A motion efficiency detection method and system, and an electronic device

Abstract

The application discloses a kind of motion efficiency detection method and system, electronic equipment, utilize sliding window to the heart rate data in current cache window is carried out sliding sampling;Every sliding, calculate the heart rate parameter in sliding window;If heart rate parameter does not satisfy aerobic condition, then from the current end time of sliding window start to re-cache heart rate data and power data;If when sliding window slides to the end time of current cache window, the heart rate parameter calculated each time all satisfy aerobic condition, then judge whether the local maximum heart rate in current cache window is greater than set threshold;If yes, start to re-cache heart rate data and power data from the time when the heart rate data in current cache window is less than set threshold;If no, estimate limit oxygen uptake according to the heart rate data and power data in current cache window;According to limit oxygen uptake, judge whether user motion efficiency is improved or decreased.The application solves the technical problem that need to configure breathing detection equipment to determine aerobic exercise.

Description

Technical Field

[0001] This invention belongs to the field of motion analysis technology, specifically, it relates to a motion efficiency detection method and system, and electronic equipment. Background Technology

[0002] Cycling efficiency refers to the user's maximum oxygen uptake level calculated based on heart rate, heart rate variability, and power output during their current exercise state. The difference between this value and the historical maximum oxygen uptake reflects changes in exercise performance.

[0003] During cycling, heart rate increases with power output until it converges to maximum heart rate. Under relatively stable physical conditions, the positive correlation between heart rate and power output remains stable, meaning real-time exercise efficiency (or maximum oxygen uptake) remains stable.

[0004] However, illness, environmental changes, lack of exercise, and fatigue can reduce a user's real-time exercise efficiency. Therefore, timely feedback on the user's current real-time exercise efficiency can help the user to make more reasonable arrangements for exercise plans.

[0005] Generally, calculating the oxygen uptake limit based on a user's real-time exercise data is a common method for indirectly estimating real-time exercise efficiency. However, this method typically requires respiratory monitoring equipment to distinguish between aerobic and anaerobic output, which has certain equipment limitations and causes inconvenience for users during exercise. Summary of the Invention

[0006] This invention provides a method for detecting exercise efficiency, which solves the technical problem in the prior art that requires the configuration of respiratory detection equipment to determine aerobic exercise.

[0007] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0008] A method for detecting exercise efficiency, comprising:

[0009] S1, caches heart rate and power data;

[0010] S2. Use a sliding window to perform sliding sampling of the heart rate data in the current cache window; calculate the heart rate parameters in the sliding window each time; the heart rate parameters include at least one of heart rate gradient and heart rate range;

[0011] S3. If the calculated heart rate parameters do not meet the set aerobic conditions, then re-cache the heart rate and power data starting from the current end time of the sliding window; return to S2.

[0012] If, when the sliding window reaches the end of the current cache window, the calculated heart rate parameters always meet the set aerobic conditions, then execute S4.

[0013] S4. Determine whether the local maximum heart rate within the current cache window is greater than the set threshold.

[0014] If the heart rate data exceeds the set threshold, then start re-caching the heart rate and power data from the moment when the heart rate data in the current cache window falls below the set threshold; return to S2;

[0015] If the value is not greater than the set threshold, the exercise within the current cache window is determined to be aerobic exercise, and S5 is executed.

[0016] S5. Estimate the maximum oxygen uptake based on the heart rate and power data in the current cache window;

[0017] S6. Determine whether the user's exercise efficiency has increased or decreased based on the estimated maximal oxygen uptake.

[0018] In some embodiments of this application, estimating the limiting oxygen uptake based on the heart rate data and power data within the current cache window specifically includes:

[0019] Calculate the average power and average heart rate within the current cache window;

[0020] Calculate the real-time oxygen uptake within the current cache window based on the average power and user weight.

[0021] The maximum oxygen uptake within the current buffer window is calculated based on the real-time oxygen uptake and average heart rate.

[0022] In some embodiments of this application, the step of determining whether the user's exercise efficiency has increased or decreased based on the estimated limiting oxygen uptake specifically includes:

[0023] After exercise, the maximum value among the estimated multiple extreme oxygen uptakes is compared with the historical maximum oxygen uptake;

[0024] If the maximum value is greater than the historical maximum oxygen uptake, it is determined that the user's exercise efficiency has improved, and the historical maximum oxygen uptake is updated using this maximum value;

[0025] If this maximum value is less than the historical maximum oxygen uptake, the user's exercise efficiency is considered to have decreased.

[0026] In some embodiments of this application, when the heart rate parameters include heart rate gradient and heart rate range...

[0027] If the heart rate gradient is greater than the gradient threshold, or the heart rate range is greater than the range threshold, then the heart rate parameters are determined not to meet the set aerobic conditions.

[0028] If the heart rate gradient is less than or equal to the gradient threshold and the heart rate range is less than or equal to the range threshold, then the heart rate parameters are determined to meet the set aerobic conditions.

[0029] In some embodiments of this application, the heart rate range is the difference between the maximum and minimum values ​​of the heart rate data within the sliding window;

[0030] The formula for calculating the heart rate gradient (grad) is:

[0031]

[0032] Where n represents the length of the sliding window;

[0033] i represents the counting point;

[0034] hr i This represents the i-th heart rate data within the sliding window;

[0035] This represents the average heart rate data within the sliding window.

[0036]

[0037] In some embodiments of this application, the threshold is set to k*hr_max;

[0038] Where k is a constant, 0.5 < k < 1;

[0039] hr_max represents the user's maximum heart rate, which is the limit of the heart rate that the user's heart can reach during exercise.

[0040] In some embodiments of this application, the formula for calculating real-time oxygen uptake (vo2) is as follows:

[0041] vo2 = (k1·p_mean + k2) / m;

[0042] Where p_mean represents the average power within the current cache window; m represents the user's weight; k1 and k2 are constants greater than 0;

[0043] The formula for calculating the limiting oxygen uptake vo2_interval_max is:

[0044]

[0045] Where 'a' represents the gain coefficient calculated from the limiting oxygen uptake;

[0046] hr_max represents the user's maximum heart rate, which is the maximum heart rate that the user's heart can reach during exercise;

[0047] hr_mean represents the average heart rate within the current cache window;

[0048] hr_rest represents the user's resting heart rate.

[0049] In some embodiments of this application, k1 = 12.1397 and k2 = 348.54.

[0050] A motion efficiency detection system, comprising:

[0051] The acquisition module is used to acquire heart rate and power data;

[0052] The aerobic exercise determination module is used to perform:

[0053] S1, caches heart rate and power data;

[0054] S2. Use a sliding window to perform sliding sampling of the heart rate data in the current cache window; calculate the heart rate parameters in the sliding window each time; the heart rate parameters include at least one of heart rate gradient and heart rate range;

[0055] S3. If the calculated heart rate parameters do not meet the set aerobic conditions, then re-cache the heart rate and power data starting from the current end time of the sliding window; return to S2.

[0056] If, when the sliding window reaches the end of the current cache window, the calculated heart rate parameters always meet the set aerobic conditions, then execute S4.

[0057] S4. Determine whether the local maximum heart rate within the current cache window is greater than the set threshold.

[0058] If the heart rate data exceeds the set threshold, then start re-caching the heart rate and power data from the moment when the heart rate data in the current cache window falls below the set threshold; return to S2;

[0059] If the value is not greater than the set threshold, then the exercise within the current cache window is determined to be aerobic exercise.

[0060] The estimation module is used to estimate the maximum oxygen uptake based on the heart rate and power data in the current cache window when the exercise in the current cache window is aerobic exercise.

[0061] The exercise efficiency assessment module is used to determine whether the user's exercise efficiency has increased or decreased based on the estimated maximal oxygen uptake.

[0062] An electronic device, comprising:

[0063] Memory, used to store computer programs;

[0064] A processor is used to invoke a computer program in the memory to execute the motion efficiency detection method.

[0065] Compared with the prior art, the advantages and positive effects of the present invention are as follows: The exercise efficiency detection method, system, and electronic device of the present invention cache heart rate data and power data; utilize a sliding window to perform sliding sampling of heart rate data within the current cache window; calculate the heart rate parameters within the sliding window with each slide; if the calculated heart rate parameters do not meet the set aerobic conditions, then re-cache the heart rate data and power data from the current end time of the sliding window; if the calculated heart rate parameters meet the set aerobic conditions each time the sliding window slides to the end time of the current cache window, then determine whether the local maximum heart rate within the current cache window is greater than a set threshold; if it is greater than the set threshold, then re-cache the heart rate data and power data from the moment when the heart rate data within the current cache window is less than the set threshold; if it is not greater than the set threshold, then determine that the exercise within the current cache window is aerobic exercise, and estimate the maximal oxygen uptake based on the heart rate data and power data within the current cache window; determine whether the user's exercise efficiency has increased or decreased based on the estimated maximal oxygen uptake. Therefore, the exercise efficiency detection method, system, and electronic device of the present invention do not require wearing a respiratory monitoring device. They can identify and filter out anaerobic exercise periods using heart rate parameters and local maximum heart rate, and then estimate the maximum oxygen uptake within the current buffer window. Moreover, since users do not need to wear a respiratory monitoring device, it is more convenient for users to exercise, improves the user experience, and solves the technical problem in the prior art that requires a respiratory monitoring device to determine aerobic exercise.

[0066] Other features and advantages of the present invention will become clearer after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. Attached Figure Description

[0067] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a flowchart of an embodiment of the motion efficiency detection method proposed in this invention;

[0069] Figure 2 This is a flowchart of some of the steps;

[0070] Figure 3 This is a flowchart of some of the steps;

[0071] Figure 4 This is a flowchart of another embodiment of the motion efficiency detection method proposed in this invention;

[0072] Figure 5 It is a system interaction diagram;

[0073] Figure 6 This is a structural block diagram of an embodiment of the motion efficiency detection system proposed in this invention. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0075] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0076] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0077] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0078] Example 1

[0079] The exercise efficiency detection method in this embodiment mainly includes the following steps, see below. Figure 1 As shown.

[0080] Acquire the user's physiological data, including user weight, user maximum heart rate, and user resting heart rate.

[0081] Acquire heart rate and power data during user exercise. Heart rate and power data at the same moment correspond one-to-one.

[0082] Step S1: Cache heart rate and power data.

[0083] Heart rate and power data are cached using a cache window.

[0084] Step S2: Use a sliding window to perform sliding sampling of the heart rate data within the current cache window; calculate the heart rate parameters within the sliding window after each slide. The heart rate parameters include at least one of heart rate gradient and heart rate range.

[0085] Step S3: Determine whether the calculated heart rate parameters do not meet the set aerobic conditions.

[0086] If the calculated heart rate parameters do not meet the set aerobic conditions, the heart rate and power data are re-cached starting from the current end time of the sliding window; then return to S2.

[0087] If, when the sliding window reaches the end of the current cache window, the calculated heart rate parameters always meet the set aerobic conditions, then execute S4.

[0088] Step S4: Determine whether the local maximum heart rate within the current cache window is greater than the set threshold.

[0089] If the local maximum heart rate is greater than the set threshold, then the heart rate and power data are re-cached starting from the moment when the heart rate data in the current cache window is less than the set threshold; then return to S2. If all heart rate data in the current cache window are greater than the set threshold, then the heart rate and power data are re-cached starting from the end of the current cache window; then return to S2.

[0090] If the local maximum heart rate is not greater than the set threshold, the exercise within the current cache window is determined to be aerobic exercise, and then S5 is executed.

[0091] Step S5: Estimate the maximum oxygen uptake based on the heart rate and power data in the current cache window.

[0092] Step S6: Determine whether the user's exercise efficiency has increased or decreased based on the estimated maximal oxygen uptake.

[0093] Throughout the exercise, multiple maximal oxygen uptakes are estimated; after the exercise, the estimated maximal oxygen uptakes are used to determine whether the user's exercise efficiency has improved or decreased.

[0094] During user exercise, heart rate and power data are cached, and a sliding window is used to sample the heart rate data within the current cache window. Each time the window slides, the heart rate parameter is calculated. If the calculated heart rate parameter does not meet the aerobic conditions, it indicates that a short burst of exercise, possibly anaerobic exercise, is likely to have occurred during that period. In this case, the period needs to be skipped, and the heart rate and power data are re-cached starting from the current end time of the sliding window.

[0095] After the sliding window slides multiple times, when it reaches the end of the current buffer window, if the calculated heart rate parameters meet the aerobic conditions each time, then it is determined whether the local maximum heart rate within the current buffer window is greater than the set threshold.

[0096] Local maximum heart rate refers to the maximum value of heart rate data within the current cache window.

[0097] If the local maximum heart rate within the current cache window is greater than the set threshold, it indicates that anaerobic exercise may occur within the current cache window. In this case, the heart rate and power data will be re-cached starting from the moment when the heart rate data within the current cache window is less than the set threshold. If all heart rate data within the current cache window are greater than the set threshold, the heart rate and power data will be re-cached starting from the end moment of the current cache window.

[0098] If the local maximum heart rate within the current buffer window is not greater than a set threshold, then the exercise within the current buffer window is determined to be aerobic exercise. Then, the maximal oxygen uptake within the current buffer window is estimated based on the heart rate and power data within the current buffer window.

[0099] The exercise efficiency detection method in this embodiment caches heart rate and power data; it uses a sliding window to sample the heart rate data within the current cache window; each time the window slides, the heart rate parameter within the sliding window is calculated; if the calculated heart rate parameter does not meet the set aerobic conditions, the heart rate and power data are re-cached starting from the current end time of the sliding window; if the calculated heart rate parameter meets the set aerobic conditions each time the sliding window reaches the end time of the current cache window, it is determined whether the local maximum heart rate within the current cache window is greater than a set threshold; if it is greater than the set threshold, the heart rate and power data are re-cached starting from the time when the heart rate data within the current cache window is less than the set threshold; if it is not greater than the set threshold, the exercise within the current cache window is determined to be aerobic exercise, and the maximal oxygen uptake (VO2 max) is estimated based on the heart rate and power data within the current cache window; the estimated VO2 max is used to determine whether the user's exercise efficiency has increased or decreased. Therefore, the exercise efficiency detection method in this embodiment does not require wearing a respiratory monitoring device; it can identify and filter out anaerobic exercise periods using heart rate parameters and local maximum heart rate, and then estimate the VO2 max within the current cache window. Moreover, since users do not need to wear respiratory detection devices, it is more convenient for users to exercise, improves the user experience, and solves the technical problem of needing to configure respiratory detection devices to determine aerobic exercise in existing technologies.

[0100] In some embodiments of this application, when the heart rate parameters include heart rate gradient and heart rate range...

[0101] If the heart rate gradient is greater than the gradient threshold, or the heart rate range is greater than the range threshold, then the heart rate parameters are determined not to meet the set aerobic conditions.

[0102] If the heart rate gradient is less than or equal to the gradient threshold and the heart rate range is less than or equal to the range threshold, then the heart rate parameters are determined to meet the set aerobic conditions.

[0103] Therefore, when the heart rate parameters include heart rate gradient and heart rate range, the heart rate parameters are only considered to meet the set aerobic conditions when the heart rate gradient is less than or equal to the gradient threshold and the heart rate range is less than or equal to the range threshold. This ensures accurate judgment and avoids misjudgment.

[0104] For example, the gradient threshold is 1.5 and the range threshold is 30.

[0105] In some embodiments of this application, when the heart rate parameter only includes the heart rate gradient, the set aerobic conditions include: less than the gradient threshold.

[0106] If the heart rate gradient is greater than the gradient threshold, it is determined that the heart rate parameters do not meet the set aerobic conditions.

[0107] If the heart rate gradient is less than or equal to the gradient threshold, then the heart rate parameter is determined to meet the set aerobic conditions.

[0108] In some embodiments of this application, when the heart rate parameter only includes the heart rate range, the set aerobic conditions include: less than the range threshold.

[0109] If the heart rate range is greater than the range threshold, the heart rate parameter is determined not to meet the set aerobic conditions.

[0110] If the heart rate range is less than or equal to the range threshold, then the heart rate parameter is determined to meet the set aerobic conditions.

[0111] In some embodiments of this application, the heart rate range is the difference between the maximum and minimum heart rate data within a sliding window.

[0112] The formula for calculating the heart rate gradient (grad) is:

[0113]

[0114] Where n represents the length of the sliding window;

[0115] i represents the counting point;

[0116] hr i This represents the i-th heart rate data within the sliding window;

[0117] This represents the average heart rate data within the sliding window.

[0118]

[0119] express The square of;

[0120] i 2 It represents the square of i.

[0121] The above formula can accurately calculate the heart rate gradient within the sliding window.

[0122] In some embodiments of this application, the threshold is set to k*hr_max.

[0123] Where k is a constant, 0.5 < k < 1. For example, k = 0.9, which means the threshold is set to 0.9 * hr_max.

[0124] hr_max represents the user's maximum heart rate, which is the limit of the heart rate that the user's heart can reach during exercise.

[0125] The threshold value is designed to be related to the user's maximum heart rate hr_max, but less than the user's maximum heart rate hr_max, and the value is appropriate.

[0126] By comparing the local maximum heart rate within the current cache window with a set threshold, it is possible to accurately determine whether the exercise within the current cache window is aerobic exercise.

[0127] In some embodiments of this application, the limiting oxygen uptake is estimated based on heart rate data and power data within the current cache window, specifically including the following steps, see [link to relevant documentation] Figure 2 As shown.

[0128] Step S51: Calculate the average power p_mean and average heart rate hr_mean within the current buffer window.

[0129] Step S52: Calculate the real-time oxygen uptake (vo2) within the current cache window based on the average power p_mean and the user's weight m.

[0130] Step S53: Calculate the maximum oxygen uptake vo2_interval_max within the current buffer window based on the real-time oxygen uptake vo2 and the average heart rate hr_mean.

[0131] The real-time oxygen uptake vo2 in the current cache window is related to the average power p_mean and user weight m in the current cache window, while the maximum oxygen uptake vo2_interval_max in the current cache window is related to the real-time oxygen uptake vo2 and average heart rate hr_mean.

[0132] By designing the above steps S51 to S53, the maximum oxygen uptake within the current buffer window can be calculated accurately, simply, and quickly.

[0133] In some embodiments of this application, the formula for calculating the real-time oxygen uptake (vo2) within the current cache window is as follows:

[0134] vo2 = (k1·p_mean + k2) / m;

[0135] Where p_mean represents the average power within the current cache window; m represents the user's weight; and k1 and k2 are constants greater than 0.

[0136] Using the above formula, the real-time oxygen uptake (VO2) can be accurately calculated using average power and user weight.

[0137] In some embodiments of this application, k1 = 12.1397 and k2 = 348.54.

[0138] That is, vo2 = (12.1397·p_mean + 348.54) / m.

[0139] By selecting the above coefficients for k1 and k2, the real-time oxygen uptake (vo2) can be accurately calculated.

[0140] In some embodiments of this application, the formula for calculating the limiting oxygen uptake vo2_interval_max within the current cache window is as follows:

[0141]

[0142] Where 'a' represents the gain coefficient for maximal oxygen uptake (VO2 max). The gain coefficient 'a' is related to the lactate threshold heart rate / maximum heart rate. For example, the gain coefficient 'a' ≈ lactate threshold heart rate / user's maximum heart rate.

[0143] vo2 represents the real-time oxygen uptake within the current cache window.

[0144] hr_max represents the user's maximum heart rate, which is the maximum heart rate that the user's heart can reach during exercise;

[0145] hr_mean represents the average heart rate within the current cache window;

[0146] hr_rest represents the user's resting heart rate.

[0147] Using the above formula, the ultimate oxygen uptake can be accurately calculated using real-time oxygen uptake, average heart rate, user's maximum heart rate, and user's resting heart rate.

[0148] In some embodiments of this application, determining whether a user's exercise efficiency has increased or decreased based on an estimated limiting oxygen uptake includes the following steps, see [link to relevant documentation] Figure 3 As shown.

[0149] Step S61: After exercise, compare the maximum value among the estimated multiple maximal oxygen uptakes with the historical maximum oxygen uptake.

[0150] From the start to the end of this exercise, multiple maximal oxygen uptakes may be estimated, and the maximum value among these multiple maximal oxygen uptakes will be compared with the historical maximum oxygen uptake.

[0151] Step S62: If the maximum value is greater than the historical maximum oxygen uptake, it is determined that the user's exercise efficiency has improved, and the historical maximum oxygen uptake is updated using the maximum value. That is, the maximum value is assigned to the historical maximum oxygen uptake.

[0152] Step S63: If the maximum value is less than the historical maximum oxygen uptake, it is determined that the user's exercise efficiency has decreased.

[0153] If the maximum value is equal to the historical maximum oxygen uptake, then the user is determined to maintain exercise efficiency.

[0154] If the storage unit does not contain the historical maximum oxygen uptake, the user can either input the historical maximum oxygen uptake themselves or estimate it using historical cycling data.

[0155] If the storage unit contains the historical maximum oxygen uptake, then the historical maximum oxygen uptake is read directly from the storage unit and compared with the maximum value among multiple extreme oxygen uptake values.

[0156] By designing steps S61 to S63, the maximum value among multiple extreme oxygen uptakes is compared with the historical maximum oxygen uptake. Based on the comparison results, it can be accurately determined whether the user's exercise efficiency has improved or decreased.

[0157] Below, in conjunction with Figure 4 The specific steps of the motion efficiency detection method of this application are illustrated through a specific embodiment.

[0158] Related variables and notes:

[0159]

[0160] (1) Heart rate / power feature extraction and anaerobic noise reduction:

[0161] This application utilizes cached heart rate and power data to indirectly estimate changes in real-time exercise efficiency by estimating changes in oxygen uptake capacity. Estimating oxygen uptake capacity requires exercise data from the aerobic output process; therefore, when caching heart rate and power data, anaerobic features need to be filtered out.

[0162] Heart rate data is collected using a heart rate monitor, and power data is collected using a power device (such as a power meter). The heart rate monitor and power device transmit the collected data to a speedometer or mobile phone. See [link to relevant documentation]. Figure 5 As shown, the motion efficiency detection method of this application is executed using a code table or a mobile phone.

[0163] Assume the buffer window is 300 seconds long. The sliding window is 11 seconds long with a sliding step of 1 second.

[0164] Judgment condition 1: Using an 11-second sliding window, perform sliding sampling on the cached 300-second heart rate sequence, and calculate the heart rate gradient and heart rate range within the sliding window. If the heart rate gradient is greater than 1.5 or the heart rate range is greater than 30, it is highly likely that a short burst of exercise (high anaerobic output) occurred during this period. In this case, skip this period and re-cache the 300-second data.

[0165]

[0166] Where grad represents the heart rate gradient within the sliding window; n represents the length of the sliding window (11 in this case); i represents the counting point; hr_edge represents the heart rate range within the sliding window; max(hr) and min(hr) represent the maximum and minimum values ​​of the heart rate data within the sliding window, respectively.

[0167] If, when the sliding window reaches the end of the current cache window, the calculated heart rate gradient is ≤1.5 and the heart rate range is ≤30, then the following judgment condition 2 is applied.

[0168] Judgment condition 2: The local maximum heart rate in the 300s cached heart rate is greater than 0.9*hr_max.

[0169] If this condition is met, the time period exceeding this threshold is the exercise process dominated by anaerobic output, and the data caching needs to be restarted at a time point less than 0.9*hr_max.

[0170] If the local maximum heart rate in the 300s cached heart rate is less than or equal to 0.9*hr_max, then calculate the average power p_mean and average heart rate hr_mean of the current 300s cached data.

[0171] (2) Real-time motion efficiency estimation:

[0172] Calculate the average power p_mean and average heart rate hr_mean within the current buffer window, and then calculate the real-time oxygen uptake vo2 based on p_mean:

[0173] vo2=(12.1397·p_mean+348.54) / m.

[0174] Based on the ratio of the average heart rate hr_mean to the maximum heart rate and resting heart rate within the current cache window, the maximal oxygen uptake is calculated inversely:

[0175]

[0176] Where vo2_interval_max is the ultimate oxygen uptake; a represents the gain coefficient of the ultimate oxygen uptake back calculation, which is related to the lactate threshold heart rate / maximum heart rate; a≈lactate threshold heart rate / maximum heart rate; hr_max and hr_rest represent the maximum heart rate and resting heart rate, respectively.

[0177] (3) Historical data comparison and exercise efficiency update:

[0178] After exercise, by comparing the calculated maximal oxygen uptake vo2_interval_max with the historical maximum oxygen uptake vo2_max, the increase or decrease in the value can indicate the improvement or decrease in the user's real-time exercise efficiency.

[0179] If the limiting oxygen uptake is greater than the historical maximum oxygen uptake, then a replacement treatment is performed:

[0180]

[0181] The exercise efficiency detection method in this embodiment belongs to the fields of smart hardware and sports health. It uses the power and heart rate during real-time cycling to estimate the user's cycling exercise efficiency by measuring the power output at the corresponding heart rate.

[0182] The exercise efficiency detection method in this embodiment relies on exercise data from the aerobic output process when estimating oxygen uptake capacity. However, anaerobic output inevitably occurs during exercise. To collect exercise periods with a high proportion of aerobic output, this application identifies and eliminates periods with a high proportion of anaerobic output using heart rate and power, avoiding the use of such features when estimating oxygen uptake capacity, thereby improving the signal-to-noise ratio of the input signal for calculating oxygen uptake capacity. Using short-delay data, the real-time oxygen uptake is calculated based on heart rate and power during this period, and combined with the user's physiological information (maximum heart rate, resting heart rate, weight, etc.), the user's oxygen uptake limit under this physical condition is estimated. The estimated oxygen uptake limit (maximum oxygen uptake) is used as a benchmark to output the user's real-time exercise efficiency.

[0183] The exercise efficiency detection method in this embodiment needs to distinguish between aerobic and anaerobic output during exercise when calculating the maximal oxygen uptake. It identifies the state of aerobic / anaerobic output based on the user's FTP and heart rate during cycling, and can estimate the user's real-time exercise efficiency without wearing a breathing detection device.

[0184] The exercise efficiency detection method in this embodiment can identify and classify anaerobic / anaerobic exercise output based on heart rate and power without the aid of respiratory detection equipment, thereby obtaining a more accurate calculation of oxygen uptake limit and improving the accuracy of real-time exercise efficiency calculation for users.

[0185] Example 2

[0186] Based on the exercise efficiency detection method of Embodiment 1, Embodiment 2 proposes an exercise efficiency detection system, including an acquisition module, an aerobic judgment module, an estimation module, and an exercise efficiency judgment module, etc. (See [link to documentation]). Figure 6 As shown.

[0187] The acquisition module is used to acquire heart rate and power data.

[0188] The aerobic exercise determination module is used to perform:

[0189] S1, caches heart rate and power data;

[0190] S2. Use a sliding window to perform sliding sampling of the heart rate data in the current cache window; calculate the heart rate parameters in the sliding window each time; the heart rate parameters include at least one of heart rate gradient and heart rate range;

[0191] S3. If the calculated heart rate parameters do not meet the set aerobic conditions, then re-cache the heart rate and power data starting from the current end time of the sliding window; return to S2.

[0192] If, when the sliding window reaches the end of the current cache window, the calculated heart rate parameters always meet the set aerobic conditions, then execute S4.

[0193] S4. Determine whether the local maximum heart rate within the current cache window is greater than the set threshold.

[0194] If the heart rate data exceeds the set threshold, then start re-caching the heart rate and power data from the moment when the heart rate data in the current cache window falls below the set threshold; return to S2;

[0195] If the value is not greater than the set threshold, then the exercise within the current cache window is determined to be aerobic exercise.

[0196] The estimation module is used to estimate the maximal oxygen uptake based on the heart rate and power data within the current cache window when the exercise within the current cache window is aerobic exercise.

[0197] The exercise efficiency assessment module is used to determine whether the user's exercise efficiency has increased or decreased based on the estimated maximal oxygen uptake.

[0198] The exercise efficiency assessment module is specifically used to: after exercise, compare the maximum value among multiple estimated maximal oxygen uptakes with the historical maximum oxygen uptake. If the maximum value is greater than the historical maximum oxygen uptake, the user's exercise efficiency is determined to have improved, and the historical maximum oxygen uptake is updated using this maximum value. If the maximum value is less than the historical maximum oxygen uptake, the user's exercise efficiency is determined to have decreased.

[0199] The exercise efficiency detection system of this embodiment is installed in a cycling computer or mobile phone. The acquisition module (heart rate / power feature extraction module) receives heart rate data and power data transmitted from the heart rate belt and power device. The acquisition module transmits the acquired heart rate data and power data to the aerobic judgment module. The aerobic judgment module (anaerobic noise reduction module) identifies and eliminates anaerobic periods. The estimation module (real-time exercise efficiency estimation module) is used to estimate the maximal oxygen uptake. The exercise efficiency judgment module (historical exercise efficiency comparison and update module) is used to determine whether the user's exercise efficiency has increased or decreased.

[0200] The specific working process of the exercise efficiency detection system has been detailed in the above exercise efficiency detection method and will not be repeated here.

[0201] The exercise efficiency detection system of this embodiment does not require wearing a respiratory monitoring device. It can identify and filter out anaerobic exercise periods using heart rate parameters and local maximum heart rate, and then estimate the maximum oxygen uptake within the current buffer window. Moreover, since users do not need to wear a respiratory monitoring device, it is more convenient for users to exercise, improves the user experience, and solves the technical problem in the prior art that requires a respiratory monitoring device to determine aerobic exercise.

[0202] Example 3

[0203] This third embodiment proposes an electronic device, including a memory, a processor, etc.

[0204] Memory is used to store computer programs.

[0205] The processor is used to call the computer program in the memory to execute the motion efficiency detection method in Embodiment 1 and achieve the technical effects of the corresponding embodiment.

[0206] Example 4

[0207] This fourth embodiment proposes a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the motion efficiency detection method in the first embodiment and achieves the technical effects of the corresponding embodiment.

[0208] The aforementioned computer storage media can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer storage media can be any available storage medium accessible to general-purpose or special-purpose computers.

[0209] In some embodiments, a computer storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in application-specific integrated circuits (ASICs). Of course, the processor and storage medium can also exist as discrete components in the device.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting motion efficiency, characterized in that: include: S1, caches heart rate and power data; S2. Use a sliding window to perform sliding sampling of the heart rate data in the current cache window; Each time the window is slid, the heart rate parameters within the sliding window are calculated; the heart rate parameters include at least one of heart rate gradient and heart rate range. S3. If the calculated heart rate parameters do not meet the set aerobic conditions, then the heart rate data and power data will be re-cached starting from the current end time of the sliding window. Return to S2; If, when the sliding window reaches the end of the current cache window, the calculated heart rate parameters always meet the set aerobic conditions, then execute S4. S4. Determine whether the local maximum heart rate within the current cache window is greater than a set threshold; the local maximum heart rate refers to the maximum value of the heart rate data within the current cache window. If the local maximum heart rate is greater than the set threshold, then the heart rate data and power data are re-cached starting from the moment when the heart rate data in the current cache window is less than the set threshold; if all heart rate data in the current cache window are greater than the set threshold, then the heart rate data and power data are re-cached starting from the end moment of the current cache window. Return to S2; If the local maximum heart rate is not greater than the set threshold, then the exercise in the current cache window is determined to be aerobic exercise, and S5 is executed; S5. Estimate the maximum oxygen uptake based on the heart rate and power data in the current cache window; S6. Determine whether the user's exercise efficiency has increased or decreased based on the estimated maximal oxygen uptake.

2. The method for detecting motion efficiency according to claim 1, characterized in that: The estimation of the ultimate oxygen uptake based on the heart rate and power data within the current cache window specifically includes: Calculate the average power and average heart rate within the current cache window; Calculate the real-time oxygen uptake within the current cache window based on the average power and user weight. The maximum oxygen uptake within the current buffer window is calculated based on the real-time oxygen uptake and average heart rate.

3. The method for detecting motion efficiency according to claim 1, characterized in that: The method of determining whether a user's exercise efficiency has improved or decreased based on the estimated maximal oxygen uptake specifically includes: After exercise, the maximum value among the estimated multiple extreme oxygen uptakes is compared with the historical maximum oxygen uptake; If the maximum value is greater than the historical maximum oxygen uptake, it is determined that the user's exercise efficiency has improved, and the historical maximum oxygen uptake is updated using this maximum value; If this maximum value is less than the historical maximum oxygen uptake, the user's exercise efficiency is considered to have decreased.

4. The method for detecting motion efficiency according to claim 1, characterized in that: When the heart rate parameters include heart rate gradient and heart rate range If the heart rate gradient is greater than the gradient threshold, or the heart rate range is greater than the range threshold, then the heart rate parameters are determined not to meet the set aerobic conditions. If the heart rate gradient is less than or equal to the gradient threshold and the heart rate range is less than or equal to the range threshold, then the heart rate parameters are determined to meet the set aerobic conditions.

5. The method for detecting motion efficiency according to claim 1, characterized in that: The heart rate range is the difference between the maximum and minimum heart rate data within the sliding window; The formula for calculating the heart rate gradient (grad) is: ； Where n represents the length of the sliding window; Indicates the counting point; Indicates the first in the sliding window Heart rate data; This represents the average heart rate data within the sliding window. =(1+2+3+……+n) / n。 6. The method for detecting motion efficiency according to claim 1, characterized in that: Set the threshold to k * hr_max; Where k is a constant, 0.5 < k < 1; hr_max represents the user's maximum heart rate, which is the limit of the heart rate that the user's heart can reach during exercise.

7. The method for detecting motion efficiency according to claim 2, characterized in that: The formula for calculating real-time oxygen uptake (VO2) is: ； Where p_mean represents the average power within the current cache window; m represents the user's weight; A constant greater than 0; The formula for calculating the limiting oxygen uptake vo2_interval_max is: ； Where 'a' represents the gain coefficient calculated from the limiting oxygen uptake; hr_max represents the user's maximum heart rate, which is the maximum heart rate that the user's heart can reach during exercise; hr_mean represents the average heart rate within the current cache window; hr_rest represents the user's resting heart rate.

8. The method for detecting motion efficiency according to claim 7, characterized in that: 。 9. A motion efficiency detection system, characterized in that: include: The acquisition module is used to acquire heart rate and power data; The aerobic exercise determination module is used to perform: S1, caches heart rate and power data; S2. Use a sliding window to perform sliding sampling of the heart rate data in the current cache window; calculate the heart rate parameters in the sliding window each time; the heart rate parameters include at least one of heart rate gradient and heart rate range; S3. If the calculated heart rate parameters do not meet the set aerobic conditions, then the heart rate data and power data will be re-cached starting from the current end time of the sliding window. Return to S2; if, when the sliding window reaches the end of the current buffer window, the calculated heart rate parameters meet the set aerobic conditions each time, then execute S4; S4. Determine whether the local maximum heart rate within the current cache window is greater than a set threshold; the local maximum heart rate refers to the maximum value of the heart rate data within the current cache window; if the local maximum heart rate is greater than the set threshold, then start re-caching the heart rate data and power data from the moment when the heart rate data within the current cache window is less than the set threshold; if all heart rate data within the current cache window are greater than the set threshold, then start re-caching the heart rate data and power data from the end moment of the current cache window. Return to S2; if the local maximum heart rate is not greater than the set threshold, then the exercise within the current cache window is determined to be aerobic exercise; The estimation module is used to estimate the maximum oxygen uptake based on the heart rate and power data in the current cache window when the exercise in the current cache window is aerobic exercise. The exercise efficiency assessment module is used to determine whether the user's exercise efficiency has increased or decreased based on the estimated maximal oxygen uptake.

10. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor for invoking a computer program in the memory to perform the method as described in any one of claims 1 to 8.

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