Detection method and related apparatus

Abstract

Provided in the present application are a detection method and a related apparatus. In the method, an emotion reference value of a user himself / herself may be first calculated by means of historical physiological data of the user, and then an emotion detection result of the user is determined by means of real-time physiological data and the emotion reference value of the user. When the emotion detection result indicates that the emotion of the user is an abnormal emotion, an abnormal-emotion prompt and an early warning may also be given in a timely manner, and corresponding emotional health improvement solutions can be used for different emotional health risk results. In this way, the emotion of the user is first recognized by means of the emotion reference value of the user himself / herself, such that an abnormal change in the emotion of the user can be recognized more accurately, thereby improving the perception of the user for his / her degree of emotional fluctuation, and reducing emotional health risks of the user.

Description

A detection method and related apparatus

[0001] This application claims priority to Chinese Patent Application No. 202411722398.X, filed on November 27, 2024, entitled "A Detection Method and Related Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of terminal and electronic technology, and in particular to a detection method and related apparatus. Background Technology

[0003] Emotions are a psychological and physiological state arising from various sensations, thoughts, and behaviors. Emotions are also a factor affecting human health. Currently, some electronic devices (e.g., watches, wristbands) can detect a user's emotions. However, current electronic devices cannot assess whether a user has emotional health risks based on detected emotions. Summary of the Invention

[0004] This application provides a detection method and related apparatus. Using the detection method provided by this application, electronic devices can assess whether a user has an emotional health risk based on the detected emotions.

[0005] In a first aspect, this application provides a detection method that can be applied to a first electronic device. The method may include: acquiring first data of a user, the first data including first physiological data and / or first behavioral data; determining an emotion detection result based on the first data and an emotion benchmark value; and generating an abnormal emotion reminder when the emotion detection result is an abnormal emotion, the abnormal emotion reminder being used to remind the user of the degree of abnormality of the emotion.

[0006] The emotion detection results can include the degree of emotional abnormality, which can be categorized as low risk, medium risk, high risk, or the probability that the user is at risk of developing an emotional disorder.

[0007] The first physiological data includes one or more of the user's heart rate, respiratory rate, heart rate variability, and body temperature acquired in real time. The first behavioral data includes the user's sleep data and / or activity data acquired during the first time period. The emotional baseline value is derived from the user's second data, which includes the second physiological data and / or the second behavioral data. The second physiological data includes one or more of the user's heart rate, respiratory rate, heart rate variability, and body temperature acquired during the second time period. The second behavioral data includes the user's sleep data and / or activity data acquired during the second time period. The start time of the second time period is earlier than the start time of the first time period.

[0008] Using the detection method provided in the first aspect, the first electronic device can assess whether a user's emotions are abnormal and whether there are any risks to their emotional health based on the physiological data acquired over a first time period. The first electronic device can also promptly alert the user to the degree of emotional abnormality, allowing the user to be aware of their emotional health in a timely manner.

[0009] In conjunction with the first aspect, in one possible implementation, the method may further include: determining an emotion classification result based on the emotion detection result. In this way, the electronic device can determine the current user's emotion category.

[0010] In conjunction with the first aspect, in one possible implementation, the emotion detection result is determined based on the first data and the emotion benchmark value. Specifically, this may include: determining a first deviation value between the first data and the emotion benchmark value; and determining the emotion detection result based on the first data and the first deviation value.

[0011] In this way, the first electronic device can determine the user's current emotion detection result based on the degree of deviation between the user's current physiological data and historical emotional levels (i.e., emotional baseline values).

[0012] In conjunction with the first aspect, in one possible implementation, the degree of emotional abnormality is any one of low risk, medium risk, and high risk; the first deviation value is the difference between the first data and the emotional benchmark value; if the difference between the first data and the emotional benchmark value is less than the first threshold, the emotional detection result is low risk; or, if the difference between the first data and the emotional benchmark value is greater than the first threshold and less than the second threshold, the emotional detection result is medium risk; or, if the difference between the first data and the emotional benchmark value is greater than the second threshold, the emotional detection result is high risk.

[0013] In this way, the first electronic device can determine whether the user's emotional abnormality is low-risk, medium-risk, or high-risk by comparing the difference between the first data and the emotional benchmark value with a set threshold.

[0014] In conjunction with the first aspect, in one possible implementation, the degree of emotional abnormality is any one of low risk, medium risk, and high risk; the first deviation value is the difference between the first data and the emotional benchmark value; if the difference between the first data and the emotional benchmark value is greater than a third threshold, and the duration of the difference between the first data and the emotional benchmark value being greater than the third threshold is less than the first duration, or if the difference between the first data and the emotional benchmark value is less than the third threshold, the emotional detection result is low risk; or if the difference between the first data and the emotional benchmark value is greater than the third threshold and less than a fourth threshold, and the duration of the difference between the first data and the emotional benchmark value being greater than the third threshold and less than the fourth threshold is greater than the first duration, the emotional detection result is medium risk; or if the difference between the first data and the emotional benchmark value is greater than the fourth threshold, and the duration of the difference between the first data and the emotional benchmark value being greater than the fourth threshold is greater than the first duration, the emotional detection result is high risk.

[0015] In some examples, the first data is acquired within the first time period, regardless of whether the duration is longer than or shorter than the first duration.

[0016] Optionally, in other examples, when the first electronic device determines the emotion detection result based on the first data collected within the first time period, it also calculates the difference between the first data collected between the first time periods and the emotion benchmark value. Ultimately, the first electronic device collectively refers to the duration for which the first data collected within the first time period and the emotion benchmark value are greater than a third threshold, as well as the duration for which the first data collected before the first time period and the emotion benchmark value are greater than the third threshold, as the duration.

[0017] In this way, the first electronic device can determine whether the user's emotional abnormality is low-risk, medium-risk, or high-risk by comparing the difference between the first data and the emotional benchmark value with a set threshold, and then combining the duration.

[0018] In conjunction with the first aspect, in one possible implementation, the degree of emotional abnormality is categorized as low risk, medium risk, or high risk; the emotional baseline value is a first interval range. When the first data point is greater than the maximum value within the first interval range of the emotional baseline value, the emotional detection result is high risk. When the first data point is within the first interval range, the emotional detection result is medium risk. When the first data point is less than the minimum value within the first interval range, the emotional detection result is low risk. Thus, even when the emotional baseline value is within a reference range, the emotional detection result can still be determined.

[0019] In conjunction with the first aspect, in one possible implementation, determining the emotion detection result based on the first data and the first deviation value may include: determining the emotion detection result based on the first data, the first deviation value, and emotion health-related data, wherein the emotion health-related data includes one or more of the user's medical history information, location information, and duration of light exposure.

[0020] Since a user's medical history, location, and duration of light exposure can all affect their physiological data (e.g., heart rate, heart rate variability, respiratory rate, and body temperature), combining emotional health-related data can more accurately determine a user's emotional state.

[0021] In conjunction with the first aspect, in one possible implementation, generating an abnormal emotion alert when the emotion detection result is an abnormal emotion may include: determining that the emotion detection result is an abnormal emotion and generating an abnormal emotion alert when the emotion detection result is medium or high risk.

[0022] This way, the abnormal emotion alert is only triggered when the user is in an abnormal emotional state, avoiding continuous alerts when the user is in a normal emotional state, which would interfere with the normal use of the primary electronic device. Furthermore, it also saves power consumption on the primary electronic device.

[0023] In conjunction with the first aspect, in one possible implementation, generating an abnormal emotion alert when the emotion detection result is an abnormal emotion may include: determining that the emotion detection result is an abnormal emotion and generating an abnormal emotion alert when the emotion detection result is high-risk and the duration of the high-risk emotion detection result is a preset duration.

[0024] This way, the system only alerts the user to emotional abnormalities when their emotions remain at a high risk for an extended period. This avoids constantly reminding the user of their emotional state when there is no actual emotional health risk, which could interfere with normal use of the primary electronic device. Furthermore, it conserves power consumption of the primary electronic device.

[0025] In conjunction with the first aspect, in one possible implementation, the emotion detection result is the probability of the risk of developing a mood disorder. When the emotion detection result is an abnormal emotion, an abnormal emotion reminder is generated, including: when the probability of the risk of developing a mood disorder is greater than a first probability value, determining that the emotion detection result is an abnormal emotion and generating an abnormal emotion reminder.

[0026] In this way, when a user's emotions suddenly become abnormal, for example, when the probability of them having a mood disorder exceeds a certain threshold, the first electronic device can promptly alert the user. This allows the user to pay attention to their emotional health in a timely manner.

[0027] In conjunction with the first aspect, in one possible implementation, after generating the abnormal emotion alert, the method may further include: displaying the abnormal emotion alert, which may include one or more of text alerts, image alerts, video alerts, and audio alerts; or, sending the abnormal emotion alert to a second electronic device.

[0028] This allows the user's family and friends to promptly notice any abnormalities in the user's emotions and pay attention to the user in a timely manner.

[0029] In conjunction with the first aspect, in one possible implementation, the method may further include: obtaining emotion detection results within a third time period and determining emotional health risk results; the emotional health risk results include one or more of the following: statistical results of abnormal emotions of the user within the third time period, the probability of the user being at risk of developing an emotional disorder, and the user's emotion tag; the statistical results of abnormal emotions include the number of times the user experienced abnormal emotions within the third time period, the location where the abnormal emotions occurred, and the time point when the abnormal emotions occurred.

[0030] In this way, users can know where and when they are prone to emotional abnormalities, and be aware of their emotional health risks in a timely manner.

[0031] In conjunction with the first aspect, in one possible implementation, after determining the emotional health risk outcome, the method may further include: generating a reminder message at the location where the user is again in an abnormal emotional state or at the time of the abnormal emotional state, the reminder message being used to remind the user to alleviate the emotion.

[0032] In this way, users can adjust their emotions in time before reaching the location where the abnormal emotion occurred or before the time when the abnormal emotion occurred, thus avoiding the recurrence of abnormal emotions.

[0033] In conjunction with the first aspect, in one possible implementation, after determining the emotional health risk outcome, the method may further include: sending the emotional health risk outcome to a third electronic device of a member of the user's family space.

[0034] In this way, family members can also be informed about the user's emotional health in a timely manner.

[0035] In a second aspect, an electronic device is provided, characterized in that it comprises: one or more processors and one or more memories; the one or more memories are respectively coupled to the one or more processors; the one or more memories are used to store computer program code, the computer program code including computer instructions; when the computer instructions are executed on the processor, the electronic device causes the electronic device to perform the method described in any possible implementation of the first aspect above.

[0036] Thirdly, a chip is provided that may include a processor and a communication interface, the communication interface being used to receive signals and transmit the signals to the processor, the processor processing the signals, such that the chip implements the method described in any possible implementation of the first aspect above.

[0037] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a computer, causes the computer to implement the method described in any possible implementation of the first aspect above.

[0038] Fifthly, a computer program product is provided that, when run on an electronic device, causes the electronic device to perform the method described in any possible implementation of the first aspect.

[0039] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0040] Figure 1 is a schematic diagram of the structure of the electronic device 100 provided in an embodiment of this application;

[0041] Figure 2 is a flowchart illustrating a detection method provided in an embodiment of this application;

[0042] Figure 3A is a schematic diagram of an emotional baseline value and an emotional deviation value provided in an embodiment of this application;

[0043] Figure 3B is a schematic diagram of another emotional baseline value and emotional deviation value provided by the embodiment of the device itself;

[0044] Figure 3C is a schematic diagram of the abnormal emotion recognition results provided in an embodiment of this application;

[0045] Figure 3D is a schematic diagram of abnormal emotion classification provided in an embodiment of this application;

[0046] Figure 3E is a schematic diagram of a two-dimensional model of emotion provided in an embodiment of this application;

[0047] Figure 4 is a waveform diagram of an emotion detection result provided in an embodiment of this application;

[0048] Figure 5 is a waveform diagram of an emotion detection result provided in an embodiment of this application;

[0049] Figure 6 is a flowchart illustrating a detection method provided in an embodiment of this application;

[0050] Figures 7A-7J are a set of user interface diagrams of the electronic device 100 provided in the embodiments of this application when performing emotion detection;

[0051] Figures 8A and 8B are schematic diagrams of a set of abnormal emotion reminder interfaces provided in the embodiments of this application;

[0052] Figures 9A and 9B are a set of user interface diagrams showing abnormal emotion reminders based on user information provided in the embodiments of this application;

[0053] Figures 10A-10C are a set of user interface diagrams provided in the embodiments of this application, which display abnormal emotion reminders based on the user's specific period and location.

[0054] Figures 11A-11C are schematic diagrams of a set of user interface diagrams for mood improvement services provided in the embodiments of this application;

[0055] Figures 12A-12F are schematic diagrams of user interfaces for a set of emotion improvement services corresponding to different abnormal emotions provided in the embodiments of this application.

[0056] Figures 13A and 13B are user interface diagrams of a set of abnormal emotion statistical results provided in the embodiments of this application;

[0057] Figure 14 is a schematic diagram of the emotion detection system 1400 provided in an embodiment of this application;

[0058] Figure 15 is a schematic diagram of the hardware structure of an electronic device 200 provided in an embodiment of this application. Detailed Implementation

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] The terminology used in the following embodiments of this application 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 include the plural expressions as well, unless the context clearly indicates otherwise. The terms “first” and “second” are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as “first” or “second” may explicitly or implicitly include one or more of that feature. “First” and “second,” etc., are used to distinguish different objects, not to describe a particular order of objects. For example, a first object and a second object are used to distinguish different objects, not to describe a particular order of objects.

[0061] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more. For example, multiple processing units refer to two or more processing units; multiple systems refer to two or more systems.

[0062] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or related scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0063] The term "and / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A existing alone, A and B existing simultaneously, and B existing alone.

[0064] To better understand the technical solutions provided in this application, before describing the technical solutions, we will first refer to the accompanying drawings to explain the electronic device 100 with a camera function to which this application applies. In the embodiments of this application, the electronic device 100 may include, but is not limited to, devices with camera functions such as mobile phones, tablets, and smartwatches. The embodiments of this application do not limit the specific form or type of the electronic device 100.

[0065] The term "user interface (UI)" used in the following embodiments of this application refers to the medium interface through which an application or operating system interacts and exchanges information with the user. It realizes the conversion between the internal form of information and the form that the user can accept. The user interface is source code written in a specific computer language such as Java or Extensible Markup Language (XML). The interface source code is parsed and rendered on the electronic device, ultimately presenting content that the user can recognize. A common form of user interface is the graphical user interface (GUI), which refers to a user interface related to computer operation displayed graphically. It can be visible interface elements such as text, icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, and widgets displayed on the screen of an electronic device.

[0066] Currently, users can identify their emotional problems using assessment scales in the medical field, such as the Self-Rating Anxiety Scale and the PHQ-9 Depression Screening Scale. However, when users assess their emotional problems using these scales, the accuracy of the final assessment results may be affected by subjective biases in the information they provide. Furthermore, early emotional health risks are often difficult for users to detect and are perceived as low-risk. If not detected and treated promptly, they may develop into mood disorders (e.g., depression, anxiety). Moreover, different users have different emotional fingerprints or profiles, making it difficult to apply uniform algorithms and parameters to different users, as each person's emotional baseline value is different. The assessment of each person's emotional health should be based on their individual emotional baseline. Therefore, evaluating the emotional health of different users using a uniform emotional baseline value may lead to inaccurate final results.

[0067] In this embodiment, the emotional baseline value is an indicator used to measure a user's emotional health; it is a specific reference value or reference range. The electronic device 100 can compare real-time data that can characterize a user's emotions (e.g., parameters such as heart rate, heart rate variability, respiratory rate, and body temperature) with the emotional baseline value to determine the user's emotional health risk.

[0068] In view of the above problems, this application provides a detection method and related apparatus.

[0069] In this embodiment, a user can use an electronic device 100 to detect emotions and determine whether there are emotional abnormalities or emotional health risks. In the detection method provided in this embodiment, the electronic device 100 collects the user's physiological data (e.g., heart rate, heart rate variability, respiratory rate, body temperature, etc.) and acquires user behavioral data (e.g., sleep data, activity level data, etc.). The electronic device 100 can determine the user's emotional health status based on one or more of the user's physiological data, and / or user behavioral data, emotional health-related data, and an emotional health risk detection algorithm. Furthermore, in this algorithm, each user has a personalized emotional baseline value. By comparing the user's personalized emotional baseline value, it is possible to more accurately determine whether the user's current emotions pose a health risk. Thus, through this detection method, the electronic device 100 can more accurately detect the user's emotional health risks. Users can also learn about their emotional health status more promptly and conveniently through the electronic device 100.

[0070] The exemplary electronic device 100 provided in the embodiments of this application will be introduced first below.

[0071] Figure 1 is a schematic diagram of the structure of the electronic device 100 provided in an embodiment of this application.

[0072] The electronic device 100 can be worn portablely by a user and has the ability to collect the user's physiological data. The electronic device 100 can be a wearable device such as a smartwatch, smart bracelet, smart glasses, or smart ring. This application embodiment does not impose any special limitations on the specific type of the electronic device 100. This application embodiment only uses a watch as an example for illustration.

[0073] As shown in Figure 1, the electronic device 100 may include: a processor 110, a wireless communication module 111, a mobile communication module 112, a sensor module 113, buttons 114, a display screen 115, a motor 116, internal memory 117, a subscriber identity module (SIM) card interface 118, a universal serial bus (USB) interface 119, a power management module 120, a battery 121, and a charging management module 122. The sensor module 113 may include a touch sensor 113A, a barometric pressure sensor 113B, a micropump 113C, an airbag 113D, a magnetic sensor 113E, a photoplethysmography (PPG) sensor 113F, an electrocardiogram (ECG) sensor 113H, a gyroscope sensor 113I, an accelerometer 113G, a positioning sensor 113J, a timing sensor 113K, and a pneumatic conduction component 113L.

[0074] 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.

[0075] Processor 110 may include one or more processing units. For example, processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0076] 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 SIM card interface, and / or a USB interface, etc.

[0077] In some embodiments, the processor 110 may also be a microcontroller unit (MCU).

[0078] The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may include multiple I2C buses. The processor 110 can couple to the touch sensor 113A, power management module 120, etc., through different I2C bus interfaces. For example, the processor 110 can couple to the touch sensor 113A through the I2C interface, enabling the processor 110 and the touch sensor 113A to communicate through the I2C bus interface, thereby realizing the touch function of the electronic device 100.

[0079] The I2S interface can be used for audio communication. The PCM interface can also be used for audio communication, sampling, quantizing, and encoding analog signals. The UART interface is a universal serial data bus used for asynchronous communication. This bus can be a bidirectional communication bus. It converts the data to be transmitted between serial and parallel communication. In some embodiments, the UART interface is typically used to connect the processor 110 and the wireless communication module 111. For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 111 via the UART interface to implement Bluetooth functionality.

[0080] The MIPI interface can be used to connect peripheral devices such as the processor 110 and the display screen 115. The MIPI interface includes a camera serial interface (CSI) and a display serial interface (DSI). The processor 110 and the display screen 115 can communicate through the DSI interface to realize the display function of the electronic device 100.

[0081] The GPIO interface is configurable via software. It can be configured as either control or data signals. The USB interface 119 is a USB standard compliant interface, specifically a Mini USB, Micro USB, or USB Type-C interface. The USB interface 119 can be used to connect a charger to charge the electronic device 100, and also for data transfer between the electronic device 100 and peripheral devices.

[0082] 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.

[0083] The charging management module 122 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 122 receives charging input from the wired charger via a USB interface 119. In some wireless charging embodiments, the charging management module 122 receives wireless charging input via the wireless charging coil of the electronic device 100. While charging the battery 121, the charging management module 122 can also supply power to the electronic device 100 via the power management module 120.

[0084] The power management module 120 connects the battery 121, the charging management module 122, and the processor 110. The power management module 120 receives input from the battery 121 and / or the charging management module 122 to power the processor 110, internal memory 117, display screen 115, and wireless communication module 111. The power management module 120 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 120 may be located within the processor 110. In other embodiments, the power management module 120 and the charging management module 122 may be housed in the same device.

[0085] The wireless communication function of the electronic device 100 can be implemented through the mobile communication module 112, the wireless communication module 111, the modem processor, and the baseband processor.

[0086] The mobile communication module 112 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 100. The mobile communication module 112 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 112 can receive electromagnetic waves via an antenna, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. In some embodiments, at least some functional modules of the mobile communication module 112 may be housed in the processor 110. In some embodiments, at least some functional modules of the mobile communication module 112 and at least some modules of the processor 110 may be housed in the same device.

[0087] The wireless communication module 111 can provide solutions for wireless communication applications on the electronic device 100, including WLAN (such as Wi-Fi networks), Bluetooth, Global Navigation Satellite System (GNSS), Frequency Modulation (FM), NFC, and Infrared (IR) technologies. The wireless communication module 111 can be one or more devices integrating at least one communication processing module. The wireless communication module 111 receives electromagnetic waves via an antenna, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to the processor 110. The wireless communication module 111 can also receive signals to be transmitted from the processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via the antenna.

[0088] Buttons 114 include a power button, volume buttons, etc. Buttons 114 can be mechanical buttons or touch-sensitive buttons. The electronic device 100 can receive button input and generate key signal inputs related to user settings and function control of the electronic device 100.

[0089] The display screen 115 is used to display images, videos, etc. The display screen 115 may include 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 quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 100 may include one or N display screens 115, where N is a positive integer greater than 1.

[0090] Motor 116 can generate vibration alerts. Motor 116 can be used for incoming call vibration alerts or for touch vibration feedback. For example, different vibration feedback effects can correspond to touch operations performed on different applications (such as taking photos, playing audio, etc.). Motor 116 can also provide different vibration feedback effects for touch operations performed on different areas of the display screen 115.

[0091] Internal memory 117 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM).

[0092] Random access memory can include static random-access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM, for example, fifth generation DDR SDRAM is generally called DDR5 SDRAM), etc.

[0093] Non-volatile memory can include disk storage devices and flash memory (FM). Flash memory can be classified according to its operating principle, including NOR FLASH, NAND FLASH, 3D NAND FLASH, etc.; according to the level of its storage cells, including single-level cell (SLC), multi-level cell (MLC), triple-level cell (TLC), quad-level cell (QLC), etc.; and according to its storage specification, including universal flash storage (UFS) and embedded multimedia card (eMMC), etc. Random access memory (RAM) can be directly read and written by the processor 110. It can be used to store executable programs (such as machine instructions) of the operating system or other running programs, and can also be used to store user and application data. Non-volatile memory can also store executable programs and user and application data, which can be pre-loaded into RAM for direct read and write by the processor 110.

[0094] The SIM card interface 118 is used to connect a SIM card. The SIM card can be inserted into or removed from the SIM card interface 118 to make contact with and separate from the electronic device 100. The electronic device 100 can support one or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 118 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 118 simultaneously. The types of these multiple cards can be the same or different. The SIM card interface 118 is also compatible with different types of SIM cards. The SIM card interface 118 is also compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to realize functions such as calls and data communication. In some embodiments, the electronic device 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

[0095] In some embodiments, the electronic device 100 may also not include the SIM card interface 118.

[0096] Touch sensor 113A, also known as a "touch device," can be disposed on display screen 115. The touch sensor 113A and display screen 115 together form a touchscreen, also known as a "touchscreen." Touch sensor 113A is used to detect touch operations applied to or near it. Touch sensor 113A can transmit the detected touch operation to the application processor to determine the type of touch event. Touch sensor 113A can provide visual output related to the touch operation through display screen 115. In other embodiments, touch sensor 113A may also be disposed on the surface of electronic device 100, in a different location than display screen 115. In some embodiments, electronic device 100 can detect touch operations in various graphical user interfaces using touch sensor 113A, such as click operations (e.g., touch operations on controls) in various graphical user interfaces, or swipe operations (up or down) in various graphical user interfaces, etc.

[0097] The pressure sensor 113B is used to measure air pressure. In some embodiments of this application, the electronic device 100 can measure the air pressure in the airbag 113D via the pressure sensor 113B. In some embodiments of this application, a portion of the pressure sensor 113B is located inside the airbag 113D for sensing the air pressure in the airbag 113D.

[0098] Micropump 113C is used for inflation and deflation. In some embodiments of this application, electronic device 100 inflates airbag 113D via micropump 113C, wherein micropump 113C and airbag 113D are connected via air passage conduit 113L. Airbag 113D is used to compress the user's blood vessels. In some embodiments, electronic device 100 can use barometric pressure sensor 113B, micropump 113C, and airbag 113D to measure the user's blood pressure and pulse rate (heart rate).

[0099] The magnetic sensor 113E includes a Hall sensor. In some embodiments of this application, the electronic device 100 can use the magnetic sensor 113E to determine whether the airbag 113D on the electronic device 100 has been removed. For example, a magnet may be disposed on the airbag 113D or on the watch strap to which the airbag 113D is connected, and the electronic device 100 can use the magnetic sensor 113E to determine the magnetic flux generated by the magnet on the airbag 113D or on the airbag 113D, thereby determining whether the airbag 113D on the electronic device 100 has been removed.

[0100] The PPG sensor 113F can be used to acquire PPG signals through the fluctuations generated by vasoconstriction to provide users with physiological data, including but not limited to: blood pressure, heart rate, blood oxygen, respiratory rate, and blood oxygen saturation (SaO2). In some embodiments, the electronic device 100 can use the PPG sensor 113F to measure the user's heart rate and heart rate interval.

[0101] ECG sensor 113H can be used to acquire ECG signals through the fluctuations generated by heart contractions to provide users with physiological data, including but not limited to: electrocardiogram and heart rate. In some embodiments, electronic device 100 can use ECG sensor 113H to measure the user's electrocardiogram and heart rate.

[0102] The gyroscope sensor 113I can be used to determine the motion posture of the electronic device 100. In some embodiments, the angular velocity of the electronic device 100 about three axes (i.e., the x, y, and z axes) can be determined by the gyroscope sensor 113I, and then the user's motion data can be calculated.

[0103] The accelerometer 113G can detect the magnitude of acceleration of the electronic device 100 in various directions (generally three axes), and thus calculate the user's motion data. It can also be used to identify the posture of the electronic device and applied to applications such as pedometers.

[0104] In some embodiments, the electronic device 100 can detect motion gestures performed by the user holding the electronic device 100, such as shaking the electronic device, through a gyroscope sensor 113I, an accelerometer sensor 113G, etc.

[0105] The positioning sensor 113J can be used to determine the orientation of the electronic device 100. It may include satellite positioning sensors, such as those from the Global Positioning System (GPS) or BeiDou, which can determine the user's location on Earth by receiving satellite signals. The positioning sensor may also include communication positioning sensors, such as base stations or Wi-Fi communication modules, which can provide base station positioning, Wi-Fi router positioning, etc. In addition, the positioning sensor may include positioning sensors such as magnetometers, which can measure the strength and direction of a magnetic field to determine the user's direction and location during movement, such as walking back and forth. It is readily understood that in some embodiments, the positioning sensor 113J is not essential but optional.

[0106] The timing sensor 113K can be used to measure time, for example, to calculate the duration of a user's movement during exercise or the duration of sleep. It is readily understood that in some embodiments, the timing sensor 113K is not essential and is optional.

[0107] Among them, the gyroscope sensor 113I, accelerometer sensor 113G, positioning sensor 113J, and timing sensor 113K can be collectively referred to as motion sensors. Motion sensors can be used to collect motion data and determine the user's motion state based on the collected motion data, and further determine the user's motion pattern based on the user's motion state. It is readily understood that in other embodiments of this application, motion sensors may include more or fewer sensors than those described above, and different sensor combinations can support different motion modes.

[0108] It is worth noting that the air passage connection component 113L can be a single component, or the air passage connection component 113L can be a combination of other hardware modules to form an air passage, or the air passage connection component 113L can be part of other components, such as part of the micro pump 113C, or part of the airbag 113D.

[0109] It is worth noting that the sensor module 113 may also include an infrared sensor, a blood oxygen sensor, etc. The blood oxygen sensor can detect the partial pressure of oxygen in the blood and convert it into a usable output signal. The micro-pump 113C can be located inside the smartwatch body, and the airbag 113D can be connected to the watch band buckle. The airbag 113D is connected to the watch face through an air vent cover. Correspondingly, the airbag 113D can be detached from the watch band or the watch face. In some examples, the micro-pump 113C, airbag 113D, and air passage connection component 113L are not essential but optional.

[0110] The methods in the embodiments of this application will be specifically described below with reference to the hardware structure of the exemplary electronic device 100 described above.

[0111] Figure 2 is a flowchart illustrating a detection method provided in an embodiment of this application. As shown in Figure 2, a detection method provided in an embodiment of this application may include the following steps:

[0112] S201. Obtain real-time user data.

[0113] When the electronic device 100 begins emotion detection, it can acquire real-time data from the user. This real-time data includes, but is not limited to, real-time physiological data and real-time behavioral data.

[0114] Electronic device 100 can acquire real-time physiological data from users, including emotion-related physiological data such as heart rate, respiratory rate, heart rate variability, and body temperature. These physiological data fluctuate with changes in the user's emotions. For example, when a user experiences excitement or agitation, their heart rate increases. When a user experiences negative emotions (such as anxiety, depression, or anger), their heart rate variability decreases. When a user experiences negative emotions (such as tension), their respiratory rate increases. When a user feels emotionally agitated, anxious, tense, or angry, their body temperature rises. Therefore, electronic device 100 can acquire emotion-related physiological data from sensors and use this data to determine the user's emotional state.

[0115] For example, electronic device 100 can acquire a user's heart rate via an ECG sensor and / or a PPG sensor. Electronic device 100 can calculate the user's heart rate variability using data acquired by the PPG sensor (e.g., the user's pulse wave signal). Electronic device 100 can acquire the user's body temperature via a body temperature sensor or an infrared sensor. Electronic device 100 can acquire the user's heart rate via a PPG sensor and the user's blood oxygen via a blood oxygen sensor; then, electronic device 100 can calculate the user's respiratory rate using the user's heart rate and blood oxygen.

[0116] It is understood that user emotion-related physiological data is not limited to heart rate, respiratory rate, heart rate variability, and body temperature, but may also include other physiological data, which this application does not limit. The embodiments of this application will be described below using heart rate, respiratory rate, heart rate variability, and body temperature as examples.

[0117] Behavioral data may include user sleep data and / or activity data. User sleep data may include one or more of the following: sleep duration, sleep start time, time to enter deep sleep, sleep end time, deep sleep duration, light sleep duration, etc. Activity data may include one or more of the following: user steps, exercise duration, exercise start time, exercise end time, etc.

[0118] Electronic device 100 can obtain user sleep data and activity data through a sports and health app used to record user sleep data and activity data. Alternatively, electronic device 100 can determine user sleep duration, sleep start time, deep sleep time, sleep end time, deep sleep duration, light sleep duration, and other data, as well as user steps, exercise duration, exercise start time, and exercise end time, through data collected by PPG sensors, accelerometers, ECG sensors, and gyroscope sensors. This application embodiment does not limit the specific method by which electronic device 100 obtains user sleep data and activity data.

[0119] In this embodiment of the application, the physiological data collected by the electronic device 100 during the time period 1 before emotion detection is called historical physiological data, and the physiological data collected during the time period 2 during emotion detection is called real-time physiological data. Similarly, the behavioral data collected by the electronic device 100 during the time period 1 before emotion detection is called historical behavioral data, and the behavioral data collected during the time period 2 during emotion detection is called real-time behavioral data.

[0120] The start time of Time Period 1 is earlier than the start time of Time Period 2, and the end time of Time Period 1 is earlier than the end time of Time Period 2. For example, the start time for the current emotion detection could be 8:00:00 on October 1, 2024. Taking Time Period 1 as one month and Time Period 2 as 3 minutes as an example, then Time Period 1 could be from 8:00:00 on September 1, 2024 to 8:00:00 on September 30, 2024, or Time Period 1 could be from 00:00:00 on September 1, 2024 to 24:00:00 on September 30, 2024, or Time Period 1 could also be from 00:00:00 on September 1, 2024 to 8:00:00 on October 1, 2024. Time Period 2 could be from 8:00:00 on October 1, 2024 to 8:03:00 on October 1, 2024. The embodiments of this application do not specify the specific time period 1 and time period 2.

[0121] Understandably, when the electronic device 100 begins detecting the user's emotions, it will display an emotion detection-related user interface. For details regarding the emotion detection-related user interface, please refer to the descriptions of Figures 7A-7J below; they will not be repeated here.

[0122] S202. Determine the user's emotion detection result based on the user's real-time data and emotion benchmark value.

[0123] Electronic device 100 can determine the user's emotion detection result based on the user's physiological data and emotional baseline values. The emotional baseline values ​​are obtained based on one or more of the user's historical physiological data, historical behavioral data, and emotional health-related data. Among them, historical behavioral data may include the user's sleep data and / or activity data, and emotional health-related data may include one or more of the following: location information, questionnaire information, medication information, and light exposure duration.

[0124] The method by which electronic device 100 acquires historical physiological data can be found in the description of the method for acquiring real-time physiological data described above. The method by which electronic device 100 acquires historical behavioral data can also be found in the description of the method for acquiring real-time behavioral data described above, and will not be repeated here.

[0125] Because human physiological data changes according to emotional states, electronic devices 100 can use this physiological data to determine the user's emotional state. However, a user's heart rate, heart rate variability, respiratory rate, and body temperature differ between sleep and wakefulness. Similarly, these data differ between active and resting states. Factors such as location, medication use, and sun exposure can also affect physiological data. For example, a user's heart rate on a day after a long run (e.g., average heart rate) differs from a day without exercise. Therefore, considering historical behavioral data and emotional health-related data when determining a user's emotional baseline can more accurately determine their emotional level over a historical period.

[0126] Electronic device 100 can obtain the user's current location information through map positioning applications. Electronic device 100 can also obtain questionnaire information from questionnaires completed on the electronic device (e.g., personality tests, mental health tests, etc.). Electronic device 100 can determine the user's medication information through medication reminder apps or healthcare apps. Electronic device 100 can determine the user's daylight hours based on the user's location (outdoor or indoor) and the day's weather data from a weather application (e.g., sunrise and sunset times on sunny days).

[0127] It is understood that electronic device 100 can obtain the user's historical physiological data, behavioral data, and emotional health-related data from other electronic devices logged into with the same user account as electronic device 100. Alternatively, electronic device 100 can obtain the user's historical physiological data, behavioral data, and emotional health-related data from a cloud server storing user data. This application embodiment does not limit the method by which electronic device 100 obtains the user's historical physiological data, behavioral data, and emotional health-related data.

[0128] Electronic device 100 can obtain an emotional baseline value based on one or more of the user's historical physiological data, behavioral data, and emotional health-related data. Specific calculation methods for the emotional baseline value include, but are not limited to, the following:

[0129] ① The electronic device 100 can accumulate and average the user's heart rate values ​​over a period of time to obtain an emotional baseline value. That is, the emotional baseline value can be the historical average heart rate value.

[0130] ② Electronic device 100 can accumulate and average the user's heart rate variability values ​​over a period of time to obtain an emotional baseline value. That is, the emotional baseline value can be the historical average heart rate variability value.

[0131] ③ The electronic device 100 can accumulate and average the user's respiratory rate values ​​over a period of time to obtain an emotional baseline value. That is, the emotional baseline value can be the historical average respiratory rate.

[0132] ④ Electronic device 100 can accumulate and average the user's body temperature values ​​over a period of time to obtain an emotional baseline value. That is, the emotional baseline value can be the historical average body temperature value.

[0133] ⑤ The electronic device 100 calculates a weighted sum of the average values ​​of different historical physiological data to obtain an emotional baseline value. For example, the electronic device 100 can obtain the emotional baseline value according to the following formula 1: Emotional Baseline Value = Historical Average Heart Rate * Weight 1 + Historical Average Heart Rate Variability * Weight 2 + Historical Average Respiratory Rate * Weight 3 + Historical Average Body Temperature * Weight 4 (Formula 1)

[0134] The weights 1, 2, 3, and 4 in Formula 1 above can be configured by the system of the electronic device 100. This application embodiment does not limit the specific values ​​of these weights 1, 2, 3, and 4.

[0135] ⑥ The electronic device 100 calculates a weighted sum of the average values ​​of different historical physiological data, different historical behavioral data, and different emotional health-related data to obtain an emotional baseline value. For example, the electronic device 100 can obtain the emotional baseline value according to the following formula 2: Emotional Baseline Value = Historical Average Heart Rate * Weight 1 + Historical Average Heart Rate Variability * Weight 2 + Historical Average Respiratory Rate * Weight 3 + Historical Average Body Temperature * Weight 4 + Historical Average Activity Level * Weight 5 + Historical Medication Reminders * Weight 6 + Historical Light Exposure Duration * Weight 7 (Formula 2)

[0136] The weights 1, 2, 3, and 4 in Formula 2 above can be found in the description of Formula 1 above, and will not be repeated here. The weights 5, 6, and 7 in Formula 2 above can be configured by the system of the electronic device 100. This application embodiment does not limit the specific values ​​of these weights 5, 6, and 7.

[0137] ⑦ The electronic device 100 can sum the weighted historical physiological data to obtain a physiological data baseline value; and sum the weighted behavioral data to obtain a behavioral data baseline value. Then, the electronic device 100 can use the physiological data baseline value and the behavioral data baseline value as an emotional baseline value. That is, the emotional baseline value can include both the physiological data baseline value and the behavioral data baseline value. For example, the physiological data baseline value can be found in Formula 3 below, and the behavioral data baseline value can be found in Formula 4 below. Physiological data baseline value = Historical average heart rate * weight 1 + Historical average heart rate variability * weight 2 + Historical average respiratory rate * weight 3 + Historical average body temperature * weight 4 (Formula 3) Behavioral data baseline value = Historical average activity level * weight 5 + Historical average sleep duration * weight 8 (Formula 4)

[0138] The weights 1, 2, 3, and 4 in Formula 3 above can be found in the description in Formula 1 above, and will not be repeated here. The weight 5 in Formula 4 can be found in the description in Formula 2 above, and will not be repeated here. The weight 8 in Formula 4 can be configured by the system of the electronic device 100, and the specific value of the weight 8 is not limited in this embodiment.

[0139] ⑧ Electronic device 100 can perform a weighted sum of long-term and short-term emotional baseline values ​​to obtain the current emotional baseline value. That is, the calculation method for the emotional baseline value can be found in Formula 5 below: Emotional Baseline Value = Long-term Emotional Baseline Value * Weight 9 + Short-term Emotional Baseline Value * Weight 10 (Formula 5)

[0140] In Formula 5 above, the long-term emotional baseline value can be calculated by the electronic device 100 based on one or more of the historical physiological data, historical behavioral data, and emotional health-related data within time period 1. The short-term emotional baseline value can be calculated based on one or more of the historical physiological data, historical behavioral data, and emotional health-related data within time period 2. Time period 1 is longer than time period 2. For example, time period 2 can be the most recent 3 days, and time period 1 can be the most recent month. Time period 1 and time period 2 are configured by the electronic device 100 system, and the specific values ​​of time period 1 and time period 2 are not limited in this application embodiment. Weights 9 and 10 are configured by the electronic device 100 system, and the specific values ​​of weights 9 and 10 are not limited in this application embodiment.

[0141] The long-term and short-term emotional baseline values ​​in method ⑧ can be obtained using any of the calculation methods described in ① to ⑦ above. It is understood that the long-term emotional baseline value is calculated using the same method as the short-term emotional baseline value.

[0142] For example, consider a mood benchmark that includes a heart rate benchmark and a heart rate variability benchmark. The calculation method for the heart rate benchmark can be found in Formula 6 below, and the calculation method for the heart rate variability benchmark can be found in Formula 7 below. Heart Rate Benchmark = Long-term Heart Rate Benchmark * Weight 11 + Short-term Heart Rate Benchmark * Weight 12 (Formula 6) Heart Rate Variability Benchmark = Long-term Heart Rate Variability Benchmark * Weight 13 + Short-term Heart Rate Variability Benchmark * Weight 14 (Formula 7)

[0143] In Formulas 6 and 7 above, the long-term heart rate baseline can be the average heart rate over time period 1, and the short-term heart rate baseline can be the average heart rate over time period 2. The long-term heart rate variability baseline can be the average heart rate variability over time period 1, and the short-term heart rate variability baseline can be the average heart rate variability over time period 2.

[0144] Weights 11, 12, 13, and 14 are configured by the system of electronic device 100. In this embodiment of the application, the specific values ​​of weights 11, 12, 13, and 14 are not limited.

[0145] This application does not limit the method by which the electronic device 100 calculates the emotional baseline value.

[0146] Optionally, in one possible implementation, a user's emotions may change due to environmental changes or specific events. Obtaining an emotional baseline value solely from historical data to judge whether a user's current emotion is abnormal may result in inaccurate abnormal emotion recognition. Therefore, the electronic device 100 can calibrate the emotional baseline value before obtaining the abnormal emotion recognition result. For example, if a user has participated in long-distance running every day for the past month, resulting in higher values ​​for physiological data such as heart rate, heart rate variability, body temperature, and respiratory rate, the emotional baseline value obtained based on this physiological data will also be higher. However, if the user did not exercise recently when their emotions were being monitored, the values ​​for these physiological data would be lower, leading to inaccurate abnormal emotion recognition results from the electronic device 100. For instance, the electronic device 100 can obtain a short-term (e.g., within 3 days) emotional baseline value from short-term physiological data, and then use this short-term emotional baseline value to correct the emotional baseline value obtained from long-term historical data (e.g., historical data within one month). In this way, abnormal emotion identification can be performed using the corrected emotional baseline value, making the abnormal emotion identification results more accurate.

[0147] For example, when electronic device 100 obtains a long-term (e.g., within one month) emotional baseline value using Formula 1 above, electronic device 100 can then obtain a short-term (e.g., within three days) emotional baseline value using Formula 1 above. Then, electronic device 100 can use Formula 5 above to correct the long-term emotional baseline value using the short-term emotional baseline value to obtain the final emotional baseline value.

[0148] It is understood that the embodiments of this application do not limit the correction method for the emotional benchmark value.

[0149] In one possible implementation, the electronic device 100 calculates an emotional baseline value (referred to as emotional baseline value 1) using historical data (such as historical physiological data, historical behavioral data, and one or more of emotional health-related data). Then, the electronic device 100 can obtain an emotional deviation value based on real-time physiological data and emotional baseline value 1. In this embodiment, the emotional deviation value can represent the degree of deviation between the current emotional value obtained from real-time physiological data and emotional baseline value 1. This emotional deviation value can be the difference between the emotional value calculated by the electronic device 100 based on real-time physiological data and emotional baseline value 1. Alternatively, the emotional deviation value can also be the standard deviation or variance between the emotional value calculated by the electronic device 100 based on real-time physiological data and emotional baseline value 1. This embodiment does not limit the calculation method of the emotional deviation value.

[0150] Electronic device 100 can obtain an emotion value based on the acquired real-time physiological data and / or real-time behavioral data, according to the calculation method of emotion benchmark 1. If emotion benchmark 1 is the average of historical heart rate values, then the emotion value is the current heart rate value, or the average heart rate value over a short period (e.g., within three minutes). If emotion benchmark 1 is calculated according to formula 1 above, then the emotion value can be calculated as shown in formula 8 below: Emotion value = Real-time heart rate value * weight 1 + Real-time heart rate variability value * weight 2 + Real-time respiratory rate * weight 3 + Real-time body temperature value * weight 4 (Formula 8)

[0151] In Formula 8, weights 1, 2, 3, and 4 can be found in the description in Formula 1, and will not be repeated here.

[0152] For example, as shown in Figure 3A, electronic device 100 can obtain the emotional baseline value 1 based on historical physiological data, behavioral data, and emotional health-related data. Historical physiological data and behavioral data can be found in the description above. Emotional health-related data can be obtained through collaboration with other apps. For example, the emotion detection app of electronic device 100 can obtain user questionnaire information from an app used to collect user questionnaire information. The emotion detection app of electronic device 100 can obtain location information from a location app. The emotion detection app of electronic device 100 can obtain user medication information from a medical health app or a medication reminder app. The electronic device 100 can obtain sunlight duration from a weather app.

[0153] As shown in Figure 3A, the emotional baseline value 1 calculated by the electronic device 100 may include a heart rate variability baseline value 1, a heart rate baseline value 1, a respiratory rate baseline value 1, and a body temperature baseline value 1. Then, the electronic device 100 can input the real-time acquired physiological data (heart rate variability, heart rate, respiratory rate, and body temperature) and the emotional baseline value 1 into model 1, and can output an emotional deviation value. This emotional deviation value may include a heart rate variability deviation value, a heart rate deviation value, a respiratory rate deviation value, and a body temperature deviation value.

[0154] For example, as shown in Figure 3B, the emotional baseline value 1 calculated by the electronic device 100 may include a physiological data baseline value 1 and a behavioral data baseline value 1. The calculation method for the physiological data baseline value 1 can be found in Formula 3 above, and the calculation method for the behavioral data baseline value 1 can be found in Formula 4 above. Then, the electronic device 100 can input the real-time acquired physiological data (heart rate variability, heart rate, respiratory rate, and body temperature) and the emotional baseline value 1 into Model 1, and can output an emotional deviation value. As shown in Figure 3B, this emotional deviation value may include physiological data deviation values ​​and behavioral data deviation values.

[0155] The model 1 shown in Figures 3A and 3B can be a machine learning-related model, such as a decision tree, logistic regression, support vector machine, etc. It can also be a deep learning-related model, such as a recurrent neural network, convolutional neural network, long short-term memory network, etc. This application embodiment does not limit the scope of model 1.

[0156] Electronic device 100 can identify abnormal emotions in a user based on at least one of the user's real-time data, emotional deviation value, and emotional health-related data, and obtain abnormal emotion identification results. For example, as shown in Figure 3C, electronic device 100 can input real-time physiological data, emotional deviation value, and emotional health-related data into model 2, which can output the user's emotion detection results.

[0157] For example, the emotion detection result may include the degree of emotional abnormality. The degree of emotional abnormality can be represented by risk levels such as low risk, medium risk, and high risk. Alternatively, the degree of emotional abnormality can also be represented by the probability of being at risk of developing an emotional disorder. For example, the final emotion detection result obtained by the electronic device 100 can be low risk, meaning the user's emotional abnormality is low risk. As another example, the emotion detection result can also be the probability of being at risk of developing an emotional disorder. If the probability of being at risk of developing an emotional disorder is 5%, it can be understood as the probability of the user's emotional abnormality being 5%. The probability of being at risk of developing an emotional disorder can refer to the probability that the user is close to developing an emotional disorder. The smaller the probability value of being at risk of developing an emotional disorder, the lower the probability that the user is close to developing an emotional disorder; conversely, the larger the probability value of being at risk of developing an emotional disorder, the higher the probability that the user is close to developing an emotional disorder.

[0158] In this embodiment of the application, the emotion detection result obtained by the electronic device 100 can be either a risk level or a probability of being at risk of developing an emotional disorder. That is, the risk level and the probability of being at risk of developing an emotional disorder are different manifestations of the emotion detection result.

[0159] In other examples, the emotion detection results obtained by the electronic device 100 may include one or more of the following: risk level, probability of having a mood disorder, and abnormal emotion classification. This application does not limit this aspect.

[0160] Alternatively, in one possible implementation, the electronic device 100 can also determine the probability of a user's risk of developing a separation disorder based on emotion detection results over a period of time. For example, the more frequently the emotion detection results are classified as medium or high risk over a period of time, the greater the probability that the electronic device 100 determines the user's risk of developing a separation disorder. Conversely, the fewer frequently the emotion detection results are classified as medium or high risk over a period of time, the lower the probability that the electronic device 100 determines the user's risk of developing a separation disorder.

[0161] Model 2 can be a machine learning-related model, such as decision trees, logistic regression, support vector machines, etc. It can also be a deep learning-related model, such as recurrent neural networks, convolutional neural networks, long short-term memory networks, etc. This application embodiment does not limit Model 2.

[0162] In one possible implementation, when the emotion deviation value (e.g., the difference between real-time physiological data and the emotion baseline value 1) is less than threshold 1, the emotion detection result output by model 2 is low risk. When the emotion deviation value is greater than threshold 1 but less than threshold 2, the emotion detection result output by model 2 is medium risk. When the emotion deviation value is greater than threshold 2, the emotion detection result output by model 2 is high risk.

[0163] Model 2 can adjust thresholds 1 and 2 based on the input emotional health-related data. For users with different emotional health-related data, the values ​​of thresholds 1 and 2 can be different. For example, electronic device 100 can determine from questionnaire information and medication reminder apps whether a user has a history of mental illness, hypertension, or hyperglycemia. For example, thresholds 1 and 2 are different for user 1 who has hypertension and needs to take hypertension medication and user 2 who does not have hypertension or other underlying diseases. For instance, threshold 1 for user 1 can be greater than threshold 1 for user 2.

[0164] In some examples, users with hypertension have higher heart rates than those without. Therefore, the threshold for users with hypertension is higher than that for those without, allowing for more accurate measurement of their emotions. In some feasible examples, the electronic device 100 can also obtain the user's location information, sleep data, light exposure data, and so on. For example, when the electronic device 100 determines that the user is pregnant based on a physiological app, or that the user is elderly based on their age, and then determines that the user is currently in an uneven location such as a hillside based on location information, it can lower thresholds 1 and 2. Furthermore, when the user's emotion detection result is determined to be of medium risk, the electronic device 100 can also activate fall detection and promptly remind the user to take precautions against falls.

[0165] For example, when a user's sleep duration is insufficient, or the user's light exposure time is insufficient, that is, when the user is always indoors, the electronic device 100 can determine that the user belongs to a group of people with a higher probability of suffering from depression. In this case, the electronic device 100 can reduce the values ​​of threshold 1 and threshold 2.

[0166] Alternatively, in another possible implementation, the electronic device 100 can determine the specific emotion detection result not only based on the comparison between the emotion deviation value and the magnitudes of threshold 1 and threshold 2, but also by combining the duration of the emotion deviation value being greater than threshold 1 or threshold 2. For example, when the emotion deviation value (e.g., the difference between immediate physiological data and the emotion baseline value 1) is greater than threshold 1, and the duration of the emotion deviation value being greater than threshold 1 within a given period is less than duration 1, the emotion detection result output by model 2 is low risk. When the immediate emotion deviation value is greater than threshold 1, and the duration of the emotion deviation value being greater than threshold 1 within a given period (e.g., 48 hours) is greater than duration 1, the emotion detection result output by model 2 is medium risk. When the emotion deviation value is greater than threshold 2, and the duration of the emotion deviation value being greater than threshold 1 within a given period (e.g., 48 hours) is greater than duration 1, the emotion detection result output by model 2 is high risk. Alternatively, when the emotion deviation value is greater than threshold 1, and the duration of the emotion deviation value being greater than threshold 1 within a certain period of time (e.g., within 48 hours) is greater than duration 2, the emotion detection result output by model 2 is high risk.

[0167] Model 2 can adjust threshold 1, threshold 2, duration 1, and duration 2 based on the input emotional health-related data. Threshold 2 is greater than threshold 1. Duration 2 is greater than duration 1. For users with different emotional health-related data, the values ​​of threshold 1, threshold 2, duration 1, and duration 2 can be different.

[0168] Optionally, in one possible implementation, when the duration of the real-time physiological data exceeding the emotional baseline value 1 is greater than a preset duration, the emotion detection result output by Model 2 is high-risk. When the real-time physiological data is greater than the emotional baseline value 1, but the duration of the real-time physiological data exceeding the emotional baseline value 1 is less than the preset duration, the emotion detection result output by Model 2 is medium-risk. When the real-time physiological data is less than or equal to the emotional baseline value 1, the emotion detection result output by Model 2 is low-risk.

[0169] For example, as shown in Figure 4, the horizontal axis represents duration in hours, and the vertical axis represents real-time physiological data (e.g., heart rate). The emotion baseline 1 can be 78, and the real-time collected physiological data is greater than 78, lasting for approximately 9 hours. The preset duration is 8 hours. When the duration of the real-time physiological data exceeding the emotion baseline is greater than the preset duration, the emotion detection result output by Model 2 is high risk.

[0170] It is understandable that Model 2 can adjust the preset duration based on the input emotional health-related data, and the preset duration can be different for different users. This application embodiment does not limit the specific value of the preset duration.

[0171] Optionally, in one possible implementation, when the difference between the real-time physiological data and the emotion benchmark 1 is greater than the amplitude threshold, the emotion detection result output by Model 2 is high-risk. When the real-time physiological data is greater than the emotion benchmark 1, but the difference between the real-time physiological data and the emotion benchmark 1 is less than the amplitude threshold, the emotion detection result output by Model 2 is medium-risk. When the real-time physiological data is less than or equal to the emotion benchmark 1, the emotion detection result output by Model 2 is low-risk.

[0172] For example, as shown in Figure 5, the horizontal axis represents duration in hours, and the vertical axis represents real-time physiological data (e.g., heart rate). The emotion baseline 1 can be 77. When the real-time physiological data is approximately 80.8 and the amplitude threshold is 3, if the fluctuation amplitude (also known as the degree of emotion deviation) between the real-time physiological data and the emotion baseline 1 is greater than the amplitude threshold, the emotion detection result output by model 2 can be high risk.

[0173] It is understandable that Model 2 can adjust this amplitude threshold based on the input emotional health-related data, and this amplitude threshold can be different for different users. This application embodiment does not limit the specific value of this amplitude threshold.

[0174] Optionally, in one possible implementation, when the current emotion value obtained by the electronic device 100 based on real-time physiological data is greater than the emotion benchmark value 1 and the emotion deviation value is greater than the threshold value 3, the emotion detection result output by model 2 is high risk. When the emotion value obtained by the electronic device 100 based on real-time physiological data is greater than the emotion benchmark value 1 and the emotion deviation value is less than the threshold value 3, the emotion detection result output by model 2 is medium risk. When the emotion value obtained by the electronic device 100 based on real-time physiological data is less than or equal to the emotion benchmark value 1 and the emotion deviation value is less than the threshold value 3, the emotion detection result output by model 2 is low risk.

[0175] Alternatively, in another possible implementation, when the current emotion value obtained by the electronic device 100 based on real-time physiological data is greater than the emotion benchmark value 1, the emotion detection result output by model 2 is high risk. When the emotion value obtained by the electronic device 100 based on real-time physiological data is equal to the emotion benchmark value 1, the emotion detection result output by model 2 is medium risk. When the emotion value obtained by the electronic device 100 based on real-time physiological data is less than the emotion benchmark value 1, the emotion detection result output by model 2 is low risk.

[0176] Alternatively, in another possible implementation, the emotion baseline value 1 can be a range, for example, range 1 [A1, A2]. When the current emotion value obtained by electronic device 100 based on real-time physiological data is greater than the maximum value in range 1 (i.e., A2), the emotion detection result output by model 2 is high risk. When the current emotion value obtained by electronic device 100 based on real-time physiological data is within range 1, the emotion detection result output by model 2 is medium risk. When the current emotion value obtained by electronic device 100 based on real-time physiological data is less than the minimum value in range 1 (i.e., A1), the emotion detection result output by model 2 is medium risk.

[0177] Optionally, the electronic device 100 can also correct the emotion benchmark value based on the emotion deviation value to obtain a corrected emotion benchmark value. When the electronic device 100 performs emotion detection next time, it can update the emotion benchmark value to the corrected emotion benchmark value. For example, the emotion benchmark value is obtained from historical heart rate values, that is, the emotion benchmark value is calculated by the above method ①. The emotion deviation value can be the difference between the instantaneous heart rate value and the emotion benchmark value. That is, the emotion deviation value is equal to the instantaneous heart rate value minus the emotion benchmark value. If the emotion deviation value is too large (for example, the emotion deviation value is much greater than the above threshold 2), the emotion benchmark value can be increased. If the emotion deviation value is too small (for example, the emotion deviation value is much smaller than the above threshold 1), the emotion benchmark value can be decreased. Since the emotion deviation value will affect the final emotion detection result, the electronic device 100 can adjust the emotion deviation value when the emotion deviation value is too large or too small, so as to make the final emotion detection result more accurate. This application embodiment does not limit the specific method of correcting the emotion benchmark value.

[0178] S203. When the user's emotion detection result is abnormal emotion, generate an abnormal emotion reminder.

[0179] Electronic device 100 can determine whether a user's current emotion is abnormal based on the user's emotion detection results. When it is determined that the user's current emotion is abnormal, it can generate an abnormal emotion alert. In this way, the user can immediately perceive that they are in an abnormal emotional state, so that the user can adjust their emotions in a timely manner.

[0180] In one possible implementation, if the user's emotion detection result includes low risk, medium risk, and high risk, then when the user's emotion detection result is medium risk or high risk, the electronic device 100 can determine that the user's current emotion is an abnormal emotion. When the user's emotion detection result is low risk, the electronic device 100 can determine that the user's current emotion is not abnormal, that is, it is a normal emotion.

[0181] In one possible implementation, if the user's emotion detection result represents a probability of risking an emotional disorder, then when the user's emotion detection result is greater than or equal to a probability threshold, the electronic device 100 can determine that the user's current emotion is an abnormal emotion. When the user's emotion detection result is less than the probability threshold, the electronic device 100 can determine that the user's current emotion is a normal emotion.

[0182] It is understood that this probability threshold can be configured by the system of the electronic device 100. For example, the probability threshold can be 50%, and the embodiments of this application do not limit the size of the probability threshold.

[0183] Optionally, in one possible implementation, the electronic device 100 can further compare the user's emotion (pleasure, unpleasantness, calmness, etc.) determined in the current emotion detection with the user's emotions detected in the previous or several previous detections. When the determined user emotion differs from the user's emotions detected in the previous or several previous detections, the electronic device 100 can determine that the user is currently experiencing an abnormal emotion. For example, if the user's emotion was pleasure in the previous or several detections, but the user's emotion is unpleasant in the current emotion detection, then the electronic device 100 can determine that the user is currently experiencing an abnormal emotion. As another example, if the user's emotion was unpleasant in the previous or several detections, but the user's emotion is pleasure in the current emotion detection, then the electronic device 100 can determine that the user is currently experiencing an abnormal emotion.

[0184] In one possible implementation, the electronic device 100 can determine the emotion classification result based on the emotion detection result.

[0185] Furthermore, in one possible implementation, when the electronic device 100 determines that the current emotion is an abnormal emotion, the electronic device 100 can also classify the abnormal emotion. For example, the types of abnormal emotions may include, but are not limited to: excessive anger, excessive excitement, excessive depression, excessive anxiety, etc. This application embodiment does not limit the types of abnormal emotions.

[0186] In other words, the emotion classification result may include, but is not limited to, any of the following: excessive liver fire, excessive excitement, excessive depression, excessive anxiety, etc.

[0187] Furthermore, in one possible implementation, as shown in Figure 3D, the electronic device 100 includes a valence model, an arousal model, and an abnormal emotion classification model. When the electronic device 100 determines that the emotion detection result is an abnormal emotion, it can input the real-time physiological data of the abnormal emotion into the valence model and the arousal model. The valence model can output the valence probability, and the arousal model can output the arousal probability. The electronic device 100 can input the valence probability and the arousal probability into the abnormal emotion classification model, which can output the type of abnormal emotion.

[0188] The valence model, arousal model, and abnormal emotion classification model can be machine learning-related models, such as decision trees, logistic regression, and support vector machines. These models can also be deep learning-related models, such as recurrent neural networks, convolutional neural networks, and long short-term memory networks. This application does not limit the specific models used in its embodiments.

[0189] Valence and arousal are two dimensions used to measure emotion. As shown in the two-dimensional model in Figure 3E, valence represents the degree of pleasure of an emotion, i.e., whether the emotion is pleasant or unpleasant. Arousal represents the activation level or intensity of an emotion. The horizontal axis in Figure 3E represents pleasure, gradually changing from unpleasant to pleasant from left to right. The horizontal axis can also be called the valence dimension. The vertical axis represents arousal, gradually changing from low to high arousal from bottom to top. The two dimensions of valence and arousal can divide emotions into four regions. In the unpleasant, low-arousal region, emotions such as sadness, frustration, boredom, and despondency may be included. In the pleasant, low-arousal region, emotions such as fatigue, ease, and satisfaction may be included. In the unpleasant, high-arousal region, emotions such as grief, anger, and fear may be included. In the pleasant, high-arousal region, emotions such as happiness, joy, excitement, and surprise may be included. It is understood that the emotion division shown in Figure 3E is only an example, and the specific division of the two dimensions of valence and arousal in this application embodiment is not limited.

[0190] Valence and arousal characteristics are the result of the combined effect of baseline calibration values ​​of various physiological indicators (including heart rate variability, heart rate, respiratory rate, and body temperature) in the model. The baseline calibration values ​​of these physiological indicators are input into the valence and arousal model to obtain the probabilities of valence and arousal. These probabilities are then input into an abnormal emotion classification model for classifying abnormal emotions. For example, the abnormal emotion classification model categorizes abnormal emotions based on valence and arousal probability into several types, such as excessive anger, excessively elevated mood, excessively depressed mood, and excessive anxiety.

[0191] In one possible implementation, the abnormal emotion is "excessive liver fire" when the valence probability is biased towards unpleasantness (e.g., the valence probability is below the threshold th1) and the arousal probability is biased towards high arousal (e.g., the arousal probability is above the threshold th2).

[0192] Alternatively, in one possible implementation, when the valence probability is biased towards unpleasantness (e.g., the valence probability is below threshold th1) and the arousal probability is biased towards low arousal (e.g., the arousal probability is below threshold th2), the abnormal emotion is "overly worried" or "overly frustrated".

[0193] Optionally, in one possible implementation, the abnormal emotion is "too difficult" when the valence probability is biased towards unpleasantness (e.g., the valence probability is below threshold th1) and the arousal probability is biased towards low arousal (e.g., the arousal probability is below threshold th3). That is, the user's current abnormal emotion can include both annoyance and a feeling of dejection. The threshold th3 is less than or equal to the threshold th2.

[0194] Alternatively, in one possible implementation, when the valence probability is biased towards pleasure (e.g., the valence probability is higher than the threshold th1) and the arousal probability is biased towards high arousal (e.g., the arousal probability is higher than the threshold th2), the abnormal emotion is "excessively high" or "excessively excited".

[0195] Alternatively, in one possible implementation, when the valence probability is in the middle and the arousal probability is biased towards extremely low arousal (e.g., arousal probability below the threshold th3), the abnormal emotion is "too tired," "feeling sleepy," or "can't keep my eyes open." That is, the user's current abnormal emotion can include both fatigue and a feeling of wanting to sleep due to exhaustion.

[0196] The thresholds th1, th2, and th3 can be configured by the electronic device 100 system. In this embodiment of the application, the specific values ​​of the thresholds th1, th2, and th3 are not limited.

[0197] It is understood that the electronic device 100 can adjust the emotional baseline value involved in this application embodiment and various parameters mentioned above (e.g., threshold 1, threshold 2, duration 1 and duration 2, threshold th1, threshold th2, threshold th3, etc.) based on the acquired user's medical history information. For example, if the electronic device 100 obtains information from the user's medical history indicating that the user currently has depression, then the electronic device 100 can lower the emotional baseline value, as well as lower the values ​​of parameters such as threshold 1, threshold 2, duration 1 and duration 2, threshold th1, threshold th2, and threshold th3.

[0198] The abnormal emotion alert generated by the electronic device 100 can be a prompt displayed on the user interface, or it can be a reminder in the form of voice, video, vibration, etc. This application embodiment does not limit the method of abnormal emotion alert. For a detailed description of the abnormal emotion alert as a prompt displayed on the user interface, please refer to the description in Figure 7F below, which will not be repeated here.

[0199] The following describes a detection method provided in this application embodiment by illustrating the corresponding changes in the user interface of the electronic device 100 when performing emotion detection.

[0200] Figure 6 illustrates an exemplary detection method provided by an embodiment of this application. As shown in Figure 6, the detection method provided by this embodiment may include the following steps:

[0201] S601. User operation 1 was detected. User operation 1 is used to enable the emotion detection function.

[0202] Electronic device 100 has the capability to provide emotion detection functionality. For example, electronic device 100 may have an application for emotion detection installed, referred to as an emotion detection application. Alternatively, the emotion detection functionality may not be provided by an emotion detection application installed in electronic device 100, but rather by a function provided by the system of electronic device 100. This application embodiment uses an electronic device 100 with an emotion detection application installed as an example for illustration.

[0203] Users can enable the emotion detection function in electronic device 100. For example, as shown in Figure 7A, electronic device 100 can display a user interface 700. In some examples, this user interface 700 can also be referred to as the always-on display interface of electronic device 100. When a user swipes up on the user interface 700 and receives the user's action, electronic device 100 can display a user interface 710.

[0204] As shown in Figure 7B, the electronic device 100 may display a user interface 710. The user interface 710 may display icons for multiple applications, such as an icon 711 for an emotion detection application, an icon 712 for a settings application, an icon for a phone application, and an icon for a camera application, etc.

[0205] The user can click the icon 711 of the emotion detection application in the user interface 710. The electronic device 100 can detect the user's action and start the emotion health detection.

[0206] In some examples, the emotion detection function may be a system function provided by the electronic device 100, and the user can click the icon 712 of the settings application to find the corresponding emotion detection function in the settings menu.

[0207] That is, in this embodiment, the user operation 1 input by the user may include an upward swipe operation in the user interface 700 and an operation of clicking the emotion detection application icon 711 in the user interface 710. Alternatively, user operation 1 may simply include the operation of clicking the emotion detection application icon 711 in the user interface 710. Or, user operation 1 input by the user may include the operation of clicking the settings application icon 712 in the user interface 710 and the operation of finding the emotion detection function in the settings menu and enabling the emotion detection function. This embodiment does not limit the specific nature of the user operation 1.

[0208] Electronic device 100 can detect the user operation 1 via a touch sensor.

[0209] S602. In response to user operation 1, display prompt message 1, prompt message 1 is used to prompt the user that the emotion detection function needs to collect the user's personal information.

[0210] In response to the user's action 1, the electronic device 100 can display a prompt message 1. This prompt message can be used to remind the user that the emotion detection function needs to collect the user's personal information, such as the user's physiological data, behavioral data, and emotional health-related data, etc.

[0211] For example, in response to user operation 1, electronic device 100 can display user interface 720 as shown in FIG7C. The user interface 720 may include a prompt message 721, which can be used to prompt that relevant information needs to be filled in to enable emotion detection. For example, as shown in FIG7C, the prompt message 721 may be "Hello, to enable abnormal emotion recognition, please log in to the Sports and Health APP and fill in the relevant information."

[0212] That is, the prompt message 1 can be the prompt message 721 shown in Figure 7C. The specific content of the prompt message 1 is not limited in this embodiment of the application.

[0213] Optionally, the user interface 720 may also include a control 722. Users can click the control 722 to log in to the sports and health app and fill in relevant information, such as user medication information, disease status, questionnaire information, etc.

[0214] S603. Display prompt message 2, which is used to prompt the user to synchronize historical data.

[0215] After the electronic device 100 displays prompt message 1, it can display prompt message 2 based on the user's operation. For example, after the user clicks control 722 in the user interface 720 shown in Figure 7C and fills in the relevant information, the electronic device 100 can display prompt message 2.

[0216] Alternatively, after the electronic device 100 finishes displaying prompt message 1, it can directly display prompt message 2.

[0217] The prompt message 2 can be used to prompt the user to synchronize historical data. For example, the prompt message 2 may include prompt message 731 shown in Figure 7D and prompt message 741 shown in Figure 7E. As shown in Figure 7D, the electronic device 100 can display a user interface 730, which may include prompt message 731. Prompt message 731 may read, "Hello, abnormal emotion recognition requires collecting historical data for a period of time; please continue wearing it." As shown in Figure 7E, the electronic device 100 can display a user interface 740, which may include prompt message 741. Prompt message 741 may read, "To ensure the accuracy of abnormal emotion recognition, please synchronize historical data in a timely manner."

[0218] It is understood that the specific content of the prompt message 2 is not limited in the embodiments of this application.

[0219] In one possible implementation, step S603 can be an optional step, that is, the electronic device 100 may not display the prompt message 2 and may synchronize historical data in the background.

[0220] S604. Display the emotional baseline value, which is obtained by the electronic device 100 based on historical data synchronized by the user.

[0221] After collecting historical data for a period of time, the electronic device 100 can calculate an emotional baseline value based on the user's synchronized historical data. The historical data may include one or more of the user's historical physiological data, behavioral data, and emotional health-related data. For details on how the electronic device 100 calculates the emotional baseline value, please refer to the description of step S202 above; it will not be repeated here.

[0222] After the electronic device 100 calculates the emotional baseline value, it can display the value. For example, as shown in FIG7F, the electronic device 100 can display a user interface 750, which may include text information 751 and graphic information 752. The text information 751 may include the text content "3-day average emotional baseline value: 80". The graphic information 752 describes the historical emotional baseline values ​​over the past 3 days; for example, the emotional baseline value on the 16th was 60, on the 17th it was 80, and on the 18th it was 100. It is understood that FIG7F showing the electronic device 100 calculating the daily emotional baseline value is merely an example, and this application embodiment does not limit whether the electronic device 100 calculates the emotional baseline value daily.

[0223] This application does not limit how the electronic device 100 displays the emotion benchmark value. The user interface 750 shown in Figure 7F is only an example and does not constitute a limitation on this application.

[0224] In one possible implementation, step S604 can also be an optional step, that is, the electronic device 100 can also calculate the emotion benchmark value in the background, but not display it.

[0225] S605. Perform emotion detection and display the emotion detection results.

[0226] The electronic device 100 can perform emotion detection based on the acquired real-time physiological data and the calculated emotion baseline value, and display the emotion detection results. For details on how the electronic device 100 performs emotion detection, please refer to step S202 and the description in Figure 3C; it will not be repeated here.

[0227] For example, the electronic device 100 may display the emotion detection result as shown in Figure 7G. Figure 7G exemplarily illustrates the user interface 760 of the electronic device 100. The user interface 760 may include text information 761 and an icon 762, as well as text information 763. The text information 761 may be used to describe the emotion detection result, for example, the emotion detection result may be "medium risk". The icon 762 is used to prompt the user that the current emotion is abnormal. The text information 763 may include "Please adjust your emotions in time".

[0228] It is understood that the embodiments of this application do not limit how the electronic device 100 displays the emotion detection results. The user interface 760 shown in Figure 7G is only an example and does not constitute a limitation on the embodiments of this application.

[0229] In one possible implementation, the electronic device 100 can continuously perform emotion detection, and only when an abnormal emotion is detected will the electronic device 100 be triggered to display the emotion detection result.

[0230] Alternatively, in another possible implementation, the electronic device 100 can detect emotion detection results over a period of time, statistically analyze the emotion detection results over that period, and display the statistical results.

[0231] S606. Determine whether the duration of the abnormal emotion exceeds the preset duration or whether the amplitude of the abnormal emotion exceeds the preset amplitude. If yes, proceed to step S607; otherwise, proceed to step S605.

[0232] The electronic device 100 can determine whether the duration of the abnormal emotion exceeds a preset time. If so, it will issue an abnormal emotion warning, that is, execute the following step S607; if not, it will continue to detect the user's emotions, that is, continue to execute step S605.

[0233] Alternatively, the electronic device 100 can determine whether the amplitude of the abnormal emotional fluctuation exceeds a preset range. If the electronic device 100 determines that the user's emotion detection result is abnormal within a short period of time, and the fluctuation of the emotion value exceeds the preset range, the electronic device 100 can issue an abnormal emotion warning, that is, execute the following step S607; otherwise, it continues to detect the user's emotion, that is, it continues to execute step S605.

[0234] S607. Issue an early warning for abnormal emotions.

[0235] When a user's abnormal emotion persists for more than a preset duration, the electronic device 100 can issue an abnormal emotion warning. For example, as shown in FIG7H, the electronic device 100 can display a user interface 770, which may include a warning icon 771 and a warning message 772. The warning message 772 reads, "Negative emotions have lasted for an extended period; please alleviate them promptly."

[0236] Optionally, the user interface 770 may also include prompt text 773, which may be "Abnormal Emotion Reminder" to indicate that the current user interface 770 is an abnormal emotion reminder interface. This application embodiment does not limit the content of the prompt text 773.

[0237] The electronic device 100 can also provide warnings through one or more of the following: voice, video, animation, and vibration. For example, while displaying the user interface 700, the electronic device 100 may issue a voice warning such as "The user's negative emotions have lasted for a long time, please alleviate them in time."

[0238] This application does not limit the form in which the electronic device 100 provides an abnormal emotion warning.

[0239] S608. Provide corresponding mood improvement services.

[0240] Optionally, the electronic device 100 can provide corresponding mood-enhancing services. For example, the electronic device 100 can display corresponding mood-enhancing service options (e.g., music, exercise, etc.) for the user to choose from. As shown in Figure 7I, the electronic device 100 can have a user interface 780, which may also include text information 781, controls 782, and controls 783. The text information 781 can be "Mood health improvement service". When the user clicks the control 782, the electronic device 100 can play mood-relieving music. When the user clicks the control 783, the electronic device 100 can display exercise training courses that can improve mood.

[0241] Electronic device 100 can determine whether a user's emotional health risk level has increased over a period of time based on multiple emotion detection results. For example, if electronic device 100 detects that multiple emotion detection results (e.g., the probability of having a mood disorder) are continuously increasing over a period of time, and the user's emotional health risk level is continuously rising, electronic device 100 can display the user's emotional health risk trend and suggest that the user seek professional help. For example, as shown in FIG7J, electronic device 100 can display the user interface 790, which may include a risk trend 791 and a prompt message 792. The risk trend 791 indicates that the user's emotional health risk is continuously increasing. The content of the prompt message 792 may be "Risk is continuously increasing, it is recommended to seek professional help."

[0242] Optionally, in this embodiment, the interface for displaying abnormal emotion alerts by the electronic device 100 may not be limited to the interface shown in FIG7G, and may have different interface forms. For example, when the electronic device 100 detects an abnormal emotion in the user, it may display the abnormal emotion alert interface shown in FIG8A or FIG8B. As shown in FIG8A, when the electronic device 100 detects that the user's emotion is negative, it may display a user interface 800. The user interface 800 may include an icon 801 and a prompt message 802. The icon 801 may be used to prompt the user that their current emotion is not good. The prompt message 802 may be "My mood has suddenly worsened, do I need to seek help?"

[0243] In some examples, an abnormal emotion alert can be displayed when a user has been consistently detected with negative or flat emotions, and then suddenly their emotions are detected as positive. This abnormal emotion alert can be used to remind the user to record positive emotional moments. For example, as shown in Figure 8B, when the electronic device 100 detects a positive user emotion, it can display a user interface 810. This user interface 810 can include an icon 811 and a prompt message 812. The icon 811 can be used to indicate to the user that their current mood is good. The prompt message 812 can be something like, "My mood has suddenly improved; do you want to record this positive moment?"

[0244] In some feasible examples, the interface and prompts displayed by the electronic device 100 when the user is experiencing emotional abnormalities can be linked to the user's medical history. If the user information includes the user's medical condition (e.g., hypertension or hyperglycemia), when the electronic device 100 detects an emotional abnormality, it can also promptly remind or warn the user to pay attention to their health, such as by taking blood pressure or blood sugar measurements.

[0245] For example, as shown in Figure 9A, electronic device 100 can determine that a user has hypertension. For example, electronic device 100 can obtain information from a medication reminder app that the user needs to take hypertension medication, thereby determining that the user has hypertension. Alternatively, if electronic device 100 obtains information from the user's personal information indicating that they have hypertension, electronic device 100 can determine that the user has hypertension. When electronic device 100 determines that the user has hypertension, and when electronic device 100 detects abnormal emotions in the user, electronic device 100 can display a user interface 900. This user interface 900 can include a prompt message 901, which is used to alert the user to the abnormal emotions and suggest that the user measure their blood pressure. For example, the prompt message 901 could be "Significant emotional fluctuations; blood pressure measurement recommended." This timely reminder helps prevent abnormal emotions from affecting the user's blood pressure health.

[0246] For example, as shown in Figure 9B, electronic device 100 can determine that a user has hyperglycemia. For example, electronic device 100 can obtain information from a medication reminder app that the user needs to take hyperglycemia medication, thereby determining that the user has hyperglycemia. Alternatively, if electronic device 100 obtains information from the user's personal information indicating that they have hyperglycemia, electronic device 100 can determine that the user has hyperglycemia. When electronic device 100 determines that a user has hyperglycemia, and detects an abnormal emotional state in the user, electronic device 100 can display a user interface 910. This user interface 910 can include a prompt message 911, which is used to alert the user to the abnormal emotional state and suggest that the user measure their blood sugar. For example, the prompt message 911 could be "Significant emotional fluctuations; blood sugar measurement recommended." This timely reminder helps prevent the user's emotional abnormalities from affecting their blood sugar health.

[0247] In other feasible examples, the interface and prompts displayed by the electronic device 100 when the user is in an emotional abnormality will change depending on the specific time period and location of the user.

[0248] For example, as shown in Figure 10A, when the electronic device 100 obtains information from the user's input or from a menstrual cycle app indicating that the user is currently pregnant, the electronic device 100 can display a user interface 1000 when it detects abnormal emotions in the user. This user interface 1000 may include a prompt message 1001, which could be something like, "Your emotions are fluctuating significantly; it is recommended to perform breathing exercises or other methods to alleviate them."

[0249] As another example, as shown in Figure 10B, when the electronic device 100 obtains information from the user's input or from a menstrual cycle app indicating that the user is currently pregnant, and obtains information from GPS location indicating that the user is outdoors, the electronic device 100 can display a user interface 1010 when it detects abnormal emotions in the user. This user interface 1010 may include a prompt message 1011, which could be something like, "Significant emotional fluctuations; fall detection recommended."

[0250] In another possible implementation, when the electronic device 100 determines that the user is pregnant, is outdoors, or detects abnormal emotional state, it can also directly activate fall detection in the background.

[0251] As another example, as shown in Figure 10C, when the electronic device 100 obtains information from the user's input or from a menstrual cycle app indicating that the user is currently in the postpartum period, and the electronic device 100 detects an abnormal emotional state in the user, it can display a user interface 1020. This user interface 1020 may include a prompt message 1021 and a control 1022. The prompt message 1021 may be something like, "Your emotions are fluctuating significantly; would you like to talk to someone?" When the user clicks the control 1022, the electronic device 100 can call a public psychological support organization, a friend, or display a phone call interface.

[0252] In this way, when a user is in a specific period of time, timely reminders of abnormal emotions can prevent the user from developing emotional disorders such as depression or anxiety due to long-term emotional abnormalities.

[0253] In some other feasible examples, electronic device 100 can also activate other electronic devices in the home to improve the user's mood when it detects abnormal user emotions.

[0254] For example, as shown in FIG11A, when the electronic device 100 establishes a communication connection with the smart light and the smart speaker, the electronic device 100 can display a user interface 1100 when it detects an abnormal user emotion. The user interface 1100 may include a prompt message 1101, a control 1102, and a control 1103. The prompt message 1101 can be used to prompt the user to turn on devices that can alleviate emotions (e.g., the smart light and the smart speaker). For example, the prompt message 1101 could be "Negative emotions have lasted for a long time; would you like to turn on the smart light and the smart speaker?" The control 1102 can be used to turn on the smart light. The control 1103 can be used to turn on the smart speaker.

[0255] As an example, as shown in FIG11B, when the electronic device 100 establishes a communication connection with the smart TV, and the electronic device 100 detects an abnormal user emotion, it can display a user interface 1110. This user interface 1110 may include a prompt message 1111 and a control 1112. The prompt message 1111 can be used to prompt the user to turn on a device that can alleviate emotions (e.g., a smart TV). For example, the prompt message 1111 could be "Negative emotions have lasted for a long time; would you like to turn on the smart TV?" The control 1112 can be used to turn on the smart TV.

[0256] As another example, as shown in FIG11C, when the electronic device 100 establishes a communication connection with the air conditioner, and the electronic device 100 detects that the user's emotional state is abnormal and the current air temperature or the user's body temperature is high, a user interface 1120 can be displayed. This user interface 1120 may include a prompt message 1121 and a control 1122. The prompt message 1121 can be used to prompt the user to turn on a device that can alleviate their emotions (e.g., an air conditioner). For example, the prompt message 1121 could be "Negative emotions have lasted for a long time; would you like to turn on the air conditioner?" The control 1122 can be used to turn on the air conditioner.

[0257] In this way, when a user is experiencing emotional distress, other electronic devices nearby can help alleviate that distress.

[0258] Furthermore, the electronic device 100 may also provide multiple mood-enhancing services for the user to choose from, allowing the user to select one service. For example, referring to Figure 7I, the user can click control 782 to play music, or click control 783 to view exercise training courses. The description of Figure 7I is provided above and will not be repeated here. Alternatively, the electronic device 100 may also display multiple controls on the interface for activating multiple mood-enhancing devices, allowing the user to select and activate one or more mood-enhancing devices.

[0259] This application does not limit the specific nature of the multiple mood-improvement services that the electronic device 100 can provide.

[0260] Optionally, in one possible implementation, if the electronic device 100 categorizes abnormal emotions, it can also display the user's abnormal emotion categorization results. Furthermore, the electronic device 100 can also provide different abnormal emotion reminders or suggestions based on different types of abnormal emotions.

[0261] For example, as shown in FIG12A, the electronic device 100 can display a user interface 1200. The user interface 1200 may include text information 1201, an icon 1202, and a prompt message 1203. The text information 1201 is used to indicate the type of abnormal emotion of the user; for example, the type of abnormal emotion could be "excessive liver fire." The icon 1202 can be used to prompt the user to turn on the air conditioner, or to indicate that the electronic device 100 has established a communication connection with the air conditioner in the user's location. Alternatively, the user can click the icon 1202, and in response to the user's operation, the electronic device 100 can send command 1 to the air conditioner. The air conditioner can then start or stop in response to command 1. The prompt message 1203 can be used to prompt the user that the negative emotion has persisted for a long time and suggest that the user turn on the air conditioner.

[0262] As exemplarily shown in FIG12B, the electronic device 100 may display a user interface 1210. The user interface 1210 may include text information 1211, an icon 1212, and a prompt message 1213. The text information 1211 indicates the type of abnormal emotion experienced by the user; for example, the type of abnormal emotion could be "excessive liver fire." The icon 1212 may be used to prompt the user to turn on the smart light, or to indicate that the electronic device 100 has established a communication connection with the smart light in the user's location. Alternatively, the user can click the icon 1212, and in response to the user's action, the electronic device 100 may send command 2 to the smart light. The smart light may turn on or off in response to command 2. The prompt message 1213 may be used to indicate that the user's heightened emotions have persisted for a long time and to suggest that the user turn on the smart light to soothe their mood.

[0263] As exemplarily shown in FIG12C, the electronic device 100 may display a user interface 1220. The user interface 1220 may include text information 1221, an icon 1222, and a prompt message 1223. The text information 1221 indicates the type of abnormal emotion experienced by the user; for example, the type of abnormal emotion may be "excessively depressed." The icon 1222 may be used to prompt the user to turn on the smart speaker, or to indicate that the electronic device 100 has established a communication connection with a smart speaker in the user's location. Alternatively, the user can click the icon 1222, and in response to the user's action, the electronic device 100 may send command 3 to the smart speaker. The smart speaker may turn on or off in response to command 3. The prompt message 1213 may be used to prompt the user that they are currently too depressed and suggest that the user turn on the smart speaker to play a cheerful song.

[0264] As an example, as shown in FIG12D, the electronic device 100 can display a user interface 1230. The user interface 1230 may include text information 1231, an icon 1232, and a prompt message 1233. The text information 1231 is used to indicate the type of abnormal emotion of the user; for example, the type of abnormal emotion could be "Do you feel it's too difficult?" The icon 1232 can be used to prompt the user to turn on the smart TV, or to indicate that the electronic device 100 has established a communication connection with the smart TV in the user's location. Alternatively, the user can click the icon 1232, and in response to the user's operation, the electronic device 100 can send command 4 to the smart TV. The smart TV can then turn on or off in response to command 4. The prompt message 1213 can be used to suggest that the user turn on the smart TV to play a funny video.

[0265] In one possible implementation, when the electronic device 100 determines that the user has a medical history, such as hypertension, the electronic device 100 can display the type of abnormal emotion the user is currently experiencing, along with relevant suggestions, when issuing an abnormal emotion alert.

[0266] For example, as shown in Figure 12E, when the electronic device 100 obtains from the medication reminder app that the user needs to take hypertension medication on time, and when the electronic device 100 detects an abnormal emotion in the user, the electronic device 100 can display a user interface 1240. This user interface 1240 can include text information 1241 and prompt information 1242. The text information 1241 can be used to indicate that the user is currently experiencing an abnormal emotion. The prompt information 1242 is used to indicate the type of abnormal emotion; for example, the type of abnormal emotion could be "excessively emotional." Optionally, the prompt information 1242 can also be used to provide suggestions from the electronic device 100, such as suggesting that the user measure their blood pressure. For example, the prompt information 1242 could be "Excessively emotional, blood pressure measurement recommended." This allows for timely reminders to the user, preventing abnormal emotions from affecting their blood pressure health.

[0267] In one possible implementation, when the electronic device 100 determines that the user is in a specific period (e.g., postpartum), the electronic device 100 can display the type of abnormal emotion the user is experiencing and related suggestions when issuing an abnormal emotion alert.

[0268] As another example, as shown in Figure 12F, when the electronic device 100 obtains information from the user's input or from a menstrual cycle app indicating that the user is currently in the postpartum period, and the electronic device 100 detects an abnormal emotional state in the user, it can display a user interface 1250. This user interface 1250 may include a prompt message 1251 and a control 1252. The prompt message 1251 may be something like, "Your current mood is too low; would you like to talk to someone?" When the user clicks the control 1252, the electronic device 100 can call a public psychological institution, a friend, or display a phone call interface.

[0269] Optionally, in one possible implementation, the electronic device 100 can acquire emotion detection results within a specific period or based on user operation, and generate emotional health risk results. For example, the specific period can be the end of the current month, and the electronic device 100 can generate abnormal emotion statistics for the current month on the last day of the current month. This application embodiment does not limit the specific period. For example, if the time period 3 is one month, that is, if the electronic device 100 generates the abnormal emotion statistics result on October 31, 2024, then the time period 3 is the period from October 1, 2024 to October 31, 2024, before the generation of the abnormal emotion statistics result. This application embodiment does not specifically limit the time period 3.

[0270] The emotional health risk results may include one or more of the following: the user's abnormal emotional statistics within time period 3, the user's probability of being at risk of developing an emotional disorder, and the user's emotional tags. The abnormal emotional statistics include the number of abnormal emotions experienced by the user within the time period 3, the location where the abnormal emotions occurred, and the time point when the abnormal emotions occurred.

[0271] For example, electronic device 100 can generate simplified and detailed versions of abnormal emotion statistics. When electronic device 100 is a wearable device such as a watch or bracelet, it can display the simplified version of the abnormal emotion statistics. For example, as shown in Figure 13A, electronic device 100 displays user interface 1300, which is used to display the simplified version of the abnormal emotion statistics. The user interface 1300 may include text information 1301, 1302, 1303, and 1304. Text information 1301 can be used to indicate that the content displayed on the current interface is the abnormal emotion statistics. Text information 1302 includes the frequency of abnormal emotions experienced by the user this month, for example, excessive anger 3 times, excessive excitement 2 times, and excessive depression 1 time. Text information 1303 can be used to indicate the user's emotion label, for example, the user's emotion label is "emotionally unstable". Text information 1304 can be used to prompt the user to go to their mobile phone to view the detailed results, that is, the detailed version of the abnormal emotion statistics.

[0272] Figure 13B exemplarily illustrates a detailed version of the abnormal emotion statistics. As shown in Figure 13B, the electronic device 200 can be a mobile phone. The electronic device 200 may display a user interface 1310, which can be used to display the detailed version of the abnormal emotion statistics. The user interface 1310 may include text information 1311, text information 1312, text information 1313, and text information 1314. Text information 1311 may include the frequency of abnormal emotions in the current month (e.g., 6 times) and / or the frequency of abnormal emotions in the past six months (e.g., 30 times). Text information 1312 includes the frequency of various abnormal emotions experienced by the user this month, for example, excessive anger 3 times, excessive excitement 2 times, and excessive depression 1 time. Text information 1313 may include the user's emotion tag and the corresponding emotional characteristics, for example, the user's emotion tag is "emotionally unstable". The emotional characteristic of "emotionally unstable" is that the user is prone to mood swings, and it prompts the user to pay immediate attention to emotional abnormalities. The text information 1314 may include the time and place of abnormal emotions occurring within the month, for example, the location and time of the first abnormal emotion (such as excessive liver fire) occurring within the month.

[0273] The simplified version of the abnormal emotion results provided in this application embodiment may include more or less content in the detailed version of the abnormal emotion results. This application embodiment does not limit the specific content of the simplified version of the abnormal emotion results in the detailed version of the abnormal emotion results.

[0274] Optionally, in one possible implementation, since the electronic device 100 has recorded when and where the user experienced abnormal emotions, when the user returns to the location where the abnormal emotion occurred, the electronic device 100 can remind the user to pay attention to the abnormal emotion and suggest that the user alleviate the emotion in a timely manner. For example, if the user experienced an abnormal emotion of excessive anger while helping their child with homework at 7 pm every evening, then before 7 pm, for example, at 6:55 pm, the electronic device 100 can remind the user to eat a dessert or perform breathing exercises to alleviate the emotion. As another example, if the user experienced an abnormal emotion of excessive excitement at location A, then when the user returns to location A, the electronic device 100 can suggest that the user listen to music or perform breathing exercises to alleviate the abnormal emotion.

[0275] Alternatively, in one possible implementation, the user can establish an emotion sharing group with other family members or friends. When the electronic device 100 detects an abnormal emotion in the user, it can share the user's emotion detection results with other members in the emotion sharing group, prompting members to pay attention to the user's emotions.

[0276] Furthermore, the electronic device 100 will only share the user's emotion detection results with members who have authorized it.

[0277] Alternatively, in one possible implementation, the electronic device 100 may also share the user's abnormal emotion statistics with other users.

[0278] This application also provides an emotion detection system. In this system, a wearable electronic device 100 can be used to collect real-time physiological data. The electronic device 100 can send the collected real-time physiological data to a more powerful portable electronic device, such as a mobile phone or tablet. The mobile phone or tablet can then perform emotion detection based on the physiological data sent by the electronic device 100.

[0279] For example, FIG14 shows a schematic diagram of an emotion detection system 1400 provided in an embodiment of this application. As shown in FIG14, the emotion detection system 1400 may include an electronic device 100 and an electronic device 200. The electronic device 100 and the electronic device 200 establish a communication connection.

[0280] Electronic device 100 can be worn by users and has the ability to collect users' physiological data. Electronic device 100 can be a smartwatch, smart bracelet, etc.

[0281] Electronic device 200 can be any of the following: mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, cellular phone, personal digital assistant (PDA), in-vehicle computer, etc.

[0282] When users need to undergo emotion testing, they can fill in personal information such as questionnaire information and medication information on electronic device 100 or electronic device 200.

[0283] The user can trigger the start of emotion detection on either electronic device 100 or electronic device 200. If the user triggers the start of emotion detection on electronic device 200, upon receiving the user's operation, electronic device 200 begins emotion detection and sends an instruction to electronic device 100 to acquire physiological data and / or behavioral data. After receiving the instruction from electronic device 200, electronic device 100 begins to collect the user's physiological data and / or behavioral data in real time. That is, electronic device 100 can execute step ① in Figure 14. Electronic device 100 can also send the collected physiological data and / or behavioral data to electronic device 200.

[0284] If a user triggers the start of emotion detection on electronic device 100, electronic device 100 responds to the user's operation by starting to collect the user's physiological data and / or behavioral data in real time. That is, electronic device 100 can execute step ① in Figure 14. The physiological data and / or behavioral data collected by electronic device 100 are sent to electronic device 200, instructing electronic device 200 to start emotion detection.

[0285] Electronic device 200 can perform emotion detection based on physiological and / or behavioral data, which is equivalent to performing step ② in Figure 14. When electronic device 200 begins emotion detection, it can first calculate an emotion baseline value. For details on how electronic device 200 calculates the emotion baseline value, please refer to the specific description of electronic device 100 calculating the emotion baseline value in step S202 above, which will not be repeated here.

[0286] The electronic device 200 contains Model 1, and inputs the acquired real-time physiological data and the calculated emotional baseline value into Model 1 to obtain the corrected emotional baseline value. For a description of Model 1, please refer to Figure 3A or Figure 3B; it will not be repeated here.

[0287] In one possible implementation, the electronic device 200 can also correct the parameters of the model 1 based on the user's physiological data, so that the accuracy of the corrected emotional baseline value shown by the model 1 becomes higher and higher.

[0288] The electronic device 200 can also obtain emotion detection results. That is, the electronic device 200 can execute step ③ shown in Figure 14. For example, the electronic device 200 can also have a model 2. The electronic device 200 can input real-time physiological data and corrected emotion benchmark values ​​into the model 2 to obtain emotion detection results. For details about the model 2 and the emotion detection results output by the model 2, please refer to the description in step S202 and Figure 3C, which will not be repeated here.

[0289] The electronic device 200 can display the emotion detection results.

[0290] Alternatively, electronic device 200 can send the emotion detection result to electronic device 100. After receiving the emotion detection result, electronic device 100 can display the emotion detection result. That is, electronic device 100 can execute step ④ in Figure 14.

[0291] In this emotion detection system, electronic device 100 or electronic device 200 can provide abnormal emotion alerts and warnings using any of the abnormal emotion alert methods shown in Figures 8A-8B, 9A-9B, and 10A-10C. Electronic device 100 or electronic device 200 can also coordinate with other nearby devices to alleviate the user's emotions when abnormal emotions are detected. See Figures 11A-11C for details, which will not be repeated here.

[0292] In one possible implementation, when electronic device 100 or electronic device 200 displays the user's emotion detection results, electronic device 100 or electronic device 200 can also display different virtual images (e.g., electronic pets) based on the emotion detection results.

[0293] In some examples, electronic device 100 or electronic device 200 displays virtual avatars with different expressions to correspond to different emotion detection results of the user. For example, if the emotion detection result represents different levels of risk (low risk, medium risk, high risk), then: when the user's emotion detection result is low risk, the virtual avatar displayed by electronic device 100 or electronic device 200 will display a smiling expression; when the user's emotion detection result is medium risk, the virtual avatar displayed by electronic device 100 or electronic device 200 will display a worried expression; and when the user's emotion detection result is high risk, the virtual avatar displayed by electronic device 100 or electronic device 200 will display a crying expression.

[0294] Optionally, in other examples, electronic device 100 or electronic device 200 displays virtual avatars of different colors to correspond to different user emotion detection results. For example, if the emotion detection result represents different levels of risk (low risk, medium risk, high risk), then: when the user's emotion detection result is high risk, the virtual avatar displayed by electronic device 100 or electronic device 200 is red; when the user's emotion detection result is medium risk, the virtual avatar displayed by electronic device 100 or electronic device 200 is green; and when the user's emotion detection result is low risk, the virtual avatar displayed by electronic device 100 or electronic device 200 is blue.

[0295] Optionally, in some further examples, electronic device 100 or electronic device 200 displays different types of virtual avatars corresponding to different user emotion detection results. For example, if the emotion detection result represents different levels of risk (low risk, medium risk, high risk), then when the user's emotion detection result is low risk, electronic device 100 or electronic device 200 displays a panda. When the user's emotion detection result is medium risk, electronic device 100 or electronic device 200 displays a wolf. When the user's emotion detection result is high risk, electronic device 100 or electronic device 200 displays a tiger.

[0296] In this embodiment, electronic device 100 or electronic device 200 can also combine different features such as the type, color, and expression of the virtual avatar to represent different emotion detection results of the user. This embodiment does not limit the specific type, color, expression, etc., of the virtual avatar displayed by electronic device 100 or electronic device 200.

[0297] Alternatively, in another possible implementation, when electronic device 100 or electronic device 200 displays the user's emotion classification result, electronic device 100 or electronic device 200 can also display different virtual avatars (e.g., virtual pets) based on the emotion classification result. For example, when the user's emotion classification result is "excessive anger," the virtual avatar displayed by electronic device 100 or electronic device 200 could be a roaring tiger. When the user's emotion classification result is "excessively high," the virtual avatar displayed by electronic device 100 or electronic device 200 could be a grinning monkey. When the user's emotion classification result is "excessively low," the virtual avatar displayed by electronic device 100 or electronic device 200 could be a puppy with drooping ears. When the user's emotion classification result is "excessively anxious," the virtual avatar displayed by electronic device 100 or electronic device 200 could be a monkey scratching its ears and cheeks.

[0298] Similarly, in this embodiment, electronic device 100 or electronic device 200 can also combine different features such as the type, color, and expression of the virtual avatar to represent different emotional classification results of the user. This embodiment does not limit the specific type, color, expression, etc., of the virtual avatar displayed by electronic device 100 or electronic device 200.

[0299] Figure 15 illustrates an exemplary hardware structure diagram of the electronic device 200.

[0300] The electronic device 200 can be various types of smart terminal devices, and the specific type of the electronic device 200 is not limited in this application embodiment. For example, the electronic device 200 can be a mobile phone, as well as a tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, etc.

[0301] As shown in Figure 15, the electronic device 200 may include a processor 210, an external memory interface 220, an internal memory 221, a USB interface 230, a charging management module 240, a power management module 241, a battery 242, an antenna 1, an antenna 2, a mobile communication module 250, a wireless communication module 260, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, a headphone jack 270D, a sensor module 280, buttons 290, a motor 291, an indicator 292, a camera 293, a display screen 294, and a SIM card interface 295, etc. The sensor module 280 may include at least one of the following: a pressure sensor 280A, a gyroscope sensor 280B, a barometric pressure sensor 280C, a magnetic sensor 280D, an accelerometer sensor 280E, a distance sensor 280F, a proximity sensor 280G, a fingerprint sensor 280H, a temperature sensor 280J, a touch sensor 280K, an ambient light sensor 280L, and a bone conduction sensor 280M.

[0302] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 200. In other embodiments of this application, the electronic device 200 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.

[0303] Processor 210 may include one or more processing units, such as application processors, modem processors, graphics processors, image signal processors, controllers, memory, video codecs, digital signal processors, baseband processors, and / or neural network processors. These different processing units may be independent devices or integrated into one or more processors. In some embodiments, electronic device 200 may also include one or more processors 210.

[0304] The controller can be the nerve center and command center of the electronic device 200. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0305] The processor 210 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 210 is a cache memory. This memory can store instructions or data that the processor 210 has just used or that are used repeatedly. If the processor 210 needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 210, and thus improves the efficiency of the electronic device 200.

[0306] In some embodiments, the processor 210 may include one or more interfaces. These interfaces may include an I2C interface, an I2S interface, a PCM interface, a UART interface, a MIPI interface, a GPIO interface, a SIM card interface 295, and / or a USB interface 230, etc.

[0307] The I2C interface is a bidirectional synchronous serial bus, including a serial data line and a serial clock line. In some embodiments, the processor 210 may include multiple I2C buses. The processor 210 can couple to the touch sensor 280K, charger, flash, camera 293, etc., through different I2C bus interfaces. For example, the processor 210 can couple to the touch sensor 280K through the I2C interface, enabling the processor 210 and the touch sensor 280K to communicate through the I2C bus interface, thereby realizing the touch function of the electronic device 200.

[0308] The I2S interface can be used for audio communication. In some embodiments, the processor 210 may include multiple I2S buses. The processor 210 can be coupled to the audio module 270 via the I2S bus to enable communication between the processor 210 and the audio module 270. In some embodiments, the audio module 270 can transmit audio signals to the wireless communication module 260 via the I2S interface to enable the function of answering phone calls through a Bluetooth headset.

[0309] The PCM interface can also be used for audio communication, sampling, quantizing, and encoding analog signals. In some embodiments, the audio module 270 and the wireless communication module 260 can be coupled via the PCM bus interface. In some embodiments, the audio module 270 can also transmit audio signals to the wireless communication module 260 via the PCM interface, enabling the function of answering phone calls through a Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

[0310] The UART interface is a universal serial data bus used for asynchronous communication. This bus can be a bidirectional communication bus. It converts the data to be transmitted between serial and parallel communication. In some embodiments, the UART interface is typically used to connect the processor 210 and the wireless communication module 260. For example, the processor 210 communicates with the Bluetooth module in the wireless communication module 260 via the UART interface to implement Bluetooth functionality. In some embodiments, the audio module 270 can transmit audio signals to the wireless communication module 260 via the UART interface to enable music playback through Bluetooth headphones.

[0311] The MIPI interface can be used to connect the processor 210 to peripheral devices such as the display screen 294 and the camera 293. The MIPI interface includes a camera serial interface and a display screen serial interface. In some embodiments, the processor 210 and the camera 293 communicate via a CSI interface to enable the electronic device 200 to capture images. The processor 210 and the display screen 294 communicate via a DSI interface to enable the electronic device 200 to display images.

[0312] The GPIO interface can be configured via software. It can be configured as a control signal or a data signal. In some embodiments, the GPIO interface can be used to connect the processor 210 to a camera 293, a display screen 294, a wireless communication module 260, an audio module 270, a sensor module 280, etc. The GPIO interface can also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, etc.

[0313] USB port 230 is a USB standard compliant interface, specifically a Mini USB port, Micro USB port, or USB Type-C port. USB port 230 can be used to connect a charger to charge electronic device 200, and can also be used for data transfer between electronic device 200 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.

[0314] 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 200. In other embodiments, the electronic device 200 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0315] The charging management module 240 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 240 receives charging input from the wired charger via a USB interface 230. In some wireless charging embodiments, the charging management module 240 receives wireless charging input via the wireless charging coil of the electronic device 200. While charging the battery 242, the charging management module 240 can also supply power to the electronic device via the power management module 241.

[0316] The power management module 241 connects the battery 242, the charging management module 240, and the processor 210. The power management module 241 receives input from the battery 242 and / or the charging management module 240, providing power to the processor 210, internal memory 221, external memory, display screen 294, camera 293, and wireless communication module 260. The power management module 241 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 241 may also be located within the processor 210. In other embodiments, the power management module 241 and the charging management module 240 may be housed in the same device.

[0317] The wireless communication function of electronic device 100 can be implemented through antenna 1, antenna 2, mobile communication module 250, wireless communication module 260, modem processor, and baseband processor.

[0318] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 200 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 a tuning switch.

[0319] The mobile communication module 250 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 200. The mobile communication module 250 may include at least one filter, switch, power amplifier, low-noise amplifier, etc. The mobile communication module 250 can receive electromagnetic waves via the antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to the modem processor for demodulation. The mobile communication module 250 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via the antenna. In some embodiments, at least some functional modules of the mobile communication module 250 may be housed in the processor 210. In some embodiments, at least some functional modules of the mobile communication module 250 and at least some modules of the processor 210 may be housed in the same device.

[0320] The modem processor may include a modulator and a demodulator. The modulator modulates the low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. The application processor outputs sound signals through an audio device (not limited to speaker 270A, receiver 270B, etc.) or displays images or videos through the display screen 294. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor 210 and may be housed in the same device as the mobile communication module 250 or other functional modules.

[0321] The wireless communication module 260 can provide solutions for wireless communication applications on the electronic device 200, including WLAN (such as Wi-Fi networks), Bluetooth, Global Navigation Satellite System, FM, NFC, infrared technology, and ultra-wideband (UWB). The wireless communication module 260 can be one or more devices integrating at least one communication processing module. The wireless communication module 260 receives electromagnetic waves via an antenna, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to the processor 210. The wireless communication module 260 can also receive signals to be transmitted from the processor 210, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via the antenna. For example, the wireless communication module 260 may include a Bluetooth module, a Wi-Fi module, etc.

[0322] In some embodiments, a portion of the antenna of the electronic device 200 is coupled to the mobile communication module 250, and another portion of the antenna is coupled to the wireless communication module 260, enabling the electronic device 200 to communicate with networks and other devices via wireless communication technology. The wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Millimeter Wave (mmWave), BT, GNSS, WLAN, NFC, FM, UWB, and / or IR technologies, etc. The GNSS may include GPS, GLONASS, BDS, QZSS, and / or SBAS.

[0323] Electronic device 200 can perform display functions, such as displaying a user's exercise and health results report, through a GPU, a display screen 294, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 294 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 210 may include one or more GPUs, which execute instructions to generate or modify display information.

[0324] Display screen 294 is used to display images, videos, etc. Display screen 294 includes a display panel. The display panel may be LCD, OLED, active-matrix organic light-emitting diode or AMOLED, FLED, MiniLED, MicroLED, Micro-OLED, QLED, etc. In some embodiments, electronic device 200 may include one or N displays screens 294, where N is a positive integer greater than 1.

[0325] In some embodiments, the display screen 294 of the electronic device 200 is larger than the display screen 115 of the electronic device 100.

[0326] Electronic device 200 can perform shooting functions through ISP, camera 293, video codec, GPU, display screen 294 and application processor.

[0327] The ISP (Image Signal Processor) is used to process data fed back from the camera 293. 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, converting it into an image visible to the naked eye. The ISP can also perform algorithmic optimization of image noise, brightness, and color. 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 293.

[0328] Camera 293 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 200 may include one or N cameras 293, where N is a positive integer greater than 1.

[0329] 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 200 selects a frequency, the DSP is used to perform Fourier transforms on the frequency energy.

[0330] Video codecs are used to compress or decompress digital video. Electronic device 200 may support one or more video codecs. Thus, electronic device 200 can play or record video in various encoding formats, such as Moving Picture Experts Group (MPEG)-1, MPEG-2, MPEG-3, MPEG-4, etc.

[0331] An NPU (Neural Processing Unit) is a neural network (NN) computing processor that, by borrowing the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, rapidly processes input information and can continuously learn on its own. NPUs enable intelligent cognitive applications in electronic devices, such as image recognition, facial recognition, speech recognition, and text understanding.

[0332] The external storage interface 220 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 200. The external memory card communicates with the processor 210 through the external storage interface 220 to perform data storage functions. For example, music, photos, videos, and other data can be stored on the external memory card.

[0333] Internal memory 221 can be used to store one or more computer programs, which include instructions. Processor 210 can execute the instructions stored in internal memory 221, thereby causing electronic device 200 to perform the data sharing methods, various functional applications, and data processing provided in some embodiments of this application. Internal memory 221 may include a program storage area and a data storage area. The program storage area may store the operating system; it may also store one or more applications (such as a gallery, contacts, etc.). The data storage area may store data created during the use of electronic device 200 (such as photos, contacts, etc.). Furthermore, internal memory 221 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.

[0334] Electronic device 200 can implement audio functions such as music playback and recording through audio module 270, speaker 270A, receiver 270B, microphone 270C, headphone jack 270D, and application processor.

[0335] The audio module 270 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 270 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 270 may be located in the processor 210, or some functional modules of the audio module 270 may be located in the processor 210.

[0336] The speaker 270A, also known as a "loudspeaker," is used to convert audio electrical signals into sound signals. The electronic device 200 can listen to music or make hands-free calls through the speaker 270A.

[0337] The receiver 270B, also known as the "earpiece," is used to convert audio electrical signals into sound signals. When the electronic device 200 answers a telephone call or voice message, the receiver 270B can be brought close to the ear to listen to the voice.

[0338] Microphone 270C, also known as a "microphone" or "voice transducer," is used to convert sound signals into electrical signals. When making a phone call or sending a voice message, the user can speak by bringing their mouth close to microphone 270C, inputting the sound signal into microphone 270C. Electronic device 200 may have at least one microphone 270C. In some embodiments, electronic device 200 may have two microphones 270C, which, in addition to collecting sound signals, can also perform noise reduction. In other embodiments, electronic device 200 may also have three, four, or more microphones 270C, which can collect sound signals, reduce noise, identify the sound source, and perform directional recording, etc.

[0339] The headphone jack 270D is used to connect wired headphones. The headphone jack 270D can be a USB 230 interface or a 3.5mm Open Mobile Terminal Platform (OMTP) standard interface, a CTIA (Cellular Telecommunications Industry Association of the USA) standard interface.

[0340] The pressure sensor 280A is used to sense pressure signals and can convert pressure signals into electrical signals.

[0341] The gyroscope sensor 280B can be used to determine the motion attitude of the electronic device 200.

[0342] The barometric pressure sensor 280C is used to measure air pressure. In some embodiments, the electronic device 200 calculates altitude using the air pressure value measured by the barometric pressure sensor 280C to assist in positioning and navigation.

[0343] The magnetic sensor 280D includes a Hall sensor. The electronic device 200 can use the magnetic sensor 280D to detect the opening and closing of the flip cover.

[0344] The accelerometer 280E can detect the magnitude of acceleration of electronic device 200 in various directions (typically three axes). When electronic device 200 is stationary, it can detect the magnitude and direction of gravity. It can also be used to identify the posture of electronic device, and can be applied to applications such as screen orientation switching and pedometers.

[0345] In this embodiment of the application, the electronic device 200 can determine the user's activity level, such as the number of steps taken, running distance, etc., by using acceleration data collected by the accelerometer 280E.

[0346] The 280F distance sensor is used to measure distance.

[0347] The proximity light sensor 280G may include, for example, a light-emitting diode (LED) and a light detector, such as a photodiode.

[0348] An ambient light sensor 280L is used to sense the ambient light intensity. Electronic device 200 can adaptively adjust the brightness of display screen 294 based on the sensed ambient light intensity.

[0349] The fingerprint sensor 280H is used to collect fingerprints. The electronic device 200 can utilize the characteristics of the collected fingerprints to achieve fingerprint unlocking, accessing application locks, taking photos with fingerprints, answering calls with fingerprints, etc.

[0350] The 280J temperature sensor is used to detect temperature.

[0351] In one possible implementation of this application embodiment, when the temperature sensor 280J is in close contact with the user's skin, it can also be used to detect the user's body temperature.

[0352] Touch sensor 280K, also known as a touch panel or touch-sensitive surface, can be located on display screen 294. The touch sensor 280K and display screen 294 together form a touchscreen, also called a "touchscreen." Touch sensor 280K detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 294. In other embodiments, touch sensor 280K may also be located on the surface of electronic device 200, in a different position than display screen 294.

[0353] The bone conduction sensor 280M can acquire vibration signals. In some embodiments, the bone conduction sensor 280M can acquire vibration signals from the vibrating bone segments of the human vocal cords. The bone conduction sensor 280M can also contact the human pulse to receive blood pressure signals. In some embodiments, the bone conduction sensor 280M can also be incorporated into headphones to form bone conduction headphones. The audio module 270 can parse the voice signals from the vibrating bone segments of the vocal cords acquired by the bone conduction sensor 280M to realize voice functionality. The application processor can parse heart rate information from the blood pressure signals acquired by the bone conduction sensor 280M to realize heart rate detection functionality.

[0354] Button 290 includes the power button, volume buttons, etc.

[0355] Motor 291 can generate vibration alerts. Motor 291 can be used for incoming call vibration alerts or for touch vibration feedback.

[0356] Indicator 292 can be an indicator light, which can be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0357] The SIM card interface 295 is used to connect a SIM card.

[0358] In this embodiment, the first data may include real-time data, the first physiological data may include real-time physiological data, and the first behavioral data may include real-time behavioral data. The second data may include historical data, the second physiological data may include historical physiological data, and the second behavioral data may include historical behavioral data.

[0359] The first time period can include time period 2, the second time period can include time period 1, and the third time period can include time period 3.

[0360] The first deviation value can include the emotional deviation value. The first probability value can include the probability threshold.

[0361] The first electronic device may include electronic device 100 and electronic device 200. When the first electronic device may include electronic device 100, the second electronic device may include electronic device 200. Alternatively, when the first electronic device may include electronic device 200, the second electronic device may include electronic device 100. The third electronic device may include the electronic device of another user who is a family member of the user of electronic device 100.

[0362] This application provides a computer program product that, when run on an electronic device, causes the electronic device to execute the technical solutions described in the above embodiments. Its implementation principle and technical effects are similar to those of the related embodiments described above, and will not be repeated here.

[0363] This application provides a readable storage medium containing instructions that, when executed by an electronic device, cause the electronic device to perform the technical solution described in the above embodiments. The implementation principle and technical effects are similar and will not be repeated here.

[0364] This application provides a chip for executing instructions. When the chip is running, it executes the technical solutions described in the above embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0365] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0366] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0367] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0368] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A detection method, characterized in that, The method is applied to a first electronic device, and the method includes: Acquire the user's first data, which includes first physiological data and / or first behavioral data; Based on the first data and the emotion benchmark value, the emotion detection result is determined; When the emotion detection result is an abnormal emotion, an abnormal emotion alert is generated, which is used to remind the user of the degree of abnormality of the emotion.

2. The method according to claim 1, characterized in that, The emotion detection results include the degree of abnormality of the emotion, which includes low risk, medium risk, high risk, or the probability that the user is at risk of having an emotional disorder.

3. The method according to any one of claims 1 or 2, characterized in that, The method further includes: Based on the emotion detection results, the emotion classification result is determined.

4. The method according to claim 3, characterized in that, The first physiological data includes one or more of the user's heart rate, respiratory rate, heart rate variability, and body temperature acquired in real time; the first behavioral data includes the user's sleep data and / or activity data acquired within a first time period. The emotional baseline value is obtained based on the user's second data, which includes second physiological data and / or second behavioral data. The second physiological data includes one or more of the user's heart rate, respiratory rate, heart rate variability, and body temperature obtained during a second time period. The second behavioral data includes the user's sleep data and / or activity data obtained during the second time period. The start time of the second time period is earlier than the start time of the first time period.

5. The method according to claim 4, characterized in that, Based on the first data and the emotion benchmark, the emotion detection result is determined, specifically including: Determine the first deviation value between the first data and the sentiment benchmark value; Based on the first data and the first deviation value, the emotion detection result is determined.

6. The method according to claim 5, characterized in that, Based on the first data and the first deviation value, the emotion detection result is determined, including: Based on the first data and the first deviation value, as well as emotional health-related data, the emotion detection result is determined. The emotional health-related data includes one or more of the user's medical history information, location information, and duration of light exposure.

7. The method according to claim 2, characterized in that, When the emotion detection result is an abnormal emotion, an abnormal emotion alert is generated, including: If the emotion detection result is medium risk or high risk, the emotion detection result is determined to be an abnormal emotion, and an abnormal emotion alert is generated.

8. The method according to claim 2, characterized in that, When the emotion detection result is an abnormal emotion, an abnormal emotion alert is generated, including: If the emotion detection result is high risk and the duration of the high risk is a preset duration, the emotion detection result is determined to be an abnormal emotion, and an abnormal emotion alert is generated.

9. The method according to claim 2, characterized in that, The emotion detection result represents the probability of the risk of developing an emotional disorder. When the emotion detection result indicates an abnormal emotion, an abnormal emotion alert is generated, including: When the probability of the risk of developing a mood disorder is greater than a first probability value, the emotion detection result is determined to be an abnormal emotion, and an abnormal emotion reminder is generated.

10. The method according to any one of claims 8 or 9, characterized in that, After generating the abnormal emotion alert, the method further includes: The abnormal emotion alert is displayed, which includes one or more of the following: text alert, image alert, video alert, and audio alert. Send the abnormal emotion alert to the second electronic device.

11. The method according to claim 10, characterized in that, The method further includes: Obtain the emotion detection results within the third time period to determine the emotional health risk results; the emotional health risk results include one or more of the following: the user's abnormal emotion statistics within the third time period, the user's probability of having an emotional disorder, and the user's emotion tag. The abnormal emotion statistics include the number of abnormal emotions experienced by the user within the third time period, the location where the abnormal emotions occurred, and the time point when the abnormal emotions occurred.

12. The method according to claim 11, characterized in that, After determining the emotional health risk outcome, the method further includes: When the user is in the same location where the abnormal emotion occurred again, or at the same time when the abnormal emotion occurred, a reminder message is generated to remind the user to alleviate the emotion.

13. The method according to claim 12, characterized in that, After determining the emotional health risk outcome, the method further includes: The emotional health risk results are sent to the third electronic devices of the user's family members.

14. An electronic device, characterized in that, It includes one or more processors; one or more memories; said one or more memories storing one or more computer programs, said one or more computer programs including instructions that, when executed by said one or more processors, cause the method of any one of claims 1 to 13 to be performed.

15. A chip, characterized in that, The chip includes a processor and a communication interface, the communication interface being used to receive signals and transmit the signals to the processor, the processor processing the signals such that the method as described in any one of claims 1 to 13 is executed.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the method as described in any one of claims 1 to 13 to be performed.

17. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 13.

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