Fatigue detection methods, equipment and storage media

Abstract

Embodiments of the present application provide a fatigue detection method, device and storage medium. The method combines the index data provided by the wearable device for calculating the fatigue level and the self-evaluation results collected by various subjective evaluation questionnaires displayed on the electronic device used by the user, and determines the fatigue level from the morning pulse resting heart rate data, the HRV data, the ACWR data and the sleep data. The four specific fatigue level determination methods are mutually complementary, and the priority order is that the morning pulse resting heart rate is higher than the HRV, the HRV is higher than the ACWR, and the ACWR is higher than the sleep. One fatigue level is selected from the fatigue levels determined from the four dimensions, so that the finally determined fatigue level is more reasonable and accurate, thereby suitable guidance suggestions can be given to the user before exercise, and damage to the body caused by excessive exercise of the user can be avoided.

Description

Technical Field

[0001] This application relates to the field of sports and health technology, and in particular to a fatigue detection method, device and storage medium. Background Technology

[0002] Before exercising, such as running, users may not be fully aware of their fatigue level, which could lead to physical discomfort and injury during exercise. Summary of the Invention

[0003] To address the aforementioned technical problems, embodiments of this application provide a fatigue detection method, device, and storage medium, aiming to accurately detect the user's fatigue level, thereby providing guidance and suggestions before the user exercises, and preventing the user from overexerting themselves and causing injury.

[0004] In a first aspect, embodiments of this application provide a fatigue detection method applied to an electronic device, wherein the electronic device and a wearable device are communicatively connected, and the wearable device is used to provide index data for calculating fatigue levels. The method includes: in response to a user's operation, acquiring wearable device readiness index data from the wearable device, the index data including one or more of morning resting heart rate data, heart rate variability (HRV) data, heart rate data during exercise, and sleep data; if the index data includes morning resting heart rate data, calculating a fatigue level based on the morning resting heart rate data; if the fatigue level calculated based on the morning resting heart rate data is the highest level of fatigue, determining the fatigue level calculated based on the morning resting heart rate data as the user's current fatigue level, where the highest level of fatigue indicates that the user is currently unsuitable for any training session.

[0005] Electronic devices, such as mobile phones and tablets.

[0006] Wearable devices, such as smartwatches and fitness trackers.

[0007] Optionally, fatigue levels can be divided into Level 0 fatigue, Level 1 fatigue, Level 2 fatigue, Level 3 fatigue, etc., with Level 3 being the highest fatigue level.

[0008] This includes responding to user actions, such as opening the exercise plan interface of a fitness and health app installed on the user's electronic device for creating exercise plans, as follows: Figure 8 Interface 10d is shown in (2).

[0009] Therefore, the fatigue level is determined by the highest priority morning resting heart rate data among the indicators provided by the wearable device for calculating the user's fatigue level. When the fatigue level determined by the morning resting heart rate data is the highest level, the fatigue level determined by the morning resting heart rate data is directly used as the user's current fatigue level. This can ensure the accuracy of the results while reducing the computational load on the electronic device.

[0010] According to the first aspect, the method also includes: when the indicator data does not include morning resting heart rate data, or the fatigue level calculated based on the morning resting heart rate data is not the highest fatigue level, but the indicator data includes heart rate variability (HRV) data, calculating the fatigue level based on the HRV data; when the fatigue level calculated based on the HRV data is the highest fatigue level, determining the fatigue level calculated based on the HRV data as the user's current fatigue level.

[0011] Therefore, when the highest level of fatigue cannot be determined based on morning resting heart rate data, HRV data is given priority. When the fatigue level determined by HRV data is the highest level of fatigue, the fatigue level determined by HRV data is directly used as the user's current fatigue level. This allows for more suitable exercise guidance based on the determined fatigue level, preventing the user from overexerting themselves and causing injury.

[0012] According to the first aspect, or any implementation of the first aspect above, the method further includes: when the indicator data does not include morning resting heart rate data, or the fatigue level calculated based on the morning resting heart rate data is not the highest fatigue level, and the indicator data does not include HRV data, or the fatigue level calculated based on the HRV data is not the highest fatigue level, but the indicator data includes heart rate data during exercise, calculating the Acute-Chronic Load Ratio (ACWR) data based on the heart rate data during exercise and the training duration of the currently completed training course; calculating the fatigue level based on the ACWR data; and when the fatigue level calculated based on the ACWR data is the highest fatigue level, determining the fatigue level calculated based on the ACWR data as the user's current fatigue level.

[0013] Therefore, when the highest level of fatigue cannot be determined based on morning resting heart rate data and HRV data, ACWR data is given priority. When the fatigue level determined by ACWR data is the highest level of fatigue, the fatigue level determined by ACWR data is directly used as the user's current fatigue level. This allows for more suitable exercise guidance based on the determined fatigue level, preventing the user from overexerting themselves and causing injury.

[0014] According to the first aspect, or any implementation of the first aspect above, the method further includes: when the indicator data does not include morning resting heart rate data, or the fatigue level calculated based on the morning resting heart rate data is not the highest fatigue level, and the indicator data does not include HRV data, or the fatigue level calculated based on the HRV data is not the highest fatigue level, and the indicator data does not include heart rate data during exercise, after the training course ends, a first questionnaire pops up, which includes multiple score options, with different score options corresponding to different levels of fatigue; the user's current training load is calculated based on the score selected by the user in the first questionnaire and the training duration; the user's current ACWR data is calculated based on the training load, historical ACWR data, and a parameter indicating the degree of decay of training load over time, where historical ACWR data is the ACWR data corresponding to the user's last completed training, and the parameter indicating the degree of decay of training load over time takes a value between 0 and 1; the fatigue level is calculated based on the ACWR data; if the fatigue level calculated based on the ACWR data is the highest fatigue level, the fatigue level calculated based on the ACWR data is determined as the user's current fatigue level.

[0015] The first questionnaire, for example Figure 17 The course feedback questionnaire pops up on the 10th page of the interface.

[0016] Therefore, even when the user is not wearing a wearable device or the wearable device does not provide heart rate data during exercise, displaying a subjective evaluation questionnaire on an electronic device ensures that ACWR data can be calculated regardless of the scenario, thereby determining the fatigue level.

[0017] According to the first aspect, or any implementation of the first aspect above, the method further includes: when the indicator data does not include morning resting heart rate data, or the fatigue level calculated based on the morning resting heart rate data is not the highest fatigue level, and the indicator data does not include HRV data, or the fatigue level calculated based on the HRV data is not the highest fatigue level, and the indicator data does not include heart rate data during exercise, or the fatigue level calculated based on the ACWR data is not the highest fatigue level, but the level calculated based on the morning resting heart rate data is lower than the highest fatigue level, the fatigue level calculated based on the morning resting heart rate data shall be determined as the user's current fatigue level.

[0018] Among them, fatigue levels that are lower than the highest level, such as fatigue level 0, fatigue level 1, or fatigue level 2, etc.

[0019] In this way, when the highest priority morning resting heart rate data can determine the fatigue level, prioritizing the use of the fatigue level determined by the morning resting heart rate data is more likely to reflect the user's actual fatigue level and is more conducive to providing appropriate exercise guidance before exercise.

[0020] According to the first aspect, or any implementation of the first aspect above, the method further includes: when the fatigue level cannot be calculated based on the morning resting heart rate data, but the indicator data includes HRV data, and the fatigue level calculated based on the HRV data is lower than the highest fatigue level, the fatigue level calculated based on the HRV data is determined as the user's current fatigue level.

[0021] In this way, when the highest priority morning resting heart rate data cannot determine the fatigue level, HRV data, which has a lower priority than the morning resting heart rate data but a higher priority than other indicators, is used to determine the fatigue level. This ensures that the determined fatigue level can better reflect the user's actual fatigue level and is conducive to providing appropriate exercise guidance before exercise.

[0022] According to the first aspect, or any implementation of the first aspect above, the method further includes: when the fatigue level cannot be calculated based on morning resting heart rate data and HRV data, but the indicator data includes heart rate data during exercise, and the fatigue level calculated based on the ACWR data calculated based on the heart rate data during exercise and the training duration is lower than the highest fatigue level, the fatigue level calculated based on the ACWR data is determined as the user's current fatigue level.

[0023] In this way, when other indicators with higher priority than ACWR data cannot determine the fatigue level, ACWR data is used to determine the fatigue level, further ensuring that the determined fatigue level can better reflect the user's actual fatigue level, which is conducive to providing appropriate exercise guidance before exercise.

[0024] According to the first aspect, or any implementation of the first aspect above, the method further includes: when the indicator data does not include morning resting heart rate data, or the fatigue level cannot be calculated based on the morning resting heart rate data, and the indicator data does not include HRV data, or the fatigue level cannot be calculated based on HRV data, and the indicator data does not include heart rate data during exercise, or the fatigue level cannot be calculated based on ACWR data, but the indicator data includes sleep data, calculating the fatigue level based on sleep data; when the fatigue level is calculated based on sleep data, determining the fatigue level calculated based on sleep data as the user's current fatigue level; when the fatigue level cannot be calculated based on sleep data, determining the lowest fatigue level as the user's current fatigue level, where the lowest fatigue level indicates that the user is not currently fatigued.

[0025] In this way, when other indicators with higher priority than sleep data cannot determine the fatigue level, sleep data is used to determine the fatigue level. Furthermore, if sleep data cannot determine the fatigue level, the user is assumed to be at the lowest fatigue level. This ensures that a fatigue level can be determined before the user exercises, regardless of the situation, and thus appropriate exercise guidance can be provided.

[0026] According to the first aspect, or any implementation of the first aspect above, the fatigue level is calculated based on sleep data, including: if the sleep data meets preset conditions, displaying a second questionnaire, which includes multiple sleep quality assessment options, with different sleep quality assessment options corresponding to different scores; and determining the fatigue level based on the score corresponding to the sleep quality option selected by the user in the second questionnaire.

[0027] The second questionnaire, for example Figure 13 The sleep quality questionnaire shown in (2) is as follows.

[0028] According to the first aspect, or any of the above implementations of the first aspect, the preset conditions are met, including: the sleep duration is less than the first preset duration; and / or, the number of nighttime awakenings is greater than or equal to the preset number; and / or, the wake-up time is earlier than the average wake-up time of the previous week than the second preset duration.

[0029] The first preset duration is, for example, 5 hours (h).

[0030] The preset number of times is, for example, 3 times.

[0031] The second preset duration is, for example, 30 minutes (min).

[0032] According to the first aspect, or any implementation of the first aspect above, the fatigue level is calculated based on the morning resting heart rate data, including: if the fluctuation of the morning resting heart rate data compared to the average morning resting heart rate of the previous week is within N1 bpm, the fatigue level is determined to be the lowest level; if the morning resting heart rate data exceeds the average morning resting heart rate of the previous week (N1 bpm~N2 bpm), a third questionnaire is displayed, which includes multiple fatigue assessment options, each corresponding to a different score; the fatigue level is determined based on the score corresponding to the fatigue assessment option selected by the user in the third questionnaire; if both the morning resting heart rate data and yesterday's morning resting heart rate data exceed the average morning resting heart rate of the previous week (N1 bpm~N2 bpm), the fatigue level is determined to be between the highest and lowest levels; if the morning resting heart rate data, yesterday's morning resting heart rate data, and the morning resting heart rate data from the day before yesterday all exceed the average morning resting heart rate of the previous week (N1 bpm~N2 bpm), the fatigue level is determined to be between the highest and lowest levels; if the morning resting heart rate data, yesterday's morning resting heart rate data, and the morning resting heart rate data from the day before yesterday all exceed the average morning resting heart rate of the previous week (N1 bpm~N2 bpm), the fatigue level is determined to be between the highest and lowest levels. At a rate of 0 bpm, the fatigue level is determined to be the highest level; when the morning resting heart rate exceeds the previous week's average morning resting heart rate by 3 bpm, the fatigue level is determined to be the highest level; where 0 <N1<N2<N3。

[0033] Among them, the third questionnaire, for example Figure 13 The Borg questionnaire shown in (1) is shown in the middle.

[0034] For specific details on calculating fatigue levels based on morning resting heart rate data, please refer to [link to relevant documentation]. Figure 12 and Figure 13 The description of the illustrated embodiment will not be repeated here.

[0035] According to the first aspect, or any implementation of the first aspect above, the fatigue level is calculated based on HRV data, including: determining the fatigue level based on the RMSSD and LF / HF ratio in the HRV data, where RMSSD refers to the root mean square of the difference between adjacent normal cardiac cycles, and the LF / HF ratio refers to the ratio of low-frequency heart rate variability to high-frequency heart rate variability.

[0036] For specific details on calculating fatigue level based on HRV data, please refer to [link / reference]. Figure 14 The description of the illustrated embodiment will not be repeated here.

[0037] Based on the first aspect, or any implementation of the first aspect above, the Acute / Chronic Load Ratio (ACWR) data is calculated based on the heart rate data during the exercise phase and the training duration of the currently completed training course. This includes: determining the heart rate interval where the heart rate data during the exercise phase falls; wherein, starting from 50% of the maximum heart rate and ending at 100% of the maximum heart rate, each 10% increment constitutes a heart rate interval, and each heart rate interval corresponds to an intensity level; determining the intensity level based on the determined heart rate interval; calculating the user's current training load based on the intensity level and training duration; and calculating the user's current ACWR data based on the training load, historical ACWR data, and a parameter indicating the degree of decay of the training load over time. The historical ACWR data is the ACWR data corresponding to the user's last completed training session, and the parameter indicating the degree of decay of the training load over time takes a value between 0 and 1.

[0038] For details on determining ACWR data based on heart rate data during exercise provided by wearable devices, and then calculating fatigue levels, please refer to [link to relevant documentation]. Figure 15 The description of the illustrated embodiment will not be repeated here.

[0039] According to the first aspect, or any implementation of the first aspect above, in response to a user's operation, the first interface is displayed in response to a user's operation on a first application, wherein the first application is an application capable of creating an exercise plan, and the first interface is the interface corresponding to the exercise plan.

[0040] The first type of application is, for example, a fitness and health app that can create exercise plans.

[0041] The first interface, for example Figure 8 Interface 10d is shown in (2).

[0042] According to the first aspect, or any implementation of the first aspect above, after determining the user's current fatigue level, the method further includes: if the user's current fatigue level is not the lowest fatigue level, pop up a first window on the first interface, and display exercise guidance content that matches the user's current fatigue level in the first window, where the lowest fatigue level indicates that the user is not currently fatigued.

[0043] The first window, for example Figure 10 The window 10d-5 shown in (1), (2) and (3) is displayed in interface 10d.

[0044] Secondly, embodiments of this application provide an electronic device. The electronic device includes: a memory and a processor, the memory and the processor being coupled; the memory stores program instructions, which, when executed by the processor, cause the electronic device to perform the methods of the first aspect or any possible implementation thereof.

[0045] Thirdly, embodiments of this application provide a computer-readable medium for storing a computer program, the computer program including instructions for performing the method in the first aspect or any possible implementation of the first aspect.

[0046] Fourthly, embodiments of this application provide a computer program including instructions for performing the method in the first aspect or any possible implementation thereof.

[0047] Fifthly, embodiments of this application provide a chip including a processing circuit and transceiver pins. The transceiver pins and the processing circuit communicate with each other via an internal connection path. The processing circuit executes the method in the first aspect or any possible implementation of the first aspect to control the receiving pin to receive signals and to control the transmitting pin to transmit signals. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating a scenario for implementing fatigue detection;

[0049] Figure 2 This is a schematic diagram of the hardware structure of an electronic device for implementing fatigue detection, as an example.

[0050] Figure 3 As shown in the example Figure 2 A schematic diagram of the software structure of the electronic device shown.

[0051] Figure 4 This is a schematic diagram of the hardware structure of another electronic device for implementing fatigue detection, as an example.

[0052] Figure 5 As shown in the example Figure 4 A schematic diagram of the software structure of the electronic device shown.

[0053] Figure 6 This is a schematic diagram illustrating an exemplary system architecture for implementing fatigue detection;

[0054] Figure 7 and Figure 8 This is an example of a user interface diagram illustrating the process of launching a fitness app to view an exercise plan;

[0055] Figure 9 This is an illustrative diagram showing course identifiers of different times and intensities;

[0056] Figure 10 This is an example diagram of a user interface that provides prompts based on different fatigue levels.

[0057] Figure 11 This is a schematic flowchart illustrating an exemplary fatigue detection method;

[0058] Figure 12 This is an illustrative diagram showing how fatigue levels are determined based on morning resting heart rate data.

[0059] Figure 13 This is an example of a user interface diagram showing the questionnaire feedback displayed when the case is determined to be case 1b based on morning resting heart rate data;

[0060] Figure 14 This is an illustrative diagram showing how fatigue levels are determined based on heart rate variability data.

[0061] Figure 15 This is an illustrative diagram showing how to determine ACWR and thus fatigue level;

[0062] Figure 16 This is an illustrative diagram illustrating another method for determining ACWR and subsequently the fatigue level.

[0063] Figure 17 For example, the target Figure 16 A user interface diagram showing the pop-up course feedback in a scenario;

[0064] Figure 18 This is an illustrative diagram showing how fatigue levels are determined based on sleep data. Detailed Implementation

[0065] 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.

[0066] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0067] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first target object" and "second target object," etc., are used to distinguish different target objects, not to describe a specific order of target objects.

[0068] 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 design that is 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. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0069] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more. For example, multiple processing units means two or more processing units; multiple systems means two or more systems.

[0070] With increasing pressure in modern life, people are paying more and more attention to their health. Currently, more and more people are improving their physical fitness through exercise. However, before exercising, such as running, users often lack sufficient awareness of their fatigue levels and are unaware of the exact impact of fatigue on running. Continuing to run at an intensity exceeding a user's safety limits when severely fatigued may pose a risk of injury.

[0071] In view of this, the embodiments of this application provide a more comprehensive fatigue detection method with multiple dimensions, which aims to accurately detect the user's fatigue level, so as to provide guidance and suggestions before the user exercises, and avoid the user from causing damage to the body due to excessive exercise.

[0072] For example, in some possible implementations, the fatigue detection method provided in this application embodiment can be applied to an electronic device. This electronic device can be any of a mobile phone, tablet computer, wearable device, or other portable device.

[0073] For example, in some other possible implementations, the fatigue detection method provided in this application embodiment can also be applied to various portable devices, such as mobile phones and smartwatches (hereinafter referred to as: watches). Through the cooperation of these two devices, a more accurate detection of the user's fatigue level can be achieved.

[0074] In this embodiment of the application, a communication connection is established between a mobile phone 100 and a watch 200 to perform data interaction, such as... Figure 1 The scenario shown provides a detailed explanation of the fatigue detection scheme.

[0075] See Figure 2 The diagram illustrates the structure of a mobile phone 100. Figure 2As shown, the mobile phone 100 may include: a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0076] The processor 110 may include one or more processing units, such as an application processor (AP), a modem, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, a neural network processing unit (NPU), etc., which will not be listed here and this application does not limit them.

[0077] Alternatively, these processing units can be independent devices, meaning that each processing unit can be viewed as a processor.

[0078] Alternatively, these processing units can be integrated into one or more processors.

[0079] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended as the only limitation on this embodiment.

[0080] The processor 110 may also include one or more interfaces. These interfaces may include inter-integrated circuit (I2C) interfaces, inter-integrated circuit sound (I2S) interfaces, pulse code modulation (PCM) interfaces, universal asynchronous receiver / transmitter (UART) interfaces, mobile industry processor interfaces (MIPI), general-purpose input / output (GPIO) interfaces, subscriber identity module (SIM) interfaces, and / or universal serial bus (USB) interfaces, etc., and are not listed here; this application does not impose any limitations on these.

[0081] The processor 110 may also include a memory for storing instructions and data. In some implementations, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

[0082] The external storage interface 120 can be used to connect an external storage card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external storage card communicates with the processor 110 through the external storage interface 120 to perform data storage functions.

[0083] Internal memory 121 can be used to store computer executable program code, including instructions. Processor 110 executes various functional applications and data processing of mobile phone 100 by running the instructions stored in internal memory 121. Internal memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application required for a function, etc. The data storage area may store data created during the use of mobile phone 100, etc. Furthermore, internal memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0084] The charging management module 140 is used to receive charging input from the charger. While charging the battery 142, the charging management module 140 can also supply power to the terminal device through the power management module 141.

[0085] The power management module 141 connects the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, providing power to the processor 110, internal memory 121, external memory, display 194, camera 193, and wireless communication module 160. The power management module 141 can also monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some implementations, the power management module 141 may be located within the processor 110. In other implementations, the power management module 141 and the charging management module 140 may be located in the same device.

[0086] The wireless communication function of mobile phone 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor.

[0087] It should be noted that antennas 1 and 2 are used to transmit and receive electromagnetic wave signals. Each antenna in mobile phone 100 can be used to cover one or more communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other implementations, the antennas can be used in conjunction with a tuning switch.

[0088] The mobile communication module 150 can provide wireless communication solutions, including 2G / 3G / 4G / 5G, for use on the mobile phone 100.

[0089] The wireless communication module 160 can provide solutions for wireless communication applications on the mobile phone 100, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. Specifically, in the technical solution provided in this embodiment, the mobile phone 100 can communicate with the watch 200 through the wireless communication module 160 and an antenna.

[0090] The audio module 170 may include a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, etc.

[0091] The sensor module 180 may include pressure sensors, gyroscope sensors, barometric pressure sensors, magnetic sensors, accelerometers, distance sensors, proximity sensors, fingerprint sensors, temperature sensors, touch sensors, ambient light sensors, bone conduction sensors, etc., which will not be listed here, and this application does not limit them.

[0092] Keypad 190 includes a power button, volume buttons, etc. Keypad 190 can be a mechanical keypad or a touch keypad. Mobile phone 100 can receive keypad input and generate keypad signal inputs related to user settings and function control of mobile phone 100.

[0093] Motor 191 can generate vibration alerts. Motor 191 can be used for incoming call vibration alerts or for touch vibration feedback.

[0094] Indicator 192 can be an indicator light, used to indicate charging status, power changes, or to indicate messages, missed calls, notifications, etc.

[0095] Camera 193 is used to capture still images or videos. In some implementations, mobile phone 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0096] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. In some implementations, the mobile phone 100 may include one or N display screens 194, where N is a positive integer greater than 1. Specifically, in the embodiments of this application, user interaction, prompts, etc., can be implemented through the display screen.

[0097] That concludes the introduction to the hardware structure of the Mobile 100. It should be understood that... Figure 2 The mobile phone 100 shown is merely an example. In a specific implementation, the mobile phone 100 may have more or fewer components than shown in the figure, may combine two or more components, or may have different component configurations. Figure 2 The various components shown can be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and / or application-specific integrated circuits.

[0098] See Figure 3 The diagram illustrates a software structure of a mobile phone 100. Before describing the software structure of the mobile phone 100, the architecture that the software system of the mobile phone 100 can adopt will be explained first.

[0099] Alternatively, the software system of Mobile 100 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture.

[0100] Optionally, the software system of mobile phone 100 includes, but is not limited to, Windows, Android and iOS systems.

[0101] For ease of explanation, this application uses the layered architecture of the Android system as an example to illustrate the software structure of the mobile phone 100.

[0102] like Figure 3 As shown, the layered architecture of the mobile phone 100 divides the software into several layers, each with a clear role and division of labor. Layers communicate with each other through software interfaces. In some implementations, the Android system of the mobile phone 100 is divided into five layers, from top to bottom: the Application (APP) layer, the Application Framework (FWK) layer, the Android runtime and system libraries, the Hardware Abstraction Layer (HAL), and the kernel layer.

[0103] The application layer can include a series of application packages.

[0104] like Figure 3 As shown, the application package may include applications such as Health, Settings, Music, Bluetooth, and Gallery, which will not be listed here, and this application does not impose any restrictions on them.

[0105] The sports and health application provided in this embodiment is an app that can be used with wearable and health devices (such as watch 200) to achieve scientific exercise monitoring and health management, and provides professional data and a rich activity experience. Users can create exercise plans through the sports and health app, making it easy to exercise according to the plan.

[0106] Furthermore, in this embodiment, the mobile phone 100 can, based on a determined level of fatigue (grade), display the exercise plan interface of the sports and health app as described in the following embodiments. Figure 8 The interface 10d shown in (2) displays coaching advice, reminding the user that their body is not suitable for exercise today, or that high-intensity exercise is not suitable. Regarding the prompt information window (window 10d-5, hereinafter referred to as window 10d-5) displayed on interface 10d based on the determined level of fatigue, it can be as follows: Figure 10 As shown in (1), (2), and (3), the specific push logic can be found in the description of the following embodiments, which will not be repeated here.

[0107] The application framework layer provides application programming interfaces (APIs) and programming frameworks (which can be described as functions) for applications in the application layer. In some embodiments, the application framework layer includes some predefined functions.

[0108] like Figure 3 As shown, the application framework layer may include a notification manager, content provider, view system, resource manager, window manager, etc.

[0109] The notification manager allows applications to display notification information in the status bar. It can be used to convey informational messages and can disappear automatically after a short time without user interaction.

[0110] Specifically, in this embodiment of the application, if it is determined that the user is fatigued, a notification message is pushed by the notification manager one hour before the time when the user usually starts exercising.

[0111] Optionally, when the mobile phone 100 and the watch 200 establish a communication connection and the message synchronization of the sports and health APP is enabled, the watch 200 will also receive this notification message.

[0112] Content providers are used to store and retrieve data, and make that data accessible to applications.

[0113] A view system includes visual controls, such as controls that display text and controls that display images.

[0114] The resource manager provides various resources for applications, such as the layout and content of various prompt message windows that need to be displayed in the interface.

[0115] The window manager is used to manage window programs. The window manager can obtain the screen size, determine if a status bar is present, lock the screen, and identify the currently displayed interface (e.g., whether it is in window 10d), etc.

[0116] The system library and runtime layer includes the system libraries and the Android Runtime. The Android Runtime includes the core libraries and the virtual machine. The Android runtime is responsible for the scheduling and management of the Android system.

[0117] The core library consists of two parts: one part is the functionalities that need to be called by the Java language, and the other part is the Android core library.

[0118] The application layer and application framework layer run in a virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0119] System libraries can include multiple functional modules. For example: surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGL ES), 2D graphics engines (e.g., SGL), etc.

[0120] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0121] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0122] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0123] Understandably, the 2D graphics engine mentioned above is a 2D drawing engine.

[0124] The HAL layer is the interface layer located between the operating system kernel and the hardware circuitry. The HAL layer includes, but is not limited to: the camera hardware abstraction layer, the display driver hardware abstraction layer, and the power supply hardware abstraction layer.

[0125] The kernel layer is the layer between hardware and software. At a minimum, the kernel layer includes sensor drivers, display drivers, camera drivers, power management drivers, etc. For example, a sensor driver can be used to output detection signals from a sensor (such as a touch sensor) to the view system, so that the view system responds to the detection signals and displays the corresponding application interface.

[0126] That concludes the introduction to the software structure of the mobile phone 100. It is understandable that... Figure 3 The layers in the illustrated software structure and the components contained in each layer do not constitute a specific limitation on the mobile phone 100. In other embodiments of this application, the mobile phone 100 may include more or fewer layers than illustrated, and each layer may include more or fewer components; this application does not impose any limitations.

[0127] See Figure 4 The example illustrates the hardware structure of a watch 200. For instance... Figure 4As shown, the watch 200 may include: processor 210, sensor 220, memory 230, antenna 240, etc.

[0128] The processor 210 can serve as the central nervous system and command center of the watch 200. The processor 210 can generate operation control signals based on the instruction opcode and timing signals to control instruction fetching and execution. 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.

[0129] The memory 230 can be used to store computer executable program code, which includes instructions. The memory 230 can be volatile memory or persistent memory. The computer executable program code stored in the memory 230 can include one or more modules, each module including a series of instruction operations. The memory 230 can include a program storage area and a data storage area.

[0130] Furthermore, the processor 210 can be configured to communicate with the memory 230 and execute a series of instructions stored in the memory 230 on the watch 200. Specifically, the processor 210 executes various functions and data processing of the watch 200 by running computer program instructions stored in the memory 230.

[0131] The sensor 220 may include, for example, a sensor for monitoring sleep, a sensor for monitoring heart rate, or other sensors capable of motion and health monitoring. These sensors can collect data on several dimensions, including sleep, morning resting heart rate, and heart rate variability (HRV), and transmit this data to the mobile phone 100. The mobile phone's health and fitness app can then determine the user's current fatigue level based on these multiple parameters and provide appropriate reminders.

[0132] The antenna 240 is used to send the data collected by the sensor 220 to the mobile phone 100 and to receive messages pushed by the sports and health APP on the mobile phone 100.

[0133] That concludes the introduction to the hardware structure of Watch 200. It should be understood that... Figure 4 The watch 200 shown is merely an example. In a real implementation, the watch 200 may have more or fewer parts than shown in the figure, may combine two or more parts, or may have different part configurations. Figure 4 The various components shown can be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and / or application-specific integrated circuits.

[0134] See Figure 5 This example illustrates a software architecture for a watch 200. Understandably, the software architecture of the watch 200 can also employ a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture. The software system includes, but is not limited to, Android and iOS systems.

[0135] For ease of explanation, this application uses the layered architecture of the Android system as an example to illustrate the software structure of the watch 200.

[0136] like Figure 3 As shown, the layered architecture of the Watch200 divides the software into several layers, each with a clear role and division of labor. Layers communicate with each other through software interfaces. In some implementations, the Android system of the Watch200 is divided into four layers, from top to bottom: the Application (APP) layer, the Application Framework (FWK) layer, the algorithm and internal libraries layer, and the kernel layer.

[0137] like Figure 5 As shown, the application package for Watch 200 may include map applications, communication applications, music players, etc.

[0138] Understandably, the communication applications mentioned above include, for example, messaging applications, call log applications, contact applications, and call applications.

[0139] See also Figure 5 For example, the application framework layer of watch 200 may include various logic control services, communication protocols, etc.

[0140] Specifically, in the embodiments of this application, in order to accurately determine the user's fatigue level from multiple dimensions such as morning resting heart rate, heart rate variability, acute-chronic workload ratio (ACWR), and sleep, the logic control service may include various logic control services such as sleep data acquisition, morning resting heart rate calculation, and heart rate variability calculation.

[0141] The communication protocol can be a pre-agreed protocol with the watch 100, which facilitates data exchange between the two according to the agreed communication protocol.

[0142] Algorithms and internal libraries are used to manage various algorithms, such as sleep algorithms, heart rate algorithms, stress algorithms, etc., various basic libraries, such as security libraries, barcode libraries, payment libraries, and protocol stacks provided by chip manufacturers, such as traditional Bluetooth protocol stacks and low-power protocol stacks.

[0143] The kernel layer includes the kernel core, the hardware abstraction layer (HAL), and the hardware driver layer. In practical applications, the kernel core can interact with the upper layers through the Cortex Microcontroller Software Interface Standard (CMSIS) API, and the hardware abstraction layer can interact with the upper layers through the HAL API.

[0144] That concludes the introduction to the software structure of Watch 200. It is understandable that... Figure 5 The layers in the illustrated software structure and the components contained in each layer do not constitute a specific limitation on the watch 200. In other embodiments of this application, the watch 200 may include more or fewer layers than illustrated, and each layer may include more or fewer components; this application does not impose any limitations.

[0145] To better understand the fatigue detection method provided in the embodiments of this application, Figure 3 The software structure shown is for mobile phone 100 and Figure 5 Based on the software structure of the watch 200 shown, combined with Figure 6 The system for implementing the fatigue detection method provided in the embodiments of this application will be described.

[0146] See Figure 6 For example, a system for implementing a fatigue detection method may include a mobile phone 100 and a watch 200.

[0147] Specifically, in this embodiment of the application, a sports and health app is installed on the mobile phone 100. Optionally, the sports and health app may integrate the logic for fatigue detection provided in this embodiment of the application, as well as the logic for providing exercise suggestions based on the determined fatigue level.

[0148] See also Figure 6 For example, the processing of integrated functions in a sports and health app can be implemented by a separate sports platform kit (tool). This application does not limit the specific implementation of the sports platform kit.

[0149] Furthermore, it should be noted that the mobile phone 100 also needs to include a software development kit (SDK) for interconnecting with the watch 200. In this embodiment, it is referred to as the Interconnection SDK, but in practical applications, it can be named as needed. In this way, the mobile phone 100, through the Interconnection SDK and using the same account, can achieve interconnected management of multiple devices, and multiple devices under the same account can also synchronize data.

[0150] See also Figure 6For example, mobile phone 100 also needs to have a communication protocol. Mobile phone 100 and watch 200 can establish a communication connection between them based on their respective communication protocols.

[0151] The watch 200 shows the logic control services, communication protocols, algorithms, etc. required to implement this case.

[0152] In addition, the display screen of watch 200 can be used to display prompts from phone 100 based on fatigue level, reminders for exercise plans, and other information.

[0153] In addition, among some possible implementations, the system used to implement the fatigue detection method may also include sports and health data cloud platforms and big data cloud platforms.

[0154] Optionally, the mobile phone 100 can interact with the sports and health data cloud platform to achieve data cloud synchronization.

[0155] Optionally, the mobile phone can also interact with the big data cloud platform to send fault or operational data to the big data cloud platform, so that technicians can locate the anomalies in the sports and health APP based on the data and perform updates, maintenance and other processing on the sports and health APP.

[0156] Based on the above system, when a user exercises using the sports and health app, the phone can determine the fatigue level based on multiple dimensions such as morning resting heart rate, heart rate variability, acute-chronic load ratio, and sleep, and provide appropriate prompts. This allows the phone to provide guidance and suggestions before the user exercises, preventing the user from overexerting themselves and causing injury.

[0157] Optionally, in one possible implementation, when the mobile phone 100 and watch 200 implement the fatigue detection method provided in the embodiments of this application, the changes in the user interface of the mobile phone 100 during the user's use of the sports and health APP, the style of the prompt information window corresponding to different fatigue levels, and the user interface of the watch 200 can be as follows: Figures 7 to 10 As shown.

[0158] See Figure 7 In example (1), the interface 10a of the mobile phone 100 displays icons of some or all of the currently installed applications. These icons include the icon 10a-1 of the Sports & Health APP.

[0159] Optionally, when the user clicks icon 10a-1, the mobile phone 100 responds to the operation by launching the Sports & Health APP. After the Sports & Health APP launches, the interface displayed on the screen will change from... Figure 7 The interface 10a shown in (1) is switched to Figure 7Interface 10b is shown in (2).

[0160] Interface 10b can be the default interface displayed after the sports and health app is launched. Optionally, interface 10b can be, for example, the interface corresponding to the "Health" option, that is, the "Health" option shown in the bottom taskbar of interface 10b (including the "Health" option, "Exercise" option, "Device" option, and "My" option) is selected (in the selected state). Figure 7 When the style shown in (2) is displayed, the corresponding interface is displayed.

[0161] See also Figure 7 In example (2), interface 10b may include various health-related function options, information, etc. For example, information related to today's steps, information related to moderate to high intensity activities, information related to activity consumption, and corresponding graphs. Also, for example, function options and information related to exercise records. Also, for example, the "Sleep" option for viewing sleep conditions. Also, for example, the "Heart Health" option for viewing heart-related data. Also, for example, the "Steps" option for viewing recent walking steps. Also, for example, the "Menstrual Cycle" option, the "Weight" option, etc.

[0162] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended to be the sole limitation of this embodiment. In practical applications, users can edit the tabs displayed on the interface 10b according to their usage habits, such as adjusting the display position of each tab, and specifying which tabs need to be shown or hidden on the interface 10b. This application does not impose any restrictions on this.

[0163] See also Figure 7 In example (2), when a user clicks the "Sports" option in interface 10b, the mobile phone 100 responds to the operation by switching from interface 10b to the interface corresponding to the "Sports" option, such as... Figure 8 The interface 10c shown in (1) indicates that the "Motion" option is selected. The bottom taskbar of the interface 10c shows the style of the "Motion" option.

[0164] See Figure 8 In example (1), interface 10c may include various sports-related options, information, etc. For example, the user's accumulated mileage from outdoor running, and various sports options (such as outdoor running, indoor running, walking, cycling, etc.). Also, for example, cards corresponding to the formulated exercise plan, such as... Figure 8 The “XX Running Plan” card shown in (1) is another example. Other examples include the “All Courses” option to view all fitness classes and the “All Plans” option to view all exercise plans.

[0165] Understandably, in the embodiments of this application, "XX Running Plan" is only an illustrative example. In actual applications, it can be named as other names as needed.

[0166] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended to be the sole limitation of this embodiment. In practical applications, other cards may also be displayed in the interface 10c, such as course ranking cards, plan recommendation cards, etc., and this application does not impose any limitations on this.

[0167] Taking a user-defined exercise plan as an example, specifically a running plan, see below. Figure 8 In example (1), the “XX Running Plan” card can display the total duration of the running plan (31 days as shown in the figure), the current progress (2 days as shown in the figure), and the course schedule for this week.

[0168] See also Figure 8 In example (1), in the "XX Running Plan" card, the weekly schedule section can be identified by different icons. For instance, a dumbbell icon can be used for core courses, which are high-intensity courses. A circle icon can be used for regular courses, which are relatively low-intensity courses.

[0169] Furthermore, since the "XX Running Plan" card displays a weekly schedule, different colors and styles can be used to distinguish between past, current, and future courses. Regarding the styles of past, current, and future course identifiers, as well as core past, current, and future course identifiers, some possible implementations could be as follows: Figure 9 As shown.

[0170] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended to be the sole limitation of this embodiment. In practical applications, other styles of icons can also be used to identify courses with different times and training intensities, and this application does not impose any restrictions on this.

[0171] by Figure 9 Taking the style shown as an example, Figure 8 The specific course schedule for this week in the “XX Running Plan” card shown in (1) is as follows: Monday (11th) has been a regular course, today (Wednesday, 13th) has been a regular course, and Friday (15th) has been a core course.

[0172] For example, when a user wants to exercise, they can click the "XX Running Plan" card. In response to this action, mobile phone 100 will redirect from interface 10c to the corresponding interface for the XX Running Plan, such as... Figure 8 Interface 10d is shown in (2).

[0173] See Figure 8 In the middle (2), for example, the interface 10d may include multiple cards, such as the daily course completion card 10d-1, the weekly course schedule card 10d-2, and the cards for all courses included in the selected date, such as the card 10d-3 corresponding to the pre-run warm-up exercise course and the card 10d-4 corresponding to the enjoyable experience run course.

[0174] In this embodiment of the application, after the fatigue level is determined according to the fatigue detection method, a prompt information window (hereinafter referred to as: window 10d-5) can be popped up in interface 10d according to the user's fatigue level, thereby realizing exercise guidance for the user.

[0175] Optionally, in this embodiment of the application, the user's state is divided into non-fatigue and fatigue. Fatigue can be divided into multiple levels as needed, such as level 1 (hereinafter referred to as: level 1 fatigue), level 2 (hereinafter referred to as: level 2 fatigue), and level 3 (hereinafter referred to as: level 3 fatigue), and non-fatigue can also be represented as level 0 fatigue.

[0176] The reminder strategies (exercise guidance) to be followed for different user states can be shown in Table 1.

[0177] Table 1 Exercise guidance for different user states

[0178]

[0179] Optionally, at level 0 fatigue, i.e., when there is no fatigue, the interface can remain as described in point 10d since no additional reminders are needed to the user. Figure 8 The style shown in (2) is as follows.

[0180] Optionally, in the case of Level 1 fatigue, a pop-up window (10d-5) in interface 10d displays content reminding the user to pay attention to their physical condition during the course and ensure sufficient rest. This content can be as follows: Figure 10 As shown in (1). That is, in this case, the style of window 10d-5 displayed in interface 10d is Figure 10 The style of (1) in the middle.

[0181] Optionally, in the case of Level 1 fatigue, the pop-up window 10d-5 in interface 10d contains information suggesting that the user should not train today or should engage in a low-intensity, regular course. This information can be as follows: Figure 10As shown in (2). That is, in this case, the style of window 10d-5 displayed in interface 10d is Figure 10 The style of (2) in the middle.

[0182] Optionally, in the case of Level 1 fatigue, the pop-up window 10d-5 in interface 10d contains information suggesting that the user should not perform any training today. This information could be as follows: Figure 10 As shown in (3). That is, in this case, the style of window 10d-5 displayed in interface 10d is Figure 10 The style of (3) in the middle.

[0183] It should be noted that when the user clicks Figure 10 After the “OK” option in window 10d-5 shown in (1), (2) or (3), the mobile phone 100 can close window 10d-5 displayed in interface 10d in response to the operation.

[0184] In addition, it should be noted that Figure 10 The courses shown in window 10d-5 in (2) are the courses for ordinary training days, i.e., training days with ordinary courses; the courses with high training intensity are the training days with core courses.

[0185] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended as the only limitation on this embodiment.

[0186] Furthermore, it should be noted that when the mobile phone 100 and the watch 200 are interconnected, and a pop-up window 10d-5 appears on interface 10d, and the watch 200 is enabled to receive notifications from the sports and health app, the watch 200 will display the prompt information in window 10d-5. The corresponding interface effects are not limited in this embodiment and will not be described here.

[0187] Regarding the fatigue detection method provided in this application embodiment, the specific processing logic on the mobile phone 100 side can be as follows: Figure 11 As shown.

[0188] S101, Mobile Phone 100 determines whether at least one of the fatigue levels, determined by morning resting heart rate, HRV, or ACWR, is level 3 fatigue.

[0189] Among them, the morning resting heart rate refers to the average resting heart rate of the user 2 hours before going to sleep.

[0190] Here, HRV refers to the minute differences that exist between successive heartbeats. In this embodiment, the fatigue level is determined based on the RMSSD (the square root of the average of the sum of squares of the differences between adjacent normal RR intervals) determined by HRV data, and the ratio of low frequency (LF) to high frequency (HF) (LF / HF).

[0191] The RR interval typically refers to the time interval between two heartbeats.

[0192] ACWR refers to the ratio of the amount of training completed in one week (acute workload or acute load) to the average amount of training completed in four weeks (average chronic workload or chronic load).

[0193] Optionally, if any one of the fatigue levels determined based on the data from these three dimensions is level 3 fatigue, then the user's current fatigue level can be determined to be level 3 fatigue.

[0194] Accordingly, if the user's current fatigue level is determined to be level 3 fatigue, a pop-up message can be displayed in the interface 10d. Figure 1 The window shown in (3) is 10d-5.

[0195] If there is no Level 3 fatigue among the fatigue levels determined based on the data from these three dimensions, the following priority can be followed (morning pulse resting heart rate > HRV > ACWR > sleep), and the data corresponding to the priority can be selected to determine the user's fatigue level according to the calculation logic corresponding to that dimension.

[0196] Optionally, once a specific fatigue level, such as level 0, level 1, or level 2 fatigue, is determined based on high-priority data, the phone 100 can stop determining the fatigue level based on low-priority data. This eliminates the need to calculate the fatigue level based on data from every dimension, reducing computational load.

[0197] S102, Mobile Phone 100 determines whether the fatigue level determined based on the morning resting heart rate data is level 0 fatigue, level 1 fatigue, or level 2 fatigue.

[0198] Optionally, in scenarios where the fatigue level determined based on the morning resting heart rate data is level 0, 1, or 2, the user's current fatigue level can be determined to be the same as that determined based on the morning resting heart rate data. Otherwise, if the fatigue level cannot be determined based on the morning resting heart rate data, the fatigue level can be determined based on HRV data, which has a lower priority than the morning resting heart rate, i.e., step S103 can be executed.

[0199] S103, Mobile Phone 100 determines whether the fatigue level determined based on HRV data is Level 1 fatigue or Level 2 fatigue.

[0200] Optionally, in scenarios where the fatigue level determined based on HRV data is level 0, level 1, or level 2 fatigue, the user's current fatigue level can be determined to be the fatigue level determined based on the HRV data. Otherwise, if the fatigue level cannot be determined based on HRV data, the fatigue level can be determined based on ACWR data, which has a lower priority than HRV, i.e., step S104 is executed.

[0201] S104, Mobile Phone 100 determines whether the fatigue level determined based on ACWR data is Level 1 fatigue or Level 2 fatigue.

[0202] Optionally, in scenarios where the fatigue level determined based on ACWR data is level 0, level 1, or level 2 fatigue, the user's current fatigue level can be determined to be the fatigue level determined based on the ACWR data. Otherwise, if the fatigue level cannot be determined based on ACWR data, the fatigue level can be determined based on sleep data with a lower priority than HRV, i.e., step S104 is executed.

[0203] S105, Mobile Phone 100 determines whether the fatigue level determined based on sleep data is Level 1 fatigue or Level 2 fatigue.

[0204] Optionally, in scenarios where the fatigue level determined based on sleep data is level 0, 1, or 2, the user's current fatigue level can be determined to be the same as the fatigue level determined by the sleep data. Otherwise, if the fatigue level cannot be determined based on sleep data, the user can be assumed to be not fatigued, i.e., the fatigue level is level 0.

[0205] In addition, it should be noted that in some possible implementations, when executing steps S102, S103, S104, and S105, it is possible to first determine whether the data of the corresponding dimension is valid, so as to further ensure the accuracy of the determined fatigue level.

[0206] For the rules governing the validity of morning resting heart rate data, HRV data, ACWR data, and sleep data, please refer to the corresponding standards; they will not be elaborated here.

[0207] Therefore, based on multi-dimensional data and prioritizing morning resting heart rate > HRV > ACWR > sleep, the mobile phone 100 determines the user's current fatigue level, thus accurately detecting the user's fatigue level while reducing unnecessary calculations.

[0208] Furthermore, the fatigue level determined by the multi-dimensional fatigue detection method provided in the embodiments of this application by the mobile phone 100 can provide guidance and suggestions suitable for the current fatigue level before the user exercises, thereby minimizing the occurrence of events that cause physical injury due to excessive exercise.

[0209] The following, in conjunction with the accompanying diagrams, explains the specific logic behind determining fatigue levels based on morning resting heart rate data, HRV data, ACWR data, and sleep data.

[0210] The following is the content for determining fatigue level based on morning resting heart rate data:

[0211] See Figure 12 For example, if a user wears watch 200 while sleeping at night, watch 200 will determine the user's morning resting heart rate, i.e., the average resting heart rate 2 hours before sleep, based on the morning resting heart rate calculation logic and heart rate algorithm.

[0212] Optionally, the user's sleep and wake times can be determined based on sleep data collected by the watch 200 over a period of time.

[0213] Furthermore, to distinguish whether the morning resting heart rate is from the first day or the second day, one possible approach is to use 8 PM as the dividing line. That is, if the sleep time is earlier than 8 PM, the obtained morning resting heart rate is determined to be from the first day (the previous day); if the sleep time is later than 8 PM, the obtained morning resting heart rate is determined to be from the second day.

[0214] In addition, it should be noted that during the user's sleep, the watch 200 can periodically record resting heart rate data, such as once per minute. This allows the average resting heart rate from the two hours prior to waking to be used as the morning resting heart rate.

[0215] In addition, it should be noted that, in order to ensure the validity of the data, for cases where the total sleep duration is less than 2 hours, the morning resting heart rate can be calculated based on the actual sleep duration.

[0216] See also Figure 12 For example, after obtaining the user's morning resting heart rate data, the watch 200 will send the user's morning resting heart rate data to the mobile phone 100 when a communication connection is established between the watch 200 and the mobile phone 100.

[0217] See also Figure 12For example, after receiving the morning resting heart rate data sent by the watch 200, the mobile phone 100 can determine the user's heart rate fluctuation range based on the morning resting heart rate data, and then determine the user's fatigue level for the day based on the heart rate fluctuation range.

[0218] It should be noted that in practical applications, users may sleep and rest multiple times a day. For situations involving multiple sleep periods within a day, when a user opens the fitness app to view their exercise plan, as shown on the phone's 100 display interface... Figure 8 When the interface 10d shown in (2) is used, in one possible implementation, the resting heart rate data of the morning pulse during the longest sleep period in multiple sleep cycles can be selected to determine the range of heart rate fluctuations of the user.

[0219] Optionally, the heart rate fluctuation range may include Figure 12 The five cases shown are as follows.

[0220] See also Figure 12 For example, if a user's heart rate today fluctuates within N1 bpm (beats per minute) compared to the average heart rate of last week, i.e., case 1a, it can be determined that the user is not fatigued today, i.e., the fatigue level is 0.

[0221] See also Figure 12 For example, in the case of a user whose heart rate today exceeds the average of last week's N1~N2 bpm (including the two endpoints) (the heart rate before today did not exceed this), i.e., case 1b, to avoid special circumstances causing temporary fluctuations in heart rate exceeding the average of last week's N1~N2 bpm, which could lead to inaccurate determination of the fatigue level, one possible implementation is to pop up a Borg questionnaire on the user interface of mobile phone 100 so that the user can conduct a self-assessment, and then determine the user's fatigue level for today based on the assessment.

[0222] It should be noted that the fatigue detection method provided in this application also includes determining the fatigue level based on sleep data. When the sleep data meets preset conditions, user intervention is still required to perform a self-assessment. Therefore, when the Borg questionnaire pops up, a sleep quality questionnaire can also be popped up simultaneously, allowing users to complete the assessment together and avoiding the need to pop up the questionnaire again later when sleep data is required to determine the fatigue level.

[0223] For example, when the heart rate fluctuation range determined based on morning resting heart rate data falls under case 1b, when the user opens the Sports & Health app and enters... Figure 8 When the interface 10d shown in (2) is displayed, the mobile phone 100 can directly jump to the following: Figure 13 The questionnaire feedback interface (interface 10e) is shown in (1).

[0224] See Figure 13 In example (1), interface 10e includes two questionnaires, such as questionnaire 10e-1 and questionnaire 10e-2. Questionnaire 10e-1 is used to determine the user's current level of fatigue, i.e., the aforementioned Borg questionnaire; questionnaire 10e-2 is used to determine the user's sleep quality last night, i.e., the aforementioned sleep quality questionnaire. The Borg questionnaire includes 11 levels of fatigue corresponding to different scores (0~10). Figure 13 Swiping up on interface 10e shown in (1) will display the complete sleep quality questionnaire. Figure 13 As shown in (2), the sleep quality questionnaire includes four options.

[0225] When a user selects an option from the Borg questionnaire and the sleep quality questionnaire shown in interface 10e based on their current physical condition, and clicks to submit option 10e-3, in the step of determining the fatigue level based on the morning resting heart rate, the mobile phone 100 will determine the current fatigue level based on the score corresponding to the fatigue level selected by the user in the Borg questionnaire.

[0226] Optionally, the relationship between user self-assessment scores obtained from the Borg questionnaire and fatigue levels can be shown in Table 2.

[0227] Table 2 shows the relationship between user self-assessment scores obtained from the Borg questionnaire and fatigue levels.

[0228]

[0229] Therefore, in case 1b, based on the user's self-assessment score obtained from the Borg questionnaire and Table 2, Mobile Phone 100 can quickly determine the user's current fatigue level.

[0230] Optionally, Table 2 can be pre-stored in the local storage medium of the mobile phone 100, such as internal memory.

[0231] See also Figure 12 For example, if a user's heart rate exceeds the average of last week's N1~N2 bpm for two consecutive days today and yesterday (i.e., case 1c), there is no need to pop up interface 10e. That is, there is no need to determine based on the user's self-assessment score. The user's fatigue level for today is directly determined to be level 2 fatigue.

[0232] See also Figure 12 For example, if a user's heart rate exceeds the average of last week's N1~N2 bpm for three consecutive days today, yesterday and the day before yesterday (i.e., case 1d), there is no need to pop up interface 10e. That is, there is no need to determine based on the user's self-assessment score. The user's fatigue level for today is directly determined to be level 3 fatigue.

[0233] See also Figure 12 For example, if a user's signaling exceeds the average of last week N3 bpm or more, it indicates that today's heart rate fluctuation is not caused by special circumstances. In this case, there is no need to pop up the interface 10e, that is, there is no need to determine based on the user's self-assessment score. The user's fatigue level today is directly determined to be level 3 fatigue.

[0234] It should be noted that N1, N2, and N3 satisfy the following relationship: 0 <N1<N2<N3。

[0235] Optionally, in some possible implementations, the value of N1 can be, for example, 5, the value of N2 can be, for example, 10, and the value of N3 can be, for example, 11.

[0236] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended to be the sole limitation of this embodiment. In practical applications, the specific values ​​of N1, N2, and N3 can be adaptively adjusted according to the user's actual situation, and this application does not impose any restrictions on this.

[0237] This allows for the determination of fatigue levels based on morning resting heart rate data.

[0238] The following is the content for determining fatigue level based on HRV data:

[0239] See Figure 14 For example, when a user wears watch 200, watch 200 will record heart rate variability data during the period when the user wears watch 200 according to heart rate variability calculation logic and heart rate algorithm, and send the heart rate variability data to mobile phone 100 by establishing a communication connection with mobile phone 100.

[0240] See also Figure 14 For example, after receiving heart rate variability data sent by watch 200, mobile phone 100 can determine the user's current fatigue level based on the heart rate variability data.

[0241] Optionally, the mobile phone 100 can determine the user's current fatigue level based on the RMSSD and LF / HF ratio determined from heart rate variability data.

[0242] RMSSD refers to the root mean square of the difference between adjacent normal cardiac cycles. It is an important indicator of heart rate variability, used to reflect the activity of the body's parasympathetic nervous system.

[0243] The LF / HF ratio refers to the ratio of low-frequency heart rate variability to high-frequency heart rate variability. Generally, a smaller LF / HF ratio indicates better autonomic nervous system function and better heart health.

[0244] The calculation of RMSSD and LF / HF ratio can be found in relevant literature on heart rate variability, and will not be elaborated here.

[0245] In addition, it should be noted that in order to ensure the accuracy of the determined fatigue level, the RMSSD, LF, HF, RR intervals and other data obtained during sleep need to be consistent with the HRV change trend.

[0246] Optionally, it can be stipulated that consistency reaches 80%.

[0247] Based on the two heart rate variability indicators, RMSSD and LF / HF ratio, several situations can be distinguished, such as... Figure 14 The five cases shown are 2a to 2e.

[0248] See also Figure 14 For example, if the RMSSD is higher than a milliseconds and the LF / HF ratio is lower than x, i.e., case 2a, it can be determined that the user is not fatigued today, i.e., the fatigue level is level 0 fatigue.

[0249] See also Figure 14 For example, for cases where the RMSSD is between b and a milliseconds (inclusive of the endpoint value) and the LF / HF ratio is between x and y (inclusive of the endpoint value), i.e., case 2b, it can be determined that the user is fatigued today and the fatigue level is level 1 fatigue.

[0250] See also Figure 14 For example, if the RMSSD is between c and b milliseconds (inclusive of the endpoint value) and the LF / HF ratio is between y and z (inclusive of the endpoint value), i.e. case 2c, it can be determined that the user is fatigued today and the fatigue level is level 2 fatigue.

[0251] See also Figure 14 For example, if the RMSSD is less than c milliseconds and the LF / HF ratio is greater than z, i.e. case 2d, it can be determined that the user is fatigued today and the fatigue level is level 3 fatigue.

[0252] See also Figure 14 For example, if the RMSSD and LF / HF ratios do not fall under case 2a, case 2b, case 2c, or case 2d, it can be determined that the user is not fatigued today, i.e., the fatigue level is 0.

[0253] It should be noted that a, b, and c satisfy the following relationship: a>b>c>0.

[0254] Optionally, in some possible implementations, the value of a can be, for example, 70, the value of b can be, for example, 50, and the value of c can be, for example, 30.

[0255] Furthermore, it should be noted that the above x, y, and z satisfy the following relationship: 0 <x<y<z。

[0256] Optionally, in some possible implementations, the value of x can be, for example, 1.0, the value of y can be, for example, 1.5, and the value of z can be, for example, 2.0.

[0257] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended to be the sole limitation of this embodiment. In practical applications, the specific values ​​of a, b, and c, as well as x, y, and z, can be adaptively adjusted according to the user's actual situation, and this application does not impose any restrictions on this.

[0258] This enabled the determination of fatigue levels based on HRV data.

[0259] The following is the content for determining fatigue level based on ACWR data:

[0260] The determination of fatigue level based on ACWR data can be divided into two scenarios: Scenario 1, where the user wears Watch 200 and calculates ACWR based on the heart rate data provided by Watch 200; and Scenario 2, where the phone 100 does not interact with Watch 200 and calculates ACWR based on the RPE value and training duration obtained from course feedback. The following will explain each scenario in conjunction with... Figure 15 and Figure 16 The following will explain scenarios 1 and 2.

[0261] Scene 1:

[0262] See Figure 15 For example, when a user is wearing watch 200, watch 200 will acquire the user's heart rate data at the end of each training session based on the heart rate data acquisition logic and heart rate algorithm, and send the acquired heart rate data to mobile phone 100.

[0263] See also Figure 15 For example, after receiving heart rate data sent by watch 200, mobile phone 100 will calculate ACWR based on the heart rate data, and then determine the fatigue level based on ACWR.

[0264] In essence, ACWR refers to the ratio of the amount of training completed in one week (acute workload or acute load) to the average amount of training completed in four weeks (average chronic workload or chronic load). That is, ACWR = the ratio of the user's acute load completed in one week to the average chronic load completed in four weeks.

[0265] Optionally, in some implementations, ACWR can be calculated using an Exponentially Weighted Moving-Average (EWMA). Specifically, the user's current ACWR can be determined according to the following formula (1).

[0266] ACWR = Training load × λa + [(1-λa) × ACWR_yesterday] Formula (1)

[0267] Here, λa represents the degree to which the training load decays over time, and its specific value is between 0 and 1.

[0268] Furthermore, the degree to which the training load, represented by λa, decays over time satisfies the following formula (2).

[0269] λa=2 / (N+1)Formula (2)

[0270] Where N is the time decay constant, which is usually the time window for acute load and chronic load. The time window for acute load is 7 days, or 1 week; the time window for chronic load is 28 days, or 4 weeks.

[0271] For example, if a user's acute load in the most recent week is 4, and the acute loads in the previous 1, 2, and 3 weeks are 1, 2, and 3 respectively, then the chronic load coupling over 4 weeks = 1 + 2 + 3 + 4 = 10, and the mean chronic load = 10 / 4 = 2.5.

[0272] The training load in formula (1) can be determined based on the pre-defined intensity level and training duration (min), specifically according to the following formula (3).

[0273] Training load = intensity level × training duration (min) Formula (3)

[0274] Optionally, intensity levels can be divided according to heart rate zones. Specifically, heart rate data from all training sessions (such as running + strength training) is aggregated and averaged. Using the training zone stimulation method, an individual's heart rate can be divided into five levels based on their maximum heart rate, with each zone increasing by 10% from 50% of their maximum heart rate.

[0275] Optionally, the intensity level can be divided into 5 heart rate zones, as shown in Table 3.

[0276] Table 3 Relationship between Heart Rate and Intensity Level

[0277]

[0278] Therefore, based on the heart rate data and Table 3 sent by the watch 200 after each training session, the mobile phone 100 can determine the training level, and then determine the training load based on the training duration and formula (3).

[0279] For example, if a user's maximum heart rate is 195 beats / min, and they exercise at a heart rate of 160 beats / min for 20 minutes, the calculated heart rate range is 160 / 195 × 100% = 82%, which falls within the range corresponding to intensity level 4. Therefore, the calculated training load is 4 × 20 = 80.

[0280] Accordingly, after determining the training load, the user's current ACWR can be quickly determined based on yesterday's ACWR (ACWR_yesterday, or the ACWR corresponding to the last training course) and formulas (1) and (2).

[0281] Optionally, ACWR can be divided into 4 intervals, which can be viewed as 4 specific cases, such as Figure 15 The examples shown are cases 3a, 3b, 3c, and 3d. These four intervals can each correspond to different fatigue levels.

[0282] The relationship between ACWR and fatigue level is shown in Table 4.

[0283] Table 4 Relationship between ACWR and Fatigue Grade

[0284]

[0285] Therefore, based on the calculated ACWR value and Table 4, Mobile Phone 100 can quickly determine the user's current fatigue level.

[0286] Optionally, Table 4 can be pre-stored in the local storage medium of the mobile phone 100, such as internal memory.

[0287] It should be noted that the above d, e, and f satisfy the following relationship: 0 <d<e<f。

[0288] Optionally, in some possible implementations, the value of d can be, for example, 1.4, the value of e can be, for example, 1.5, and the value of f can be, for example, 2.0.

[0289] It should be understood that the above description is merely an example provided to better understand the technical solution of this embodiment, and is not intended to be the sole limitation of this embodiment. In practical applications, the specific values ​​of d, e, and f can be adaptively adjusted according to the user's actual situation, and this application does not impose any restrictions on this.

[0290] Thus, when the mobile phone 100 interacts with the watch 200, the ACWR is calculated based on the heart rate data provided by the watch 200, thereby determining the fatigue level.

[0291] Scene 2:

[0292] See Figure 16 For example, if the user is not wearing the watch 200, or if there is no communication connection between the watch 200 and the mobile phone 100, the mobile phone 100 can pop up a course feedback form, such as the rating of perceived exertion (REP) scale, after each training session. Then, based on the RPE value selected by the user in the RPE table and the training duration (min), the ACWR is calculated, and the fatigue level is determined based on the ACWR.

[0293] Optionally, the RPE table can be displayed in a way that, for example, is an interface corresponding to the end of the training course. Figure 17 A pop-up window 10f-1 appears in the interface 10f shown.

[0294] Optionally, window 10f-1 provides a space for users to perform a self-assessment. The selected RPE value can include 11 scores from 0 to 10, with each score corresponding to a description... Figure 10 The descriptions corresponding to the same score in the Borg questionnaire shown in (1) are similar, and will not be repeated here.

[0295] Optionally, each RPE score can be considered as an intensity level. That is, in the absence of watch 200, mobile phone 100 can be determined according to the following formula (4).

[0296] Training load = RPE value × training duration (min) Formula (4)

[0297] Therefore, without the interaction between the mobile phone 100 and the watch 200, the user's current ACWR can be quickly determined based on yesterday's ACWR (ACWR_yesterday, or the ACWR corresponding to the last training course) and the formulas (1) and (2) given in scenario 1.

[0298] Accordingly, after determining ACWR, based on Table 4 provided in Scenario 1, mobile phone 100 can quickly determine the user's current fatigue level.

[0299] This allows for the calculation of ACWR based on the RPE value obtained from course feedback and training duration, thereby determining the fatigue level.

[0300] The following is the content for determining fatigue levels based on sleep data:

[0301] See Figure 18For example, if a user wears watch 200 while sleeping, watch 200 will acquire the user's sleep data during sleep based on sleep data acquisition logic and sleep algorithms.

[0302] Optionally, the watch 200 can send sleep data to the mobile phone 100 with which it has established a communication connection, either on a timer or after detecting that the user has woken up.

[0303] See also Figure 18 For example, after receiving sleep data sent by watch 200, mobile phone 100 will determine whether the user's sleep meets any one or more of the following three conditions based on the received sleep data.

[0304] Condition 1: Sleep time < first preset duration;

[0305] Condition 2: Number of nighttime awakenings ≥ preset number;

[0306] Condition 3: The average wake-up time of the previous week is earlier than the second preset time.

[0307] Optional, the first preset duration is, for example, 5 hours (h).

[0308] Optionally, the preset number of times is, for example, 3 times.

[0309] Optionally, the second preset duration is, for example, 30 minutes.

[0310] Optionally, when the mobile phone 100 determines that the user's sleep meets at least one of the above three conditions based on sleep data, and the user opens the sports and health APP and is on interface 10d, it will jump from interface 10d to interface 10e shown in 13, and then determine the fatigue level based on the results of the user's selection in the sleep quality questionnaire in interface 10e.

[0311] It should be understood that the priority of using sleep data to determine fatigue level is lower than that of using morning resting heart rate data. Therefore, if situation 1b occurs during the stage of determining fatigue level using morning resting heart rate data, where the sleep quality questionnaire pops up along with the borg questionnaire, meaning both questionnaires are displayed simultaneously on interface 10e, then when entering the stage of determining fatigue level based on sleep data, there is no need to jump back to interface 10e. Instead, the fatigue level can be determined directly based on the user's selection in the sleep quality questionnaire on interface 10e, as in situation 1b.

[0312] pass Figure 10As shown in the interface 10e in (2), the sleep quality questionnaire includes four options. In practical applications, these four options can be mapped to four specific situations, such as very good corresponding to situation 4a, good corresponding to situation 4b, poor corresponding to situation 4c, and very poor corresponding to situation 4d. Among these, these four situations can correspond to different scores, and different scores can correspond to different fatigue levels.

[0313] Optionally, the scores for each option in the sleep quality questionnaire, and the relationship between different scores and fatigue levels, can be shown in Table 4.

[0314] Table 5 shows the scores for each option in the sleep quality questionnaire, and the relationship between different scores and fatigue levels.

[0315]

[0316] Therefore, if the sleep data meets any one or more of the above three conditions, based on the options selected by the user in the sleep quality questionnaire and Table 5, Mobile Phone 100 can quickly determine the user's current fatigue level.

[0317] Optionally, Table 5 can be pre-stored in the local storage medium of the mobile phone 100, such as internal memory.

[0318] This allows for the determination of fatigue levels based on sleep data.

[0319] The beneficial effects of the fatigue detection method provided in this application embodiment are as follows:

[0320] In the fatigue detection method provided in this application embodiment, health-related data collected by a smart wearable device, such as the watch 200 mentioned in the above embodiment, including morning resting heart rate data, HRV data, heart rate data (for ACWR calculation), and sleep data, are combined with self-evaluation results collected from various subjective evaluation questionnaires displayed on a user's smart device, such as a mobile phone 100. This method complements four specific fatigue level determination methods: determining fatigue level using morning resting heart rate data, HRV data, ACWR data, and sleep data. Furthermore, a fatigue level is selected from these four dimensions according to the priority order: morning resting heart rate > HRV, HRV > ACWR, and ACWR > sleep. This makes the final determined fatigue level more reasonable and accurate, enabling appropriate guidance and suggestions to be provided before the user exercises, thus preventing excessive exercise from causing injury to the body.

[0321] Furthermore, it should be noted that the embodiments of this application mainly take the running scenario as an example. In practical applications, the fatigue detection method provided by the embodiments of this application can also be applied to other sports scenarios, such as cycling, swimming, mountain climbing, etc., and this application does not limit it.

[0322] Furthermore, it is understood that, in order to achieve the aforementioned functions, the electronic device includes hardware and / or software modules corresponding to the execution of each function. Based on the algorithmic steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware-driven or software-driven manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in conjunction with the embodiments, but such implementation should not be considered beyond the scope of this application.

[0323] Furthermore, it should be noted that, in practical application scenarios, the fatigue detection methods provided in the above embodiments, implemented by electronic devices, can also be executed by a chip system included in the electronic device. This chip system may include a processor. The chip system can be coupled to a memory, enabling it to call a computer program stored in the memory during runtime to implement the steps executed by the electronic device. The processor in this chip system can be an application processor or a non-application processor.

[0324] In addition, this application embodiment also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device performs the above-mentioned related method steps to implement the fatigue detection method in the above embodiment.

[0325] In addition, this application also provides a computer program product that, when run on an electronic device, causes the electronic device to perform the above-mentioned related steps to implement the fatigue detection method in the above embodiments.

[0326] In addition, embodiments of this application also provide a chip (which may also be a component or module), which may include one or more processing circuits and one or more transceiver pins; wherein the transceiver pins and the processing circuits communicate with each other through internal connection paths, and the processing circuits execute the above-mentioned related method steps to implement the fatigue detection method in the above embodiments, so as to control the receiving pin to receive signals and control the transmitting pin to transmit signals.

[0327] Furthermore, as can be seen from the above description, the electronic devices, computer-readable storage media, computer program products, or chips provided in the embodiments of this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0328] 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.

Claims

1. A fatigue detection method characterized by, The method is applied to an electronic device, the electronic device is in communication connection with a wearable device, the wearable device is used to provide index data of a fatigue level, and the method comprises the following steps: In response to an operation of a user, the wearable device is used to obtain the index data prepared by the wearable device, the index data comprising one or more of morning pulse resting heart rate data, heart rate variability (HRV) data, heart rate data of a movement stage, and sleep data; In a case where the index data comprises the morning pulse resting heart rate data, a fatigue level is calculated according to the morning pulse resting heart rate data; In a case where the fatigue level calculated according to the morning pulse resting heart rate data is a highest fatigue level, the fatigue level calculated according to the morning pulse resting heart rate data is determined as the current fatigue level of the user, and the highest fatigue level indicates that the user is currently not suitable for any training course; The calculation of the fatigue level further comprises the following steps: In a case where the heart rate data of the movement stage is obtained from the wearable device, first acute and chronic work rate ratio (ACWR) data is calculated according to the heart rate data of the movement stage and the training duration of a currently completed training course; A fatigue level is calculated according to the first ACWR data; Or, In a case where the heart rate data of the movement stage is not obtained from the wearable device, a first questionnaire is popped up after the training course is completed, the first questionnaire comprising multiple score options, and different score options corresponding to different fatigue levels; The current training load of the user is calculated according to the score option selected by the user in the first questionnaire and the training duration of the currently completed training course; Second ACWR data of the user is calculated according to the training load, historical ACWR data corresponding to the last completed training course of the user, and a degree parameter indicating the attenuation of the training load over time, the degree parameter having a value between 0 and 1; A fatigue level is calculated according to the second ACWR data.

2. The method of claim 1, wherein, The method further comprises the following steps: In a case where the index data does not comprise the morning pulse resting heart rate data, or the fatigue level calculated according to the morning pulse resting heart rate data is not the highest fatigue level, but the index data comprises heart rate variability (HRV) data, a fatigue level is calculated according to the HRV data; In a case where the fatigue level calculated according to the HRV data is the highest fatigue level, the fatigue level calculated according to the HRV data is determined as the current fatigue level of the user.

3. The method of claim 2, wherein, The method further comprises the following steps: in a case where the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated according to the morning pulse resting heart rate data is not the highest level of fatigue level, and the index data also does not include the HRV data, or the fatigue level calculated according to the HRV data is not the highest level of fatigue level, but the index data includes the heart rate data in the exercise stage, the fatigue level is calculated according to the first ACWR data; in a case where the fatigue level calculated according to the first ACWR data is the highest level of fatigue level, the fatigue level calculated according to the first ACWR data is determined as the current fatigue level of the user.

4. The method of claim 3, wherein, The method further comprises: in a case where the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated according to the morning pulse resting heart rate data is not the highest level of fatigue level, and the index data also does not include the HRV data, or the fatigue level calculated according to the HRV data is not the highest level of fatigue level, and the heart rate data in the exercise stage is not acquired, the fatigue level is calculated according to the second ACWR data; in a case where the fatigue level calculated according to the second ACWR data is the highest level of fatigue level, the fatigue level calculated according to the second ACWR data is determined as the current fatigue level of the user.

5. The method of claim 4, wherein, The method further comprises: in a case where the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated according to the morning pulse resting heart rate data is not the highest level of fatigue level, and the index data also does not include the HRV data, or the fatigue level calculated according to the HRV data is not the highest level of fatigue level, and the index data does not include the heart rate data in the exercise stage, or the fatigue level calculated according to the first ACWR data is not the highest level of fatigue level, or the fatigue level calculated according to the second ACWR data is not the highest level of fatigue level, the fatigue level calculated according to the morning pulse resting heart rate data is determined as the current fatigue level of the user.

6. The method of claim 5, wherein, The method further comprises: in a case where the fatigue level cannot be calculated according to the morning pulse resting heart rate data, but the index data includes the HRV data, and the fatigue level calculated according to the HRV data is lower than the highest level of fatigue level, the fatigue level calculated according to the HRV data is determined as the current fatigue level of the user.

7. The method of claim 3, wherein, The method further comprises: in a case where the fatigue level cannot be calculated according to the morning pulse resting heart rate data and the HRV data, but the index data includes the heart rate data in the exercise stage, and the fatigue level calculated according to the first ACWR data is lower than the highest level of fatigue level, the fatigue level calculated according to the first ACWR data is determined as the current fatigue level of the user.

8. The method of claim 7, wherein, The method further comprises: In a case where the index data does not include the morning pulse resting heart rate data, or a fatigue level cannot be calculated according to the morning pulse resting heart rate data, and the index data does not include the HRV data, or a fatigue level cannot be calculated according to the HRV data, and the index data does not include the heart rate data of the exercise stage, or a fatigue level cannot be calculated according to the first ACWR data, or a fatigue level cannot be calculated according to the second ACWR data, but the index data includes sleep data, a fatigue level is calculated according to the sleep data; In a case where a fatigue level is calculated according to the sleep data, the fatigue level calculated according to the sleep data is determined as the current fatigue level of the user; In a case where a fatigue level cannot be calculated according to the sleep data, a lowest fatigue level is determined as the current fatigue level of the user, the lowest fatigue level indicating that the user is not currently fatigued.

9. The method of claim 8, wherein, The fatigue level calculated according to the sleep data includes: In a case where sleep data meets a preset condition, a second questionnaire is displayed, the second questionnaire including a plurality of sleep quality evaluation options, different sleep quality evaluation options corresponding to different scores; A fatigue level is determined according to a score corresponding to a sleep quality option selected by the user in the second questionnaire.

10. The method of claim 9, wherein, The preset condition includes: The sleep duration is less than a first preset duration; and / or, The number of night awakenings is greater than or equal to a preset number; and / or, The wake-up time is earlier than the average wake-up time of the previous week by a second preset duration.

11. The method according to any one of claims 2 to 6, characterized in that, The fatigue level calculated according to the HRV data includes: A fatigue level is determined according to an RMSSD in the HRV data and an LF / HF ratio, the RMSSD being a root mean square of adjacent normal cardiac cycle differences, and the LF / HF ratio being a ratio of low-frequency heart rate variability and high-frequency heart rate variability.

12. The method of claim 1 or 3, wherein, The first acute chronic workload ratio ACWR data calculated according to the heart rate data of the exercise stage and the training duration of the currently completed training course includes: A heart rate interval in which the heart rate data of the exercise stage is located is determined; wherein, starting from 50% maximum heart rate to 100% maximum heart rate, each 10% is a heart rate interval, and each heart rate interval corresponds to an intensity level; An intensity level is determined according to the determined heart rate interval; A training load of the user is calculated according to the intensity level and the training duration; The first ACWR data of the user is calculated according to the training load, historical ACWR data, and a parameter indicating the degree of attenuation of the training load over time, the historical ACWR data being ACWR data corresponding to the user after the last training, and the parameter indicating the degree of attenuation of the training load over time having a value between 0 and 1.

13. The method according to any one of claims 1 to 10, characterized in that, The response to the operation of the user includes: In response to the operation of the user on a first application, a first interface is displayed, the first application being an application capable of formulating an exercise plan, and the first interface being an interface corresponding to the exercise plan.

14. The method of claim 13, wherein, After determining the current fatigue level of the user, the method further comprises: In the case that the current fatigue level of the user is not the lowest fatigue level, a first window is popped up on the first interface, and movement guidance content matching the current fatigue level of the user is displayed in the first window, the lowest fatigue level indicating that the user is not currently fatigued.

15. An electronic device, comprising: The electronic device comprises a memory and a processor, which are coupled; the memory stores program instructions, and the program instructions are executed by the processor to enable the electronic device to perform the fatigue detection method according to any one of claims 1 to 14.

16. A computer-readable storage medium, characterized in that, The computer program is executed on the electronic device to enable the electronic device to perform the fatigue detection method according to any one of claims 1 to 14.

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