Method for evaluating effect of anaerobic training, intelligent wearable device and storage medium

Abstract

The application discloses an effect evaluation method of anaerobic training, an intelligent wearable device and a storage medium. The method comprises the following steps: continuously detecting the average heart rate value of a user in a preset period of anaerobic training, calculating a maximum heart rate percentage according to the average heart rate value, calculating a maximum oxygen uptake percentage according to the maximum heart rate percentage, calculating an oxygen deficit increment value according to the maximum oxygen uptake percentage, accumulating the oxygen deficit increment value in the anaerobic training to obtain the oxygen deficit of the anaerobic training, fusing the oxygen deficit and the user activity level of the user to calculate a fusion feature of the anaerobic training, and fitting the fusion feature to obtain the anaerobic training effect of the anaerobic training. Thus, the objective oxygen deficit feature data and the activity state of the user can be utilized to accurately obtain the measurement value of the anaerobic training effect, the data is objective and accurate, and the method has strong practicability and easy use.

Description

Technical Field

[0001] This application relates to the field of sports monitoring technology, and in particular to a method for evaluating the effectiveness of anaerobic training, a smart wearable device, and a storage medium. Background Technology

[0002] Repeated high-intensity interval training (such as anaerobic training, HIIT, etc.) has a particularly beneficial effect on anaerobic capacity. This is because combining high intensity with intervals leads to post-exercise excess oxygen consumption, which in turn accelerates the body's metabolic rate. After high-intensity anaerobic training, the body's metabolic rate can remain elevated for up to 48 hours. This means that even after leaving the gym, fat burning continues.

[0003] Many users, after a period of anaerobic training, want to know how effective their anaerobic training is and want more accurate and objective data for measurement, but there is currently no good solution. Summary of the Invention

[0004] This application provides a method for evaluating the effectiveness of anaerobic training, a terminal device, and a computer-readable storage medium to solve the technical problem of inconvenient testing of the effectiveness of anaerobic training in the prior art.

[0005] The first aspect of this application provides a method for evaluating the effectiveness of anaerobic training, which includes:

[0006] It continuously monitors the user's average heart rate during a preset period of anaerobic training and calculates the percentage of maximum heart rate based on the average heart rate.

[0007] The percentage of maximum oxygen uptake is calculated based on the percentage of maximum heart rate.

[0008] The oxygen deficit increment is calculated based on the percentage of maximum oxygen uptake.

[0009] The oxygen deficit during anaerobic training is accumulated by adding up the increments.

[0010] The oxygen deficit is calculated by integrating it with the user's activity level to obtain the integrated characteristics of anaerobic training.

[0011] The fusion features are fitted and calculated to obtain the anaerobic training effect of anaerobic training.

[0012] The detection of the user's average heart rate during a preset period of anaerobic training also includes:

[0013] The preset period is T seconds, and the current time is denoted as t. The average of the instantaneous heart rate values ​​within the time window (tT, t) is calculated to obtain the average heart rate value at the current time t, where t is in seconds.

[0014] The calculation of the percentage of maximum heart rate based on the average heart rate includes:

[0015] Percentage of maximum heart rate = Average heart rate / User's maximum heart rate

[0016] Among them, the user's maximum heart rate is determined based on the user's age.

[0017] The calculation of the percentage of maximum oxygen uptake based on the percentage of maximum heart rate per second includes:

[0018] %VO2max = f(HRmax),

[0019] Where %VO2max is an expression for the percentage of maximum oxygen uptake.

[0020] %HRmax is an expression for the percentage of maximum heart rate, and f is a linear function.

[0021] The oxygen deficit increment, calculated based on the percentage of maximum oxygen uptake, includes:

[0022] Calculate the anaerobic amplification factor m, and then calculate the amplified value of the maximum oxygen uptake percentage (modified intensity) based on the amplification factor m.

[0023] The modified intensity value is fitted and calculated to obtain the oxygen deficit increment.

[0024] Where modified intensity = m * %VO2max,

[0025] m = d * %VO2max^2 * delta_met,

[0026] delta_met=e*0.15*(%VO2max[t]-%VO2max[t-1]),

[0027] %VO2max[t] is the percentage of maximum oxygen uptake at the current time t, %VO2max[t-1] is the percentage of maximum oxygen uptake one second before the current time t, and d and e are adjustment coefficients.

[0028] in,

[0029] The modified intensity value is fitted to obtain the oxygen deficit increment value, including:

[0030] Oxygen deficit increment = x * modified intensity^2 * y

[0031] The attenuation coefficient x and the prediction coefficient y are the fitting coefficients.

[0032] The calculation of oxygen deficit in conjunction with the user's activity level to obtain the fusion characteristics of anaerobic training includes:

[0033]

[0034] The median level of user activity status is the median of user activity levels.

[0035] The process of fitting and calculating the fusion features to obtain the anaerobic training effect includes:

[0036] Anaerobic training effect = a * fusion feature + c

[0037] Where a and c are the fitting coefficients.

[0038] The methods for evaluating the effectiveness of anaerobic training also include: displaying the effects of anaerobic training on the display interface; the methods for displaying the effects of anaerobic training include: text, charts, and videos.

[0039] On the other hand, a smart wearable device is provided, including a memory and a processor coupled to each other, wherein the processor is used to execute program instructions stored in the memory to implement any of the above-mentioned methods for evaluating the effectiveness of anaerobic training.

[0040] On the other hand, a computer-readable storage medium is provided, on which program instructions are stored, which, when executed by a processor, implement any of the above-mentioned methods for evaluating the effectiveness of anaerobic training.

[0041] This application offers the following advantages: The anaerobic training effectiveness evaluation method of this application continuously monitors heart rate within a preset period to calculate and determine the user's oxygen deficit characteristics. Based on the correspondence between these oxygen deficit characteristics and the user's activity level, the anaerobic training effect is calculated. This allows for the accurate acquisition of a metric for anaerobic training effectiveness using objective oxygen deficit characteristic data and the user's own activity level. The data is objective, accurate, and possesses strong practicality and ease of use. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the technical solutions of this application.

[0043] Figure 1 This is a flowchart illustrating an embodiment of the anaerobic training effectiveness evaluation method provided in this application;

[0044] Figure 2 This is a schematic flowchart of an embodiment of obtaining the oxygen deficit increment provided in this application;

[0045] Figure 3This is a flowchart illustrating another embodiment of the anaerobic training effectiveness evaluation method provided in this application;

[0046] Figure 4 This is a schematic diagram of the framework of an embodiment of the anaerobic training effect evaluation device provided in this application;

[0047] Figure 5 This is a schematic diagram of the framework of an embodiment of the smart wearable device provided in this application;

[0048] Figure 6 This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0049] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0050] In the following description, specific details such as particular system architectures, interfaces, and technologies are presented for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of this application.

[0051] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, "many" in this document means two or more. Moreover, the term "at least one" in this document means any combination of at least two of any one or more of a plurality of objects. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0052] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the anaerobic training effectiveness evaluation method provided in this application. The execution entity of the anaerobic training effectiveness evaluation method in this embodiment can be a terminal device. For example, the anaerobic training effectiveness evaluation method can be executed by a terminal device, a server, or other processing devices. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. In some possible implementations, the anaerobic training effectiveness evaluation method can be implemented by a processor calling computer-readable instructions stored in memory. For example, the terminal device in this embodiment can also be a smart wearable device, such as a smart bracelet, smartwatch, or other device used to display exercise information.

[0053] Specifically, such as Figure 1 As shown, the method for evaluating the effectiveness of anaerobic training according to embodiments of this disclosure may include the following steps:

[0054] S1: Continuously monitors the user's average heart rate during a preset period of anaerobic training, and calculates the percentage of maximum heart rate based on the average heart rate.

[0055] S2: Calculate the percentage of maximum oxygen uptake based on the percentage of maximum heart rate;

[0056] S3: Calculate the oxygen deficit increment based on the percentage of maximum oxygen uptake;

[0057] S4: Accumulate the oxygen deficit increment during anaerobic training to obtain the oxygen deficit during anaerobic training.

[0058] S5: Combine oxygen deficit with the user's activity level to obtain the fusion characteristics of anaerobic training;

[0059] S6: Fit and calculate the fusion features to obtain the anaerobic training effect of anaerobic training.

[0060] Anaerobic training effect characterizes the magnitude of the benefits to the body after high-intensity exercise. Repeated high-intensity interval training (10-120 seconds) has a particularly beneficial effect on anaerobic capacity. The inventors discovered through research that anaerobic training effect can be calculated based on indicators such as heart rate and personal information during exercise, helping people effectively assess the anaerobic benefits of training.

[0061] This solution continuously monitors heart rate over a preset period to calculate and determine the user's oxygen deficit characteristics. Based on the correlation between these oxygen deficit characteristics and the user's activity level, it calculates the anaerobic training effect. Therefore, it can accurately measure the anaerobic training effect using objective oxygen deficit data and the user's own activity level.

[0062] Among them, VO2 max is the maximum amount of oxygen a person's body can use during exercise. It can indicate the body's ability to inhale, circulate, and use oxygen, and it depends on important body organs such as the heart, lungs, and muscles, as well as the blood and blood vessels that connect these organs. Therefore, it can indicate the body's overall health status.

[0063] Furthermore, maximal accumulated oxygen deficit, a sports medicine term published in 2019, refers to the difference between the theoretical oxygen demand and the actual oxygen uptake when the human body completes maximal intensity exercise. In this method, the oxygen deficit is obtained by accumulating multiple increments of oxygen deficit during a single anaerobic training session. This oxygen deficit can thus identify a single anaerobic training session; the larger the oxygen deficit value, the better the training effect.

[0064] Furthermore, user activity levels represent different levels of each user's specific activity status, and are strongly correlated with the individual, influenced by factors such as gender, age, and exercise habits. For example, user activity levels can be divided into 1-10. User A's user activity level can be obtained based on their total exercise time in the previous week, thus reflecting whether user A exercises frequently. If user A's user activity level is 10, it means that user A exercised frequently last week.

[0065] Due to individual differences, the same exercise intensity will produce different effects on different users. Furthermore, if two different users have the same oxygen deficit value after exercise, it does not mean that they achieved the same effect from their anaerobic training. Therefore, a more accurate and useful calculation can be obtained by combining the user's activity level and oxygen deficit.

[0066] Furthermore, since accurate VO2 max testing requires specialized equipment in a laboratory or gym and involves high-intensity exercise, the cost is high and cannot meet users' needs for real-time and convenient access. Therefore, this solution uses heart rate detection and corresponding algorithms to calculate oxygen deficit, which is more convenient. Users only need to wear a smart wearable device (such as a fitness tracker) that can continuously monitor heart rate to perform real-time detection and calculation.

[0067] Furthermore, since the oxygen deficit values ​​obtained from heart rate monitoring and related algorithms, as well as the fusion features obtained from the aforementioned fusion calculations, are theoretical values ​​and will differ somewhat from the actual values, this solution also provides a fitting calculation to eliminate the difference between theoretical and actual values ​​to a certain extent. Specifically, the fitting operation can employ linear fitting, where the fitting coefficients are obtained through training with a large number of samples. The training samples can be detected in laboratories or gyms using specialized equipment, such as specialized breathing machines, to measure the VO2 max of a large number of different groups of people after high-intensity exercise, in order to obtain accurate VO2 max data and oxygen deficit data.

[0068] The detection of the user's average heart rate during a preset period of anaerobic training also includes:

[0069] The preset period is T seconds, and the current time is denoted as t. The average of the instantaneous heart rate values ​​within the time window (tT, t) is calculated to obtain the average heart rate value at the current time t, where t is in seconds.

[0070] For specific details, please refer to Figure 2 This is a flowchart for calculating the oxygen deficit increment, where T = 30.

[0071] One example of obtaining the oxygen deficit increment is described below:

[0072] A user's exercise session lasts for x seconds. Obtain all oxygen deficit increments using the following steps:

[0073] 1. Given the current time 1 (s), obtain the oxygen deficit increment at time 1s;

[0074] 2. The current time is t (in seconds). Take a 30-second time window (t-30, t) to obtain a maximum oxygen uptake percentage. After fitting and calculating based on the maximum oxygen uptake percentage, obtain an oxygen deficit increment for the current time t.

[0075] 3. With time t+1 (s), take a 30s time window (t-29, t+1) to obtain a maximum oxygen uptake percentage. After fitting and calculating based on the maximum oxygen uptake percentage, obtain an oxygen deficit increment for the current time t+1 (s).

[0076] By using a 30-second window and a 1-second step size, we can obtain x oxygen deficit increments within one exercise cycle, and sum them up to obtain the total oxygen deficit value of 1 for this exercise.

[0077] Input a total oxygen deficit value and a user level, and calculate the anaerobic training effect of this exercise through fusion.

[0078] The above steps represent the process of one user's exercise session. By repeating these steps across multiple samples, multiple corresponding anaerobic training effects can be obtained.

[0079] The calculation of the percentage of maximum heart rate based on the average heart rate includes:

[0080] Percentage of maximum heart rate = Average heart rate / User's maximum heart rate

[0081] Among them, the user's maximum heart rate is determined based on the user's age.

[0082] Optionally, the user's maximum heart rate = 220 - age, and further, a corresponding adjustment coefficient can be added.

[0083] The calculation of the percentage of maximum oxygen uptake based on the percentage of maximum heart rate per second includes:

[0084] %VO2max = f(HRmax),

[0085] Where %VO2max is an expression for the percentage of maximum oxygen uptake.

[0086] %HRmax is an expression for the percentage of maximum heart rate, and f is a linear function.

[0087] Optionally, %VO2max = 1.408%HRmax - 45.1.

[0088] The oxygen deficit increment, calculated based on the percentage of maximum oxygen uptake, includes:

[0089] Calculate the anaerobic amplification factor m, and then calculate the amplified value of the maximum oxygen uptake percentage (modified intensity) based on the amplification factor m.

[0090] The modified intensity value is fitted and calculated to obtain the oxygen deficit increment.

[0091] Where modified intensity = m * %VO2max,

[0092] m = d * %VO2max^2 * delta_met,

[0093] delta_met=e*0.15*(%VO2max[t]-%VO2max[t-1]),

[0094] %VO2max[t] is the percentage of maximum oxygen uptake at the current time t, %VO2max[t-1] is the percentage of maximum oxygen uptake one second before the current time t, and d and e are adjustment coefficients.

[0095] Alternatively, the values ​​of the adjustment coefficients d and e are set as follows:

[0096] m=1.3841*%VO2max^2*delta_met,

[0097] delta_met=5*0.15*(%VO2max[t]-%VO2max[t-1]),

[0098] %VO2max[t] is the percentage of maximum oxygen uptake at the current time t, and %VO2max[t-1] is the percentage of maximum oxygen uptake in the second before the current time t.

[0099] For specific details, please refer to Figure 2 This is a flowchart illustrating the specific process for calculating the oxygen deficit increment.

[0100] The process involves fitting the amplified value (modified intensity) to obtain the oxygen deficit increment, which is calculated as: Oxygen deficit increment = x * modified intensity^2 * y, where the attenuation coefficient x and the prediction coefficient y are the fitting coefficients. Adjusting these fitting coefficients helps avoid extreme values ​​or other special cases in the oxygen deficit increment, thus ensuring it better conforms to statistical laws.

[0101] Therefore, it can be seen that the first fitting calculation was used when calculating the oxygen deficit increment. The attenuation coefficient x and the prediction coefficient y are fitting coefficients obtained after training with a large number of samples. Once obtained, they are not changed, and in actual user use, these coefficients are fixed values.

[0102] The calculation of oxygen deficit in conjunction with the user's activity level to obtain the fusion characteristics of anaerobic training includes:

[0103] Fusion The median level of user activity status is the median of user activity levels.

[0104] For example:

[0105] If user activity level = 10 and user activity status intermediate level = 5, then the fusion feature = oxygen deficit.

[0106]

[0107] If user activity level = 8 and user activity status intermediate level = 5, then the fusion feature = oxygen deficit.

[0108]

[0109] If the user activity level is 5 and the intermediate user activity status level is 5, then the fusion feature is oxygen deficit × 1.

[0110] If the user activity level is 1 and the intermediate user activity status level is 5, then the fusion feature is oxygen deficit × 5.

[0111] Therefore, it can be seen that in the scheme of this application, the higher the user's activity level, the smaller the correction of its fusion feature value relative to the original oxygen deficit. In fact, this is consistent with the real-world scenario: when a user exercises frequently, the effect of a single anaerobic training session will not be very significant; while when a user does not exercise frequently, the effect of a single anaerobic training session will be more significant.

[0112] The process of fitting and calculating the fusion features to obtain the anaerobic training effect includes: Anaerobic training effect = a * fusion features + c, where a and c are fitting coefficients.

[0113] Therefore, a second fitting calculation was used when calculating the effect of anaerobic training. Here, 'a' and 'c' are fitting coefficients obtained after training with a large number of samples; once obtained, they are not changed, and in actual user use, these coefficients are fixed values.

[0114] Furthermore, other fitting coefficients can be added to further refine the final result, making it closer to the real-world scenario. For example: Anaerobic training effect = a * fusion feature + Where a, b, and c are the fitting coefficients.

[0115] The value range corresponding to the anaerobic training effect is [0.0, 5.0]. 5.0 represents the best effect, that is, the user's anaerobic training has achieved a significant effect, while 0.0 represents the least effect, that is, the user's anaerobic training has achieved a very small effect, which can be ignored.

[0116] The methods for evaluating the effectiveness of anaerobic training also include: displaying the effects of anaerobic training on the display interface; the methods for displaying the effects of anaerobic training include: text, charts, and videos.

[0117] Furthermore, voice prompts can be used to remind users of the results of anaerobic training.

[0118] Further, refer to Figure 3 This application also provides an anaerobic training effect evaluation device, including a control unit 102, a data storage unit 105, a display unit 106, a heart rate monitoring unit 104, an oxygen deficit calculation unit 101, and a fusion calculation unit 103.

[0119] The heart rate monitoring unit 104 receives instructions from the control unit 102 to perform heart rate monitoring and stores the monitoring data in the data storage unit 105.

[0120] The oxygen deficit calculation unit 101 receives instructions from the control unit 102 and performs oxygen deficit calculation.

[0121] The fusion computing unit 103 receives instructions from the control unit 102 and performs fusion computing and fitting.

[0122] The control unit 102 stores the fusion calculation fitting result (i.e., the anaerobic training effect) to the data storage unit 105.

[0123] The display unit receives instructions from the control unit 102, reads the anaerobic training effect from the data storage unit, and displays it. Furthermore, it can query historical records to display historical anaerobic training effects, such as statistical data for the day, week, month, and year.

[0124] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0125] On the other hand, a smart wearable device is provided, including a memory and a processor coupled to each other, wherein the processor is used to execute program instructions stored in the memory to implement any of the above-mentioned methods for evaluating the effectiveness of anaerobic training.

[0126] Specifically, please refer to Figure 5 The smart wearable device 200 described in the embodiments of this application may specifically include a processor 210 and a memory 220. The memory 220 is coupled to the processor 210.

[0127] Processor 210 is used to control the operation of smart wearable device 200. Processor 210 can also be referred to as CPU (Central Processing Unit). Processor 210 may be an integrated circuit chip with signal processing capabilities. Processor 210 can also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. The general-purpose processor can be a microprocessor, or processor 210 can be any conventional processor.

[0128] Memory 220 is used to store computer programs and may be RAM, ROM, or other types of storage devices. Specifically, the memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory is used to store at least one line of program code.

[0129] The processor 210 is used to execute computer programs stored in the memory 220 to implement the methods described in the various method embodiments of this application.

[0130] In some embodiments, the smart wearable device 200 may further include a peripheral device interface 230 and at least one peripheral device. The processor 210, memory 220, and peripheral device interface 230 can be connected via a bus or signal lines. Each peripheral device can be connected to the peripheral device interface 230 via a bus, signal lines, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 240, a display screen 250, an audio circuit 260, and a power supply 270.

[0131] Peripheral device interface 230 can be used to connect at least one I / O (Input / output) related peripheral device to processor 210 and memory 220. In some embodiments, processor 210, memory 220 and peripheral device interface 230 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 210, memory 220 and peripheral device interface 230 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0132] The radio frequency (RF) circuit 240 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 240 communicates with communication networks and other communication devices via electromagnetic signals; it is the communication circuit of the smart wearable device 200. The RF circuit 240 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 240 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 240 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 240 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0133] Display screen 250 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 250 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 210 for processing. In this case, display screen 250 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 250, located on the front panel of the smart wearable device 200; in other embodiments, there may be at least two display screens, respectively located on different surfaces of the smart wearable device 200 or in a folded design; in still other embodiments, display screen 250 may be a flexible display screen, located on a curved or folded surface of the smart wearable device 200. Furthermore, display screen 250 may even be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 250 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0134] The audio circuit 260 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting them into electrical signals that are input to the processor 210 for processing, or to the radio frequency circuit 240 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the smart wearable device 200. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 210 or the radio frequency circuit 240 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 260 may also include a headphone jack.

[0135] Power source 270 is used to power the various components in smart wearable device 200. Power source 270 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power source 270 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0136] For a detailed description of the functions and execution processes of each functional module or component in the embodiments of the smart wearable device of this application, please refer to the descriptions in the above-mentioned method embodiments of this application, which will not be repeated here.

[0137] In the embodiments provided in this application, it should be understood that the disclosed smart wearable devices and methods can be implemented in other ways. For example, the embodiments of smart wearable devices described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0139] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] In another aspect, this application provides a computer-readable storage medium storing a computer program that can be executed by a processor to implement any of the methods described above.

[0141] Please see Figure 6If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in computer-readable storage medium 300. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions / computer programs to cause a smart wearable device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, as well as electronic devices such as computers, mobile phones, laptops, tablets, and cameras that have the aforementioned storage media.

[0142] The execution process of program data in a computer-readable storage medium can be described with reference to the above-described method embodiments of this application, and will not be repeated here.

[0143] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0144] In summary, this application has the following beneficial effects: by continuously monitoring the heart rate within a preset period, the user's oxygen deficit characteristics are calculated and determined, and the anaerobic training effect is calculated based on the correspondence between the oxygen deficit characteristics and the user's activity level. Therefore, objective oxygen deficit characteristic data and the user's own activity status can be used to accurately obtain the measurement value of anaerobic training effect. The data is objective, accurate, and has strong practicality and ease of use.

[0145] Furthermore, since accurate VO2 max testing requires specialized equipment in a laboratory or gym and involves high-intensity exercise, the cost is high and cannot meet users' needs for real-time and convenient access. Therefore, this solution uses heart rate detection and corresponding algorithms to calculate oxygen deficit, which is more convenient. Users only need to wear a smart wearable device (such as a fitness tracker) that can continuously monitor heart rate to perform real-time detection and calculation.

[0146] Furthermore, since the oxygen deficit value obtained from heart rate detection and related algorithms and the fusion feature obtained from the above fusion calculation are both theoretical values ​​and will differ from the true values, this application also provides fitting calculation to eliminate the difference between theoretical and true values ​​to a certain extent.

[0147] Furthermore, this application employs a two-stage fitting process: first, the oxygen deficit increment is calculated, and second, the final anaerobic training effect is calculated based on the fusion features, thereby making the final anaerobic training effect more closely resemble the real-world scenario.

[0148] Furthermore, when combining user activity levels for fusion calculation, individual differences and statistical patterns of users are fully considered, and the median of user activity levels is introduced for correction, so that the final anaerobic training effect is closer to the real scenario.

[0149] Furthermore, multiple display methods are provided to show the effects of anaerobic training, thereby improving the user experience.

[0150] Furthermore, by saving historical data, users' needs to view historical training results can be met.

Claims

1. A method for evaluating the effectiveness of anaerobic training, characterized in that, The methods for evaluating the effectiveness of anaerobic training include: The system continuously monitors the user's average heart rate during a preset period of anaerobic training and calculates the percentage of maximum heart rate based on the average heart rate. The percentage of maximum oxygen uptake is calculated based on the percentage of maximum heart rate. The oxygen deficit increment is calculated based on the percentage of maximum oxygen uptake. The oxygen deficit increment during the anaerobic training is accumulated to obtain the oxygen deficit during the anaerobic training. The oxygen deficit is fused with the user's activity level to obtain the fusion characteristics of the anaerobic training. The fusion features are fitted and calculated to obtain the anaerobic training effect of the anaerobic training. The calculation of the oxygen deficit increment based on the maximum oxygen uptake percentage includes: Calculate the anaerobic amplification factor m, and calculate the amplified value (modified intensity) of the maximum oxygen uptake percentage based on the amplification factor m; The modified intensity value is fitted and calculated to obtain the oxygen deficit increment. Where modified intensity = m * %VO2max, m=d*%VO2max^2*delta_met, delta_met=e*(%VO2max[t]-%VO2max[t-1]), %VO2max[t] is the percentage of the maximum oxygen uptake at the current time t, %VO2max[t-1] is the percentage of the maximum oxygen uptake one second before the current time t, and d and e are adjustment coefficients; The step of fitting and calculating the modified intensity value to obtain the oxygen deficit increment includes: Oxygen deficit increment = x * modified intensity^2 * y The attenuation coefficient x and the prediction coefficient y are the fitting coefficients.

2. The method for evaluating the effect of anaerobic training according to claim 1, characterized in that, The method for detecting the user's average heart rate during a preset period of anaerobic training also includes: The preset period is T seconds, and the current time is denoted as t. The instantaneous heart rate values ​​within the time window (tT, t) are averaged to obtain the average heart rate value at the current time t, where t is in seconds.

3. The method for evaluating the effect of anaerobic training according to claim 2, characterized in that, The calculation of the maximum heart rate percentage based on the average heart rate includes: Maximum heart rate percentage = Average heart rate / User's maximum heart rate The user's maximum heart rate is determined based on the user's age.

4. The method for evaluating the effect of anaerobic training according to claim 3, characterized in that, The calculation of the percentage of maximum oxygen uptake based on the percentage of maximum heart rate per second includes: %VO2max=f(HRmax), Wherein, %VO2max is an expression for the percentage of maximum oxygen uptake. %HRmax is an expression for the percentage of the maximum heart rate, and f is a linear function.

5. The method for evaluating the effect of anaerobic training according to claim 4, characterized in that, The step of fusing the oxygen deficit with the user's activity level to obtain the fusion characteristics of the anaerobic training includes: Fusion features = , The median level of user activity status is the median of the user activity levels.

6. The method for evaluating the effect of anaerobic training according to claim 5, characterized in that, The step of fitting and calculating the fusion features to obtain the anaerobic training effect includes: Anaerobic training effect = a * fusion feature + c Where a and c are the fitting coefficients.

7. The method for evaluating the effect of anaerobic training according to claim 1, characterized in that, The method for evaluating the effect of anaerobic training further includes: displaying the effect of anaerobic training on a display interface; the methods for displaying the effect of anaerobic training include: text, charts, and videos.

8. A smart wearable device, characterized in that, The method includes a memory and a processor coupled to each other, the processor being used to execute program instructions stored in the memory to implement the anaerobic training effect evaluation method according to any one of claims 1 to 7.

9. A computer-readable storage medium having program instructions stored thereon, characterized in that, When the program instructions are executed by the processor, they implement the anaerobic training effect evaluation method according to any one of claims 1 to 7.

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