Fatigue detection method and device and storage medium

Abstract

The embodiment of the invention provides a fatigue detection method and device and a storage medium. According to the method, index data which is provided by wearable equipment and is used for calculating a fatigue level and self-evaluation results which are collected by various subjective evaluation questionnaires and displayed on electronic equipment used by a user are combined; four specific fatigue level determination modes of determining a fatigue level by morning pulse resting heart rate data, determining a fatigue level by HRV data, determining a fatigue level by ACWR data and determining a fatigue level by sleep data are mutually complemented, and according to a priority sequence that the morning pulse resting heart rate priority is higher than HRV, the HRV priority is higher than ACWR, and the ACWR priority is higher than sleep, the morning pulse resting heart rate is determined according to the sequence that the morning pulse resting heart rate priority is higher than HRV, the HRV priority is higher than ACWR, and the sleep priority is higher than ACWR. According to the method, one fatigue level is selected from the fatigue levels determined in the four dimensions, so that the finally determined fatigue level is more reasonable and accurate, proper guidance suggestions can be given before the user exercises, and damage to the body caused by excessive exercise of the user is avoided.

Description

Technical Field

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

[0002] Before exercising, such as running, users may not have enough perception of their fatigue level, and may experience physical discomfort during exercise due to fatigue, resulting in physical injuries. Summary of the Invention

[0003] To solve the above technical problems, embodiments of this application provide a fatigue detection method, device, and storage medium, aiming to accurately detect the fatigue level of users, so as to give guiding suggestions before users exercise and avoid physical injuries caused by excessive exercise of users.

[0004] In a first aspect, embodiments of this application provide a fatigue detection method, which is applied to an electronic device. The electronic device is communicatively connected to a wearable device, and the wearable device is used to provide index data for calculating the fatigue level. The method includes: in response to a user's operation, obtaining the index data ready in the wearable device from the wearable device, where the index data includes one or more of morning pulse resting heart rate data, heart rate variability (HRV) data, heart rate data during the exercise phase, and sleep data; in the case where the index data includes morning pulse resting heart rate data, calculating the fatigue level according to the morning pulse resting heart rate data; in the case where the fatigue level calculated according to the morning pulse resting heart rate data is the highest fatigue level, determining the fatigue level calculated according to the morning pulse resting heart rate data as the user's current fatigue level, and the highest fatigue level indicates that the user is currently not suitable for any training courses.

[0005] Among them, the electronic device, such as a mobile phone, a tablet computer, etc.

[0006] Among them, the wearable device, such as a smart watch, a bracelet, etc.

[0007] Optionally, the fatigue level can be divided into level 0 fatigue, level 1 fatigue, level 2 fatigue, level 3 fatigue, etc. Among them, the highest fatigue level, for example, is level 3 fatigue.

[0008] Among them, in response to a user's operation, for example, opening the exercise plan interface of a sports health APP installed by the user in the electronic device for formulating an exercise plan, as follows Figure 8 The interface 10d shown in (2) below.

[0009] Thus, by determining the fatigue level based on the morning pulse resting heart rate data with the highest priority among the index data for calculating the user's fatigue level provided by the wearable device, and when the fatigue level determined based on the morning pulse resting heart rate data is the highest level of fatigue, directly taking the fatigue level determined by the morning pulse resting heart rate data as the user's current fatigue level, it is possible to not only ensure the accuracy of the result but also reduce the computational load of the electronic device.

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

[0011] Thus, in the case where the highest level of fatigue cannot be determined based on the morning pulse resting heart rate data, the HRV data is preferentially considered. When the fatigue level determined by the HRV data is the highest level of fatigue, directly taking the fatigue level determined by the HRV data as the user's current fatigue level, so that more appropriate exercise guidance for the user's current physical state can be given according to the determined fatigue level, avoiding damage to the body caused by the user's excessive exercise.

[0012] According to the first aspect, or any one of the implementation manners of the above first aspect, the method further includes: in the case where the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated based on the morning pulse resting heart rate data is not the highest level of fatigue, and the index data also does not include the HRV data, or the fatigue level calculated based on the HRV data is not the highest level of fatigue, but the index data includes the heart rate data during the exercise phase, calculating the acute - chronic workload ratio (ACWR) data based on the heart rate data during the exercise phase and the training duration of the currently completed training session; calculating the fatigue level based on the ACWR data; in the case where the fatigue level calculated based on the ACWR data is the highest level of fatigue, determining the fatigue level calculated based on the ACWR data as the user's current fatigue level.

[0013] Thus, in the case where the highest level of fatigue cannot be determined based on both the morning pulse resting heart rate data and the HRV data, the ACWR data is preferentially considered. When the fatigue level determined by the ACWR data is the highest level of fatigue, directly taking the fatigue level determined by the ACWR data as the user's current fatigue level, so that more appropriate exercise guidance for the user's current physical state can be given according to the determined fatigue level, avoiding damage to the body caused by the user's excessive exercise.

[0014] According to the first aspect, or any implementation of the above first aspect, the method further includes: when the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated based on the morning pulse resting heart rate data is not the highest level of fatigue, and the index data also does not include the HRV data, or the fatigue level calculated based on the HRV data is not the highest level of fatigue, and the index data also does not include the heart rate data during the exercise phase, after the training session ends, a first questionnaire is popped up. The first questionnaire includes multiple score options, and different score options correspond to different fatigue levels; according to the score selected by the user in the first questionnaire and the training duration, the current training load of the user is calculated; according to the training load, the historical ACWR data, and the parameter indicating the degree of attenuation of the training load over time, the current ACWR data of the user is calculated. The historical ACWR data is the ACWR data corresponding to the user after the last training session. The value of the parameter indicating the degree of attenuation of the training load over time is between 0 and 1; the fatigue level is calculated based on the ACWR data; when the fatigue level calculated based on the ACWR data is the highest level of fatigue, the fatigue level calculated based on the ACWR data is determined as the current fatigue level of the user.

[0015] Among them, the first questionnaire, for example Figure 17 is the course feedback questionnaire popped up on the interface 10f.

[0016] Thus, when the user does not wear the wearable device, or the wearable device does not provide the heart rate data during the exercise phase, by displaying the subjective evaluation questionnaire on the electronic device, it can be ensured that the ACWR data can be calculated in any scenario, and then the fatigue level can be determined.

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

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

[0019] In this way, when the fatigue level can be determined based on the morning pulse resting heart rate data with the highest priority, the fatigue level determined using the morning pulse resting heart rate data is preferred, which can better reflect the actual fatigue degree of the user and is beneficial to providing appropriate exercise guidance before exercise.

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

[0021] In this way, when the fatigue level cannot be determined based on the morning pulse resting heart rate data with the highest priority, the HRV data with a priority lower than the morning pulse resting heart rate data but higher than other index data is preferably used to determine the fatigue level, ensuring that the determined fatigue level can better reflect the actual fatigue degree of the user and is beneficial to providing appropriate exercise guidance before exercise.

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

[0023] In this way, when other index data with a priority higher than the ACWR data cannot determine the fatigue level, the ACWR data is used to determine the fatigue level, further ensuring that the determined fatigue level can better reflect the actual fatigue degree of the user and is beneficial to providing appropriate exercise guidance before exercise.

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

[0025] In this way, when other metric data with a higher priority than sleep data cannot determine the fatigue level, sleep data is used to determine the fatigue level. And when the sleep data cannot determine the fatigue level, it is defaulted that the user belongs to the lowest level of fatigue, so as to ensure that a fatigue level can be determined in any case before the user exercises, and then appropriate exercise guidance can be given.

[0026] According to the first aspect, or any one of the implementation manners of the above first aspect, calculating the fatigue level according to the sleep data includes: when the sleep data meets the preset conditions, displaying a second questionnaire, the second questionnaire includes multiple sleep quality evaluation options, and different sleep quality evaluation options correspond to different scores; determining the fatigue level according to the score corresponding to the sleep quality option selected by the user in the second questionnaire.

[0027] Among them, the second questionnaire, for example Figure 13 the sleep quality questionnaire shown in (2) below.

[0028] According to the first aspect, or any one of the implementation manners of the above first aspect, meeting the preset conditions includes: the sleep duration is less than the first preset duration; and / or, the number of night 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 by the second preset duration.

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

[0030] Among them, the preset number, for example, is 3 times.

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

[0032] According to the first aspect, or any implementation of the above first aspect, calculating the fatigue level based on the morning pulse resting heart rate data includes: when the fluctuation of the morning pulse resting heart rate data from the average morning pulse resting heart rate of the previous week is within N1 bpm, determining that the fatigue level is the lowest fatigue level; when the morning pulse resting heart rate data exceeds the average morning pulse resting heart rate of the previous week by N1 bpm to N2 bpm, presenting the third questionnaire, where the third questionnaire includes multiple fatigue degree evaluation options, and different fatigue degree evaluation options correspond to different scores; determining the fatigue level according to the score corresponding to the fatigue degree option selected by the user in the third questionnaire; when both the morning pulse resting heart rate data and the morning pulse resting heart rate data of yesterday exceed the average morning pulse resting heart rate of the previous week by N1 bpm to N2 bpm, determining that the fatigue level is a fatigue level between the highest level and the lowest level; when the morning pulse resting heart rate data, the morning pulse resting heart rate data of yesterday, and the morning pulse resting heart rate data of the day before yesterday all exceed the average morning pulse resting heart rate of the previous week by N1 bpm to N2 bpm, determining that the fatigue level is the highest fatigue level; when the morning pulse resting heart rate data exceeds the average morning pulse resting heart rate of the previous week by N3 bpm, determining that the fatigue level is the highest fatigue level; where 0 < N1 < N2 < N3.

[0033] Among them, the third questionnaire, for example Figure 13 the borg questionnaire shown in (1) below.

[0034] Regarding the specific details of calculating the fatigue level based on the morning pulse resting heart rate data, reference can be made to Figure 12 and Figure 13 the descriptions of the embodiments shown, which will not be elaborated here.

[0035] According to the first aspect, or any implementation of the above first aspect, calculating the fatigue level based on the HRV data includes: determining the fatigue level according to the RMSSD and LF / HF ratio in the HRV data, where RMSSD refers to the root mean square of the differences 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] Regarding the specific details of calculating the fatigue level based on the HRV data, reference can be made to Figure 14 the descriptions of the embodiments shown, which will not be elaborated here.

[0037] According to the first aspect, or any implementation of the above first aspect, calculate the acute-chronic workload ratio (ACWR) data based on the heart rate data of the exercise phase and the training duration of the currently completed training session, including: determining the heart rate range in which the heart rate data of the exercise phase is located; wherein, starting from 50% of the maximum heart rate to 100% of the maximum heart rate, each increase of 10% is a heart rate range, and each heart rate range corresponds to an intensity level; determining the intensity level according to the determined heart rate range; calculating the user's current training load according to the intensity level and the training duration; calculating the user's current ACWR data according to the training load, the historical ACWR data, and the parameter indicating the degree of attenuation of the training load over time, the historical ACWR data being the ACWR data corresponding to the user after the last completed training, and the value of the parameter indicating the degree of attenuation of the training load over time being between 0 and 1.

[0038] Regarding the specific details of determining the ACWR data based on the heart rate data of the exercise phase provided by the wearable device and then calculating the fatigue level, reference can be made to Figure 15 the description of the embodiments shown, which will not be elaborated here for the time being.

[0039] According to the first aspect, or any implementation of the above first aspect, in response to the user's operation, including: in response to the user's operation on the first application, display the first interface, the first application being an application capable of formulating an exercise plan, and the first interface being the interface corresponding to the exercise plan.

[0040] Among them, the first application, such as a sports health APP capable of formulating an exercise plan.

[0041] Among them, the first interface, such as Figure 8 the interface 10d shown in (2) below.

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

[0043] Among them, the first window, such as Figure 10 the window 10d-5 displayed in the interface 10d shown in (1), (2), and (3) below.

[0044] In a second aspect, an embodiment of the present application provides an electronic device. The electronic device includes: a memory and a processor, the memory and the processor being coupled; the memory stores program instructions, and when the program instructions are executed by the processor, the electronic device executes the instructions of the method in the first aspect or any possible implementation of the first aspect.

[0045] In a third aspect, an embodiment of the present application provides a computer-readable medium for storing a computer program, where the computer program includes instructions for executing the method in the first aspect or any possible implementation manner of the first aspect.

[0046] In a fourth aspect, an embodiment of the present application provides a computer program, where the computer program includes instructions for executing the method in the first aspect or any possible implementation manner of the first aspect.

[0047] In a fifth aspect, an embodiment of the present application provides a chip, which includes a processing circuit and transceiver pins. Among them, the transceiver pins and the processing circuit communicate with each other through an internal connection path, and the processing circuit executes the method in the first aspect or any possible implementation manner of the first aspect to control the receiving pin to receive a signal and control the sending pin to send a signal. Description of the Drawings

[0048] Figure 1 A schematic diagram of a scenario for implementing fatigue detection shown by way of example;

[0049] Figure 2 A schematic diagram of the hardware structure of an electronic device for implementing fatigue detection shown by way of example;

[0050] Figure 3 Shown by way of example Figure 2 A schematic diagram of the software structure of the shown electronic device;

[0051] Figure 4 A schematic diagram of the hardware structure of another electronic device for implementing fatigue detection shown by way of example;

[0052] Figure 5 Shown by way of example Figure 4 A schematic diagram of the software structure of the shown electronic device;

[0053] Figure 6 A schematic diagram of a system architecture for implementing fatigue detection shown by way of example;

[0054] Figure 7 And Figure 8 A schematic diagram of a user interface involved in the process of starting the Sports Health APP to view the exercise plan shown by way of example;

[0055] Figure 9 A schematic diagram of course identifiers with different intensities at different times shown by way of example;

[0056] Figure 10 A schematic diagram of a user interface for giving prompt information according to different fatigue levels shown by way of example;

[0057] Figure 11 Flow schematic diagram of an exemplary fatigue detection method;

[0058] Figure 12 Schematic diagram for determining fatigue level based on morning pulse resting heart rate data;

[0059] Figure 13 Schematic diagram of the user interface of the questionnaire feedback displayed when it is determined to be Case 1b based on morning pulse resting heart rate data;

[0060] Figure 14 Schematic diagram for determining fatigue level based on heart rate variability data;

[0061] Figure 15 Schematic diagram for determining ACWR and then determining fatigue level;

[0062] Figure 16 Another schematic diagram for determining ACWR and then determining fatigue level;

[0063] Figure 17 For exemplary illustration of Figure 16 Schematic diagram of the user interface for popping up course feedback for the scenario of;

[0064] Figure 18 Schematic diagram for determining fatigue level based on sleep data. Detailed implementation manners

[0065] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0066] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone.

[0067] The terms "first" and "second" in the description and claims of the embodiments of the present application are used to distinguish different objects, rather than to describe a specific order of the objects. For example, the first target object and the second target object are used to distinguish different target objects, rather than to describe a specific order of the target objects.

[0068] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0069] In the description of the embodiments of the present application, unless otherwise specified, "a plurality of" means two or more. For example, a plurality of processing units means two or more processing units; a plurality of systems means two or more systems.

[0070] With the increasing pressure of modern life, people are paying more and more attention to their own health. Currently, more and more people improve their physical fitness through fitness. However, before fitness, such as running, users do not have enough perception of their fatigue level and do not know the exact impact of fatigue on running. In the case of very severe fatigue, continuing to perform running exercises with an intensity exceeding the user's safety range may pose an injury risk to the user.

[0071] In view of this, the embodiments of the present application provide a more comprehensive multi-dimensional fatigue detection method, aiming to accurately detect the fatigue level of users, so as to give guiding suggestions before users' fitness and avoid damage to the body caused by excessive exercise of users.

[0072] Exemplarily, in some possible implementation manners, the fatigue detection method provided by the embodiments of the present application can be applied to an electronic device. The electronic device can be any one of portable devices such as a mobile phone, a tablet computer, a wearable device, etc.

[0073] Exemplarily, in some other possible implementation manners, the fatigue detection method provided by the embodiments of the present application can also be applied to multiple portable devices, such as a mobile phone and a smart watch (hereinafter simply referred to as: watch). Through the cooperation of these two devices, the fatigue level of users can be detected more accurately.

[0074] The embodiments of the present application establish a communication connection between the mobile phone 100 and the watch 200 to perform data interaction, such as Figure 1 shown in the scenario, to specifically illustrate the fatigue detection solution.

[0075] See Figure 2 , which exemplarily shows a schematic structural diagram of a mobile phone 100. As Figure 2As shown in the figure, 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, a button 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. For example, the processor 110 may include an application processor (AP), a modem processor (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. There is no need to list them one by one here, and this application does not limit this.

[0077] Optionally, these processing units may be independent devices, that is, each processing unit can be regarded as a processor.

[0078] Optionally, these processing units may also be integrated in one or more processors.

[0079] It should be understood that the above description is only an example listed for better understanding the technical solution of this embodiment and does not serve as the only limitation to this embodiment.

[0080] The processor 110 may further include one or more interfaces. Among them, the interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc. There is no need to list them one by one here, and this application does not limit this.

[0081] A memory may also be provided in the processor 110 for storing instructions and data. In some implementation manners, the memory in the processor 110 is a cache memory. This memory can save the instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to use this instruction or data again, it can directly call it 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 memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to implement the storage capacity expansion of the mobile phone 100. The external memory card communicates with the processor 110 through the external memory interface 120 to implement the data storage function.

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

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

[0085] The power management module 141 is used to connect the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives inputs from the battery 142 and / or the charging management module 140 and supplies power to the processor 110, the internal memory 121, the external memory, the display screen 194, the camera 193, the wireless communication module 160, etc. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage, impedance). In some other implementation manners, the power management module 141 can also be disposed in the processor 110. In some other implementation manners, the power management module 141 and the charging management module 140 can also be disposed in the same device.

[0086] The wireless communication function of the mobile phone 100 can be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modulation and demodulation processor, and the baseband processor, etc.

[0087] It should be noted that the antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization rate of the antennas. For example: the antenna 1 can be multiplexed as the diversity antenna of the wireless local area network. In some other implementation manners, the antenna can be used in combination with a tuning switch.

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

[0089] The wireless communication module 160 can provide solutions for wireless communications 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), infrared technology (IR), etc. applied to the mobile phone 100. Specifically, in the technical solution provided by the embodiments of the present application, the mobile phone 100 can communicate with the smartwatch 200 through the wireless communication module 160 and the 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 a pressure sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, a bone conduction sensor, etc. There is no need to list them one by one here, and this application does not limit this.

[0092] The button 190 includes a power-on button, a volume button, etc. The button 190 may be a mechanical button or a touch button. The mobile phone 100 may receive button inputs and generate button signal inputs related to the user settings and function controls of the mobile phone 100.

[0093] The motor 191 may generate a vibration prompt. The motor 191 may be used for incoming call vibration prompts or for touch vibration feedback.

[0094] The indicator 192 may be an indicator light and may be used to indicate the charging state, the change in battery level, or may also be used to indicate messages, missed calls, notifications, etc.

[0095] The camera 193 is used to capture still images or videos. In some implementation manners, the 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 implementation manners, 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, interactions with the user, prompt messages, etc. may be implemented through the display screen.

[0097] This is the end of the introduction to the hardware structure of the mobile phone 100. It should be understood that Figure 2 The illustrated mobile phone 100 is only an example. In specific implementations, the mobile phone 100 may have more or fewer components than those shown in the figure, may combine two or more components, or may have different component configurations. Figure 2 The various components shown may 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 , which exemplarily shows a schematic diagram of the software structure of a mobile phone 100. Before explaining the software structure of the mobile phone 100, first explain the architecture that the software system of the mobile phone 100 may adopt.

[0099] Optionally, the software system of the mobile phone 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservices architecture, or a cloud architecture.

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

[0101] For the sake of convenience of description, in the embodiments of the present application, the Android system with a layered architecture is taken as an example to exemplarily illustrate the software structure of the mobile phone 100.

[0102] As Figure 3 shown, the layered architecture of the mobile phone 100 divides the software into several layers, and each layer has a clear role and division of labor. The layers communicate with each other through software interfaces. In some implementation manners, the Android system of the mobile phone 100 is divided into five layers, from top to bottom, namely the application layer, the application framework layer, the Android runtime and system libraries, the hardware abstraction layer (HAL), and the kernel layer.

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

[0104] As Figure 3 shown, the application packages may include applications such as sports health, settings, music, Bluetooth, and gallery. Here, they are not listed one by one, and the present application does not limit this.

[0105] Among them, the sports health application is an APP provided by the embodiments of the present application, which can cooperate with wearable and health devices (such as the watch 200) to achieve scientific sports monitoring and health management, and provide professional data and rich activity experiences. Through the sports health APP, users can formulate sports plans to facilitate exercising according to the sports plans.

[0106] In addition, in the embodiments of the present application, the mobile phone 100 may, according to the determined fatigue level (grade), on the sports plan interface of the sports health APP, such as the interface 10d shown in (2) in the following embodiments Figure 8 display coach suggestions, prompting the user that the body is not suitable for exercise today, or not suitable for high-intensity exercise. Regarding the prompt information window (window 10d-5 mentioned below) displayed on the interface 10d according to the determined fatigue level, it may be as shown in (1), (2), and (3) in Figure 10 . The specific push logic can refer to the description of the following embodiments and will not be elaborated here.

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

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

[0109] The notification manager enables an application to display notification information in the status bar. It can be used to convey messages of the notification type and can disappear automatically after a short stay without user interaction.

[0110] Specifically, in the embodiments of this application, when it is determined that the user is fatigued, before the time when the user is accustomed to starting exercise, for example, one hour before, a prompt message is pushed by the notification manager.

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

[0112] The content provider is used to store and obtain data and enable these data to be accessible to applications.

[0113] The view system includes visual controls, such as controls for displaying text, controls for displaying pictures, etc.

[0114] The resource manager provides various resources for applications, such as the layouts and contents of various prompt message windows that need to be displayed in the interface 10d.

[0115] The window manager is used to manage window programs. The window manager can obtain the size of the display screen, determine whether there is a status bar, lock the screen, the current display interface (such as determining whether it is in the interface 10d), etc.

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

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

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

[0119] The system library can include multiple functional modules. For example: surface manager, media libraries, 3D (three-dimensional) graphics processing library (such as OpenGL ES), 2D (two-dimensional) graphics engine (such as SGL), etc.

[0120] The surface manager is used to manage the display subsystem and provides the fusion of 2D and 3D layers for multiple applications.

[0121] The media library supports the playback and recording of multiple common audio and video formats, as well as static image files, etc. The media library can support multiple audio and video coding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

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

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

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

[0125] The kernel layer is a layer between the hardware and the software. The kernel layer at least includes sensor drivers, display drivers, camera drivers, power management drivers, etc. Exemplarily, the sensor driver can be used to output the detection signal of a sensor (such as a touch sensor) to the view system, so that the view system displays the corresponding application interface in response to the detection signal.

[0126] This is the end of the introduction to the software structure of the mobile phone 100. Understandably, Figure 3 The layers in the shown software structure and the components included in each layer do not constitute a specific limitation on the mobile phone 100. In other embodiments of the present application, the mobile phone 100 may include more or fewer layers than shown, and each layer may include more or fewer components, which are not limited in the present application.

[0127] See Figure 4 , an exemplary hardware structure of a watch 200 is shown. As Figure 4As shown, the smartwatch 200 may include: a processor 210, a sensor 220, a memory 230, an antenna 240, etc.

[0128] Among them, the processor 210 may be the nerve center and command center of the smartwatch 200. The processor 210 may generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching instructions and executing instructions. A memory may also be provided in the processor 210 for storing instructions and data. In some embodiments, the memory in the processor 210 is a cache memory.

[0129] Among them, the memory 230 may be used to store computer-executable program codes, and the executable program codes include instructions. The memory 230 may be a volatile memory or a persistent memory. The computer-executable program codes stored in the memory 230 may include one or more modules, and each module may include a series of instruction operations. The memory 230 may include a program storage area and a data storage area.

[0130] Furthermore, the processor 210 may be configured to communicate with the memory 230 to execute a series of instruction operations in the memory 230 on the smartwatch 200. Among them, the processor 210 executes various functions and data processing of the smartwatch 200 by running the computer program instructions stored in the memory 230.

[0131] Among them, the sensor 220 may include, for example, sensors for monitoring sleep, sensors for monitoring heart rate, etc., which can perform motion monitoring and health monitoring. These sensors can collect data on several dimensions such as sleep, morning pulse resting heart rate, and heart rate variability (HRV), and transmit this data to the mobile phone 100 so that the sports health APP of the mobile phone 100 can determine the user's current fatigue level based on parameters of multiple dimensions and then make reasonable reminders.

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

[0133] The introduction of the hardware structure of the smartwatch 200 ends here. It should be understood that Figure 4 The smartwatch 200 shown is only an example. In specific implementations, the smartwatch 200 may have more or fewer components than those shown in the figure, may combine two or more components, or may have different component configurations. Figure 4 The various components shown may 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] Refer to Figure 5 for an exemplary illustration of the software structure of a watch 200. Understandably, the software structure of the watch 200 can also adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservices architecture, or a cloud architecture. The software system includes, but is not limited to, Android systems and iOS systems, etc.

[0135] For ease of explanation, in the embodiments of the present application, taking the Android system with a layered architecture as an example, the software structure of the watch 200 is exemplarily described.

[0136] As Figure 3 shown, the layered architecture of the watch 200 divides the software into several layers, and each layer has a clear role and division of labor. The layers communicate with each other through software interfaces. In some implementation manners, the Android system of the watch 200 is divided into 4 layers, from top to bottom are the Application (APP) layer, the Application Framework (FWK) layer, the algorithms and internal libraries, and the kernel layer.

[0137] As Figure 5 shown, the application package of the watch 200 may include a map application, a communication application, a music player, etc.

[0138] Understandably, the above-mentioned communication application, for example, includes an information application, a call record application, a contacts application, a call application, etc.

[0139] Continue to refer to Figure 5 for an example, the application framework layer of the watch 200 may include various logic control services, communication protocols, etc.

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

[0141] Among them, the communication protocol can be a communication protocol agreed with the watch 100, so as to facilitate the data interaction between the two according to the agreed communication protocol.

[0142] The 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 classes, barcodes, payment-related basic libraries, and protocol stacks provided by chip manufacturers, such as traditional Bluetooth protocol stacks, low-power protocol stacks, etc.

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

[0144] This concludes the introduction to the software structure of the watch 200. It can be understood that Figure 5 the layers in the shown software structure and the components included 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 shown, and each layer may include more or fewer components, which are not limited in this application.

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

[0146] Referring to Figure 6 , exemplarily, the system for implementing the fatigue detection method may include the mobile phone 100 and the watch 200.

[0147] Specifically, in the embodiments of this application, a sports health APP is installed in the mobile phone 100. Optionally, the sports health APP may integrate the logic for performing fatigue detection provided in the embodiments of this application, and the logic for providing exercise suggestions according to the determined fatigue level.

[0148] Continuing to refer to Figure 6 , exemplarily, the processing of the integrated functions of the sports health APP can be implemented by a separate sports middle platform Kit (tool). The specific implementation of the sports middle platform Kit is not limited in this application.

[0149] In addition, it should be noted that the mobile phone 100 also needs to include a Software Development Kit (SDK) for realizing the interconnection with the watch 200. In the embodiments of this application, it is called the interconnection SDK, but in actual applications, it can be named according to needs. In this way, through the interconnection SDK, the mobile phone 100 can realize the interconnection management of multiple devices using the same account, and the data of multiple devices under the same account can also be synchronized.

[0150] Continuing to refer to Figure 6, illustratively, a communication protocol is also required in the mobile phone 100. The mobile phone 100 and the watch 200 can establish a communication connection between the two based on their respective communication protocols.

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

[0152] In addition, for the watch 200, its display screen can be used to display prompt information made by the mobile phone 100 according to the fatigue level, course reminder information in the exercise plan, etc.

[0153] In addition, in some possible implementations, the system for implementing the fatigue detection method may also include a sports health data cloud platform and a big data cloud platform, etc.

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

[0155] Optionally, the mobile phone 100 can also interact with the big data cloud platform to send the management data of faults or operations to the big data cloud platform, so that technical personnel can locate the abnormalities of the sports health APP based on the management data and update, maintain, etc. the sports health APP.

[0156] Based on the above system, when a user uses the sports health APP to exercise, the mobile phone 100 can determine the fatigue level based on multiple dimensions such as morning pulse resting heart rate, heart rate variability, acute and chronic load ratio, sleep, etc., and make appropriate prompt information, so as to provide guidance and suggestions before the user exercises to avoid damage to the user's body due to excessive exercise.

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

[0158] See also Figure 7 In (1), for example, icons of some or all currently installed applications are displayed in the interface 10a of the mobile phone 100. These icons include an icon 10a-1 of a sports health APP.

[0159] Optionally, when the user clicks the icon 10a-1, the mobile phone 100 will start the sports health APP in response to the operation. After the sports health APP is started, the interface displayed on the display screen will change from Figure 7 The interface 10a shown in (1) is switched to Figure 7The interface 10b shown in (2).

[0160] Among them, the interface 10b can be the interface that is default displayed after the sports and health APP is launched. Optionally, the interface 10b is, for example, the interface corresponding to the "Health" option, that is, when the "Health" option shown in the bottom task bar (including the "Health" option, "Sports" option, "Device" option, "My" option) in the interface 10b is selected (in the Figure 7 style shown in (2)), the corresponding interface.

[0161] Continue to refer to Figure 7 (2). Exemplarily, the interface 10b may include various health-related function options, information, etc. For example, information related to the number of steps today, information related to moderate-to-high intensity, information related to activity consumption, and corresponding graphics. Also for example, function options and information corresponding to sports 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 the recent number of walking steps. Also for example, the "Physiological Cycle" option, "Weight" option, etc.

[0162] It should be understood that the above description is only an example listed for better understanding the technical solution of this embodiment and does not serve as the sole limitation of this embodiment. In actual applications, users can, according to their usage habits, edit the option cards displayed in the interface 10b, such as adjusting the display positions of the option cards, the option cards to be displayed and hidden in the interface 10b, etc., and this application does not make any restrictions on this.

[0163] Continue to refer to Figure 7 (2). Exemplarily, when the user clicks the "Sports" option in the interface 10b, the mobile phone 100 responds to this operation and will switch from the interface 10b to the interface corresponding to the "Sports" option, such as Figure 8 the interface 10c shown in (1). Among them, the style of the "Sports" option shown in the bottom task bar in the interface 10c indicates that the "Sports" option is selected.

[0164] Refer to Figure 8 (1). Exemplarily, the interface 10c may include various sports-related options, information, etc. For example, the cumulative kilometers of outdoor running by the user, various sports options (such as outdoor running, indoor running, walking, cycling, etc.). Also for example, the card corresponding to the formulated sports plan, such as Figure 8 the "XX Running Plan" card shown in (1). Also for example, the "All Courses" option for viewing all fitness courses, the "All Plans" option for viewing all sports plans, etc.

[0165] Understandably, in the embodiments of the present application, the "XX Running Plan" is only for illustrative purposes. In actual applications, it can be named other names according to needs.

[0166] It should be understood that the above description is only an example listed for better understanding the technical solution of this embodiment and does not serve as the sole limitation of this embodiment. In actual applications, other cards can also be displayed in the interface 10c, such as course ranking cards, plan recommendation cards, etc. The present application places no restrictions on this.

[0167] Taking the running plan formulated by the user as an example of the running plan, continue to refer to Figure 8 In (1) below, exemplarily, the "XX Running Plan" card can display the total cycle of the formulated running plan (31 days as shown in the figure), the current progress (2 days as shown in the figure), and the course arrangements for this week.

[0168] Continue to refer to Figure 8 In (1) below, exemplarily, in the part of the course arrangements for this week in the "XX Running Plan" card, different icons can be used to identify the sports courses. For example, for core courses, that is, courses with a relatively high training intensity, a dumbbell icon can be used. For ordinary courses, that is, courses with a relatively low training intensity, a circle icon can be used.

[0169] In addition, since the "XX Running Plan" card shows the course arrangements for one week. In order to distinguish the courses in the past time, the current time, and the future time, different colors and styles can be used for identification. Regarding the styles of the ordinary course identification in the past time, the ordinary course identification at the current time, the ordinary course identification in the future time, the core course identification in the past time, the core course identification at the current time, and the core course identification in the future time, in some possible implementation manners, they can be as Figure 9 shown.

[0170] It should be understood that the above description is only an example listed for better understanding the technical solution of this embodiment and does not serve as the sole limitation of this embodiment. In actual applications, other styles of icons can also be used to identify the courses with different times and different training intensities. The present application places no restrictions on this.

[0171] Taking Figure 9 the shown style as an example, Figure 8 in the specific case of the course arrangements for this week shown in the "XX Running Plan" card in (1) below: The ordinary course was arranged on the past Monday (the 11th), the ordinary course is arranged today (Wednesday, the 13th), and the core course is arranged on the upcoming Friday (the 15th).

[0172] Exemplarily, when the user wants to exercise, they can click on the "XX Running Plan" card, and in response to this operation, the mobile phone 100 will jump from the interface 10c to the interface corresponding to the XX Running Plan, such as Figure 8 the interface 10d shown in (2) of

[0173] See Figure 8 in (2) of

[0174] In the embodiment of the present application, after determining the fatigue level according to the fatigue detection method, a prompt information window (subsequently referred to as: window 10d-5) can be popped up in the interface 10d according to the user's fatigue level, so as to realize the exercise guidance for the user.

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

[0176] Regarding different user states, the reminder strategies (exercise guidance) that follow can be as shown in Table 1.

[0177] Table 1 Exercise guidance corresponding to different user states

[0178] Fatigue level Exercise guidance Level 0 fatigue No reminder Level 1 fatigue Remind the user to pay attention to their physical condition during the course and ensure sufficient rest Level 2 fatigue It is recommended that the user not train today or take a regular course with a low training intensity Level 3 fatigue It is recommended that the user not perform any training today

[0179] Optionally, in the case of level 0 fatigue, that is, in the non-fatigued situation, since there is no need to give the user additional reminders, the interface 10d can remain in the style as Figure 8 shown in (2) of

[0180] Optionally, in the case of level 1 fatigue, the relevant content for reminding the user to pay attention to their physical state during the course and ensure sufficient rest in the window 10d-5 popped up in the interface 10d can be as Figure 10 shown in (1) of Figure 10 That is, in this case, the style of the window 10d-5 displayed in the interface 10d is the style of

[0181] Optionally, in the case of level 1 fatigue, the relevant content for suggesting that the user does not train today or trains in a relatively low-intensity ordinary course in the window 10d-5 popped up in the interface 10d can be as Figure 10As shown in (2) in the figure. That is, in this case, the style of the window 10d-5 displayed in the interface 10d is Figure 10 the style shown in (2) in the figure.

[0182] Optionally, in the case of level 1 fatigue, the relevant content for suggesting that the user does not perform any training today in the window 10d-5 popped up in the interface 10d can be as Figure 10 shown in (3) in the figure. That is, in this case, the style of the window 10d-5 displayed in the interface 10d is Figure 10 the style shown in (3) in the figure.

[0183] It should be noted that when the user clicks on the Figure 10 "Got it" option in the window 10d-5 shown in (1), (2), or (3) in the figure, the mobile phone 100 can close the window 10d-5 displayed in the interface 10d in response to this operation.

[0184] In addition, it should also be noted that Figure 10 the courses on ordinary training days shown in the window 10d-5 shown in (2) in the figure, that is, the training days with ordinary courses arranged; the courses with high training intensity, that is, the training days with core courses arranged.

[0185] It should be understood that the above description is only an example listed for better understanding of the technical solution of this embodiment and does not serve as the sole limitation of this embodiment.

[0186] In addition, it should be noted that when the mobile phone 100 and the smartwatch 200 are fused and interconnected, and the window 10d-5 pops up in the interface 10d, and the smartwatch 200 has enabled receiving notification messages of the sports health APP, the smartwatch 200 will display the prompt information in the window 10d-5. Regarding the corresponding interface effects, the embodiments of this application do not make any restrictions and will not be described here either.

[0187] Regarding the fatigue detection method provided by the embodiments of this application, the specific processing logic on the mobile phone 100 side can be as Figure 11 shown.

[0188] S101, the mobile phone 100 determines whether at least one of the fatigue levels determined according to the morning pulse resting heart rate, or HRV, or ACWR is level 3 fatigue.

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

[0190] Among them, HRV refers to the minute differences between successive heartbeats. In this embodiment, specifically, the RMSSD (the square root of the mean of the sum of the squares of the differences between adjacent normal R-R intervals), the ratio of low frequency (LF) to high frequency (HF) (LF / HF) are determined based on the HRV data to determine the fatigue level.

[0191] Among them, the R-R interval generally refers to the time interval between two heartbeats.

[0192] Among them, ACWR refers to the ratio of the training volume (acute workload or acute load) completed within 1 week to the average training volume (average chronic workload or average chronic load mean) completed within 4 weeks.

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

[0194] Correspondingly, when it is determined that the user's current fatigue level is level 3 fatigue, a window 10d-5 as shown in (3) in Figure 1 can pop up in the interface 10d.

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

[0196] Optionally, when a specific fatigue level, such as level 0 fatigue, or level 1 fatigue, or level 2 fatigue, is determined based on the data of the high priority, the mobile phone 100 can stop the operation of determining the fatigue level based on the data of the low priority. In this way, it is not necessary to calculate the fatigue level based on the data of each dimension, reducing the calculation amount.

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

[0198] Optionally, in the scenario where the fatigue level determined based on the morning pulse resting heart rate data is level 0 fatigue, or level 1 fatigue, or level 2 fatigue, it can be determined that the user's current fatigue level is the fatigue level determined based on the morning pulse resting heart rate data. Otherwise, that is, when the fatigue level cannot be determined based on the morning pulse resting heart rate data, the fatigue level can be determined based on the HRV data with a priority level lower than that of the morning pulse resting heart rate, that is, step S103 is executed.

[0199] S103, the mobile phone 100 determines whether the fatigue level determined according to the HRV data is level 1 fatigue or level 2 fatigue.

[0200] Optionally, in the scenario where the fatigue level determined according to the HRV data is level 0 fatigue, or level 1 fatigue, or level 2 fatigue, it can be determined that the user's current fatigue level is the fatigue level determined according to the HRV data. Otherwise, that is, in the case where the fatigue level cannot be determined according to the HRV data, the fatigue level can be determined according to the ACWR data which has a priority level one lower than that of the HRV, that is, step S104 is executed.

[0201] S104, the mobile phone 100 determines whether the fatigue level determined according to the ACWR data is level 1 fatigue or level 2 fatigue.

[0202] Optionally, in the scenario where the fatigue level determined according to the ACWR data is level 0 fatigue, or level 1 fatigue, or level 2 fatigue, it can be determined that the user's current fatigue level is the fatigue level determined according to the ACWR data. Otherwise, that is, in the case where the fatigue level cannot be determined according to the ACWR data, the fatigue level can be determined according to the sleep data which has a priority level one lower than that of the HRV, that is, step S104 is executed.

[0203] S105, the mobile phone 100 determines whether the fatigue level determined according to the sleep data is level 1 fatigue or level 2 fatigue.

[0204] Optionally, in the scenario where the fatigue level determined according to the sleep data is level 0 fatigue, or level 1 fatigue, or level 2 fatigue, it can be determined that the user's current fatigue level is the fatigue level determined according to the sleep data. Otherwise, that is, in the case where the fatigue level cannot be determined according to the sleep data, it can be defaulted that the user is not fatigued currently, that is, the fatigue level is level 0 fatigue.

[0205] In addition, it should be noted that in some possible implementation manners, when executing step S102, step S103, step S104, and step S105, it can be first determined whether the data in the corresponding dimension is valid, further ensuring the accuracy of the determined fatigue level.

[0206] For the judgment rules on whether the morning pulse resting heart rate data, HRV data, ACWR data, and sleep data are valid, reference can be made to the corresponding standards, which will not be elaborated here.

[0207] Thus, the mobile phone 100 determines the user's current fatigue level according to multi-dimensional data and in the priority order of morning pulse resting heart rate > HRV > ACWR > sleep, so as to accurately detect the user's fatigue degree and reduce unnecessary calculations.

[0208] In addition, the fatigue level determined by the multi-dimensional fatigue detection method provided by the embodiment of the present application in the mobile phone 100 can give guiding suggestions suitable for the current fatigue level before the user exercises, so as to avoid as much as possible the occurrence of events where the user's excessive exercise causes damage to the body.

[0209] The following will combine with the accompanying drawings to separately illustrate the specific logics for determining the fatigue level based on the morning pulse resting heart rate data, the HRV data, the ACWR data, and the sleep data.

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

[0211] Refer to Figure 12 , for example, when the user wears the watch 200 at night while sleeping, the watch 200 will determine the morning pulse resting heart rate of the user according to the morning pulse resting heart rate calculation logic and the heart rate algorithm, etc., that is, the average resting heart rate in the 2 hours before going to bed.

[0212] Optionally, regarding the time of going to bed, it can be determined according to the sleep data collected by the watch 200 for a period of time in the past by the user.

[0213] In addition, in order to distinguish whether the morning pulse resting heart rate is of the first day or the second day, in some possible implementation manners, 8 o'clock in the evening every day can be selected as the dividing line. That is to say, when the time of going to bed is earlier than 8 o'clock in the evening, the determined morning pulse resting heart rate is of the first day (the previous day); when the time of going to bed is later than 8 o'clock in the evening, the determined morning pulse resting heart rate is of the second day.

[0214] In addition, it should also be noted that during the user's sleep, the watch 200 can periodically record the resting heart rate data, such as recording once every minute. In this way, after the user wakes up, the average value of the resting heart rate in the 2 hours before going to bed can be used as the morning pulse resting heart rate.

[0215] In addition, it should also be noted that in order to ensure the data validity, for the case where the entire sleep duration is less than 2 hours, the morning pulse resting heart rate can be calculated according to the actual sleep time.

[0216] Continue to refer to Figure 12 , for example, after the watch 200 obtains the morning pulse resting heart rate data of the user today, when a communication connection is established between the watch 200 and the mobile phone 100, it will send the morning pulse resting heart rate data of the user today to the mobile phone 100.

[0217] Continue to refer to Figure 12, Exemplarily, after the mobile phone 100 receives the morning pulse resting heart rate data sent by the smartwatch 200, it can determine the heart rate fluctuation range of the user based on the morning pulse resting heart rate data, and then determine the fatigue level of the user today according to the heart rate fluctuation range.

[0218] It should be noted that in actual applications, the user may sleep and rest multiple times on the same day. For the situation where there are multiple sleeps in a day, when the user opens the sports health APP to view the exercise plan, such as the interface 10d shown in the display interface of the mobile phone 100 Figure 8 as shown in (2), in a possible implementation manner, the morning pulse resting heart rate data of the sleep with the longest sleep duration among multiple sleeps can be selected to determine the heart rate fluctuation range of the user.

[0219] Optionally, the heart rate fluctuation range may include Figure 12 the 5 situations shown in

[0220] Continuing to refer to Figure 12 , Exemplarily, for the situation where the user's heart rate today fluctuates within N1 bpm (beat per minute) from the average value of the heart rate last week, that is, situation 1a, it can be determined that the user is not fatigued today, that is, the fatigue level is level 0 fatigue.

[0221] Continuing to refer to Figure 12 , Exemplarily, for the situation where the user's heart rate today exceeds the average value of last week by N1 - N2 bpm (including both end values) (the heart rate before today did not exceed), that is, situation 1b, in order to avoid the heart rate temporarily fluctuating beyond the average value of last week by N1 - N2 bpm due to special circumstances, resulting in inaccurate determination of the fatigue level. In a possible implementation manner, a Borg questionnaire can be popped up on the user interface of the mobile phone 100 for the user to conduct self - assessment, and then the fatigue level of the user today can be determined according to the assessment results.

[0222] It should be noted that since the fatigue detection method provided in the embodiments of the present application also includes determining the fatigue level based on the dimension of sleep data. When the sleep data meets the preset conditions, user intervention is also required to enter self - assessment. Therefore, when the Borg questionnaire is popped up, a sleep quality questionnaire can also be popped up together to facilitate the user to complete the assessment together, avoiding popping up the questionnaire again when the sleep data is needed to determine the fatigue level later.

[0223] Exemplarily, when the heart rate fluctuation range determined according to the morning pulse resting heart rate data belongs to situation 1b, when the user opens the sports health APP and enters Figure 8 the interface 10d shown in (2), the mobile phone 100 can directly jump to the questionnaire feedback interface (interface 10e) shown in (1) of Figure 13 as shown.

[0224] See Figure 13 In (1), by way of example, the interface 10e includes 2 questionnaires, such as questionnaire 10e-1 and questionnaire 10e-2. Among them, questionnaire 10e-1 is a questionnaire for determining the user's current fatigue level, that is, the borg questionnaire mentioned above; questionnaire 10e-2 is a questionnaire for determining the user's sleep quality last night, that is, the sleep quality questionnaire mentioned above. Among them, the borg questionnaire includes 11 levels of fatigue corresponding to different scores (0-10 in sequence). In Figure 13 In the interface 10e shown in (1), by swiping up, the complete sleep quality questionnaire can be displayed. As Figure 13 As shown in (2), the sleep quality questionnaire includes 4 options.

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

[0226] Optionally, the relationship between the user's self-assessment score obtained from the borg questionnaire and the fatigue level can be as shown in Table 2.

[0227] Table 2 Relationship between the user's self-assessment score obtained from the borg questionnaire and the fatigue level

[0228] User self - assessment score (including endpoint values) Fatigue level 0 - 3 points Level 0 fatigue 4 points Level 1 fatigue 5 - 7 points Level 2 fatigue 8 points and above Level 3 fatigue

[0229] Thus, in the case of 1b, according to the user's self-assessment score obtained from the borg questionnaire and Table 2, the 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 the internal memory.

[0231] Continue to refer to Figure 12 , by way of example, for the case where the heart rates of the user for two consecutive days, today and yesterday, both exceed the weekly average by N1-N2 bpm, that is, case 1c, there is no need to pop up the interface 10e, that is, it is not necessary to determine according to the user's self-assessment score, and it is directly determined that the user's fatigue level today is level 2 fatigue.

[0232] Continue to refer to Figure 12 , by way of example, for the case where the heart rates of the user for three consecutive days, today, yesterday and the day before yesterday, all exceed the weekly average by N1-N2 bpm, that is, case 1d, there is no need to pop up the interface 10e, that is, it is not necessary to determine according to the user's self-assessment score, and it is directly determined that the user's fatigue level today is level 3 fatigue.

[0233] Continue to refer to Figure 12 , for example, when the user's signaling exceeds the average value of last week by 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 interface 10e, that is, it is not necessary to determine according to the user's self-assessment score. It is directly determined that the user's fatigue level today is level 3 fatigue.

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

[0235] Optionally, in some possible implementation manners, 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 only an example listed for better understanding of the technical solution of this embodiment and does not serve as the only limitation of this embodiment. In actual applications, the specific values of N1, N2, and N3 can be adaptively adjusted according to the actual situation of the user, and this application does not limit this.

[0237] Thus, the fatigue level is determined according to the morning pulse resting heart rate data.

[0238] The following is the content of determining the fatigue level according to the HRV data:

[0239] Refer to Figure 13 , for example, when the user wears the watch 200, the watch 200 will record the heart rate variability data during the period when the user wears the watch 200 according to the heart rate variability calculation logic and heart rate algorithm, etc., and send the heart rate variability data to the mobile phone 100 by establishing a communication connection with the mobile phone 100.

[0240] Continue to refer to Figure 13 , for example, after receiving the heart rate variability data sent by the watch 200, the mobile phone 100 can determine the user's current fatigue level according to the heart rate variability data.

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

[0242] Among them, RMSSD refers to the root mean square of the differences between adjacent normal cardiac cycles. It is an important index of heart rate variability and is used to reflect the activity of the body's parasympathetic nerve.

[0243] Among them, the LF / HF ratio refers to the ratio of low-frequency heart rate variability to high-frequency heart rate variability. Usually, the smaller the LF / HF ratio, the better the function of the user's autonomic nervous system and the better the heart health status.

[0244] Regarding the calculation of RMSSD and LF / HF ratio, it can be based on the relevant literature of 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 data such as RMSSD, LF, HF, RR intervals, etc. measured during sleep need to be consistent with the HRV change trend.

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

[0247] Based on the two heart rate variability indexes of RMSSD and LF / HF ratio, multiple situations can be divided, such as Figure 14 the five situations of situation 2a to situation 2e shown in

[0248] Continue to refer to Figure 13 , for example, for the situation where RMSSD is higher than a milliseconds and the LF / HF ratio is lower than x, that is, situation 2a, it can be determined that the user is not fatigued today, that is, the fatigue level is grade 0 fatigue.

[0249] Continue to refer to Figure 13 , for example, for the situation where RMSSD is between b and a milliseconds (including the endpoint values) and the LF / HF ratio is between x and y (including the endpoint values), that is, situation 2b, it can be determined that the user is fatigued today, and the fatigue level is grade 1 fatigue.

[0250] Continue to refer to Figure 13 , for example, for the situation where RMSSD is between c and b milliseconds (including the endpoint values) and the LF / HF ratio is between y and z (including the endpoint values), that is, situation 2c, it can be determined that the user is fatigued today, and the fatigue level is grade 2 fatigue.

[0251] Continue to refer to Figure 13 , for example, for the situation where RMSSD is lower than c milliseconds and the LF / HF ratio is higher than z, that is, situation 2d, it can be determined that the user is fatigued today, and the fatigue level is grade 3 fatigue.

[0252] Continue to refer to Figure 13 , for example, when RMSSD and LF / HF ratio do not belong to situation 2a, or situation 2b, or situation 2c, or situation 2d, it can be determined that the user is not fatigued today, that is, the fatigue level is grade 0 fatigue.

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

[0254] Optionally, in some possible implementation manners, 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] In addition, it should be noted that the above x, y, and z satisfy the following relationship: 0 < x < y < z.

[0256] Optionally, in some possible implementation manners, 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 only an example listed for better understanding of the technical solution of this embodiment, and does not serve as 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 actual situation of the user, and this application does not limit this.

[0258] Thus, the determination of the fatigue level according to the HRV data is achieved.

[0259] The following is the content of determining the fatigue level according to the ACWR data:

[0260] Regarding the determination of the fatigue level according to the ACWR data, it can be divided into two scenarios: when the user wears the watch 200, calculates the ACWR based on the heart rate data provided by the watch 200, and then determines the fatigue level (subsequently referred to as: Scenario 1); and when the mobile phone 100 does not interact with the watch 200, calculates the ACWR based on the RPE value and training duration obtained from the course feedback, and then determines the fatigue level (subsequently referred to as: Scenario 2). The following will respectively describe Figure 15 and Figure 16 , and explain Scenario 1 and Scenario 2.

[0261] Scenario 1:

[0262] Referring to Figure 15 , exemplarily, when the user wears the watch 200, the watch 200 will obtain the heart rate data of the user at the end of each training according to the acquisition logic related to the heart rate data and the heart rate algorithm, etc., and send the obtained heart rate data to the mobile phone 100.

[0263] Continuing to refer to Figure 15 , exemplarily, after receiving the heart rate data sent by the watch 200, the mobile phone 100 will calculate the ACWR based on the heart rate data, and then determine the fatigue level according to the ACWR.

[0264] It can be understood that ACWR refers to the ratio of the training volume (acute workload or acute load) completed within 1 week and the average training volume (average chronic workload or chronic load average value) completed within 4 weeks. That is, ACWR = the ratio of the acute load completed by the user within 1 week to the chronic load average value completed within 4 weeks.

[0265] Optionally, in some implementations, the calculation of ACWR can be performed using the Exponentially Weighted Moving-Average (EWMA) method. 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] Where λa represents the degree of attenuation of the training load over time, and the specific value ranges between 0 and 1.

[0268] In addition, the degree of attenuation of the training load represented by λa satisfies the following formula (2).

[0269] λa = 2 / (N + 1) Formula (2)

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

[0271] For example, if the acute load of the user in the recent 1 week is 4, and the acute loads corresponding to the previous 1, 2, and 3 weeks are 1, 2, and 3 respectively. Then the chronic load coupling for 4 weeks = 1 + 2 + 3 + 4 = 10, and the chronic load average = 10 / 4 = 2.5.

[0272] Among them, the training load in formula (1) can be determined according to the pre-divided intensity levels and training duration (min), specifically according to the following formula (3).

[0273] Training Load = Intensity Level × Training Duration (min) Formula (3)

[0274] Optionally, the intensity levels can be divided according to heart rate intervals. Specifically, based on the heart rate data of all trainings (such as running + strength), the average value is calculated. Using the training interval stimulation amount method, according to the individual's maximum heart rate, the individual's heart rate can be divided into five levels, starting from 50% of the maximum heart rate and increasing by 10% for each interval.

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

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

[0277] Intensity level Heart rate range 1 50% - 60HRmax 2 60% - 70HRmax 3 70% - 80HRmax 4 80% - 90HRmax 5 90% - 100HRmax

[0278] Thus, based on the heart rate data and Table 3 sent by the smartwatch 200 after each training session, the mobile phone 100 can determine the training level, and further determine the training load according to the training duration and Formula (3).

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

[0280] Correspondingly, after determining the training load, based on the ACWR of yesterday (ACWR_yesterday, or the ACWR corresponding to the previous training session) and Formulas (1) and (2), the mobile phone 100 can quickly determine the user's current ACWR.

[0281] Optionally, the ACWR can be divided into 4 intervals, and these 4 intervals can be regarded as 4 specific situations respectively, such as Figure 15 Situation 3a, Situation 3b, Situation 3c, and Situation 3d shown in []. And these 4 intervals can respectively correspond to different fatigue levels.

[0282] Regarding the relationship between ACWR and fatigue level, it can be as shown in Table 4.

[0283] Table 4 Relationship between ACWR and Fatigue Level

[0284] ACWR value Fatigue level ACWR<d Level 0 fatigue d ≤ ACWR < e Level 1 fatigue e ≤ ACWR ≤ f Level 2 fatigue ACWR > f Level 3 fatigue

[0285] Thus, based on the calculated value of ACWR and Table 4, the 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 the 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 implementation manners, 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 descriptions are only examples listed for better understanding the technical solutions of this embodiment, and do not serve as the only limitation to this embodiment. In practical applications, the specific values of d, e, and f can be adaptively adjusted according to the actual situation of the user, and this application does not limit this.

[0290] Thus, in the case of interaction between the mobile phone 100 and the smartwatch 200, the ACWR is calculated based on the heart rate data provided by the smartwatch 200, and then the fatigue level is determined.

[0291] Scenario 2:

[0292] See Figure 16 , for example, in the case where the user does not wear the smartwatch 200 or there is no communication connection established between the smartwatch 200 and the mobile phone 100, the mobile phone 100 can pop up a course feedback form, such as a rating of perceived exertion (RPE) scale, after each training session of the user, and then calculate the ACWR based on the RPE value selected by the user in the RPE form and the training duration (min), and then determine the fatigue level according to the ACWR.

[0293] Optionally, the way to pop up the RPE form can be, for example, to pop up a window 10f-1 in the interface corresponding to the end of the training course, such as the interface 10f shown in Figure 17 the interface 10f shown.

[0294] Optionally, for the user to conduct self-assessment in the window 10f-1, the selected RPE values can include 11 scores from 0 to 10, and the description corresponding to each score is similar to the description corresponding to the same score in the borg questionnaire shown in (1) of Figure 10 and will not be elaborated here.

[0295] Optionally, each RPE score can be regarded as an intensity level. That is, in the absence of the smartwatch 200, the mobile phone 100 can be based on the following formula (4).

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

[0297] Thus, in the case where the mobile phone 100 does not interact with the smartwatch 200, based on the ACWR of yesterday (ACWR_yesterday, or the ACWR corresponding to the previous training course) and the formulas (1) and (2) given in Scenario 1, the current ACWR of the user can be quickly determined.

[0298] Correspondingly, after determining the ACWR, according to Table 4 given in Scenario 1, the mobile phone 100 can quickly determine the current fatigue level of the user.

[0299] Thus, it is realized to calculate the ACWR based on the RPE value and training duration obtained from the course feedback, and then determine the fatigue level.

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

[0301] See Figure 18, Exemplarily, when the user wears the watch 200 while sleeping, the watch 200 will obtain the sleep data of the user during sleep according to the sleep data acquisition logic, sleep algorithm, etc.

[0302] Optionally, the watch 200 can be timed, or after detecting that the user wakes up, send the sleep data to the mobile phone 100 with which a communication connection is established.

[0303] Continue to refer to Figure 18 , Exemplarily, after receiving the sleep data sent by the watch 200, the mobile phone 100 will determine whether the user's sleep meets any one or more of the following three conditions according to the received sleep data.

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

[0305] Condition 2: The number of awakenings at night ≥ the preset number of times;

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

[0307] Optionally, 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 (min).

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

[0311] It should be understood that the priority of the mobile phone 100 using sleep data to determine the fatigue level is lower than the priority of using morning pulse resting heart rate data to determine the fatigue level. Therefore, if in the stage of using morning pulse resting heart rate data to determine the fatigue level, situation 1b occurs, the sleep quality questionnaire will be popped up together when the borg questionnaire is popped up, that is, both questionnaires are displayed in interface 10e at the same time. Therefore, when entering the link of determining the fatigue level according to sleep in the case where the fatigue level has not been determined based on the morning pulse resting heart rate data, HRV data, and ACWR data, there is no need to jump to interface 10e again, and the fatigue level can be directly determined according to the results selected by the user in the sleep quality questionnaire in interface 10e in situation 1b of the user.

[0312] Through Figure 10As can be seen from the interface 10e shown in (2), the sleep quality questionnaire includes 4 options. In practical applications, these 4 options can be corresponded to 4 specific situations. For example, very good corresponds to situation 4a, better corresponds to situation 4b, worse corresponds to situation 4c, and very bad corresponds to situation 4d. Among them, these 4 situations can correspond to different scores, and different scores can correspond to different fatigue levels.

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

[0314] Table 5 The scores corresponding to each option in the sleep quality questionnaire, and the relationship between different scores and fatigue levels

[0315] Sleep quality questionnaire options User self - assessment score Fatigue level Very good 0 points Level 0 fatigue Better 1 point Level 0 fatigue Worse 2 points Level 1 fatigue Very poor 3 points Level 2 fatigue

[0316] Thus, when the sleep data meets any one or more of the above three conditions, according to the option selected by the user in the sleep quality questionnaire and Table 5, the mobile phone 100 can quickly determine the current fatigue level of the user.

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

[0318] Thus, the determination of the fatigue level according to the sleep data is realized.

[0319] Beneficial effects of the fatigue detection method provided by the embodiments of the present application:

[0320] In the fatigue detection method provided by the embodiments of the present application, by combining the health-related data collected by the smart wearable device, such as the morning pulse resting heart rate data, HRV data, heart rate data (calculating ACWR), sleep data, etc. as described in the above embodiments, with the self-evaluation results collected by various subjective evaluation questionnaires displayed on the smart device used by the user, such as the mobile phone 100, the 4 specific fatigue level determination methods of determining the fatigue level by morning pulse resting heart rate data, determining the fatigue level by HRV data, determining the fatigue level by ACWR data, and determining the fatigue level by sleep data are mutually complementary, and in accordance with the priority order that the morning pulse resting heart rate has a higher priority than HRV, HRV has a higher priority than ACWR, and ACWR has a higher priority than sleep, a fatigue level is selected from the fatigue levels determined from these 4 dimensions, making the finally determined fatigue level more reasonable and accurate, so as to be able to give appropriate guidance suggestions before the user exercises, and avoid the user from causing damage to the body due to excessive exercise.

[0321] In addition, it should be noted that the embodiments of this application mainly take the running scenario as an example. In actual 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. This application does not limit this.

[0322] In addition, it can be understood that in order for the electronic device to implement the above functions, it includes the corresponding hardware and / or software modules for executing each function. Combining the algorithm steps of each example described in the embodiments disclosed in this article, this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving the hardware 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 combination with the embodiments, but such implementation should not be considered to exceed the scope of this application.

[0323] In addition, it should be noted that in the actual application scenario, the fatigue detection method provided by the above-mentioned embodiments implemented by the electronic device can also be executed by a chip system included in the electronic device. Among them, the chip system can include a processor. The chip system can be coupled with the memory, so that when the chip system runs, it calls the computer program stored in the memory to implement the steps executed by the above-mentioned electronic device. Among them, the processor in the chip system can be an application processor or a processor other than an application processor.

[0324] In addition, the embodiments of this application also provide a computer-readable storage medium. Computer instructions are stored in the computer storage medium. When the computer instructions run on the electronic device, the electronic device is enabled to execute the above-mentioned related method steps to implement the fatigue detection method in the above-mentioned embodiments.

[0325] In addition, the embodiments of this application also provide a computer program product. When the computer program product runs on the electronic device, the electronic device is enabled to execute the above-mentioned related steps to implement the fatigue detection method in the above-mentioned embodiments.

[0326] In addition, the embodiments of this application also provide a chip (which can also be a component or a module). The chip can include one or more processing circuits and one or more transceiver pins; among them, 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-mentioned embodiments to control the receiving pin to receive signals and control the sending pin to send signals.

[0327] In addition, as can be seen from the above description, the electronic device, computer-readable storage medium, computer program product or chip provided by the embodiments of the present application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be elaborated here.

[0328] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A fatigue detection method, characterized in that, Applied to an electronic device, the electronic device is communicatively connected to a wearable device, and the wearable device is used to provide index data for calculating a fatigue level. The method includes: In response to a user's operation, obtain the index data for which the wearable device is ready from the wearable device. The index data includes one or more of morning pulse resting heart rate data, heart rate variability (HRV) data, heart rate data during a movement phase, and sleep data. When the index data includes the morning pulse resting heart rate data, calculate the fatigue level based on the morning pulse resting heart rate data. When the fatigue level calculated based on the morning pulse resting heart rate data is the highest level of fatigue, which indicates that the user is currently not suitable for any training courses, determine the fatigue level calculated based on the morning pulse resting heart rate data as the user's current fatigue level.

2. The method according to claim 1, wherein The method further includes: When the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated based on the morning pulse resting heart rate data is not the highest level of fatigue, but the index data includes HRV data, calculate the fatigue level based on the HRV data. When the fatigue level calculated based on the HRV data is the highest level of fatigue, determine the fatigue level calculated based on the HRV data as the user's current fatigue level.

3. The method according to claim 2, wherein The method further includes: When the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated based on the morning pulse resting heart rate data is not the highest level of fatigue, and the index data does not include the HRV data, or the fatigue level calculated based on the HRV data is not the highest level of fatigue, but the index data includes heart rate data during the movement phase, calculate acute to chronic workload ratio (ACWR) data based on the heart rate data during the movement phase and the training duration of the currently completed training course. Calculate the fatigue level based on the ACWR data. When the fatigue level calculated based on the ACWR data is the highest level of fatigue, determine the fatigue level calculated based on the ACWR data as the user's current fatigue level.

4. The method according to claim 3, characterized in that, The method further includes: When the index data does not include the morning pulse resting heart rate data, or the fatigue level calculated based on the morning pulse resting heart rate data is not the highest level of fatigue, and the index data does not include the HRV data, or the fatigue level calculated based on the HRV data is not the highest level of fatigue, and the index data does not include the heart rate data during the movement phase, after ending the training course, pop up a first questionnaire, which includes multiple score options, and different score options correspond to different fatigue degrees. Calculate the user's current training load based on the score selected by the user in the first questionnaire and the training duration. Calculate the user's current ACWR data based on the training load, historical ACWR data, and a degree parameter indicating the degree of decay of the training load over time, where the historical ACWR data is the ACWR data corresponding to the user after the last training session, and the value of the degree parameter indicating the degree of decay of the training load over time ranges between 0 and 1; Calculate the fatigue level based on the ACWR data; When the fatigue level calculated based on the ACWR data is the highest-level fatigue level, determine the fatigue level calculated based on the ACWR data as the user's current fatigue level.

5. The method according to claim 4, characterized in that, The method further includes: When the metric data does not include the morning pulse resting heart rate data, or the fatigue level calculated based on the morning pulse resting heart rate data is not the highest-level fatigue level, and the metric data also does not include the HRV data, or the fatigue level calculated based on the HRV data is not the highest-level fatigue level, and the metric data also does not include the heart rate data during the exercise phase, or the fatigue level calculated based on the ACWR data is not the highest-level fatigue level, but the fatigue level calculated based on the morning pulse resting heart rate data is lower than the highest-level fatigue level, determine the fatigue level calculated based on the morning pulse resting heart rate data as the user's current fatigue level.

6. The method according to claim 5, characterized in that, The method further includes: When a fatigue level cannot be calculated based on the morning pulse resting heart rate data, but the metric data includes the HRV data, and the fatigue level calculated based on the HRV data is lower than the highest-level fatigue level, determine the fatigue level calculated based on the HRV data as the user's current fatigue level.

7. The method according to claim 6, wherein The method further includes: When a fatigue level cannot be calculated based on both the morning pulse resting heart rate data and the HRV data, but the metric data includes the heart rate data during the exercise phase, and the fatigue level calculated based on the ACWR data calculated from the heart rate data during the exercise phase and the training duration is lower than the highest-level fatigue level, determine the fatigue level calculated based on the ACWR data as the user's current fatigue level.

8. The method according to claim 7, wherein The method further includes: When the metric data does not include the morning pulse resting heart rate data, or a fatigue level cannot be calculated based on the morning pulse resting heart rate data, and the metric data also does not include the HRV data, or a fatigue level cannot be calculated based on the HRV data, and the metric data also does not include the heart rate data during the exercise phase, or a fatigue level cannot be calculated based on the ACWR data, but the metric data includes sleep data, calculate the fatigue level based on the sleep data; When a fatigue level is calculated based on the sleep data, determine the fatigue level calculated based on the sleep data as the user's current fatigue level; In the case where the fatigue level cannot be calculated based on the sleep, the lowest fatigue level is determined as the current fatigue level of the user, and the lowest fatigue level indicates that the user is not currently fatigued.

9. The method according to claim 8, characterized in that The calculating the fatigue level according to the sleep data includes: When the sleep data meets the preset conditions, a second questionnaire is displayed, and the second questionnaire includes multiple sleep quality evaluation options, and different sleep quality evaluation options correspond to different scores; According to the score corresponding to the sleep quality option selected by the user in the second questionnaire, the fatigue level is determined.

10. The method according to claim 9, wherein The meeting the preset conditions includes: The sleep duration is less than the first preset duration; And / or, The number of night 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 by the second preset duration.

11. The method according to any one of claims 1 to 5, characterized in that The calculating the fatigue level according to the morning pulse resting heart rate data includes: When the fluctuation of the morning pulse resting heart rate data compared with the average value of the morning pulse resting heart rate of the previous week is within N1 bpm, the fatigue level is determined as the lowest fatigue level; When the morning pulse resting heart rate data exceeds the average value of the morning pulse resting heart rate of the previous week by N1 bpm to N12 bpm, a third questionnaire is displayed, and the third questionnaire includes multiple fatigue degree evaluation options, and different fatigue degree evaluation options correspond to different scores; According to the score corresponding to the fatigue degree option selected by the user in the third questionnaire, the fatigue level is determined; When both the morning pulse resting heart rate data and the morning pulse resting heart rate data of yesterday exceed the average value of the morning pulse resting heart rate of the previous week by N1 bpm to N12 bpm, the fatigue level is determined as the fatigue level between the highest level and the lowest level; When the morning pulse resting heart rate data, the morning pulse resting heart rate data of yesterday and the morning pulse resting heart rate data of the day before yesterday all exceed the average value of the morning pulse resting heart rate of the previous week by N1 bpm to N12 bpm, the fatigue level is determined as the highest fatigue level; When the morning pulse resting heart rate data exceeds the average value of the morning pulse resting heart rate of the previous week by N3 bpm, the fatigue level is determined as the highest fatigue level; where 0 < N1 < N2 < N3.

12. The method according to any one of claims 2 to 6, characterized in that, The calculating the fatigue level according to the HRV data includes: According to the RMSSD and LF / HF ratio in the HRV data, the fatigue level is determined. The RMSSD refers to the root mean square of the differences 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.

13. The method according to claim 3, or any one of claims 5 to 7, characterized in that The calculating the acute-chronic workload ratio ACWR data according to the heart rate data in the exercise stage and the training duration of the currently completed training session includes: Determine the heart rate range in which the heart rate data in the exercise stage is located; where, starting from 50% of the maximum heart rate to 100% of the maximum heart rate, each increase of 10% is a heart rate range, and each heart rate range corresponds to an intensity level; According to the determined heart rate range, determine the intensity level; According to the intensity level and the training duration, calculate the current training load of the user; Calculate the user's current ACWR data according to the training load, historical ACWR data, and a degree parameter indicating the degree of attenuation of the training load over time. The historical ACWR data is the ACWR data corresponding to the user after the last training session, and the value of the degree parameter indicating the degree of attenuation of the training load over time is between 0 and 1.

14. The method according to any one of claims 1 to 13, characterized in that The responding to the user's operation includes: In response to the user's operation on a first application, display a first interface, where the first application is an application capable of formulating an exercise plan, and the first interface is the interface corresponding to the exercise plan.

15. The method according to claim 14, wherein After determining the user's current fatigue level, the method further includes: In the case where the user's current fatigue level is not the lowest fatigue level, a first window is popped up on the first interface, and the first window displays exercise guidance content matching the user's current fatigue level. The lowest fatigue level indicates that the user is not currently fatigued.

16. An electronic device, characterized in that, The electronic device includes: a memory and a processor, the memory and the processor are coupled; the memory stores program instructions, and when the program instructions are executed by the processor, the electronic device executes the fatigue detection method according to any one of claims 1 to 15.

17. A computer-readable storage medium, characterized in that, It includes a computer program, and when the computer program runs on an electronic device, the electronic device executes the fatigue detection method according to any one of claims 1 to 15.

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