Fat consumption speed estimation method and system, interaction system and code table

Abstract

The invention discloses a fat consumption speed estimation method and system, an interaction system and a code table. The estimation method comprises the steps that the oxygen uptake speed vo2 (i) at the ith moment in the movement process is estimated; on the basis of a preset relationship between the maximum oxygen uptake percentage% vo2max at the ith moment and the maximum oxygen uptake percentage at the ith moment and the contribution ratio of fat in energy supply, estimating the contribution ratio faixid (i) at the ith moment; estimating a fat gain coefficient lambda; and estimating the fat consumption speed vfat (i) at the ith moment by using the lambda, the fat (i), the vo2 (i) and the Mbody. The exercise intensity, the carbon water intake and the exercise duration of the user influencing fat oxidation are considered, the fat consumption speed is estimated based on the heart rate data and / or the power data of the user, external connection of respiration monitoring equipment is avoided, and the riding experience of the user is improved while the requirement for obtaining fat consumption of the user is met.

Description

Technical Field

[0001] The present invention relates to the field of sports health technology, and in particular to a fat consumption estimation method, an estimation system and an estimation interaction system. Background Art

[0002] Fat consumption is an important sports health indicator. Generally, the fat consumption during exercise can be estimated by using a respiratory monitoring device to calculate the respiratory exchange rate during exercise. 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 a certain extent.

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

[0004] In order to solve the above technical problems, the present invention provides a fat consumption rate estimation method, which takes into account the user's exercise intensity, carbohydrate intake and exercise duration that affect fat oxidation, and estimates the fat consumption rate based on the user's heart rate data and / or power data, avoiding the need for external respiratory monitoring equipment, thereby meeting the user's need to obtain fat consumption and improving the user's cycling experience.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: The present application relates to a method for estimating fat consumption rate, comprising: During an exercise, based on the user's heart rate data and / or power data, the user can estimate the i Oxygen uptake rate at the moment VO2 ( i ); Based on i Maximum oxygen uptake percentage at that moment VO2max and i The preset relationship between the maximum oxygen uptake percentage and the contribution of fat in energy supply at the moment is used to estimate the i Contribution ratio of time Fat Oxid ( i ) ； Estimate the fat gain coefficient λ, specifically: 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, based on % VO2max The first preset relationship between t and λ is used to estimate λ; when carbon water is consumed before exercise and t does not reach the first preset period, based on % VO2maxand the second preset relationship of λ, estimate λ; when carbon water is consumed before exercise and t reaches the first preset period, based on % VO2max A third preset relationship with λ, estimating λ; Using λ, Fat Oxid ( i ), VO2 ( i )and M Body , estimated i Fat burning rate at a time V Fat ( i ).

[0006] In some embodiments of the present application, based on the heart rate data and / or power data of the exercise user, the first i Oxygen uptake rate at the moment VO2 ( i ), specifically: Based on user i Cycling heart rate at the moment hr ( i ) and the user’s physiological information to estimate the i Oxygen uptake rate at the moment VO2 ( i );or Based on user i Cycling power at the moment Power ( i ), user weight M Body Vehicle weight M Bake , estimated i Oxygen uptake rate at the moment VO2 ( i ).

[0007] In some embodiments of the present application, based on the user's i Cycling heart rate at the moment hr ( i ) and the user’s physiological information to estimate the i Oxygen uptake rate at the moment VO2 ( i ), specifically: ; in, α 1 represents the heart rate oxygen uptake gain experimental coefficient, HR Max 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 .

[0008] In some embodiments of the present application, the maximum heart rate HR Max is a preset fixed value, or; Maximum heart rate HR Max The user's age is used for calculation, specifically: .

[0009] In some embodiments of the present application, based on the user's i Cycling power at the moment Power ( i ), user weight M_ Body Vehicle weight M Bake , estimated i Oxygen uptake rate at the moment VO2 ( i ), specifically: VO2 ( i )= α 2 ·Power ( i) / (M Body + M Bake)+β ; in, α 2 represents the power oxygen uptake gain experimental coefficient, and β represents the basic oxygen uptake consumption correction experimental coefficient.

[0010] In some embodiments of the present application, based on i Maximum oxygen uptake percentage at that moment VO2max and i The preset relationship between the maximum oxygen uptake percentage and the contribution of fat in energy supply at the moment is used to estimate the i Contribution ratio of time Fat_ Oxid ( i ), specifically: Fat Oxid ( i )= k·( % VO2max) l +m ; % VO2max = VO2 ( i ) / VO2max ; in, k is the gain coefficient, l is the exponential parameter, m is the bias correction, and k , l and m All are obtained from experiments. VO2max It indicates the maximum oxygen uptake rate during an exercise.

[0011] In some embodiments of the present application, the fat consumption rate estimation method further includes: When the time interval Δt between the current movement and the last continuous movement does not reach the second preset time period, the compensation value is used to adjust the first i Fat burning rate at a time V Fat ( i ) steps to take to make compensation; The smaller the difference between the time interval Δt and the second preset time period, the larger the corresponding compensation value, and the compensation value is greater than 1.

[0012] The fat consumption rate estimation method provided in some embodiments of the present application has the following advantages and beneficial effects: (1) Estimating the fat consumption rate based on the user's heart rate data and / or power data, without the need to wear external respiratory equipment, thus improving the user's cycling experience; (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). Factors that affect fat oxidation, such as exercise intensity, carbohydrate intake, and exercise duration, are also taken into account to accurately estimate the real-time fat consumption rate.

[0013] The present application also relates to a fat consumption rate estimation system, comprising: The oxygen uptake rate estimation module is used to estimate the oxygen uptake rate of the first exercise process based on the heart rate data and / or power data of the exercise user during an exercise process. i Oxygen uptake rate at the moment VO2 ( i ); Contribution ratio estimation module, which is based on the i Maximum oxygen uptake percentage at that moment VO2max and i The preset relationship between the maximum oxygen uptake percentage and the contribution of fat in energy supply at the moment is used to estimate the first i Contribution ratio of time Fat Oxid ( i ); The fat gain coefficient estimation module is used to estimate the fat gain coefficient λ, specifically: 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, based on % VO2max and t and the first preset relationship between λ, estimate λ; when carbon water is consumed before exercise and t does not reach the first preset period, based on % VO2max and the second preset relationship of λ, estimate λ; when carbon water is consumed before exercise and t reaches the first preset period, based on % VO2max A third preset relationship with λ, estimating λ; The fat consumption rate estimation module uses λ, Fat Oxid ( i ), VO2 ( i )and M Body , estimated i Fat burning rate at a time V Fat ( i ).

[0014] Some embodiments of the present application relate to a cycling computer, in which the fat consumption rate estimation system as described above is configured.

[0015] The embodiment of the present application also relates to a fat consumption rate estimation interactive system, comprising: A data collection unit, which is used to output the cycling heart rate and / or cycling power of the exercise user during exercise; A terminal, which receives the cycling heart rate and / or cycling power; Execute the fat consumption rate estimation method as described above to estimate the fat consumption rate; or After estimating the fat consumption rate, use the fat consumption rate to calculate the amount of fat consumed during exercise; The estimated fat consumption speed and / or the amount of fat consumed during exercise is displayed on the front end of the terminal.

[0016] After reading the specific embodiments of the present invention in conjunction with the accompanying drawings, other features and advantages of the present invention will become more clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a flow chart of an embodiment of the fat consumption rate estimation method proposed in the present application; Figure 2 is a block diagram of an embodiment of a fat consumption rate estimation system proposed in the present application; Figure 3 It is a hardware block diagram of an embodiment of the fat consumption rate estimation interactive system proposed in this application.

[0019] Reference numerals: 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

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0022] In the description of the present invention, 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. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0023] 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 the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0024] In order to solve the problem of needing to wear an external respiratory monitoring device to calculate the respiratory exchange rate during exercise and then estimate the fat consumption, some embodiments of the present application involve a fat consumption rate estimation method, which is based on the heart rate data and / or power data of the exercise user, and takes into account the exercise intensity, carbohydrate intake and exercise duration that affect fat oxidation. There is no need to wear an external respiratory monitoring device, so that a reliable and accurate estimation of the fat consumption rate can be achieved, which facilitates the measurement of the exercise health status of the exercise user.

[0025] In some embodiments of the present application, see Figure 1 , relates to a method for estimating fat consumption rate.

[0026] Figure 1 A flow chart showing a method for estimating fat consumption rate, Figure 2 The fat consumption rate estimation system is shown. The fat consumption rate estimation method is implemented based on the fat consumption rate estimation system. As follows, the fat consumption rate estimation method will be described in conjunction with the fat consumption rate estimation system.

[0027] refer to Figure 1 And combined Figure 2 , the fat consumption rate estimation method is described in detail as follows.

[0028] S1: During an exercise, based on the user's heart rate data and / or power data, estimate the i Oxygen uptake rate at the moment VO2 ( i ).

[0029] This oxygen uptake rate VO2 ( i ) can be achieved by the oxygen uptake rate estimation module 300 in the fat consumption rate estimation system.

[0030] Exercise intensity affects fat consumption. Heart rate or oxygen uptake can indicate the exercise intensity of the user. Heart rate is also related to oxygen uptake. Therefore, the user's exercise intensity can be obtained by estimating the oxygen uptake speed.

[0031] In some embodiments of the present application, real-time heart rate data and / or power data may be used to estimate oxygen uptake rate in real time.

[0032] When using heart rate data, it is necessary to combine the user's maximum heart rate HR Max and physiological information, and is estimated using formula (1).

[0033] The user's physiological information here includes the user's weight M Body , user age, user height and resting heart rate HR Rest .

[0034] (1).

[0035] in, α 1 Represents the heart rate oxygen uptake gain experimental coefficient.

[0036] For example, α 1 The value is around 15.3.

[0037] In some embodiments of the present application, the user's maximum heart rate HR Max A fixed value that can be entered in advance by the user.

[0038] In some embodiments of the present application, the maximum heart rate HR Max The user age of the sports user can be estimated using the following formula (2).

[0039] (2).

[0040] Maximum heart rate as above HR Max The calculation of depends on age. Therefore, people of similar age may have large differences in heart rate. In this case, the maximum heart rate obtained by formula (2) is HR Max It is not accurate enough.

[0041] Therefore, in some embodiments of the present application, in the cycling sports mode, the maximum heart rate can be estimated based on the historical cycling data sequence with heart rate data and power data. HR Max The specific estimation process is as follows.

[0042] S11: Acquire power data and heart rate data of the exercise user from a historical cycling data sequence of the exercise user.

[0043] The historical cycling data sequence of a sports user may include heart rate data, power data, speed data, etc. Therefore, when heart rate data and power data are needed, the historical cycling data sequence may be filtered.

[0044] S12: Filter out a power cache sequence and a heart rate cache sequence based on the acquired power data and heart rate data.

[0045] During cycling, the heart rate will increase as the intensity of exercise increases. When the output power exceeds the power threshold, FTP (Functional Threshold Power), the heart rate will increase further until the power value reaches Map (Maximal aerobic power), the heart rate will converge to the maximum level, and the heart rate in this state is close to the maximum heart rate.

[0046] Since the positive correlation between power and heart rate is relatively strong during the period from moderate-intensity exercise to heart rate convergence, if you want to estimate the maximum heart rate, the power corresponding to the heart rate needs to be screened to avoid the problem of poor accuracy in maximum heart rate estimation caused by the introduction of power data at low power.

[0047] in, FTP Refers to the average power output of a user riding at full effort for one hour;Map Refers to the power output value corresponding to the maximum heart rate during exercise.

[0048] In some embodiments of the present application, the screened power is limited to a value greater than a first preset value and FTP The product of can be used to filter out lower power, which helps to accurately fit the following functional relationship.

[0049] As mentioned above, the heart rate data and power data in the historical cycling data sequence can form a fit file. The specific filtering method is as follows.

[0050] (1) For the fit file, further screening is performed to filter out the files whose extraction size reaches the first preset value and power threshold FTP The power data of the product of the two is collected, and the heart rate data at the corresponding moment is sampled synchronously.

[0051] The first preset value may be preset as needed. As described above, in order to improve the accuracy of the maximum heart rate estimation, the first preset value may be set between 50% and 70%.

[0052] In some embodiments of the present application, the first preset value may be selected to be 60%.

[0053] That is, filter the power data to have a power value greater than 60%* FTP power data, and heart rate data of the filtered power data.

[0054] In some embodiments of the present application, due to the need to use FTP Therefore, users are required to provide FTP If it cannot be provided, it is necessary to go through the patent publication number CN117688275A involved FTP Calculation method solution FTP .

[0055] CN117688275A involves FTP The calculation method is not described here in detail and is incorporated into this article by reference.

[0056] (2) Using a sliding window method, the average power and average heart rate in each window are sampled. After the entire fit file is slid, the average power under each window forms the power cache sequence described above. Power Batch , and the average heart rate under each window forms the heart rate cache sequence Heatreat Batch .

[0057] The calculation method is shown in the following formula (3).

[0058] (3).

[0059] In some embodiments of the present application, a 30 s sliding window and a 1 s step are used for average sampling.

[0060] Among them, as in (3) above, i The sliding window i Move, k is the counting point, representing 1 to 30 in the sliding window.

[0061] Power Indicates power data, Heatreat Indicates heart rate data. Power Batch(i) represents the average power of the i-th sliding window, Heatreat Batch(i) represents the average heart rate of the i-th sliding window.

[0062] S13: using the screened power buffer sequence and heart rate buffer sequence to fit the functional relationship between the maximum heart rate and the maximum power.

[0063] According to the filtered power data and heart rate data, it can be seen that heart rate has a strong positive correlation with power.

[0064] That is, when the output power is higher, there will also be a correspondingly higher heart rate.

[0065] Therefore, when there are enough data in the power cache sequence and the heart rate cache sequence in S12, the cached heart rate data and power data are used to perform function fitting, and this functional relationship refers to the linear relationship between the maximum power and the maximum heart rate.

[0066] In some embodiments of the present application, the power buffer sequence described above is used Power Batch Power data in Power Batch(i) and heart rate buffer sequence Heatreat Batch Heart rate data in Heatreat Batch(i) , based on the least squares method, the fitting parameters used to fit the functional relationship are calculated Grad and Bias , so that the maximum heart rate = Grad *Maximum Power+ Bias .

[0067] Among them, the fitting parameters Grad and Bias It is obtained using the following formula (4).

[0068] (4).

[0069] in, n represents the length of the sliding window, represents the average value of the power buffer sequence, Represents the average value of the heart rate buffer sequence.

[0070] S14: Based on the preset model, using the user weight, user height and power threshold of the sports user FTP , estimate the corresponding maximum aerobic power Map .

[0071] As mentioned above, the power reaches the maximum aerobic power Map After that, the heart rate will converge to the maximum level close to the maximum heart rate. Therefore, if the fitting function relationship between heart rate and power is known, Map A more accurate estimate can be made, and then the functional relationship can be used to reversely calculate the maximum heart rate.

[0072] Therefore, it is possible to reliably and accurately estimate Map , which helps to estimate your maximum heart rate reliably and accurately.

[0073] because Map For sports users FTP and physiological information (including user height h and user weight M Body ) is related, therefore, the sports user will be established in advance Map and its user's weight M Body , User height h and power threshold.

[0074] The preset model is obtained by multiple users outputting extreme sports. Map ,and FTP , User height h and user weight M Body The fitted model is an empirical model, as shown in the following formula (5).

[0075] (5).

[0076] in, q1 , q2 and q3 are all fitting coefficients, for example, q1 can be 0.7326, q2 It can be 44.13, q3 It can be 22.035.

[0077] The estimated maximum aerobic power is given by Map process.

[0078] First, obtain the user's height from the user's physiological information h and user weight M Body .

[0079] Secondly, determine whether there is any advance input FTPIf not, use the method described in Patent Publication No. CN117688275A as described above to FTP Perform calculations.

[0080] After that, after getting the user's height h and user weight M Body and FTP Then, according to formula (5), we can get Map .

[0081] S15: Based on functional relationship and maximum aerobic power Map , obtain the corresponding heart rate as the estimated maximum heart rate.

[0082] As described above, the heart rate corresponding to the maximum aerobic power is considered to be the maximum heart rate. Therefore, based on the functional relationship obtained in S13 and the function obtained in S14, Map , get the maximum heart rate HR Max .

[0083] HR Max = Grad * Map + Bias .

[0084] After the sports user rides multiple times, the physical data changes, and the user's FTP will also change, so based on the new FTP Back-calculated maximum heart rate HR Max This will lead to calculation errors.

[0085] Therefore, the present application updates the power threshold based on the physical fitness changes of the sports user. FTP , enabling reliable estimation of maximum heart rate HR Max .

[0086] In some embodiments of the present application, it is necessary to obtain the speed power of the corresponding riding, and to estimate the oxygen uptake VO2_ Power right FTP Update, after update FTP Used to filter the data in the next round of S12.

[0087] In some embodiments of the present application, the following formula (6) is used to FTP to update.

[0088] (6).

[0089] in, Power Indicates the power recorded during the user's cycling process. HR Max Indicates the maximum heart rate of the user ，k1 , k2 andk3 are experimental coefficients, k This is the heart rate to oxygen conversion coefficient. This value will change with the user's exercise capacity. HR Power is the average heart rate corresponding to the window of maximum average power obtained by sliding window.

[0090] In some embodiments of the present application, obtaining HR Power A 30s sliding window and a 1s step sliding are used.

[0091] After calculating the average power of 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 HR Power .

[0092] In some embodiments of the present application, for example, k1 It can be 12.24, k2 can be 350, and k3 It can be 15.024.

[0093] When testing data from different sports users, the experimental coefficient k There will also be differences.

[0094] Calculating oxygen uptake rate using power data VO2 ( i ), it is necessary to combine the user's weight M Body and vehicle weight M_ Bake , and is estimated using formula (7).

[0095] VO2 ( i )= α 2 ·Power ( i) / (M Body + M Bake)+β (7).

[0096] in, Power ( i ) represents the power at the i-th moment, α 2 represents the power oxygen uptake gain experimental coefficient, β represents the basic oxygen uptake consumption correction experimental coefficient, α 2 and β are experimentally obtained coefficients. For example, in the present application, α2 is between 1.5 and 2.3, and β is around 7 ml / kg / min.

[0097] S2: Based on i Maximum oxygen uptake percentage at that moment VO2max and iThe preset relationship between the maximum oxygen uptake percentage and the contribution of fat in energy supply at the moment is used to estimate the i Contribution ratio of time Fat Oxid ( i ).

[0098] This contribution ratio Fat Oxid ( i ) can be achieved by the contribution ratio estimation module 500 in the fat consumption rate estimation system.

[0099] In some embodiments of the present application, the real-time energy consumption is first estimated using the oxygen uptake rate, and then the contribution ratio of fat in energy supply is calculated to indirectly calculate the fat consumption rate.

[0100] During aerobic exercise, the contribution of fat in energy supply will first increase and then decrease with the intensity of exercise, and the maximum contribution ratio can reach about 50%.

[0101] In some embodiments of the present application, the real-time maximum oxygen uptake percentage % VO2max A function fitting is performed in advance between the contribution ratio of fat in energy supply and the two to obtain a preset relationship between the two, see formula (8).

[0102] Fat Oxid ( i )= k·( % VO2max) l +m (8).

[0103] in, k is the gain coefficient, l is the exponential parameter, m is the bias correction, and k , l and m All are obtained from experiments. Fat_ Oxid ( i ) indicates the i The contribution ratio of fat in energy supply at each moment.

[0104] For example, k The value is around -1.05. l The value is around 3.01. m The value is between 0.55 and 0.65.

[0105] Therefore, when obtaining the real-time maximum oxygen uptake percentage VO2max By comparing with the above preset relationship, the real-time contribution ratio of fat in energy supply can be obtained.

[0106] The above percentage of maximum oxygen uptake VO2max Oxygen uptake rateVO2 ( i ) and maximum oxygen uptake rate VO2max Ratio.

[0107] In some embodiments of the present application, there is a fixed maximum oxygen uptake rate during an exercise. VO2max The maximum oxygen uptake rate may be manually input by the user.

[0108] S3: Estimate the fat gain coefficient λ.

[0109] The estimation process of the fat gain coefficient λ can be implemented by the fat gain coefficient estimation module 400 in the fat consumption rate estimation system.

[0110] Exercise intensity, carbohydrate intake, and running duration are all factors that affect fat oxidation. Intake of carbohydrates before exercise inhibits fat oxidation during low- and medium-intensity exercise, and the fat decomposition rate will decrease by 40% to 50%. Intake of carbohydrates before exercise will affect the fat oxidation rate for at least 6 hours. Intake of carbohydrates 30 minutes after the start of exercise will reduce the fat oxidation rate by less, about 20%. However, carbohydrates have little effect on fat decomposition during high-intensity exercise.

[0111] Generally speaking, after exercise lasts for 30 to 40 minutes (before this, glycogen plays a dominant role in energy supply, accounting for more than 80%, while fat accounts for only 15 to 20%), the oxidation rate of fat will increase significantly. When the exercise intensity is around 50% of the maximum oxygen uptake (corresponding to about 70% of the maximum heart rate), the absolute fat consumption rate reaches the maximum.

[0112] Therefore, taking exercise intensity, carbohydrate intake, and running duration into account when estimating the fat gain coefficient λ can correct the contribution of fat in energy supply to obtain accurate fat consumption.

[0113] (1) When no carbohydrates are consumed before exercise and the exercise duration t reaches the first preset period, λ is set to 1.

[0114] In some embodiments of the present application, the first preset time period can be set according to different users, for example, 30 minutes.

[0115] (2) If no carbohydrates are consumed before exercise and the exercise duration t does not reach the first preset period, based on % VO2max The first preset relationship between t and λ is used to estimate λ.

[0116] In some embodiments of the present application, the first preset relationship refers to formula (9).

[0117] λ = α3 ·%VO2max + β 3 ·t + γ 3 (9).

[0118] α 3 is the first exercise intensity gain coefficient, β 3 is the motion time gain coefficient, γ 3 Represents bias gain. These coefficients can be obtained experimentally and are fixed coefficients when used.

[0119] (3) When carbohydrate intake is taken before exercise and t does not reach the first preset period, based on % VO2max and a second preset relationship between λ and λ, estimating λ.

[0120] In some embodiments of the present application, the second preset relationship refers to formula (10).

[0121] λ = α 4 ·%VO2max (10).

[0122] α 4 It is the second exercise intensity gain coefficient, which can be obtained through experiments and is a fixed coefficient when used.

[0123] (4) When carbohydrate intake is reached before exercise and t reaches the first preset period, based on % VO2max and a third preset relationship of λ, estimating λ.

[0124] In some embodiments of the present application, the third preset relationship refers to formula (11).

[0125] λ = 0.8+ α 5 ·%VO2max (11).

[0126] α 5 It is the third exercise intensity gain coefficient, which can be obtained through experiments and is a fixed coefficient when used.

[0127] S4: Using λ, Fat Oxid ( i ), VO2 ( i )and M Body , estimated i Fat burning rate at a time V Fat ( i ).

[0128] This fat consumption rate V Fat ( i ) can be implemented by the fat consumption rate estimation module 600 in the fat consumption rate estimation system.

[0129] The real-time oxygen uptake rate can be used to estimate the real-time energy consumption. By accumulating the real-time energy consumption during exercise time, the total energy consumption can be obtained, and it can be used Fat Oxid ( i ) and λ estimate the contribution of fat consumption to real-time energy expenditure, that is, the fat consumption rate.

[0130] Therefore, the fat consumption rate can be calculated using the following formula (12): V Fat ( i ) for estimation.

[0131] V Fat ( i )= λ·Fat Oxid ( i ) ·VO2 ( i ) ·M Body / 3.5 / 3600 (12).

[0132] Since 1 metabolic equivalent is equivalent to 3.5 ml of oxygen consumed per kilogram of body weight per minute, it can be based on the oxygen uptake rate VO2 ( i ), energy consumption e ( i ) for estimation.

[0133] That is, the energy consumption at the i-th moment is e ( i )for: e ( i )= VO2 ( i ) ·M Body / 3.5 / 3600 .

[0134] During a single exercise session, the rate of fat consumption V Fat ( i ) to accumulate the amount of fat consumed during this exercise.

[0135] In some embodiments of the present application, if the time interval △t between two adjacent exercises of the exercising user does not reach the second preset time period, it is equivalent to that the current exercise is a continuation of the previous exercise, and the fat consumption rate does not need to increase slowly from low to high again. At this time, the fat consumption rate needs to be compensated.

[0136] 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.

[0137] Based on the time interval △t between two consecutive movements, the compensation value can be set, and the contribution ratio can be calculated by using the compensation value. Fat_ Oxid ( i ) for compensation.

[0138] The compensation value may be obtained according to a preset compensation function, or may be set according to a time interval Δt.

[0139] For example, when 6 hours is selected in the second preset time period, if the time interval △t is greater than or equal to 6 hours, the current exercise is considered to be a new exercise, that is, the fat consumption rate increases slowly from low to high again; if the time interval △t is less than 6 hours, the current exercise is considered to be a continuation of the previous exercise, that is, the fat consumption rate does not need to increase slowly from low to high again.

[0140] 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 larger the corresponding compensation value.

[0141] You can set the compensation function corresponding to the compensation value f (△t-6), the function takes the difference between △t and the second preset time period as a variable, and when the variable is in the positive interval, the compensation function f (△t-6) is 1, when the change is in the negative range, the compensation function f (△t-6) is a decreasing function greater than 1.

[0142] That is, V Fat ( i )= f (△t-6) ·λ·fat_oxid ( i ) ·vo2 ( i ) M_body / 3.5 / 3600 (13).

[0143] In some embodiments of the present application, see Figure 3 , and also relates to an interactive system for estimating fat consumption rate.

[0144] The fat consumption rate estimation interactive system includes a data acquisition unit 100 and a terminal 200 .

[0145] The data collection unit 100 is used to output the cycling heart rate and / or cycling power of the exercise user during exercise.

[0146] The terminal 200 may be a smart device (eg, a mobile phone, a smart watch, a pad) that is communicatively connected to the data acquisition unit 100 .

[0147] The fat consumption rate estimation method as described above is integrated in the terminal 200 for estimating the fat consumption rate.

[0148] The data collection unit 100 may be a heart rate collection device 110 (eg, a heart rate belt) for collecting heart rate data of a sports user and sending the data to the terminal 200 .

[0149] When the terminal 200 has the heart rate data, it estimates the oxygen uptake rate by using the heart rate data when executing the fat consumption rate estimating method.

[0150] The data collection unit 100 may be a power collection device 120 (eg, a power meter) for collecting power data of a sports user and sending the data to the terminal 200 .

[0151] When the power data is available, the terminal 200 estimates the oxygen uptake rate using the power data when executing the fat consumption rate estimating method.

[0152] The data collection unit 100 may also include a heart rate collection device 110 (eg, a heart rate belt) and a power collection device 120 (eg, a power meter).

[0153] When executing the fat consumption rate estimation method, the terminal 200 may estimate the oxygen uptake rate using power data, or estimate the oxygen uptake rate using heart rate data.

[0154] The fat consumption speed or fat consumption amount estimated by the terminal 200 can be displayed on the front end of the terminal 200 for easy viewing by the exercising user.

[0155] Among them, the fat consumption rate is the fat consumption in one second. Therefore, during exercise, the accumulated fat consumption rate is used to calculate the fat consumption.

[0156] In some embodiments of the present application, a cycling meter (not shown) is also involved, which can record the heart rate data and / or power data of the cycling user, and has a built-in fat consumption speed estimation system as described above, which can display the fat consumption speed and / or fat consumption amount on the cycling meter during the user's cycling exercise, making it convenient for the exercise user to view.

[0157] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for a person skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions claimed to be protected by the present invention.

Claims

1. A method for estimating fat consumption rate, characterized in that: include: During an exercise, based on the user's heart rate data and / or power data, the user can estimate the i Oxygen uptake rate at the moment vo2 ( i ); Based on i Maximum oxygen uptake percentage at that moment vo2max and i The preset relationship between the maximum oxygen uptake percentage and the contribution of fat in energy supply at the moment is used to estimate the i Contribution ratio of time fat_oxid ( i ) ； Estimate the fat gain coefficient λ, specifically: 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, based on % vo2max The first preset relationship between t and λ is used to estimate λ; when carbon water is consumed before exercise and t does not reach the first preset period, based on % vo2max and the second preset relationship of λ, estimate λ; when carbon water is consumed before exercise and t reaches the first preset period, based on % vo2max A third preset relationship with λ, estimating λ; Using λ, fat_oxid ( i ), vo2 ( i )and M_body , estimated i Fat burning rate at a time v_fat ( i ).

2. The method for estimating fat consumption rate according to claim 1, characterized in that: Based on the heart rate data and / or power data of the exercise user, the i Oxygen uptake rate at the moment vo2 ( i ), specifically: Based on user i Cycling heart rate at the moment hr ( i ) and the user’s physiological information to estimate the i Oxygen uptake rate at the moment vo2 ( i ); or Based on user i Cycling power at the moment power ( i ), user weight M_body Vehicle weight M_bake , estimated i Oxygen uptake rate at the moment vo2 ( i ).

3. The method for estimating fat consumption rate according to claim 2, characterized in that: Based on user i Cycling heart rate at the moment hr ( i ) and the user’s physiological information to estimate the i Oxygen uptake rate at the moment vo2 ( i ), specifically: ； in, α 1 represents the heart rate oxygen uptake gain experimental coefficient, hr_max 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 is a preset fixed value, or; Maximum heart rate hr_max The user's age is used for calculation, specifically: 。 5. The method for estimating fat consumption rate according to claim 2, characterized in that: Based on user i Cycling power at the moment power ( i) , User weight M_body Vehicle weight M_bake , estimated i Oxygen uptake rate at the moment vo2 ( i ), specifically: vo2 ( i )= α 2 ·power ( i) / (M_body + M_bake)+β ； in, α 2 represents the power oxygen uptake gain experimental coefficient, and β represents the basic oxygen uptake consumption correction experimental coefficient.

6. The method for estimating fat consumption rate according to claim 1, characterized in that: Based on i Maximum oxygen uptake percentage at that moment vo2max and i The preset relationship between the maximum oxygen uptake percentage and the contribution of fat in energy supply at the moment is used to estimate the i Contribution ratio of time fat_oxid ( i ), specifically: fat_oxid ( i )= k·( % vo2max) l +m ； % vo2max = vo2 ( i ) / vo2max ； in, k is the gain coefficient, l is the exponential parameter, m is the bias correction, and k , l and m All are obtained from experiments. vo2max It indicates the maximum oxygen uptake rate during an exercise.

7. The method for estimating fat consumption rate according to claim 1, characterized in that: The fat consumption rate estimation method further comprises: When the time interval Δt between the current movement and the last continuous movement does not reach the second preset time period, the compensation value is used to adjust the first i Fat burning rate at a time v_fat ( i ) steps to take to make compensation; 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 the difference between the time interval △t and the second preset time period is less than zero, the compensation value is greater than 1 and the smaller the difference, the larger the corresponding compensation value.

8. A fat consumption rate estimation system, characterized in that: include: The oxygen uptake rate estimation module is used to estimate the oxygen uptake rate of the first exercise process based on the heart rate data and / or power data of the exercise user during an exercise process. i Oxygen uptake rate at the moment vo2 ( i ); Contribution ratio estimation module, which is based on the i Maximum oxygen uptake percentage at that moment vo2max and i The preset relationship between the maximum oxygen uptake percentage and the contribution of fat in energy supply at the moment is used to estimate the first i Contribution ratio of time fat_ oxid ( i ); The fat gain coefficient estimation module is used to estimate the fat gain coefficient λ, specifically: 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, based on % vo2max The first preset relationship between t and λ is used to estimate λ; when carbon water is consumed before exercise and t does not reach the first preset period, based on % vo2max and the second preset relationship of λ, estimate λ; when carbon water is consumed before exercise and t reaches the first preset period, based on % vo2max A third preset relationship with λ, estimating λ; The fat consumption rate estimation module uses λ, fat_oxid ( i ), vo2 ( i )and M_body , estimated i Fat burning rate at a time v_fat ( i ).

9. An interactive system for estimating fat consumption rate, characterized in that: include: A data collection unit, which is used to output the cycling heart rate and / or cycling power of the exercise user during exercise; A terminal, which receives the cycling heart rate and / or cycling power; Execute the fat consumption rate estimation method according to any one of claims 1 to 7 to estimate the fat consumption rate; or After estimating the fat consumption rate, use the fat consumption rate to calculate the amount of fat consumed during exercise; The estimated fat consumption speed and / or the amount of fat consumed during exercise is displayed on the front end of the terminal.

10. A code table, characterized in that: The fat consumption rate estimation system as claimed in claim 8 is configured in the code table.

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