Method and device for analyzing running exercise condition

Abstract

The invention discloses a method and a device for analyzing running exercise conditions. The method comprises the steps that inspection data and test data are collected through a sensor laid on a runway, the inspection data are used for obtaining a stiffness coefficient and a baseline offset, the test data are used for obtaining a plurality of accelerations, and an acceleration time curve is obtained; basic parameters are obtained, wherein the basic parameters comprise the weight, time and stride frequency of an athlete; according to the acceleration and the basic parameters, obtaining a step length, an acceleration change rate, a data relationship between the acceleration and the step length and a data relationship between the acceleration and a stride frequency; acceleration ability and dynamic performance are evaluated, and athlete ability is optimized. Through the method and the device, the problem of incomplete analysis of running conditions of athletes in related technologies is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sports science, in particular, especially relates to a method and device for analyzing running conditions. BACKGROUND

[0002] In the field of sports training, improving athlete performance and reducing sports injuries has always been a research focus. Traditional training methods mainly rely on the experience of coaches, lacking precise data support. With the development of technology, pressure sensors are increasingly used in the field of sports, but existing technologies have many shortcomings. On the one hand, data is not fully utilized, and the relationship between pressure data and athlete performance and injury risk is not fully analyzed. On the other hand, there is a lack of personalized training program development methods, which cannot meet the specific needs of different athletes.

[0003] In view of the above problems existing in the related art, no effective solutions have been proposed so far. SUMMARY

[0004] The main purpose of the present application is to provide a method and device for analyzing running conditions, to at least solve the problem of incomplete analysis of athlete running conditions in the related art.

[0005] In order to achieve the above purpose, according to one aspect of the present application, a method for analyzing running conditions is provided. The method comprises: collecting test data and test data through sensors laid on a track, the test data including charge throughput corresponding to test weight and test time, the test data being used to obtain stiffness coefficient and baseline offset, the test data being test time corresponding to charge throughput, the test data being used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and obtaining an acceleration time curve; obtaining basic parameters, the basic parameters including athlete weight, time and step frequency; obtaining step length, acceleration change rate, data relationship between acceleration and step length, and data relationship between acceleration and step frequency according to the acceleration and the basic parameters; evaluating acceleration ability and dynamic performance, and optimizing athlete ability.

[0006] Optionally, the stiffness coefficient and the baseline offset are obtained according to a plurality of sets of test data, and the formula is wherein, is the test weight, is the stiffness coefficient, is the baseline offset, ; the test data is obtained, the instantaneous force is calculated, and the calculation formula is wherein, is the instantaneous force, is the charge throughput corresponding to the test time, is the test time.

[0007] Optionally, multiple accelerations are calculated, and an acceleration-time curve is obtained based on the acceleration and the test time. The calculation formula is as follows: ,in, For instantaneous force, For acceleration, For the athlete's weight.

[0008] Optionally, the step size is calculated using the following formula: ,in, The initial velocity, For acceleration, Step size, The final velocity; calculate the instantaneous velocity, obtain the instantaneous velocity-time curve, and the calculation formula is as follows. ,in, Instantaneous velocity The initial velocity, For time The corresponding acceleration; calculate the displacement, obtain the displacement-time curve, and the calculation formula is as follows. ,in, For displacement, This is the initial displacement. For time The corresponding instantaneous velocity, For time The corresponding acceleration; differentiate the formula corresponding to the acceleration-time curve to obtain the rate of change of acceleration, and obtain the graph of the rate of change of acceleration. The formula is: ,in, This represents the rate of change of acceleration.

[0009] Optionally, obtain athletes The group's observation data was substituted into the linear model. , to obtain the predicted value Obtain the sum of squared residuals The observed data includes step size and acceleration. ,in, For residuals, For predicted values, The first regression coefficient, The second regression coefficient, For the first Step size of group data For the first Acceleration of the set of data; about Taking the partial derivative, we get ;right about Taking the partial derivative, we get ,in, average acceleration, average step length.

[0010] Optionally, a correlation coefficient is calculated, the correlation coefficient representing a relationship between the acceleration and the step frequency, the calculation formula being: wherein, the correlation coefficient, the acceleration of the first group, the step frequency of the first group, the average acceleration, the average step frequency, .

[0011] Optionally, the average acceleration of a starting stage is calculated, the calculation formula being: wherein, the average acceleration of the starting stage, the time of the starting stage, the formula of the acceleration with respect to time; the acceleration weight, the step length weight and the step frequency weight are obtained through principal component analysis, and the dynamic performance index is calculated, the calculation formula being: wherein, , the dynamic performance index, the acceleration weight, the step length weight, the step frequency weight; the values of the acceleration weight, the step length weight and the step frequency weight are compared, and the item corresponding to the lower value is strengthened. , and

[0012] According to another aspect of the present application, a device for analyzing a running motion condition is provided. The device comprises: a collection unit for collecting inspection data and test data through a sensor laid on a track, the inspection data comprising a charge passing amount corresponding to an inspection weight and an inspection time, the inspection data being used to obtain a stiffness coefficient and a baseline offset, and the test data being a charge passing amount corresponding to a test time, the test data being used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset to obtain an acceleration-time curve; a first obtaining unit for obtaining basic parameters, the basic parameters comprising a weight of an athlete, a time and a step frequency; a second obtaining unit for obtaining a step length, an acceleration change rate, a data relationship between the acceleration and the step length, and a data relationship between the acceleration and the step frequency according to the acceleration and the basic parameters; and an evaluation unit for evaluating an acceleration ability and a dynamic performance, and optimizing an athlete's ability.

[0013] ​To achieve the above object, according to another aspect of the present application, there is provided a computer readable storage medium comprising a stored program, wherein the program performs any one of the above-mentioned methods for analyzing a running motion condition.

[0014] According to another aspect of the present application, there is provided an electronic device comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise a method for performing any one of the above-mentioned methods for analyzing a running motion condition.

[0015] According to the present application, the following steps are adopted: collecting test data and test data through sensors laid on a track, the test data comprising charge passing amount corresponding to test weight and test time, the test data being used to obtain a stiffness coefficient and a baseline offset, the test data being test time corresponding charge passing amount, the test data being used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset to obtain an acceleration time curve; obtaining basic parameters, the basic parameters comprising athlete weight, time and step frequency; obtaining step length, acceleration change rate, data relationship between acceleration and step length, and data relationship between acceleration and step frequency according to the acceleration and the basic parameters; evaluating acceleration ability and dynamic performance, and optimizing athlete ability, which solves the problem of incomplete analysis of athlete running conditions in the related art, and thus achieves the effect of targeted optimization of weak directions of athletes. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0017] Figure 1 is a flowchart of a method for analyzing a running motion condition according to an embodiment of the present application;

[0018] Figure 2 is a structural block diagram of a device for analyzing a running motion condition according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0020] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should fall into the protection scope of the present application.

[0021] It should be noted that the terms "first", "second", and the like in the description and claims of the present application and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0022] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should fall into the protection scope of the present application.

[0023] In the present embodiment, a method for analyzing running motion conditions running on a mobile terminal, a computer terminal or the like is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0024] Figure 1 is a flowchart of a method for analyzing running motion conditions according to an embodiment of the present application. As shown in Figure 1 the method comprises the following steps:

[0025] In step S101, test data and test data are collected by sensors laid on a track. The test data includes the amount of charge passing corresponding to the test weight and the test time, and the test data is used to obtain the stiffness coefficient and the baseline offset. The test data is the amount of charge passing corresponding to the test time, and the test data is used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and an acceleration time curve is obtained.

[0026] Specifically, on a standard track, a sensor array is laid out at a density of at least one piezoelectric sensor per 7 cm to ensure coverage of the main area where the athlete runs. The sensors are connected to a data acquisition device, and a collection program is started to record the raw data charge throughput and time synchronously when the athlete is training or testing. The collected raw data is imported into data processing software, and the Kalman filtering algorithm is used to filter the raw data to remove high-frequency noise. The raw data is normalized according to the normalization formula, and the processed data is stored.

[0027] In step S102, the basic parameters are obtained, including the weight of the athlete, the time, and the step frequency.

[0028] Specifically, the weight of the athlete during training can be considered as a constant. The step frequency is calculated according to the formula p = n / t min (n is the number of steps in t min minutes), and the unit is steps / minute.

[0029] In step S103, the step length, the acceleration change rate, the data relationship between acceleration and step length, and the data relationship between acceleration and step frequency are obtained based on the acceleration and the basic parameters.

[0030] In step S104, the acceleration ability and the dynamic performance are evaluated, and the athlete's ability is optimized.

[0031] Specifically, the average acceleration is based on the "time accumulation effect of acceleration", which is equivalent to a "uniform acceleration simplified model" by integrating and averaging calculation, and accurately measures the "overall effect" of acceleration ability, solving the problem of "how to quantitatively compare the dynamic acceleration process"; the dynamic performance index is based on the objective weight distribution of principal component analysis, integrating the three core dimensions of "acceleration, step length, and step frequency", realizing the "multi-index scientific integration and quantitative comparison" of running dynamic performance, and solving the problem of "how to accurately evaluate the synergistic effect of multiple factors". The combination of the two builds a complete tool chain from "kinematics micro-analysis" (acceleration ability) to "dynamic performance macro-evaluation" (multi-index synergy). Coaches and athletes can upgrade "experience-driven training" to "data-driven scientific training" through these quantitative indicators, accurately optimize acceleration skills, improve dynamic output efficiency, and ultimately achieve breakthroughs in competitive performance.

[0032] In an optional embodiment, the stiffness coefficient and the baseline offset are obtained based on multiple sets of test data, according to the formula wherein, is the test weight, is the stiffness coefficient, is the baseline offset, The test data is obtained, and the instantaneous force is calculated according to the formula wherein, is the instantaneous force, is the charge throughput corresponding to the test time, is the test time.

[0033] Specifically, when obtaining the stiffness coefficient and the baseline offset, a plurality of objects with known weights are dropped freely onto a runway on which sensors have been laid, and the charge throughput transmitted by the sensors is obtained. A plurality of sets of data can be summarized as a linear function, so that the stiffness coefficient and the baseline offset can be obtained.

[0034] In an alternative embodiment, a plurality of accelerations are calculated, and an acceleration-time curve is obtained according to the accelerations and the test time, and the calculation formula is wherein, is the instantaneous force, is the acceleration, is the weight of the athlete.

[0035] Specifically, force analysis: vertical direction , is the vertical force, and the horizontal force is the resultant force , is the horizontal friction force. Acceleration calculation: the instantaneous acceleration is calculated by , and the acceleration-time curve is generated by recording .

[0036] In an alternative embodiment, the step length is calculated, and the calculation formula is: wherein, is the initial speed, is the acceleration, is the step length; the instantaneous speed is calculated, and an instantaneous speed-time curve is obtained, and the calculation formula is wherein, is the instantaneous speed, is the initial speed, is the time corresponding to the acceleration; the displacement is calculated, and a displacement-time curve is obtained, and the calculation formula is wherein, is the displacement, is the initial displacement, is the time corresponding to the instantaneous speed, is the time corresponding to the acceleration; the derivative of the formula corresponding to the acceleration-time curve is obtained, and the acceleration change rate is obtained, and the formula is: wherein, is the acceleration change rate. ​​

[0037] Specifically, the athlete's running speed Displacement distance at various times and the rate of change of acceleration ①Time The x-axis represents the rate of change of acceleration. Using the vertical axis as the ordinate, a graph of the rate of change of acceleration can be obtained, allowing for the study of the rate of change of acceleration over time. The x-axis represents velocity. Using the vertical axis as the ordinate, a time-velocity graph can be obtained to study the instantaneous velocity of an athlete. ③ Time x-axis represents displacement Using the vertical axis as the ordinate, a time-displacement graph can be obtained to study the changes in the athlete's motion displacement. Through the data and graphs obtained above, trends can be studied, data magnitudes compared, outliers identified, and the athlete's motion process captured, thus providing a direct and comprehensive assessment of the athlete's dynamic motion performance.

[0038] Furthermore, in the kinematic analysis of athletes running, we often first make the simplified assumption of uniformly accelerated linear motion (the actual running process involves complex situations such as muscle force exertion and ground interaction, but the assumption of uniformly accelerated linear motion can provide an effective approximation for preliminary analysis). The initial velocity is known. speed at a certain moment acceleration According to the core formula in kinematics, the velocity-displacement formula... The stride length *l* can be calculated. The stride length of the athlete at different times can be obtained using kinematic formulas, expressed in terms of time. With l as the horizontal axis and step length l as the vertical axis, a time-step length research image can be obtained to study the changes in the athlete's step length and monitor the movement trajectory.

[0039] In one alternative embodiment, the athlete is obtained. The group's observation data was substituted into the linear model. , to obtain the predicted value Obtain the sum of squared residuals The observed data includes step size and acceleration. ,in, For residuals, For predicted values, The first regression coefficient, The second regression coefficient, For the first Step size of group data For the first Acceleration of the set of data; about Taking the partial derivative, we get ;right Regarding Take partial derivative to get where, is the average acceleration, is the average step size.

[0040] Specifically, let be a set of observation data , substitute each set of data into the linear model to get the predicted value ( ). The residual , the sum of squared residuals is defined as: find the appropriate and so that is minimized.

[0041] Because is a binary quadratic function about and , and the square term coefficient is positive, its graph is an open upward parabolic surface, there is only one minimum value. According to the knowledge of calculus, to find the minimum value, take partial derivative of and respectively, and let the partial derivative be , solve the equation set to get and .

[0042] Take partial derivative of and let it be : take partial derivative of about :

[0043] ;

[0044] Simplify to get: ;

[0045] Further expand: ;

[0046] Arrange to get: ;

[0047] where, is the average value of step size, is the average value of acceleration.

[0048] Take partial derivative of and let it be : substitute into , and take partial derivative of (also can directly take partial derivative of original about Taking partial derivative, the process is as follows: directly to Regarding Taking partial derivative:

[0049] ;

[0050] Simplifying: ;

[0051] Substitute into the above formula: ;

[0052] Expand the brackets: ;

[0053] Split the summation term: ;

[0054] Move terms to solve : ;

[0055] To simplify the calculation, the numerator and denominator can be further transformed (using the properties of summation): the numerator (because ); the denominator (by the same reasoning, ).

[0056] Therefore, the final calculation formula for and is: , .

[0057] In an alternative embodiment, the correlation coefficient representing the relationship between acceleration and step frequency is calculated, and the calculation formula is: where is the correlation coefficient, is the acceleration of the th group, is the step frequency of the th group, is the average acceleration, is the average step frequency, .

[0058] Specifically, it is assumed that the athlete maintains a nearly constant speed running state within a time period of . At this time, the speed can be derived from the step length and the step frequency :

[0059] When running, "speed" is an abstract physical quantity, but step length and step frequency are concrete movement characteristics that athletes can intuitively control and sensors can easily collect. From the "time-displacement" nature: step frequency represents "steps per minute", then the total displacement run in 1 minute is "steps × displacement per step", that is: step; step length represents "displacement per step", then the total displacement run in 1 minute is "steps × displacement per step", that is: (If you need to convert to "m / s", you can further divide by 60, that is , but the unit does not affect the linear relationship when analyzing the correlation, so the original form is retained.)

[0060] The core value of this formula is to translate the abstract speed into two observable and trainable movement indicators: "step length + step frequency", so that the correlation between acceleration and step frequency can be applied to actual movement characteristics, and a concrete bridge is established for subsequent analysis.

[0061] acceleration Describes the speed of change, in the classical uniform acceleration motion, the relationship between speed and time is: , where: is the initial speed (unit: meters / minute or meters / second, which needs to be unified with ), is the motion time (unit: minutes or seconds), is the acceleration (unit: meters / minute² or meters / second²).

[0062] This is the most basic definition of kinematics. The effect of acceleration on speed is essentially "linear accumulation (uniform acceleration scenario) or instantaneous change (variable acceleration scenario, which can be extended to )". Whether the running is uniform acceleration or variable acceleration, acceleration will inevitably affect the coordinated adjustment of step frequency / step length by changing speed, because speed has been associated with step frequency and step length.

[0063] Combining the "speed-step length-step frequency" relationship , the speed is substituted into the acceleration formula to establish an indirect relationship between acceleration and step frequency : .

[0064] If you want to study the "effect of acceleration on step frequency", you can organize the step frequency about expression (assuming step length is relatively stable, or analyzing the impact of change separately), which embodies the correlation logic between the two due to "speed change". Choose as the basis is to first grasp the core logic of "acceleration changing speed", and then anchor the variable relationship with a simplified model. Subsequent expansion can be made to more complex movement scenarios.

[0065] ​Linear correlation coefficient: for quantitative analysis of acceleration and step frequency , the Pearson correlation coefficient is introduced . It is a classical standardized tool in statistics to measure the degree of linear correlation between two variables. The adaptability comes from the fact that a set of motion data is collected, each set of data is ( the th set of acceleration, is the corresponding step frequency), define: ( the average value of acceleration), ( the average value of step frequency).

[0066] The formula for the correlation coefficient is: ;

[0067] Molecular logic: , measures the "co-deviation" between and : if is less than the mean value (or at the same time), the product is positive, and the cumulative sum is greater, indicating a stronger positive linear correlation; otherwise, it is negative. This step captures the essence of whether the "trend of change" of the two variables is consistent.

[0068] Denominator logic: , "standardize" the numerator, eliminate the influence of dimension (such as acceleration unit is m / s², step frequency is step / minute) and data fluctuation amplitude, so that the coefficient range is limited to . This step allows kinematic indicators of different dimensions to be compared fairly in terms of correlation, and is the key to converting physical quantity correlation into a statistically interpretable index.

[0069] Basic relationship (formula ): use "step length, step frequency" to translate abstract speed into a concrete form, so that the correlation between acceleration and step frequency can be applied to the indicators that can be intervened in training, which is the first step from "motion phenomenon to physical quantity analysis".

[0070] Dynamic correlation (formula ): anchor the kinematic bottom logic of "acceleration change speed", establish the relationship between acceleration and speed through a simplified model, and then relate step frequency through speed to build a complete variable chain.

[0071] Quantitative measurement (formula ): choose the Pearson correlation coefficient to quantify the "degree of linear correlation between acceleration and step frequency" using standardized statistical tools, solve the problem of "how to convert physical quantity correlation into interpretable data conclusions", and connect kinematic analysis with data-driven training strategies.

[0072] ​In an alternative embodiment, the average acceleration of the starting phase is calculated, according to the formula: wherein, is the average acceleration of the starting phase, is the time of the starting phase, is the formula for acceleration with respect to time.

[0073] In particular, the acceleration capacity is the core embodiment of the efficiency of the speed improvement of the athlete during running, and its essence is the "accumulation effect of acceleration with time". In order to accurately capture this dynamic process, the average acceleration is introduced as a quantitative indicator. The formula is derived based on the integral idea, and the physical meaning is the average contribution of acceleration to the change in speed within the time interval, and the specific form is: .

[0074] Acceleration is the "instantaneous rate of change of speed with respect to time", that is, the basic definition of kinematics ( is the instantaneous speed at time ). The integral of over the time interval is essentially the calculation of the total change in speed through "micro-cumulative": dividing the time into an infinite number of small time intervals , the acceleration in each can be approximately regarded as a "constant value ", and the change in speed within this micro time interval is . Summing up (i.e. integrating) all the micro changes gives the total speed change: wherein, is the final speed at time , and is the initial speed at the initial time . The integral result clearly shows the cumulative effect of acceleration over time, which is equivalent to the difference between the final speed and the initial speed, and directly reflects the "absolute value of speed improvement" in the acceleration process.

[0075] In actual running, the acceleration often fluctuates with time due to factors such as force rhythm, fatigue, etc. (e.g. the acceleration is large at the starting stage of sprint, and gradually decreases in the later stage). To simplify the analysis and quantify the "overall acceleration effect", the variable acceleration process needs to be equivalent to the "uniform acceleration process with constant acceleration", i.e. to find an "average acceleration ", which produces exactly the same "total speed change" as the actual variable acceleration motion within the same time . According to the formula of uniform acceleration motion, if the constant acceleration Motion, the change in velocity is (due to initial velocity) When constant, final velocity The change in velocity is Combining the physical meaning of the integral term (the total change in velocity is...), ), can be combined into a single equation: Therefore, the average acceleration can be solved: .

[0076] This derivation transforms "complex variable acceleration motion" into "simple uniform acceleration equivalent model", shifting the assessment of acceleration ability from "instantaneous fluctuations" to "overall effect", making it easier for coaches and athletes to intuitively understand and compare acceleration performance at different times or for different individuals.

[0077] After the sprinter starts Taking the internal acceleration process as an example: actual acceleration It may exhibit a curve that "rises rapidly and then declines" (e.g.) hour , hour , hour , hour ) Calculated by integration Assuming the result is (That is, the speed increased within 3 seconds) Substituting into the average acceleration formula, we get... .

[0078] This means that although the actual acceleration changes constantly, the equivalent average acceleration effect of the athlete during these 3 seconds is the same as that achieved by using... The effect of "constant acceleration and continuous acceleration" is exactly the same. This indicator can intuitively compare the "average efficiency" of different athletes' starting acceleration, or the changes in acceleration ability of the same athlete at different training stages.

[0079] In an optional embodiment, the acceleration weight, step size weight, and step frequency weight are obtained using principal component analysis to calculate the dynamic performance index. The calculation formula is as follows: ,in, , For power performance index, For acceleration weights, Step size weight, Step frequency weighting; comparison , as well as The values ​​are used to strengthen training for items with lower values.

[0080] Dynamic performance is a comprehensive reflection of power output, movement efficiency, and rhythm coordination during running, influenced by multiple factors such as acceleration (mechanical drive), stride length (amplitude of movement), and stride frequency (frequency of movement). To achieve the scientific integration and quantitative comparison of multiple indicators, a dynamic performance index is constructed. The formula is based on Principal Component Analysis (PCA) for weight allocation, and takes the following form:

[0081] acceleration This reflects the "mechanical root of speed improvement," directly related to muscle explosive power and neuromuscular activation efficiency, and is the core manifestation of "power output intensity"; stride length. Step frequency reflects the "displacement capacity of a single stride," and is influenced by leg strength, joint flexibility, and range of motion, embodying the "efficiency of converting power into displacement." It reflects the "number of steps per unit time", which is constrained by the speed of neural recruitment and motor coordination, and embodies the "rhythmic efficiency of power output".

[0082] These three elements comprehensively cover the core components of running dynamic performance from the dimensions of "mechanical drive - displacement conversion - rhythm coordination," ensuring the index... It can comprehensively depict the athlete's dynamic performance.

[0083] Weight determination: The mathematical principles of Principal Component Analysis (PCA). Weights It is not a subjective setting, but rather an "objective deduction" based on a large amount of sample data through Principal Component Analysis (PCA), a multivariate statistical method. The core logic is to identify the indicator dimensions that have the most significant impact on running performance.

[0084] Data Collection and Standardization: Collecting "Acceleration" Data from Multiple Groups of Athletes Step length Step frequency "Data, while recording corresponding running performance indicators (such as sprint speed, split time, etc.). The raw data is standardized (e.g., subtracting the mean, dividing by the standard deviation) to eliminate dimensional differences (e.g.,..." Units are , for , for This ensures that different indicators "participate in the analysis fairly".

[0085] Variance contribution analysis: The covariance matrix and eigenvalues ​​are calculated using PCA to analyze... The variance contribution of the three indicators, i.e. the proportion of each indicator that can "explain the variation in running performance (such as score difference)". The higher the variance contribution, the more critical the indicator is to the power performance. For example: if the analysis finds that the variance contribution of acceleration is 60%, it means that 60% of the difference in running performance can be explained by the change in acceleration; stride length contributes 30%, and step frequency contributes 10%.

[0086] Weight distribution and standardization: convert "variance contribution" to weight in proportion to ensure . Taking the above assumptions as an example, the weight distribution is: In this way, the power performance index can be expressed as: .

[0087] This weight distribution ensures that can focus on the "main variation direction" of the data (i.e. the most critical factor affecting power performance) to the greatest extent, making the index have the value of "scientific quantification and objective comparison".

[0088] Specifically, based on the pressure data and the established model, the pressure peak, action time, and pressure center trajectory of each step of the athlete are analyzed, and the special pressure model of different projects is compared to determine whether the athlete's movement pattern is reasonable. Regularly collect the training data of the athletes and input them into the established model for analysis. According to the movement pattern diagnosis results, individualized training plans are developed for the athletes, such as arranging specific starting technique training courses and adjusting the training intensity of step frequency and stride length. During the training process, the load and fatigue indicators are monitored in real time, and the training arrangement is adjusted in a timely manner. For athletes with a history of injury, the risk of injury and rehabilitation are closely monitored, and professional advice is provided for rehabilitation training. Coaches and athletes can view the analysis report and training recommendations through the interface of the intelligent training system, adjust the training strategy according to the feedback, and continuously optimize the training effect.

[0089] In addition, the core value of the power performance index lies in "multi-dimensional integration" and "quantitative decision-making":

[0090] Scenario 1: Individual training optimization: by monitoring the data and index of athletes, the strengths and weaknesses can be accurately located: if a certain athlete is high but contribution is low, it means that "insufficient stride length" is a potential weakness and leg strength and movement amplitude training need to be strengthened; if contribution is low, focus can be placed on neural coordination training (such as high-frequency stepping and rhythm running) to improve step frequency efficiency.

[0091] Scenario 2: Group competition screening: compare different athletes Index, quickly identify "power performance comprehensive advantage": athlete A: , Athlete B: , By Comparison, it can be directly judged that the power performance of athlete B is better (although the acceleration is slightly lower, but the step length and step frequency contribute more), which provides data basis for competition lineup selection.

[0092] Scenario 3: Training effect tracking:

[0093] Long-term monitoring of the change of the same athlete can quantitatively evaluate the effectiveness of the training program: if the training after a certain period, 10%, 5%, 3%, the weight calculation growth rate can judge the "comprehensive improvement effect of power performance of the current training", and then optimize the subsequent training plan.

[0094] The present application can comprehensively and accurately analyze the sports performance of athletes by multi-dimensional data acquisition and advanced modeling algorithm, and provide scientific decision basis for coaches. Compared with the traditional experience training method, the training efficiency can be significantly improved, and the athletes can improve the performance faster. The damage risk prediction and rehabilitation evaluation function based on pressure data can timely find the potential sports damage risk, take preventive measures in advance, and provide quantitative guidance for the rehabilitation process of athletes, which helps to reduce the incidence of sports damage and shorten the rehabilitation period. The intelligent training system realizes the closed-loop feedback optimization of the training process, and the athletes and coaches can obtain personalized training suggestions in real time, improve the pertinence and scientificity of the training, and promote the development of sports training to intelligent and precise direction.

[0095] The embodiment of the present application also provides a device for analyzing running sports. It should be noted that the device for analyzing running sports of the embodiment of the present application can be used to execute the method for analyzing running sports provided by the embodiment of the present application. The device is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiment is preferably implemented in software, hardware, or a combination of software and hardware can also be implemented and conceived.

[0096] The device for analyzing running sports provided by the embodiment of the present application is introduced below.

[0097] Figure 2is a structural block diagram of a device for analyzing a running motion condition according to an embodiment of the present application. As shown in Figure 2 the device comprises: an acquisition unit 201 configured to acquire test data and test data by using a sensor arranged on a track, the test data comprising a charge passing amount corresponding to a test weight and a test time, the test data being used to obtain a stiffness coefficient and a baseline offset, the test data being used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and an acceleration-time curve being obtained; a first obtaining unit 202 configured to obtain basic parameters, the basic parameters comprising a weight of an athlete, a time, and a step frequency; a second obtaining unit 203 configured to obtain a step length, an acceleration change rate, a data relationship between the acceleration and the step length, and a data relationship between the acceleration and the step frequency according to the accelerations and the basic parameters; and an evaluation unit 204 configured to evaluate an acceleration ability and a dynamic performance, and to optimize an athlete's ability.

[0098] The acquisition unit comprises: a first obtaining sub-unit configured to obtain the stiffness coefficient and the baseline offset according to a plurality of sets of test data, a formula being wherein, the test weight is W, the stiffness coefficient is K, the baseline offset is B, test data is obtained, an instantaneous force is calculated, and a formula being wherein, the instantaneous force is F, the charge passing amount corresponding to the test time is Q, the test time is t.

[0099] The acquisition unit comprises: a first calculating sub-unit configured to calculate a plurality of accelerations, to obtain an acceleration-time curve according to the accelerations and the test time, and a formula being wherein, the instantaneous force is F, the acceleration is a, the weight of the athlete is W.

[0100] The second obtaining unit comprises: a second calculating sub-unit configured to calculate a step length, and a formula being: wherein, an initial speed is v0, the acceleration is a, the step length is s, a final speed is v; and a third calculating sub-unit configured to calculate an instantaneous speed, to obtain an instantaneous speed-time curve, and a formula being wherein, the instantaneous speed is v, the initial speed is v0, the time is t The corresponding acceleration; the fourth calculation subunit is used to calculate displacement and obtain the displacement-time curve. The calculation formula is as follows: ,in, For displacement, This is the initial displacement. For time The corresponding instantaneous velocity, For time The corresponding acceleration; the fifth calculation subunit is used to differentiate the formula corresponding to the acceleration-time curve to obtain the rate of change of acceleration and to obtain the acceleration rate of change image. The formula is: ,in, This represents the rate of change of acceleration.

[0101] The second acquisition unit includes: acquiring athletes The group's observation data was substituted into the linear model. , to obtain the predicted value Obtain the sum of squared residuals The observed data includes step size and acceleration. ,in, For residuals, For predicted values, The first regression coefficient, The second regression coefficient, For the first Step size of group data For the first Acceleration of the set of data; about Taking the partial derivative, we get ;right about Taking the partial derivative, we get ,in, For average acceleration, This represents the average step size.

[0102] The second acquisition unit includes: calculating the correlation coefficient, which represents the relationship between acceleration and step frequency, and the calculation formula is: ,in, The correlation coefficient is... For the first The acceleration of the group For the first The group's step frequency For average acceleration, The average step frequency, .

[0103] The evaluation unit includes: calculating the average acceleration during the starting phase, using the following formula; ,in, The average acceleration in the starting stage, The time in the starting stage, The formula of acceleration about time; the acceleration weight, the step length weight, and the step frequency weight are obtained by principal component analysis method, the dynamic performance index is calculated, and the formula is: Wherein, , The dynamic performance index, The acceleration weight, The step length weight, The step frequency weight; the values of , And The item corresponding to the lower value is strengthened.

[0104] The device for analyzing the running situation includes a processor and a memory, the above-mentioned collection unit 201 and the like are stored in the memory as program units, and the corresponding functions are realized by the processor executing the above-mentioned program units stored in the memory. The above-mentioned modules are located in the same processor; or the above-mentioned modules are located in different processors in any combination.

[0105] The processor includes a core, and the core retrieves the corresponding program unit from the memory. The core can be set to one or more, and the technical problem that the running situation of athletes is not fully analyzed in the related art is solved by adjusting the core parameters.

[0106] The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0107] The embodiment of the present application provides a computer readable storage medium, the computer readable storage medium includes a stored program, wherein when the program runs, the device where the computer readable storage medium is located executes the method for analyzing the running situation.

[0108] Specifically, a method for analyzing the running situation includes:

[0109] In step S101, test data and test data are collected by sensors laid on the track. The test data includes the test weight and the test time corresponding to the charge throughput, and is used to obtain the stiffness coefficient and the baseline offset. The test data is the charge throughput corresponding to the test time, and is used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and an acceleration time curve is obtained. Specifically, on a standard track, at least according to the density of 7cm installed piezoelectric sensors, a sensor array is laid to ensure that the main area of the athlete's running is covered. Connect the sensor with the data acquisition device, start the collection program, and record the original data charge throughput and time synchronously when the athlete is training or testing. The collected original data is imported into the data processing software, and the Kalman filtering algorithm is used to filter the original data to remove high-frequency noise. The original data is normalized according to the normalization formula, and the processed data is stored.

[0110] In step S102, the basic parameters are obtained, including the weight of the athlete, the time and the step frequency.

[0111] Specifically, the weight of the athlete during training can be regarded as a constant. Step frequency: the calculation formula is p=n / t min (n is t min steps per minute.

[0112] In step S103, the step length, the acceleration change rate, the data relationship between the acceleration and the step length, and the data relationship between the acceleration and the step frequency are obtained according to the acceleration and the basic parameters.

[0113] In step S104, the acceleration ability and the dynamic performance are evaluated, and the athlete's ability is optimized.

[0114] Specifically, the average acceleration is based on the "time accumulation effect of acceleration", which is equivalent to a "uniform acceleration simplified model" by integrating and averaging calculation, and accurately measures the "overall effect" of the acceleration ability, solving the problem of "how to quantitatively compare the dynamic acceleration process"; the dynamic performance index is based on the objective weight distribution of principal component analysis, integrating the three core dimensions of "acceleration, step length, and step frequency", realizing the "multi-index scientific integration and quantitative comparison" of running dynamic performance, and solving the problem of "how to accurately evaluate the synergistic effect of multiple factors". The combination of the two builds a complete tool chain from "microscopic analysis of kinematics" (acceleration ability) to "macroscopic evaluation of dynamic performance" (multi-index synergy). Coaches and athletes can upgrade "experience-driven training" to "data-driven scientific training" through these quantitative indicators, accurately optimize acceleration skills, improve dynamic output efficiency, and ultimately achieve breakthroughs in competitive performance.

[0115] Optionally, the stiffness coefficient and the baseline offset are obtained according to a plurality of test data, and the formula is wherein, is the weight, is the stiffness coefficient, is the baseline offset, ; obtaining test data, calculating instantaneous force, the calculation formula is wherein, is the instantaneous force, is the test time corresponding to the charge throughput, is the test time. Optionally, a plurality of accelerations are calculated, and an acceleration-time curve is obtained according to the acceleration and the test time, and the calculation formula is wherein, is the instantaneous force, is the acceleration, is the weight of the athlete.

[0116] Optionally, a step length is calculated, and the calculation formula is: wherein, is the initial speed, is the acceleration, is the step length, is the end speed; calculating the instantaneous speed, obtaining the instantaneous speed-time curve, and the calculation formula is wherein, is the instantaneous speed, is the initial speed, is the time corresponding to the acceleration; calculating the displacement, obtaining the displacement-time curve, and the calculation formula is wherein, is the displacement, is the initial displacement, is the time corresponding to the instantaneous speed, is the time corresponding to the acceleration; the derivative of the formula corresponding to the acceleration-time curve is obtained, and the acceleration change rate is obtained, and the formula is: wherein, is the acceleration change rate.

[0117] Optionally, the observation data of the athlete group is obtained, and the observation data is substituted into the linear model to obtain the predicted value , and the residual sum of squares is obtained, wherein the observation data includes the step length and the acceleration, wherein, is the residual, is the predicted value, is the first regression coefficient, is the second regression coefficient, the step length of the first group of data, the acceleration of the first group of data; the partial derivative of with respect to is obtained as ; the partial derivative of with respect to is obtained as , wherein is the average acceleration, is the average step length.

[0118] Optionally, a correlation coefficient is calculated, the correlation coefficient representing the relationship between the acceleration and the step frequency, and the calculation formula is: , wherein is the correlation coefficient, is the acceleration of the first group of data, is the step frequency of the first group of data, is the average acceleration, is the average step frequency, .

[0119] Optionally, the average acceleration of the starting stage is calculated, and the calculation formula is: , wherein is the average acceleration of the starting stage, is the time of the starting stage, is the formula of the acceleration with respect to the time; the acceleration weight, the step length weight and the step frequency weight are obtained through principal component analysis, and the dynamic performance index is calculated, and the calculation formula is: , wherein , is the dynamic performance index, is the acceleration weight, is the step length weight, is the step frequency weight; the values of , and are compared, and the item corresponding to the lower value is strengthened.

[0120] An embodiment of the present application provides a processor used for running a program, wherein the processor is used for running the method for analyzing a running motion condition.

[0121] Specifically, a method for analyzing a running motion condition comprises:

[0122] Step S101, collecting test data and test data through the sensor array laid on the track, the test data including the test weight and the test time corresponding charge throughput, the test data being used to obtain the stiffness coefficient and the baseline offset, the test data being the charge throughput corresponding to the test time, the test data being used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and obtaining an acceleration time curve;

[0123] Specifically, on a standard track, at least according to the density of 7cm per piezoelectric sensor, the sensor array is laid to ensure that the main area of the athlete running is covered. The sensor is connected with the data acquisition device, and the collection program is started. When the athlete is training or testing, the original data charge throughput and time are recorded synchronously. The collected original data is imported into the data processing software, and the Kalman filtering algorithm is used to filter the original data to remove high-frequency noise. The original data is normalized according to the normalization formula, and the processed data is stored.

[0124] Step S102, obtaining basic parameters, including the weight of the athlete, the time and the step frequency;

[0125] Specifically, the weight of the athlete during training can be regarded as a constant. Step frequency: the calculation formula is p=n / t min (n is t min steps per minute.

[0126] Step S103, obtaining the step length, the acceleration change rate, the data relationship between the acceleration and the step length, and the data relationship between the acceleration and the step frequency according to the acceleration and the basic parameters;

[0127] Step S104, evaluating the acceleration ability and the dynamic performance, and optimizing the athlete's ability.

[0128] Specifically, the average acceleration is based on the "time accumulation effect of acceleration". By integrating and mean calculation, the "complex variable acceleration movement" is equivalent to the "uniform acceleration simplified model", which accurately measures the "overall effect" of the acceleration ability, and solves the problem of "how to quantitatively compare the dynamic acceleration process". The dynamic performance index is based on the objective weight distribution of principal component analysis, which integrates the three core dimensions of "acceleration, step length and step frequency", realizes the "multi-index scientific integration and quantitative comparison" of running dynamic performance, and solves the problem of "how to accurately evaluate the synergistic effect of multiple factors". The combination of the two builds a complete tool chain from "kinetics micro-analysis" (acceleration ability) to "dynamic performance macro-evaluation" (multi-index synergy). Coaches and athletes can upgrade "experience-driven training" to "data-driven scientific training" through these quantitative indicators, accurately optimize acceleration skills, improve dynamic output efficiency, and ultimately achieve breakthroughs in competitive performance.

[0129] Optionally, the stiffness coefficient and the baseline offset are obtained according to a plurality of test data, and a formula is wherein, is the test weight, is the stiffness coefficient, is the baseline offset, test data are obtained, and the instantaneous force is calculated, and a formula is wherein, is the instantaneous force, is the test time corresponding to the charge throughput, is the test time.

[0130] Optionally, a plurality of accelerations are calculated, and the acceleration-time curve is obtained according to the acceleration and the test time, and a formula is wherein, is the instantaneous force, is the acceleration, is the weight of the athlete.

[0131] Optionally, the step length is calculated, and a formula is wherein, is the initial speed, is the acceleration, is the step length, is the end speed; the instantaneous speed is calculated, and the instantaneous speed-time curve is obtained, and a formula is wherein, is the instantaneous speed, is the initial speed, is the time corresponding to the acceleration; the displacement is calculated, and the displacement-time curve is obtained, and a formula is wherein, is the displacement, is the initial displacement, is the time corresponding to the instantaneous speed, is the time corresponding to the acceleration; the derivative of the formula corresponding to the acceleration-time curve is obtained, and the acceleration change rate is obtained, and a formula is wherein, is the acceleration change rate.

[0132] Optionally, the observation data of the athlete group are obtained, the observation data are substituted into the linear model , the predicted value is obtained, and the residual sum of squares is obtained, wherein the observation data include the step length and the acceleration, wherein, is the residual. is a predicted value, is a first regression coefficient, is a second regression coefficient, is a step length of the first group of data, is an acceleration of the first group of data; is a step length of the second group of data, is an acceleration of the second group of data; is a partial derivative with respect to, is a partial derivative with respect to, ; is a partial derivative with respect to, is a partial derivative with respect to, wherein, is an average acceleration, is an average step length.

[0133] Optionally, a correlation coefficient is calculated, the correlation coefficient representing a relationship between the acceleration and the step frequency, the calculation formula being: wherein, is a correlation coefficient, is an acceleration of the first group, is a step frequency of the first group, is an acceleration of the second group, is a step frequency of the second group, is an average acceleration, is an average step frequency.

[0134] Optionally, an average acceleration of a starting stage is calculated, the calculation formula being: wherein, is an average acceleration of the starting stage, is a time of the starting stage, is an acceleration formula with respect to time; through principal component analysis, acceleration weight, step length weight, and step frequency weight are obtained, and a dynamic performance index is calculated, the calculation formula being: wherein, , is a dynamic performance index, is an acceleration weight, is a step length weight, is a step frequency weight; values of, , and are compared, and a project corresponding to a lower value is strengthened.

[0135] ​The embodiment of the present application provides a device, the device comprises a processor, a memory and a program stored on the memory and executable on the processor, when the processor executes the program, at least the following steps are realized: collecting test data and test data through a sensor laid on a runway, the test data comprises a test weight and a test time corresponding electric charge passing amount, the test data is used for obtaining a stiffness coefficient and a baseline offset, the test data is a test time corresponding electric charge passing amount, and the test data is used for obtaining a plurality of accelerations according to the stiffness coefficient and the baseline offset, and an acceleration time curve is obtained; a basic parameter is obtained, the basic parameter comprises an athlete weight, a time and a step frequency; a step length, an acceleration change rate, a data relationship between the acceleration and the step length and a data relationship between the acceleration and the step frequency are obtained according to the acceleration and the basic parameter; acceleration ability and dynamic performance are evaluated, and the athlete ability is optimized. The device in the paper can be a server, a PC, a PAD, a mobile phone and the like.

[0136] Optionally, the stiffness coefficient and the baseline offset are obtained according to a plurality of test data, and a formula is , wherein, is the test weight, is the stiffness coefficient, is the baseline offset, ; test data is obtained, an instantaneous force is calculated, and a calculation formula is , wherein, is the instantaneous force, is the test time corresponding electric charge passing amount, is the test time.

[0137] Optionally, a plurality of accelerations are calculated, an acceleration time curve is obtained according to the acceleration and the test time, and a calculation formula is , wherein, is the instantaneous force, is the acceleration, is the athlete weight.

[0138] Optionally, a step length is calculated, and a calculation formula is: , wherein, is an initial speed, is the acceleration, is the step length, is a final speed; an instantaneous speed is calculated, an instantaneous speed time curve is obtained, and a calculation formula is , wherein, is the instantaneous speed, is the initial speed, is the time corresponding acceleration; displacement is calculated, a displacement time curve is obtained, and a calculation formula is , wherein, is the displacement, This is the initial displacement. For time The corresponding instantaneous velocity, For time The corresponding acceleration; differentiate the formula corresponding to the acceleration-time curve to obtain the rate of change of acceleration, and obtain the graph of the rate of change of acceleration. The formula is: ,in, This represents the rate of change of acceleration.

[0139] Optionally, obtain athletes The group's observation data was substituted into the linear model. , to obtain the predicted value Obtain the sum of squared residuals The observed data includes step size and acceleration. ,in, For residuals, For predicted values, The first regression coefficient, The second regression coefficient, For the first Step size of group data For the first Acceleration of the set of data; about Taking the partial derivative, we get ;right about Taking the partial derivative, we get ,in, For average acceleration, This represents the average step size.

[0140] Optionally, the correlation coefficient is calculated, which represents the relationship between acceleration and step frequency. The calculation formula is as follows: ,in, The correlation coefficient is... For the first The acceleration of the group For the first The group's step frequency For average acceleration, The average step frequency, .

[0141] Optionally, the average acceleration during the starting phase is calculated using the following formula; ,in, The average acceleration during the starting phase. This refers to the time during the starting phase. The formula for acceleration with respect to time is given; the acceleration weight, step size weight, and step frequency weight are obtained through principal component analysis, and the dynamic performance index is calculated using the following formula: wherein, , is a power performance index, is an acceleration weight, is a step length weight, is a step frequency weight; the values of , and , the items corresponding to the lower values of which are emphasized in the training.

[0142] The application also provides a computer program product, when executed on a data processing device, is adapted to execute the program of at least the following method steps: collecting test data and test data through the sensor laid on the runway, the test data including the test weight and the test time corresponding to the charge passing amount, the test data is used to obtain the stiffness coefficient and the baseline offset, the test data is the charge passing amount corresponding to the test time, the test data is used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and an acceleration time curve is obtained; obtaining basic parameters, the basic parameters including the weight of the athlete, the time and the step frequency; obtaining the step length, the acceleration change rate, the data relationship between the acceleration and the step length, and the data relationship between the acceleration and the step frequency according to the acceleration and the basic parameters; evaluating the acceleration ability and the power performance, and optimizing the athlete's ability.

[0143] Optionally, the stiffness coefficient and the baseline offset are obtained according to a plurality of test data, and the formula is wherein, is the test weight, is the stiffness coefficient, is the baseline offset, ; obtaining test data, calculating the instantaneous force, and the calculation formula is wherein, is the instantaneous force, is the charge passing amount corresponding to the test time, is the test time.

[0144] Optionally, a plurality of accelerations are calculated, and the acceleration time curve is obtained according to the acceleration and the test time, and the calculation formula is wherein, is the instantaneous force, is the acceleration, is the weight of the athlete.

[0145] Optionally, the step length is calculated, and the calculation formula is: wherein, is the initial speed, is the acceleration, is the step length, The final velocity; calculate the instantaneous velocity, obtain the instantaneous velocity-time curve, and the calculation formula is as follows. ,in, Instantaneous velocity The initial velocity, For time The corresponding acceleration; calculate the displacement, obtain the displacement-time curve, and the calculation formula is as follows. ,in, For displacement, This is the initial displacement. For time The corresponding instantaneous velocity, For time The corresponding acceleration; differentiate the formula corresponding to the acceleration-time curve to obtain the rate of change of acceleration, and obtain the graph of the rate of change of acceleration. The formula is: ,in, This represents the rate of change of acceleration.

[0146] Optionally, obtain athletes The group's observation data was substituted into the linear model. , to obtain the predicted value Obtain the sum of squared residuals The observed data includes step size and acceleration. ,in, For residuals, For predicted values, The first regression coefficient, The second regression coefficient, For the first Step size of group data For the first Acceleration of the set of data; about Taking the partial derivative, we get ;right about Taking the partial derivative, we get ,in, For average acceleration, This represents the average step size.

[0147] Optionally, the correlation coefficient is calculated, which represents the relationship between acceleration and step frequency. The calculation formula is as follows: ,in, The correlation coefficient is... For the first The acceleration of the group For the first The group's step frequency For average acceleration, The average step frequency, .

[0148] Optionally, the average acceleration of the starting stage is calculated, and the calculation formula is: wherein, is the average acceleration of the starting stage, is the time of the starting stage, is the formula of acceleration about time; the acceleration weight, the step length weight, and the step frequency weight are obtained by principal component analysis, and the dynamic performance index is calculated, and the calculation formula is: wherein, , is the dynamic performance index, is the acceleration weight, is the step length weight, is the step frequency weight; the values of , and are compared, and the item corresponding to the lower value is strengthened.

[0149] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be realized by general computing devices, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and they can be realized by program codes executable by the computing devices, so that they can be stored in storage devices and executed by the computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into individual integrated circuit modules, or multiple modules or steps can be manufactured into a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0150] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer usable storage media containing computer usable program codes (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0151] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0152] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0153] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0154] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0155] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM) for storing structural information and / or instruction data. Access and control of memory can be facilitated by a Memory Controller. Note that not all of the software described herein need to be stored on the same computer or device. Some software can be stored on a computer readable medium that is separate from the computer or device.

[0156] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0157] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0158] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. Those skilled in the art can make various changes and modifications to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of analyzing a running motion, characterized by, The method comprises the following steps: Collecting test data and test data through sensors laid on a track, the test data comprising test weight and test time corresponding charge passing amount, the test data being used to obtain stiffness coefficient and baseline offset, the test data being test time corresponding charge passing amount, the test data being used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and obtaining an acceleration-time curve; Obtaining basic parameters, the basic parameters comprising athlete weight, time and step frequency; Obtaining step length, acceleration change rate, data relationship between acceleration and step length, and data relationship between acceleration and step frequency according to the acceleration and the basic parameters; Evaluating acceleration ability and dynamic performance, and optimizing athlete ability.

2. The method of claim 1, wherein, The method comprises the following steps: According to multiple sets of the test data, a stiffness coefficient and a baseline offset are obtained, and a formula is wherein, is the test weight, is the stiffness coefficient, is the baseline offset, is the charge throughput corresponding to the test time; The test data is acquired, and the instantaneous force is calculated, and the calculation formula is wherein, is the instantaneous force, is the charge passing amount corresponding to the test time, is the test time.

3. The method of claim 2, wherein, Collecting test data and test data through sensors laid on a track, the test data being used to obtain stiffness coefficient and baseline offset, the test data being used to obtain a plurality of accelerations, and obtaining an acceleration-time curve, comprising: The acceleration is calculated according to the acceleration and the test time, and the formula is Wherein, The instantaneous force is F, The acceleration is a, The weight of the athlete is W.

4. The method of claim 1, wherein, Collecting test data and test data through sensors laid on a track, the test data being used to obtain stiffness coefficient and baseline offset, the test data being used to obtain a plurality of accelerations, and obtaining an acceleration-time curve, comprising: The step length is calculated by the formula: wherein is the initial velocity, is the acceleration, is the step length, is the end velocity; The instantaneous speed is calculated, and a time curve of the instantaneous speed is obtained, and the calculation formula is wherein, is the instantaneous speed, is the initial speed, is the time corresponding acceleration; The displacement is calculated, and a displacement-time curve is obtained, and the formula is wherein, is the displacement, is the initial displacement, is the time is the corresponding instantaneous velocity, is the time is the corresponding acceleration; Derivation of the formula corresponding to the acceleration-time curve obtains the acceleration rate of change, and obtains the acceleration rate of change image, and the formula is: Wherein, is the acceleration rate of change.

5. The method of claim 1, wherein Obtaining step length, acceleration change rate, data relationship between acceleration and step length, and data relationship between acceleration and step frequency according to the acceleration and the basic parameters, comprising: Acquire athletes The group's observation data is substituted into the linear model. , to obtain the predicted value Obtain the sum of squared residuals The observed data includes step size and acceleration. ,in, For residuals, The predicted value, The first regression coefficient, The second regression coefficient, For the first Step size of group data For the first Acceleration of the data set; To With Partial derivatives are taken, resulting in ; To With respect to The partial derivative is obtained where, is the average acceleration, is the average step size.

6. The method of claim 1, wherein, Obtaining step length, acceleration change rate, data relationship between acceleration and step length, and data relationship between acceleration and step frequency according to the acceleration and the basic parameters, comprising: a correlation coefficient representing a relationship between the acceleration and the step frequency is calculated by the following equation: wherein is the correlation coefficient, is the acceleration of the first group, is the step frequency of the first group, is the average acceleration, is the average step frequency, .

7. The method of claim 1, wherein, Obtaining step length, acceleration change rate, data relationship between acceleration and step length, and data relationship between acceleration and step frequency according to the acceleration and the basic parameters, comprising: The average acceleration of the starting phase is calculated by the formula: wherein is the average acceleration of the starting phase, is the time of the starting phase, is the formula of the acceleration with respect to time. The acceleration weight, the step length weight and the step frequency weight are obtained by principal component analysis method, the dynamic performance index is calculated, and the calculation formula is: wherein, , the dynamic performance index, the acceleration weight, the step length weight, the step frequency weight; Comparing , and the values, the items for which the lower values correspond are reinforced.

8. An apparatus for analyzing a running motion, characterized by Evaluating acceleration ability and dynamic performance, and optimizing athlete ability, comprising: The method comprises the following steps: A collecting unit is configured to collect test data and test data through sensors laid on a track, the test data comprising test weight and test time corresponding charge passing amount, the test data being used to obtain stiffness coefficient and baseline offset, the test data being test time corresponding charge passing amount, the test data being used to obtain a plurality of accelerations according to the stiffness coefficient and the baseline offset, and obtaining an acceleration-time curve; A first obtaining unit is configured to obtain basic parameters, the basic parameters comprising athlete weight, time and step frequency; A second obtaining unit is configured to obtain step length, acceleration change rate, data relationship between acceleration and step length, and data relationship between acceleration and step frequency according to the acceleration and the basic parameters; 9. A computer-readable storage medium, characterized in that, An evaluating unit is configured to evaluate acceleration ability and dynamic performance, and optimize athlete ability.

10. An electronic device, comprising: The computer readable storage medium comprises a stored program, wherein the program controls the device where the computer readable storage medium is located to execute the method for analyzing running motion condition according to any one of claims 1 to 7 when the program is running. The method comprises the following steps: One or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including programs to perform the method of any one of claims 1-7 to analyze a running motion.

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