A method, system, interactive system and code table for estimating the rate of fat loss

Abstract

This invention discloses a method, system, interactive system, and code table for estimating the rate of fat consumption. The estimation method includes: estimating the rate of fat consumption during exercise. i oxygen uptake rate at any given time vo2 ( i Based on the first i Maximum oxygen uptake percentage at any given time % vo2max and the i The preset relationship between the percentage of maximum oxygen uptake and the contribution of fat to energy supply at a given time is used to estimate the first... i Contribution ratio at any moment fat_oxid ( i ) ； Estimate the fat gain coefficient λ; utilize λ, fat_oxid ( i ), vo2 ( i )and M_body Estimate the first i fat burning rate at any time v_fat ( i This invention takes into account the user's exercise intensity, carbohydrate intake, and exercise duration, which affect fat oxidation. Based on the user's heart rate and / or power data, it estimates the rate of fat consumption, avoiding the need for external respiratory monitoring devices. This satisfies the user's need to obtain information on fat consumption while improving the user's cycling experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sports health, and in particular to a fat consumption estimation method, an estimation system and an estimation interaction system. BACKGROUND

[0002] Fat consumption is an important sports health indicator. Generally, the respiratory exchange ratio during exercise is calculated using a respiratory monitoring device, and the fat consumption during the exercise can be estimated. This method requires an external respiratory monitoring device, which is difficult to implement in outdoor cycling scenarios, and wearing a respiratory monitoring device affects the user's exercise experience to some extent.

[0003] Therefore, there is an urgent need for a method that can obtain the fat consumption of a user during cycling without an external respiratory monitoring device. SUMMARY

[0004] To solve the above technical problems, the present application provides a fat consumption speed estimation method, which considers the user's exercise intensity, carbohydrate intake and exercise duration that affect fat oxidation, estimates the fat consumption speed based on the user's heart rate data and / or power data, avoids external respiratory monitoring devices, meets the user's demand for obtaining fat consumption, and improves the user's cycling experience.

[0005] To achieve the above application purposes, the present application adopts the following technical solutions:

[0006] The present application relates to a fat consumption speed estimation method, comprising:

[0007] During a movement, based on the heart rate data and / or power data of the moving user, the oxygen uptake speed at the first moment during the movement is estimated. i vo2 i

[0008] Based on the maximum oxygen uptake percentage at the first moment and the preset relationship between the maximum oxygen uptake percentage at the first moment and the contribution ratio of fat to energy supply, the contribution ratio at the first moment is estimated. i vo2max i i fat_oxid i ；

[0009] The fat gain coefficient λ is estimated, specifically:

[0010] When no carbohydrates are consumed before exercise and the exercise duration t reaches the first preset period, λ is set to 1; when no carbohydrates are consumed before exercise and the exercise duration t does not reach the first preset period, λ is estimated based on vo2max ​​​​​​​​​The first preset relationship between t and λ is used to estimate λ; when carbohydrates are ingested before exercise and t has not reached the first preset time period, based on % vo2max The second preset relationship with λ is used to estimate λ; when carbohydrates are ingested before exercise and t reaches the first preset time period, based on % vo2max The third presupposed relation to λ is used to estimate λ;

[0011] Using λ, fat_oxid ( i ), vo2 ( i )and M_body Estimate the first i fat burning rate at any time v_fat ( i ).

[0012] In some embodiments of this application, the first step during exercise is estimated based on the heart rate data and / or power data of the exercise user. i oxygen uptake rate at any given time vo2 ( i Specifically:

[0013] Based on user number i Heart rate during cycling hr ( i And the user's physiological information, to estimate the first i oxygen uptake rate at any given time vo2 ( i );or

[0014] Based on user number i Cycling power at any time power ( i User weight M_body and vehicle weight M_bake Estimate the first i oxygen uptake rate at any given time vo2 ( i ).

[0015] In some embodiments of this application, based on user number i Heart rate during cycling at any time hr ( i And the user's physiological information, to estimate the first i oxygen uptake rate at any given time vo2 ( i Specifically:

[0016] ;

[0017] in, α 1 This represents the experimental coefficient for heart rate oxygen uptake gain. hr_maxrepresents a maximum heart rate of the user, the physiological information of the user includes a weight of the user M_body , an age age of the user and a resting heart rate hr_rest .

[0018] In some embodiments of the present application, the maximum heart rate hr_max is a preset fixed value, or;

[0019] The maximum heart rate hr_max is calculated by using the age age of the user, specifically as follows:

[0020] .

[0021] In some embodiments of the present application, based on a riding power i at the first moment t1, a weight of the user power i , a weight of the vehicle M_ body , an oxygen uptake rate M_bake at the first moment t1 is estimated, specifically as follows: i vo2 i

[0022] vo2 ( i )= α 2 ·power ( i) / (M_body + M_bake)+β ;

[0023] Wherein, α 2 represents a power oxygen uptake gain test coefficient, and β represents a basic oxygen consumption correction test coefficient.

[0024] In some embodiments of the present application, based on a maximum oxygen uptake percentage i at the first moment t1 and a preset relationship between the maximum oxygen uptake percentage at the first moment t1 and a contribution ratio of fat in energy supply at the first moment t1, the contribution ratio vo2max at the first moment t1 is estimated, specifically as follows: i i fat_ oxid ( i ), specifically as follows:

[0025] fat_oxid ( i )= k·( % vo2max) l +m ;

[0026] % vo2max = vo2 ( i ) / ​​​​​​vo2max ;

[0027] in, k This is the gain coefficient. l For exponential parameters, m For bias correction, and k , l and m All data were obtained experimentally. vo2max It represents the maximum oxygen uptake rate during a single exercise session.

[0028] In some embodiments of this application, the method for estimating the rate of fat consumption further includes:

[0029] When the time interval Δt between the current movement and the previous consecutive movement does not reach the second preset time interval, the compensation value is used to adjust the... i fat burning rate at any time v_fat ( i The steps for compensation;

[0030] The smaller the difference between the time interval Δt and the second preset time interval, the larger the corresponding compensation value, and the compensation value is greater than 1.

[0031] The fat consumption rate estimation method provided in this application has the following advantages and beneficial effects in some embodiments:

[0032] (1) Fat consumption rate is estimated based on the heart rate data and / or power data of the exercise user, without the need to wear external breathing equipment, thus improving the cycling experience of the exercise user;

[0033] (2) The contribution of fat to energy supply is estimated based on exercise intensity (oxygen uptake is related to exercise intensity, so oxygen uptake can represent exercise intensity), and factors such as exercise intensity, carbohydrate intake and exercise duration that affect fat oxidation are also considered to accurately estimate the real-time fat consumption rate.

[0034] This application also relates to a system for estimating the rate of fat consumption, comprising:

[0035] The oxygen uptake rate estimation module is used to estimate the rate of oxygen uptake during exercise, based on the user's heart rate and / or power data. i oxygen uptake rate at any given time vo2 ( i );

[0036] The contribution ratio estimation module is based on the first i Maximum oxygen uptake percentage at any given time % vo2max and the i A preset relationship between the percentage of maximum oxygen uptake and the contribution of fat to energy supply at a given time is used to estimate the first step during exercise.i Contribution ratio at any moment fat_oxid ( i );

[0037] The fat gain coefficient estimation module is used to estimate the fat gain coefficient λ. Specifically, when no carbohydrates were consumed before exercise and the exercise duration t reaches a first preset time period, λ is set to 1; when no carbohydrates were consumed before exercise and the exercise duration t does not reach the first preset time period, λ is based on %. vo2max The first preset relationship between t and λ is used to estimate λ; when carbohydrates are ingested before exercise and t has not reached the first preset time period, based on % vo2max The second preset relationship with λ is used to estimate λ; when carbohydrates are ingested before exercise and t reaches the first preset time period, based on % vo2max The third presupposed relation to λ is used to estimate λ;

[0038] The fat consumption rate estimation module utilizes λ, fat_oxid ( i ), vo2 ( i )and M_body Estimate the first i fat burning rate at any time v_fat ( i ).

[0039] Some embodiments of this application relate to a code table configured with the fat consumption rate estimation system described above.

[0040] Embodiments of this application also relate to an interactive system for estimating fat consumption rate, comprising:

[0041] The data acquisition unit is used to output the cycling heart rate and / or cycling power of the user during exercise.

[0042] The terminal receives the cycling heart rate and / or cycling power;

[0043] Estimate the rate of fat consumption using the method described above; or

[0044] After estimating the rate of fat burning, the amount of fat burned during exercise is calculated using the rate of fat burning.

[0045] The estimated fat burning rate and / or the amount of fat burned during exercise are displayed at the front end of the terminal.

[0046] Other features and advantages of the present invention will become clearer after reading the detailed embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings described in the following embodiments are some embodiments of the present application, and other drawings can be obtained by those of ordinary skill in the art without creative effort based on these drawings.

[0048] Figure 1 is a flow chart of the fat consumption rate estimation method embodiment proposed in the present application;

[0049] Figure 2 is a block diagram of the fat consumption rate estimation system embodiment proposed in the present application;

[0050] Figure 3 is a hardware block diagram of the fat consumption rate estimation interactive system embodiment proposed in the present application.

[0051] Reference signs:

[0052] 100, data acquisition unit; 110, heart rate acquisition device; 120, power acquisition device; 200, terminal; 300, oxygen uptake rate estimation module; 400, fat gain coefficient estimation module; 500, contribution ratio estimation module; 600, fat consumption rate estimation module. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments.

[0054] Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of protection of the present application. In the description of the present application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0055] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0056] 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 technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0057] To address the issue of needing to wear external respiratory monitoring devices to calculate the respiratory exchange rate during exercise and thus estimate fat consumption, some embodiments of this application involve a method for estimating the rate of fat consumption. This method is based on the exercise user's heart rate and / or power data, and takes into account the exercise intensity, carbohydrate intake, and exercise duration that affect fat oxidation. It eliminates the need for external respiratory monitoring devices, enabling reliable and accurate estimation of the rate of fat consumption and facilitating the measurement of the exercise user's exercise health status.

[0058] In some embodiments of this application, see Figure 1 This involves a method for estimating the rate of fat consumption.

[0059] Figure 1 A flowchart illustrating the method for estimating the rate of fat consumption is shown. Figure 2 A fat consumption rate estimation system is shown. The fat consumption rate estimation method is implemented based on this system, and the method will be described below in conjunction with the fat consumption rate estimation system.

[0060] refer to Figure 1 And combined Figure 2 The method for estimating the rate of fat consumption is described in detail below.

[0061] S1: During a single exercise session, based on the user's heart rate and / or power data, estimate the [number]th [unit / item] of the exercise. i oxygen uptake rate at any given time vo2 ( i ).

[0062] This oxygen uptake rate vo2 ( i The estimation process can be implemented by the oxygen uptake rate estimation module 300 in the fat consumption rate estimation system.

[0063] The exercise intensity has an impact on fat consumption, and the heart rate or the oxygen uptake can represent the exercise intensity of the exercise user, and the heart rate is also related to the oxygen uptake, so the exercise intensity of the user can be obtained by estimating the oxygen uptake rate.

[0064] In some embodiments of the present application, the oxygen uptake rate can be estimated in real time by using real-time heart rate data and / or power data.

[0065] When using heart rate data, it is necessary to combine the maximum heart rate of the user hr_max and the physiological information to estimate by using formula (1).

[0066] The physiological information of the user here includes the weight of the user M_body , the age of the user age, the height of the user, and the resting heart rate hr_rest .

[0067] (1).

[0068] Wherein, α 1 represents the heart rate oxygen uptake gain test coefficient.

[0069] For example, α 1 The value is about 15.3.

[0070] In some embodiments of the present application, the maximum heart rate of the user hr_max can be a fixed value input by the user in advance.

[0071] In some embodiments of the present application, the maximum heart rate hr_max can be calculated by using the following formula (2) by using the user age of the exercise user.

[0072] (2).

[0073] As the maximum heart rate hr_max is calculated by using the age, there may be a case that the ages are similar but the heart rates are quite different, so the maximum heart rate hr_max obtained by using formula (2) is not accurate enough.

[0074] Therefore, in some embodiments of the present application, in the cycling exercise mode, the maximum heart rate hr_max can be estimated based on the historical cycling data sequence with heart rate data and power data, and the specific estimation process is as follows.

[0075] S11: Obtain the power data and heart rate data of the exercise user from the historical cycling data sequence of the exercise user.

[0076] The historical riding data sequence of the sports user can include heart rate data, power data, speed data, etc., so that the historical riding data sequence can be filtered when heart rate data and power data are needed.

[0077] S12: Based on the obtained power data and heart rate data, filter out the power cache sequence and the heart rate cache sequence.

[0078] During the riding process, the heart rate will increase with the increase of the exercise intensity, and when the output power exceeds the power threshold value FTP (Functional Threshold Power, Functional Threshold Power), the heart rate will further increase until the power value reaches Map (Maximal aerobic power, Maximal aerobic power), and the heart rate converges to the maximum level, and the heart rate in this state is approximately equal to the maximum heart rate.

[0079] Because the positive correlation between power and heart rate is relatively strong during the period from moderate-intensity exercise state to heart rate convergence, if the maximum heart rate is to be estimated, the power corresponding to the heart rate needs to be filtered to avoid the problem of poor accuracy of maximum heart rate estimation caused by low-power data introduction.

[0080] Wherein, FTP refers to the average power output by the user during one hour of full effort riding; Map refers to the power value corresponding to the output at the maximum heart rate during exercise.

[0081] In some embodiments of the present application, the filtered power is greater than the product of the first preset value and FTP The product of the first preset value and the power threshold value, which helps to accurately realize the fitting of the following function relationship.

[0082] As described above, the heart rate data and power data in the historical riding data sequence can form a fit file. The specific filtering method is as follows.

[0083] (1) For the fit file, further filtering is performed to extract power data with a size reaching the product of the first preset value and the power threshold value FTP , and simultaneously sampling the heart rate data at the corresponding time.

[0084] Wherein, the first preset value can be preset according to the needs, and as described above, in order to improve the accuracy of the maximum heart rate estimation, the first preset value can be set to be between 50% and 70%.

[0085] In some embodiments of the present application, the first preset value can be selected as 60%.

[0086] That is, the power data with a power value greater than 60%*FTP power data, and heart rate data of the screened power data.

[0087] In some embodiments of the present application, since it is necessary to use FTP , it is necessary for the user to provide FTP in advance. If it cannot be provided, it is necessary to calculate FTP by the calculation method involved in the patent publication CN117688275A FTP .

[0088] The calculation method involved in the patent publication CN117688275A power_batch will not be repeated here, and is incorporated by reference.

[0089] (2) The average power and average heart rate in each window are sampled by sliding window. After sliding through the entire fit file, the average power under each window forms the power buffer sequence heatreat_batch , and the average heart rate under each window forms the heart rate buffer sequence power .

[0090] The calculation method is shown in formula (3) as follows.

[0091] (3).

[0092] In some embodiments of the present application, a sliding window of 30s and a step of 1s are used for average sampling.

[0093] wherein, as in (3) above, i represents the i th movement of the sliding window, k is the count point, representing 1-30 in the sliding window.

[0094] heatreat represents the power data, power_batch(i) represents the heart rate data, heatreat_batch(i) represents the average power of the i th sliding window, power_batch represents the average heart rate of the i th sliding window.

[0095] S13: The function relationship between the maximum heart rate and the maximum power is fitted by using the screened power buffer sequence and heart rate buffer sequence.

[0096] According to the screened power data and heart rate data, it can be seen that there is a strong positive correlation between heart rate and power.

[0097] That is, in the case of larger output power, there will also be a corresponding larger heart rate.

[0098] Therefore, when there are enough data in the power cache sequence and the heart rate cache sequence in S12, the function fitting is performed by using the cached heart rate data and the power data, and the function relationship refers to the linear relationship between the maximum power and the maximum heart rate.

[0099] In some embodiments of the present application, the power data in the power cache sequence and the heart rate data in the heart rate cache sequence as described above are used to calculate fitting parameters in the function relationship based on the least square method power_batch(i) heatreat_batch heatreat_batch(i) grad , so that the maximum heart rate bias = a grad * maximum power + b. bias grad .

[0100] wherein the fitting parameters a and b are obtained by using formula (4) as follows. bias FTP

[0101] (4).

[0102] wherein, n represents the length of the sliding window, n represents the average value of the power cache sequence, represents the average value of the heart rate cache sequence.

[0103] S14: based on the preset model, the user weight, the user height and the power threshold value of the sports user are used to estimate the corresponding maximum aerobic power Map . Map

[0104] As described above, after the power reaches the maximum aerobic power Map , the heart rate converges to the maximum level close to the maximum heart rate, therefore, if the fitting function relationship between the heart rate and the power is obtained, the maximum heart rate can be inversely calculated by using the function relationship. Map

[0105] Therefore, the reliable and accurate estimation of the maximum heart rate is helpful to the reliable and accurate estimation of the maximum heart rate. Map

[0106] Since the maximum heart rate is related to the physiological information of the sports user, including the user height FTP and the user weight M_body , the maximum heart rate of the sports user is established in advance in relation to the user weight h , the user height Map and the physiological information of the sports user. M_body Map h ​​​​​​​​​​​​a preset model among the maximum oxygen power, the power threshold value and the preset model.

[0107] The preset model is obtained by multiple users outputting extreme sports FTP , and the user height M_body , the user weight h and the user body fat percentage Map are fitted into the model.

[0108] The model belongs to an empirical model, as shown in the following formula (5).

[0109] wherein, q1 , q2 and q3 are fitting coefficients, for example, q1 may be 0.7326, q2 may be 44.13, q3 may be 22.035.

[0110] The process of estimating the maximum oxygen power M_body is as follows.

[0111] First, the user height h and the user weight FTP in the physiological information of the sports user are obtained.

[0112] Secondly, it is determined whether there is an early input FTP , if not, the method disclosed in the above-mentioned patent No. CN117688275A is used to calculate M_body .

[0113] After obtaining the user height h and the user weight FTP and Map , according to formula (5), the maximum oxygen power Map can be obtained.

[0114] S15: Based on the function relationship and the maximum oxygen power Map , the corresponding heart rate is obtained as the estimated maximum heart rate.

[0115] As mentioned above, the corresponding heart rate under the maximum oxygen power is considered as the maximum heart rate, therefore, based on the function relationship obtained in S13 and the maximum oxygen power hr_max obtained in S14, the maximum heart rate hr_max is obtained.

[0116] grad = Map * bias + FTP .

[0117] After multiple rides, the fitness data of sports users changed, and the users' physical fitness data changed. FTP It will also change, therefore, based on the new hr_max Reverse calculation of maximum heart rate FTP This can lead to calculation errors.

[0118] Therefore, this application updates the power threshold based on the changes in the physical fitness of the exercise user. hr_max To achieve reliable estimation of maximum heart rate vo2_ .

[0119] In some embodiments of this application, it is necessary to obtain the speed-power ratio of the corresponding cycling motion and estimate oxygen uptake. power FTP right FTP Updated, after the update FTP Used in the next round of S12 to filter the data.

[0120] In some embodiments of this application, the following formula (6) is used for... power Update.

[0121] (6).

[0122] in, hr_max This indicates the power recorded by the user during cycling. hr_power This indicates the maximum heart rate of the exercise user. ，k1 , k2 and k3 All are experimental coefficients. k This is the heart rate-to-oxygen uptake coefficient, a value that varies with the user's exercise capacity. hr_power This represents the average heart rate corresponding to the window of maximum average power obtained using a sliding window method.

[0123] In some embodiments of this application, obtaining hr_power The time frame uses a 30-second sliding window and a 1-second step sliding.

[0124] After calculating the average power for each window, the maximum average power is obtained, and the average heart rate at the time corresponding to the maximum average power is recorded as [the value of the average heart rate]. vo2 .

[0125] In some embodiments of this application, for example, k1 It can be 12.24. k2 It can be 350, and k3 It can be 15.024.

[0126] When testing data from different sports users, the experimental coefficients... k Differences will also occur.

[0127] When calculating the oxygen uptake rate using power data M_body ( i ), the user's body weight bake and the vehicle weight M_ vo2 need to be combined to estimate using formula (7).

[0128] ·power ( i )= α 2 i) / (M_body ( M_bake)+β + power (7)。

[0129] wherein, vo2max ( i ) represents the power at the i-th moment, α 2 represents the power oxygen uptake gain test coefficient, and β represents the basal oxygen consumption correction test coefficient, α 2 Both α and β are test coefficients, for example, in the present application, α2 is between 1.5 and 2.3, and β is about 7 ml / kg / min.

[0130] S2: estimating the contribution ratio i ( fat_oxid ) at the i-th moment based on the maximum oxygen uptake percentage i at the i-th moment and the preset relationship between the maximum oxygen uptake percentage and the contribution ratio of fat to energy supply at the i-th moment. i fat_oxid This estimation process of the contribution ratio i ( ) can be realized by the contribution ratio estimation module 500 in the fat consumption rate estimation system.

[0131] vo2max In some embodiments of the present application, the oxygen uptake rate is first estimated to estimate the real-time energy consumption, and then the contribution ratio of fat to energy supply is calculated to indirectly calculate the fat consumption rate. i During aerobic exercise, the contribution ratio of fat to energy supply will first increase and then decrease with the exercise intensity, and the maximum contribution ratio can reach about 50%.

[0132] In some embodiments of the present application, the real-time maximum oxygen uptake percentage

[0133] and the contribution ratio of fat to energy supply are pre-functionally fitted to obtain the preset relationship between the two, see formula (8).

[0134] fat_oxid (

[0135] vo2max) ( i ​​)= k·( % fat_ l +m (8).

[0136] in, k This is the gain coefficient. l For exponential parameters, m For bias correction, and k , l and m All data were obtained experimentally. oxid vo2max ( i ) indicates the first i The proportion of fat in energy supply at any given time.

[0137] For example, k The value is around -1.05. l The value is around 3.01. m The value ranges from 0.55 to 0.65.

[0138] Therefore, in obtaining the real-time maximum oxygen uptake percentage (%) vo2max After establishing the above-mentioned preset relationship, the real-time contribution ratio of fat to energy supply can be obtained.

[0139] The above-mentioned maximum oxygen uptake percentage vo2 oxygen uptake rate vo2max ( i ) and maximum oxygen uptake rate vo2max The ratio.

[0140] In some embodiments of this application, there is a fixed maximum oxygen uptake rate during a single exercise session. vo2max The maximum oxygen uptake rate can be manually entered by the user.

[0141] S3: Estimate the fat gain coefficient λ.

[0142] The estimation process of this fat gain coefficient λ can be achieved by the fat gain coefficient estimation module 400 in the fat consumption rate estimation system.

[0143] Exercise intensity, carbohydrate intake, and duration of exercise all influence fat oxidation. Carbohydrate intake before exercise inhibits fat oxidation during low- to moderate-intensity exercise, reducing the fat breakdown rate by 40% to 50%, and this pre-exercise carbohydrate intake affects the fat oxidation rate for at least 6 hours. Carbohydrate intake 30 minutes after exercise begins reduces the fat oxidation rate by only about 20%. However, carbohydrates have little effect on fat breakdown during high-intensity exercise.

[0144] Generally, the fat oxidation rate will increase significantly after the exercise duration of 30-40 minutes (before which glycogen plays a dominant role in energy supply, accounting for more than 80%, while fat accounts for only 15-20%), and the absolute consumption rate of fat reaches the maximum when the exercise intensity is about 50% of the maximum oxygen uptake (corresponding to about 70% of the maximum heart rate).

[0145] Therefore, by considering the exercise intensity, carbohydrate intake, and exercise duration when estimating the fat gain coefficient λ, the contribution of fat to energy supply can be corrected to obtain an accurate fat consumption.

[0146] (1) When no carbohydrates are consumed before exercise and the exercise duration t reaches the first preset period, λ is set to 1.

[0147] In some embodiments of the present application, the first preset period can be set according to different users, for example, 30 min.

[0148] (2) When no carbohydrates are consumed before exercise and the exercise duration t does not reach the first preset period, λ is estimated based on the first preset relationship between and t and λ. λ=α

[0149] In some embodiments of the present application, the first preset relationship is shown in equation (9).

[0150] ·%vo2max 3 γ + β 3 ·t + γ 3 (9).

[0151] α 3 is the first exercise intensity gain coefficient, β 3 is the exercise time gain coefficient, vo2max 3 represents the bias gain, and these coefficients can be obtained through experiments and are fixed coefficients when used.

[0152] (3) When carbohydrates are consumed before exercise and t does not reach the first preset period, λ is estimated based on the second preset relationship between and λ. λ=α

[0153] In some embodiments of the present application, the second preset relationship is shown in equation (10).

[0154] ·%vo2max 4 vo2max (10). ​​

[0155] α 4 The second exercise intensity gain coefficient can be obtained through experiments and is a fixed coefficient in use.

[0156] (4) When the carbohydrate is taken before exercise and t reaches the first preset period, λ is estimated based on a third preset relationship between % and λ. λ=

[0157] In some embodiments of the present application, the third preset relationship refers to formula (11).

[0158] ·%vo2max 0.8+ α 5 fat_oxid (11).

[0159] α 5 The third exercise intensity gain coefficient can be obtained through experiments and is a fixed coefficient in use.

[0160] S4: using λ, vo2 ( i ), M_body ( i ) and v_fat , estimate the fat consumption rate i ( v_fat ) at the moment t. i

[0161] The estimation process of the fat consumption rate fat_oxid ( i ) can be realized by the fat consumption rate estimation module 600 in the fat consumption rate estimation system.

[0162] The real-time energy consumption can be estimated using the real-time oxygen uptake rate, the cumulative real-time energy consumption in the exercise time can be obtained, and the total energy consumption can be obtained. The contribution of fat consumption to the real-time energy consumption, i.e. the fat consumption rate, can be estimated using v_fat ( i ) and λ.

[0163] Therefore, the fat consumption rate v_fat ( i ) can be estimated using the following formula (12).

[0164] λ·fat_oxid ( i )= ·vo2 ( i ) ·M_body / 3.5 / 3600 ( i ) vo2 (12).

[0165] ​​Since 1 metabolic equivalent corresponds to the consumption of 3.5 ml of oxygen per minute per kg of body weight, the energy consumption vo2 ( i ) can be estimated based on the oxygen uptake velocity e ( i ).

[0166] That is, the energy consumption e ( i ) at the i-th moment is: e ( i )= ·M_body / 3.5 / 3600 ( i ) v_fat .

[0167] During the exercise period of one exercise, the fat consumption rate fat_ ( i ) is accumulated, and the fat consumption amount in the current exercise process can be obtained.

[0168] In some embodiments of the present application, if the time interval △t of the adjacent two exercises does not reach the second preset time period, the fat consumption rate does not need to be slowly increased from low to high, which is equivalent to that the current exercise is a continuation of the last exercise, and the fat consumption rate needs to be compensated.

[0169] The second preset time period can be set according to actual conditions; the intake of carbohydrates before exercise will affect the fat oxidation rate for at least 6 hours, so for example, the second preset time period can be selected as 6 hours.

[0170] Based on the time interval △t of the two consecutive exercises, a compensation value can be set to compensate the contribution ratio oxid v_fat ( i ).

[0171] The compensation value can be obtained according to a preset compensation function, or can be set according to the time interval △t.

[0172] For example, when the second preset time period is selected as 6 hours, if the time interval △t is greater than or equal to 6 hours, it is considered that the current exercise is a new exercise, that is, the fat consumption rate is slowly increased from low to high; if the time interval △t is less than 6 hours, it is considered that the current exercise is a continuation of the last exercise, that is, the fat consumption rate does not need to be slowly increased from low to high.

[0173] When the difference between the time interval △t and the second preset time period is greater than or equal to zero, the compensation value is 1, and when it is less than zero, the compensation value is greater than 1, and the smaller the difference, the greater the corresponding compensation value.

[0174] A compensation function corresponding to the compensation value can be setf (△t-6), the function taking the difference between △t and the second preset time period as a variable, when the variable is in a positive interval, the compensation function f (△t-6) is 1, when the variable is in a negative interval, the compensation function f (△t-6) is a decreasing function greater than 1.

[0175] That is, ​ ( i )= f (△t-6) • λ • fat_oxid ( i ) • vo2 ( i ) • M_body / 3.5 / 3600 (13).

[0176] In some embodiments of the present application, referring to Figure 3 , also relates to a fat consumption rate estimation interactive system.

[0177] The fat consumption rate estimation interactive system comprises a data acquisition unit 100 and a terminal 200.

[0178] The data acquisition unit 100 is configured to output the cycling heart rate and / or cycling power of the sports user during the sports process.

[0179] The terminal 200 can be a smart device (such as a mobile phone, a smart watch, a pad) communicatively connected with the data acquisition unit 100.

[0180] The fat consumption rate estimation method as described above is integrated in the terminal 200, and is configured to perform fat consumption rate estimation.

[0181] The data acquisition unit 100 can be a heart rate acquisition device 110 (such as a heart rate belt), configured to acquire the heart rate data of the sports user and send the heart rate data to the terminal 200.

[0182] When the heart rate data is available, the terminal 200 utilizes the heart rate data to estimate the oxygen uptake rate when performing the fat consumption rate estimation method.

[0183] The data acquisition unit 100 can be a power acquisition device 120 (such as a power meter), configured to acquire the power data of the sports user and send the power data to the terminal 200.

[0184] When the power data is available, the terminal 200 utilizes the power data to estimate the oxygen uptake rate when performing the fat consumption rate estimation method.

[0185] The data acquisition unit 100 can comprise the heart rate acquisition device 110 (such as a heart rate belt) and the power acquisition device 120 (such as a power meter).

[0186] The terminal 200 can estimate the oxygen uptake rate using the power data or estimate the oxygen uptake rate using the heart rate data when performing the fat consumption rate estimation method.

[0187] The fat consumption rate or the fat consumption amount estimated by the terminal 200 can be displayed on the front end of the terminal 200 for the user to view.

[0188] The fat consumption rate is the fat consumption amount per second, so the cumulative fat consumption rate is calculated to obtain the fat consumption amount during the exercise.

[0189] In some embodiments of the present application, a code table (not shown) is also involved, which can record the heart rate data and / or power data of the cycling user, and is internally provided with the fat consumption rate estimation system as described above, so as to display the fat consumption rate or and / or the fat consumption amount on the code table during the cycling exercise of the user, for the user to view conveniently.

[0190] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, the technical solutions recorded in the foregoing embodiments can still be modified by those of ordinary skill in the art, or some technical features thereof can be replaced equivalently; and such modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions claimed by the present application.

Claims

1. A method for estimating the rate of fat consumption, characterized in that, include: During a single exercise session, based on the user's heart rate and / or power data, estimate the [number]th [unit / item] of the exercise. i oxygen uptake rate at any given time vo2 ( i ); Based on the i Maximum oxygen uptake percentage at any given time % vo2max use fat oxidase ( i )= k·( % vo2max) l +m Estimate the first i The proportion of fat in energy supply at any given time fat oxidase ( i ),in,% vo2max = vo2 ( i ) / vo2max , k This is the gain coefficient. l For exponential parameters, m For bias correction, and k , l and m All data were obtained experimentally. vo2max Indicates the maximum oxygen uptake rate during a single exercise session; The fat gain coefficient λ is estimated as follows: When no carbohydrates were consumed before exercise and the exercise duration t reached the first preset time period, λ was set to 1; when no carbohydrates were consumed before exercise and the exercise duration t did not reach the first preset time period, the formula was used. λ=α 3 %vo2max + β 3 ·t + γ 3 Estimate λ; when carbohydrates are ingested before exercise and t has not reached the first preset time period, use... λ=α 4 %vo2max Estimate λ; when carbohydrates are ingested before exercise and t reaches the first preset time period, use... λ= 0.8+ α 5 %vo2max Estimate λ; Based on λ, fat oxidase ( i ), vo2 ( i )and M_body ,use v_fat ( i )= λ·fat_oxid ( i ) ·vo2 ( i ) ·M_ body / 3.5 / 3600 Estimate the first i fat burning rate at any time v_fat ( i ); in, α 3 The first motion intensity gain coefficient, β 3 This is the motion time gain coefficient. γ 3 Indicates the bias gain. α 4 This is the second motion intensity gain coefficient. α 5 This is the third motion intensity gain coefficient.

2. The method for estimating fat consumption rate according to claim 1, characterized in that, Based on the exercise user's heart rate and / or power data, estimate the first [unit / phase] during exercise. i oxygen uptake rate at any given time vo2 ( i Specifically: Based on user number i Heart rate data at any time hr ( i And the user's physiological information, to estimate the first i oxygen uptake rate at any given time vo2 ( i ); or Based on user number i Power data at time power ( i User weight M_body and vehicle weight M_bake Estimate the first i oxygen uptake rate at any given time vo2 ( i ).

3. The method for estimating fat consumption rate according to claim 2, characterized in that, Based on user number i Heart rate data at any time hr ( i And the user's physiological information, to estimate the first i oxygen uptake rate at any given time vo2 ( i Specifically: ； in, α 1 This represents the experimental coefficient for heart rate oxygen uptake gain. hr_max This indicates the user's maximum heart rate; the user's physiological information includes the user's weight. M_body User age and resting heart rate hr_rest .

4. The method for estimating fat consumption rate according to claim 3, characterized in that, Maximum heart rate hr_max For a preset fixed value, or; Maximum heart rate hr_max The calculation is performed using the user's age, specifically:

5. The method for estimating fat consumption rate according to claim 2, characterized in that, Based on user number i Power data at time power ( i) User weight M_body and vehicle weight M_bake Estimate the first i oxygen uptake rate at any given time vo2 ( i Specifically: vo2 ( i )= α 2 ·power ( i) / (M_body + M_bake)+β ； in, α 2 β represents the experimental coefficient for power oxygen uptake gain, and β represents the experimental coefficient for correction of baseline oxygen uptake consumption.

6. The method for estimating fat consumption rate according to claim 1, characterized in that, The method for estimating the rate of fat consumption also includes: When the time interval Δt between the current movement and the previous consecutive movement does not reach the second preset time interval, the compensation value is used to adjust the... i fat burning rate at any time v_fat ( i The steps for compensation; When the difference between the time interval Δt and the second preset time interval is greater than or equal to zero, the compensation value is 1. When the difference between the time interval Δt and the second preset time interval is less than zero, the compensation value is greater than 1, and the smaller the difference, the greater the corresponding compensation value.

7. A system for estimating the rate of fat consumption, characterized in that, include: The oxygen uptake rate estimation module is used to estimate the rate of oxygen uptake during exercise, based on the user's heart rate and / or power data. i oxygen uptake rate at any given time vo2 ( i ); The contribution ratio estimation module is based on the first i Maximum oxygen uptake percentage at any given time % vo2max ,use fat oxidase ( i )= k· ( % vo2max) l +m Estimate the first step during the motion. i The proportion of fat in energy supply at any given time fat oxidase ( i Estimate, of which, % vo2max = vo2 ( i ) / vo2max , k This is the gain coefficient. l For exponential parameters, m For bias correction, and k , l and m All data were obtained experimentally. vo2max Indicates the maximum oxygen uptake rate during a single exercise session; The fat gain coefficient estimation module is used to estimate the fat gain coefficient λ. Specifically, when no carbohydrates were consumed before exercise and the exercise duration t reaches a first preset time period, λ is set to 1; when no carbohydrates were consumed before exercise and the exercise duration t does not reach the first preset time period, the formula is used. λ=α 3 %vo2max + β 3 ·t + γ 3 Estimate λ; when carbohydrates are ingested before exercise and t has not reached the first preset time period, use... λ=α 4 %vo2max Estimate λ; when carbohydrates are ingested before exercise and t reaches the first preset time period, use... λ= 0.8+ α 5 %vo2max Estimate λ; The fat consumption rate estimation module is based on λ. fat oxidase ( i ), vo2 ( i )and M_body ,use v_fat ( i )= λ· fat oxidase ( i ) ·vo2 ( i ) ·M_body / 3.5 / 3600 Estimate the first i fat burning rate at any time v_fat ( i ); in, α 3 The first motion intensity gain coefficient, β 3 This is the motion time gain coefficient. γ 3 Indicates the bias gain. α 4 This is the second motion intensity gain coefficient. α 5 This is the third motion intensity gain coefficient.

8. An interactive system for estimating fat consumption rate, characterized in that, include: The data acquisition unit is used to output the heart rate data and / or power data of the exercise user during exercise; The terminal receives the heart rate data and / or power data; The fat consumption rate is estimated by performing the fat consumption rate estimation method according to any one of claims 1 to 6; or After estimating the rate of fat burning, the amount of fat burned during exercise is calculated using the rate of fat burning. The estimated fat burning rate and / or the amount of fat burned during exercise are displayed at the front end of the terminal.

9. A code table, characterized in that, The code table is equipped with a fat consumption rate estimation system as described in claim 7.

Images