Muscle fatigue quantification and prediction method based on output power and residual energy

Abstract

The invention discloses a muscle fatigue quantification and prediction method based on output power and residual energy, and belongs to the technical field of sports sciences, and the method comprises the following steps: S1, selecting upper limb motion information and a surface electromyogram signal sEMG of biceps brachii muscle in the bending and lifting process of a single-arm dumbbell as analysis objects, calculating the output power of the biceps brachii muscle in the movement process based on a Hill muscle mechanical model, and analyzing the change rule of the muscle output power in the muscle fatigue aggravation process according to the change condition of the muscle output power and the subjectively judged muscle fatigue degree; s2, constructing an improved CP model adaptive to the current physiological state; and S3, a muscle fatigue degree quantification and prediction model is constructed, accurate positioning of the muscle moderate fatigue stage is achieved, the effectiveness and feasibility of the muscle fatigue degree quantification and prediction model are verified in the dumbbell single arm bending and lifting experiment, the muscle fatigue state can be accurately described, and accurate quantification of the muscle fatigue state is achieved.

Description

Technical Field

[0001] The invention belongs to the field of sports science and technology, and in particular relates to a muscle fatigue quantification and prediction method based on output power and residual energy. Background Art

[0002] With the increase in the number of sub-healthy people, indoor convenient fitness exercises have received more and more attention. With the continuation of fitness exercises, muscles gradually develop from a non-fatigue state to a moderate fatigue or even an over-fatigue state, and muscle fatigue will lead to an increase in the amplitude of muscle force fluctuations and a decrease in control complexity, which will in turn reduce the stability and adaptability of the muscle system. This change not only has a negative impact on motor control, sports mechanics and motor coordination, but also reduces the effect of fitness training, increases the risk of failure of sports tasks, and increases the possibility of sports injuries. Therefore, accurate quantification of muscle fatigue is particularly important, which not only helps to achieve scientific planning and safety monitoring of fitness exercises, but also has an extremely significant role in promoting the development of scientific fitness. At present, the research on muscle fatigue quantification is mainly carried out from the time-frequency characteristics of surface electromyography signals and muscle residual energy. Among them, the continuous and soothing changes in muscle residual energy lay the foundation for the quantification and prediction of muscle fatigue.

[0003] At present, fatigue quantification research based on muscle energy changes usually mainly uses the hyperbolic relationship between muscle output power and duration. However, this method requires the collection of multiple sets of experimental data to fit the key parameters required by the model, and cannot adapt well to the physiological state affected by multiple factors such as health status, temperature and humidity changes. In addition, this method is currently mainly used to analyze the overall sports performance of multiple muscles and tissues in the human body, but in actual exercise, multiple muscle tissues will not reach a fatigue state at the same time, so it is impossible to accurately quantify muscle fatigue. At the same time, non-professional fitness people often use moderate muscle fatigue to achieve bodybuilding, muscle gain or improve endurance. Under the premise of ensuring safety, the key to improving fitness efficiency lies in the rational use of muscle fatigue, which is to ensure that the muscles fully release energy in the moderate fatigue stage, and to prevent them from falling into an excessive fatigue state and causing muscle damage.

[0004] Therefore, there is a need for a muscle fatigue quantification and prediction method based on output power and residual energy that can accurately characterize the muscle fatigue state. Summary of the invention

[0005] The purpose of the present invention is to provide a muscle fatigue quantification and prediction method based on output power and residual energy, which can accurately characterize the muscle fatigue state and achieve accurate quantification of the muscle fatigue state.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for quantifying and predicting muscle fatigue based on output power and remaining energy, comprising the following steps:

[0008] Step S1: Select the upper limb movement information and the surface electromyogram signal sEMG of the biceps brachii during the single-arm dumbbell curl as the analysis object, calculate the output power of the biceps brachii during the movement based on the Hill muscle mechanics model, and analyze the change law of the muscle output power during the process of muscle fatigue aggravation according to the change of the muscle output power and the subjectively judged muscle fatigue degree;

[0009] Step S2: Establish a critical power CP model for a single muscle tissue according to the output power and sustainable time of the biceps brachii under different exercise intensities, use the muscle output power of the non-fatigued exercise cycle to fit the virtual true value of the muscle output power required under the current physiological state, and construct an improved CP model that adapts to the current physiological state;

[0010] Step S3: Based on the change laws of muscle output power and remaining energy, construct a muscle fatigue degree quantification and prediction model, realize the accurate positioning of the moderate muscle fatigue stage, and verify the effectiveness and feasibility of the muscle fatigue degree quantification and prediction model in the single-arm dumbbell curl experiment.

[0011] A further improvement of the technical solution of the present invention is that in step S1, the Hill mechanics model is based on the ultrastructure of the muscle and includes a contractile unit, a parallel elastic unit, a series elastic unit, and a pennation angle

[0012] A further improvement of the technical solution of the present invention is that in step S1, by collecting the surface electromyogram signal of the biceps brachii and the movement state information of the forearm during the upper limb movement and using them as the input signals of the Hill muscle mechanics model, the output power of the biceps brachii during the movement is calculated. The muscle mechanics model formula is as follows:

[0013]

[0014] In the formula, a represents muscle activation, F max represents the maximum muscle force that can be generated during isometric muscle contraction, v max represents the maximum contraction speed of muscle fibers, which is related to the optimal muscle fiber length of the muscle and represents the normalized muscle fiber contraction speed correlation function.

[0015] A further improvement of the technical solution of the present invention is that the step S2 includes the following steps:

[0016] Step S201: Through multiple groups of experiments, obtain the output power and sustainable time of the biceps brachii reaching muscle fatigue under different exercise intensities in the same physiological state;

[0017] Step S202: Using the muscle output power and sustainable time, establish the critical power CP model formula as follows:

[0018]

[0019] In the formula, P represents the average power during the entire duration, T lim represents the sustainable time at the current average output power, CP represents the critical output power that can maintain long-term muscle movement, and W′ represents the available work beyond CP;

[0020] Step S203: Convert the CP model into a linear form to reduce the complexity of the parameter fitting process. The formula of the linear CP model is as follows:

[0021] W = CP × t + W'

[0022] In the formula, t represents the time required from the start of the movement to the muscle over-fatigue stage, and W represents the total energy consumed during the process from the start of the movement to the muscle over-fatigue;

[0023] Step S204: Calculate the average value for several bicep curl cycles with relatively stable muscle output power in the early stage of the movement, and recognize this average value as the virtual true value of the muscle output power required to complete the fitness task under the current physical condition. It is considered that this average value can represent the current physical condition, and construct a linear model between W′ and this virtual true value to obtain an improved CP model.

[0024] A further improvement of the technical solution of the present invention lies in: in step S204, adjust W′ in the CP model through the average muscle output power to enable the CP model to adapt to changes in the physical condition. Among them, the calculation formula of the average muscle output power is as follows:

[0025]

[0026] In the formula, P dzi represents the average power consumed in the i-th bicep curl cycle, m and n respectively represent the m-th and n-th bicep curl cycles, and P tru represents the virtual true value of the muscle output power required to complete the fitness task under the current physical condition;

[0027] The formula of the improved CP model is as follows:

[0028]

[0029] In the formula, k and b represent the coefficients of the linear model.

[0030] A further improvement of the technical solution of the present invention is that in step S3, muscle output power and residual energy are used as judgment basis respectively to accurately divide muscle fatigue into three stages: no fatigue, moderate fatigue and excessive fatigue.

[0031] A further improvement of the technical solution of the present invention is that the step S3 includes the following steps:

[0032] Step S301: constructing a muscle residual energy recovery model during intermittent exercise based on an exponential energy recovery model to calculate the muscle residual energy during exercise in real time;

[0033] Assuming that fatigue exercise consumes W' during the time 0 to u, and stops exercising after time u to enter the recovery phase, the complete energy consumption and muscle residual energy recovery model during this interval exercise is as follows:

[0034]

[0035] Where W' bal (t) represents the remaining W′ at time t;

[0036] Step S302: using muscle output power as a criterion for determining whether the muscle has entered a moderate fatigue stage, so as to achieve the division of muscle fatigue stages, and selecting muscle output power as a first threshold point as a dividing point between a non-fatigue state and a moderate fatigue state;

[0037] Step S303: Determine the mean and standard deviation of the muscle output power, and set a threshold range of the muscle output power. When the muscle output power exceeds the threshold range, it is determined that the muscle has reached a moderate fatigue state.

[0038] Step S304: using the remaining muscle energy as a prediction criterion for entering an excessive fatigue state;

[0039] Step S305: verifying the performance of the muscle fatigue degree quantification and prediction model in terms of fatigue degree quantification and prediction capability during intermittent exercise through a dumbbell single-arm curl experiment.

[0040] A further improvement of the technical solution of the present invention is that in step S303, the muscle output power threshold range determination formula is as follows:

[0041] P mean_n =A1×P mean_n-1 +A2×P n-1

[0042] Where P mean_n P represents the theoretical true value of the muscle output power required for the nth curling action. nIt represents the actual muscle output power value of the nth bicep curl movement, and A1 and A2 represent iteration factors;

[0043] The calculation formula for the muscle output power threshold range is as follows:

[0044] P thr = k × P mean_n

[0045] In the formula, P thr represents the threshold of the muscle output power required for the nth bicep curl, and k represents the threshold coefficient.

[0046] A further improvement of the technical solution of the present invention is that in step S304, W' is used as the judgment criterion for the second threshold, and it is proposed to compare W' bal with the required W' during a single movement in real time after the end of a single movement. When it is found that W' bal is less than the required W', it is considered that the muscle is about to enter a state of excessive fatigue.

[0047] Due to the adoption of the above technical solution, the technical progress achieved by the present invention is:

[0048] The present invention provides a method for quantifying and predicting muscle fatigue based on output power and remaining energy. By exploring the variation laws of muscle output power during fatigue exercise and muscle remaining energy under different physiological states, it effectively solves the problems such as misjudging the fatigue degree and inability to adapt to physiological changes during the process of muscle fatigue quantification, realizes the accurate quantitative characterization of muscle fatigue state, and effectively ensures the personal safety of fitness enthusiasts during exercise.

[0049] The present invention is based on surface electromyogram signals and elbow joint movement data. It calculates muscle output power through the Hill model, combines subjective fatigue degree judgment to obtain the variation law of muscle output power during the fatigue aggravation process, and on this basis, proposes an improved CP model based on muscle average output power, thereby improving the calculation accuracy of muscle remaining energy; First, muscle fatigue directly leads to a decrease in muscle contraction ability. Therefore, muscle output power can be used as an intuitive indicator to reflect the fatigue degree. Studying the variation law of muscle output power during the muscle fatigue aggravation process can reveal the fatigue evolution mechanism of muscles in a continuous contraction state, thus providing a theoretical basis for human movement science; Second, based on the improved CP model, the present invention realizes the accurate prediction of muscle fatigue degree by calculating the muscle remaining energy during exercise, makes up for the deficiency of existing methods in the long-term prediction of fatigue degree, and provides a new idea for safety guarantee during exercise and fitness; Finally, the present invention combines the energy recovery model to calculate the recovery of muscle energy during intermittent exercise, providing effective data support for the scientific planning of fitness exercises such as dumbbell bicep curl exercises and personalized fitness guidance.

[0050] By conducting research on the core key technologies of muscle fatigue quantification and energy control, the present invention realizes the real-time quantification of muscle fatigue degree and state prediction during fitness exercises, providing new theories and technologies for fatigue monitoring and fitness guidance, which is of great significance for improving the health level of the people. Description of the Drawings

[0051] Figure 1 is the flowchart of the muscle fatigue quantification and prediction method of the present invention;

[0052] Figure 2 is the structural diagram of the Hill muscle mechanics model in the present invention;

[0053] Figure 3 is the graph of the change law of muscle output power in the present invention;

[0054] Figure 4 is the schematic diagram of the muscle output power-duration curve (CP model) in the present invention;

[0055] Figure 5 is the graph of the quantification result of muscle fatigue degree in the present invention. Detailed Description of the Invention

[0056] The following further describes the present invention in detail with reference to the embodiments:

[0057] As Figure 1 shown, the present invention provides a muscle fatigue quantification and prediction method based on output power and remaining energy, including the following steps:

[0058] Step S1: Select the upper limb movement information and the surface electromyogram signal sEMG of the biceps brachii during the single-arm dumbbell curl as the analysis object, calculate the output power of the biceps brachii during the movement based on the Hill muscle mechanics model, and analyze the change law of the muscle output power during the process of muscle fatigue aggravation according to the change of muscle output power and the subjectively judged muscle fatigue degree, as Figure 3 shown; among them, a simplified upper limb model is constructed based on the Hill muscle mechanics model, considering the complexity of the human bone and muscle tissues, and at the same time reducing the complexity of the calculation of muscle output power, which is more conducive to muscle mechanics analysis, as Figure 2 shown, the Hill mechanics model is based on the ultrastructure of the muscle, including a contractile unit, a parallel elastic unit, a series elastic unit, and a pennation angle

[0059] Specifically, by collecting the surface electromyogram signal of the biceps brachii and the movement state information of the forearm during the upper limb movement process, and using them as the input signals of the Hill muscle mechanics model, calculate the output power of the biceps brachii during the movement process. Among them, the muscle mechanics model formula is as follows:

[0060]

[0061] In the formula, a represents muscle activation, F max represents the maximum muscle force that can be generated during isometric muscle contraction, and v max represents the maximum contraction speed of muscle fibers, which is related to the optimal muscle fiber length of the muscle and represents the normalized muscle fiber contraction speed-related function;

[0062] Step S2: According to the output power and sustainable time of the biceps brachii at different exercise intensities, establish a critical power (CP) model for a single muscle tissue. Use the muscle output power during non-fatiguing exercise cycles to fit the virtual true value of the muscle output power required under the current physiological state, and construct an improved CP model that adapts to the current physiological state; use the improved CP model to calculate the remaining energy during exercise, and predict the degree of muscle fatigue during exercise, which is used as a judgment criterion for fatigue quantification and prediction;

[0063] The hyperbolic form of the muscle output power-duration relationship is a basic characteristic of human muscles. The hyperbolic form presented by the CP model enables the model to include two key parameters, the critical power CP and the super-CP limited available work W'. These two parameters can characterize the inherent characteristics of muscle output power and energy determined by an individual's physiological structure. At the same time, W' can also be used as a criterion for judging the degree of muscle fatigue. After fitting the CP model through multiple experiments, the remaining W' during fitness exercise can be calculated in real time, thereby realizing the real-time judgment of the degree of muscle fatigue;

[0064] Specifically, it includes the following steps:

[0065] Step S201: Through multiple groups of experiments, obtain the output power and sustainable time of the biceps brachii at different exercise intensities when muscle fatigue is reached under the same physiological state;

[0066] Step S202: Use the muscle output power and sustainable time to establish the critical power CP model formula as follows:

[0067]

[0068] In the formula, P represents the average power during the entire duration, and T lim represents the sustainable time at the current average output power, CP represents the critical output power that can maintain long-term muscle movement, and W' represents the super-CP limited available work;

[0069] Step S203: Convert the CP model into a linear form to reduce the complexity of the parameter fitting process. The formula for the linear CP model is as follows:

[0070] W = CP × t + W'

[0071] In the formula, t represents the time required from the start of the movement to the stage of muscle over-fatigue, and W represents the total energy consumed during the process from the start of the movement to muscle over-fatigue;

[0072] Step S204: As can be seen from the construction process of the above CP model, this CP model cannot adapt to the changes in physiological states. In view of the above defects, in this step, the average value is obtained for several bicep curl cycles with relatively stable muscle output power in the pre-exercise period, and this average value is recognized as the virtual true value of the muscle output power required to complete the fitness task under the current body physiological state. It is considered that this average value can represent the current physiological state, and then a linear model between W' and this virtual true value is constructed to obtain an improved CP model;

[0073] Specifically, W' in the CP model is adjusted through the average muscle output power to enable the CP model to adapt to the changes in physiological states. Among them, the calculation formula for the average muscle output power is as follows:

[0074]

[0075] In the formula, P dzi represents the average power consumed in the i-th bicep curl cycle, m and n respectively represent the m-th and n-th bicep curl cycles, and P tru represents the virtual true value of the muscle output power required to complete the fitness task under the current physiological state;

[0076] Such as Figure 4 shown, the formula for the improved CP model is as follows:

[0077]

[0078] In the formula, k and b represent the coefficients of the linear model;

[0079] Step S3: Based on the change laws of muscle output power and remaining energy, construct a muscle fatigue degree quantification and prediction model to achieve accurate positioning of the stage of moderate muscle fatigue, that is, use muscle output power and remaining energy as judgment bases respectively, and accurately divide muscle fatigue into three stages: non-fatigue, moderate fatigue, and over-fatigue, and verify the effectiveness and feasibility of the muscle fatigue degree quantification and prediction model in the single-arm dumbbell bicep curl experiment;

[0080] Specifically, it includes the following steps:

[0081] Step S301: Based on the exponential form energy recovery model, construct a muscle remaining energy recovery model during intermittent exercise to calculate the muscle remaining energy during the movement in real time. Only W' in the CP model is used, and no other variables need to be fitted;

[0082] Assuming that fatigue exercise consumes W' during the time 0 to u, and stops exercising after time u to enter the recovery phase, the complete energy consumption and muscle residual energy recovery model during this interval exercise is as follows:

[0083]

[0084] Where W' bal (t) represents the remaining W′ at time t;

[0085] Step S302: using muscle output power as a criterion for determining whether the muscle has entered a moderate fatigue stage, so as to achieve the division of muscle fatigue stages, and selecting muscle output power as the first threshold point, as the dividing point between non-fatigue and moderate fatigue states; this is because, during isobaric contraction, as muscle fatigue continues to intensify, complex changes such as an increase in the number of recruited muscle fibers, a decrease in metabolic efficiency, and changes in mechanical properties result, which cause muscle performance to decline and output power fluctuations to intensify. In view of this, changes in muscle output power can intuitively reflect the degree of muscle fatigue, so muscle output power is selected as the first threshold point;

[0086] Step S303: Determine the mean and standard deviation of the muscle output power, and set a threshold range of the muscle output power. When the muscle output power exceeds the threshold range, it is determined that the muscle has reached a moderate fatigue state.

[0087] The formula for determining the muscle output power threshold range is as follows:

[0088] P mean_n =A1×P mean_n-1 +A2×P n-1

[0089] Where P mean_n P represents the theoretical true value of the muscle output power required for the nth curling action. n It represents the actual muscle power output value of the nth curling action, and A1 and A2 represent iteration factors;

[0090] The calculation formula for the muscle output power threshold range is as follows:

[0091] P thr =k×P mean_n

[0092] Where P thr represents the threshold of muscle output power required for the nth curl, and k represents the threshold coefficient;

[0093] Step S304: Using the remaining muscle energy as a prediction criterion for entering an over-fatigue state, that is, using W' as a judgment criterion for the second threshold, and proposing to change W' in real time after an action is completed.bal Compare with the required W' during a single biceps curl movement process. When it is found that W' bal is less than the required W', it is considered that the muscle is about to enter the state of over-fatigue; taking W' in the CP model as the criterion for judging muscle over-fatigue can not only adapt to the current physiological state, but also avoid the problem of volatility. At the same time, the characteristic that W' remains constant under different task requirements also lays a foundation for muscle fatigue prediction;

[0094] Step S305: Verify the performance of the muscle fatigue degree quantification and prediction model in quantifying and predicting the fatigue degree during intermittent movement through a single-arm dumbbell biceps curl experiment;

[0095] In the experimental verification process, subjects are selected for dumbbell biceps curl tests; first, the CP model is fitted by collecting multiple sets of dumbbell biceps curl data. Subsequently, surface electromyogram signals and inertial signals during the biceps curl process are collected in real time and uploaded to the host computer for real-time processing. According to the above steps, the fatigue degree is quantified and predicted based on muscle output power and remaining energy; a typical verification experiment is as Figure 5 shown: The muscle output power shows that the subject reaches a moderate fatigue state at the 12th biceps curl cycle; the remaining energy model predicts that it will enter the over-fatigue state at the 17th biceps curl cycle; from Figure 5 this, it can be seen that significant jumps in muscle output power occur at the 12th and 17th biceps curls, which are completely consistent with the above division of fatigue stages; in addition, the quantified and predicted results output by the model are consistent with the subjective fatigue assessment of the subject, further verifying the accuracy of this method;

[0096] In response to the problems of changes in body state and the volatility of muscle output power, this step proposes an adaptive dynamic threshold adjustment technology based on virtual true value learning. Select the output power in the non-fatigued state of the muscle in the early stage, and approximate the virtual true value of the current required muscle output power through an iterative algorithm to achieve real-time and accurate monitoring of different physiological states. Set the power change threshold range based on the mean and standard deviation in statistical methods, and construct a muscle fatigue degree judgment model.

[0097] It can be understood that the present invention is described through some embodiments. Those skilled in the art know that without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. In addition, under the teaching of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the scope protected by the present invention.

Claims

1. A method for quantifying and predicting muscle fatigue based on output power and remaining energy, characterized in that It includes the following steps: Step S1: Select the upper limb movement information and the surface electromyogram signal sEMG of the biceps brachii during the single-arm dumbbell curl as the analysis object, calculate the output power of the biceps brachii during the movement based on the Hill muscle mechanics model, and analyze the change law of the muscle output power during the process of muscle fatigue aggravation according to the change of muscle output power and the subjectively judged muscle fatigue degree; Step S2: According to the output power and sustainable time of the biceps brachii at different exercise intensities, establish a critical power CP model for a single muscle tissue, use the muscle output power of the non-fatigue exercise cycle to fit the virtual true value of the muscle output power required in the current physiological state, and construct an improved CP model adapted to the current physiological state; Step S3: Based on the change laws of muscle output power and remaining energy, construct a muscle fatigue degree quantification and prediction model, realize the accurate positioning of the moderate muscle fatigue stage, and verify the effectiveness and feasibility of the muscle fatigue degree quantification and prediction model in the single-arm dumbbell curl experiment.

2. The muscle fatigue quantification and prediction method based on output power and remaining energy according to claim 1, wherein: In step S1, the Hill mechanical model is based on the ultrastructure of muscle, including contractile units, parallel elastic units, series elastic units, and pennation angle 3. A method for quantifying and predicting muscle fatigue based on output power and remaining energy according to claim 2, characterized in that: In step S1, by collecting the surface electromyogram signal of the biceps brachii and the forearm movement state information during the upper limb movement and using them as the input signals of the Hill muscle mechanics model, calculate the output power of the biceps brachii during the movement. The muscle mechanics model formula is as follows: Where a represents muscle activation, F max represents the maximum muscle force that can be generated during isometric muscle contraction, v max represents the maximum contraction speed of muscle fibers, which is related to the optimal muscle fiber length of the muscle and represents the normalized muscle fiber contraction speed related function.

4. A method for quantifying and predicting muscle fatigue based on output power and remaining energy according to claim 3, characterized in that: The said step S2 includes the following steps: Step S201: Through multiple groups of experiments, obtain the output power and sustainable time of the biceps brachii at different exercise intensities until muscle fatigue in the same physiological state; Step S202: Use the muscle output power and sustainable time to establish the critical power CP model formula as follows: where P represents the average power over the entire duration, T lim represents the sustainable time at the current average output power, CP represents the critical output power for maintaining long-term muscle movement, and W′ represents the available work beyond CP; Step S203: Convert the CP model into a linear form to reduce the complexity of the parameter fitting process. The formula of the linear CP model is as follows: W = CP × t + W' In the formula, t represents the time required from the start of the movement to the stage of muscle over-fatigue, and W represents the total energy consumed from the start of the movement to the process of muscle over-fatigue; Step S204: Calculate the average value of several curl cycles with relatively stable muscle output power in the early stage of the movement, and recognize this average value as the virtual true value of the muscle output power required to complete the fitness task in the current body physiological state. It is considered that this average value can represent the current physiological state, and construct a linear model between W′ and this virtual true value to obtain an improved CP model.

5. A method for quantifying and predicting muscle fatigue based on output power and remaining energy according to claim 4, characterized in that: In step S204, adjust W′ in the CP model through the average muscle output power to achieve the adaptation of the CP model to the change of physiological state. The calculation formula of the average muscle output power is as follows: where P dzi represents the average power consumed in the i-th bicep curl cycle, m and n respectively represent the m-th and n-th bicep curl cycles, and P tru represents the virtual true value of the muscle output power required to complete the fitness task in the current physiological state; The formula of the improved CP model is as follows: In the formula, k and b represent the linear model coefficients.

6. A method for quantifying and predicting muscle fatigue based on output power and remaining energy according to claim 5, characterized in that: In step S3, use the muscle output power and remaining energy as the judgment basis respectively, and accurately divide muscle fatigue into three stages: non-fatigue, moderate fatigue, and over-fatigue.

7. A method for quantifying and predicting muscle fatigue based on output power and remaining energy according to claim 6, characterized in that: The said step S3 includes the following steps: Step S301: Based on the exponential form energy recovery model, construct a muscle remaining energy recovery model during intermittent exercise to calculate the muscle remaining energy during the movement in real time; Assume that during the time period from 0 to u, fatigue exercise consumes W′, and after the moment u, the exercise stops and enters the recovery stage. The complete energy consumption and muscle remaining energy recovery model during this intermittent exercise period is shown as follows: Where, W' bal (t) represents the remaining W′ at time t; Step S302: Use the muscle output power as the judgment criterion for the muscle to enter the moderate fatigue stage to achieve the division of the muscle fatigue stage. Select the muscle output power as the first threshold point, which is the demarcation point between the non-fatigue and moderate fatigue states; Step S303: Measure the mean and standard deviation of the muscle output power, and set the threshold range of the muscle output power. When the muscle output power exceeds the threshold range, it is determined that the muscle reaches the moderate fatigue state; Step S304: Use the remaining muscle energy as the prediction criterion for the upcoming over-fatigue state; Step S305: Verify the performance of the muscle fatigue degree quantification and prediction model in terms of the quantification and prediction ability of the fatigue degree during the intermittent exercise through the dumbbell single-arm curl experiment.

8. A method for quantifying and predicting muscle fatigue based on output power and remaining energy according to claim 7, characterized in that: In step S303, the formula for determining the threshold range of the muscle output power is shown as follows: P mean_n = A1 × P mean_n-1 + A2 × P n-1 where P mean_n represents the theoretical true value of the muscle output power required for the nth bicep curl motion, and P n represents the actual muscle output power value of the nth bicep curl motion, and A1 and A2 represent iteration factors; The calculation formula for the threshold range of the muscle output power is shown as follows: P thr = k × P mean_n where P thr represents the threshold of the muscle output power required for the nth bicep curl, and k represents the threshold coefficient.

9. A method for quantifying and predicting muscle fatigue based on output power and remaining energy according to claim 8, characterized in that: In step S304, W' is used as the judgment criterion for the second threshold, and it is proposed to update W' in real time after the end of one action. bal Compare it with the W' required during one bicep curl exercise. When it is found that W' bal is less than the required W', it is considered that the muscle is about to enter a state of over-fatigue.

Images