yellow.

[0189] As an example, referring to Figures 13 and 18, in response to an operation applied to interface 211, watch 20 can display interface 216 as shown in Figure 18. This interface 216 can be used to display the user's insomnia status during the current month. For example, the user experienced insomnia for more than 20 days in October.

[0190] As an implementation, to help users understand their sleep patterns, the labels for dates with insomnia and dates without insomnia in interface 216 can be different. For example, in interface 216, if a user did not experience insomnia on October 2nd but did on October 9th, the background of the cell for October 2nd could be white, and the background of the cell for October 9th could be yellow. Similarly, to help users better understand each instance of insomnia, the labels used to indicate insomnia on the dates in October can differ based on the duration and severity of the insomnia. For example, if a user's insomnia lasted 1.5 hours on October 10th and 4.2 hours on October 15th, the cell representing October 10th in interface 216 could be light yellow, and the cell representing October 15th could be dark yellow.

[0191] The above example, using the user interface of watch 20, illustrates the detection results of the user's sleep problems by watch 20. In some examples, watch 20 can also send the sleep problem detection results to other electronic devices (such as mobile phones). After receiving the sleep problem detection results, the electronic device can display interface 217 as shown in Figure 19.

[0192] In some examples, interface 217 can be used to display the detection results of sleep problems.

[0193] For example, the detection results of the sleep problem may include information about the user's sleep profile, and may also include information about potential sleep problems the user may have.

[0194] As an example, sleep profile information may include one or more of the following: "Nighttime Sleep Score," "Sleep Duration," or "Target Sleep Duration." For a more detailed explanation of sleep profile information, please refer to the description above.

[0195] As an example, the information about the sleep problem may include one or more of the following: the type of sleep problem, the subtype of the sleep problem, or the severity of the sleep problem.

[0196] The types of sleep problems can include one or more of the following: insomnia, sleep apnea, restless legs syndrome, bruxism, sleepwalking, REM sleep behavior disorder, or circadian rhythm disorder; the severity of sleep problems can include any of the following: healthy (no insomnia), mild insomnia, moderate insomnia, or severe insomnia; the subtypes of sleep problems can include one or more of the following: psychological insomnia, behavioral insomnia, environmental insomnia, physiological insomnia, jet lag insomnia, or drug- or substance-induced insomnia.

[0197] For detailed information on sleep problems, please refer to the description above; it will not be repeated here.

[0198] The content displayed on interface 217 is roughly similar to the content displayed on the interfaces shown in Figures 3 to 18 above. For details, please refer to the content above.

[0199] In the example above, the electronic device (watch 20) can determine potential sleep problems by acquiring and analyzing the user's physiological and / or environmental data. To more accurately and reliably determine potential sleep problems, the electronic device can acquire additional information related to the user's sleep patterns beyond what has been described above, and analyze the user's sleep based on this information. The following examples will continue to focus on watch 20.

[0200] In some examples, Watch 20 can combine a user's historical sleep data to determine the severity and type of sleep problems the user may have.

[0201] As an example, a user's historical sleep parameters and historical physiological data can be used to determine the user's historical sleep patterns. In order to utilize this data, the watch 20 needs to obtain permission to save this data and permission to read this data.

[0202] For example, watch 20 can display interface 218 as shown in Figure 20. This interface 218 can be called a permission request interface, which can be used to obtain permission to save and read the aforementioned historical sleep parameters and historical physiological data. For example, this interface 218 can display a prompt message similar to "Allow the device to save and read historical sleep data?". This interface 218 may include controls 301 and 302. In response to the user's selection of control 301, watch 20 can obtain the aforementioned permission; in response to the user's selection of control 302, watch 20 cannot obtain the aforementioned permission.

[0203] With the aforementioned permissions granted, watch 20 can save and read historical sleep parameters and / or historical physiological data. Based on this data, it can determine information such as reference ranges for sleep parameters, and thus determine potential sleep problems of the user based on these reference ranges and measured values ​​of sleep parameters. Without the aforementioned permissions granted, watch 20 can determine potential sleep problems of the user based on measured values ​​of sleep parameters. The methods used by electronic devices to determine the severity and type of potential sleep problems of a user will be explained in detail below and will not be elaborated upon here.

[0204] In some examples, the watch 20 can combine the user's lifestyle to determine the severity and type of sleep problems the user may have.

[0205] For example, a user's schedule and / or calendar may include planned events that may occur in the future. Therefore, information such as schedules and / or calendars indicates to some extent how a user plans their future time, and these planned events or arrangements may affect the assessment process of the user's sleep problems.

[0206] As a possible approach, Watch 20 can determine a user's schedule and / or calendar arrangements by accessing one or more of the following reference information: SMS messages, calendar information, alarm clock messages, memos, to-do lists, notes, or subscription service information. Alternatively, the aforementioned reference information can be used to determine planned events that may occur in the future. To access this reference information, Watch 20 needs permission to read it.

[0207] For example, watch 20 can display interface 219 as shown in Figure 21. This interface 219 can be called a permission request interface, which can be used to obtain one or more permissions to read the aforementioned reference information. For example, interface 219 can display a prompt message similar to "Allow the device to access calendar, SMS, and alarm clock information?". Interface 219 may include controls 303 and 304. In response to the user selecting control 303, watch 20 can obtain the aforementioned permissions; in response to the user selecting control 304, watch 20 cannot obtain the aforementioned permissions.

[0208] When watch 20 has the aforementioned permissions, it can read one or more of the aforementioned reference information and determine whether the user's lifestyle has changed, or in other words, whether a planned event affecting sleep parameters has occurred, and combine this information with the measured values ​​of the sleep parameters to determine potential sleep problems. When the aforementioned permissions are not available, watch 20 can determine potential sleep problems based on the measured values ​​of the sleep parameters. The methods by which electronic devices determine the severity and type of potential sleep problems based on the aforementioned information will be explained in detail below and will not be elaborated upon here.

[0209] In some examples, in the event of a change in the user's lifestyle, or in the event of a planned event that may affect sleep parameters, watch 20 may display interface 220 as shown in Figure 22. In response to an operation performed on interface 220, watch 20 may determine whether the aforementioned planned event has occurred.

[0210] For example, the interface 220 may include information indicating a change in the user's sleep patterns. The interface 220 may also include information about a planned event, which may include the reason for the change in the user's sleep patterns. In response to a confirmation operation on the information on the interface 220, the watch 20 may determine that a planned event has occurred; in response to a cancellation operation on the information on the interface 220, the watch 20 may determine that no planned event has occurred.

[0211] For example, interface 220 can display a prompt message such as "Do you need to take a flight to City A this morning?" Interface 220 can also include controls 305 and 306. In response to the user's selection of control 305, watch 20 can determine that the user needs to take a flight to City A that day; in response to the user's selection of control 306, watch 20 can determine that the user does not need to take a flight to City A that day.

[0212] Similar to the planned events mentioned above, some unexpected events may also affect a user's sleep patterns, thereby affecting the results of sleep problem detection.

[0213] Here, "unexpected events" refers to events that are different from the planned events mentioned above, events that are not arranged in advance by the user, or in other words, events that the user cannot predict in advance.

[0214] For example, an unexpected event may affect a user's sleep environment, thereby affecting their sleep. For instance, an unexpected event may affect factors such as temperature, humidity, light, and noise in the user's sleep environment.

[0215] For example, an emergency could be construction work at a site near a user's residence during their sleep, which could cause significant noise and disrupt their sleep.

[0216] For example, a sudden event could be a heavy rainstorm during a user's sleep, which could cause fluctuations in temperature and humidity in the user's sleep environment, thus affecting the user's sleep.

[0217] In some examples, for unexpected events, watch 20 can also display an interface similar to 220 described above. Watch 20 can determine whether an unexpected event has occurred based on the user's response to this interface 220. For example, interface 220 may display "Were you awakened by the rainstorm last night?" In response to the user's confirmation, watch 20 can determine that the user's sleep was affected by the unexpected event; in response to the user's cancellation, watch 20 can determine that the user's sleep was not affected by the unexpected event.

[0218] One possibility is that the user made multiple planned events on the same day. In this case, the interface 220 can be used to determine whether each of the multiple planned events has occurred.

[0219] One possibility is that multiple emergencies can occur during sleep. In this case, the interface 220 can be used to determine whether each of the multiple emergencies has occurred.

[0220] One possibility is that a change in a user's sleep pattern may be affected by both planned events and unexpected events. In this case, the aforementioned interface 220 can be used to determine whether the planned events and unexpected events have occurred separately.

[0221] In other words, the interface 220 described above can be used to determine whether one or more emergencies have occurred, and / or, the interface 220 described above can be used to determine whether one or more planned events have occurred.

[0222] In some examples, watch 20 may include interface 221 as shown in Figure 23, which can uniformly manage the authorization process of interfaces 218, 219 and similar interfaces mentioned above.

[0223] For example, interface 221 may include a function switch 501, which can be used to determine whether to grant the watch 20 the permissions shown in interfaces 218 and 219 above. In response to the user turning on the function switch 501, the watch 20 may be granted the permissions shown in interfaces 218 and 219 above; in response to the user turning off the function switch 501, the watch 20 may be deprived of the permissions shown in interfaces 218 and 219 above.

[0224] With the user granting the watch 20 the aforementioned permissions, the watch 20 can more accurately detect potential sleep problems. In this scenario, the function switch 501 in interface 221 can be referred to as the "precise detection" function switch 501. When the "precise detection" function switch 501 is enabled, the watch 20 can obtain more information related to the user's sleep patterns, thereby more accurately identifying potential sleep problems.

[0225] Because the watch 20 stores and retrieves data more frequently when the aforementioned function switch 501 is on, the watch 20 needs to process a larger amount of data and consumes more power during sleep detection. As a possible solution, the aforementioned function switch 501 can be turned off by default, and the user can manually turn it on, or in other words, the user can manually enable the aforementioned "precise detection" function.

[0226] Figure 24 illustrates a method for detecting sleep problems provided in an embodiment of this application. The electronic device can determine the degree and type of insomnia based on the user's physiological data and / or sleep parameters.

[0227] S101, The electronic device acquires the user's physiological data.

[0228] For example, the physiological data mentioned above may include one or more of the following: heart rate, blood oxygen content, respiratory rate, body temperature, brain waves, or activity status.

[0229] In some examples, a user's physiological data may include physiological data during sleep.

[0230] One possibility is that the electronic device may include one or more sensors that can detect the aforementioned physiological data. In this case, the electronic device can obtain the user's physiological data by reading and processing the data detected by these sensors. For example, the electronic device may be a wearable device such as a wristband, watch, ring, or smart glasses.

[0231] For example, an electronic device may include a photoplethysmography (PPG) sensor, which can be used to detect a user's heart rate, blood oxygen saturation, maximum oxygen uptake or pressure level, respiratory rate, etc.

[0232] For example, an electronic device may include an accelerometer (ACC) which can be used to determine information such as whether a user is in motion and the type of motion.

[0233] For example, an electronic device may include a temperature sensor that can be used to measure a user's body temperature.

[0234] One possibility is that the electronic device can acquire the aforementioned physiological data from other electronic devices. Alternatively, the electronic device can acquire the user's physiological data through network communication. For example, the electronic device could be a mobile phone, tablet, or foldable electronic device, and it can acquire the aforementioned physiological data from devices such as wearable devices.

[0235] As a feasible approach, in order to improve the efficiency of determining physiological data, electronic devices can preprocess the data detected by the aforementioned sensors. For example, electronic devices can filter out data with a high signal-to-noise ratio and good signal quality for use in determining physiological data.

[0236] S102, the electronic device determines sleep parameters based on the user's physiological data.

[0237] In some examples, the sleep parameters mentioned above may include one or more of the following: sleep duration, sleep latency, wakefulness after falling asleep, number of wakefulness after falling asleep, deep sleep duration, deep sleep continuity, sleep efficiency, light sleep duration, REM sleep duration, total deep sleep duration, or average deep sleep segment duration, etc.

[0238] Among them, sleep duration can be understood as the total time a user is in a sleep state during sleep; sleep latency can be understood as the time required from when a user starts trying to fall asleep to when they actually enter a sleep state; wake-up time after falling asleep can be understood as the total duration of all awake (wake) states after a user enters a sleep state; and number of wake-up times after falling asleep can be understood as the total number of times a user enters a sleep state.

[0239] In some examples, a user's physiological data may include physiological data during sleep and physiological data outside of sleep, and electronic devices can determine sleep parameters based on the user's physiological data during sleep.

[0240] For example, electronic devices can determine a user's sleep onset time, sleep onset time, and total sleep duration based on the aforementioned physiological data and in conjunction with sleep detection algorithms.

[0241] For example, electronic devices can determine a user's bedtime and alighting time based on the aforementioned physiological data and in conjunction with a bed rest detection algorithm.

[0242] For example, electronic devices can determine information such as deep sleep duration, deep sleep ratio, wake-up time after falling asleep, and number of wake-ups after falling asleep based on the above physiological data and in combination with sleep stage detection algorithms.

[0243] For example, electronic devices can determine a user's sleep latency and sleep efficiency based on sleep onset time, sleep end time, total sleep duration, bedtime, and bedtime.

[0244] For example, a user's heart rate is typically lower during sleep than during non-sleep periods, and heart rate patterns differ across different sleep stages (light sleep, deep sleep, or REM sleep). By detecting changes in a user's heart rate, it is possible to determine the different sleep stages, the duration of each stage, and the total sleep duration.

[0245] For example, users typically exhibit less physical activity during sleep than during non-sleep periods. Detecting a user's physical activity level can determine whether the user is asleep or the duration of sleep. Generally, prolonged periods of physical stillness may indicate that the user is asleep. During sleep, if the user's physical activity level exceeds a preset threshold, the electronic device can determine that the user is awake during the night (e.g., experiencing insomnia).

[0246] For example, a user's breathing rate during sleep is different from that during non-sleep periods. By detecting a user's breathing rate, it can be determined whether the user is asleep or the duration of sleep.

[0247] For example, a user's body temperature (such as skin temperature) is usually lower at night than during the day, and a user's body temperature can also be used to determine whether a user is asleep or to determine the duration of sleep.

[0248] In some examples, electronic devices can also combine environmental data to determine one or more of the aforementioned sleep parameters.

[0249] For example, electronic devices can determine whether a user is asleep by detecting changes in brightness in the sleep environment, thereby helping to determine the duration of sleep. For instance, if the ambient brightness in the sleep environment is less than or equal to a brightness threshold, the electronic device can determine that the user may be asleep.

[0250] For example, electronic devices can determine whether a user is asleep by monitoring the power consumption changes of other electronic devices, thus helping to determine the duration of sleep. For instance, if the power consumption of all electronic devices in a room is close to their standby power consumption, the electronic devices can determine that the user may be asleep.

[0251] S103, the electronic device determines the range Ra and / or the range Rb.

[0252] The aforementioned range Ra can be used to determine the degree of a user's insomnia, or in other words, the range Ra can be used to determine whether a user has insomnia, and, if so, to determine the severity of the user's insomnia.

[0253] In some examples, the range Ra may include the range Ra1 and / or the range Ra2, wherein the range Ra1 may be used to indicate the range of values ​​for physiological data under a preset level of insomnia; and the range Ra2 may be used to indicate the range of values ​​for sleep parameters under a preset level of insomnia.

[0254] As an example, the range Ra can be a set A0, which can contain subsets A1 and A2. Subset A1 can include subsets A11, A12, A13, and A14, and subset A2 can include subsets A21, A22, A23, and A24.

[0255] A0 = {A1, A2};

[0256] A1={A11, A12, A13, A14};

[0257] A2={A21, A22, A23, A24}.

[0258] The aforementioned subset A1 can be used to represent the range Ra1, and subset A2 can be used to represent the range Ra2. Specifically, subset A11 corresponds to physiological data under normal sleep conditions, and subsets A12, A13, and A14 correspond to physiological data under mild, moderate, and severe insomnia conditions, respectively. Subset A21 corresponds to sleep parameters under normal sleep conditions, and subsets A22, A23, and A24 correspond to sleep parameters under mild, moderate, and severe insomnia conditions, respectively.

[0259] For example, for the case of "mild insomnia", the range Ra1 can be the range of values ​​for physiological data such as heart rate, emotional stress, and body temperature; the range Ra2 can be the range of values ​​for sleep parameters such as sleep duration, sleep onset time, and deep sleep duration.

[0260] For example, in cases of mild insomnia, the heart rate can range from V11 to V12, the emotional stress can range from V13 to V14, the sleep duration can range from V15 to V16, and the time to fall asleep can range from V17 to V18.

[0261] In one possible implementation, the range Ra1 can be determined based on the values ​​of physiological data from multiple users.

[0262] For example, the lower limit of the range Ra1 can be the minimum value of physiological data among M users, and the upper limit of the range Ra1 can be the maximum value of physiological data among M users, where M is a positive integer.

[0263] For example, among 10,000 users with moderate insomnia, the minimum body temperature measured during sleep can be V21 and the maximum body temperature can be V22. In this case, the lower limit of the above range Ra1 can be determined as V21 and the upper limit of the range Ra1 as V22.

[0264] For example, among 30,000 users with severe insomnia, the minimum heart rate measured during sleep can be V23 and the maximum heart rate can be V24. In this case, the lower limit of the above range Ra1 can be determined as V23 and the upper limit of the range Ra1 as V24.

[0265] For example, the lower limit of the range Ra1 can be: the average of the physiological data of N users minus the standard deviation of the physiological data of the N users, and the upper limit of the range Ra1 can be: the average of the physiological data of N users plus the standard deviation of the physiological data of the N users, where N is a positive integer.

[0266] For example, among 6,000 users with mild insomnia, the average blood oxygen content measured during sleep can be V25, and the standard deviation can be σ1. In this case, the lower limit of the range Ra1 is V25-σ1, and the upper limit of the range Ra1 is V25+σ1.

[0267] For example, among 100,000 users with normal sleep patterns, the average respiratory rate measured during sleep can be V26, and the standard deviation can be σ2. In this case, the lower limit of the range Ra1 is V26-σ2, and the upper limit of the range Ra1 is V26+σ2.

[0268] In one possible implementation, the range Ra1 can be determined based on the user's historical physiological data.

[0269] For example, the lower limit of the range Ra1 can be the minimum value of the measured physiological data of the user during sleep within a preset time period Tr1, and the upper limit of the range Ra1 can be the maximum value of the measured physiological data of the user within the preset time period Tr1.

[0270] For example, if the minimum body temperature measured during a user's sleep over seven consecutive days is V27 and the maximum body temperature is V28, then the lower limit of the range Ra1 can be determined as V27 and the upper limit of the range Ra1 as V28.

[0271] For example, if the minimum heart rate measured during a user's sleep over a continuous 30-day period can be V31 and the maximum heart rate can be V32, then the lower limit of the range Ra1 can be determined as V31 and the upper limit of the range Ra1 as V32.

[0272] For example, the lower limit of the range Ra1 can be the average value of the measured physiological data of the user within the preset time period Tr2 minus the standard deviation, and the upper limit of the range Ra1 can be the average value of the measured physiological data of the user within the preset time period Tr2 plus the standard deviation.

[0273] For example, if the average blood oxygen content measured during sleep over seven consecutive days can be V33 and the standard deviation can be σ3, then the lower limit of the range Ra1 is V33-σ3 and the upper limit of the range Ra1 is V11+σ3.

[0274] For example, if the average respiratory rate during sleep is measured over 30 consecutive days, the standard deviation can be V34, and the standard deviation can be σ4. In this case, the lower limit of the range Ra1 is V34-σ4, and the upper limit of the range Ra1 is V34+σ4.

[0275] Since different users have different physical conditions, the measured values ​​of physiological data of different users will vary greatly. Compared with determining the range Ra1 based on the physiological data of multiple users, this solution determines the range Ra1 by using the historical data of individual users' physiological data. This is beneficial to improving the efficiency of electronic devices in processing physiological data and improving the efficiency of electronic devices in determining the range Ra1.

[0276] In one possible implementation, the range Ra2 can be determined based on the values ​​of sleep parameters for multiple users.

[0277] For example, the lower limit of the range Ra2 can be the minimum value of the sleep parameter among M users, and the upper limit of the range Ra2 can be the maximum value of the sleep parameter among M users, where M is a positive integer.

[0278] For example, among 50,000 users with severe insomnia, the minimum and maximum deep sleep durations detected can be V35. In this case, the lower limit of the range Ra2 can be determined as V35 and the upper limit of the range Ra2 as V36.

[0279] For example, among 60,000 users with moderate insomnia, the minimum sleep duration detected could be V37 and the maximum sleep duration could be V38. In this case, the lower limit of the range Ra2 can be determined as V37 and the upper limit of the range Ra2 as V38.

[0280] For example, the lower limit of the range Ra2 can be: the average value of sleep parameters among N users minus the standard deviation of sleep parameters among the N users, and the upper limit of the range Ra2 can be: the average value of sleep parameters among N users plus the standard deviation of sleep parameters among the N users, where N is a positive integer.

[0281] For example, among 200,000 users with mild insomnia, the average sleep latency detected can be V41, and the standard deviation can be σ5. In this case, the lower limit of the range Ra2 is V41-σ5, and the upper limit of the range Ra2 is V41+σ5.

[0282] For example, among 100,000 users with moderate insomnia, the average duration of wakefulness after falling asleep can be V42, and the standard deviation can be σ6. In this case, the lower limit of the range Ra2 is V42-σ6, and the upper limit of the range Ra2 is V42+σ6.

[0283] In one possible implementation, the range Ra2 can be determined based on historical data of the user's sleep parameters.

[0284] For example, the lower limit of the range Ra2 can be the minimum value of the measured sleep parameter of the user within the preset time period Tr1, and the upper limit of the range Ra2 can be the maximum value of the measured sleep parameter of the user within the preset time period Tr1.

[0285] For example, if the minimum deep sleep duration detected over seven consecutive days is V43 and the maximum deep sleep duration is V44, then the lower limit of the range Ra2 can be determined as V43 and the upper limit of the range Ra2 as V44.

[0286] For example, if the minimum sleep duration detected over a consecutive 30-day period is V45 and the maximum sleep duration is V46, then the lower limit of the range Ra2 can be determined as V45 and the upper limit of the range Ra2 as V46.

[0287] For example, the lower limit of the range Ra2 can be the average value of the measured sleep parameters of the user within the preset time period Tr2 minus the standard deviation, and the upper limit of the range Ra2 can be the average value of the measured sleep parameters of the user within the preset time period Tr2 plus the standard deviation.

[0288] For example, if the average value of the sleep latency detected over seven consecutive days is V47 and the standard deviation is σ7, then the lower limit of the range Ra2 is V47-σ7 and the upper limit of the range Ra2 is V47+σ7.

[0289] For example, if the average duration of wakefulness after falling asleep is measured over a continuous 30-day period, the standard deviation can be V48, and the standard deviation can be σ8. In this case, the lower limit of the range Ra2 is V48-σ8, and the upper limit of the range Ra2 is V48+σ8.

[0290] Since different users have different sleep habits, the measured values ​​of sleep parameters will vary greatly. Compared with determining the range Ra2 based on the sleep parameters of multiple users, this solution determines the range Ra2 by using the historical data of the individual user's sleep parameters. This is beneficial to improving the efficiency of electronic devices in processing sleep parameters and determining the range Ra2.

[0291] In cases where a user is diagnosed with insomnia, the range Rb can be used to determine the type of insomnia the user has.

[0292] In some examples, the range Rb may include range Rb1 and / or range Rb2, wherein range Rb1 may be used to indicate the range of values ​​for physiological data under a preset insomnia type; and range Rb2 may be used to indicate the range of values ​​for sleep parameters under a preset insomnia type.

[0293] As an example, the scope Rb can be a set B0, which can contain subsets B1 and B2. Subset B1 can include subsets B11 and B12, and subset B2 can include subsets B21, B22, and B23, that is:

[0294] B0 = {B1, B2};

[0295] B1 = {B11, B12};

[0296] B2 = {B21, B22, B23}.

[0297] The aforementioned subset B1 can be used to represent the range Rb1, and subset B2 can be used to represent the range Rb2. Specifically, subset B11 corresponds to the physiological data under the first type of insomnia, and subset B12 corresponds to the physiological data under the second type of insomnia; subset B21 corresponds to the sleep parameters under the first type of insomnia, and subsets B22 and B23 correspond to the sleep parameters under the second and third types of insomnia, respectively.

[0298] For example, for insomnia with difficulty falling asleep, the range Rb1 can be the range of values ​​for physiological data such as heart rate and respiratory rate; the range Rb2 can be the range of values ​​for sleep parameters such as sleep latency and sleep efficiency.

[0299] For example, for insomnia with difficulty falling asleep, the heart rate can be in the range of V51 to V52, the respiratory rate can be in the range of V53 to V54, the sleep latency can be in the range of V55 to V56, and the sleep efficiency can be in the range of V57 to V58.

[0300] In one possible implementation, the range Rb1 can be determined based on the values ​​of physiological data from multiple users.

[0301] For example, the lower limit of the range Rb1 can be the minimum value of physiological data among M users, and the upper limit of the range Rb1 can be the maximum value of physiological data among M users, where M is a positive integer.

[0302] For example, the lower limit of the range Rb1 can be: the average of the physiological data of N users minus the standard deviation of the physiological data of the N users, and the upper limit of the range Rb1 can be: the average of the physiological data of N users plus the standard deviation of the physiological data of the N users, where N is a positive integer.

[0303] In one possible implementation, the range Rb1 can be determined based on the user's historical physiological data.

[0304] For example, the lower limit of the range Rb1 can be the minimum value of the measured physiological data of the user during sleep within a preset time period Tr1, and the upper limit of the range Rb1 can be the maximum value of the measured physiological data of the user within the preset time period Tr1.

[0305] For example, the minimum value of the range Rb1 can be the average value of the measured physiological data of the user within the preset time period Tr2 minus the standard deviation, and the maximum value of the range Rb1 can be the average value of the measured physiological data of the user within the preset time period Tr2 plus the standard deviation.

[0306] Because different users have different physical conditions, the measured values ​​of physiological data from different users will vary greatly. Compared with determining the range Rb1 based on the physiological data of multiple users, this solution determines the range Rb1 by using the historical data of individual users' physiological data. This is beneficial to improving the efficiency of electronic devices in processing physiological data and improving the efficiency of electronic devices in determining the range Rb1.

[0307] In one possible implementation, the range Rb2 can be determined based on the values ​​of sleep parameters for multiple users.

[0308] For example, the lower limit of the range Rb2 can be the minimum value of the sleep parameter among M users, and the upper limit of the range Rb2 can be the maximum value of the sleep parameter among M users, where M is a positive integer.

[0309] For example, the lower limit of the range Rb2 can be: the average value of sleep parameters among N users minus the standard deviation of sleep parameters among the N users, and the upper limit of the range Rb2 can be: the average value of sleep parameters among N users plus the standard deviation of sleep parameters among the N users, where N is a positive integer.

[0310] In one possible implementation, the range Rb2 can be determined based on historical data of the user's sleep parameters.

[0311] For example, the lower limit of the range Rb2 can be the minimum value of the measured sleep parameter of the user within the preset time period Tr1, and the upper limit of the range Rb2 can be the maximum value of the measured sleep parameter of the user within the preset time period Tr1.

[0312] For example, the lower limit of the range Rb2 can be the average value of the measured sleep parameters of the user within the preset time period Tr2 minus the standard deviation, and the upper limit of the range Rb2 can be the average value of the measured sleep parameters of the user within the preset time period Tr2 plus the standard deviation.

[0313] Since different users have different sleep habits, the measured values ​​of sleep parameters will vary greatly. Compared with determining the range Rb2 based on the sleep parameters of multiple users, this solution determines the range Rb2 by using the historical data of each user's sleep parameters. This is beneficial to improving the efficiency of electronic devices in processing sleep parameters and in determining the range Rb2.

[0314] The methods for determining the ranges Rb1 and Rb2 are similar to those for determining the ranges Ra1 and Ra2 mentioned earlier. For details, please refer to the relevant content on the methods for determining the ranges Ra1 and Ra2 mentioned earlier.

[0315] In some examples, the aforementioned ranges Ra and Rb can be dynamically adjusted based on the user's actual sleep patterns. This will be explained in detail later and will not be elaborated here.

[0316] In some examples, by detecting users' physiological data and establishing a connection between the physiological data and the users' actual sleep experience, it is possible to determine the values ​​of users' physiological data under different degrees of insomnia and under different types of insomnia.

[0317] Similarly, by detecting users' sleep parameters and establishing a relationship between these parameters and users' actual sleep experiences, it is possible to determine the values ​​of these sleep parameters under different levels of insomnia and under different types of insomnia.

[0318] As an implementation, a user's actual sleep experience can be determined through one or more of the following methods: Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Generalized Anxiety Disorder Scale (GAD-7), Fatigue Severity Scale (FSS), Polysomnography (PSG), Morning-Evening Questionnaire (MEQ), Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS), or Sleep Diary, etc.

[0319] In other words, based on the detection of the user's physiological data and sleep parameters, one or more of the above methods can determine the user's actual perceived level and type of insomnia. This allows for the determination of the correspondence between different ranges of physiological data values ​​and different levels of insomnia, different ranges of physiological data values ​​and different types of insomnia, different ranges of sleep parameter values ​​and different levels of insomnia, and different ranges of sleep parameter values ​​and different types of insomnia. Therefore, the range Ra for determining sleep level and the range Rb for determining insomnia type more closely reflect the user's actual sleep experience, resulting in more accurate sleep problem detection results from electronic devices.

[0320] S104, The electronic device determines the degree of insomnia based on sleep parameters and / or physiological data and the range Ra.

[0321] In some examples, the range Ra may include range Ra1 and range Ra2, where range Ra1 may correspond to physiological data and range Ra2 may correspond to sleep parameters.

[0322] In some examples, different insomnia levels correspond to different ranges Ra1 and / or Ra2. Based on this, electronic devices can determine the degree of insomnia according to the range of physiological data and / or the range of sleep parameters.

[0323] For example, an electronic device can determine the user's level of insomnia based on the user's sleep parameters and range Ra2.

[0324] Typically, in a state of insomnia, sleep parameters such as the length of sleep latency, the number of awakenings after falling asleep, the duration of awakenings after falling asleep, the time to wake up, and the duration or proportion of deep sleep will also be abnormal. These sleep parameters will also exhibit different changes depending on the severity of insomnia.

[0325] For example, in a state of insomnia, a user's sleep latency will be longer than in a normal sleep state. In other words, in a state of insomnia, the user's sleep latency will exceed a certain threshold. Put simply, electronic devices can determine the user's level of insomnia based on the range of sleep latency duration.

[0326] For example, compared to normal sleep, the variation in deep sleep duration is smaller in cases of mild insomnia and larger in cases of severe insomnia. Therefore, by detecting the duration of deep sleep during sleep, the severity of a user's insomnia can be roughly determined. In other words, electronic devices can determine the degree of a user's insomnia based on the range of deep sleep duration.

[0327] For example, an electronic device can determine the user's level of insomnia based on the user's physiological data and range Ra1.

[0328] Typically, during insomnia, physiological data such as heart rate, stress, and body temperature undergo significant changes, and physiological data such as activity levels also become abnormal. These physiological data will exhibit different changes depending on the severity of insomnia.

[0329] For example, during insomnia, a person's heart rate can be higher than during normal sleep. In other words, during insomnia, a user's heart rate can be greater than or equal to a heart rate threshold. Therefore, by detecting a user's heart rate during sleep, it's possible to roughly determine whether the user is experiencing insomnia or normal sleep.

[0330] For example, compared to normal sleep, the change in heart rate variability is smaller in users with mild insomnia and larger in users with severe insomnia. Therefore, by detecting heart rate variability during sleep, the severity of a user's insomnia can be roughly determined.

[0331] For example, an electronic device can determine the degree of a user's insomnia by combining the user's sleep parameters and range Ra2 with physiological data and range Ra1.

[0332] For example, electronic devices can determine a user's level of insomnia by combining the duration of the user's sleep latency with the range of the user's heart rate during sleep.

[0333] For example, electronic devices can determine a user's level of insomnia by combining the range of deep sleep duration with the range of heart rate variability during sleep.

[0334] For example, a sleep latency of 10 minutes or less is considered normal sleep (not insomnia); a sleep latency between 10 and 30 minutes is considered mild insomnia; a sleep latency between 10 and 60 minutes is considered moderate insomnia; and a sleep latency exceeding 60 minutes is considered severe insomnia.

[0335] For example, if you wake up 0 times after falling asleep, it is considered a normal sleep state; if you wake up 1 or 2 times after falling asleep, and each awakening is short (e.g., 5 to 15 minutes), it is considered mild insomnia; if you wake up 3 to 5 times after falling asleep, and each awakening is longer (e.g., 15 to 30 minutes), it is considered moderate insomnia; and if you wake up more than 5 times after falling asleep, and each awakening is very long (e.g., more than 30 minutes), it is considered severe insomnia.

[0336] For example, if sleep duration falls within the normal range, it is classified as normal sleep; if sleep duration is slightly less than the normal range, it is classified as mild insomnia; if sleep duration is significantly less than the normal range, it is classified as moderate insomnia; and if sleep duration is far less than the normal range, it is classified as severe insomnia. For instance, the normal sleep duration range is 7 to 9 hours. Sleep duration within this range is classified as normal sleep; 5 to 6 hours is classified as mild insomnia; 4 to 5 hours is classified as moderate insomnia; and less than 4 hours is classified as severe insomnia.

[0337] For example, a sleep efficiency (the ratio of actual sleep duration to bedtime) greater than 85% is defined as normal sleep; a sleep efficiency between 80% and 85% is defined as mild insomnia; a sleep efficiency between 70% and 80% is defined as moderate insomnia; and a sleep efficiency less than 70% is defined as severe insomnia.

[0338] In some examples, electronic devices can determine the degree of insomnia based on insomnia assessment models.

[0339] For example, the insomnia severity assessment model described above may include one or more of the following classification algorithms: decision tree, random forest, gradient boosting decision trees (GBDT), or extreme gradient boosting (XGBoost). By using physiological data and / or sleep parameters as input to the insomnia severity assessment model, the electronic device can determine which value range the physiological data and / or sleep parameters belong to using the aforementioned classification algorithms.

[0340] For example, the above-mentioned insomnia severity assessment model may include: a correspondence S0 between the range of values ​​for physiological data and / or the range of values ​​for sleep parameters and the severity of insomnia. For given physiological data and given sleep parameters, based on determining which range of values ​​the physiological data and / or the sleep parameters belong to, and in conjunction with the aforementioned correspondence S0, it is possible to determine whether the user suffers from insomnia and, if so, the severity of the insomnia problem.

[0341] For example, for "normal sleep state", "mild insomnia", "moderate insomnia" and "severe insomnia", the corresponding ranges of the sleep parameter Sa1 are Rg1, Rg2, Rg3 and Rg4, respectively. When the value of the sleep parameter Sa1 is z1, the insomnia severity assessment model can determine which of the above four ranges z1 belongs to, thereby determining the corresponding degree of insomnia.

[0342] It should be noted that there can be multiple sleep parameters, and each sleep parameter has a different value range corresponding to different degrees of insomnia. The algorithm evaluation model can combine multiple different sleep parameters to determine the degree of insomnia. The ranges Rg1, Rg2, Rg3, and Rg4 mentioned above can be regarded as examples of subsets A21, A22, A23, and A24 mentioned above, respectively.

[0343] Similarly, for "normal sleep state," "mild insomnia," "moderate insomnia," and "severe insomnia," the corresponding physiological data Sa2 values ​​range from Rg5 to Rg6, Rg7, and Rg8, respectively. When the sleep parameter Sa2 is z2, the insomnia severity assessment model can determine which of the four ranges z2 belongs to, thus determining the corresponding degree of insomnia.

[0344] It should be noted that there can be multiple physiological data points, and each type of physiological data point has a different value range corresponding to different degrees of insomnia. The insomnia severity assessment model can combine multiple different physiological data points to determine the degree of insomnia. The ranges Rg5, Rg6, Rg7, and Rg8 mentioned above can be regarded as examples of subsets A11, A12, A13, and A14 mentioned above, respectively.

[0345] Similarly, for "normal sleep state," "mild insomnia," "moderate insomnia," and "severe insomnia," the corresponding ranges for sleep parameter Sa3 and physiological data Sa4 are (ranges Rg11 and Rg21), (ranges Rg12 and Rg22), (ranges Rg13 and Rg23), and (ranges Rg14 and Rg24), respectively. When the sleep parameter Sa3 is z3 and the physiological data is z4, the insomnia severity assessment model can determine which of the four ranges (z3 and z4) each belongs to, thus determining the corresponding degree of insomnia.

[0346] In some examples, the insomnia severity assessment model may include the correspondence between the range of values ​​of physiological data and / or the range of values ​​of sleep parameters and whether or not insomnia exists (S1), and the insomnia severity assessment model may also include the correspondence between the range of values ​​of physiological data and / or the range of values ​​of sleep parameters and the degree of insomnia (S2).

[0347] For example, by using physiological data and / or sleep parameters as input to an insomnia severity assessment model, and combining binary classification algorithms such as logistic regression with the aforementioned correspondence S1, the electronic device can determine whether the user is in a state of insomnia. In the case of insomnia, by using one or more of the aforementioned classification algorithms and combining the aforementioned correspondence S2, the electronic device can determine the severity of the user's insomnia problem.

[0348] Figure 25 exemplarily illustrates the process of determining a user's insomnia level using the aforementioned insomnia type assessment model. The electronic device determines the user's sleep parameters based on their physiological data. The physiological data and / or sleep parameters can serve as input to the insomnia type assessment model, which can output the user's insomnia level.

[0349] In one possible implementation, the insomnia assessment model may contain fewer parameters, require less energy to run, or have lower data processing requirements for the device running the assessment model. In this case, the insomnia assessment model can run on a wearable electronic device such as a watch 20.

[0350] In one possible implementation, the insomnia assessment model may contain a large number of parameters, require high energy consumption to run, or have high data processing capabilities for the device running the assessment model. In this case, the insomnia assessment model may be run on other electronic devices connected to the watch 20. The watch 20 may be used to collect the user's physiological data and transmit this physiological data to the aforementioned electronic devices.

[0351] In one possible implementation, when determining the degree of insomnia, the weight of sleep parameters is W1, and the weight of physiological data is W2, with W1 being greater than W2. In other words, the electronic device can use sleep parameters as the primary reference data and physiological data as the secondary reference data when determining the degree of insomnia.

[0352] In some examples, the range Ra may also include a range Ra3, which may correspond to environmental parameters. The electronic device can also determine the user's level of insomnia based on the environmental parameters and the range Ra3.

[0353] For example, the environmental parameters mentioned above may include one or more of the following: temperature of the sleep environment, humidity of the sleep environment, noise of the sleep environment, light of the sleep environment, or use of electronic devices in the sleep environment.

[0354] One possibility is that environmental factors may affect the user's level of insomnia. In this case, electronic devices can also determine the user's level of insomnia by measuring the range of environmental parameters.

[0355] For example, if the duration of electronic device use before bed exceeds a certain threshold, and the time interval between lights out and falling asleep is greater than a certain time interval threshold, the user's insomnia may be more severe. Conversely, if the duration of electronic device use before bed is less than or equal to the threshold, and the time interval between lights out and falling asleep is less than or equal to the threshold, the user's insomnia may be milder.

[0356] For example, if the ambient noise level is higher than the loudness threshold, the ambient temperature is outside the reference temperature range, or the change in ambient temperature is greater than the fluctuation threshold during sleep, the user's insomnia may be more severe. Conversely, if the ambient noise level is lower than or equal to the loudness threshold, the ambient temperature is within the reference temperature range, and the change in ambient temperature is less than or equal to the fluctuation threshold during sleep, the user's insomnia may be milder.

[0357] In some examples, the electronic device can determine the user's degree of insomnia by combining sleep parameters and range Ra2 with environmental parameters and range Ra3; or, the electronic device can determine the user's degree of insomnia by combining physiological data and range Ra1 with environmental parameters and range Ra3; or, the electronic device can determine the user's degree of insomnia by combining sleep parameters and range Ra2, physiological data and range Ra1, and environmental parameters and range Ra3.

[0358] For example, electronic devices can determine a user's level of insomnia by combining the range of ambient noise during sleep with the range of the number of times the user wakes up after falling asleep.

[0359] For example, electronic devices can determine a user's level of insomnia by combining the range of ambient light during sleep with the range of blood oxygen levels after the user falls asleep.

[0360] For example, electronic devices can determine a user's level of insomnia by combining the range of ambient temperature during sleep, the range of wakefulness after the user falls asleep, and the user's heart rate during sleep.

[0361] S105, when it is determined that the user has insomnia, the electronic device determines the type of insomnia based on sleep parameters and / or physiological data and the range Rb.

[0362] In some examples, the range Rb may include range Rb1 and range Rb2, where range Rb1 may correspond to physiological data and range Rb2 may correspond to sleep parameters.

[0363] In some examples, different insomnia types correspond to different ranges Rb1 and / or Rb2. Based on this, electronic devices can determine the insomnia type according to the range of physiological data and / or the range of sleep parameters.

[0364] Sleep parameters can reflect a user's sleep patterns or insomnia symptoms to some extent. Based on these parameters, electronic devices determine the type of insomnia a user has, which can also be understood as determining the type of insomnia based on the user's insomnia symptoms.

[0365] For example, an electronic device can determine the type of insomnia based on sleep parameters and the range Rb2.

[0366] For example, if the sleep latency is greater than 30 minutes and the sleep efficiency is less than 80%, electronic devices can be used to identify insomnia with difficulty falling asleep.

[0367] For example, if a person wakes up twice or more after falling asleep, and the duration of awakening after falling asleep is greater than 30 minutes, electronic devices can identify the condition as sleep maintenance difficulty type insomnia or night awakening type insomnia.

[0368] For example, if someone wakes up 1-2 hours earlier than their normal bedtime and has difficulty falling back asleep, electronic devices can identify this as early awakening insomnia.

[0369] For example, if the continuity of deep sleep is less than a preset threshold and the proportion of deep sleep is less than 15%, electronic devices can identify it as insomnia due to decreased sleep quality.

[0370] For example, an electronic device can determine the type of insomnia based on physiological data and the range Rb1.

[0371] Physiological data can reflect the causes of a user's insomnia to some extent. Based on this, electronic devices determine the type of insomnia based on the physiological data, which can also be understood as determining the type of insomnia based on the cause of the user's insomnia.

[0372] For example, if pre-sleep emotional stress exceeds the stress threshold and heart rate variability shows an abnormal high-frequency to low-frequency ratio, it is usually due to insomnia caused by anxiety, stress, or other psychological factors. In this case, electronic devices can be identified as psychogenic insomnia.

[0373] For example, if a record shows the intake of substances such as caffeine, alcohol, or drugs before bedtime, and sleep quality is significantly reduced, the electronic device can be identified as causing insomnia due to the drug or substance.

[0374] For example, if the sleep apnea index is greater than the index threshold, the decrease in oxygen saturation during sleep is greater than the amplitude threshold, or the heart rate fluctuation is greater than the fluctuation threshold, it may be related to physiological conditions or diseases such as pain or sleep apnea syndrome. In this case, electronic devices can be identified as physiological insomnia.

[0375] For example, in cases of circadian rhythm disorders, where the time between falling asleep and an individual's biological clock cycle is greater than a threshold, or the time between falling out of sleep and an individual's biological clock cycle is greater than a threshold, it may be due to circadian rhythm disturbances caused by cross-time zone travel or shift work. This usually manifests as difficulty falling asleep or waking up early in the morning. In such cases, electronic devices can be used to identify jet lag insomnia.

[0376] In one possible implementation, during the process of determining the type of insomnia, the weight of sleep parameters is W1, and the weight of physiological data is W2, with W1 being greater than W2. In other words, electronic devices can use sleep parameters as primary reference data and physiological data as secondary reference data when determining the degree of insomnia.

[0377] For example, an electronic device can combine sleep parameters and range Rb2 with physiological data and range Ra1 to determine the user's type of insomnia.

[0378] For example, electronic devices can determine a user's type of insomnia based on the range of sleep latency duration and the user's heart rate during sleep.

[0379] For example, electronic devices can determine a user's type of insomnia based on the range of deep sleep continuity and the user's heart rate variability during sleep.

[0380] In some examples, the range Rb may also include a range Rb3, which may correspond to environmental parameters. The electronic device may also determine the user's insomnia type based on the environmental parameters and the range Rb3.

[0381] For example, the environmental parameters mentioned above may include one or more of the following: temperature of the sleep environment, humidity of the sleep environment, noise of the sleep environment, light of the sleep environment, or use of electronic devices in the sleep environment.

[0382] One possibility is that the user's insomnia may be caused by environmental factors. In this case, electronic devices can also determine the type of insomnia by measuring the range of environmental parameters.

[0383] For example, if the duration of electronic device use before bed exceeds the time threshold, and the time interval between lights out and falling asleep is greater than the time interval threshold, it may be related to bad sleep habits such as using electronic devices before bed, frequent late nights, or irregular sleep schedules. In this case, electronic device use can be identified as behavioral insomnia.

[0384] For example, if during sleep, the ambient noise level is higher than the loudness threshold, the ambient temperature is lower than the first temperature threshold or higher than the second temperature threshold, or the change in ambient temperature is greater than the fluctuation threshold, then electronic devices can be identified as causing environmental insomnia.

[0385] In some examples, the electronic device can determine the user's level of insomnia by combining sleep parameters and range Rb2 with environmental parameters and range Rb3; or, the electronic device can determine the user's level of insomnia by combining physiological data and range Rb1 with environmental parameters and range Rb3; or, the electronic device can determine the user's level of insomnia by combining sleep parameters and range Rb2, physiological data and range Rb1, and environmental parameters and range Rb3.

[0386] For example, an electronic device can determine a user's insomnia type by combining the range of ambient noise during sleep with the range of the number of times the user wakes up after falling asleep. For instance, if the ambient noise during sleep is less than a loudness threshold and the number of times the user wakes up after falling asleep is greater than a frequency threshold, the user can be identified as having difficulty maintaining sleep.

[0387] For example, an electronic device can determine a user's type of insomnia by combining the range of ambient light during sleep with the range of the user's heart rate after falling asleep.

[0388] For example, electronic devices can determine a user's insomnia type by combining the range of ambient temperature during sleep, the range of wakefulness after the user falls asleep, and the user's heart rate variability during sleep.

[0389] In some examples, electronic devices can simultaneously determine the degree and type of insomnia based on physiological data and / or sleep parameters. In other words, step S104 above and step S105 here can be performed in the same step.

[0390] In some examples, the results of sleep problem detection may also include suggestions for improving the user's sleep.

[0391] For example, for insomnia caused by difficulty falling asleep, the corresponding suggestions for improving sleep problems could be: engaging in relaxation exercises such as deep breathing and meditation to relieve anxiety and promote sleep. Alternatively, avoiding consuming stimulants such as caffeine and nicotine before bed, and avoiding strenuous exercise or stimulating activities.

[0392] For example, for insomnia with difficulty maintaining sleep or frequent awakenings at night, the corresponding suggestions for improving sleep problems could be: if you wake up for more than 20 minutes at night, get up and engage in quiet activities (such as reading) to avoid thinking too much in bed. Alternatively, avoid long naps during the day, drink less water at night, and reduce the number of times you wake up at night to use the toilet.

[0393] For example, for early morning awakening insomnia, the corresponding suggestions for improving sleep problems can be: avoid getting up immediately after waking up in the morning or engaging in activities too early, in order to reduce the reinforcing effect of early awakening on the biological clock.

[0394] For example, for insomnia caused by poor sleep quality, the corresponding suggestions for improving sleep problems can be: regular exercise (avoiding strenuous exercise within 3 hours before bedtime), maintaining a good sleep environment, and avoiding severe emotional fluctuations before bedtime to promote deep sleep.

[0395] For example, for psychogenic insomnia, the corresponding suggestions for improving sleep problems could be: if insomnia is related to psychological problems such as anxiety or depression, it is recommended to seek professional psychological counseling or cognitive behavioral therapy. Alternatively, regulate emotions, such as reducing stress and anxiety through meditation, mood diaries, and emotional catharsis.

[0396] For example, for behavioral insomnia, corresponding suggestions for improving sleep problems could be: avoid using electronic devices before bed, especially mobile phones and computers, to reduce the impact of blue light on sleep; or, establish good pre-sleep habits, such as only going to bed when sleepy. Alternatively, avoid engaging in activities unrelated to sleep in bed, such as watching TV or working.

[0397] For example, for environmental insomnia, corresponding suggestions for improving sleep problems could be: optimize the sleep environment by ensuring the bedroom is quiet, dark, and comfortable, and using tools such as blackout curtains, earplugs, and white noise machines to reduce environmental disturbances. Alternatively, choose suitable mattresses, pillows, and other bedding to ensure comfort.

[0398] For example, for insomnia caused by drugs or substances, the corresponding advice for improving sleep problems could be: avoid taking medications that affect sleep close to bedtime.

[0399] For example, for physiological insomnia, the corresponding advice for improving sleep problems could be: if insomnia is caused by physiological problems or diseases, it is recommended to treat the primary disease first.

[0400] For example, for jet lag-related insomnia, corresponding suggestions for improving sleep problems could include: adjusting sleep patterns by gradually adjusting sleep time to adapt to the new time zone before traveling, or gradually changing sleep patterns while working shifts. Alternatively, light therapy can be used to adjust circadian rhythms by exposing oneself to bright light at appropriate times to help regulate the biological clock.

[0401] Figure 26 illustrates a method for detecting sleep problems provided in an embodiment of this application. The ranges Ra and Rb for determining the degree and / or type of insomnia in a user can be adjusted based on the user's actual sleep patterns. Based on this, the electronic device can combine the adjusted ranges Ra and Rb to determine the degree and / or type of insomnia in the user, resulting in more accurate and reliable sleep problem detection results output by the electronic device.

[0402] S201, the electronic device determines the first parameter and range Rc.

[0403] In some examples, the first parameter can be the user's physiological data.

[0404] For example, the physiological data mentioned above may include one or more of the following: heart rate, blood oxygen content, respiratory rate, body temperature, brain waves, or activity status.

[0405] In some examples, the range Rc can be a range of values ​​corresponding to the aforementioned physiological data. For example, the range Rc can be the aforementioned range Ra1 or range Rb1. In other words, the range Rc can be used to determine the degree or type of insomnia.

[0406] For example, the range Rc can be used to determine the severity of a user's insomnia, such as mild insomnia, moderate insomnia, or severe insomnia.

[0407] For example, the range Rc can be used to determine the type of insomnia a user has, such as psychogenic insomnia, behavioral insomnia, or physiological insomnia.

[0408] In some examples, the range Rc can be the baseline of physiological data, or in other words, the range Rc can be used to indicate the user's physiological condition in the absence of external influences.

[0409] For example, the first parameter can be blood oxygen content, and the range Rc can be the range of measured blood oxygen content of user A during sleep over 7 consecutive days.

[0410] For example, the first parameter can be the respiratory rate, and the range Rc can be the range of measured respiratory rates of user B during sleep over a continuous 30-day period.

[0411] For example, the baseline of physiological data can reflect the overall situation of a user's physiological data over a period of time, or in other words, the baseline of physiological data can be determined based on the user's physiological data over a period of time.

[0412] It should be noted that the baseline of physiological data can reflect a user's lifestyle habits and living conditions. When a user's lifestyle habits change, the baseline of their physiological data may also change. In other words, the baseline of physiological data can be dynamic.

[0413] In one possible implementation, the range Rc can be determined based on the values ​​of the aforementioned physiological data of multiple users within the user group. Alternatively, the range Rc can be used to indicate the values ​​of the aforementioned physiological data of multiple users within the user group.

[0414] For example, the first parameter can be core body temperature, and the range Rc can be the range of core body temperature values ​​for multiple users in the user group during insomnia (e.g., 36.5℃ to 37.5℃).

[0415] For example, the first parameter can be emotional stress, and the range Rc can be the range of emotional stress values ​​for multiple users in the user group under the state of psychogenic insomnia.

[0416] For example, the first parameter can be heart rate, and the range Rc can be the range of heart rate values ​​for multiple users in the user group under mild, moderate, or severe insomnia.

[0417] In one possible implementation, the range Rc can be determined based on the user's historical physiological data. Alternatively, the range Rc can be used to indicate the historical detection status of the user's physiological data.

[0418] One feasible approach is to determine the range Rc based on the average and standard deviation of historical measured values ​​of physiological data over a period of time. Specifically, the lower limit of the range Rc can be the average of the physiological data over a preset time period minus the standard deviation of the physiological data over a preset time period, and the upper limit of the range Rc can be the average of the physiological data over a preset time period plus the standard deviation of the physiological data over a preset time period.

[0419] For example, the first parameter can be blood oxygen content. The average blood oxygen content measured by user A during sleep over 7 consecutive days is V61, and the standard deviation is δ1. Then, the minimum blood oxygen content in the range Rc can be V61-δ1, and the maximum blood oxygen content can be V61+δ1.

[0420] For example, the first parameter could be heart rate variability. User B experienced multiple episodes of psychogenic insomnia over a consecutive 30-day period. During these episodes of psychogenic insomnia, the mean heart rate variability of User B was measured to be V63, with a standard deviation of δ2. Therefore, the lower limit of the range Rc could be V63 - δ2, and the upper limit of the range Rc could be V63 + δ2.

[0421] In some examples, the first parameter can be a sleep parameter.

[0422] For example, the sleep parameter may include one or more of the following: sleep duration, sleep latency, wakefulness after falling asleep, number of wakefulness after falling asleep, deep sleep duration, deep sleep continuity, sleep efficiency, light sleep duration, REM sleep duration, total deep sleep duration or average deep sleep segment duration, etc.

[0423] In some examples, the range Rc can be a range of values ​​corresponding to the sleep parameter. For example, the range Rc can be the aforementioned range Ra2 or range Rb2. Alternatively, the range Rc can be used to determine the degree or type of insomnia.

[0424] For example, the range Rc can be used to determine the severity of a user's insomnia, such as mild insomnia, moderate insomnia, or severe insomnia.

[0425] For example, the range Rc can be used to determine the type of insomnia a user has, such as difficulty falling asleep, difficulty sleeping, or early awakening insomnia.

[0426] In some examples, the range Rc can be a baseline for sleep parameters, or in other words, the range Rc can be used to indicate a user's sleep patterns in the absence of external influences.

[0427] For example, the first parameter can be the proportion of deep sleep, and the range Rc can be the range of values ​​for the actual proportion of deep sleep of user A over 7 consecutive days.

[0428] For example, the first parameter can be sleep efficiency, and the range Rc can be the range of values ​​for user B's measured sleep efficiency over 30 consecutive days.

[0429] For example, the baseline of sleep parameters can reflect the overall situation of a user's sleep parameters over a period of time, or in other words, the baseline of sleep parameters can be determined based on the user's sleep parameters over a period of time.

[0430] It should be noted that the baseline of sleep parameters can reflect a user's lifestyle habits and living conditions. When a user's lifestyle habits change, the baseline of their sleep parameters may also change. In other words, the baseline of sleep parameters can be dynamic.

[0431] In one possible implementation, the range Rc can be determined based on the values ​​of the aforementioned sleep parameters for multiple users within the user group. Alternatively, the range Rc can be used to indicate the values ​​of the aforementioned sleep parameters for multiple users within the user group.

[0432] For example, the first parameter can be sleep duration, and the range Rc can be the range of sleep duration values ​​for multiple users in the user group (such as 6 hours to 9 hours).

[0433] For example, the first parameter can be the duration of deep sleep, and the range Rc can be the range of deep sleep duration for multiple users in the user group (e.g., 1 hour to 2 hours).

[0434] For example, the first parameter can be the time when you go to sleep, and the range Rc can be the range of the times when you go to sleep for multiple users in the user group (such as 5 a.m. to 7 a.m.).

[0435] In one possible implementation, the range Rc can be determined based on historical data of the user's sleep parameters. Alternatively, the range Rc can be used to indicate the historical detection status of the user's sleep parameters.

[0436] One feasible approach is for the electronic device to determine the range Rc based on the average and standard deviation of the historical measured values ​​of the sleep parameter. Specifically, the lower limit of the range Rc can be the average value of the sleep parameter over a preset time period minus the standard deviation of the sleep parameter over a preset time period, and the upper limit of the range Rc can be the average value of the sleep parameter over a preset time period plus the standard deviation of the sleep parameter over a preset time period.

[0437] For example, the first parameter can be the proportion of deep sleep. Through actual testing, the average proportion of deep sleep of user A over 7 consecutive days is V65, and the standard deviation is δ3. Then the lower limit of the range Rc can be V65-δ3, and the upper limit of the range Rc can be V65+δ3.

[0438] For example, the first parameter can be the duration of wakefulness after falling asleep. By statistically analyzing the duration of wakefulness after falling asleep for multiple days over a continuous 30-day period, we can obtain the following: the average duration of wakefulness after falling asleep is V67, and the standard deviation is δ4. Therefore, the lower limit of the range Rc can be V67-δ4, and the upper limit of the range Rc can be V67+δ4.

[0439] S202, Electronic equipment detection of occasional events.

[0440] Here, an occasional event can refer to an event that affects the measured value of the user's aforementioned first parameter, or in other words, an occasional event can include an event that may cause the measured value of the first parameter to change.

[0441] For example, an unplanned event can be an event that the user has arranged in advance, such as a planned event mentioned above. Alternatively, an unplanned event can be an event that the user cannot predict, such as a sudden event mentioned above.

[0442] One possibility is that an isolated incident could be the cause of the user's insomnia. In other words, an isolated incident could cause the user to go from not having insomnia to having insomnia.

[0443] For example, an occasional event could be environmental noise exceeding a loudness threshold during sleep, which might cause insomnia, and the corresponding type of insomnia might be environmental insomnia. Another occasional event could be a user's travel plans for the next morning, which might cause the user to go to bed too early, leading to early awakening insomnia.

[0444] One possibility is that the incidental event does not cause insomnia in the user. That is, while the measured value of the first parameter may change in the event of an incidental event, this change does not necessarily mean the user is experiencing insomnia. Conversely, the user may experience insomnia even without the incidental event.

[0445] In some examples, electronic devices can determine whether an incident has occurred based on information such as the user's travel plans and / or calendar arrangements.

[0446] As a possible approach, electronic devices can determine a user's schedule and / or calendar arrangements by obtaining one or more of the following reference information: SMS messages, calendar information, alarm clock information, memo information, to-do list information, notes information, or subscription service information, etc.

[0447] For example, text messages can contain ticket information for a user's travel, such as airplanes or trains, which electronic devices can use to determine the user's travel plans.

[0448] For example, calendar information, memo information, and to-do list information can contain a user's work plans for a future period of time, and electronic devices can use this information to determine the user's time arrangements for the future.

[0449] For example, notes or subscription service information may include push notification times from social media and news services, which electronic devices can use to determine when users might be disturbed by notifications.

[0450] For example, the electronic device may display the interface 217 mentioned above, and in response to the user's authorization confirmation operation, the electronic device may obtain the above information.

[0451] As a possible approach, based on the information above indicating the possibility of an unplanned event, the electronic device can confirm whether the event has occurred. Alternatively, the electronic device can determine whether an unplanned event has occurred based on the user's response.

[0452] For example, the electronic device can display the interface 220 mentioned above. In response to the user's confirmation operation, the electronic device can determine that an incidental event has occurred. Based on this, the change in the first parameter may be related to the aforementioned incidental event. In response to the user's cancellation operation on the interface 220, the electronic device can determine that the detected change in the first parameter is unrelated to the aforementioned incidental event.

[0453] As a feasible approach, electronic devices can communicate with detection devices to obtain information on whether an incidental event has occurred. For example, the electronic device could be the watch 20 mentioned earlier, and the detection device could be a mobile phone with a communication connection to the watch 20. The mobile phone can read the aforementioned reference information, determine whether an incidental event has occurred based on the reference information, and send the result to the watch 20.

[0454] In some examples, incidental events may cause the first parameter to deviate from the baseline of the user's physiological data or the baseline of sleep parameters.

[0455] For example, an occasional event could be an early alarm clock at 6 a.m., and the user's reference range for bedtime could be 7:30 a.m. This occasional event might cause the user to go to bed earlier than the reference range.

[0456] For example, an incidental event could be the schedule for the next day, which might cause the user's emotional stress during sleep to be higher than the reference range for emotional stress.

[0457] For example, an isolated event could be a nighttime downpour, which could cause users to wake up more often after falling asleep, exceeding the reference range for the number of times a user should wake up after falling asleep.

[0458] In some examples, incidental events may cause changes in a user's level and / or type of insomnia.

[0459] For example, an unexpected event could be a last-minute meeting at night that might cause a user to use electronic devices for more than a certain amount of time before falling asleep, thus changing the user's insomnia type from early awakening insomnia to difficulty falling asleep insomnia.

[0460] For example, an isolated incident could be excessive caffeine intake before bed, which could cause a user's mild insomnia to progress to severe insomnia.

[0461] S203, The range Rd of the electronic equipment is defined.

[0462] Here, the range Rd can be understood as the adjusted range Rc. The value of the range Rd can be the same as or different from the value of the range Rc. In other words, the adjusted range Rc can coincide with the original range Rc.

[0463] In some examples, the range Rc is the baseline of the physiological data, and the range Rd can be the adjusted baseline of the physiological data; or, the range Rc is the baseline of the sleep parameters, and the range Rd can be the adjusted baseline of the sleep parameters.

[0464] Given that each user has different lifestyle habits and physical conditions, dynamically adjusting the baseline of the user's physiological data and / or sleep parameters based on the user's actual sleep situation can more clearly and accurately analyze the user's sleep habits, which is conducive to realizing personalized assessment of the user's sleep situation and enabling electronic devices to provide users with more reasonable and targeted sleep improvement suggestions.

[0465] In some examples, the range Rc is the range Ra1, range Ra2, range Rb1 or range Rb2 mentioned above, and the range Rd can be the corresponding adjusted range Ra1, adjusted range Ra2, adjusted range Rb1 or adjusted range Rb2.

[0466] Ranges Ra1 and Ra2 can be used to determine the user's insomnia level, while ranges Rb1 and Rb2 can be used to determine the user's insomnia type. Given that each user's lifestyle and physical condition are different, one or more of these ranges are dynamically adjusted based on the user's actual sleep patterns. The electronic device assesses the user's sleep based on the adjusted ranges, and the output results for insomnia level and / or insomnia type are more consistent with the user's sleep habits. Based on the assessment results output by the electronic device, users can more accurately understand changes in their personal sleep patterns, which helps them take more appropriate improvement measures.

[0467] For example, the electronic device can determine the range Rd based on the user's actual sleep status (including measured values ​​of physiological data and / or measured values ​​of sleep parameters) and the range Rc. That is, the electronic device can continuously adjust the baseline of physiological data and / or the baseline of sleep parameters, as well as the range of values ​​for physiological data and / or sleep parameters used to determine the user's degree and type of insomnia, based on the user's actual sleep status.

[0468] As one implementation, the electronic device can determine the range Rd according to the flowchart shown in Figure 27.

[0469] One possibility is that the first parameter falls within the range Rc. In this case, the electronic device can determine that the user is in a normal sleep state, and the electronic device can determine the range Rd based on the first parameter and the range Rc.

[0470] For example, the range Rc can be the average of the measured values ​​of the first parameter over the previous N days, and the range Rd can be the average of the measured values ​​of the first parameter over the previous N days and the measured values ​​of the first parameter on the (N+1)th day (N is a positive integer).

[0471] One possible scenario is that the first parameter does not belong to the range Rc and an isolated event occurs. In this case, the electronic device can determine that the deviation of the first parameter from the range Rc may be related to the aforementioned isolated event. In this case, the first parameter can be ignored and the range Rc is not updated. In other words, in this case, the range Rd can be the same as the range Rc.

[0472] One possible scenario is that the first parameter does not fall within the range Rc, and no incidental event has occurred. In this case, the electronic device can determine that the deviation of the first parameter from the range Rc is not caused by the aforementioned incidental event. In this case, the electronic device can determine the range Rd based on the first parameter and the range Rc.

[0473] For example, the range Rc can be the average of the measured values ​​of the first parameter over the previous M days, and the range Rd can be the average of the measured values ​​of the first parameter over the previous M days and the measured values ​​of the first parameter on the (M+1)th day (M is a positive integer).

[0474] Taking heart rate as an example, before the adjustment, the heart rate ranges corresponding to normal sleep, mild insomnia, moderate insomnia and severe insomnia were 60 to 100 beats per minute, 70 to 80 beats per minute, 80 to 90 beats per minute and 90 to 100 beats per minute, respectively.

[0475] One possible scenario is that the user experienced moderate insomnia for 20 out of a 30-day period, with their heart rate actually measured at around 75 beats per minute during sleep, unaffected by other factors (such as the aforementioned unforeseen events). In this case, the range Rd (adjusted range) can be determined based on the range Rc (the range before adjustment) and the user's measured heart rate during sleep. As an example, after adjustment, the heart rate ranges corresponding to normal sleep, mild insomnia, moderate insomnia, and severe insomnia are: 55 to 95 beats per minute, 60 to 70 beats per minute, 70 to 80 beats per minute, and 80 to 100 beats per minute, respectively.

[0476] Based on the range Rc, the user is diagnosed with mild insomnia, which does not reflect the user's actual sleep experience; based on the range Rd, the user is diagnosed with moderate insomnia, which is more consistent with the user's actual sleep experience.

[0477] Taking sleep latency as an example, before the adjustment, the sleep latency of insomnia with difficulty falling asleep could be 15 to 30 minutes.

[0478] One possible scenario is that the user's sleep latency remained around 20 minutes for 7 days without being affected by other factors (such as the aforementioned incidental events), and the user did not actually experience "difficulty falling asleep." In this case, the range Rd can be determined based on the range Rc mentioned above and the user's actual sleep latency duration. As an example, the adjusted sleep latency duration for difficulty falling asleep insomnia could be between 25 and 45 minutes.

[0479] Based on the range Rc before adjustment, it can be determined that the user has difficulty falling asleep, which does not match the user's actual sleep experience; based on the range Rd, it can be determined that the user does not have difficulty falling asleep, which is more in line with the user's actual sleep experience.

[0480] In one possible implementation, the electronic device can determine the baseline of the adjusted sleep parameters and / or the baseline of the adjusted physiological data according to the flowchart shown in Figure 27 based on the user's actual sleep status. On this basis, the adjusted range Ra and / or the adjusted range Rb are determined according to the baseline of the adjusted sleep parameters and / or the baseline of the adjusted physiological data.

[0481] The methods used by electronic devices to determine the baselines of adjusted sleep parameters and adjusted physiological data are described above and will not be repeated here. The following example illustrates how electronic devices determine the adjusted range Ra and / or adjusted range Rb based on the baselines of adjusted sleep parameters and / or adjusted physiological data.

[0482] It should be noted that the method by which electronic devices determine the adjusted range Ra and / or adjusted range Rb based on the baseline of the adjusted sleep parameters and / or the baseline of the adjusted physiological data can also be understood as: the method of determining the range Ra and / or range Rb based on the baseline of the sleep parameters and / or the baseline of the physiological data. The following example uses the method for determining the range Ra as an example for illustration; the method for determining the range Rb can be followed accordingly.

[0483] For example, the range Ra can be determined based on the baseline and the offset, or in other words, the electronic device can add a certain offset to the baseline to obtain the range Ra.

[0484] For example, an electronic device detects a user's heart rate during sleep over 30 days and obtains the user's actual sleep experience of experiencing mild insomnia multiple times during these 30 days. Based on the measured heart rate during sleep, a baseline of 75 beats per minute can be determined. Then, combining this with the actual sleep experience, adding a -5 beats per minute offset to the baseline (75 beats per minute) yields the lower limit of the heart rate range corresponding to mild insomnia, and adding a +5 beats per minute offset to the baseline (75 beats per minute) yields the upper limit. Therefore, the heart rate range for mild insomnia can be determined to be 70 to 80 beats per minute.

[0485] It should be noted that while adjusting the value range of sleep parameters or physiological data corresponding to one level of insomnia, electronic devices can also adjust the value ranges of sleep parameters or physiological data corresponding to other levels of insomnia. In other words, in the example above, in addition to determining the heart rate value range corresponding to mild insomnia based on the baseline and offset, the electronic device can also determine the heart rate value range corresponding to moderate insomnia and the heart rate value range corresponding to severe insomnia.

[0486] For example, electronic devices can detect the duration of a user's sleep latency over 7 days and obtain the user's sleep experience during these 7 days, indicating that the user did not have difficulty falling asleep. Based on the measured value of the user's sleep latency over 7 days, the user's sleep latency duration can be determined to be 20 minutes. Based on this, combined with the user's actual sleep experience, by adding a 5-minute offset to the baseline (20 minutes), the lower limit of the sleep latency duration corresponding to difficulty falling asleep insomnia can be obtained. By adding a 25-minute offset to the baseline (20 minutes), the upper limit of the sleep latency duration corresponding to difficulty falling asleep insomnia can be obtained. Thus, the sleep latency duration of difficulty falling asleep insomnia can be determined to be between 25 minutes and 45 minutes. Similarly, by adding a -5-minute offset to the baseline (20 minutes), the lower limit of the sleep latency duration without insomnia can be obtained, and by adding a +5-minute offset to the baseline (20 minutes), the upper limit of the sleep latency duration without insomnia can be obtained. Thus, it can be determined that the sleep latency duration in the case of no insomnia is 20 to 25 minutes.

[0487] For example, the range Ra can be determined based on the baseline and the offset ratio, or in other words, the electronic device can multiply the baseline by a certain offset ratio to obtain the range Ra.

[0488] For example, according to the flowchart shown in Figure 27, the baseline heart rate of a user during sleep is adjusted from 50 beats per minute to 60 beats per minute. Correspondingly, the lower limit of the heart rate range corresponding to mild insomnia can be adjusted from 70 beats per minute to 60 beats per minute. The upper limit of the heart rate range corresponding to mild insomnia can be adjusted from 80 beats per minute to [missing value]. Therefore, the heart rate range for mild insomnia can be determined to be 84 to 96 beats per minute.

[0489] It should be noted that while adjusting the value range of sleep parameters or physiological data corresponding to one level of insomnia, electronic devices can also adjust the value ranges of sleep parameters or physiological data corresponding to other levels of insomnia. In other words, in the example above, in addition to determining the heart rate value range corresponding to mild insomnia based on the baseline and offset, the electronic device can also determine the heart rate value range corresponding to moderate insomnia and the heart rate value range corresponding to severe insomnia.

[0490] For example, if a user's sleep latency is adjusted from 20 minutes to 25 minutes, the lower limit of the sleep latency corresponding to difficulty falling asleep insomnia can be adjusted from 25 minutes to... The upper limit of the sleep latency time corresponding to insomnia with difficulty falling asleep can be adjusted from 45 minutes to This allows us to determine that the sleep latency for difficulty falling asleep type insomnia ranges from 31.25 minutes to 56.25 minutes.

[0491] Using the method provided in the above example, electronic devices can update the evaluation indicators used to determine the severity and type of insomnia in a timely manner based on the measured values ​​and reference ranges of the user's physiological data, the measured values ​​and reference ranges of sleep parameters, and whether any incidental events occur. The updated evaluation indicators are closer to the user's actual sleep situation and are more conducive to accurately determining the severity and type of insomnia.

[0492] Based on the same inventive concept, as shown in FIG28, this application embodiment also provides a sleep problem detection device 2800. This device 2800 may possess the functions of the electronic device described in the above method embodiments and may be used to execute the steps performed by the functions of the electronic device in the above method embodiments. This function may be implemented by hardware, or by software or hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions.

[0493] In one possible implementation, the sleep problem detection device 2800 may include an acquisition module 2810 and a processing module 2820, which are coupled to each other.

[0494] In some examples, the acquisition module 2810 can be used to support the electronic device in the foregoing embodiments in acquiring user taps or drags of desktop cards, etc.

[0495] The processing module 2820 is used to support the electronic device in performing the processing actions in the above method embodiments, such as displaying the target page after adding the target desktop card.

[0496] Optionally, the sleep problem detection device 2800 may further include a storage unit 2830 for storing the program code and data of the sleep problem detection device 2800.

[0497] Figure 29 illustrates an electronic device 2900 provided in an embodiment of this application. As shown, the electronic device 2900 includes at least one processor 2910 and a transceiver 2920. The processor 2910 is coupled to a memory and is used to execute instructions stored in the memory to control the transceiver 2920 to transmit and / or receive signals.

[0498] Optionally, the electronic device 2900 also includes a memory 2930 for storing instructions.

[0499] In some embodiments, the processor 2910 and the memory 2930 can be combined into a single processing device, with the processor 2910 executing program code stored in the memory 2930 to achieve the aforementioned functions. Specifically, the memory 2930 can be integrated into the processor 2910 or independent of it.

[0500] In some embodiments, transceiver 2920 may include a receiver and a transmitter.

[0501] The transceiver 2920 may further include an antenna, and the number of antennas may be one or more. The transceiver 2920 may be a communication interface or an interface circuit.

[0502] When the electronic device 2900 is a chip, the chip includes a transceiver module and a processing module. The transceiver module can be an input / output circuit or a communication interface; the processing module can be a processor, microprocessor, or integrated circuit integrated on the chip.

[0503] This embodiment also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device performs the aforementioned method steps to implement the sleep problem detection method in the above embodiment.

[0504] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the sleep problem detection method described in the above embodiment.

[0505] Furthermore, embodiments of this application also provide an apparatus, which may specifically be a chip, component, or module. The apparatus may include a connected processor and a memory. The memory stores computer execution instructions. When the apparatus is running, the processor can execute the computer execution instructions stored in the memory to cause the chip to perform the sleep problem detection methods described in the above-described method embodiments.

[0506] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0507] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0508] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0509] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0510] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0511] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0512] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting sleep problems, applied to electronic devices, characterized in that, include: Obtaining users' physiological data; Sleep parameters are determined based on the physiological data; Display a first interface, which includes the degree and / or type of insomnia; The degree and type of insomnia are determined based on the sleep parameters.

2. The method according to claim 1, characterized in that, The degree and / or type of insomnia are determined based on the sleep parameters and the physiological data.

3. The method according to claim 1 or 2, characterized in that, Before displaying the first interface, the method further includes: If the physiological data falls within a first range and / or the sleep parameters fall within a second range, the degree of insomnia is determined; and / or, If the physiological data falls within the third range and / or the sleep parameters fall within the fourth range, the type of insomnia is determined.

4. The method according to claim 3, characterized in that, The first range and the third range are determined based on historical data of the physiological data within a first preset time period; and / or, The second range and the fourth range are determined based on historical data of the sleep parameters within a second preset time period.

5. The method according to claim 3 or 4, characterized in that, The physiological data includes a first parameter, or the sleep parameters include a first parameter, and the method further includes: It is determined that the first parameter does not belong to the reference range, and the reference range is determined based on the historical data of the first parameter within a third preset time period; Under preset conditions, one or more of the reference range, the first range, the second range, the third range, or the fourth range are adjusted according to the first parameter.

6. The method according to claim 5, characterized in that, The preset conditions include: no planned event has occurred, and / or no sudden event has occurred, wherein both the planned event and the sudden event can cause a change in the first parameter.

7. The method according to claim 6, characterized in that, The method further includes: Obtain reference information, which is used to determine the planned event.

8. The method according to claim 7, characterized in that, The reference information includes one or more of the following: SMS messages, calendar information, alarm clock information, memo information, to-do list information, notes information, or subscription service information.

9. The method according to claim 6, characterized in that, The method further includes: A second interface is displayed, which is used to request confirmation as to whether the planned event and / or the unexpected event have occurred.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: Accept the first operation applied to the first interface; A third interface is displayed, which includes statistical information about the sleep problem.

11. The method according to any one of claims 1 to 10, characterized in that, The method further includes: Accept the second operation applied to the first interface; A fourth interface is displayed, which includes suggestions for improving sleep problems, corresponding to the type of insomnia.

12. The method according to any one of claims 1 to 11, characterized in that, The level of insomnia is used to indicate the severity of the sleep problem; and / or, The insomnia type is used to indicate the symptoms corresponding to the insomnia type and / or the cause of the insomnia type.

13. The method according to any one of claims 1 to 12, characterized in that, The degree of insomnia includes any of the following: no insomnia, mild insomnia, moderate insomnia, or severe insomnia; and / or, The types of insomnia include one or more of the following: psychological insomnia, behavioral insomnia, environmental insomnia, drug- or substance-induced insomnia, physiological insomnia, jet lag insomnia, difficulty falling asleep, difficulty maintaining sleep, early awakening insomnia, or poor sleep quality insomnia.

14. The method according to any one of claims 1 to 13, characterized in that, The degree and / or type of insomnia are also determined based on one or more of the following: noise in the sleep environment, temperature in the sleep environment, humidity in the sleep environment, light in the sleep environment, or the use of electronic devices in the sleep environment.

15. An electronic device, characterized in that, It includes a processor and a memory, the memory being used to store program instructions, and the processor being used to invoke the program instructions to perform the method of any one of claims 1 to 14.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program code that, when executed on a computer, causes the method of any one of claims 1 to 14 to be performed.

17. A chip, comprising a processor, characterized in that, The processor is used to read instructions stored in memory, and when the processor executes the instructions, it causes the chip to implement the method of any one of claims 1 to 14.

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