Motion efficiency detection method and system and electronic equipment

Abstract

The invention discloses an exercise efficiency detection method and system and electronic equipment. The exercise efficiency detection method comprises the steps that a sliding window is used for conducting sliding sampling on heart rate data in a current cache window; the heart rate parameter in the sliding window is calculated every time the sliding window slides; if the heart rate parameter does not meet the aerobic condition, the heart rate data and the power data are cached again from the current ending moment of the sliding window; if the heart rate parameter calculated each time meets the aerobic condition when the sliding window slides to the end moment of the current cache window, whether the local maximum heart rate in the current cache window is larger than a set threshold value or not is judged; if yes, the heart rate data and the power data are cached again from the moment when the heart rate data in the current cache window is smaller than the set threshold value; if not, estimating the limit oxygen uptake according to the heart rate data and the power data in the current cache window; and judging whether the exercise efficiency of the user is improved or reduced according to the limit oxygen uptake. The technical problem that breathing detection equipment needs to be configured to judge aerobic exercise is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of motion analysis, and in particular, relates to a motion efficiency detection method and system, and electronic equipment. Background Art

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

[0003] During cycling, the heart rate will increase as the power increases until it converges to the maximum heart rate. When physical fitness does not change much, the positive correlation between heart rate and power is stable, that is, the real-time exercise efficiency is stable (or expressed as a stable maximum oxygen uptake level).

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

[0005] Generally speaking, back-calculating the oxygen uptake limit based on the user's real-time exercise data is a commonly used method to indirectly estimate real-time exercise efficiency. However, using this method to measure generally requires breathing detection equipment to distinguish aerobic / anaerobic output, which has certain equipment limitations and causes inconvenience to users. Summary of the invention

[0006] The present invention provides a method for detecting exercise efficiency, which solves the technical problem in the prior art that a breathing detection device needs to be configured to determine aerobic exercise.

[0007] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] A motion efficiency detection method, comprising:

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

[0010] S2. Sliding sampling is performed on the heart rate data in the current cache window using a sliding window; each time the sliding window is slid, a heart rate parameter in the sliding window is calculated; the heart rate parameter includes at least one of a heart rate gradient and a heart rate range;

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

[0012] If the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current buffer window, S4 is executed;

[0013] S4, determining whether the local maximum heart rate in the current buffer window is greater than a set threshold;

[0014] If it is greater than the set threshold, the heart rate data and power data are re-cached from the moment when the heart rate data in the current cache window is less than the set threshold; return to S2;

[0015] If it is not greater than the set threshold, it is determined that the exercise in the current cache window is aerobic exercise, and S5 is executed;

[0016] S5, estimating the limit oxygen uptake according to the heart rate data and power data in the current cache window;

[0017] S6. Determine whether the user's exercise efficiency is improved or decreased based on the estimated maximum oxygen uptake.

[0018] In some embodiments of the present application, estimating the limit oxygen uptake according to the heart rate data and power data in the current cache window specifically includes:

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

[0020] Calculate the real-time oxygen uptake in the current cache window according to the average power and the user's weight;

[0021] The limit oxygen uptake in the current cache window is calculated according to the real-time oxygen uptake and the average heart rate.

[0022] In some embodiments of the present application, judging whether the user's exercise efficiency is improved or decreased according to the estimated limit oxygen uptake specifically includes:

[0023] After exercise, the maximum of the multiple estimated VO2 max was compared with the historical VO2 max.

[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 the maximum value;

[0025] If the maximum value is less than the historical maximum oxygen uptake, it is determined that the user's exercise efficiency has decreased.

[0026] In some embodiments of the present application, when the heart rate parameters include heart rate gradient and heart rate extreme difference,

[0027] If the heart rate gradient is greater than the gradient threshold, or the heart rate extreme difference is greater than the extreme difference threshold, it is determined that the heart rate parameters do not meet the set aerobic conditions;

[0028] If the heart rate gradient is less than or equal to the gradient threshold, and the heart rate extreme difference is less than or equal to the extreme difference threshold, it is determined that the heart rate parameters meet the set aerobic conditions.

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

[0030] The calculation formula of 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 Represents the i-th heart rate data in the sliding window;

[0035] Represents the average value of the heart rate data within the sliding window;

[0036]

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

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

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

[0040] In some embodiments of the present application, the calculation formula for real-time oxygen uptake vo2 is:

[0041] vo2=(k 1 p_mean+k 2 ) / m;

[0042] Where p_mean represents the average power in the current cache window; m represents the user weight; k 1 , k 2 is a constant greater than 0;

[0043] The calculation formula for the maximum oxygen uptake vo2_interval_max is:

[0044]

[0045] Where a represents the back-calculation gain coefficient of the limit oxygen uptake;

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

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

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

[0049] In some embodiments of the present application, k 1 =12.1397, k 2 =348.54.

[0050] A motion efficiency detection system, comprising:

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

[0052] Aerobic judgment module, used to perform:

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

[0054] S2. Sliding sampling is performed on the heart rate data in the current cache window using a sliding window; each time the sliding window is slid, a heart rate parameter in the sliding window is calculated; the heart rate parameter includes at least one of a heart rate gradient and a heart rate range;

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

[0056] If the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current buffer window, S4 is executed;

[0057] S4, determining whether the local maximum heart rate in the current buffer window is greater than a set threshold;

[0058] If it is greater than the set threshold, the heart rate data and power data are re-cached from the moment when the heart rate data in the current cache window is less than the set threshold; return to S2;

[0059] If it is not greater than the set threshold, it is determined that the exercise in the current cache window is aerobic exercise;

[0060] An estimation module, used for estimating the limit oxygen uptake according to the heart rate data and power data in the current cache window when the exercise in the current cache window is aerobic exercise;

[0061] The exercise efficiency judgment module is used to judge whether the user's exercise efficiency is improved or decreased based on the estimated limit oxygen uptake.

[0062] An electronic device, comprising:

[0063] Memory for storing computer programs;

[0064] The processor is used to call the 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: the exercise efficiency detection method and system, and the electronic device of the present invention cache heart rate data and power data; use a sliding window to slide and sample the heart rate data in the current cache window; calculate the heart rate parameters in the sliding window each time it slides; if the calculated heart rate parameters do not meet the set aerobic conditions, re-cache the heart rate data and power data from the current end time of the sliding window; if the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current cache window, determine whether the local maximum heart rate in the current cache window is greater than a set threshold; if it is greater than the set threshold, re-cache the heart rate data and power data from the moment when the heart rate data in the current cache window is less than the set threshold; if it is not greater than the set threshold, determine that the exercise in the current cache window is aerobic exercise, and estimate the limit oxygen uptake based on the heart rate data and power data in the current cache window; determine whether the user's exercise efficiency is improved or decreased based on the estimated limit oxygen uptake. Therefore, the exercise efficiency detection method and system and electronic device of the present invention do not need to wear a breathing detection device. The heart rate parameters and the local maximum heart rate can be used to identify and filter out the anaerobic exercise period, and then the limit oxygen uptake in the current cache window can be estimated. Moreover, since the user does not need to wear a breathing detection device, it is more convenient for the user to exercise, improves the user experience, and solves the technical problem of the prior art that a breathing detection device needs to be configured to determine aerobic exercise.

[0066] Other features and advantages of the present invention will become more apparent after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0068] Figure 1 is a flow chart of an embodiment of the motion efficiency detection method proposed by the present invention;

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

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

[0071] Figure 4 is a flow chart of another embodiment of the motion efficiency detection method proposed by the present invention;

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

[0073] Figure 6 It is a structural block diagram of an embodiment of the motion efficiency detection system proposed by the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present 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 understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0077] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0078] Embodiment 1

[0079] The motion efficiency detection method of this embodiment mainly includes the following steps: Figure 1 shown.

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

[0081] Get the user's heart rate data and power data during exercise. The heart rate data and power data at the same time correspond one to one.

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

[0083] Use the cache window to cache heart rate data and power data.

[0084] Step S2: Sliding sampling is performed on the heart rate data in the current buffer window by using a sliding window; each time the sliding window is slid, the heart rate parameters in the sliding window are calculated. The heart rate parameters include at least one of the heart rate gradient and the 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 data and power data are re-cached starting from the current end time of the sliding window; then the process returns to S2.

[0087] If the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current buffer window, S4 is executed.

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

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

[0090] If the local maximum heart rate is not greater than the set threshold, it is determined that the exercise in the current buffer window is aerobic exercise, and then S5 is executed.

[0091] Step S5: estimating the limit oxygen uptake according to the heart rate data and power data in the current buffer window.

[0092] Step S6: Determine whether the user's exercise efficiency is improved or reduced based on the estimated maximum oxygen uptake.

[0093] During the entire exercise, multiple maximum oxygen uptake values ​​will be estimated; after the exercise, the user's exercise efficiency will be judged whether it has increased or decreased based on the estimated maximum oxygen uptake values.

[0094] During the user's exercise, the user's heart rate data and power data are cached, and the sliding window is used to slide the heart rate data in the current cache window. The heart rate parameters are calculated once each slide. If the calculated heart rate parameters do not meet the aerobic conditions, it means that a short burst of exercise is very likely to have occurred during this period, and anaerobic exercise may have occurred. In this case, it is necessary to skip this period, that is, re-cache the heart rate data and power data starting from the current end time of the sliding window.

[0095] After the sliding window slides multiple times, the sliding window slides to the end time of the current cache window. If the heart rate parameters calculated each time meet the aerobic conditions, it is determined whether the local maximum heart rate in the current cache window is greater than the set threshold.

[0096] The local maximum heart rate refers to the maximum value of the heart rate data in the current buffer window.

[0097] If the local maximum heart rate in the current cache window is greater than the set threshold, it means that anaerobic exercise may occur in the current cache window, and the heart rate data and power data will be re-cached from the moment 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, the heart rate data and power data will be re-cached from the end moment of the current cache window.

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

[0099] The method for detecting the exercise efficiency of this embodiment is to cache the heart rate data and power data; use the sliding window to perform sliding sampling on the heart rate data in the current cache window; calculate the heart rate parameters in the sliding window each time it slides; if the calculated heart rate parameters do not meet the set aerobic conditions, re-cache the heart rate data and power data from the current end time of the sliding window; if the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current cache window, then determine whether the local maximum heart rate in the current cache window is greater than the set threshold; if it is greater than the set threshold, re-cache the heart rate data and power data from the moment when the heart rate data in the current cache window is less than the set threshold; if it is not greater than the set threshold, determine that the exercise in the current cache window is aerobic exercise, and estimate the limit oxygen uptake based on the heart rate data and power data in the current cache window; and determine whether the user's exercise efficiency is improved or decreased based on the estimated limit oxygen uptake. Therefore, the method for detecting the exercise efficiency of this embodiment does not require wearing a respiratory detection device, and can use the heart rate parameters and the local maximum heart rate to identify and filter out the anaerobic exercise period, and then estimate the limit oxygen uptake in the current cache window. Moreover, since users do not need to wear breathing detection equipment, it is more convenient for users to exercise, which improves the user experience and solves the technical problem in the prior art that breathing detection equipment needs to be configured to determine aerobic exercise.

[0100] In some embodiments of the present application, when the heart rate parameters include the heart rate gradient and the heart rate extreme difference,

[0101] If the heart rate gradient is greater than the gradient threshold, or the heart rate extreme difference is greater than the extreme difference threshold, it is determined that the heart rate parameters do not meet the set aerobic conditions.

[0102] If the heart rate gradient is less than or equal to the gradient threshold, and the heart rate extreme difference is less than or equal to the extreme difference threshold, it is determined that the heart rate parameters meet the set aerobic conditions.

[0103] Therefore, when the heart rate parameters include the heart rate gradient and the heart rate extreme difference, the heart rate parameters are judged to meet the set aerobic conditions only when the heart rate gradient is less than or equal to the gradient threshold and the heart rate extreme difference is less than or equal to the extreme difference threshold, so that the judgment is accurate and misjudgment is avoided.

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

[0105] In some embodiments of the present application, when the heart rate parameter only includes the heart rate gradient, the set aerobic conditions include: less than a 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, it is determined that the heart rate parameters meet the set aerobic conditions.

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

[0109] If the heart rate extreme difference is greater than the extreme difference threshold, it is determined that the heart rate parameter does not meet the set aerobic conditions;

[0110] If the heart rate extreme difference is less than or equal to the extreme difference threshold, it is determined that the heart rate parameter meets the set aerobic conditions.

[0111] In some embodiments of the present application, the heart rate range is the difference between the maximum and minimum values ​​of the heart rate data in the sliding window.

[0112] The calculation formula of 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 Represents the i-th heart rate data in the sliding window;

[0117] Represents the average value of the heart rate data within the sliding window;

[0118]

[0119] express The square of

[0120] i 2 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 the present application, the threshold is set to k*hr_max.

[0123] Wherein, k is a constant, 0.5<k<1. For example, k=0.9, that is, the threshold value is set to 0.9*hr_max.

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

[0125] By designing the set threshold to be related to the user's maximum heart rate hr_max and smaller than the user's maximum heart rate hr_max, the value is appropriate.

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

[0127] In some embodiments of the present application, the limit oxygen uptake is estimated based on the heart rate data and power data in the current cache window, specifically including the following steps, see Figure 2 shown.

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

[0129] Step S52: Calculate the real-time oxygen uptake vo2 in the current cache window according to the average power p_mean and the user's weight m.

[0130] Step S53: Calculate the limit oxygen uptake vo2_interval_max in the current cache window according to 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 the user weight m in the current cache window, and the limit oxygen uptake vo2_interval_max in the current cache window is related to the real-time oxygen uptake vo2 and the average heart rate hr_mean.

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

[0133] In some embodiments of the present application, the calculation formula for the real-time oxygen uptake vo2 in the current cache window is:

[0134] vo2=(k 1 p_mean+k 2 ) / m;

[0135] Where p_mean represents the average power in the current cache window; m represents the user weight; k 1 , k 2 is a constant greater than 0.

[0136] Through the above formula, the real-time oxygen uptake vo2 can be accurately calculated using average power and user weight.

[0137] In some embodiments of the present application, k 1 =12.1397, k 2 =348.54.

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

[0139] k1 , k 2 By selecting the above coefficients, the real-time oxygen uptake vo2 can be accurately calculated.

[0140] In some embodiments of the present application, the calculation formula of the limit oxygen uptake vo2_interval_max in the current cache window is:

[0141]

[0142] Wherein, a represents the limit oxygen uptake back-calculation gain coefficient. The limit oxygen uptake back-calculation gain coefficient a is related to the lactate threshold heart rate / maximum heart rate. For example, the limit oxygen uptake back-calculation gain coefficient a≈lactate threshold heart rate / user's maximum heart rate.

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

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

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

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

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

[0148] In some embodiments of the present application, judging whether the user's exercise efficiency is improved or decreased according to the estimated limit oxygen uptake includes the following steps: Figure 3 shown.

[0149] Step S61: After the exercise is finished, the maximum value of the estimated multiple limit oxygen uptakes is compared with the historical maximum oxygen uptake.

[0150] From the beginning to the end of this exercise, multiple limit oxygen uptakes may be estimated, and the maximum value of the multiple limit oxygen uptakes is 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 maximum value is used to update the historical maximum oxygen uptake. 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, it is determined that the user maintains exercise efficiency.

[0154] If the historical maximum oxygen uptake is not stored in the storage unit, the user inputs the historical maximum oxygen uptake by himself or estimates it using historical riding data.

[0155] If a historical maximum oxygen uptake is stored in the storage unit, the historical maximum oxygen uptake is directly read from the storage unit and then compared with a maximum value of the multiple limit oxygen uptakes.

[0156] By designing steps S61 to S63, the maximum value of multiple limit oxygen uptakes is compared with the historical maximum oxygen uptake, and based on the comparison result, it can be accurately determined whether the user's exercise efficiency is improved or reduced.

[0157] Next, combine Figure 4 , the specific steps of the motion efficiency detection method of the present application are explained through a specific embodiment.

[0158] Related variables and comments:

[0159]

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

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

[0162] Use a heart rate belt to collect heart rate data, and use a power device (such as a power meter) to collect power data. The heart rate belt and power device transmit the collected data to a cycling computer or mobile phone. See Figure 5 The exercise efficiency detection method of the present application is performed using a stopwatch or a mobile phone.

[0163] Assume that the length of the cache window is 300 seconds, the length of the sliding window is 11 seconds, and the sliding step is 1 second.

[0164] Judgment condition 1: Use an 11s sliding window to perform sliding sampling on the cached 300s 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 very likely that a short burst of exercise (with a high proportion of anaerobic output) occurred during this period, then skip this period and re-cache 300s of data.

[0165]

[0166] Wherein, grad represents the heart rate gradient in the sliding window; n represents the length of the sliding window (here is 11), i represents the counting point; hr_edge represents the extreme difference of heart rate in the sliding window; max(hr) and min(hr) represent the maximum and minimum values ​​of the heart rate data in the sliding window, respectively.

[0167] If the heart rate gradient calculated each time is ≤1.5 and the heart rate range is ≤30 when the sliding window slides to the end time of the current cache window, the following judgment condition 2 is performed.

[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 greater than this threshold is an exercise process dominated by anaerobic output, and the data needs to be cached again 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, the average power p_mean and average heart rate hr_mean of the current 300s cached data are calculated.

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

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

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

[0174] According to the ratio of the average heart rate hr_mean in the current cache window to the maximum heart rate and resting heart rate, the limit oxygen uptake is calculated:

[0175]

[0176] Among them, vo2_interval_max is the limit oxygen uptake; a represents the limit oxygen uptake back-calculation gain coefficient, 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 the exercise, by comparing the calculated limit oxygen uptake vo2_interval_max with the historical maximum oxygen uptake vo2_max, the increase or decrease in the value can be used to obtain the increase or decrease in the user's real-time exercise efficiency.

[0179] If the limit oxygen uptake is greater than the historical maximum oxygen uptake, alternative treatment is performed:

[0180]

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

[0182] The exercise efficiency detection method of this embodiment needs to rely on the exercise data of the aerobic output process when estimating the oxygen uptake capacity. However, it is inevitable that anaerobic output will be mixed in the exercise process. In order to collect the exercise period with a high proportion of aerobic output, this application identifies and eliminates the period with a high proportion of anaerobic output through heart rate and power, and avoids using such features when estimating the oxygen uptake capacity, so as to improve the signal-to-noise ratio of the input signal for solving the oxygen uptake capacity. Through the short-delay data, the real-time oxygen uptake is calculated according to the heart rate and power of this period, and combined with the user's physiological information (maximum heart rate, resting heart rate, weight, etc.), the oxygen uptake limit of the user in this physical state is estimated. Using the estimated value of the oxygen uptake limit (limit oxygen uptake) as a measure, the real-time exercise efficiency of the user is output.

[0183] The exercise efficiency detection method of this embodiment needs to distinguish between aerobic and anaerobic outputs during exercise when back-calculating the limit oxygen uptake. The state of aerobic / anaerobic output is recognized based on the user's FTP and heart rate during riding, and the user's real-time exercise efficiency can be estimated without wearing a breathing detection device.

[0184] The exercise efficiency detection method of this embodiment can identify and divide anaerobic exercise output according to heart rate and power without the aid of breathing detection equipment, thereby obtaining a more accurate calculation of the oxygen uptake limit, thereby improving the accuracy of the user's real-time exercise efficiency calculation.

[0185] Embodiment 2

[0186] Based on the design of the exercise efficiency detection method in the first embodiment, the second embodiment proposes an exercise efficiency detection system, including an acquisition module, an aerobic judgment module, an estimation module, an exercise efficiency judgment module, etc. Figure 6 shown.

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

[0188] Aerobic judgment module, used to perform:

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

[0190] S2. Sliding sampling is performed on the heart rate data in the current cache window using a sliding window; each time the sliding window is slid, a heart rate parameter in the sliding window is calculated; the heart rate parameter includes at least one of a heart rate gradient and a heart rate range;

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

[0192] If the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current buffer window, S4 is executed;

[0193] S4, determining whether the local maximum heart rate in the current buffer window is greater than a set threshold;

[0194] If it is greater than the set threshold, the heart rate data and power data are re-cached from the moment when the heart rate data in the current cache window is less than the set threshold; return to S2;

[0195] If it is not greater than the set threshold, it is determined that the exercise in the current cache window is aerobic exercise.

[0196] The estimation module is used to estimate the limit oxygen uptake according to the heart rate data and power data in the current cache window when the exercise in the current cache window is aerobic exercise.

[0197] The exercise efficiency judgment module is used to judge whether the user's exercise efficiency is improved or decreased based on the estimated limit oxygen uptake.

[0198] The exercise efficiency judgment module is specifically used to: after the exercise is finished, compare the maximum value of the estimated multiple limit oxygen uptake 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 the maximum value. If the maximum value is less than the historical maximum oxygen uptake, it is determined that the user's exercise efficiency has decreased.

[0199] The exercise efficiency detection system of this embodiment is installed in a cycle table or a mobile phone, wherein an acquisition module (heart rate / power feature extraction module) receives heart rate data and power data transmitted by a heart rate belt and a power device, and the acquisition module transmits the acquired heart rate data and power data to an aerobic judgment module. The aerobic judgment module (anaerobic noise reduction module) identifies and eliminates anaerobic periods, and the estimation module (real-time exercise efficiency estimation module) is used to estimate the limit oxygen uptake, and the exercise efficiency judgment module (historical exercise efficiency comparison and update module) is used to judge whether the user's exercise efficiency is improved or reduced.

[0200] The specific working process of the motion efficiency detection system has been described in detail in the above-mentioned motion efficiency detection method and will not be repeated here.

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

[0202] Embodiment 3

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

[0204] Memory, 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 the first embodiment and realize the technical effect of the corresponding embodiment.

[0206] Embodiment 4:

[0207] The fourth embodiment of the present invention proposes a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the motion efficiency detection method in the first embodiment is implemented, and the technical effects of the corresponding embodiment are achieved.

[0208] The above-mentioned computer storage medium can be implemented by 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 memory, flash memory, magnetic disk or optical disk. Computer storage medium can be any available storage medium that can be accessed by general or special-purpose computers.

[0209] In some embodiments, a computer storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist in the device as discrete components.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A motion efficiency detection method, characterized in that: include: S1, cache heart rate data and power data; S2, using a sliding window to perform sliding sampling on the heart rate data in the current cache window; Each time the sliding is performed, a heart rate parameter in the sliding window is calculated; the heart rate parameter includes at least one of a heart rate gradient and a heart rate range; S3. If the calculated heart rate parameters do not meet the set aerobic conditions, re-caching the heart rate data and power data from the current end time of the sliding window; Return to S2; If the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current buffer window, S4 is executed; S4, determining whether the local maximum heart rate in the current buffer window is greater than a set threshold; If it is greater than the set threshold, the heart rate data and power data are re-cached from the moment when the heart rate data in the current cache window is less than the set threshold; Return to S2; If it is not greater than the set threshold, it is determined that the exercise in the current cache window is aerobic exercise, and S5 is executed; S5, estimating the limit oxygen uptake according to the heart rate data and power data in the current cache window; S6. Determine whether the user's exercise efficiency is improved or decreased based on the estimated maximum oxygen uptake.

2. The motion efficiency detection method according to claim 1, characterized in that: The estimating the limit oxygen uptake according to the heart rate data and power data in the current cache window specifically includes: Calculate the average power and average heart rate in the current cache window; Calculate the real-time oxygen uptake in the current cache window according to the average power and the user's weight; The limit oxygen uptake in the current cache window is calculated according to the real-time oxygen uptake and the average heart rate.

3. The motion efficiency detection method according to claim 1, characterized in that: The step of judging whether the user's exercise efficiency is improved or decreased according to the estimated limit oxygen uptake specifically includes: After exercise, the maximum of the multiple estimated VO2 max was compared with the historical VO2 max. 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; If the maximum value is less than the historical maximum oxygen uptake, it is determined that the user's exercise efficiency has decreased.

4. The motion efficiency detection method according to claim 1, characterized in that: When the heart rate parameters include heart rate gradient and heart rate extreme difference, If the heart rate gradient is greater than the gradient threshold, or the heart rate extreme difference is greater than the extreme difference threshold, it is determined that the heart rate parameters do not meet the set aerobic conditions; If the heart rate gradient is less than or equal to the gradient threshold, and the heart rate extreme difference is less than or equal to the extreme difference threshold, it is determined that the heart rate parameters meet the set aerobic conditions.

5. The motion efficiency detection method according to claim 1, characterized in that: The heart rate range is the difference between the maximum and minimum values ​​of the heart rate data in the sliding window; The calculation formula of heart rate gradient grad is: Where n represents the length of the sliding window; i represents the counting point; hr i Represents the i-th heart rate data in the sliding window; Represents the average value of the heart rate data within the sliding window; 6. The motion efficiency detection method according to claim 1, characterized in that: Set threshold = k*hr_max; Wherein, k is a constant, 0.5<k<1; hr_max represents the user's maximum heart rate, which is the heart rate limit that the user's heart can reach during exercise.

7. The motion efficiency detection method according to claim 2, characterized in that: The calculation formula for real-time oxygen uptake vo2 is: vo2=(k1·p_mean+k2) / m; Where p_mean represents the average power in the current cache window; m represents the user weight; k1 and k2 are constants greater than 0; The calculation formula for the maximum oxygen uptake vo2_interval_max is: Where a represents the back-calculation gain coefficient of the limit oxygen uptake; hr_max indicates the user's maximum heart rate, which is the heart rate limit that the user's heart can reach during exercise; hr_mean represents the average heart rate in the current cache window; hr_rest indicates the user's resting heart rate.

8. The motion efficiency detection method according to claim 7, characterized in that: k1=12.1397, k2=348.

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9. A motion efficiency detection system, characterized in that: include: An acquisition module is used to acquire heart rate data and power data; Aerobic judgment module, used to perform: S1, cache heart rate data and power data; S2. Sliding sampling is performed on the heart rate data in the current cache window using a sliding window; each time the sliding window is slid, a heart rate parameter in the sliding window is calculated; the heart rate parameter includes at least one of a heart rate gradient and a heart rate range; S3. If the calculated heart rate parameters do not meet the set aerobic conditions, re-caching the heart rate data and power data from the current end time of the sliding window; Return to S2; if the heart rate parameters calculated each time meet the set aerobic conditions when the sliding window slides to the end time of the current cache window, execute S4; S4, determining whether the local maximum heart rate in the current cache window is greater than a set threshold; if greater than the set threshold, re-caching the heart rate data and power data from the moment when the heart rate data in the current cache window is less than the set threshold; Return to S2; if it is not greater than the set threshold, it is determined that the exercise in the current cache window is aerobic exercise; An estimation module, used for estimating the limit oxygen uptake according to the heart rate data and power data in the current cache window when the exercise in the current cache window is aerobic exercise; The exercise efficiency judgment module is used to judge whether the user's exercise efficiency is improved or decreased based on the estimated limit oxygen uptake.

10. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to call the computer program in the memory to execute the method according to any one of claims 1 to 8.

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