# Application of Multiagent Technology in Intelligent Distributed Sports Training Simulation System

Authors: Dongfang Nie; Jiannan Liu
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

Multiagent technology, as a conceptual model commonly used in intelligent systems, has now had a lot of related research, and it is also widely used in sports training simulation systems. This paper mainly studies the development of the sports training simulation system based on agent technology. This paper proposes the motion perception system of multiagent technology in sports training. In the research, this paper introduces the related concepts and methods of agent in detail. In order to better simulate the changes of human joints during exercise with the system, this article uses a variety of comparison methods. Among them, the experiment of the imaging analysis module is carried out through the simulation of the simulation system. The experimental results show that the role of multiagent in the simulation system is huge. In linear motion, the accuracy of joint extraction has reached more than 95%. The extraction of joints in the curve movement reached more than 80%, indicating that it can not only complete the relevant processing adaptively but also predict and correct the exercise process of the trainer. Therefore, in the research of sports training simulation system, more attention should be paid to the research of curve motion.


---

## Body

## 1. Introduction

### 1.1. Background

Modern competitive sports are rapidly developing towards high, difficult, and sophisticated, and the role of science and technology in promoting the development of competitive sports has become increasingly apparent. At present, competitive sports have developed to a fairly high level. In training simulation of competitive sports, the research hotspot of system simulation in recent years is the object-oriented simulation method. At present, there is no complete sports video analysis software and computer analysis software for human body motion in China. In addition, it does not have the ability to handle multiple input devices. With the continuous advancement of related technologies, virtual reality technology will be popularized. It provides effective and brand-new training methods of athletes through virtual training venues, intelligent interface technology, physiology, and multisensor technology. It should be pointed out that sports training simulation research has just started, providing an effective platform for technical communication and exchanges with athletes and coaches and also providing application space for the expansion of information science. The multiagent system is distributed and autonomous, which can save the energy consumption of the entire system while completing the adaptation to the environment. In the early days, the multiagent system replaced the traditional distributed control system with intelligent control and solved unstructured problems that were difficult to solve by conventional methods by imitating human behavior and thought. It can also accomplish larger and more complex tasks through collaboration. Although intelligent control methods such as fuzzy control were also introduced to/into the later period, most of the research on the application of agent technology to simulation systems stayed in the theoretical research stage.


### 1.2. Significance

Physical training is vital to the role of athletes. With the development of technology, the sports training simulation system has also been continuously developed, which is very useful for the training of athletes. In developed countries such as the United States and Canada, they started early, and their use of the sports training simulation system has brought obvious effects. For example, in diving, although the strength of the national team is top, the lack of unified standards such as a simulation system makes it difficult to achieve the purpose of passing on excellent training experience and skills. The simulation system can store motion data. Good standards can be used as template training, and errors can be found in time. Now, related technologies have become mature, so it is meaningful to carry out research on sports training simulation systems.


### 1.3. Related Work

Although there are not many previous studies on the sports training simulation system, there are still some studies on the realization of some functions, such as video acquisition system and motion analysis system. Higher, faster, and stronger is the purpose of the Olympic Games and the goal of sports. In order to achieve this goal, many related researches and applications for sports training have spontaneously emerged. Hui believes that in the swimming training process, coaches mostly use traditional methods to train students, and the training effect is not good [1]. Rhodes et al. examined the speed curves of elite wheelchair rugby (WCR) players in game simulation training exercises with different player numbers and batting time rules. Use an RF-based indoor tracking system for monitoring during the 5-month training period. Experiments have proved that by reducing the number of players on the field and the hitting time to 15 seconds, the coach can significantly improve the speed curve of WCR elite players in match simulation training [2]. In order to improve the safety control ability of dragon boat sports physical training, Yin and Wang used the advantages of machine learning in data analysis and feature mining in dragon boat sports training and proposed a machine learning-based dragon boat sports physical training safety mode control model [3]. Gokeler et al. were immersed in a virtual reality environment to evaluate the impact of ACL reconstruction (ACLR) on the biomechanics of the patient’s knee joint. After ACLR, the patient was immersed in a virtual reality environment and showed knee biomechanics similar to CTRL [4]. When Kłodowski et al. evaluated sports training methods, first, they performed a motion analysis of the object. Based on experimental motion data (motion analysis and multibody simulation), it created two models: rotation control model and torque control model. For comparison, it created a model with motion input from the literature [5]. Zhang and others created an autonomous karate team player (KaraKter) based on the simulation system. This role can be used for VR-based training and research of karate team players. For real-time interaction with KaraKter, human athletes are tracked in a virtual environment. Evaluation shows that all experts consider the system to be very useful in the training of karate players [6]. In terms of multiagent technology, Laatabi and others believe that with the development of powerful computers and the availability of massive data, the use of agent-based models to model and simulate the world is one of the key disciplines emerging in the field of computing today [7]. Mahalaxmi and Esther proposed an improved ant colony optimization routing algorithm for IoT based on multiagent technology. The experimental results show that the network simulator NS-2 is used to evaluate the performance of the proposed algorithm program [8]. In general, the research on sports training simulation systems is more on the opposite of image processing, and there is no simulation training for sports systems, which is urgently needed for trainers.


### 1.4. Innovation

In this paper, a motion perception system based on multiagent technology in motion training is proposed, and the system has a good effect on joint extraction of linear motion, and the system can help motion simulation training. This paper discusses the application research of multiagent technology in intelligent distributed sports training simulation system. Although there are not many predecessors of sports training simulation system, there are many excellent researches. This article is based on previous research and made some innovations: (1) the system design emphasizes the role of multiagent technology, which is easy to find in the article. This article uses a lot of pen and ink to describe the application of agent technology in the simulation system. (2) For the link of the simulation system to simulate the motion engine, an intelligent distributed pressure sensor is specially added. This is of great significance for complementing the data in the simulation system and better simulating the joint points of motion. (3) Principal component analysis is used in data analysis, and all components are analyzed in the algorithm of traditional simulation system. Principal component analysis is proposed in this article, which will greatly reduce the amount of data for system analysis.


## 1.1. Background

Modern competitive sports are rapidly developing towards high, difficult, and sophisticated, and the role of science and technology in promoting the development of competitive sports has become increasingly apparent. At present, competitive sports have developed to a fairly high level. In training simulation of competitive sports, the research hotspot of system simulation in recent years is the object-oriented simulation method. At present, there is no complete sports video analysis software and computer analysis software for human body motion in China. In addition, it does not have the ability to handle multiple input devices. With the continuous advancement of related technologies, virtual reality technology will be popularized. It provides effective and brand-new training methods of athletes through virtual training venues, intelligent interface technology, physiology, and multisensor technology. It should be pointed out that sports training simulation research has just started, providing an effective platform for technical communication and exchanges with athletes and coaches and also providing application space for the expansion of information science. The multiagent system is distributed and autonomous, which can save the energy consumption of the entire system while completing the adaptation to the environment. In the early days, the multiagent system replaced the traditional distributed control system with intelligent control and solved unstructured problems that were difficult to solve by conventional methods by imitating human behavior and thought. It can also accomplish larger and more complex tasks through collaboration. Although intelligent control methods such as fuzzy control were also introduced to/into the later period, most of the research on the application of agent technology to simulation systems stayed in the theoretical research stage.


## 1.2. Significance

Physical training is vital to the role of athletes. With the development of technology, the sports training simulation system has also been continuously developed, which is very useful for the training of athletes. In developed countries such as the United States and Canada, they started early, and their use of the sports training simulation system has brought obvious effects. For example, in diving, although the strength of the national team is top, the lack of unified standards such as a simulation system makes it difficult to achieve the purpose of passing on excellent training experience and skills. The simulation system can store motion data. Good standards can be used as template training, and errors can be found in time. Now, related technologies have become mature, so it is meaningful to carry out research on sports training simulation systems.


## 1.3. Related Work

Although there are not many previous studies on the sports training simulation system, there are still some studies on the realization of some functions, such as video acquisition system and motion analysis system. Higher, faster, and stronger is the purpose of the Olympic Games and the goal of sports. In order to achieve this goal, many related researches and applications for sports training have spontaneously emerged. Hui believes that in the swimming training process, coaches mostly use traditional methods to train students, and the training effect is not good [1]. Rhodes et al. examined the speed curves of elite wheelchair rugby (WCR) players in game simulation training exercises with different player numbers and batting time rules. Use an RF-based indoor tracking system for monitoring during the 5-month training period. Experiments have proved that by reducing the number of players on the field and the hitting time to 15 seconds, the coach can significantly improve the speed curve of WCR elite players in match simulation training [2]. In order to improve the safety control ability of dragon boat sports physical training, Yin and Wang used the advantages of machine learning in data analysis and feature mining in dragon boat sports training and proposed a machine learning-based dragon boat sports physical training safety mode control model [3]. Gokeler et al. were immersed in a virtual reality environment to evaluate the impact of ACL reconstruction (ACLR) on the biomechanics of the patient’s knee joint. After ACLR, the patient was immersed in a virtual reality environment and showed knee biomechanics similar to CTRL [4]. When Kłodowski et al. evaluated sports training methods, first, they performed a motion analysis of the object. Based on experimental motion data (motion analysis and multibody simulation), it created two models: rotation control model and torque control model. For comparison, it created a model with motion input from the literature [5]. Zhang and others created an autonomous karate team player (KaraKter) based on the simulation system. This role can be used for VR-based training and research of karate team players. For real-time interaction with KaraKter, human athletes are tracked in a virtual environment. Evaluation shows that all experts consider the system to be very useful in the training of karate players [6]. In terms of multiagent technology, Laatabi and others believe that with the development of powerful computers and the availability of massive data, the use of agent-based models to model and simulate the world is one of the key disciplines emerging in the field of computing today [7]. Mahalaxmi and Esther proposed an improved ant colony optimization routing algorithm for IoT based on multiagent technology. The experimental results show that the network simulator NS-2 is used to evaluate the performance of the proposed algorithm program [8]. In general, the research on sports training simulation systems is more on the opposite of image processing, and there is no simulation training for sports systems, which is urgently needed for trainers.


## 1.4. Innovation

In this paper, a motion perception system based on multiagent technology in motion training is proposed, and the system has a good effect on joint extraction of linear motion, and the system can help motion simulation training. This paper discusses the application research of multiagent technology in intelligent distributed sports training simulation system. Although there are not many predecessors of sports training simulation system, there are many excellent researches. This article is based on previous research and made some innovations: (1) the system design emphasizes the role of multiagent technology, which is easy to find in the article. This article uses a lot of pen and ink to describe the application of agent technology in the simulation system. (2) For the link of the simulation system to simulate the motion engine, an intelligent distributed pressure sensor is specially added. This is of great significance for complementing the data in the simulation system and better simulating the joint points of motion. (3) Principal component analysis is used in data analysis, and all components are analyzed in the algorithm of traditional simulation system. Principal component analysis is proposed in this article, which will greatly reduce the amount of data for system analysis.


## 2. Agent Technology and Simulation Method

### 2.1. Multiagent Technology

The original concept of agent technology is derived from the field of artificial intelligence. Its concept involves a wide range. Generally speaking, the definition of its authority is divided into weak definition and strong definition [9]. In the weak definition, to include the characteristics of autonomy, social ability, etc., the specific program that can complete the given work is the agent. In a strong definition, it is a computing unit with a control problem solving mechanism [10], an individual with knowledge and ability to solve practical problems. It can be a robot, an expert system, a process, a calculation module, or a solution unit [11]. Agent technology is a hot spot in the current recommendation system industry [12].As an agent model, it can have the following characteristics:(i)
Autonomous ability: it adopts multithreaded realization method, realizes its own functions through continuous actions, and has self-control ability. It can regulate its own behavior and state without the direct intervention of humans or related agents [13]. It can transfer from this place to other places on the same network while ensuring its internal state remains unchanged(ii)
Social ability: it solves problems through information exchange and communication with users, resources, or other agents. And it uses agent communication language and message mode for information exchange and behavioral collaboration [14]. It can find a more reasonable reasoning method to solve its current problems based on the knowledge and experience existing in its current database(iii)
Response ability: agent will automatically recognize changes in the environment, be able to perceive the environment, and take corresponding actions according to the changes, so as to achieve the ability to respond to the environment. When it thinks it can solve the problem it wants to solve, it always tries its best to accomplish this intention [15](iv)
Proactiveness: controlling action and behavior planning through changes in the state of the mind, which regulates the environment and behavior [16]. It can be actively target-driven according to its own and global knowledge and finally can adapt to the new environment by changing its willIt is precisely because of the relevant capabilities of the agent that it can play an important role in analysis in the system, and it is also because of the multiagent technology composed of each single agent that the functions of the simulation system can be realized.In application, multiagent technology is generally used. Because of the characteristics of a single agent, when forming a multiagent structure, all submodules can communicate and collaborate to complete a series of complex problems. It is similar to simulating the human way of thinking and the nature of human social collaboration, so it is also more suitable for an open and dynamic social environment to deal with similar problems that require automatic evolution [17]. Multiagent generally has the following structures.
(1)
Reactive agentThe reactive type does not have any internal state and does not include symbolic world models and complex reasoning. It is analyzed by the reasoner and planner with the support of the knowledge base. When the external environment changes, it cannot be obtained directly through perception. This is because it does not have any learning ability, but it executes prespecified conditions-action rules. Although its structure is simple, the function is single, and the response speed is fast [18]. The structure is shown in Figure 1.
(2)
Deliberate agentFigure 1
Reactive agent.The deliberate agent is a kind of consciousness system. This structure determines that the intelligence of this type of agent is relatively high, but it requires a lot of time and accuracy. The external environment is complex, the internal state symbolization is complicated, and it cannot solve the situation of limited resources [19]. This greatly affects the system efficiency [20]. Based on the deliberate agent, the BDI theoretical model is the most widely used. It adds three different factors to make the model more considerate, so it can handle many complex problems. However, there are not many applications for it, so this article will not describe it too much [21]. The structure is shown in Figure 2.
(3)
Hybrid agentFigure 2
Deliberate agentNeither the purely single structure nor the best structure mode of the agent. This structure combines the deliberate and reactive architectures and combines the nonclassical and classic technologies of artificial intelligence technology. It is currently a more popular one [22, 23]. It has both the ability to deal with complex environments in a deliberate manner and the advantages of high reactive efficiency, which makes people gradually agree with the hybrid agent structure [24]. The structure is shown in Figure 3.Figure 3
Hybrid agent.


### 2.2. Sports Training Simulation System

Computer simulation technology is based on a variety of disciplines and theories, and the application of simulation technology in Chinese sports has developed relatively quickly. The sports video image analysis system can collect and analyze the actual movement of the human body to construct a realistic training virtual environment, and then, the computer drives the virtual person on the screen according to the recorded data [25–28]. The real-time processing part of the system mainly uses background separation technology to obtain the foreground part of the video, but the operation is complicated, and it is difficult to learn. It requires us to emphasize the practicality of the simulation system [29]. However, due to its high computational complexity or the need to manually set cumbersome parameters, it is difficult to meet actual needs. It is difficult to develop a sports training simulation system, and less than 50% of them have good practicality. For example, parameters such as the physical characteristics of the human body can be selected interactively, and technical actions can be realistically simulated, designed, and analyzed in a three-dimensional manner. Compared with developed countries, application is still relatively backward [30].There are a large number of applications of virtual reality technology in training simulation, of which the two most critical parts are the modeling of the motion of the simulation object and the construction of the virtual environment. The main goal of virtual human research is to truly reflect the geometry of human beings, while motion modeling needs to be based on the dynamics of the simulated object. Through the establishment of a certain process and a certain system model, a series of experiments are carried out to understand the behavior of the system based on the model or the different operating strategies of the system. In order to make a breakthrough from the traditional theory based on reductionism and linear thinking, the comprehensive application and perfection of training simulation needs further research, and it is currently in the stage of theoretical maturity and social practice. National and international computer networks have emerged one after another. The communication standards between the subjects and the behavior design of the subjects have become more mature, and the relationship between the running time and the number of subjects has increased exponentially [31]. In exploring an efficient operation mode, the sports industry environment is taking shape. It needs the support of the network to facilitate the transmission of sports information and the analysis of sports data. This process achieves the prediction and analysis of the sports system.


## 2.1. Multiagent Technology

The original concept of agent technology is derived from the field of artificial intelligence. Its concept involves a wide range. Generally speaking, the definition of its authority is divided into weak definition and strong definition [9]. In the weak definition, to include the characteristics of autonomy, social ability, etc., the specific program that can complete the given work is the agent. In a strong definition, it is a computing unit with a control problem solving mechanism [10], an individual with knowledge and ability to solve practical problems. It can be a robot, an expert system, a process, a calculation module, or a solution unit [11]. Agent technology is a hot spot in the current recommendation system industry [12].As an agent model, it can have the following characteristics:(i)
Autonomous ability: it adopts multithreaded realization method, realizes its own functions through continuous actions, and has self-control ability. It can regulate its own behavior and state without the direct intervention of humans or related agents [13]. It can transfer from this place to other places on the same network while ensuring its internal state remains unchanged(ii)
Social ability: it solves problems through information exchange and communication with users, resources, or other agents. And it uses agent communication language and message mode for information exchange and behavioral collaboration [14]. It can find a more reasonable reasoning method to solve its current problems based on the knowledge and experience existing in its current database(iii)
Response ability: agent will automatically recognize changes in the environment, be able to perceive the environment, and take corresponding actions according to the changes, so as to achieve the ability to respond to the environment. When it thinks it can solve the problem it wants to solve, it always tries its best to accomplish this intention [15](iv)
Proactiveness: controlling action and behavior planning through changes in the state of the mind, which regulates the environment and behavior [16]. It can be actively target-driven according to its own and global knowledge and finally can adapt to the new environment by changing its willIt is precisely because of the relevant capabilities of the agent that it can play an important role in analysis in the system, and it is also because of the multiagent technology composed of each single agent that the functions of the simulation system can be realized.In application, multiagent technology is generally used. Because of the characteristics of a single agent, when forming a multiagent structure, all submodules can communicate and collaborate to complete a series of complex problems. It is similar to simulating the human way of thinking and the nature of human social collaboration, so it is also more suitable for an open and dynamic social environment to deal with similar problems that require automatic evolution [17]. Multiagent generally has the following structures.
(1)
Reactive agentThe reactive type does not have any internal state and does not include symbolic world models and complex reasoning. It is analyzed by the reasoner and planner with the support of the knowledge base. When the external environment changes, it cannot be obtained directly through perception. This is because it does not have any learning ability, but it executes prespecified conditions-action rules. Although its structure is simple, the function is single, and the response speed is fast [18]. The structure is shown in Figure 1.
(2)
Deliberate agentFigure 1
Reactive agent.The deliberate agent is a kind of consciousness system. This structure determines that the intelligence of this type of agent is relatively high, but it requires a lot of time and accuracy. The external environment is complex, the internal state symbolization is complicated, and it cannot solve the situation of limited resources [19]. This greatly affects the system efficiency [20]. Based on the deliberate agent, the BDI theoretical model is the most widely used. It adds three different factors to make the model more considerate, so it can handle many complex problems. However, there are not many applications for it, so this article will not describe it too much [21]. The structure is shown in Figure 2.
(3)
Hybrid agentFigure 2
Deliberate agentNeither the purely single structure nor the best structure mode of the agent. This structure combines the deliberate and reactive architectures and combines the nonclassical and classic technologies of artificial intelligence technology. It is currently a more popular one [22, 23]. It has both the ability to deal with complex environments in a deliberate manner and the advantages of high reactive efficiency, which makes people gradually agree with the hybrid agent structure [24]. The structure is shown in Figure 3.Figure 3
Hybrid agent.


## 2.2. Sports Training Simulation System

Computer simulation technology is based on a variety of disciplines and theories, and the application of simulation technology in Chinese sports has developed relatively quickly. The sports video image analysis system can collect and analyze the actual movement of the human body to construct a realistic training virtual environment, and then, the computer drives the virtual person on the screen according to the recorded data [25–28]. The real-time processing part of the system mainly uses background separation technology to obtain the foreground part of the video, but the operation is complicated, and it is difficult to learn. It requires us to emphasize the practicality of the simulation system [29]. However, due to its high computational complexity or the need to manually set cumbersome parameters, it is difficult to meet actual needs. It is difficult to develop a sports training simulation system, and less than 50% of them have good practicality. For example, parameters such as the physical characteristics of the human body can be selected interactively, and technical actions can be realistically simulated, designed, and analyzed in a three-dimensional manner. Compared with developed countries, application is still relatively backward [30].There are a large number of applications of virtual reality technology in training simulation, of which the two most critical parts are the modeling of the motion of the simulation object and the construction of the virtual environment. The main goal of virtual human research is to truly reflect the geometry of human beings, while motion modeling needs to be based on the dynamics of the simulated object. Through the establishment of a certain process and a certain system model, a series of experiments are carried out to understand the behavior of the system based on the model or the different operating strategies of the system. In order to make a breakthrough from the traditional theory based on reductionism and linear thinking, the comprehensive application and perfection of training simulation needs further research, and it is currently in the stage of theoretical maturity and social practice. National and international computer networks have emerged one after another. The communication standards between the subjects and the behavior design of the subjects have become more mature, and the relationship between the running time and the number of subjects has increased exponentially [31]. In exploring an efficient operation mode, the sports industry environment is taking shape. It needs the support of the network to facilitate the transmission of sports information and the analysis of sports data. This process achieves the prediction and analysis of the sports system.


## 3. Construction of Sports Training Simulation System

### 3.1. Related Technology

In the construction of the training simulation system, an important link is the extraction of moving objects. This article introduces the use of motion camera images to extract the joints of moving objects, including image preprocessing and data analysis.Ti is the pixel of the extraction target, and Ii is the original pixel. There is a big difference between the gray value of the moving target in the foreground of the moving target and the gray value of the background, and the gray value of the moving object itself will not have a big difference, as in Equation (1), where a represents the grayscale of the moving target and b represents the grayscale value.
(1)Tia,b=260,Iia,b−Ii+1a,b≥D,0,Iia,b−Ii+1a,b>D.Compare the pixels of the binarized image with the pixels of the original image to achieve the return of the pixel value, as shown in(2)Iia,b=0,ifTia,b=0,Iia,b,else.A more effective method is adopted, that is, the images of adjacent frames are sequentially superimposed to form the bottom plateEp, as shown in
(3)Epa,b=S0a,b,H0a,b,C0a,bT.For the pixels that have been assigned on the original base plate, the algorithms of formula (4) and formula (5) are used:
(4)Epa,b′=Epa,b+Eka,b2,(5)Epa,b′=Spa,b+Ska,b2,Hpa,b+Hka,b2,Cpa,bT.Among them,Ea,b represents the adoption of pixels, and its product represents the expectation of the formula.Figure4 shows a schematic diagram of the algorithm extracting joints. When analyzing sports data, it is impossible for the computer to extract all the information. Therefore, it mainly extracts important data of its arm and knee joint. In Figure 4, the trainer’s side view is recorded, which is also the position of the computer. Because one of its arms will be blocked, it needs to be simulated by algorithms. After categorizing the elbow joint data, it is combined with the motion data for analysis. Knee joints are subjected to fluctuation analysis, and the integrated motion data is saved and entered into the system for retention.Figure 4
Schematic diagram of extracting joints.


### 3.2. Design of Virtual Human Walking Engine Based upon ICA

Autocorrelation (ICA) technology can propose outliers in the data and make the simulator more stable. We use autocorrelation(ICA) technology to extract independent motion components from motion data, which correspond to meaningful motion features. And these motion characteristics can describe complex human motion. Similar to the partial autocorrelation (PCA) method, the design idea of the ICA-based virtual human walking engine is by modifying the independent motion components in the ICA domain, that is, operating the basis vectors representing the motion feature space, various types of motion can be generated.(1)
For certain dataAssume that the motion dataϕt of the three-dimensional human body is represented as shown in
(6)ϕt=ϕ1t,ϕ2t,⋯,ϕntT.In the ICA model, the mean value of the motion data must be zero, as shown in(7)ϕ¯t=ϕt−Eϕt.Applying the ICA algorithm, it can be represented by a series of independent components as shown in(8)ϕ¯t=Ast.The purpose of ICA is to find a matrixB, expressed as
(9)nt=Bϕ¯t.In order to simplify the calculation, as in(10)mt=Vϕ¯t.After applying the PCA algorithm, Equation (9) is rewritten as
(11)nt=WTmt.Therefore, the independent componentyt can be expressed as
(12)nt=WTmt=WTVϕ¯t=WTVϕt−Eϕt.The independent components of the exercise data correspond to different styles of exercise, as in(13)v1,2=v1e1+v2e2.Among them,v1 and v2 represent two different styles, and the selection is judged by the decision-making agent.As shown in Table1, the choices of different styles are represented by binary codes, so that the algorithm will be more convenient.Table 1
Different style selection table.
PCAabcnStyle 10110Style 21011Style1111After the transfer operation of the independent components, the formula becomes(14)v2′=v1+v2s−v1sTes.Then, simulate the collected data, as shown in Figure5.Figure 5
The generation of the walking engine with the motion data of objectx in the motion database.Figure5 shows the analysis and processing of exercise data. For data with objects, the goal is clear. Taking into account the agent’s ability to adapt to the external environment, its generating walking engine is reliable. When performing PCA analysis in Figure 5, two different divisions were selected. For style 1, it is the data when walking, and for style 2, it is the data when running. The system distinguishes according to the algorithm and enters the data into the corresponding data set.(2) For data without objectsWhen there is no movement data of objectx in the movement database, but a small amount of movement data of object x is collected in advance, calculate the Mahalanobis distance first, because there is no strong connection in the nonobject data, so the Euclidean distance is not considered, as shown in
(15)dnϕ1,ϕ2=∑iλ12αin−αip2∑iλ12.Calculate the correlation coefficient between objectx and object i, as shown in
(16)γx,p=dnϕ1,ϕ2−1∑q=1dnϕ1,ϕ2−1,q=1,2,3,⋯,i,(17)γx,p=∑i=1kωx,p,xik,q=1,2,3,⋯,i.Calculate the PCA coefficient of objectx according to the PCA coefficient of object 1~i and the PCA coefficient of attribute s, as shown in
(18)αix,s=∑i=1kγx,pαip,s.The whole process is shown in Figure6.Figure 6
The generation of the walking engine without the motion data of objectx in the motion database.Figure6 is the processing of motion data without objects. Compared with data processing with objects, it adds the step of creating objects. It can be seen from Figure 6 that because the data is objectless, each object of i is established in the first step. In order to have the correct assignment, in the early stage, there is one data for one object. When i objects are reached, the data is filtered into the previously generated objects, and the objects are directly assigned automatically.Table2 shows more reflection terms used in the construction of the agent system, which are listed here.Table 2
Description of main parameters.
ParameterIllustrateInformNotify other agents that the statement in the message is correctAgreeAgent accepts the request and agrees to do a certain jobFailureNotify other agents that a certain task has been executed, and the execution result is a failureQueryAsk for the correctness of the message


### 3.3. Platform Construction

After the introduction of 2.1, we know that the multiagent system has good learning ability, and a human-like social relationship can be formed between a single agent, harmonious, and mutual assistance. Then, this part of the content will design the overall system, through the cooperation of various parts of the agent, to achieve the desired effect of simulated sports training.(1)
Interface agentInterface agent, as a human-computer interaction interface, accepts the planner’s task, then passes it to the functional agent, and requires the planner to input necessary physical and test information. After the plan is made, inform the planner of the plan and make a scientific explanation according to the needs of users. According to the characteristics of problem handling and human-computer interaction components, there must be a corresponding explanation mechanism inside the interface agent.(2)
Blackboard control agentSince the blackboard control agent is the core of the entire multiagent system, the functional agent and decision-making agent are both regarded as the knowledge source in the blackboard system. It can determine the type of problem according to user needs and play a role in the central nervous system. It does a good job of uploading and distributing, passing the decision-making information to the decision-making machine and the processing information to the processor.(3)
Functional agentThe functional agent categorizes the tasks passed from the blackboard, divides them into multiple small tasks according to actual needs, and assigns them to the corresponding one or more decision-making agents for execution. As a functional agent, it also has other deployment functions. The functional agent also monitors the blackboard and also provides queries on the execution of the system at any time.(4)
Decision agentThe decision-making agent class uses a computer and uses a series of algorithms such as a decision tree to give the decision to assign to the task. For some repetitive and simple tasks, it will simply handle it by itself. It can be said that the task of decision-making agent is the highest, and it plays an indispensable role in the system.Table3 is a comparison table of the responsibilities of decision-makers and executors. It can be found that decision-makers play more of the role of judgment and assignment.Table 3
Comparison table of main responsibilities of decision-makers and executives.
Decision-makerExecutorCreate and publish a task bookReceiving the task bookReceive and evaluate bids from performersAccording to ability to judge whether to accept the taskPick the right bidderSend tender notice and create tenderEstablish a contract with the winning bidderIf you win the bid, enter into a contract with the managerSupervise the execution of tasks and check the execution resultsPerform tasks and report results as required by managersFigure7 shows the functional structure of the design system. This is a system structure that is deeply designed based on the user’s use, and the interface agent is directly facing the user. It handles simple functions through its own feedback and displays the main page to the user as the interface display of the system. The time blackboard in the middle was also mentioned in the introduction to the blackboard agent. As the core of the system, the blackboard plays an important role in uploading and distributing. As can be seen from the structure diagram in Figure 7, all agents must be assembled on the blackboard. It can be seen as central nervous system, which performs the functions of distribution and processing. It is the functional agent, which cooperates with the decision-making agent at the bottom to do various tasks on the blackboard. They can even learn from each other, help each other, and jointly complete some adaptive tasks.Figure 7
The functional structure diagram of the system.


## 3.1. Related Technology

In the construction of the training simulation system, an important link is the extraction of moving objects. This article introduces the use of motion camera images to extract the joints of moving objects, including image preprocessing and data analysis.Ti is the pixel of the extraction target, and Ii is the original pixel. There is a big difference between the gray value of the moving target in the foreground of the moving target and the gray value of the background, and the gray value of the moving object itself will not have a big difference, as in Equation (1), where a represents the grayscale of the moving target and b represents the grayscale value.
(1)Tia,b=260,Iia,b−Ii+1a,b≥D,0,Iia,b−Ii+1a,b>D.Compare the pixels of the binarized image with the pixels of the original image to achieve the return of the pixel value, as shown in(2)Iia,b=0,ifTia,b=0,Iia,b,else.A more effective method is adopted, that is, the images of adjacent frames are sequentially superimposed to form the bottom plateEp, as shown in
(3)Epa,b=S0a,b,H0a,b,C0a,bT.For the pixels that have been assigned on the original base plate, the algorithms of formula (4) and formula (5) are used:
(4)Epa,b′=Epa,b+Eka,b2,(5)Epa,b′=Spa,b+Ska,b2,Hpa,b+Hka,b2,Cpa,bT.Among them,Ea,b represents the adoption of pixels, and its product represents the expectation of the formula.Figure4 shows a schematic diagram of the algorithm extracting joints. When analyzing sports data, it is impossible for the computer to extract all the information. Therefore, it mainly extracts important data of its arm and knee joint. In Figure 4, the trainer’s side view is recorded, which is also the position of the computer. Because one of its arms will be blocked, it needs to be simulated by algorithms. After categorizing the elbow joint data, it is combined with the motion data for analysis. Knee joints are subjected to fluctuation analysis, and the integrated motion data is saved and entered into the system for retention.Figure 4
Schematic diagram of extracting joints.


## 3.2. Design of Virtual Human Walking Engine Based upon ICA

Autocorrelation (ICA) technology can propose outliers in the data and make the simulator more stable. We use autocorrelation(ICA) technology to extract independent motion components from motion data, which correspond to meaningful motion features. And these motion characteristics can describe complex human motion. Similar to the partial autocorrelation (PCA) method, the design idea of the ICA-based virtual human walking engine is by modifying the independent motion components in the ICA domain, that is, operating the basis vectors representing the motion feature space, various types of motion can be generated.(1)
For certain dataAssume that the motion dataϕt of the three-dimensional human body is represented as shown in
(6)ϕt=ϕ1t,ϕ2t,⋯,ϕntT.In the ICA model, the mean value of the motion data must be zero, as shown in(7)ϕ¯t=ϕt−Eϕt.Applying the ICA algorithm, it can be represented by a series of independent components as shown in(8)ϕ¯t=Ast.The purpose of ICA is to find a matrixB, expressed as
(9)nt=Bϕ¯t.In order to simplify the calculation, as in(10)mt=Vϕ¯t.After applying the PCA algorithm, Equation (9) is rewritten as
(11)nt=WTmt.Therefore, the independent componentyt can be expressed as
(12)nt=WTmt=WTVϕ¯t=WTVϕt−Eϕt.The independent components of the exercise data correspond to different styles of exercise, as in(13)v1,2=v1e1+v2e2.Among them,v1 and v2 represent two different styles, and the selection is judged by the decision-making agent.As shown in Table1, the choices of different styles are represented by binary codes, so that the algorithm will be more convenient.Table 1
Different style selection table.
PCAabcnStyle 10110Style 21011Style1111After the transfer operation of the independent components, the formula becomes(14)v2′=v1+v2s−v1sTes.Then, simulate the collected data, as shown in Figure5.Figure 5
The generation of the walking engine with the motion data of objectx in the motion database.Figure5 shows the analysis and processing of exercise data. For data with objects, the goal is clear. Taking into account the agent’s ability to adapt to the external environment, its generating walking engine is reliable. When performing PCA analysis in Figure 5, two different divisions were selected. For style 1, it is the data when walking, and for style 2, it is the data when running. The system distinguishes according to the algorithm and enters the data into the corresponding data set.(2) For data without objectsWhen there is no movement data of objectx in the movement database, but a small amount of movement data of object x is collected in advance, calculate the Mahalanobis distance first, because there is no strong connection in the nonobject data, so the Euclidean distance is not considered, as shown in
(15)dnϕ1,ϕ2=∑iλ12αin−αip2∑iλ12.Calculate the correlation coefficient between objectx and object i, as shown in
(16)γx,p=dnϕ1,ϕ2−1∑q=1dnϕ1,ϕ2−1,q=1,2,3,⋯,i,(17)γx,p=∑i=1kωx,p,xik,q=1,2,3,⋯,i.Calculate the PCA coefficient of objectx according to the PCA coefficient of object 1~i and the PCA coefficient of attribute s, as shown in
(18)αix,s=∑i=1kγx,pαip,s.The whole process is shown in Figure6.Figure 6
The generation of the walking engine without the motion data of objectx in the motion database.Figure6 is the processing of motion data without objects. Compared with data processing with objects, it adds the step of creating objects. It can be seen from Figure 6 that because the data is objectless, each object of i is established in the first step. In order to have the correct assignment, in the early stage, there is one data for one object. When i objects are reached, the data is filtered into the previously generated objects, and the objects are directly assigned automatically.Table2 shows more reflection terms used in the construction of the agent system, which are listed here.Table 2
Description of main parameters.
ParameterIllustrateInformNotify other agents that the statement in the message is correctAgreeAgent accepts the request and agrees to do a certain jobFailureNotify other agents that a certain task has been executed, and the execution result is a failureQueryAsk for the correctness of the message


## 3.3. Platform Construction

After the introduction of 2.1, we know that the multiagent system has good learning ability, and a human-like social relationship can be formed between a single agent, harmonious, and mutual assistance. Then, this part of the content will design the overall system, through the cooperation of various parts of the agent, to achieve the desired effect of simulated sports training.(1)
Interface agentInterface agent, as a human-computer interaction interface, accepts the planner’s task, then passes it to the functional agent, and requires the planner to input necessary physical and test information. After the plan is made, inform the planner of the plan and make a scientific explanation according to the needs of users. According to the characteristics of problem handling and human-computer interaction components, there must be a corresponding explanation mechanism inside the interface agent.(2)
Blackboard control agentSince the blackboard control agent is the core of the entire multiagent system, the functional agent and decision-making agent are both regarded as the knowledge source in the blackboard system. It can determine the type of problem according to user needs and play a role in the central nervous system. It does a good job of uploading and distributing, passing the decision-making information to the decision-making machine and the processing information to the processor.(3)
Functional agentThe functional agent categorizes the tasks passed from the blackboard, divides them into multiple small tasks according to actual needs, and assigns them to the corresponding one or more decision-making agents for execution. As a functional agent, it also has other deployment functions. The functional agent also monitors the blackboard and also provides queries on the execution of the system at any time.(4)
Decision agentThe decision-making agent class uses a computer and uses a series of algorithms such as a decision tree to give the decision to assign to the task. For some repetitive and simple tasks, it will simply handle it by itself. It can be said that the task of decision-making agent is the highest, and it plays an indispensable role in the system.Table3 is a comparison table of the responsibilities of decision-makers and executors. It can be found that decision-makers play more of the role of judgment and assignment.Table 3
Comparison table of main responsibilities of decision-makers and executives.
Decision-makerExecutorCreate and publish a task bookReceiving the task bookReceive and evaluate bids from performersAccording to ability to judge whether to accept the taskPick the right bidderSend tender notice and create tenderEstablish a contract with the winning bidderIf you win the bid, enter into a contract with the managerSupervise the execution of tasks and check the execution resultsPerform tasks and report results as required by managersFigure7 shows the functional structure of the design system. This is a system structure that is deeply designed based on the user’s use, and the interface agent is directly facing the user. It handles simple functions through its own feedback and displays the main page to the user as the interface display of the system. The time blackboard in the middle was also mentioned in the introduction to the blackboard agent. As the core of the system, the blackboard plays an important role in uploading and distributing. As can be seen from the structure diagram in Figure 7, all agents must be assembled on the blackboard. It can be seen as central nervous system, which performs the functions of distribution and processing. It is the functional agent, which cooperates with the decision-making agent at the bottom to do various tasks on the blackboard. They can even learn from each other, help each other, and jointly complete some adaptive tasks.Figure 7
The functional structure diagram of the system.


## 4. System Effect Analysis

### 4.1. PCA-Based Motion Attribute Changes

The simulation system is to project the captured joint data to the fitting person. Real-time feedback and simulation after data collection are carried out through the multiagent technology, and the height and data of the simulated person can be corrected to produce different effects. Judging from the experimental results, although the system simulation engine is in different data simulating humans, the simulated actions are somewhat distorted, and the results achieved are not like real humans. But in terms of the overall effect, the designed model has a relatively high degree of capture for the simulant in different scenes, and the flow of the movement can be regarded as a comparative flow, and the control of the system can also achieve better results. The main problem is that it cannot perform a good simulation of objects moving in a curve. When the object moves in a curved line, the motion of the simulated human is often greatly distorted to an unrecognizable level, so it is very difficult to change from a curve to a straight one. Therefore, the simulation system can play a greater role in linear motion, but not in curved motion. As shown in Figure8, generally 2 to 4 principal components are required. This is because in the collected data, more information is obtained from the principal components. The speed of movement can be obtained through changes in the movement of the knee joint. It does not need other data, so we can analyze the principal components, so as to achieve the purpose of improving the calculation efficiency.Figure 8
Proportion of principal components in data information.In Figure9, the representation of each joint in the system is unique; some of the movement details are not described. It can be seen that the handling of the arms is still relatively stiff. This is a virtual human supplemented by the system because part of the joints are blocked. In this virtual person, most of the details have already been described, especially the lower body. For the upper body, there are many content nodes, which makes the processing inappropriate.Figure 9
Collection of walking data of a sample.In Figure10, the recording is data collection in motion, the frequency is adjusted to 30 frames, and the principal component is used for analysis, so the processing effect will be much better. Although it still cannot analyze the curve movement, the capture of the linear movement is very smooth. In the record of the curve, because the direction vector of the sampling point is always changing, the main joint points are easily occluded. The processing capacity of the decision-making agent and the functional agent will increase, and if they cannot handle it, they will be obfuscated, resulting in a series of constant data points. It leads to little change in the movement range or extremely exaggerated movements.Figure 10
Data used after exercise.


### 4.2. Joint Force Analysis

The force analysis of the joints is collected by the sensors of the intelligent distributed system, and the force analysis is made on the left and right elbow joints and the left and right knee joints, respectively. The purpose is to obtain the force on the joints of the human body during movement, so as to supplement the data of the occluded part of the imaging system. In the experiment, two sets of data were tested. They were the control group when walking and the experimental group when running.In Figure11, the joint force is roughly the same when running and walking, but it is clearly found that the upper limit of the force when running is higher. The force data can be used to measure a state during exercise, which can then be provided to the system as a data supplement. In this way, with the support of data, there will be traces for the processing of the decision-making agent.Figure 11
Joint force diagram.


## 4.1. PCA-Based Motion Attribute Changes

The simulation system is to project the captured joint data to the fitting person. Real-time feedback and simulation after data collection are carried out through the multiagent technology, and the height and data of the simulated person can be corrected to produce different effects. Judging from the experimental results, although the system simulation engine is in different data simulating humans, the simulated actions are somewhat distorted, and the results achieved are not like real humans. But in terms of the overall effect, the designed model has a relatively high degree of capture for the simulant in different scenes, and the flow of the movement can be regarded as a comparative flow, and the control of the system can also achieve better results. The main problem is that it cannot perform a good simulation of objects moving in a curve. When the object moves in a curved line, the motion of the simulated human is often greatly distorted to an unrecognizable level, so it is very difficult to change from a curve to a straight one. Therefore, the simulation system can play a greater role in linear motion, but not in curved motion. As shown in Figure8, generally 2 to 4 principal components are required. This is because in the collected data, more information is obtained from the principal components. The speed of movement can be obtained through changes in the movement of the knee joint. It does not need other data, so we can analyze the principal components, so as to achieve the purpose of improving the calculation efficiency.Figure 8
Proportion of principal components in data information.In Figure9, the representation of each joint in the system is unique; some of the movement details are not described. It can be seen that the handling of the arms is still relatively stiff. This is a virtual human supplemented by the system because part of the joints are blocked. In this virtual person, most of the details have already been described, especially the lower body. For the upper body, there are many content nodes, which makes the processing inappropriate.Figure 9
Collection of walking data of a sample.In Figure10, the recording is data collection in motion, the frequency is adjusted to 30 frames, and the principal component is used for analysis, so the processing effect will be much better. Although it still cannot analyze the curve movement, the capture of the linear movement is very smooth. In the record of the curve, because the direction vector of the sampling point is always changing, the main joint points are easily occluded. The processing capacity of the decision-making agent and the functional agent will increase, and if they cannot handle it, they will be obfuscated, resulting in a series of constant data points. It leads to little change in the movement range or extremely exaggerated movements.Figure 10
Data used after exercise.


## 4.2. Joint Force Analysis

The force analysis of the joints is collected by the sensors of the intelligent distributed system, and the force analysis is made on the left and right elbow joints and the left and right knee joints, respectively. The purpose is to obtain the force on the joints of the human body during movement, so as to supplement the data of the occluded part of the imaging system. In the experiment, two sets of data were tested. They were the control group when walking and the experimental group when running.In Figure11, the joint force is roughly the same when running and walking, but it is clearly found that the upper limit of the force when running is higher. The force data can be used to measure a state during exercise, which can then be provided to the system as a data supplement. In this way, with the support of data, there will be traces for the processing of the decision-making agent.Figure 11
Joint force diagram.


## 5. Conclusions

Nowadays, both virtual technology and computer computing system have made great progress. It is now fully qualified for the design of sports training simulation systems, but the domestic emphasis on related technologies is not particularly enough, and there are technical gaps in some key places, such as video presentation technology. This is actually detrimental to the development of domestic simulation systems. This article starts with the introduction of agent technology. Multiagent technology is very important for the design of the system in this article. It emphatically uses the characteristics of the agent, and uses the agent to complete the corresponding functions. Therefore, the focus has been devoted to describing the principle of agent, and then, the trainer’s exercise simulation system is used for running. Human body joints during walking are extracted and simulated for data. After experimentation, although the system can simulate a linear motion engine well, the analysis effect of curved motion is not particularly good. However, because of the intelligent distributed sensor system, it can provide the simulation system with missing data by sensing the joint pressure. This kind of curve movement can also have a good simulation effect. The content of this article is limited by space and knowledge, the research on simulation system is relatively simple, and only part of the content can be realized. However, it is hoped that it can be strengthened in the follow-up research, and it will also provide some reference for the latecomers.



---

*Source: 8286371-2022-03-23.xml*