# A Data Mining-Based Model for Evaluating Tennis Players’ Training Movements

Authors: Hang Chen
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

This paper uses data mining technology to mathematically model the training movements of tennis players, establish a three-dimensional data information database of athletes utilizing depth imaging, analyze the data with data mining algorithms, and derive the results after comparative evaluation and analysis with a database of movement characteristics of tennis dribblers. This paper uses video observation and mathematical modeling to construct a tennis player training action evaluation model, which provides a reference basis for tennis players to improve and enhance their tactical level; it can also provide a reference for the development of sports training special theory of tennis projects and enrich the tactical diagnosis method of tennis matches. To improve the accuracy of 3D human pose estimation, this paper adopts a 3D skeleton point extraction method based on RGBD images; for the action alignment problem, this paper uses a dynamic time warping (DTW) algorithm; for the similarity measure, this paper gives a Pearson correlation coefficient method based on the joint point features of human parts. This paper aims to conduct a systematic theoretical analysis of tennis players’ training movements based on theories and methods such as system science theory and social network analysis. On this basis, the characteristics of tennis training technology development are analyzed from a combination of qualitative and quantitative perspectives, while the development of tennis player training is explored based on tracking observations of tennis player movement training, and finally, the attack and service characteristics of tennis training are analyzed to better provide some reference for the sustainable development of tennis.


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## Body

## 1. Introduction

With the application of sports video analytics, the training videos of players can be evaluated, and computer vision and image analysis in sports applications optimize the results of sports video analysis [1]. The intelligent analysis of sports video is used in the technical analysis of individuals, which focuses on the individual ability, movement normality, and physical fitness of the athletes, and the detection, tracking, and movement analysis of the athletes through sports video promote the improvement of the technical level [2]. Human pose estimation provides the basis for action analysis, and the use of deep learning has improved the accuracy of human pose estimation, and sports video analysis based on human pose estimation is developing rapidly. Because the raw data of the sensor only has three-axis acceleration and angular velocity, it cannot show the characteristics of swing motion well. To better show the characteristics of the motion action, after the interception of the action window is completed, it is necessary to perform feature extraction and feature selection on the intercepted window data. Feature extraction is to further extract the action features from the original inertial sensor data of the action window that has been intercepted. Action standardization system is a type of sports video analysis, collecting players’ training videos, decomposing the recorded actions through multidimensionality, getting players’ real action data, using algorithms for analyzing videos, comparing and synthesizing them, which can assist players to find the gap with standard actions, improving training for the shortcomings of their actions, and improving the intuitiveness of training and the rapidity of feedback; at the same time, standardized training movements protect the health of athletes. [3].Nowadays, with the high level of competition in the tennis world, the flexibility of techniques and tactics, and the fast offensive and defensive transitions, many technical factors affect the winning and losing of matches. The players are getting closer and closer to each other, so it is difficult to find out the key technical indicators that affect the results of the matches from the technical data of only a few matches. A good way to do this is through a statistical analysis of high-level tennis matches in the world today [4]. By quantifying the technical indicators of tennis matches, it is possible to delineate the key components and to reflect objectively the athletic status of the players at the time. In sports research, it is very important to find out the interconnection between various factors from the huge amount of data and to discover the dynamic changes of many factors so that they can provide accurate and valuable information for people’s use. The coach user logs in to the system to manage the athlete’s training records, view the athlete’s basic movement training and the degree of improvement in each training, grasp the athlete’s training trend, and facilitate the development of training plans. At the same time, coaches need to manage the categories of uploaded actions and collect standard actions of different categories to provide references for athletes’ uploading actions.In the background of the new era, the tedious data can be analyzed by big data quickly and get accurate results, the analysis of data has entered our life, and different data analysis mode and method have different effects. Using big data analysis in the tennis game, though the organization of some game data of each player and different analysis methods, and finally get intuitive results, in which the use of big data analysis methods to find out the number of key technical indicators of each player, for each player and coach, has the scientific and practical significance of complementing the shortcomings [5]. Data mining is also translated as data exploration, data mining. It is one of the steps in Knowledge-Discovery in Databases (KDD). It is the process of automatically searching for information with special relationships (belonging to association rule learning) hidden in a large amount of data. One of the techniques used to find patterns in a large amount of data is the processing of data using SPSS software. Based on the technical and tactical statistics collected from women’s singles matches, the winning indexes of the players are selected and analyzed, and a scientific mathematical model is established to provide scientific guidance for women’s training and matches, as well as to provide a theoretical and practical basis for scientific prediction of their own technical and tactical abilities. The analysis of the technical indexes of the four Grand Slam women’s singles matches is of great significance to help recognize the winning rules of tennis matches and guide tennis training. The tennis player action specification system based on RGBD proposed in this paper faces the user groups of athletes, coaches, and administrators. Coaches need to collect standard actions, and athletes need to collect corresponding types of test actions. While understanding yourself and your opponent, you can play your strengths to suppress your opponent, limit your opponent’s play, and increase your chances of winning [6]. For this reason, the study of statistical analysis of excellent women’s tennis singles matches is very relevant to improving the level of women’s competitive tennis.


## 2. Related Works

Research into sports video analytics systems has developed rapidly in recent years, using a wide range of technologies, and many companies have launched their own developed sports video analytics systems. Various sports organizations and sports teams use video to analyze the effects of training and competition, providing a reference for the rationalization of sports training [7]. These systems can be applied in both individual and team sports to develop individual training plans by analyzing the movements and trajectories of athletes: by tracking the trajectories of team members, they analyze the tactical use of the team and provide coaches with appropriate tactical analysis. At present, when analyzing players and teams in mainstream commercial systems, the system is not highly intelligent and requires a lot of manual assistance work, which needs to be further improved in terms of analysis efficiency and ease of use. Most domestic sports video analysis systems focus on soccer, with less analysis and application for other sports such as tennis [8].From the combination of mathematical discipline vision and table tennis players’ stride and other aspects, the artificial fish swarm algorithm in mathematical discipline is used to modify and innovate it, and the ideal result is obtained after continuous calculation, the rule model of table tennis technical and tactical features classification based on artificial fish swarm algorithm is proposed, the example of excellent table tennis players’ game combined with artificial fish swarm algorithm is analyzed, and the final result shows that the artificial fish swarm algorithm with the improved artificial fish swarm algorithm model has a greater advantage in mining table tennis technical and tactical features than the existing algorithms in terms of quality and mining effect compared to association rule data mining in table tennis technical and tactical features [9]. By analyzing the data between technical indicators using association rules to mine the information, the association analysis theory, and the characteristics of technical and tactical decisions in tennis matches, the association problem between the hitting point and the points scored in the technical and tactical characteristics of tennis is obtained and the data in the technical and tactical characteristics are mined and analyzed. The results show that there is an inherent connection between hitting points and points scored in the technical and tactical indexes of tennis matches, which provides a scientific basis for future research on the technical and tactical aspects of tennis matches and the improvement of the athletic level of players [10].The development of sports video analytics systems has driven further improvements in sports video analytics. The analysis content has changed from the underlying features to the higher-level semantic features. The shot classification problem in soccer video, using the color change of the soccer field and edge detection to distinguish the class of shots in the video frame, improves the accuracy of shot detection; the detection of redundant data in basketball game video, using the color feature of the main area of the basketball court and the multiple area segmentation method, can detect the corresponding game video more accurately; the audio information is recorded in tennis matches, using the Gaussian mixture model and the hierarchical language model to detect audio sequences and use the maximum entropy Markov model to match tennis events; in basketball matches the video frames are divided into two categories according to the scoreboard, and the text and video frames are aligned using webcast text analysis to extract semantic events in scoreboard frames and slow motion playback in nonscoreboard frames, which increases the detection rate; hierarchical data association algorithms track the trajectory of the ball in tennis matches and according to the tennis trajectories to annotate the match process; sports include single player and team sports, and the analysis focuses on different types of sports videos [11]. Gray relational analysis is a method of quantitative description and comparison of the development and change of a system to analyze the degree of influence of various factors on the result. As a subsequence, find the degree of correlation between each subsequence and the parent sequence, and draw a conclusion based on the correlation. In team sports, the focus is on the tactics used by both sides and evaluating the performance of the team in the match; in single sports, the main analysis is on the body posture and movement accuracy of the athletes to assist in training and evaluating the performance of the athletes.


## 3. Data Mining-Based Model Construction for Evaluating Tennis Players’ Training Movements

### 3.1. Data Mining Model Construction

Before designing and developing the movement specification system, preliminary survey work is required to understand the requirements of the players, coaches, and other groups regarding an online movement specification system and design the system based on the surveyed requirements. In this paper, the proposed RGBD-based movement specification system for tennis players faces user groups such as players, coaches, and administrators. The coaches need to collect the standard movements and the athletes need to collect the test movements of corresponding categories [12]. Athletes can upload videos of basic movements, which include service, forward and backward draw, and chipping. Feedback results are obtained through system analysis to improve the accuracy of basic movements. The coach user logs into the system to manage the training records of the athlete, view the basic movements training and the improvement degree of each training, grasp the training trend of the athlete, and facilitate the formulation of training plans. At the same time, coaches need to manage the categories of uploaded movements and collect different categories of standard movements to provide a reference for the movements uploaded by athletes. The administrator maintains system users and assigns permissions and is responsible for different functions according to different users; it owns all the functions of the system, manages user logins, ensures the normal operation of the system, and records abnormalities of the system [13].With the system requirement analysis obtained in the previous section, the functional description of the RGBD-based basketball player movement specification system implementation is as follows.


#### 3.1.1. Registration Function

When the users register, they need to select the category they belong to and fill in basic information such as user name, after successful registration; after the administrator’s information verification, they can log in to the system to have different functions according to the category of personnel.


#### 3.1.2. Action Acquisition

Athletes and coaches can achieve the acquisition of test actions and standard actions through the acquisition equipment and provide data for uploading the corresponding actions. When establishing diagnostic indicators, a reasonable design and construction should be carried out according to the winning rules of competitive tennis competitions, with the goal of reflecting the players’ tactical capabilities comprehensively and objectively. Therefore, to ensure the comprehensiveness, rationality, and accuracy of diagnostic indicators, the construction of the tactical diagnostic indicator system must follow the following basic principles: scientific principles, systematic principles, purpose principles, independence principles, operability principles, and dynamics, the principle of sex. The use case diagram design of the data acquisition module is shown in Figure1.Figure 1
Use case diagram of the data acquisition module.Since the raw data of the sensor only has three-axis acceleration and angular velocity cannot represent the characteristics of the swing action well, to better represent the characteristics of the motion action, after completing the interception of the motion window, feature extraction and feature selection need to be performed on the intercepted window data. Feature extraction is the further extraction of motion features for the original inertial sensor data of the intercepted motion window. The features extracted in this paper mainly include the time domain features and frequency domain features of the motion data. The above-extracted features are obtained from the acceleration and acceleration within the action window, except for the signal amplitude which is one-dimensional data for each of acceleration, and the mean and variance of motion intensity which is extracted by combining the data of all axes and the dimensionality are also one dimensional. All other features are extracted from the three-axis acceleration for three-dimensional data and the three-axis angular velocity for three-dimensional data, resulting in 76 dimensions for the features extracted for each action window. In addition to the commonly used statistical features such as mean and variance, several other sensor data features such as absolute mean square deviation, energy, motion intensity, and signal amplitude are extracted in this paper and proved to be very effective features.(1)RMSfx=1n∑x=1nxn3.The whole process of action recognition includes data acquisition, window interception, feature extraction and selection, and recognition algorithm research. This chapter focuses on feature extraction and dimensionality reduction of window data and investigates a two-level recognition algorithm based on a decision tree algorithm for grip mode recognition and random forest for the swing action. This paper is dedicated to the study of an algorithm that can be used to recognize tennis swing actions in real-time [14]. As mentioned above, after fixing the sensor on the tennis racket, there are two grip styles, and to eliminate the influence of grip style on the recognition of swing action, the recognition algorithm studied in this paper is divided into two layers, the first layer first identifies the grip style of the player when he swings, and the second layer identifies the swing action based on the identified grip style. It is necessary to study the classification method for identifying the grip style first, and after identifying the grip style, it is necessary to build a classifier for swing action recognition based on a certain grip style. Therefore, in this paper, we propose a decision tree-based recognition method of grip style and a random forest-based recognition method of swing action, as shown in Figure 2.Figure 2
The process of recognition of the swing action.Gray correlation analysis is a quantitative description and comparison of the development of a system to analyze the degree of influence of each factor on the results, the core of which is to establish a parent series of changes over time following certain rules, the changes in each assessment object over time as a subsequence, and the degree of correlation between each subsequence and the parent series, according to the size of the correlation to conclude. The gray correlation analysis method has wide applicability to sampling content and applies to both the processing of small sample data and the analysis of large sample data. Gray correlation analysis is to explore the main and superior relationship between factors by calculating the gray correlation degree and then exploring the regular characteristics between subsystems. In this paper, the overall athletic ability (probability of winning) of players’ matches in the time dimension (2011–2017) and spatial dimension (different tournaments: Australian Open, US Open, and ATP Year-End Finals) are used as the parent series and tennis technical and tactical diagnostic indicators are used as the subseries for gray correlation analysis.(2)ξk=1xik+xjk−0.125γ.The action database is designed according to the functional requirements of athletes, involving four tables: an action category table, including action name and id; an action frame table, including action id, video frame, and human id, whether the standard action; a skeleton table, including key point coordinates, action frame id, and frame id; an action comparison table, including action comparison result table, including skeleton id and analysis result. The specific E-R diagram is shown in Figure3.Figure 3
Action database E-R diagram.The distribution of the world’s outstanding male tennis players’ scores on hard courts is mainly concentrated in the first 6 shots, accounting for 72.78% of the total. Specifically, the use of tactics and scoring characteristics of each shooting show the characteristics of three stages. The first stage includes beats 1-2; the second stage includes beats 3–6; and the third stage includes beats 7 and thereafter (hereinafter referred to as beat 7). The differences in the use rate and scoring rate of each tactical tactics illustrate the differences in the characteristics and scoring effects of the three stages of tactical use. The tactical diagnosis system of tennis matches is composed of a series of diagnostic indicators and corresponding evaluation criteria, through which the diagnostic indicators are evaluated to reflect the overall situation of judging and identifying targets. When establishing the diagnostic indexes, it should be reasonably designed and constructed according to the law of winning competitive tennis matches and to reflect the tactical ability of the players comprehensively and objectively. Therefore, to ensure the comprehensiveness, rationality, and accuracy of the diagnostic indexes, the construction of the tactical diagnostic index system must follow the following basic principles: scientific principle, systematic principle, purposeful principle, independence principle, operability principle, and dynamic principle. The theoretical basis of the diagnostic index system of this study is the theory of system science, the theory of item group training, the theory of tennis special training, the theory of evaluation of three indexes, the theory of the basic unit competitive process of the item group holding the racket across the net, and the theory of tennis technique and tactics analysis; at the same time, we should focus on the combination of theory and practice, and the indexes have both theoretical value and can accept the test of practice to ensure that the evaluation results of science should have a fully scientific basis [15]. In addition, in the process of constructing the tactical index system of men’s tennis singles, the diagnostic indexes must be determined by scientific and reasonable statistical analysis methods, with the correct theory and mathematical means for qualitative and quantitative analysis of statistical data results to ensure the scientific and validity of the tactical diagnostic index system of tennis and to obtain relatively real and objective diagnostic results.(3)E=a1101a2−10−11.


### 3.2. Design of Movement Specification System for Tennis Players

The basic characteristics of the tactical use of each racket in men’s tennis singles matches are analyzed. The scoring rate and the usage rate are the most basic evaluation parameters for the technical and tactical analysis of the match with a racket across the net. The analysis of the usage rate and score rate of the world’s best men’s tennis players in hard-court matches can grasp the characteristics and rules of the tactical use of men’s players through the characteristics of the tactical use of each racket (combination racket). In general, the distribution of the number of points scored and lost by the world’s best men tennis players in hard-court matches is mainly concentrated in the first 6 rackets, accounting for 72.78% of the total. Specifically, the tactical use and scoring characteristics of each shot show three stages [16]. The problem of lens classification in football videos uses the color change and edge detection of the football field to distinguish the types of shots in the video frame, which improves the accuracy of the shot detection; the detection of redundant data in the basketball game video uses the color characteristics of the main area of the basketball field and the multiple region segmentation method can more accurately detect the corresponding game video; the audio information recorded in the tennis game uses the Gaussian mixture model and the layered language model to detect the audio sequence, and the maximum entropy Markov model is used to match the tennis event. The first stage includes shots 1-2; the second stage includes shots 3–6; the third stage includes shots 7 and beyond (later referred to as shot 7). The differences in tactical use and scoring rate of each beat illustrate the differences in tactical use characteristics and scoring effects in the three phases. In the first phase (shots 1 and 2), the scoring rate was 57.41%, the cumulative usage rate was 34.26%, and the contribution rate was 39.33%. The effectiveness of the tactical use in the game of holding a racket spaced net sports is influenced by both the scoring rate and the usage rate; when the scoring rate is higher than 50%, the higher usage rate has a greater positive impact on winning the game; when the scoring rate is lower than 50%, the higher usage rate has a greater negative impact on winning the game, as shown in Figure 4.Figure 4
Diagram of the usage rate and cumulative usage rate of each racket of the best men’s tennis players on hard courts.The serve is the only technique that can be played actively without the interference of the opponent’s return, and the player can serve any spin and any drop according to his subjective intention. The absolute advantage of the serving side, serving style, and performance changes, tactical effectiveness, is the guarantee of the service; on the other hand, for the receiving side, facing the unpredictable serve, the choice of receiving tactics is in a passive position. The receiving side must not only take the initiative to attack under the condition of avoiding errors but also must actively defend under the condition of being unable to attack to inhibit the opponent’s attack after serving and at the same time to create conditions for the attack after receiving the serve. It shows that the ability of the receiving side to receive the service is the key link to achieve breakage in tennis. Serve and receive tactics have a relatively independent tactical status compared to other hitting tactics of each shot. The ability to score directly on serve and receive tactics is the dominant factor in winning tennis matches. Therefore, the 1st-2nd racket can be divided into one phase, the serve-receive tactical phase, to achieve a diagnosis of the player’s serve and receive level.(4)w1=1−ki∑1+ki.According to the principles of the technique, the muscles involved in backhand one-handed pumping technique movements include trunk muscles,the trunk muscles involved in backhand one-handed pumping technique include erector spine (extension) upper limb muscles, and the upper limb muscles involved in backhand one-handed pumping technique include rhomboids, deltoids, supraspinatus, radial wrist long extensors, radial wrist short extensors, and ulnar wrist extensors. Of these, the erector spinae allows for the posterior extension of the spine, the trapezius allows for scapular elevation and retraction, and the deltoid and supraspinatus allow for the abduction of the upper arm at the shoulder joint. These two muscles play the most important role in the power of the backhand one-handed pumping technique [17]. The backhand two-handed stroke is used more than the backhand one-handed pump and hides the trajectory of the ball, making it difficult for the opponent to determine the direction and rotation of the ball. According to the technical principles of the movement, the muscles involved in the backhand two-handed stroke are the same as in the backhand one-handed stroke but mobilize two major groups of muscles: the upper limb flexors of the backhand and the upper limb extensors of the forehand, which are limited in their contraction, and the trunk muscles (as in the backhand one-handed stroke). In addition, the upper limb muscles involved in the backhand two-handed pumping technique movement include the pectoralis major, anterior deltoid, biceps, and brachioradialis. Of these, the pectoralis major and anterior deltoid are involved in the forward flexion of the upper arm process; the biceps, brachialis, and brachioradialis allow the forearm to be flexed at the elbow joint. This paper uses the 10-fold cross-validation method to obtain the average recognition rate of the algorithm to verify the pros and cons of the classification algorithm. The specific method is to randomly divide all the sample sets into 10 parts, and each time 9 parts of the data are taken as the training set, 1 part of the data is used as the validation set.After 10 times tests, the average recognition rate is finally calculated.(5)Ci=1−Di−2Di+−Di−_.Using the VideoGraphic image recording and analysis methodology: using state-of-the-art digital technology to evaluate the dynamics of the technology and the movement technique in three-dimensional (3D) space, this method has been confirmed as reliable by the PMG Institute (Medical Research Facility Accuracy Testing Institute) of GAZ. The method collects various motion parameters such as practice, speed, velocity, and angle of movement. The movement of the athlete in three dimensions can be analyzed utilizing digital camera footage and specialized software. Using the speed check speed tester: the speed of the ball as it crosses the tester can be measured to calculate the fast force of the batted ball. Using the test method of hitting ball fast power, based on the data collected and analyzed, this study interviewed tennis coaches, teachers, and referees with questionnaires to select the most suitable test method for evaluating tennis hitting ball fast power [18]. The results of the questionnaire concluded that it is important to use the experimental method of indirect test of hitting ball fast power. The reason is that this method is simpler, does not require too much complex testing equipment, and is close to the specific training activities of coaches and athletes. It is also one of the most used methods in sports measurement today. Among the direct testing methods, the video analysis method and the velocimeter method had more accurate results, but only obtained the same results at the 87.5% level as at the 93.75% level . This may be due to the high facility requirements, as shown in Table 1.Table 1
Results of the selection of indicators of playing fast power.
OrderIndexNumber of timesNumber of times1Touch time (t)892Power to pass to the ball (F)92893Ball speed (v)674Maximum height joint (G)50495The speed of the wrist joint (m/s)0.30.35


## 3.1. Data Mining Model Construction

Before designing and developing the movement specification system, preliminary survey work is required to understand the requirements of the players, coaches, and other groups regarding an online movement specification system and design the system based on the surveyed requirements. In this paper, the proposed RGBD-based movement specification system for tennis players faces user groups such as players, coaches, and administrators. The coaches need to collect the standard movements and the athletes need to collect the test movements of corresponding categories [12]. Athletes can upload videos of basic movements, which include service, forward and backward draw, and chipping. Feedback results are obtained through system analysis to improve the accuracy of basic movements. The coach user logs into the system to manage the training records of the athlete, view the basic movements training and the improvement degree of each training, grasp the training trend of the athlete, and facilitate the formulation of training plans. At the same time, coaches need to manage the categories of uploaded movements and collect different categories of standard movements to provide a reference for the movements uploaded by athletes. The administrator maintains system users and assigns permissions and is responsible for different functions according to different users; it owns all the functions of the system, manages user logins, ensures the normal operation of the system, and records abnormalities of the system [13].With the system requirement analysis obtained in the previous section, the functional description of the RGBD-based basketball player movement specification system implementation is as follows.


### 3.1.1. Registration Function

When the users register, they need to select the category they belong to and fill in basic information such as user name, after successful registration; after the administrator’s information verification, they can log in to the system to have different functions according to the category of personnel.


### 3.1.2. Action Acquisition

Athletes and coaches can achieve the acquisition of test actions and standard actions through the acquisition equipment and provide data for uploading the corresponding actions. When establishing diagnostic indicators, a reasonable design and construction should be carried out according to the winning rules of competitive tennis competitions, with the goal of reflecting the players’ tactical capabilities comprehensively and objectively. Therefore, to ensure the comprehensiveness, rationality, and accuracy of diagnostic indicators, the construction of the tactical diagnostic indicator system must follow the following basic principles: scientific principles, systematic principles, purpose principles, independence principles, operability principles, and dynamics, the principle of sex. The use case diagram design of the data acquisition module is shown in Figure1.Figure 1
Use case diagram of the data acquisition module.Since the raw data of the sensor only has three-axis acceleration and angular velocity cannot represent the characteristics of the swing action well, to better represent the characteristics of the motion action, after completing the interception of the motion window, feature extraction and feature selection need to be performed on the intercepted window data. Feature extraction is the further extraction of motion features for the original inertial sensor data of the intercepted motion window. The features extracted in this paper mainly include the time domain features and frequency domain features of the motion data. The above-extracted features are obtained from the acceleration and acceleration within the action window, except for the signal amplitude which is one-dimensional data for each of acceleration, and the mean and variance of motion intensity which is extracted by combining the data of all axes and the dimensionality are also one dimensional. All other features are extracted from the three-axis acceleration for three-dimensional data and the three-axis angular velocity for three-dimensional data, resulting in 76 dimensions for the features extracted for each action window. In addition to the commonly used statistical features such as mean and variance, several other sensor data features such as absolute mean square deviation, energy, motion intensity, and signal amplitude are extracted in this paper and proved to be very effective features.(1)RMSfx=1n∑x=1nxn3.The whole process of action recognition includes data acquisition, window interception, feature extraction and selection, and recognition algorithm research. This chapter focuses on feature extraction and dimensionality reduction of window data and investigates a two-level recognition algorithm based on a decision tree algorithm for grip mode recognition and random forest for the swing action. This paper is dedicated to the study of an algorithm that can be used to recognize tennis swing actions in real-time [14]. As mentioned above, after fixing the sensor on the tennis racket, there are two grip styles, and to eliminate the influence of grip style on the recognition of swing action, the recognition algorithm studied in this paper is divided into two layers, the first layer first identifies the grip style of the player when he swings, and the second layer identifies the swing action based on the identified grip style. It is necessary to study the classification method for identifying the grip style first, and after identifying the grip style, it is necessary to build a classifier for swing action recognition based on a certain grip style. Therefore, in this paper, we propose a decision tree-based recognition method of grip style and a random forest-based recognition method of swing action, as shown in Figure 2.Figure 2
The process of recognition of the swing action.Gray correlation analysis is a quantitative description and comparison of the development of a system to analyze the degree of influence of each factor on the results, the core of which is to establish a parent series of changes over time following certain rules, the changes in each assessment object over time as a subsequence, and the degree of correlation between each subsequence and the parent series, according to the size of the correlation to conclude. The gray correlation analysis method has wide applicability to sampling content and applies to both the processing of small sample data and the analysis of large sample data. Gray correlation analysis is to explore the main and superior relationship between factors by calculating the gray correlation degree and then exploring the regular characteristics between subsystems. In this paper, the overall athletic ability (probability of winning) of players’ matches in the time dimension (2011–2017) and spatial dimension (different tournaments: Australian Open, US Open, and ATP Year-End Finals) are used as the parent series and tennis technical and tactical diagnostic indicators are used as the subseries for gray correlation analysis.(2)ξk=1xik+xjk−0.125γ.The action database is designed according to the functional requirements of athletes, involving four tables: an action category table, including action name and id; an action frame table, including action id, video frame, and human id, whether the standard action; a skeleton table, including key point coordinates, action frame id, and frame id; an action comparison table, including action comparison result table, including skeleton id and analysis result. The specific E-R diagram is shown in Figure3.Figure 3
Action database E-R diagram.The distribution of the world’s outstanding male tennis players’ scores on hard courts is mainly concentrated in the first 6 shots, accounting for 72.78% of the total. Specifically, the use of tactics and scoring characteristics of each shooting show the characteristics of three stages. The first stage includes beats 1-2; the second stage includes beats 3–6; and the third stage includes beats 7 and thereafter (hereinafter referred to as beat 7). The differences in the use rate and scoring rate of each tactical tactics illustrate the differences in the characteristics and scoring effects of the three stages of tactical use. The tactical diagnosis system of tennis matches is composed of a series of diagnostic indicators and corresponding evaluation criteria, through which the diagnostic indicators are evaluated to reflect the overall situation of judging and identifying targets. When establishing the diagnostic indexes, it should be reasonably designed and constructed according to the law of winning competitive tennis matches and to reflect the tactical ability of the players comprehensively and objectively. Therefore, to ensure the comprehensiveness, rationality, and accuracy of the diagnostic indexes, the construction of the tactical diagnostic index system must follow the following basic principles: scientific principle, systematic principle, purposeful principle, independence principle, operability principle, and dynamic principle. The theoretical basis of the diagnostic index system of this study is the theory of system science, the theory of item group training, the theory of tennis special training, the theory of evaluation of three indexes, the theory of the basic unit competitive process of the item group holding the racket across the net, and the theory of tennis technique and tactics analysis; at the same time, we should focus on the combination of theory and practice, and the indexes have both theoretical value and can accept the test of practice to ensure that the evaluation results of science should have a fully scientific basis [15]. In addition, in the process of constructing the tactical index system of men’s tennis singles, the diagnostic indexes must be determined by scientific and reasonable statistical analysis methods, with the correct theory and mathematical means for qualitative and quantitative analysis of statistical data results to ensure the scientific and validity of the tactical diagnostic index system of tennis and to obtain relatively real and objective diagnostic results.(3)E=a1101a2−10−11.


## 3.1.1. Registration Function

When the users register, they need to select the category they belong to and fill in basic information such as user name, after successful registration; after the administrator’s information verification, they can log in to the system to have different functions according to the category of personnel.


## 3.1.2. Action Acquisition

Athletes and coaches can achieve the acquisition of test actions and standard actions through the acquisition equipment and provide data for uploading the corresponding actions. When establishing diagnostic indicators, a reasonable design and construction should be carried out according to the winning rules of competitive tennis competitions, with the goal of reflecting the players’ tactical capabilities comprehensively and objectively. Therefore, to ensure the comprehensiveness, rationality, and accuracy of diagnostic indicators, the construction of the tactical diagnostic indicator system must follow the following basic principles: scientific principles, systematic principles, purpose principles, independence principles, operability principles, and dynamics, the principle of sex. The use case diagram design of the data acquisition module is shown in Figure1.Figure 1
Use case diagram of the data acquisition module.Since the raw data of the sensor only has three-axis acceleration and angular velocity cannot represent the characteristics of the swing action well, to better represent the characteristics of the motion action, after completing the interception of the motion window, feature extraction and feature selection need to be performed on the intercepted window data. Feature extraction is the further extraction of motion features for the original inertial sensor data of the intercepted motion window. The features extracted in this paper mainly include the time domain features and frequency domain features of the motion data. The above-extracted features are obtained from the acceleration and acceleration within the action window, except for the signal amplitude which is one-dimensional data for each of acceleration, and the mean and variance of motion intensity which is extracted by combining the data of all axes and the dimensionality are also one dimensional. All other features are extracted from the three-axis acceleration for three-dimensional data and the three-axis angular velocity for three-dimensional data, resulting in 76 dimensions for the features extracted for each action window. In addition to the commonly used statistical features such as mean and variance, several other sensor data features such as absolute mean square deviation, energy, motion intensity, and signal amplitude are extracted in this paper and proved to be very effective features.(1)RMSfx=1n∑x=1nxn3.The whole process of action recognition includes data acquisition, window interception, feature extraction and selection, and recognition algorithm research. This chapter focuses on feature extraction and dimensionality reduction of window data and investigates a two-level recognition algorithm based on a decision tree algorithm for grip mode recognition and random forest for the swing action. This paper is dedicated to the study of an algorithm that can be used to recognize tennis swing actions in real-time [14]. As mentioned above, after fixing the sensor on the tennis racket, there are two grip styles, and to eliminate the influence of grip style on the recognition of swing action, the recognition algorithm studied in this paper is divided into two layers, the first layer first identifies the grip style of the player when he swings, and the second layer identifies the swing action based on the identified grip style. It is necessary to study the classification method for identifying the grip style first, and after identifying the grip style, it is necessary to build a classifier for swing action recognition based on a certain grip style. Therefore, in this paper, we propose a decision tree-based recognition method of grip style and a random forest-based recognition method of swing action, as shown in Figure 2.Figure 2
The process of recognition of the swing action.Gray correlation analysis is a quantitative description and comparison of the development of a system to analyze the degree of influence of each factor on the results, the core of which is to establish a parent series of changes over time following certain rules, the changes in each assessment object over time as a subsequence, and the degree of correlation between each subsequence and the parent series, according to the size of the correlation to conclude. The gray correlation analysis method has wide applicability to sampling content and applies to both the processing of small sample data and the analysis of large sample data. Gray correlation analysis is to explore the main and superior relationship between factors by calculating the gray correlation degree and then exploring the regular characteristics between subsystems. In this paper, the overall athletic ability (probability of winning) of players’ matches in the time dimension (2011–2017) and spatial dimension (different tournaments: Australian Open, US Open, and ATP Year-End Finals) are used as the parent series and tennis technical and tactical diagnostic indicators are used as the subseries for gray correlation analysis.(2)ξk=1xik+xjk−0.125γ.The action database is designed according to the functional requirements of athletes, involving four tables: an action category table, including action name and id; an action frame table, including action id, video frame, and human id, whether the standard action; a skeleton table, including key point coordinates, action frame id, and frame id; an action comparison table, including action comparison result table, including skeleton id and analysis result. The specific E-R diagram is shown in Figure3.Figure 3
Action database E-R diagram.The distribution of the world’s outstanding male tennis players’ scores on hard courts is mainly concentrated in the first 6 shots, accounting for 72.78% of the total. Specifically, the use of tactics and scoring characteristics of each shooting show the characteristics of three stages. The first stage includes beats 1-2; the second stage includes beats 3–6; and the third stage includes beats 7 and thereafter (hereinafter referred to as beat 7). The differences in the use rate and scoring rate of each tactical tactics illustrate the differences in the characteristics and scoring effects of the three stages of tactical use. The tactical diagnosis system of tennis matches is composed of a series of diagnostic indicators and corresponding evaluation criteria, through which the diagnostic indicators are evaluated to reflect the overall situation of judging and identifying targets. When establishing the diagnostic indexes, it should be reasonably designed and constructed according to the law of winning competitive tennis matches and to reflect the tactical ability of the players comprehensively and objectively. Therefore, to ensure the comprehensiveness, rationality, and accuracy of the diagnostic indexes, the construction of the tactical diagnostic index system must follow the following basic principles: scientific principle, systematic principle, purposeful principle, independence principle, operability principle, and dynamic principle. The theoretical basis of the diagnostic index system of this study is the theory of system science, the theory of item group training, the theory of tennis special training, the theory of evaluation of three indexes, the theory of the basic unit competitive process of the item group holding the racket across the net, and the theory of tennis technique and tactics analysis; at the same time, we should focus on the combination of theory and practice, and the indexes have both theoretical value and can accept the test of practice to ensure that the evaluation results of science should have a fully scientific basis [15]. In addition, in the process of constructing the tactical index system of men’s tennis singles, the diagnostic indexes must be determined by scientific and reasonable statistical analysis methods, with the correct theory and mathematical means for qualitative and quantitative analysis of statistical data results to ensure the scientific and validity of the tactical diagnostic index system of tennis and to obtain relatively real and objective diagnostic results.(3)E=a1101a2−10−11.


## 3.2. Design of Movement Specification System for Tennis Players

The basic characteristics of the tactical use of each racket in men’s tennis singles matches are analyzed. The scoring rate and the usage rate are the most basic evaluation parameters for the technical and tactical analysis of the match with a racket across the net. The analysis of the usage rate and score rate of the world’s best men’s tennis players in hard-court matches can grasp the characteristics and rules of the tactical use of men’s players through the characteristics of the tactical use of each racket (combination racket). In general, the distribution of the number of points scored and lost by the world’s best men tennis players in hard-court matches is mainly concentrated in the first 6 rackets, accounting for 72.78% of the total. Specifically, the tactical use and scoring characteristics of each shot show three stages [16]. The problem of lens classification in football videos uses the color change and edge detection of the football field to distinguish the types of shots in the video frame, which improves the accuracy of the shot detection; the detection of redundant data in the basketball game video uses the color characteristics of the main area of the basketball field and the multiple region segmentation method can more accurately detect the corresponding game video; the audio information recorded in the tennis game uses the Gaussian mixture model and the layered language model to detect the audio sequence, and the maximum entropy Markov model is used to match the tennis event. The first stage includes shots 1-2; the second stage includes shots 3–6; the third stage includes shots 7 and beyond (later referred to as shot 7). The differences in tactical use and scoring rate of each beat illustrate the differences in tactical use characteristics and scoring effects in the three phases. In the first phase (shots 1 and 2), the scoring rate was 57.41%, the cumulative usage rate was 34.26%, and the contribution rate was 39.33%. The effectiveness of the tactical use in the game of holding a racket spaced net sports is influenced by both the scoring rate and the usage rate; when the scoring rate is higher than 50%, the higher usage rate has a greater positive impact on winning the game; when the scoring rate is lower than 50%, the higher usage rate has a greater negative impact on winning the game, as shown in Figure 4.Figure 4
Diagram of the usage rate and cumulative usage rate of each racket of the best men’s tennis players on hard courts.The serve is the only technique that can be played actively without the interference of the opponent’s return, and the player can serve any spin and any drop according to his subjective intention. The absolute advantage of the serving side, serving style, and performance changes, tactical effectiveness, is the guarantee of the service; on the other hand, for the receiving side, facing the unpredictable serve, the choice of receiving tactics is in a passive position. The receiving side must not only take the initiative to attack under the condition of avoiding errors but also must actively defend under the condition of being unable to attack to inhibit the opponent’s attack after serving and at the same time to create conditions for the attack after receiving the serve. It shows that the ability of the receiving side to receive the service is the key link to achieve breakage in tennis. Serve and receive tactics have a relatively independent tactical status compared to other hitting tactics of each shot. The ability to score directly on serve and receive tactics is the dominant factor in winning tennis matches. Therefore, the 1st-2nd racket can be divided into one phase, the serve-receive tactical phase, to achieve a diagnosis of the player’s serve and receive level.(4)w1=1−ki∑1+ki.According to the principles of the technique, the muscles involved in backhand one-handed pumping technique movements include trunk muscles,the trunk muscles involved in backhand one-handed pumping technique include erector spine (extension) upper limb muscles, and the upper limb muscles involved in backhand one-handed pumping technique include rhomboids, deltoids, supraspinatus, radial wrist long extensors, radial wrist short extensors, and ulnar wrist extensors. Of these, the erector spinae allows for the posterior extension of the spine, the trapezius allows for scapular elevation and retraction, and the deltoid and supraspinatus allow for the abduction of the upper arm at the shoulder joint. These two muscles play the most important role in the power of the backhand one-handed pumping technique [17]. The backhand two-handed stroke is used more than the backhand one-handed pump and hides the trajectory of the ball, making it difficult for the opponent to determine the direction and rotation of the ball. According to the technical principles of the movement, the muscles involved in the backhand two-handed stroke are the same as in the backhand one-handed stroke but mobilize two major groups of muscles: the upper limb flexors of the backhand and the upper limb extensors of the forehand, which are limited in their contraction, and the trunk muscles (as in the backhand one-handed stroke). In addition, the upper limb muscles involved in the backhand two-handed pumping technique movement include the pectoralis major, anterior deltoid, biceps, and brachioradialis. Of these, the pectoralis major and anterior deltoid are involved in the forward flexion of the upper arm process; the biceps, brachialis, and brachioradialis allow the forearm to be flexed at the elbow joint. This paper uses the 10-fold cross-validation method to obtain the average recognition rate of the algorithm to verify the pros and cons of the classification algorithm. The specific method is to randomly divide all the sample sets into 10 parts, and each time 9 parts of the data are taken as the training set, 1 part of the data is used as the validation set.After 10 times tests, the average recognition rate is finally calculated.(5)Ci=1−Di−2Di+−Di−_.Using the VideoGraphic image recording and analysis methodology: using state-of-the-art digital technology to evaluate the dynamics of the technology and the movement technique in three-dimensional (3D) space, this method has been confirmed as reliable by the PMG Institute (Medical Research Facility Accuracy Testing Institute) of GAZ. The method collects various motion parameters such as practice, speed, velocity, and angle of movement. The movement of the athlete in three dimensions can be analyzed utilizing digital camera footage and specialized software. Using the speed check speed tester: the speed of the ball as it crosses the tester can be measured to calculate the fast force of the batted ball. Using the test method of hitting ball fast power, based on the data collected and analyzed, this study interviewed tennis coaches, teachers, and referees with questionnaires to select the most suitable test method for evaluating tennis hitting ball fast power [18]. The results of the questionnaire concluded that it is important to use the experimental method of indirect test of hitting ball fast power. The reason is that this method is simpler, does not require too much complex testing equipment, and is close to the specific training activities of coaches and athletes. It is also one of the most used methods in sports measurement today. Among the direct testing methods, the video analysis method and the velocimeter method had more accurate results, but only obtained the same results at the 87.5% level as at the 93.75% level . This may be due to the high facility requirements, as shown in Table 1.Table 1
Results of the selection of indicators of playing fast power.
OrderIndexNumber of timesNumber of times1Touch time (t)892Power to pass to the ball (F)92893Ball speed (v)674Maximum height joint (G)50495The speed of the wrist joint (m/s)0.30.35


## 4. Analysis of Results

### 4.1. Implementation of the Action System

The tactical use of men’s hard-court singles tennis was divided into the receive-and-serve tactical segment (2 shots), the catch-and-snap tactical segment (4 and 6 shots), and the holding tactical segment (8th shot) based on the characteristics of the variation of the score and usage rate graphs of each shot in the receive-and-serve set. To further verify the rationality of the division, the variability of the tactical performance of each racket in the receiving set was analyzed. The percentage of the total number of points was scored by each racket in a good men’s tennis hard-court match. First, the highest percentage of the total number of points scored was accounted for by the 1st racket score at 26.31%, nearly one-third. The KMO and Bartlett sphericity tests show that the KMO value is 0.692, which is greater than 0.5, which is between 0 and 1, which is the KMO measure of sampling appropriateness. The Bartlett sphericity test value df = 91, and the associated probability P ≤ 0.001, indicating that it is very much in the significant level, the null hypothesis is therefore rejected and the data are considered suitable for factor analysis. Every basic unit of the competitive process in tennis begins with the serve, and the implementation of serve tactics is the only tennis tactic in the game that is not interfered with by the opponent, and relying on quality serves to gain an advantage or even score directly is an important tactic among professional tennis players. Therefore, a diagnosis of the tactical aspects of the service is an essential part of the process. Secondly, the first 6 shots accounted for 77.2% of the total points scored, and the use of the first 6 shots tactics played the first role in winning the match. Thirdly, the percentage of points scored on each shot of service is generally higher than that of the total points scored on each shot of receive serve, indicating that the scoring advantage of service is significantly higher than that of receiving services, and the hold of service is crucial to winning the match. Fourth, the proportion of the first 3 shots of the services to the total score is as high as 50.2%, which is more than half of the total score, and the use of the first 3 shots of the service is the key to the service hold and even the match win. Therefore, the first level diagnostic index of articulation and stealing section can be distinguished between serve and receive, which are serve and steal (3 and 5 shots) and receive and steal (4 and 6 shots), respectively, as shown in Figure 5Figure 5
Diagram of the percentage of total points scored by the racket in good men’s tennis hard-court matches.There are two ways of recognizing the grip of an athlete when he does a swing: one is to recognize the swing directly without considering the grip; the other is to recognize the grip of the athlete first and then recognize the swing based on some grip [19]. To eliminate the influence of grip mode on the recognition result, this paper adopts the approach of recognizing the grip mode first and then the swing action. The final decision tree algorithm is used to identify the grip pattern, and the data features from the training set are used to construct the C4.5 tree, which eventually forms the decision tree model. In this paper, the average recognition rate of the algorithm is obtained through a 10-fold cross-validation method to validate the classification algorithm by dividing all the sample sets into 10 randomly, taking 9 of the data as the training set and 1 as the validation set each time, and so on 10 times, and finally calculating the average recognition rate. The decision tree algorithm used in this paper can achieve an average recognition rate of 99.41% for grip patterns, so it can be used for grip pattern recognition before swing action recognition. In sports applications, computer vision and image analysis optimize the results of sports video analysis. Intelligent analysis of sports videos is used in personal technical analysis, mainly for athletes’ personal abilities, movement norms, and physical fitness. The detection, tracking, and movement analysis of athletes through sports videos has promoted the improvement of technical levels.In this paper, we establish the prediction model of the total score in different field games according to the gray system theory. Gray system theory is mainly through the generation of part of the known information and the extraction of valuable information, from this processed information to obtain the technical indicators affecting the total score in the game and to understand its evolution law, but the prediction is through the exploration of things in the past to understand the future development trend of things, and the development of things is constantly changing.So any prediction of the future pair will not be accurate, but it will provide some way of solving problems for our plans. The original data quasisequence is a prerequisite for building the GM (1,1) model [20]. Therefore, to ensure the feasibility of the modeling approach, the necessary tests must first be done on the raw discrete data. Human body pose estimation provides a basis for action analysis. The use of deep learning improves the accuracy of human body pose estimation. Sports video analysis based on human body pose estimation is developing rapidly. If the raw discrete data series satisfy the smoothness condition, the model can be built directly, for calculation and prediction; otherwise, the raw discrete data must be preprocessed with data variation techniques to make them satisfy the modeling conditions.(6)X0=X10+X20+⋯+Xn0.The data acquisition system used in this paper is designed to transmit data via low-power Bluetooth (BLE), which is supported by Android since version 4.3. BLE is based on the GATT (Generic Attribute Profile) protocol, which describes an ATT-based service framework that defines a procedure and data format for data transfer based on service and characteristic [21]. The procedure defines the discovery, reading, writing, notifying, indicating, and configuring the broadcast of a characteristic. A low-power Bluetooth device can define many services and a service can be understood as a collection of functions. Each different service in the device has a 128 bit UUID as a separate flag for this service. Under the service, there are many independent data items, which are called characteristic, and each characteristic also has a unique UUID as an identifier. In Android development, after establishing a Bluetooth connection, sending data to peripheral devices via Bluetooth is writing data to the value field in these characteristics; sending data from peripheral devices to mobile phones is listening for changes in the value field in these characteristics, and if there is a change, the phone’s BLE API will receive a call-back for listening. Women’s tennis players’ first-serve success rates in the four Grand Slam tournaments they played in 2015 were overall good and above the overall level. But, poor scoring efficiency has led to hard-fought matches and even defeats. The competitive level of athletes is getting closer and closer. Therefore, it is difficult to accurately find the key technical indicators that affect the outcome of the game from the technical data of only a few games. It is a good way to conduct statistical analysis and research on the high-level tennis matches in the world today. The technical indicators obtained by quantifying the tennis match can be divided into key parts and at the same time can objectively reflect the competitive state of the athletes at that time. Having a quality service also requires a good rhythm to tip the scales in your favor. The first-serve percentage and second-serve percentage are an indication of the strength of the service in the match to ensure that your service is not controlled by the opponent to have a chance to win. Also, the overall women’s singles players in our country do not have a high serve-receive percentage and most of them are below the overall level. The serve-receive percentage is the lowest standard value within the four indicators screened, which indicates that the overall level of serve-receive percentage is not high and shows the importance of keeping one’s served.


### 4.2. System Performance Analysis

Based on the experimental movements captured in the previous section, they were analyzed in the movement specification module. First, four action clips are selected as key actions, which present the overall process of shooting a basketball. When each action sequence is uploaded, the system looks up the keyframes corresponding to the standard action according to the category of the action. The system’s recognition accuracy of the key points varies less significantly with the video input size, and the running time increases with the size of the video. On the one hand, as the resolution of each frame of the color map is 1920 × 1080, it is higher in the accuracy of the detected joints, but the detection time also increases: on the other hand, DTW is used in the system for action specification, and as the video size increases, the DTW detection time is longer, resulting in a longer running time on each frame. Therefore, the system is not yet able to achieve a real-time action specification effect. Analyzing the big data of the technical indicators of the four Grand Slam women’s singles competitions based on various factors will help to understand the winning rules of tennis matches and guide tennis training is of great significance. While understanding yourself and your opponents, use your own advantages to suppress your opponents, limit your opponent’s performance, and increase your chances of winning.The KMO and Bartlett’s sphericity test showed that the KMO value 0.692 is greater than 0.5; this value is between 0 and 1 for the KMO measure of sampling appropriateness and Bartlett’s sphericity test value df = 91 with a companion probability P ≤ 0.001 which indicates that it is in the very significant level; therefore, the null hypothesis is rejected and the data are considered suitable for factor analysis. The overall variables were tested while the results showed that the degree of extraction was not the same. The factors were extracted in descending order according to the height of the factors: net score rate, first-serve success rate, second-serve average speed, break rate, first-serve average speed, second-serve score rate, a double fault on serve, unforced error, total score, first-serve score rate, fastest speed on serve, winning score, Aces ball, and receiving serve. The cumulative contribution of the 8 common factors reached 86.79%, which can summarize more than 85% of the information of the original variables, so it can be considered that the 8 common factors can explain most of the variables. These systems can be applied to individual projects and team projects to develop personal training plans by analyzing the movements and trajectories of athletes: by tracking team members’ trajectories, analyzing the team’s tactical use, and providing coaches with appropriate tactical analysis. Usually, the graph shows a clear break between the steep slope of the large factor and the gentle tails of the remaining factors, while generally the main factors are selected on a very steep slope, but the top 8 factors are selected based on the criterion of 85% factor contribution. This is shown in Figure 6.Figure 6
Factor eigenvalue gravel plot.After continuous calculations, the ideal result is obtained. A rule model for the classification of table tennis technical and tactical features based on the artificial fish school algorithm is proposed. An example of the combination of an excellent table tennis player game and the artificial fish school algorithm is analyzed. The result shows that artificial fish compared with the association rule data mining in the technical and tactical characteristics of table tennis, the swarm algorithm is better than the existing algorithms in mining technical and tactical characteristics of table tennis. The function of the sports analysis module is to analyze the sportsman’s sports data to analyze the sportsman’s sports situation and display it to the user. The module mainly includes sports goal setting and completion, comprehensive evaluation, and ball wind analysis. The design principle of the goal setting and completion is that the goal set by the user is saved in the local database and uploaded to the cloud, and the completion status of the goal is displayed by comparing the goal set with the sports situation and calculating the percentage of completion status. The goal field in the movement data table represents the movement goal set by the user. Users can set the exercise time of the week, the number of swings, calories burned, and exercise frequency on the the exercise goal completion screen, exercise goal setting screen, and the sliding of the SeekBar. The user’s goal completion is displayed in the form of a percentage. In addition to being able to display training data and set exercise goals, the software also analyzes the exercise data of the exerciser, thus making it easier for the user to know the details of the exercise over time and enabling the user to understand the shortcomings of their skill level from the analysis of the exercise data. The sports analysis screen is a general analysis of the sport, which analyzes the number of swings, movement time, swing speed, etc., of each swing technique of the athlete over a period to obtain such data as swing performance, maximum speed, reaction power index, explosive power index, endurance index, duration, and overall performance index, which is presented to the user in the form of a spider web graph. The competitive process of every basic unit of a tennis game starts with serving. The implementation of serving tactics is the only tennis tactic that is not interfered by the opponent in the game. Among professional tennis players, relying on high-quality serve to gain an advantage or even direct score is one. The technical style of the sport is analyzed based on the number of offensive and defensive shots of the athlete, where snapping and high and long are specified as offensive shots. The trend of the user’s movement in a week is analyzed, firstly, the information such as movement time, number of swings, energy consumption, and maximum speed in a week is counted, and then the movement trend in a week is reflected in the form of a curve, as shown in Figure7.Figure 7
Trend of weekly movements.


## 4.1. Implementation of the Action System

The tactical use of men’s hard-court singles tennis was divided into the receive-and-serve tactical segment (2 shots), the catch-and-snap tactical segment (4 and 6 shots), and the holding tactical segment (8th shot) based on the characteristics of the variation of the score and usage rate graphs of each shot in the receive-and-serve set. To further verify the rationality of the division, the variability of the tactical performance of each racket in the receiving set was analyzed. The percentage of the total number of points was scored by each racket in a good men’s tennis hard-court match. First, the highest percentage of the total number of points scored was accounted for by the 1st racket score at 26.31%, nearly one-third. The KMO and Bartlett sphericity tests show that the KMO value is 0.692, which is greater than 0.5, which is between 0 and 1, which is the KMO measure of sampling appropriateness. The Bartlett sphericity test value df = 91, and the associated probability P ≤ 0.001, indicating that it is very much in the significant level, the null hypothesis is therefore rejected and the data are considered suitable for factor analysis. Every basic unit of the competitive process in tennis begins with the serve, and the implementation of serve tactics is the only tennis tactic in the game that is not interfered with by the opponent, and relying on quality serves to gain an advantage or even score directly is an important tactic among professional tennis players. Therefore, a diagnosis of the tactical aspects of the service is an essential part of the process. Secondly, the first 6 shots accounted for 77.2% of the total points scored, and the use of the first 6 shots tactics played the first role in winning the match. Thirdly, the percentage of points scored on each shot of service is generally higher than that of the total points scored on each shot of receive serve, indicating that the scoring advantage of service is significantly higher than that of receiving services, and the hold of service is crucial to winning the match. Fourth, the proportion of the first 3 shots of the services to the total score is as high as 50.2%, which is more than half of the total score, and the use of the first 3 shots of the service is the key to the service hold and even the match win. Therefore, the first level diagnostic index of articulation and stealing section can be distinguished between serve and receive, which are serve and steal (3 and 5 shots) and receive and steal (4 and 6 shots), respectively, as shown in Figure 5Figure 5
Diagram of the percentage of total points scored by the racket in good men’s tennis hard-court matches.There are two ways of recognizing the grip of an athlete when he does a swing: one is to recognize the swing directly without considering the grip; the other is to recognize the grip of the athlete first and then recognize the swing based on some grip [19]. To eliminate the influence of grip mode on the recognition result, this paper adopts the approach of recognizing the grip mode first and then the swing action. The final decision tree algorithm is used to identify the grip pattern, and the data features from the training set are used to construct the C4.5 tree, which eventually forms the decision tree model. In this paper, the average recognition rate of the algorithm is obtained through a 10-fold cross-validation method to validate the classification algorithm by dividing all the sample sets into 10 randomly, taking 9 of the data as the training set and 1 as the validation set each time, and so on 10 times, and finally calculating the average recognition rate. The decision tree algorithm used in this paper can achieve an average recognition rate of 99.41% for grip patterns, so it can be used for grip pattern recognition before swing action recognition. In sports applications, computer vision and image analysis optimize the results of sports video analysis. Intelligent analysis of sports videos is used in personal technical analysis, mainly for athletes’ personal abilities, movement norms, and physical fitness. The detection, tracking, and movement analysis of athletes through sports videos has promoted the improvement of technical levels.In this paper, we establish the prediction model of the total score in different field games according to the gray system theory. Gray system theory is mainly through the generation of part of the known information and the extraction of valuable information, from this processed information to obtain the technical indicators affecting the total score in the game and to understand its evolution law, but the prediction is through the exploration of things in the past to understand the future development trend of things, and the development of things is constantly changing.So any prediction of the future pair will not be accurate, but it will provide some way of solving problems for our plans. The original data quasisequence is a prerequisite for building the GM (1,1) model [20]. Therefore, to ensure the feasibility of the modeling approach, the necessary tests must first be done on the raw discrete data. Human body pose estimation provides a basis for action analysis. The use of deep learning improves the accuracy of human body pose estimation. Sports video analysis based on human body pose estimation is developing rapidly. If the raw discrete data series satisfy the smoothness condition, the model can be built directly, for calculation and prediction; otherwise, the raw discrete data must be preprocessed with data variation techniques to make them satisfy the modeling conditions.(6)X0=X10+X20+⋯+Xn0.The data acquisition system used in this paper is designed to transmit data via low-power Bluetooth (BLE), which is supported by Android since version 4.3. BLE is based on the GATT (Generic Attribute Profile) protocol, which describes an ATT-based service framework that defines a procedure and data format for data transfer based on service and characteristic [21]. The procedure defines the discovery, reading, writing, notifying, indicating, and configuring the broadcast of a characteristic. A low-power Bluetooth device can define many services and a service can be understood as a collection of functions. Each different service in the device has a 128 bit UUID as a separate flag for this service. Under the service, there are many independent data items, which are called characteristic, and each characteristic also has a unique UUID as an identifier. In Android development, after establishing a Bluetooth connection, sending data to peripheral devices via Bluetooth is writing data to the value field in these characteristics; sending data from peripheral devices to mobile phones is listening for changes in the value field in these characteristics, and if there is a change, the phone’s BLE API will receive a call-back for listening. Women’s tennis players’ first-serve success rates in the four Grand Slam tournaments they played in 2015 were overall good and above the overall level. But, poor scoring efficiency has led to hard-fought matches and even defeats. The competitive level of athletes is getting closer and closer. Therefore, it is difficult to accurately find the key technical indicators that affect the outcome of the game from the technical data of only a few games. It is a good way to conduct statistical analysis and research on the high-level tennis matches in the world today. The technical indicators obtained by quantifying the tennis match can be divided into key parts and at the same time can objectively reflect the competitive state of the athletes at that time. Having a quality service also requires a good rhythm to tip the scales in your favor. The first-serve percentage and second-serve percentage are an indication of the strength of the service in the match to ensure that your service is not controlled by the opponent to have a chance to win. Also, the overall women’s singles players in our country do not have a high serve-receive percentage and most of them are below the overall level. The serve-receive percentage is the lowest standard value within the four indicators screened, which indicates that the overall level of serve-receive percentage is not high and shows the importance of keeping one’s served.


## 4.2. System Performance Analysis

Based on the experimental movements captured in the previous section, they were analyzed in the movement specification module. First, four action clips are selected as key actions, which present the overall process of shooting a basketball. When each action sequence is uploaded, the system looks up the keyframes corresponding to the standard action according to the category of the action. The system’s recognition accuracy of the key points varies less significantly with the video input size, and the running time increases with the size of the video. On the one hand, as the resolution of each frame of the color map is 1920 × 1080, it is higher in the accuracy of the detected joints, but the detection time also increases: on the other hand, DTW is used in the system for action specification, and as the video size increases, the DTW detection time is longer, resulting in a longer running time on each frame. Therefore, the system is not yet able to achieve a real-time action specification effect. Analyzing the big data of the technical indicators of the four Grand Slam women’s singles competitions based on various factors will help to understand the winning rules of tennis matches and guide tennis training is of great significance. While understanding yourself and your opponents, use your own advantages to suppress your opponents, limit your opponent’s performance, and increase your chances of winning.The KMO and Bartlett’s sphericity test showed that the KMO value 0.692 is greater than 0.5; this value is between 0 and 1 for the KMO measure of sampling appropriateness and Bartlett’s sphericity test value df = 91 with a companion probability P ≤ 0.001 which indicates that it is in the very significant level; therefore, the null hypothesis is rejected and the data are considered suitable for factor analysis. The overall variables were tested while the results showed that the degree of extraction was not the same. The factors were extracted in descending order according to the height of the factors: net score rate, first-serve success rate, second-serve average speed, break rate, first-serve average speed, second-serve score rate, a double fault on serve, unforced error, total score, first-serve score rate, fastest speed on serve, winning score, Aces ball, and receiving serve. The cumulative contribution of the 8 common factors reached 86.79%, which can summarize more than 85% of the information of the original variables, so it can be considered that the 8 common factors can explain most of the variables. These systems can be applied to individual projects and team projects to develop personal training plans by analyzing the movements and trajectories of athletes: by tracking team members’ trajectories, analyzing the team’s tactical use, and providing coaches with appropriate tactical analysis. Usually, the graph shows a clear break between the steep slope of the large factor and the gentle tails of the remaining factors, while generally the main factors are selected on a very steep slope, but the top 8 factors are selected based on the criterion of 85% factor contribution. This is shown in Figure 6.Figure 6
Factor eigenvalue gravel plot.After continuous calculations, the ideal result is obtained. A rule model for the classification of table tennis technical and tactical features based on the artificial fish school algorithm is proposed. An example of the combination of an excellent table tennis player game and the artificial fish school algorithm is analyzed. The result shows that artificial fish compared with the association rule data mining in the technical and tactical characteristics of table tennis, the swarm algorithm is better than the existing algorithms in mining technical and tactical characteristics of table tennis. The function of the sports analysis module is to analyze the sportsman’s sports data to analyze the sportsman’s sports situation and display it to the user. The module mainly includes sports goal setting and completion, comprehensive evaluation, and ball wind analysis. The design principle of the goal setting and completion is that the goal set by the user is saved in the local database and uploaded to the cloud, and the completion status of the goal is displayed by comparing the goal set with the sports situation and calculating the percentage of completion status. The goal field in the movement data table represents the movement goal set by the user. Users can set the exercise time of the week, the number of swings, calories burned, and exercise frequency on the the exercise goal completion screen, exercise goal setting screen, and the sliding of the SeekBar. The user’s goal completion is displayed in the form of a percentage. In addition to being able to display training data and set exercise goals, the software also analyzes the exercise data of the exerciser, thus making it easier for the user to know the details of the exercise over time and enabling the user to understand the shortcomings of their skill level from the analysis of the exercise data. The sports analysis screen is a general analysis of the sport, which analyzes the number of swings, movement time, swing speed, etc., of each swing technique of the athlete over a period to obtain such data as swing performance, maximum speed, reaction power index, explosive power index, endurance index, duration, and overall performance index, which is presented to the user in the form of a spider web graph. The competitive process of every basic unit of a tennis game starts with serving. The implementation of serving tactics is the only tennis tactic that is not interfered by the opponent in the game. Among professional tennis players, relying on high-quality serve to gain an advantage or even direct score is one. The technical style of the sport is analyzed based on the number of offensive and defensive shots of the athlete, where snapping and high and long are specified as offensive shots. The trend of the user’s movement in a week is analyzed, firstly, the information such as movement time, number of swings, energy consumption, and maximum speed in a week is counted, and then the movement trend in a week is reflected in the form of a curve, as shown in Figure7.Figure 7
Trend of weekly movements.


## 5. Conclusion

The analysis of data has entered every aspect of people’s lives, and its use in sports is not a new thing. In tennis, the quantification of the game into data for analysis is already well established. Using data to analyze the key factors to help athletes improve their game is a highly relevant method. Scientifically constructing a tactical diagnostic index system for tennis matches, systematically diagnosing, and evaluating the tactical strength of athletes, and effectively monitoring the level of tactical development of athletes are an important guarantee for improving athletes’ competitive level. The tennis tactical diagnostic index system, evaluation criteria, and the use of different methods to diagnose and evaluate the tactical strength of athletes using diagnostic indexes constructed in this study provide ideas and methods for quantitative analysis of tactical performance in men’s tennis singles matches. However, there are some shortcomings. In the subsequent research, the system of tactical diagnostic indicators applicable to red clay and grass court matches can continue to be improved, while the diagnostic methods of women players’ tactics should be given focused attention. With the development of tennis tactics and the evolution of game rule reform, the constructed tactical diagnostic index system should be enriched and improved continuously in practice.



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*Source: 8950732-2022-02-21.xml*