A method of back muscle training control and training apparatus with adaptive resistance

Abstract

The embodiment of the application provides a back muscle training control method and a training instrument with adaptive resistance, and the method comprises the following steps: in the back muscle training process, determining the sliding average value A of the real-time load parameter corresponding to the continuous W trend determination window quantity based on the average centripetal velocity MPV of the pull rope acquired in real time, wherein W is the trend determination window quantity determined based on the training mode; determining the trend slope K by linear regression fitting based on the W As; determining the target adjustment coefficient alpha based on the A, the K and an error threshold E in the case that the A and the load interval [T L , T U ] corresponding to the training mode are not matched; and adjusting the initial resistance determined based on the training mode by using the alpha, so that the current adaptive resistance can make the A stably located in the load interval [T L , T U ] corresponding to the training mode, and the absolute value of the K is less than or equal to the E, wherein the initial resistance is determined based on the control current, the damping coefficient and the radius of the pull rope.

Description

Technical Field

[0001] This application relates to the field of fitness equipment, specifically to an adaptive resistance back muscle training control method and training device. Background Technology

[0002] With the increasing health awareness of the general public, fitness enthusiasts have a greater need for personalized control of training intensity, and rehabilitation patients have a greater need for safe control of movement load. Traditional training equipment lacks an intuitive force feedback mechanism to meet training load requirements, making it difficult to quantify training effects. Summary of the Invention

[0003] The purpose of this application is to provide an adaptive resistance back muscle training control method and training device, and the specific technical solution adopted is as follows:

[0004] Firstly, an adaptive resistance back muscle training control method is provided, the method comprising:

[0005] During back muscle training, the sliding average value A of the real-time load parameter corresponding to the number of consecutive W trend judgment windows is determined based on the real-time acquired average centripetal velocity (MPV) of the rope. Here, W is the number of trend judgment windows determined based on the training mode and is an integer greater than or equal to 2.

[0006] Based on the W moving averages A, the trend slope K is obtained by linear regression fitting.

[0007] Determine the moving average A and the load interval [T] corresponding to the training mode. L T U In the case of mismatch, the target adjustment coefficient α is determined based on the moving average A, the trend slope K, and the error threshold E, wherein the error threshold E is determined based on the training mode;

[0008] The initial resistance determined based on the training mode is adjusted using the target adjustment coefficient α. The resulting current adaptive resistance ensures that the sliding average value A is stable within the load range [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E. The initial resistance is determined based on the control current, damping coefficient, and radius of the pull rope.

[0009] Secondly, an adaptive resistance back muscle training device is provided, the back muscle training device comprising: a walking fixation module, a training module, and an intelligent control display module;

[0010] The walking fixation module is located at the bottom of the back muscle training device and is used to realize the movement and fixation of the back muscle training device.

[0011] The training module is located inside the back muscle training device and is used for rope pulling force detection and upper limb training movements.

[0012] The intelligent control display module is used to display the preset training mode. During the back muscle training process, the sliding average value A of the real-time load parameter corresponding to the number of consecutive W trend judgment windows of the training is determined based on the real-time acquired average centripetal velocity MPV of the rope. Here, W is the number of trend judgment windows determined based on the training mode, which is an integer greater than or equal to 2.

[0013] Based on the W moving averages A, the trend slope K is obtained by linear regression fitting.

[0014] Determine the moving average A and the load interval [T] corresponding to the training mode. L T U In the case of mismatch, the target adjustment coefficient α is determined based on the moving average A, the trend slope K, and the error threshold E, wherein the error threshold E is determined based on the training mode;

[0015] The initial resistance determined based on the training mode is adjusted using the target adjustment coefficient α so that the sliding average value A is stably within the load range [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E, wherein the initial resistance is determined based on the control current, damping coefficient and the radius of the pull rope.

[0016] Thirdly, an adaptive resistance back muscle training control device is provided, the device comprising:

[0017] The acquisition module is used to determine the sliding average value A of the real-time load parameter corresponding to the number of consecutive W trend judgment windows during back muscle training, based on the real-time acquired average centripetal velocity (MPV) of the pull rope. Here, W is the number of trend judgment windows determined based on the training mode and is an integer greater than or equal to 2.

[0018] The fitting module is used to obtain the trend slope K by linear regression fitting based on W moving averages A;

[0019] The determination module is used to determine the load interval [T] corresponding to the moving average A and the training mode. L T U In the case of mismatch, the target adjustment coefficient α is determined based on the moving average A, the trend slope K, and the error threshold E, wherein the error threshold E is determined based on the training mode;

[0020] The adjustment module is used to adjust the initial resistance determined based on the training mode using the target adjustment coefficient α, so that the current adaptive resistance can make the sliding average value A stably within the load range [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E, wherein the initial resistance is determined based on the control current, damping coefficient and the radius of the pull rope.

[0021] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0022] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0023] This application has the following beneficial effects: it achieves precise quantification of training effects through load quantification and adaptive resistance adjustment, automatically optimizes parameters for different training goals and provides real-time feedback, ensures that resistance adjustment conforms to biomechanical characteristics, effectively reduces the risk of sports injury, and improves training effects. Attached Figure Description

[0024] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A schematic diagram of the internal and bottom structure of an adaptive resistance intelligent quantitative back muscle training device provided in this application embodiment;

[0026] Figure 2 A schematic diagram of the external structure of an adaptive resistance intelligent quantitative back muscle training device provided in an embodiment of this application;

[0027] Figure 3 A schematic diagram of the walking fixation component of an adaptive resistance intelligent quantitative back muscle training device provided in an embodiment of this application;

[0028] Figure 4 A schematic diagram illustrating the implementation process of an adaptive resistance back muscle training control method provided in this application embodiment;

[0029] Figure 5 A schematic diagram of an adaptive resistance back muscle training control device provided in an embodiment of this application;

[0030] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an adaptive resistance back muscle training control method proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined from any suitable form.

[0032] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0035] Figure 1 This application provides a schematic diagram of the internal and bottom structure of an adaptive resistance intelligent quantitative back muscle training device, as shown in the embodiments below. Figure 1 As shown, the training device 1 is equipped with a first support 12 and a second support 13. A high-precision encoder 14 and a current-adjustable magnetic powder brake 15 are mounted on the first support 12, and a DC geared motor 16 is mounted on the second support 13. A connecting rod 17 is provided between the two supports, and an upper reel 18 is mounted on the connecting rod 17. A lower reel 19 is installed between the DC geared motor 16 and the magnetic powder brake 15. A pull rope 20 runs inside and outside the training device 1, with one end connected to the upper reel 18 and the other end connected to the lower reel 19.

[0036] The bottom of the training device 1 is equipped with a left front swivel wheel 2, a right front swivel wheel 3, a left rear swivel wheel 4, and a right rear swivel wheel 5 for moving and turning the training device. Fixing components are installed at the bottom and inside of the training device 1, including a left front suction cup 6, a right front suction cup 7, a left rear suction cup 8, and a right rear suction cup 9. An internal telescopic motor 11 is installed, connected to a chassis 21 equipped with a negative pressure generator 10. The chassis 21 is equipped with the left front suction cup 6, right front suction cup 7, left rear suction cup 8, and right rear suction cup 9.

[0037] Figure 2 This is a schematic diagram of the external structure of an adaptive resistance intelligent quantitative back muscle training device provided in an embodiment of this application, as shown below. Figure 2 As shown, the bottom of the training device 1 is equipped with a left front swivel wheel 2, a right front swivel wheel 3, a left rear swivel wheel 4, and a right rear swivel wheel 5 for moving and turning the training device. Fixing components are installed at the bottom and inside of the training device 1, including a left front suction cup 6, a right front suction cup 7, a left rear suction cup 8, and a right rear suction cup 9. A pull rope 20 runs through the inside and outside of the training device 1, as shown... Figure 1 As shown, one end of the pull rope 20 is connected to the upper reel 18, and the other end is connected to the lower reel 19. The trainee performs upper limb training by pulling the pull rope 20, and the encoder 14 monitors the linear velocity and linear acceleration of the pull rope in real time.

[0038] like Figure 2 The training device 1 shown is equipped with an intelligent control display module 22 on its top, which supports three preset training modes (muscle building mode, strength / explosive power mode, and shaping / endurance mode). Each mode corresponds to different %1RM target range, safe tension threshold, exercise speed range, initial resistance parameters and load closed-loop control parameters, and can dynamically adjust the magnetic powder brake current according to real-time training data to achieve closed-loop control.

[0039] Figure 3 This application provides a schematic diagram of the walking fixation component of an adaptive resistance intelligent quantitative back muscle training device, as shown in the embodiments of this application. Figure 3 As shown, the walking and fixing module of the training device 1 includes a walking component and a fixing component. The walking component includes a left front swivel wheel 2, a right front swivel wheel 3, a left rear swivel wheel 4, and a right rear swivel wheel 5 installed at the bottom, used to realize the movement and turning of the training device. The fixing component is installed at the bottom and inside of the training device 1, including a left front suction cup 6, a right front suction cup 7, a left rear suction cup 8, and a right rear suction cup 9. A telescopic motor 11 is installed inside, and the telescopic motor is connected to a chassis 21 with a negative pressure generator 10. The left front suction cup 6, right front suction cup 7, left rear suction cup 8, and right rear suction cup 9 are installed on the chassis 21.

[0040] Before training begins, such as Figure 1The telescopic motor 11 drives the chassis 21 to move the suction cup downwards and firmly adhere it to the ground, ensuring the stability of the equipment during training. After training, the telescopic motor 11 drives the chassis 21 upwards, causing the suction cup to detach from the ground for easy movement.

[0041] This application provides an intelligent control method for an adaptive resistance intelligent quantitative back muscle training device, which can be achieved through the following steps:

[0042] Step 1: Training preparation and equipment setup.

[0043] Before starting training, the user turns on the device, such as... Figure 1 The telescopic motor 11 inside the training device drives the chassis 21, which is connected to the negative pressure generator 10, to move downwards. This causes the left front suction cup 6, right front suction cup 7, left rear suction cup 8, and right rear suction cup 9, which are mounted on the chassis 21, to contact the ground and form a tight suction. This ensures that the training device remains stable during subsequent training and avoids affecting training safety and data accuracy due to equipment movement or shaking.

[0044] Step 2: Select training mode.

[0045] During implementation, users can use methods such as Figure 2 The interactive interface of the intelligent control display module 22 shown allows users to select one of three preset training modes. The system will then call the corresponding core parameter set based on the selected mode, including the %1RM target interval [T]. L T U The system includes preset continuous comparison quantity S, mutation elimination threshold δ, trend judgment window number W, and error threshold E. The intelligent control display module 22 has at least three preset training modes: muscle building mode, strength / explosive power mode, and shaping / endurance mode.

[0046] For example, in muscle-building mode, the preset %1RM target range is 60% to 80% 1RM; the load closed-loop control parameters are selected with sample size S=10, mutation elimination threshold δ=4%, trend judgment window number W=5, and error threshold E=0.08%, which aims to guide users to use the best combination of load and speed to stabilize the training load within the target %1RM range and efficiently promote muscle volume growth.

[0047] In the power / explosive power mode, the preset %1RM target range is 40% to 60%1RM; the load closed-loop control parameters are selected with sample size S=8, mutation elimination threshold δ=3%, trend judgment window size W=4, and error threshold E=0.1%. By emphasizing the higher speed weight in the centripetal phase, the user is guided to achieve rapid force exertion within the corresponding %1RM range, thus developing the maximum power and explosive power output capability.

[0048] In the shaping / endurance mode, the target range of %1RM is preset to 50% to 65%1RM; the load closed-loop control parameters are selected as follows: sample size S=12, mutation elimination threshold δ=5%, trend judgment window number W=6, and error threshold E=0.05%. With a relatively low safe load combined with the target %1RM range, users are guided to conduct long-term, controlled training to improve muscle endurance and muscle shaping effect.

[0049] Step 3: Initial resistance setting.

[0050] The system automatically configures the initial control current based on the selected training mode. I The initial resistance is set according to the selected training mode using the following formula (1). :

[0051] (1);

[0052] in, k The damping coefficient is... r The radius of the rope.

[0053] Step 4: Real-time load calculation.

[0054] When the user pulls the rope 20, the encoder 14, with a detection frequency of not less than 100Hz, detects the average centripetal speed MPV of the rope in real time, and calculates the user's real-time load %1RM based on the following formula (2):

[0055] %1RM=a×MPV²+b×MPV+c(2);

[0056] Where %1RM is the percentage of the current training load relative to the individual's maximum repetition weight (1RM), and a, b, and c are regression coefficients specific to the training exercise. For the lat pulldown exercise, the regression coefficients can be set to the following values: a=7.89, b=-70.12, c=111.6.

[0057] Step 5: Intelligent analysis and dynamic resistance adjustment.

[0058] The system executes a load closed-loop control process, and the load closed-loop control parameters for each mode are selected within the preset value range.

[0059] Collect the Sth real-time %1RM data (S ranges from 7 to 12), and calculate the median Md; remove abrupt changes in Md whose relative deviation exceeds δ (δ ranges from 3% to 5%). If the remaining valid sample size is ≥ S / 2, calculate the moving average A; otherwise, collect the Sth data again.

[0060] Update the sample pool according to the rule of "removing the earliest sample and adding the newly collected sample", restart the mutation rejection process, calculate W consecutive moving averages (W ranges from 4 to 6), and obtain the trend slope K through linear regression fitting. The calculation formula (3) is as follows:

[0061] (3);

[0062] Where W is the number of trend determination windows, A i is the i th moving average.

[0063] According to whether the moving average A is within the target interval [T L , T U of the selected mode, and the relationship between the trend slope K and the error threshold E, calculate the adjustment coefficient α according to the following rules:

[0064] When A < T L , if K < -2E, then α = α1 (0.95); if -2E ≤ K < -E, then α = α2 (0.98); if -E ≤ K < 0, then α = α3 (0.995).

[0065] When A > T U , if K > 2E, then α = α4 (1.05); if E < K ≤ 2E, then α = α5 (1.02); if 0 < K ≤ E, then α = α6 (1.005).

[0066] When T L ≤ A ≤ T U and |K| > E, if K > 0, then α = α7 (1.002); if K < 0, then α = α8 (0.998).

[0067] Where α1 is the first adjustment coefficient; α2 is the second adjustment coefficient; α3 is the third adjustment coefficient; α4 is the fourth adjustment coefficient; α5 is the fifth adjustment coefficient; α6 is the sixth adjustment coefficient; α7 is the seventh adjustment coefficient; α8 is the eighth adjustment coefficient;<{

[0068] Update the damping according to the following formula (4):

[0069] F 阻尼N+1 = F 阻尼N × α (4);

[0070] When the user pulls the pull rope again, based on the updated damping, detect the magnitude of the average centripetal velocity MPV of the pull rope to realize real-time adjustment of the magnitude of %1RM;

[0071] Repeat the above steps until A is stably within [T L , T UIf |K|≤E within the interval, then the current damping F remains unchanged.

[0072] Step 6: Quantify and output the training effect.

[0073] During training, the intelligent control display module (22) outputs the cumulative load (kg) in real time. The calculation method is as follows: the sum of the products of the actual load for each group and the number of times each group is completed, the average %1RM (arithmetic mean of all valid %1RM data throughout the training process), the MPV adaptation ratio (i.e., the percentage of times the MPV falls within the corresponding mode adaptation range out of the total number of completions), and the fatigue index (i.e., the ratio of the average MPV of the last round to the average MPV of the first round multiplied by 100%).

[0074] The cumulative load can be calculated using the following formula (5):

[0075] Cumulative load = Σ (actual load per group × number of times per group is completed) (5);

[0076] The MPV adaptation ratio can be calculated using the following formula (6):

[0077] (6);

[0078] The fatigue index can be calculated using the following formula (7):

[0079] Fatigue index = (average MPV of the last round ÷ average MPV of the first round) × 100% (7);

[0080] After training, the system automatically generates a quantitative report, which clearly defines the %1RM achievement rate within the training time (the percentage of time within the effective training time that the %1RM is in the target range), the estimated strength improvement (the percentage of the user's %1RM improvement within one month predicted by a model based on training load, cumulative repetitions, and historical data), the neuromuscular efficiency improvement ratio (calculated through MPV stability and %1RM response speed, reflecting the improvement in the efficiency of nerve control over muscles), and the training load rationality score (a score of 0 to 100 points given based on the user's preset goals, body data, and training performance), thus achieving objective quantification and data-driven presentation of training effects.

[0081] Step 7: Training ends and automatic rope reel in.

[0082] The encoder (14) continuously detects the linear velocity V of the pull rope. When the duration of V=0 exceeds a preset threshold T seconds, the system determines that the training action has stopped. Subsequently, the system controls the current of the adjustable magnetic powder brake (15) to return to zero, so that F 阻尼=0, and at the same time start the DC geared motor (16) to reverse, and smoothly retract the pull rope (20) to the initial winding position to complete the equipment reset.

[0083] The beneficial effects of this invention after adopting the above solution are as follows: It achieves precise quantification of training effects through an encoder and a %1RM quantization model; based on a preset %1RM target range and a scientific load calculation method, it automatically optimizes parameters and provides real-time feedback for different training goals, effectively reducing the risk of sports injuries; the innovative walking fixation module combines omnidirectional wheels and a suction cup system, balancing mobility and training stability; the user interface is intuitive and user-friendly, and the automatic rope retraction function and three training modes make the device suitable for various scenarios such as home, gym, and rehabilitation institutions, comprehensively improving the scientific nature of training and user experience.

[0084] This application provides an adaptive resistance back muscle training control method, such as... Figure 4 As shown, this can be achieved through the following steps:

[0085] Step S410: During back muscle training, the sliding average value A of the real-time load parameter corresponding to the number of consecutive W trend judgment windows is determined based on the real-time acquired average centripetal velocity MPV of the rope, where W is the number of trend judgment windows determined based on the training mode and is an integer greater than or equal to 2.

[0086] During implementation, users can, for example, Figure 2 The interactive interface of the intelligent control display module 22 shown allows users to select one of three preset training modes. The system will then call the corresponding core parameter set based on the selected mode, including the %1RM target interval [T]. L T U The system includes preset continuous comparison quantity S, mutation elimination threshold δ, trend judgment window number W, and error threshold E. The intelligent control display module 22 has at least three preset training modes: muscle building mode, strength / explosive power mode, and shaping / endurance mode.

[0087] For example, W is set to 5 in muscle-building mode, 4 in strength / explosive power mode, and 6 in shaping / endurance mode.

[0088] Afterwards, the training system automatically configures the initial control current according to the selected training mode. I The initial resistance is set according to the selected training mode using the above formula (1). .

[0089] User-driven actions, such as Figure 1 When the rope 20 is pulled, the encoder 14 with a detection frequency of not less than 100Hz detects the average centripetal speed MPV of the rope in real time, and calculates the user's real-time load %1RM based on the above formula (2).

[0090] Based on the selected training mode, the number of trend judgment windows W is determined, and the moving average A of the W real-time load parameters for training is determined based on the user's real-time load %1RM.

[0091] Step S420: Based on the W moving averages A, obtain the trend slope K through linear regression fitting;

[0092] During implementation, W moving averages A can be input into the above formula (3), and the trend slope K can be obtained by linear regression fitting.

[0093] Step S430: Determine the moving average A and the load interval [T] corresponding to the training mode. L T U In the case of mismatch, the target adjustment coefficient α is determined based on the moving average A, the trend slope K, and the error threshold E, wherein the error threshold E is determined based on the training mode;

[0094] During implementation, the moving average A and the load interval [T] corresponding to the training mode are determined. L T U In the event of a mismatch, preset adjustment rules can be used to divide the range of K values, and different adjustment coefficients α can be set for K in different ranges.

[0095] Step S440: Adjust the initial resistance determined based on the training mode using the target adjustment coefficient α. The resulting current adaptive resistance ensures that the sliding average value A is stably within the load range [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E. The initial resistance is determined based on the control current, damping coefficient, and radius of the pull rope.

[0096] During implementation, the target adjustment coefficient α can be input into the above formula (4) to obtain the adjusted adaptive resistance. When the user pulls the rope again, based on the updated adaptive damping, the magnitude of the average centripetal velocity MPV of the rope is detected to achieve real-time adjustment of the magnitude of %1RM; the above steps S430 are repeated using the adjustment rules to obtain the target adjustment coefficient α used to adjust the damping until A stabilizes at [T L T U If |K|≤E within the interval, then the current damping F remains unchanged.

[0097] The system can dynamically adjust the magnetic powder brake current based on real-time training data (target adjustment coefficient α) to achieve closed-loop control.

[0098] In this embodiment, the training effect is accurately quantified by load quantification and adaptive resistance adjustment. Parameters are automatically optimized for different training goals and real-time feedback is provided to ensure that resistance adjustment conforms to biomechanical characteristics, effectively reducing the risk of sports injury and improving training effect.

[0099] In some embodiments, the step S410 above, "determining the sliding average A of W real-time load parameters during back muscle training based on the real-time acquired average centripetal velocity (MPV) of the cable," can be achieved through the following steps:

[0100] Step 411: Within a trend determination window, collect S average centripetal velocities (MPVs) that satisfy a preset number of consecutive comparisons, where S is determined based on the training mode and is an integer greater than or equal to 1.

[0101] Here, S can be determined based on the training mode pre-selected by the user. For example, S is 10 in muscle-building mode, 8 in strength / power mode, and 12 in shaping / endurance mode.

[0102] During implementation, when the user pulls the pull rope 20, the encoder 14, with a detection frequency of not less than 100Hz, detects the average centripetal speed (MPV) of the pull rope S in real time.

[0103] Step 412: Based on the deviation threshold δ, determine the number of valid samples greater than the sample threshold in the S average centripetal velocity (MPV), wherein the deviation threshold δ is determined based on the training mode, and the sample threshold is determined based on S.

[0104] Here, δ can be determined based on the user's pre-selected training mode. For example, δ is 4% in muscle-building mode, 3% in strength / power mode, and 5% in shaping / endurance mode.

[0105] During implementation, mutation values ​​with relative deviations exceeding δ are removed from the S average centripetal velocities (MPVs). If the remaining number of valid samples is greater than or equal to a sample threshold (e.g., S / 2), step 413 is executed to calculate the moving average. If the remaining number of valid samples is less than S / 2, the Sth data collection is performed again, and the sample pool is updated according to the rule of "removing the earliest sample and adding newly collected samples," restarting the mutation removal process.

[0106] Step 413: Determine the moving average A corresponding to one of the trend determination windows based on the valid samples, so as to determine the W moving averages A corresponding to W trend determination windows.

[0107] Here, we can calculate W consecutive moving averages A. For example, in muscle-building mode, if W is 5, then we can calculate 5 consecutive moving averages A.

[0108] During implementation, the effective sample values ​​can be summed and then divided by the number of samples to obtain the moving average A.

[0109] In this embodiment, the trend determination window and effective sample screening ensure that the moving average A value accurately reflects the real load state, which is in line with the "load-adaptation" theory in exercise physiology. The deviation threshold δ set based on the training mode can remove outliers from the collected S MPVs, ensuring the quality of the selected effective samples.

[0110] In some embodiments, step 412 above, "determining the number of valid samples greater than the sample threshold based on the deviation threshold δ among the S average centripetal velocities (MPVs)," can be achieved through the following steps:

[0111] Step 4121: Determine the median value Md of the S average centripetal velocities MPV;

[0112] Here, the median is an important statistical indicator used to describe the central tendency of a dataset. Its calculation method is simple and it is highly robust against interference, making it particularly suitable for datasets with outliers. In MPV (mean concentric velocity) analysis of back muscle training, the median can serve as a robust benchmark, effectively filtering out instantaneous fluctuations or measurement errors.

[0113] The median is the value in the middle when a set of data is arranged in ascending order. It is not affected by extreme values, is more stable than the mean, strictly reflects the middle position of the data, and is suitable for asymmetric distributions.

[0114] Step 4122: Remove abrupt changes in the S average centripetal velocities (MPVs) that deviate from Md and whose relative deviation exceeds the deviation threshold δ, to obtain valid samples;

[0115] During implementation, samples whose relative deviation from Md exceeds the deviation threshold δ can be considered as mutation samples, while the remaining valid samples are within the preset deviation range.

[0116] Step 4123: Determine that the number of valid samples is greater than or equal to the sample threshold.

[0117] Here, the sample threshold can be determined based on S, for example, S / 2. Then, a number greater than S / 2 can be identified as valid samples.

[0118] If the number of valid samples is less than the sample threshold, sample data will be collected again until the number of valid samples is greater than or equal to the sample threshold.

[0119] In this embodiment, mutant samples are removed using the median value Md. Since the median value is the middle value when a set of data is arranged in ascending order, it is unaffected by extreme values, more stable than the mean, and can accurately reflect the middle position of the data, making it suitable for asymmetric distributed sample data.

[0120] In some embodiments, step S430 above, "determining the moving average A and the load interval corresponding to the training mode [T]", is described. L T U In the event of a mismatch, determining the target adjustment coefficient α based on the moving average A, the trend slope K, and the error threshold E can be achieved through the following steps:

[0121] Step A: Determine that the sliding average value A is less than T. L ;

[0122] The sliding average value A is less than T L This allows us to determine if the current resistance setting is too high and cannot meet the user's training objectives.

[0123] Step B: If the trend slope K is less than -2E, the first adjustment coefficient α1 is determined as the target adjustment coefficient α.

[0124] During implementation, when A <T L If K < -2E, then α = α1. For example, α1 can be 0.95. That is, you can choose the coefficient α1 that is furthest from 1 (such as 0.95) to quickly reduce the resistance and bring A back to the safe range.

[0125] Step C: If the trend slope K is greater than or equal to -2E and less than -E, then the second adjustment coefficient α2 is determined as the target adjustment coefficient α.

[0126] During implementation, when A <T L If -2E ≤ K < -E, then α = α². For example, α² can be 0.98. That is, a moderate coefficient α² (such as 0.98) can be chosen to moderately reduce resistance.

[0127] Step D: If the trend slope K is greater than or equal to -E and less than 0, the third adjustment coefficient α3 is determined as the target adjustment coefficient α.

[0128] During implementation, when A <T L If -E ≤ K < 0, then α = α3. For example, α3 can be 0.995. That is, you can choose the coefficient α3 closest to 1 (such as 0.995) to fine-tune the resistance and avoid overreaction.

[0129] Among them, α1, α2, and α3 increase in sequence and are all less than 1.

[0130] Here, since K set in steps B, C, and D is getting closer and closer to 0, that is, the change is getting smaller and smaller, α1, α2, and α3 can be set to increase in sequence and approach 1, that is, the adjustment of the resistance is getting smaller and smaller.

[0131] In the embodiment of the present application, it is determined that the sliding average value A is less than T L In this case, three intervals are set based on the relationship between K and E, corresponding to three different adjustment coefficients respectively. In this way, adopting the segmented PID control idea, different K value intervals correspond to different adjustment coefficients, achieving the balance between fast response and overshoot suppression. The intelligent matching of resistance adjustment is realized, so that the user can meet the load interval corresponding to the training mode based on the resistance training after adjustment.

[0132] In some embodiments, in the above step S430, "when it is determined that the sliding average value A does not match the load interval [T L , T U corresponding to the training mode, the target adjustment coefficient α is determined based on the sliding average value A, the trend slope K, and the error threshold E" can be implemented through the following steps:

[0133] Step A: Determine that the sliding average value A is greater than T U ;

[0134] When the sliding average value A is greater than T U , it can be determined that the currently set resistance is insufficient and cannot meet the user's training goal.

[0135] Step B: When it is determined that the trend slope K is greater than 2E, the fourth adjustment coefficient α4 is determined as the target adjustment coefficient α;

[0136] In the implementation process, when A > T U , if K > 2E, then α = α4. For example, α4 can take a value of 1.05. The coefficient α4 (such as 1.05) farthest from 1 can be selected to quickly increase the resistance to make A return to the safe interval.

[0137] Step C: When it is determined that the trend slope K is greater than E and less than or equal to 2E, the fifth adjustment coefficient α5 is determined as the target adjustment coefficient α;

[0138] In the implementation process, when A > T U , if E < K ≤ 2E, then α = α5. For example, α5 can take a value of 1.02. That is, the medium coefficient α5 (such as 1.02) can be selected to moderately increase the resistance.

[0139] Step D: When it is determined that the trend slope K is greater than 0 and less than E, determine the sixth adjustment coefficient α6 as the target adjustment coefficient α;

[0140] During implementation, when A > T U If 0 < K ≤ E, then α = α6(1.005). For example, α6 can take the value of 1.005. That is, the coefficient α6 closest to 1 (such as 1.005) can be selected to fine-tune the resistance to avoid overreaction.

[0141] Among them, α4, α5, and α6 decrease in sequence and are all greater than 1. <00​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​Step C: If K is less than 0, determine the eighth adjustment coefficient α8 as the target adjustment coefficient α;

[0150] If K < 0, then α = α8 (0.998), that is, the resistance is finely adjusted, so that K can meet the error threshold.

[0151] Among them, α7 is greater than 1, and α8 is less than 1.

[0152] In this embodiment of the application, when it is determined that A meets the preset range, the adjustment system is determined by comparing the magnitudes of K and E to fine-tune the resistance so that K meets the error threshold.

[0153] In some embodiments, during back muscle training using a training device, at least one of the following parameters is output: cumulative load parameter, average load parameter, MPV adaptation percentage, and fatigue index, wherein the cumulative load parameter is determined based on the load of each set of ropes; the average load represents the average load of each set of ropes; the MPV adaptation percentage represents the percentage of times the MPV falls within the adaptation range of the corresponding mode out of the total number of completed times; and the fatigue index is determined based on the MPV of the last set of ropes and the MPV of the first set of ropes.

[0154] Here, the cumulative load can be calculated using the above formula (5); the MPV adaptation ratio can be calculated using the above formula (6); and the fatigue index can be calculated using the above formula (7).

[0155] After training, the system automatically generates a quantitative report, which clearly defines the %1RM achievement rate within the training time (the percentage of time within the effective training time that the %1RM is in the target range), the estimated strength improvement (the percentage of the user's %1RM improvement within one month predicted by a model based on training load, cumulative repetitions, and historical data), the neuromuscular efficiency improvement ratio (calculated through MPV stability and %1RM response speed, reflecting the improvement in the efficiency of nerve control over muscles), and the training load rationality score (a score of 0 to 100 points given based on the user's preset goals, body data, and training performance), thus achieving objective quantification and data-driven presentation of training effects.

[0156] In this embodiment, the training device can output at least one of the following parameters in real time: cumulative load parameter, average load parameter, MPV adaptation ratio, and fatigue index. This allows users to understand the training status based on the displayed parameters, improving the scientific nature of the training and the user experience.

[0157] This application provides a rope tension force measurement training device (adaptive resistance back muscle training device), which includes three core modules: a walking fixed module, a training module, and an intelligent control and display module.

[0158] The walking and fixing module is located at the bottom of the back muscle trainer and is used to enable the movement and fixation of the back muscle trainer.

[0159] The training module, located inside the back muscle trainer, is used for rope tension testing and upper limb training movements.

[0160] The intelligent control display module is used to display the preset training mode. During the back muscle training process, the sliding average value A of W real-time load parameters is determined based on the real-time acquisition of the average centripetal velocity MPV of the cable. Here, W is an integer greater than or equal to 2, which is based on the preset number of trend judgment windows.

[0161] Based on the W moving averages A, the trend slope K is obtained by linear regression fitting.

[0162] Determine the moving average A and the load interval [T] corresponding to the training mode. L T U In the event of a mismatch, the target adjustment coefficient α is determined based on the moving average A, the trend slope K, and the error threshold E.

[0163] The initial resistance determined based on the training mode is adjusted using the target adjustment coefficient α, so that the sliding average value A matches the load interval [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E.

[0164] In this embodiment, the training effect is accurately quantified by load quantification and adaptive resistance adjustment. Parameters are automatically optimized for different training goals and real-time feedback is provided to ensure that resistance adjustment conforms to biomechanical characteristics, effectively reducing the risk of sports injury and improving training effect.

[0165] In some embodiments, the walking and fixing module includes a walking component and a fixing component, such as... Figure 1 and 3 As shown, the walking assembly includes a left front swivel wheel 2, a right front swivel wheel 3, a left rear swivel wheel 4, and a right rear swivel wheel 5;

[0166] The fixing components include a left front suction cup 6, a right front suction cup 7, a left rear suction cup 8, and a right rear suction cup 9 disposed at the bottom of the back muscle training device, and a telescopic motor 11, a negative pressure generator 10, and a chassis 21 disposed inside the back muscle training device; wherein, the telescopic motor 11 is connected to the chassis 21 on which the negative pressure generator 10 is disposed, and the left front suction cup 6, the right front suction cup 7, the left rear suction cup 8, and the right rear suction cup 9 are all mounted on the chassis 21;

[0167] The chassis 21 is driven to move downward by the telescopic motor 11, so that the left front suction cup 6, the right front suction cup 7, the left rear suction cup 8, and the right rear suction cup 9 are attached to the ground and fixed. It is then moved upward so that the left front suction cup 6, the right front suction cup 7, the left rear suction cup 8, and the right rear suction cup 9 are removed from the ground and move.

[0168] This application provides an innovative walking fixation module that combines omnidirectional wheels and a suction cup system, balancing ease of movement with training stability.

[0169] In some embodiments, such as Figure 1 As shown, the training device is internally equipped with a first support 12 and a second support 13.

[0170] The first bracket 12 is equipped with an encoder 14 and a current-adjustable magnetic powder brake 15;

[0171] The second bracket 13 is equipped with a DC geared motor 16;

[0172] A connecting rod 17 is provided between the first bracket 12 and the second bracket 13, and the connecting rod 17 is provided with an upper cable reel 18;

[0173] A lower winding reel 19 is provided between the DC geared motor 16 and the adjustable magnetic powder brake 15;

[0174] The pull rope 20 runs inside and outside the training device, with its two ends connected to the upper reel 18 and the lower reel 19, respectively. The encoder 14 is used to detect the average centripetal speed (MPV) of the pull rope in real time.

[0175] The training device provided in this embodiment is suitable for various scenarios such as home, gym and rehabilitation institution, and comprehensively improves the scientific nature of training and user experience.

[0176] This application provides an adaptive resistance back muscle training control device. Please refer to [link to relevant documentation]. Figure 5 The device 500 includes:

[0177] The acquisition module 510 is used to determine the sliding average value A of the real-time load parameter corresponding to the number of consecutive W trend judgment windows during back muscle training based on the real-time acquired average centripetal velocity MPV of the pull rope, where W is the number of trend judgment windows determined based on the training mode and is an integer greater than or equal to 2.

[0178] The fitting module 520 is used to obtain the trend slope K by linear regression fitting based on W moving averages A;

[0179] Module 530 is used to determine the moving average A and the load interval [T] corresponding to the training mode. L T UIn the case of mismatch, the target adjustment coefficient α is determined based on the moving average A, the trend slope K, and the error threshold E, wherein the error threshold E is determined based on the training mode;

[0180] The adjustment module 540 is used to adjust the initial resistance determined based on the training mode using the target adjustment coefficient α, so that the current adaptive resistance can make the sliding average value A stably within the load range [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E, wherein the initial resistance is determined based on the control current, damping coefficient and the radius of the pull rope.

[0181] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. For example, as shown... Figure 6 As shown, the computer device 600 includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602, wherein when the processor 602 executes the computer program 603, the computer device can execute any of the adaptive resistance back muscle training control methods described above.

[0182] Furthermore, this application also protects a control device, which may include a memory and a processor. The memory stores executable program code, and the processor is used to call and execute the executable program code to perform an adaptive resistance back muscle training control method provided in this application. This application can divide the control device into functional modules based on the above method examples. For example, each module may correspond to a specific function, or two or more functions may be integrated into a single processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this application is illustrative and only represents a logical functional division; other division methods may exist in actual implementation. It should also be noted that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here. It should be understood that the control device provided in this application is used to execute the above-mentioned adaptive resistance back muscle training control method, and therefore can achieve the same effect as the above-mentioned implementation method. When using integrated units, the control device may include a processing module and a storage module. When the control device is applied to a block device, the processing module can be used to control and manage the actions of the block device. The storage module can be used to support block devices in executing mutual program code, etc. The processing module can be a processor or controller, which can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and microprocessors, etc., and the storage module can be a memory.

[0183] Furthermore, the control device provided in the embodiments of this application may specifically be a chip, component, or module. The chip may include a connected processor and a memory; wherein, the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the adaptive resistance back muscle training control method provided in the above embodiments. The embodiments of this application also provide a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, it causes the computer to execute the aforementioned related method steps to implement the adaptive resistance back muscle training control method provided in the above embodiments.

[0184] This application also provides a computer program product. When the computer program product is run on a computer, it causes the computer to execute the aforementioned related steps to achieve the adaptive resistance back muscle training control method provided in the above embodiments. The control device, computer-readable storage medium, computer program product, or chip provided in this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here. Through the description of the above embodiments, those skilled in the art can understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the control device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided in this application, it should be understood that the disclosed control device and method can be implemented in other ways. For example, the control device embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another control device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, control device or unit, and can be electrical, mechanical or other forms.

[0185] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multiple task processing and parallel processing are possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. The above content is only a specific implementation of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application.

[0186] The intelligent quantitative back muscle trainer (adaptive resistance back muscle trainer) is a physical hardware device that can implement an adaptive resistance back muscle training control method.

[0187] An adaptive resistance back muscle training control device is a virtual device capable of implementing an adaptive resistance back muscle training control method.

[0188] A computer device is a physical hardware device capable of implementing a computer program product, including a computer-readable storage medium.

Claims

1. A back muscle training control method of adaptive resistance, characterized by, The method comprises: In the process of back muscle training, the sliding average values A of W real-time load parameters of the training are determined based on the real-time average centripetal velocity MPV of the pull rope, wherein W is the number of trend determination windows determined based on the training mode, and is an integer greater than or equal to 2; A trend slope K is obtained by linear regression fitting based on the W sliding average values A; determining a target adjustment coefficient a based on the moving average value A, the trend slope K, and an error threshold value E, in a case where it is determined that the moving average value A and a load range [T L , T U ] corresponding to the training pattern do not match, wherein the error threshold value E is determined based on the training pattern; An initial resistance determined based on the training mode is adjusted by using the target adjustment coefficient α to obtain a current adaptive resistance, which can make the sliding average value A stable in the load interval [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E, wherein the initial resistance is determined based on the control current, the damping coefficient and the radius of the pull rope.

2. The method of claim 1, wherein, The method further comprises: In the process of back muscle training, the sliding average values A of W real-time load parameters of the training are determined based on the real-time average centripetal velocity MPV of the pull rope, wherein W is the number of trend determination windows determined based on the training mode, and is an integer greater than or equal to 2; In a trend determination window, S average centripetal velocities MPV satisfying a preset continuous comparison number are collected, wherein S is determined based on the training mode and is an integer greater than or equal to 1; Based on a deviation threshold δ, effective samples with a number greater than a sample threshold are determined from the S average centripetal velocities MPV, wherein the deviation threshold δ is determined based on the training mode, and the sample threshold is determined based on S; 3. The method of claim 2, wherein, Based on the effective samples, a sliding average value A corresponding to one of the trend determination windows is determined to determine W sliding average values A corresponding to W trend determination windows. Based on a deviation threshold δ, effective samples with a number greater than a sample threshold are determined from the S average centripetal velocities MPV, wherein the deviation threshold δ is determined based on the training mode, and the sample threshold is determined based on S. The median value Md of the S average centripetal velocities MPV is determined; In the S average centripetal velocities MPV, mutant values with a relative deviation exceeding the deviation threshold δ from the median value Md are removed to obtain effective samples; 4. The method of claim 1, wherein, The determining the target adjustment coefficient a based on the sliding average value A, the trend slope K and an error threshold E in a case where the sliding average value A and the load interval [T L , T U ] corresponding to the training mode do not match comprises: determining that the moving average A is less than T L ; It is determined that the number of the effective samples is greater than or equal to the sample threshold. In a case where the trend slope K is less than -2E, a first adjustment coefficient α1 is determined as the target adjustment coefficient α; In a case where the trend slope K is greater than or equal to -2E and less than -E, a second adjustment coefficient α2 is determined as the target adjustment coefficient α; In a case where the trend slope K is greater than or equal to -E and less than 0, a third adjustment coefficient α3 is determined as the target adjustment coefficient α; 5. The method of claim 4, wherein, Wherein, α1, α2, α3 increase in turn and are all less than 1. determining that the moving average A is greater than T U ; The method further comprises: In a case where the trend slope K is greater than 2E, a fourth adjustment coefficient α4 is determined as the target adjustment coefficient α; In a case where the trend slope K is greater than E and less than or equal to 2E, a fifth adjustment coefficient α5 is determined as the target adjustment coefficient α; In a case where the trend slope K is greater than 0 and less than E, a sixth adjustment coefficient α6 is determined as the target adjustment coefficient α; 6. The method of claim 4, wherein, Wherein, α4, α5, α6 decrease in turn and are all greater than 1. determining that the moving average A is greater than or equal to T L and less than or equal to T U while the absolute value of the trend slope K is greater than E; The method further comprises: In a case where K is greater than 0, a seventh adjustment coefficient α7 is determined as the target adjustment coefficient α; determining the eighth adjustment coefficient a8 as the target adjustment coefficient a when it is determined that k is less than 0; wherein a7 is greater than 1 and a8 is less than 1.

7. The method according to any one of claims 1 to 6, characterized in that, The method further comprises: During the back muscle training process, outputting at least one of the following parameters: a cumulative load parameter, an average load parameter, an MPV adaptation proportion, and a fatigue index, wherein the cumulative load parameter is determined based on the load of each group of pull ropes; the average load represents the average load of each group of pull ropes; the MPV adaptation proportion represents the percentage of the number of times that the MPV falls within the corresponding mode adaptation interval to the total number of completions; and the fatigue index is determined based on the MPV of the last group of pull ropes and the MPV of the first group of pull ropes.

8. A back muscle training apparatus of self-adapting resistance, characterized in that, The back muscle training instrument comprises a walking fixing module, a training module, and an intelligent control display module. The walking fixing module is arranged at the bottom of the back muscle training instrument and is used to realize the movement and fixation of the back muscle training instrument. The training module is arranged inside the back muscle training instrument and is used for rope-pulling force detection and upper limb training action execution. The intelligent control display module is used to display a preset training mode, and during the back muscle training process, a sliding average value A of real-time load parameters corresponding to W continuous trend determination windows is determined based on the real-time acquisition of the average centripetal velocity MPV of the pull rope, wherein W is the number of trend determination windows determined based on the training mode and is an integer greater than or equal to 2. A trend slope K is obtained by linear regression fitting based on W sliding average values A. determining a target adjustment coefficient a based on the moving average value A, the trend slope K, and an error threshold value E, in a case where it is determined that the moving average value A and a load range [T L , T U ] corresponding to the training pattern do not match, wherein the error threshold value E is determined based on the training pattern; The initial resistance determined based on the training mode is adjusted using the target adjustment coefficient a, so that the sliding average value A is stably within the load interval [TL, TU] corresponding to the training mode, and the absolute value of the trend slope K is less than or equal to the error threshold E, wherein the initial resistance is determined based on the control current, the damping coefficient, and the radius of the pull rope.

9. The training apparatus of claim 8 wherein, The walking fixing module comprises a walking assembly and a fixing assembly. The walking assembly comprises a left front universal wheel (2), a right front universal wheel (3), a left rear universal wheel (4), and a right rear universal wheel (5). The fixing assembly comprises a left front suction disc (6), a right front suction disc (7), a left rear suction disc (8), and a right rear suction disc (9) arranged at the bottom of the back muscle training instrument, and a telescopic motor (11), a negative pressure generator (10), and a chassis (21) arranged inside the back muscle training instrument; wherein the telescopic motor (11) is connected with the chassis (21) where the negative pressure generator (10) is arranged, and the left front suction disc (6), the right front suction disc (7), the left rear suction disc (8), and the right rear suction disc (9) are all mounted on the chassis (21). The left front suction disc (6), the right front suction disc (7), the left rear suction disc (8), and the right rear suction disc (9) are adsorbed and fixed to the ground by driving the chassis (21) to move downward using the telescopic motor (11), and are moved by separating from the ground by moving upward.

10. The training apparatus of claim 8 wherein, First and second supports (12) and (13) are arranged inside the training instrument. The first support (12) is provided with an encoder (14) and a current adjustable magnetic powder brake (15); The second support (13) is provided with a DC speed reduction motor (16); A connecting rod (17) is arranged between the first support (12) and the second support (13), and the connecting rod (17) is provided with an upper pulley (18); A lower pulley (19) is arranged between the DC speed reduction motor (16) and the adjustable magnetic powder brake (15); A pull rope (20) passes through the inside and outside of the training instrument, and two ends thereof are connected with the upper pulley (18) and the lower pulley (19) respectively, and the encoder (14) is used for detecting the average centripetal velocity MPV of the pull rope in real time.

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