# Aerobics Characteristics Analysis and Auxiliary Training Efficiency Improvement Method Based on Deep Learning

Authors: Jiwen Wang; Bingxue Wang; Shuli Wan
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Aerobic exercise is the predecessor of aerobics. Aerobics is a kind of young and energetic sports, although it is only is popular in recent years, but because of its strong ornamental, very beautiful, so many people like it. Now, the characteristic of sports development is to integrate competitive sports into schools, and the establishment of high-level sports teams in Chinese colleges and universities is the need of the current development trend and adapts to the trend of the times. Many colleges and universities have invested a lot of time, money, and energy. It is to set up a high-level sports team. It has provided great help to physical education and athletes. Therefore, it is very beneficial to the sustainable development of aerobics of high-level sports teams in colleges and universities to compare, analyze, and study the aerobic training methods of high-level sports teams in colleges and universities, and find out the existing problems in current training, correct them in time, and find out more suitable training methods.


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## Body

## 1. Introduction

An opponent is established for the deep learning system applied to image object recognition, and the system parameters are used to find the minimum disturbance of the input image so that the system can misclassify with high confidence. We use this method to construct and deploy an opponent of a deep learning system applied to music content analysis. We find that the convolution architecture is more robust than the majority vote system based on a single classified audio frame [1]. Large-scale gene expression profiles have been widely used to describe the response of cells to various disease conditions, genetic interference, and so on. Although the cost of genome-wide expression profiles has been steadily decreasing, it is still very expensive to generate a compilation of expression profiles on thousands of samples. Deep learning is still better than linear regression with a relative improvement of 6.57% and achieves lower error on 81.31% of target genes [2]. The emergence of electronic medical records with large electronic image databases and the progress of deep neural networks with machine learning provide unique opportunities to achieve milestones in automated image analysis. As a new image classification technology, deep learning technology has high classification accuracy and effectiveness [3]. A long-term memory network is the most advanced technology for sequence learning. They are not often applied to financial time-series forecasting, but they are essentially suitable for this field. We find that the daily return rate of the LSTM network is 0.46%, and the Sharpe ratio before transaction cost is 5.8, which is superior to memoryless classification methods, namely, random forest, deep neural network, and logistic regression classifier [4]. We propose a consistency-aware depth learning (CADL) framework for personnel rerecognition in camera networks. Under the framework of deep learning, we use this consistency perception information to automatically learn feature representation and image matching under certain consistency constraints. Experimental results show that the performance of this method is greatly improved, which is much better than the existing methods [5]. A new scalable training method for deep learning machines is proposed, which utilizes data parallelism through incremental block training and intrablock parallel optimization, and stabilizes the learning process through block model update filtering. Compared with the traditional random gradient descent training based on a small batch on a single GPU, we have achieved linear acceleration of 16 GPUs on the LSTM task and 64 GPUs on the DNN task, and the recognition accuracy has not decreased or improved [6]. Defect prediction is a very interesting topic, especially at the change level. Deep learning is a research hotspot in the field of machine learning. Whether deep learning can be used to improve the performance of just-in-time defect prediction has not been studied. The experimental results show that in 4 of the 6 items, this is statistically significantly higher than Yang et al.’s method [7]. The practical success of deep neural networks has not matched the theoretical progress of satisfactorily explaining their behavior. In this study, the information bottleneck theory of deep learning is studied. By combining the analysis results with the simulation results, we prove that the information plane trajectory is mainly a function of neural nonlinearity [8]. Finding the parameters that minimize the loss function is the core of many machine learning methods. The random gradient descent algorithm is widely used and provides the most advanced results for many problems [8]. Nowadays, many diseases are gradually becoming younger, and stroke is one of them. This study found that aerobics can assist in the treatment of stroke [9]. Aerobics can make people’s joints and muscles get good exercise, thus reducing symptoms such as joint sprain and muscle strain. This study found that aerobics is a means to improve the important system of the body and improve the efficiency of the body system through physical exercise [10]. This study found that aerobics can reduce overweight and adipose tissue, coordinate human limbs, and strengthen explosive force and flexibility [11]. Because aerobics has many benefits to people’s health, people like this sport more and more. It can cultivate students’ innovative abilities, so it is necessary to improve teachers’ professional skills in the teaching process [12]. This study uses imagery training to improve the teaching method of aerobics. The research shows that this method can improve our understanding of aerobics and adjust our emotions [13]. In this study, yoga, aerobics, and aerobics combined with yoga training are divided into groups. The analysis results show that the fitness effect of yoga and aerobics on women is better than that of single training [14].


## 2. An Analysis of the Problems Existing in the Traditional Aerobics Teaching Mode

### 2.1. Action Name Confusion

Under the traditional aerobics teaching, students learn to cope with examinations, which is very common. At the end of the assessment, the actions are forgotten. After the course, 90% of the students cannot answer the terms, basic action names, and action methods of aerobics. Even in the process of learning, it will be found that the focus of contact with this subject is on the imitation learning and memory of complete sets of movements, ignoring the repeated practice and memory of basic movements, resulting in poor standardization of the final complete sets of movements. For example, kicking and bouncing are indistinguishable, word step and man step are indistinguishable, and the transition from low-impact step to high-impact step-bouncing kick and bouncing kick jump, leg sucking, and leg sucking jump are indistinguishable, which are caused by unclear basic action concepts, unclear action names, or confusion, which will not be conducive to subsequent learning and memory.


### 2.2. Poor Standardization of Movements

One of the most basic elements of aerobics is that the movements should be elastic, due to lack of practice and poor coordination of limbs. For example, when sucking legs in the last step, the thighs are uneven, the toes are hooked, and the crotch is swung forward. In the “V” step, the center of gravity fluctuates too much, wraps the legs outward, and swings the hip joints in turn. These are the problems caused by unclear action essentials. Therefore, the essentials and norms of basic movements should be the focus of learning at the beginning, instead of rushing to remember the sequence of complete sets of movements.


### 2.3. Insufficient Movement Practice

In the process of students’ learning, you will find that some students’ poor movement quality is due to the lack of exercise, which leads to poor physical coordination. After a month’s class with the same teacher and the same basic movement training methods, it is finally found that the coordination and standardization of students’ movements improved by the main items are better than those of students in general classes, no matter which set of exercises. Therefore, the number of exercises is the decisive factor to change the coordination of movements, that is, the so-called quantitative change causes qualitative change. In addition, the quality of flexibility is congenital, but there is a lot of room for change the day after tomorrow. In the learning process of basic movements, continuous and special training for the completion of movements will greatly improve and enhance the standardization of movements. For example, lifting and side lifting are the worst standardized movements for beginners, and it is difficult to get in place at once. However, after repeated targeted positioning control exercises, increasing muscle memory, and shoulder joint flexibility exercises, these movements, especially lifting movements, have been greatly improved.


## 2.1. Action Name Confusion

Under the traditional aerobics teaching, students learn to cope with examinations, which is very common. At the end of the assessment, the actions are forgotten. After the course, 90% of the students cannot answer the terms, basic action names, and action methods of aerobics. Even in the process of learning, it will be found that the focus of contact with this subject is on the imitation learning and memory of complete sets of movements, ignoring the repeated practice and memory of basic movements, resulting in poor standardization of the final complete sets of movements. For example, kicking and bouncing are indistinguishable, word step and man step are indistinguishable, and the transition from low-impact step to high-impact step-bouncing kick and bouncing kick jump, leg sucking, and leg sucking jump are indistinguishable, which are caused by unclear basic action concepts, unclear action names, or confusion, which will not be conducive to subsequent learning and memory.


## 2.2. Poor Standardization of Movements

One of the most basic elements of aerobics is that the movements should be elastic, due to lack of practice and poor coordination of limbs. For example, when sucking legs in the last step, the thighs are uneven, the toes are hooked, and the crotch is swung forward. In the “V” step, the center of gravity fluctuates too much, wraps the legs outward, and swings the hip joints in turn. These are the problems caused by unclear action essentials. Therefore, the essentials and norms of basic movements should be the focus of learning at the beginning, instead of rushing to remember the sequence of complete sets of movements.


## 2.3. Insufficient Movement Practice

In the process of students’ learning, you will find that some students’ poor movement quality is due to the lack of exercise, which leads to poor physical coordination. After a month’s class with the same teacher and the same basic movement training methods, it is finally found that the coordination and standardization of students’ movements improved by the main items are better than those of students in general classes, no matter which set of exercises. Therefore, the number of exercises is the decisive factor to change the coordination of movements, that is, the so-called quantitative change causes qualitative change. In addition, the quality of flexibility is congenital, but there is a lot of room for change the day after tomorrow. In the learning process of basic movements, continuous and special training for the completion of movements will greatly improve and enhance the standardization of movements. For example, lifting and side lifting are the worst standardized movements for beginners, and it is difficult to get in place at once. However, after repeated targeted positioning control exercises, increasing muscle memory, and shoulder joint flexibility exercises, these movements, especially lifting movements, have been greatly improved.


## 3. Collaborative Filtering Algorithm

The collaborative filtering algorithm, as the most practical and popular recommendation algorithm at present, makes use of the collaborative wisdom among users to make recommendation judgments. Generally speaking, collaborative filtering algorithms are divided into two ways: nearest neighbor-based collaborative filtering and model-based collaborative filtering.


### 3.1. Collaborative Filtering Algorithm Based on Nearest Neighbor

The basic idea of the nearest neighbor-based collaborative filtering algorithm is as follows: in order to recommend the potentially interesting content to a specific user, first, other users with some similar characteristics are found, and based on the content objects that similar users are interested in, the content objects are pushed to specific users. Therefore, the nearest neighbor-based collaborative filtering algorithm mainly includes two main steps: (1) to find the user set similar to the target user, and (2) find the objects that users like in this collection and the target users have not made a relevant evaluation and recommend them to the target users. First, the user-object rating matrix is used to represent the user’s historical behavior. For userI, the vector ruler Ri∗ (representing the matrix R and the i-th row) is used to represent the user’s historical behavior. Next, we need to find out the user set similar to user I. In order to find out the similar users, researchers put forward two commonly used similarity measurement methods: Jaccard similarity and cosine similarity.Jaccard similarity can be expressed as formula (1) as follows:(1)JaccardSimilarity=Wi∩WjWi∩Wj.Cosine similarity can be expressed as formula (2) as follows:(2)CosineSimilarity=Ri∗⋅Rj∗Ri∗×Rj∗,where Ri∗ and Rj∗ represent the historical behavior of users I and J in the user-object scoring matrix, ⋅ represents the inner product of vectors, and ⋅ represents the modulus of vectors.After determining the similarity measurement method,K users with the highest similarity with the target user I are formed into a similar user set Ui,k. Finally, formula (3) is used to calculate the score prediction of the object whose evaluation is not made by the target user I.(3)R^is=∑j∈Ui,k∩NswijRjs,R^is represents the evaluation prediction of user I on object S, wij represents the similarity between user I and user J, Rjs represents the score of user J on object S, Ui,k represents the collection of K users most similar to user I, and Ns represents the collection of users who have evaluated article S.After predicting the ratings of the target userI for all potential recommendation targets (objects that make evaluations), K objects with the highest predicted ratings are recommended to user I.The nearest neighbor-based collaborative filtering algorithm is also called the user-based collaborative filtering algorithm, because the overall idea of its algorithm is to calculate the user set most similar to the target user from the user’s point of view, then calculate the score prediction of the target user to the potential recommendation target, and finally recommend it. In addition to the user-based collaborative filtering algorithm, there is also an object-based collaborative filtering algorithm, whose ideas and steps are almost the same as those of the user-based collaborative filtering algorithm. The object-based collaborative filtering algorithm is to find the object that is most similar to the target object from the perspective of the object, then predict the prediction score between the object and the user, and finally recommend it.The user-based collaborative filtering algorithm needs to maintain a user similarity matrix whose size ism × m, and the object-based collaborative filtering algorithm needs to maintain an object similarity matrix whose size is n × n. From the storage point of view, if the number of users is huge, it needs a lot of storage space to use user CF. Similarly, if the number of items is large, using item CF also requires a large maintenance cost.


### 3.2. Model-Based Collaborative Filtering Algorithm

In October 2006, Netflix, a DVD retailer, announced a competition in which anyone who invented a new method 10% better than its existing movie recommendation algorithm, CineMatch, won a seven-figure prize. The algorithm that shines brilliantly in the competition is the model-based collaborative filtering algorithm, which is also called the LFM hidden factor model or probability matrix decomposition model. The gradual implicit factor model has become the most common algorithm in recommendation algorithm and also become the most important model in recommendation algorithm research.The PMF model directly models the user-object evaluation matrixR, making R≈UTV. The algorithm uses two parameter matrices U and V to describe the features of users and objects, where K is used to describe the dimension of feature vectors of users and goods. The parameters in U and V are trained by using the score data made by users in R, then, the vacant score in R is predicted by using R≈UTV, and finally, the object with the highest predicted score is selected as the recommendation object for the target users. The whole algorithm can be regarded as a process of matrix factorization, so the LFM model is often called matrix factorization model.The mathematical expression of score prediction in PMF is the following formula:(4)rij=Ui∗⋅V∗j,where rij represents the score of user I on object J, Ui∗ represents the i-th row of U matrix, that is, the eigenvector of user I, and V∗j represents the j-th column of V matrix, that is, the eigenvector of object J. Formula (4) represents the construction method of the scoring model in PMF. In order to solve the parameter matrices U and V, the PMF model constructs a loss function J(U, V) as follows:(5)JU,V=∑Rij∈SUi∗⋅V∗j−rij2+λ1UF2+λ2VF2.The first term in formula (5) is a fidelity term, which ensures that the parameter matrices U and V are consistent with the training data, while the last two terms are regular terms or constraint terms, and the matrix Frobenius norm is generally used as the regular term, where S represents the set of non-null scoring items in the user-object scoring matrix, indicating that the loss function JU,V only fits the non-null items in the user-object scoring matrix; λ1 and λ2 are regularity parameters, which are used to adjust the degree of strong-weak relationship between fidelity term and regularity term. The solution problem of LFM is transformed into the following optimization problem, as shown in formula (6).(6)U,V=argminu,vJU,V.In order to solve formula (6), the most common optimization algorithm, batch gradient descent method, is introduced. The BGD is an iterative optimization algorithm by constantly updating each element Uim and Vmj in the U and V matrices, so that each update can approach the minimum value of the objective function JU,V. The updating rules are formulas (7) and (8).(7)Uimn+1:=Uimn−∂∂UimJU,V∗α,(8)Vmjn+1=Vmjn−∂∂VmjJU,V∗α,N in the above formulas (7) and (8) denotes the current number of iterations, and α is the learning rate or iteration step. Then, the partial derivatives of the objective function J(U, V) with respect to Uim and Vmj are solved, as shown in formulas (9) and (10).(9)∂∂UimJU,V=∑j2VmjUi∗⋅V∗j−rij+2λ1Uim,(10)∂∂VmjJU,V=∑j2UimUi∗⋅V∗j−rij+2λ1Vmj.The complete iterative formula of BGD can be obtained by bringing formulas (9) and (10) into (7) and (8).There are four hyperparameters in the PMF model, including the number of hidden factorsk, regularization parameters λ1 and λ2, and learning rate α, where k describes the size of the model, which is generally related to the density of data, λ1 and λ2 describe the strength of regularization terms, which are generally used to overcome the overfitting problem of the model, and α represents the adjustment degree and convergence rate of each update. Usually, the selection of super-parameters needs to be decided by experiments, and there are also cross-validations or Bayesian methods to help select super-parameters.Subsequently, the probability model of PMF and its derivation process are explained. Because the essence of the PMF model is to fit theR of score data, the PMF is a regression model on the whole. Based on the basic modeling idea of the regression model, it is assumed that the prediction error obeys the normal distribution with an expectation of 0, as shown in formulas (11) and (12).(11)ε∼N0,σ2,(12)ε=rij−Ui∗V∗j.As shown in formula (12), the error is equal to the difference between the predicted value and the true value, from which the probability of the occurrence of the true value can be deduced as shown in formula (13).(13)prij|Ui∗,V∗j,σ=12πδexp−rij−Ui∗⋅V∗j22δ2.Formula (13) is arranged into a matrix expression as shown in formula (14).(14)pR|U,V,σ=∏i=1m∏j=1m12πδexp−rij−Ui∗⋅V∗j22δ2Iij.The goal of the model is to solveU and V, so the Bayesian formula is used to find the conditional probability expression of U and V and R, where Iij is the indicator function, indicating whether rij is empty, as shown in formula (15).(15)pU,V|R=pR|U,VpUpVpR∞pR|U,VpUpV.Then, it is assumed that the parameters inU and V also obey a normal distribution with an expectation of 0 as shown in formula (16).(16)pU|σU2=∏i=1mNUi|0,σU2I,pV|σU2=∏i=1mNV|i0,σV2I.According to formulas (14)–(16), conditional probability expressions for U and V and R can be obtained from the maximum a posteriori estimation framework. In order to simplify the calculation, the logarithmic form of the conditional probability will be obtained here, as shown in formula (17).(17)InpU,V|R,σ,σU,σV=−12σ2∑i=1m∑j=1mIijrij−Ui∗⋅V∗j2−12σU2∑i=1mUI∗TUi∗−12σU2∑j=1mVI∗TVi∗−12∑i=1m∑j=1mIijInσ2+nkInσU2+mkInσV2+C.N denotes the number of users in the system, M denotes the number of objects in the system, K denotes the number of implicit factors, and formula (18) can be obtained by sorting out formula (17).(18)E=12∑i=1m∑j=1mIijrij−Ui∗⋅V∗j2+λU2∑i=1mUi∗F2+λV2∑j=1mV∗jF2.According to the definition of the MAP framework, the solution goal is to find the parametersU and V so that the value in formula (7) is as large as possible, and the value of E in formula (18) is as small as possible. It can be found that the objective function and solution objective in formula (18) are consistent with those in the previous formula (5).


## 3.1. Collaborative Filtering Algorithm Based on Nearest Neighbor

The basic idea of the nearest neighbor-based collaborative filtering algorithm is as follows: in order to recommend the potentially interesting content to a specific user, first, other users with some similar characteristics are found, and based on the content objects that similar users are interested in, the content objects are pushed to specific users. Therefore, the nearest neighbor-based collaborative filtering algorithm mainly includes two main steps: (1) to find the user set similar to the target user, and (2) find the objects that users like in this collection and the target users have not made a relevant evaluation and recommend them to the target users. First, the user-object rating matrix is used to represent the user’s historical behavior. For userI, the vector ruler Ri∗ (representing the matrix R and the i-th row) is used to represent the user’s historical behavior. Next, we need to find out the user set similar to user I. In order to find out the similar users, researchers put forward two commonly used similarity measurement methods: Jaccard similarity and cosine similarity.Jaccard similarity can be expressed as formula (1) as follows:(1)JaccardSimilarity=Wi∩WjWi∩Wj.Cosine similarity can be expressed as formula (2) as follows:(2)CosineSimilarity=Ri∗⋅Rj∗Ri∗×Rj∗,where Ri∗ and Rj∗ represent the historical behavior of users I and J in the user-object scoring matrix, ⋅ represents the inner product of vectors, and ⋅ represents the modulus of vectors.After determining the similarity measurement method,K users with the highest similarity with the target user I are formed into a similar user set Ui,k. Finally, formula (3) is used to calculate the score prediction of the object whose evaluation is not made by the target user I.(3)R^is=∑j∈Ui,k∩NswijRjs,R^is represents the evaluation prediction of user I on object S, wij represents the similarity between user I and user J, Rjs represents the score of user J on object S, Ui,k represents the collection of K users most similar to user I, and Ns represents the collection of users who have evaluated article S.After predicting the ratings of the target userI for all potential recommendation targets (objects that make evaluations), K objects with the highest predicted ratings are recommended to user I.The nearest neighbor-based collaborative filtering algorithm is also called the user-based collaborative filtering algorithm, because the overall idea of its algorithm is to calculate the user set most similar to the target user from the user’s point of view, then calculate the score prediction of the target user to the potential recommendation target, and finally recommend it. In addition to the user-based collaborative filtering algorithm, there is also an object-based collaborative filtering algorithm, whose ideas and steps are almost the same as those of the user-based collaborative filtering algorithm. The object-based collaborative filtering algorithm is to find the object that is most similar to the target object from the perspective of the object, then predict the prediction score between the object and the user, and finally recommend it.The user-based collaborative filtering algorithm needs to maintain a user similarity matrix whose size ism × m, and the object-based collaborative filtering algorithm needs to maintain an object similarity matrix whose size is n × n. From the storage point of view, if the number of users is huge, it needs a lot of storage space to use user CF. Similarly, if the number of items is large, using item CF also requires a large maintenance cost.


## 3.2. Model-Based Collaborative Filtering Algorithm

In October 2006, Netflix, a DVD retailer, announced a competition in which anyone who invented a new method 10% better than its existing movie recommendation algorithm, CineMatch, won a seven-figure prize. The algorithm that shines brilliantly in the competition is the model-based collaborative filtering algorithm, which is also called the LFM hidden factor model or probability matrix decomposition model. The gradual implicit factor model has become the most common algorithm in recommendation algorithm and also become the most important model in recommendation algorithm research.The PMF model directly models the user-object evaluation matrixR, making R≈UTV. The algorithm uses two parameter matrices U and V to describe the features of users and objects, where K is used to describe the dimension of feature vectors of users and goods. The parameters in U and V are trained by using the score data made by users in R, then, the vacant score in R is predicted by using R≈UTV, and finally, the object with the highest predicted score is selected as the recommendation object for the target users. The whole algorithm can be regarded as a process of matrix factorization, so the LFM model is often called matrix factorization model.The mathematical expression of score prediction in PMF is the following formula:(4)rij=Ui∗⋅V∗j,where rij represents the score of user I on object J, Ui∗ represents the i-th row of U matrix, that is, the eigenvector of user I, and V∗j represents the j-th column of V matrix, that is, the eigenvector of object J. Formula (4) represents the construction method of the scoring model in PMF. In order to solve the parameter matrices U and V, the PMF model constructs a loss function J(U, V) as follows:(5)JU,V=∑Rij∈SUi∗⋅V∗j−rij2+λ1UF2+λ2VF2.The first term in formula (5) is a fidelity term, which ensures that the parameter matrices U and V are consistent with the training data, while the last two terms are regular terms or constraint terms, and the matrix Frobenius norm is generally used as the regular term, where S represents the set of non-null scoring items in the user-object scoring matrix, indicating that the loss function JU,V only fits the non-null items in the user-object scoring matrix; λ1 and λ2 are regularity parameters, which are used to adjust the degree of strong-weak relationship between fidelity term and regularity term. The solution problem of LFM is transformed into the following optimization problem, as shown in formula (6).(6)U,V=argminu,vJU,V.In order to solve formula (6), the most common optimization algorithm, batch gradient descent method, is introduced. The BGD is an iterative optimization algorithm by constantly updating each element Uim and Vmj in the U and V matrices, so that each update can approach the minimum value of the objective function JU,V. The updating rules are formulas (7) and (8).(7)Uimn+1:=Uimn−∂∂UimJU,V∗α,(8)Vmjn+1=Vmjn−∂∂VmjJU,V∗α,N in the above formulas (7) and (8) denotes the current number of iterations, and α is the learning rate or iteration step. Then, the partial derivatives of the objective function J(U, V) with respect to Uim and Vmj are solved, as shown in formulas (9) and (10).(9)∂∂UimJU,V=∑j2VmjUi∗⋅V∗j−rij+2λ1Uim,(10)∂∂VmjJU,V=∑j2UimUi∗⋅V∗j−rij+2λ1Vmj.The complete iterative formula of BGD can be obtained by bringing formulas (9) and (10) into (7) and (8).There are four hyperparameters in the PMF model, including the number of hidden factorsk, regularization parameters λ1 and λ2, and learning rate α, where k describes the size of the model, which is generally related to the density of data, λ1 and λ2 describe the strength of regularization terms, which are generally used to overcome the overfitting problem of the model, and α represents the adjustment degree and convergence rate of each update. Usually, the selection of super-parameters needs to be decided by experiments, and there are also cross-validations or Bayesian methods to help select super-parameters.Subsequently, the probability model of PMF and its derivation process are explained. Because the essence of the PMF model is to fit theR of score data, the PMF is a regression model on the whole. Based on the basic modeling idea of the regression model, it is assumed that the prediction error obeys the normal distribution with an expectation of 0, as shown in formulas (11) and (12).(11)ε∼N0,σ2,(12)ε=rij−Ui∗V∗j.As shown in formula (12), the error is equal to the difference between the predicted value and the true value, from which the probability of the occurrence of the true value can be deduced as shown in formula (13).(13)prij|Ui∗,V∗j,σ=12πδexp−rij−Ui∗⋅V∗j22δ2.Formula (13) is arranged into a matrix expression as shown in formula (14).(14)pR|U,V,σ=∏i=1m∏j=1m12πδexp−rij−Ui∗⋅V∗j22δ2Iij.The goal of the model is to solveU and V, so the Bayesian formula is used to find the conditional probability expression of U and V and R, where Iij is the indicator function, indicating whether rij is empty, as shown in formula (15).(15)pU,V|R=pR|U,VpUpVpR∞pR|U,VpUpV.Then, it is assumed that the parameters inU and V also obey a normal distribution with an expectation of 0 as shown in formula (16).(16)pU|σU2=∏i=1mNUi|0,σU2I,pV|σU2=∏i=1mNV|i0,σV2I.According to formulas (14)–(16), conditional probability expressions for U and V and R can be obtained from the maximum a posteriori estimation framework. In order to simplify the calculation, the logarithmic form of the conditional probability will be obtained here, as shown in formula (17).(17)InpU,V|R,σ,σU,σV=−12σ2∑i=1m∑j=1mIijrij−Ui∗⋅V∗j2−12σU2∑i=1mUI∗TUi∗−12σU2∑j=1mVI∗TVi∗−12∑i=1m∑j=1mIijInσ2+nkInσU2+mkInσV2+C.N denotes the number of users in the system, M denotes the number of objects in the system, K denotes the number of implicit factors, and formula (18) can be obtained by sorting out formula (17).(18)E=12∑i=1m∑j=1mIijrij−Ui∗⋅V∗j2+λU2∑i=1mUi∗F2+λV2∑j=1mV∗jF2.According to the definition of the MAP framework, the solution goal is to find the parametersU and V so that the value in formula (7) is as large as possible, and the value of E in formula (18) is as small as possible. It can be found that the objective function and solution objective in formula (18) are consistent with those in the previous formula (5).


## 4. Experimental Analysis

### 4.1. Performance Comparison

In order to improve the accuracy of our later experiments, we have performed 10 experiments on the number of parameters, learning ability, and complexity of DL, DBN, CNN, and RNN, and compared the performance of the four algorithms, as shown in Figures1–3.Figure 1
Comparison of the number of parameters.Figure 2
Comparison of learning ability.Figure 3
Comparison of complex capabilities.Looking at Figures1–3, we can clearly see that the DL algorithm has the strongest performance ability in terms of parameter number, learning ability, and complexity, so we choose the DL algorithm for the next experiment.


### 4.2. Analysis of the Proportion of Completed Points and Reduced Point Units in the Complete Set of Competitive Events

#### 4.2.1. Complete the Proportion Analysis of Subunit and Subunit

In the evaluation part of the completion of the rules, the units of each action content reduction unit are different. The difficulty/skill is to reduce the points once for each complete action, the operation action is to reduce the points once for each eight-beat action, and the main content (transition connection, cooperation, and lifting action) is to reduce the points once for each action. According to expert interviews, the principle of completing score reduction is to take the first grade of score reduction when multiple errors of the same grade (deviating from the degree of perfect completion) occur at the same time, and when errors of different grades occur, the light is avoided and more attention is paid.Table1 is obtained by video analysis and writing of the men’s singles final set of the 15th World Championships. It can be seen from the following table that the number of difficulty reduction units in each group of men’s singles is 80, and the proportion (40.00%) is the highest among all action contents. Followed by exercises, the total number is 65 (33.50%). Finally, there are transition connections, with a total number of 53 (26.50%).Table 1
Statistics on the number of reduced units in men’s singles.
RankingDifficult movementExercise actionTransition connectionTotal11087252108523310962541098275108725610972671086248108725Total806553198Percentage40.00%33.50%26.50%Table2 is obtained by video analysis and writing of the women’s singles final set in the 15th World Championships. As can be seen from the following table, the number of difficulty reduction units in each group of women’s singles is 80, accounting for 39.60%, which is the highest among all movements, followed by exercises, with a total number of 67 (33.17%).Table 2
Statistics of the number of women’s singles reduced units.
RankingDifficult movementExercise actionTransition connectionTotal11086242109827310882641086245108725610982771097268108523Total806755202Percentage39.60%33.17%27.23%Table3 is obtained by video analysis and writing of the mixed doubles final set of the 15th World Championships. It can be seen from the following table that the number of difficulty reduction units in each group of mixed doubles is 72, accounting for 33.80%, which is the highest among all action contents. Then, there are transition connections, with a total number of 36 (16.90%) and, finally, cooperation, with a total number of 33 (15.49%). Lifting is the fixed content of collective projects, so each set has a reduction unit.Table 3
Statistics on the number of reduced units in mixed doubles in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal1984412629844126399541284986512959853126696551267985412789924125Total726436338213Percentage33.80%30.05%27.23%15.49%3.76%Table4 is obtained by video analysis and writing of the 15th World Championships three-person final set. As can be seen from the following table, the number of difficulty reduction units in each group of the three people is 72, accounting for 33.49%, which is the highest among all action contents. Followed by exercises, the total number is 66 (30.70%). Then, there are transition connections, with a total number of 48 (16.90%) and, finally, cooperation, with a total number of 21 (9.77%).Table 4
Statistics on the number of reduction units completed by three people in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal1998212929843125399721284988312959852125698621267985312689854127Total726648218215Percentage33.49%30.70%22.33%9.77%3.72%Table5 is obtained by video analysis and writing of the five-person final set of the 15th World Championships. As can be seen from the following table, the number of difficulty reduction units in each group of five people is 72, accounting for 32.73%, which is the highest among all action contents, followed by exercises, with a total number of 69 (31.36%); then, there are transition connections, with a total number of 42 (19.09%) and, finally, cooperation, with a total number of 29 (13.18%).Table 5
Statistics of the number of reduced units completed.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal18945127298541273986412849844126581053127699731297885412689962127Total696942298217Percentage32.73%31.36%22.33%9.77%3.64%


#### 4.2.2. Overall Analysis of Point Reduction Units in Complete Sets of Competitive Events

By writing 40 sets of videos, the number of units for completing points and reducing points of different action contents in each competition routine is obtained, and Table5 is calculated according to the average of the total number of individual items. As can be seen from Table 6, the average number of difficulty units in single events is 10 and that in collective events is 9. There are more than 8 units in each routine on average; because, in this cycle rule, it is required that there must be 8 complete operation units in each set, among which the average value of five people is the highest (8.63) and the average value of mixed doubles is the lowest (8). In the rules of this cycle, it is required that there must be no less than 4G+ actions in each set, and only G+ can be expressed by transitional connections in single events. Therefore, the average value of transitional connections in single events is significantly higher than that in collective events. In collective events, the average value of three-person exercises (6) is the highest and closest to single events, while mixed doubles events are the lowest (4.5). In cooperation, the average number of units in mixed doubles is the highest (4.13), while that in triple doubles is the lowest (2.63).Table 6
Statistics on the number of reduced point units completed in the finals of the 15th World Championships.
Difficult movementExercise actionTransition connectionCollaborationLifting actionMen's singles108.386.63Women’s singles108.386.88Mixed doubles984.54.131Three persons98.2562.631Five people98.635.253.631Figure4 is obtained by adding the total data of each item and calculating the percentage. It can be seen that the number of units with difficulty to complete the reduction is the largest, accounting for 35.81%.Figure 4
Percentage statistics of completed point reduction units.


### 4.3. Complete the Statistics and Analysis of Points and Reductions

The reduction statistics studied in this section are based on the rules, and the error between the final reduction scores and the actual reduction scores of the complete set is within the allowable range of the rules.It can be seen from Table7 that the action content with the most point reduction in men’s singles is the difficulty action, and the total difficulty reduction is 7.2, accounting for 73.47% and, finally, the transition connection, with a total reduction of 0.7, accounting for 7.14%.Table 7
Statistics on the reduction of points in the contents of men’s singles in the 15th World Championships.
RankingDifficult movementExercise actionTransition connection10.50.20.120.90.1030.90.2040.50.70.2510.20.160.80.20.371.20.2081.40.10Total7.21.90.7Percentage73.47%19.39%7.14%It can be seen from Table8 that the action content with the most point reduction in men’s singles is the difficulty action, and the total difficulty reduction is 9.0, accounting for 73.77% and, finally, the transition connection, with a total reduction of 1.4, accounting for 11.48%.Table 8
Statistics on the reduction of points in the completion of each movement content of women’s singles.
RankingDifficult movementExercise actionTransition connection1100.220.90.20.130.90.40.341.20.10.1510.20.161.10.30.271.10.30.281.80.30.2Total91.81.4Percentage73.77%14.75%11.48%From Table9, it can be seen that the highest ranking of mixed doubles is the difficulty movement, and the lowest ranking is the transition connection. The total difficulty reduction is 6.3, accounting for 57.27%. The total reduction score of consistency is 2.1, accounting for 19.09%. The total reduction score of maneuvering movements is 1.3, accounting for 11.82%. The total reduction of cooperation is 0.3, accounting for 2.73%. The total deduction of overlink is 0.1, accounting for 0.91.Table 9
Statistics on the reduction of points for each action content of mixed doubles.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionConsistency10.8000.10.10.120.90.1000.10.330.50.100.10.30.340.80.100.10.10.350.90.10.1000.2610.3000.10.170.40.5000.10.5810.1000.10.3Total6.31.30.10.30.92.1Percentage57.27%11.82%0.91%2.73%8.18%19.09%It can be seen from Table10 that the contents of the three-person project from high to low are difficulty action, consistency, operation action, lifting action, cooperation, and transition connection, and the total difficulty reduction is 6.1, accounting for 51.69%. The total consistency reduction is 2.2, accounting for 18.64%. The total reduction of maneuvering movements is 1.5, accounting for 12.71%. The total reduction of cooperation is 0.7, accounting for 5.93%. The total reduction of transition connection is 0.5, accounting for 4.24%.Table 10
Statistics on the reduction of points in each action content of the three people in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionConsistency10.60.20000.420.70.20.200.10.230.50.30.10.10.10.340.60.20.20.10.10.250.90.200.10.10.260.70.100.20.10.470.60.300.20.30.281.500000.3Total6.11.50.50.70.82.2Percentage51.69%12.71%4.24%5.93%6.78%18.64%From Table11, it can be seen that the contents of the five-person project from high to low are difficulty action, consistency, operation action, cooperation, lifting action, and transition connection. The total reduction score of difficulty action is 6.8, accounting for 56.20%. The total reduction score of sex is 2.6, accounting for 21.49%. The total reduction of cooperation is 0.7, accounting for 5.79%. The total reduction of lifting action is 0.5, accounting for 4.13%. The total reduction score of transition connection is 0.3, accounting for 2.48%.Table 11
Statistics of score reduction of action content completion.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionConsistency10.80.200.100.420.90.2000.10.230.70.200.10.10.340.70.100.100.550.60.200.10.10.46100.2000.371.10.10.10.10.10.2810.200.20.10.3Total6.81.20.30.70.52.6Percentage56.20%9.92%2.48%5.79%4.13%21.49%Figure5 is the statistics of the percentage of points reduced in the final of a single event. As can be seen from the following figure, the difficulty is to complete the action content with the largest proportion of point reduction, accounting for 74% of the total point reduction, followed by exercises, accounting for 17% and, finally, transition connection, accounting for 9%.Figure 5
Statistics on the proportion of point reduction completed by a single project.Figure6 is the statistics of point reduction in the final completion of collective events. As can be seen from the following figure, the highest ranking for reducing points is the difficult action, and the lowest ranking is the transition connection. Among them, the proportion of difficult movements is 55%.Figure 6
Statistics on the proportion of point reduction completed by collective projects.This study found that in collective events, the proportion of consistency reduction is second only to the proportion of difficulty reduction, and collective events should be paid attention to in training. Exercise movement is a complete set of movement content second only to difficulty reduction, and the reduction of exercise movement is mainly posture reduction. Athletes should also pay attention to the training of basic skills while improving the complete set of difficulties.


## 4.1. Performance Comparison

In order to improve the accuracy of our later experiments, we have performed 10 experiments on the number of parameters, learning ability, and complexity of DL, DBN, CNN, and RNN, and compared the performance of the four algorithms, as shown in Figures1–3.Figure 1
Comparison of the number of parameters.Figure 2
Comparison of learning ability.Figure 3
Comparison of complex capabilities.Looking at Figures1–3, we can clearly see that the DL algorithm has the strongest performance ability in terms of parameter number, learning ability, and complexity, so we choose the DL algorithm for the next experiment.


## 4.2. Analysis of the Proportion of Completed Points and Reduced Point Units in the Complete Set of Competitive Events

### 4.2.1. Complete the Proportion Analysis of Subunit and Subunit

In the evaluation part of the completion of the rules, the units of each action content reduction unit are different. The difficulty/skill is to reduce the points once for each complete action, the operation action is to reduce the points once for each eight-beat action, and the main content (transition connection, cooperation, and lifting action) is to reduce the points once for each action. According to expert interviews, the principle of completing score reduction is to take the first grade of score reduction when multiple errors of the same grade (deviating from the degree of perfect completion) occur at the same time, and when errors of different grades occur, the light is avoided and more attention is paid.Table1 is obtained by video analysis and writing of the men’s singles final set of the 15th World Championships. It can be seen from the following table that the number of difficulty reduction units in each group of men’s singles is 80, and the proportion (40.00%) is the highest among all action contents. Followed by exercises, the total number is 65 (33.50%). Finally, there are transition connections, with a total number of 53 (26.50%).Table 1
Statistics on the number of reduced units in men’s singles.
RankingDifficult movementExercise actionTransition connectionTotal11087252108523310962541098275108725610972671086248108725Total806553198Percentage40.00%33.50%26.50%Table2 is obtained by video analysis and writing of the women’s singles final set in the 15th World Championships. As can be seen from the following table, the number of difficulty reduction units in each group of women’s singles is 80, accounting for 39.60%, which is the highest among all movements, followed by exercises, with a total number of 67 (33.17%).Table 2
Statistics of the number of women’s singles reduced units.
RankingDifficult movementExercise actionTransition connectionTotal11086242109827310882641086245108725610982771097268108523Total806755202Percentage39.60%33.17%27.23%Table3 is obtained by video analysis and writing of the mixed doubles final set of the 15th World Championships. It can be seen from the following table that the number of difficulty reduction units in each group of mixed doubles is 72, accounting for 33.80%, which is the highest among all action contents. Then, there are transition connections, with a total number of 36 (16.90%) and, finally, cooperation, with a total number of 33 (15.49%). Lifting is the fixed content of collective projects, so each set has a reduction unit.Table 3
Statistics on the number of reduced units in mixed doubles in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal1984412629844126399541284986512959853126696551267985412789924125Total726436338213Percentage33.80%30.05%27.23%15.49%3.76%Table4 is obtained by video analysis and writing of the 15th World Championships three-person final set. As can be seen from the following table, the number of difficulty reduction units in each group of the three people is 72, accounting for 33.49%, which is the highest among all action contents. Followed by exercises, the total number is 66 (30.70%). Then, there are transition connections, with a total number of 48 (16.90%) and, finally, cooperation, with a total number of 21 (9.77%).Table 4
Statistics on the number of reduction units completed by three people in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal1998212929843125399721284988312959852125698621267985312689854127Total726648218215Percentage33.49%30.70%22.33%9.77%3.72%Table5 is obtained by video analysis and writing of the five-person final set of the 15th World Championships. As can be seen from the following table, the number of difficulty reduction units in each group of five people is 72, accounting for 32.73%, which is the highest among all action contents, followed by exercises, with a total number of 69 (31.36%); then, there are transition connections, with a total number of 42 (19.09%) and, finally, cooperation, with a total number of 29 (13.18%).Table 5
Statistics of the number of reduced units completed.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal18945127298541273986412849844126581053127699731297885412689962127Total696942298217Percentage32.73%31.36%22.33%9.77%3.64%


### 4.2.2. Overall Analysis of Point Reduction Units in Complete Sets of Competitive Events

By writing 40 sets of videos, the number of units for completing points and reducing points of different action contents in each competition routine is obtained, and Table5 is calculated according to the average of the total number of individual items. As can be seen from Table 6, the average number of difficulty units in single events is 10 and that in collective events is 9. There are more than 8 units in each routine on average; because, in this cycle rule, it is required that there must be 8 complete operation units in each set, among which the average value of five people is the highest (8.63) and the average value of mixed doubles is the lowest (8). In the rules of this cycle, it is required that there must be no less than 4G+ actions in each set, and only G+ can be expressed by transitional connections in single events. Therefore, the average value of transitional connections in single events is significantly higher than that in collective events. In collective events, the average value of three-person exercises (6) is the highest and closest to single events, while mixed doubles events are the lowest (4.5). In cooperation, the average number of units in mixed doubles is the highest (4.13), while that in triple doubles is the lowest (2.63).Table 6
Statistics on the number of reduced point units completed in the finals of the 15th World Championships.
Difficult movementExercise actionTransition connectionCollaborationLifting actionMen's singles108.386.63Women’s singles108.386.88Mixed doubles984.54.131Three persons98.2562.631Five people98.635.253.631Figure4 is obtained by adding the total data of each item and calculating the percentage. It can be seen that the number of units with difficulty to complete the reduction is the largest, accounting for 35.81%.Figure 4
Percentage statistics of completed point reduction units.


## 4.2.1. Complete the Proportion Analysis of Subunit and Subunit

In the evaluation part of the completion of the rules, the units of each action content reduction unit are different. The difficulty/skill is to reduce the points once for each complete action, the operation action is to reduce the points once for each eight-beat action, and the main content (transition connection, cooperation, and lifting action) is to reduce the points once for each action. According to expert interviews, the principle of completing score reduction is to take the first grade of score reduction when multiple errors of the same grade (deviating from the degree of perfect completion) occur at the same time, and when errors of different grades occur, the light is avoided and more attention is paid.Table1 is obtained by video analysis and writing of the men’s singles final set of the 15th World Championships. It can be seen from the following table that the number of difficulty reduction units in each group of men’s singles is 80, and the proportion (40.00%) is the highest among all action contents. Followed by exercises, the total number is 65 (33.50%). Finally, there are transition connections, with a total number of 53 (26.50%).Table 1
Statistics on the number of reduced units in men’s singles.
RankingDifficult movementExercise actionTransition connectionTotal11087252108523310962541098275108725610972671086248108725Total806553198Percentage40.00%33.50%26.50%Table2 is obtained by video analysis and writing of the women’s singles final set in the 15th World Championships. As can be seen from the following table, the number of difficulty reduction units in each group of women’s singles is 80, accounting for 39.60%, which is the highest among all movements, followed by exercises, with a total number of 67 (33.17%).Table 2
Statistics of the number of women’s singles reduced units.
RankingDifficult movementExercise actionTransition connectionTotal11086242109827310882641086245108725610982771097268108523Total806755202Percentage39.60%33.17%27.23%Table3 is obtained by video analysis and writing of the mixed doubles final set of the 15th World Championships. It can be seen from the following table that the number of difficulty reduction units in each group of mixed doubles is 72, accounting for 33.80%, which is the highest among all action contents. Then, there are transition connections, with a total number of 36 (16.90%) and, finally, cooperation, with a total number of 33 (15.49%). Lifting is the fixed content of collective projects, so each set has a reduction unit.Table 3
Statistics on the number of reduced units in mixed doubles in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal1984412629844126399541284986512959853126696551267985412789924125Total726436338213Percentage33.80%30.05%27.23%15.49%3.76%Table4 is obtained by video analysis and writing of the 15th World Championships three-person final set. As can be seen from the following table, the number of difficulty reduction units in each group of the three people is 72, accounting for 33.49%, which is the highest among all action contents. Followed by exercises, the total number is 66 (30.70%). Then, there are transition connections, with a total number of 48 (16.90%) and, finally, cooperation, with a total number of 21 (9.77%).Table 4
Statistics on the number of reduction units completed by three people in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal1998212929843125399721284988312959852125698621267985312689854127Total726648218215Percentage33.49%30.70%22.33%9.77%3.72%Table5 is obtained by video analysis and writing of the five-person final set of the 15th World Championships. As can be seen from the following table, the number of difficulty reduction units in each group of five people is 72, accounting for 32.73%, which is the highest among all action contents, followed by exercises, with a total number of 69 (31.36%); then, there are transition connections, with a total number of 42 (19.09%) and, finally, cooperation, with a total number of 29 (13.18%).Table 5
Statistics of the number of reduced units completed.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionTotal18945127298541273986412849844126581053127699731297885412689962127Total696942298217Percentage32.73%31.36%22.33%9.77%3.64%


## 4.2.2. Overall Analysis of Point Reduction Units in Complete Sets of Competitive Events

By writing 40 sets of videos, the number of units for completing points and reducing points of different action contents in each competition routine is obtained, and Table5 is calculated according to the average of the total number of individual items. As can be seen from Table 6, the average number of difficulty units in single events is 10 and that in collective events is 9. There are more than 8 units in each routine on average; because, in this cycle rule, it is required that there must be 8 complete operation units in each set, among which the average value of five people is the highest (8.63) and the average value of mixed doubles is the lowest (8). In the rules of this cycle, it is required that there must be no less than 4G+ actions in each set, and only G+ can be expressed by transitional connections in single events. Therefore, the average value of transitional connections in single events is significantly higher than that in collective events. In collective events, the average value of three-person exercises (6) is the highest and closest to single events, while mixed doubles events are the lowest (4.5). In cooperation, the average number of units in mixed doubles is the highest (4.13), while that in triple doubles is the lowest (2.63).Table 6
Statistics on the number of reduced point units completed in the finals of the 15th World Championships.
Difficult movementExercise actionTransition connectionCollaborationLifting actionMen's singles108.386.63Women’s singles108.386.88Mixed doubles984.54.131Three persons98.2562.631Five people98.635.253.631Figure4 is obtained by adding the total data of each item and calculating the percentage. It can be seen that the number of units with difficulty to complete the reduction is the largest, accounting for 35.81%.Figure 4
Percentage statistics of completed point reduction units.


## 4.3. Complete the Statistics and Analysis of Points and Reductions

The reduction statistics studied in this section are based on the rules, and the error between the final reduction scores and the actual reduction scores of the complete set is within the allowable range of the rules.It can be seen from Table7 that the action content with the most point reduction in men’s singles is the difficulty action, and the total difficulty reduction is 7.2, accounting for 73.47% and, finally, the transition connection, with a total reduction of 0.7, accounting for 7.14%.Table 7
Statistics on the reduction of points in the contents of men’s singles in the 15th World Championships.
RankingDifficult movementExercise actionTransition connection10.50.20.120.90.1030.90.2040.50.70.2510.20.160.80.20.371.20.2081.40.10Total7.21.90.7Percentage73.47%19.39%7.14%It can be seen from Table8 that the action content with the most point reduction in men’s singles is the difficulty action, and the total difficulty reduction is 9.0, accounting for 73.77% and, finally, the transition connection, with a total reduction of 1.4, accounting for 11.48%.Table 8
Statistics on the reduction of points in the completion of each movement content of women’s singles.
RankingDifficult movementExercise actionTransition connection1100.220.90.20.130.90.40.341.20.10.1510.20.161.10.30.271.10.30.281.80.30.2Total91.81.4Percentage73.77%14.75%11.48%From Table9, it can be seen that the highest ranking of mixed doubles is the difficulty movement, and the lowest ranking is the transition connection. The total difficulty reduction is 6.3, accounting for 57.27%. The total reduction score of consistency is 2.1, accounting for 19.09%. The total reduction score of maneuvering movements is 1.3, accounting for 11.82%. The total reduction of cooperation is 0.3, accounting for 2.73%. The total deduction of overlink is 0.1, accounting for 0.91.Table 9
Statistics on the reduction of points for each action content of mixed doubles.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionConsistency10.8000.10.10.120.90.1000.10.330.50.100.10.30.340.80.100.10.10.350.90.10.1000.2610.3000.10.170.40.5000.10.5810.1000.10.3Total6.31.30.10.30.92.1Percentage57.27%11.82%0.91%2.73%8.18%19.09%It can be seen from Table10 that the contents of the three-person project from high to low are difficulty action, consistency, operation action, lifting action, cooperation, and transition connection, and the total difficulty reduction is 6.1, accounting for 51.69%. The total consistency reduction is 2.2, accounting for 18.64%. The total reduction of maneuvering movements is 1.5, accounting for 12.71%. The total reduction of cooperation is 0.7, accounting for 5.93%. The total reduction of transition connection is 0.5, accounting for 4.24%.Table 10
Statistics on the reduction of points in each action content of the three people in the 15th World Championships.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionConsistency10.60.20000.420.70.20.200.10.230.50.30.10.10.10.340.60.20.20.10.10.250.90.200.10.10.260.70.100.20.10.470.60.300.20.30.281.500000.3Total6.11.50.50.70.82.2Percentage51.69%12.71%4.24%5.93%6.78%18.64%From Table11, it can be seen that the contents of the five-person project from high to low are difficulty action, consistency, operation action, cooperation, lifting action, and transition connection. The total reduction score of difficulty action is 6.8, accounting for 56.20%. The total reduction score of sex is 2.6, accounting for 21.49%. The total reduction of cooperation is 0.7, accounting for 5.79%. The total reduction of lifting action is 0.5, accounting for 4.13%. The total reduction score of transition connection is 0.3, accounting for 2.48%.Table 11
Statistics of score reduction of action content completion.
RankingDifficult movementExercise actionTransition connectionCollaborationLifting actionConsistency10.80.200.100.420.90.2000.10.230.70.200.10.10.340.70.100.100.550.60.200.10.10.46100.2000.371.10.10.10.10.10.2810.200.20.10.3Total6.81.20.30.70.52.6Percentage56.20%9.92%2.48%5.79%4.13%21.49%Figure5 is the statistics of the percentage of points reduced in the final of a single event. As can be seen from the following figure, the difficulty is to complete the action content with the largest proportion of point reduction, accounting for 74% of the total point reduction, followed by exercises, accounting for 17% and, finally, transition connection, accounting for 9%.Figure 5
Statistics on the proportion of point reduction completed by a single project.Figure6 is the statistics of point reduction in the final completion of collective events. As can be seen from the following figure, the highest ranking for reducing points is the difficult action, and the lowest ranking is the transition connection. Among them, the proportion of difficult movements is 55%.Figure 6
Statistics on the proportion of point reduction completed by collective projects.This study found that in collective events, the proportion of consistency reduction is second only to the proportion of difficulty reduction, and collective events should be paid attention to in training. Exercise movement is a complete set of movement content second only to difficulty reduction, and the reduction of exercise movement is mainly posture reduction. Athletes should also pay attention to the training of basic skills while improving the complete set of difficulties.


## 5. Conclusions

The society attaches great importance to physical education and sports training. There are many kinds of modern sports, and aerobics is one of the sports. It plays an important role in losing weight, strengthening the body, and exercising cardiopulmonary function. Therefore, it is necessary to strengthen aerobics teaching and training, realize the integrated development of theoretical teaching and skill training, analyze the problems existing in the concrete practice of aerobics teaching and training mode in time, actively explore and study the construction methods of aerobics teaching and training mode, and continuously improve the overall level and quality of aerobics teaching and training. In the dimension of completing point reduction, through the statistics and analysis of 40 sets of final videos of competitive events in the 15th World Championships, it is found that difficult movements account for the largest proportion of the number of units completing point reduction and completing point reduction. The highest score for a single-player project is the difficulty action, and the lowest is the transition connection. Difficult movements rank highest in the scores of collective events.



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*Source: 6073855-2022-08-02.xml*