# Assistant Training System of Teenagers’ Physical Ability Based on Artificial Intelligence

Authors: Cong Du
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

The rapid development of artificial intelligence technology makes it widely used in various fields. In order to more scientifically assist teenagers in physical training, this paper develops a set of teenagers’ physical training system based on artificial intelligence technology. Firstly, the experimental platform is built, and the sensor nodes are connected with the test host through the serial port to collect data to the experimental platform. The system consists of target detection module, data analysis module, and human posture estimation module. The background modeling method based on vibe model is used to form the target detection module, and the canny edge detection algorithm is used to form the data analysis module. Finally, the posture auxiliary index is established to estimate the human posture. This paper makes a systematic application test on a youth sports team. The experimental group was trained with artificial intelligence-based physical training system, while the control group was trained with traditional training methods. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30 s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again. The experimental results show that the intelligent assistant system established in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. After six weeks of training, the standing long jump of the experimental group has been improved by 20.97 cm, the 50 meters dash has been accelerated by 1.21 s, the 30 second single swing rope has been increased by 13.76, the pull-up has been increased by 1.41, and the squat 1RM has been increased by 15.16. This shows that the auxiliary training system based on artificial intelligence can help young athletes improve their physical quality and enhance their sports skills.


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## Body

## 1. Introduction

### 1.1. Background Significance

Although China’s sports industry has been more rapid development, the athletes’ physical training is always not satisfactory. Improper physical training methods and schemes will not only lead to the training to not reach the expected effect and athletes not able to play their true level in the competition but also may cause harm to the athletes’ body [1]. Especially, for the athletes in their youth, their physical functions and skills are in the stage of development [2]. A scientific physical training system will undoubtedly provide solutions to these problems. In this paper, based on artificial intelligence technology, the construction of intelligent physical assistant training system is of great significance to strengthen the effect of physical training for teenagers.


### 1.2. Related Work

Physical training has become an integral part of the European education system because it brings knowledge and insights centered on the principles and concepts of learning skills in the 21st century. Roliak A O analyzed the structure, content, and goal of basic sports in Danish professional teacher training system [3]. Taking mountaineering training as an example, Chen h introduced the development and design of virtual reality technology in sports training courses [4]. First, he described the overall design of the mountaineering training system based on virtual reality technology and then introduced the implementation process of each part. On this basis, he put forward the autonomous learning training mode of the system and, finally, carried out the control experiment to verify the effectiveness of the system.With the development of computer technology, artificial intelligence technology is widely used in the evaluation system. Decision support system is a computer-based information system. The development of sports evaluation decision system can provide scientific support for sports training. Xie puts forward a method to optimize the analysis of physical education teaching evaluation system and the design of intelligent evaluation system. The system has multiple teaching management modules, which can realize dynamic physical education evaluation and reasonably arrange students’ daily training plan [5]. The traditional training system based on case teaching is to carry out strength training for aerobics special movements according to the analysis of previous competitions and training cases. The training results cannot be evaluated intelligently and accurately, and the performance of dynamic analysis is poor. In view of this problem, Jia designed the core training system of aerobics special strength quality based on artificial intelligence to realize the intelligent training of aerobics special strength quality [6]. Through the research of fuzzy normal form system, the intelligent functions of optimization and decision-making of intelligent fuzzy network are realized. He uses FIR filter to deal with the phase distortion in the process of signal transmission and realizes the information management of trainees and statistical query of training results through the information management module. His research provides a technical reference for the construction of the experimental platform in this paper.


### 1.3. Innovative Points in This Paper

In order to improve the quality of physical assistive training for teenagers and cultivate higher quality young athletes for Chinese sports, this paper studies the physical assistive training system for teenagers based on artificial intelligence technology. The innovations of this paper are as follows: (1) the experimental platform is successfully built. The sensor nodes are connected with the test host through the serial port, and the data can be transmitted to the experimental platform after collecting. (2) The function template of the designed intelligent system includes three modules: target detection module, data analysis module, and human posture estimation module. (3) Through the comparison of training experimental data, the intelligent auxiliary training system constructed in this paper can improve the physical quality level of young athletes and help them improve their sports skills.


## 1.1. Background Significance

Although China’s sports industry has been more rapid development, the athletes’ physical training is always not satisfactory. Improper physical training methods and schemes will not only lead to the training to not reach the expected effect and athletes not able to play their true level in the competition but also may cause harm to the athletes’ body [1]. Especially, for the athletes in their youth, their physical functions and skills are in the stage of development [2]. A scientific physical training system will undoubtedly provide solutions to these problems. In this paper, based on artificial intelligence technology, the construction of intelligent physical assistant training system is of great significance to strengthen the effect of physical training for teenagers.


## 1.2. Related Work

Physical training has become an integral part of the European education system because it brings knowledge and insights centered on the principles and concepts of learning skills in the 21st century. Roliak A O analyzed the structure, content, and goal of basic sports in Danish professional teacher training system [3]. Taking mountaineering training as an example, Chen h introduced the development and design of virtual reality technology in sports training courses [4]. First, he described the overall design of the mountaineering training system based on virtual reality technology and then introduced the implementation process of each part. On this basis, he put forward the autonomous learning training mode of the system and, finally, carried out the control experiment to verify the effectiveness of the system.With the development of computer technology, artificial intelligence technology is widely used in the evaluation system. Decision support system is a computer-based information system. The development of sports evaluation decision system can provide scientific support for sports training. Xie puts forward a method to optimize the analysis of physical education teaching evaluation system and the design of intelligent evaluation system. The system has multiple teaching management modules, which can realize dynamic physical education evaluation and reasonably arrange students’ daily training plan [5]. The traditional training system based on case teaching is to carry out strength training for aerobics special movements according to the analysis of previous competitions and training cases. The training results cannot be evaluated intelligently and accurately, and the performance of dynamic analysis is poor. In view of this problem, Jia designed the core training system of aerobics special strength quality based on artificial intelligence to realize the intelligent training of aerobics special strength quality [6]. Through the research of fuzzy normal form system, the intelligent functions of optimization and decision-making of intelligent fuzzy network are realized. He uses FIR filter to deal with the phase distortion in the process of signal transmission and realizes the information management of trainees and statistical query of training results through the information management module. His research provides a technical reference for the construction of the experimental platform in this paper.


## 1.3. Innovative Points in This Paper

In order to improve the quality of physical assistive training for teenagers and cultivate higher quality young athletes for Chinese sports, this paper studies the physical assistive training system for teenagers based on artificial intelligence technology. The innovations of this paper are as follows: (1) the experimental platform is successfully built. The sensor nodes are connected with the test host through the serial port, and the data can be transmitted to the experimental platform after collecting. (2) The function template of the designed intelligent system includes three modules: target detection module, data analysis module, and human posture estimation module. (3) Through the comparison of training experimental data, the intelligent auxiliary training system constructed in this paper can improve the physical quality level of young athletes and help them improve their sports skills.


## 2. Artificial Intelligence Technology and Auxiliary Training Method of Physical Fitness

### 2.1. Motion Recognition Algorithm

#### 2.1.1. Bone Data Filtering

The basic data of motion recognition is the joint data provided by the bone tracking system, but most of the joint data returned by the bone tracking system will have some noise. So, it is necessary to denoise and filter the data. Bone data belongs to time series data, there will be a certain time interval between each frame data, and the joint position data will have a jump change [7]. There will be noise and peak tip in time series, so the filtering method is needed to smooth the data.The data filter of joint position can eliminate unnecessary noise and jitter, obtain smooth and accurate joint position data, and avoid the phenomenon of data delay. If the difference between the input data and the output data of the filter is less than the threshold, the filtered output is equivalent to the input [8]. Otherwise, the output change will be limited. The jitter filter variant of exponential filter can be used to suppress the input change, as shown in(1)X^n=Xn,Xn−X^n−1<z,ϖXn+1−ϖX^n−1,Xn−X^n−1≥z,where z is the threshold. In addition, the median filter can eliminate the peak, and the median can limit the change of input, as shown in(2)X^n=Xn,Xn−Xm<z,Xm,Xn−Xm<z,where Xm is the median value of the last n input data. The data filtered by jitter clearing can be processed by using double exponential smoothing filter. The double exponential smoothing filter, as shown in (3) and (4), respectively, corresponds to the trend of input data and the filtered smoothing output:(3)Q:an=λX^n−X^n−1+1−λan−1,(4)S:X^n=ηXn+1−ηX^n−1+an−1,where λandη are parameters that control the weight of input data. Bone data will become smoother after filtering peak tip and noise, and the data will be more accurate in describing bone features.


#### 2.1.2. Finite State Machine Based on Bone Spatial Features

In the past, the traditional analytic geometry method was always used to calculate the angle of three-dimensional space, but this method needs to consider the boundary conditions, so the calculation is very complex. The space vector rule does not need to consider the boundary conditions, first maps the space coordinate system to the conventional mathematical coordinate system, and uses the vector method [9]. The coordinates of PQ two points are x,y,zandx′,y′,z′ respectively, so the transformation of vector is shown in(5)PQ⟶=x′−x,y′−y,z′−z.The vectors between the joints of the human body can be transformed in the above way. By calculating the angle between the two vectors, the angle between the joints can be obtained. Using the space vector method, we can extract the spatial features of bones and use them as the state description of creating the finite state machine, which can achieve more accurate and complete motion recognition [10].The function of FSM is to describe the state sequence of the target in the period and the influence of external events on the state sequence. The main characteristics of FSM are the discreteness and finiteness of states, which are widely used in modeling. The components of FSM include state, transition, detector, and event [11]. When the transition condition is satisfied, the state will change, and the detector will detect the state transition condition in real time.


#### 2.1.3. Dynamic Time Warping

Based on the idea of dynamic programming, dynamic time warping (DTW) measures the similarity of two discrete time series to find the minimum matching path [12]. The reference templates of two time series are given as follows:(6)X=X1,X2...Xi...XI,(7)Y=Y1,Y2...Yj...YJ,where ij represents the timing label of the reference template X and the timing label of the test template Y, respectively. The distance between the corresponding points of two sequences can be calculated by Euclidean distance, as shown in(8)da,b=Xa−Yb2.Dynamic programming is based on the local optimal path to achieve the global optimal path. Thenth element of regular path is defined as follows:(9)Wn=i,jn.Regular paths need to satisfy the constraints of boundary conditions, monotonicity, and continuity. When the constraint conditions are satisfied, the path with the least cost of regularization is selected, and the calculation is shown in(10)DTW=X,Y=min∑r=1RWrR.According to the setR value, from the matching similarity of the current node and the maximum similarity of the direct cumulative similarity, the formula of the regular path is as follows:(11)Dai,bj=dXi,Yj+minDai,bj+1,Dai+1,bj,Dai+1,bj+1.


### 2.2. Human Pose Estimation Algorithm

#### 2.2.1. Human Pose Estimation Based on Model

In the model-based human pose estimation method, the joint tree or other complex model of the human object is established first, and then, the parameters of human activity are obtained to realize the estimation of human pose. The preparation stage of model-based human pose estimation is to build a human model based on prior knowledge, so as to effectively constrain the feature change space, improve the computational efficiency, and further narrow the search range of matching [13]. The skeleton model is the simplest one in the human body structure model. The joints are represented by points and the bones are represented by line segments, as shown in Figure 1.Figure 1
Skeleton model diagram.In the observation phase of model-based human pose estimation, it is necessary to project the three-dimensional human model to the image plane and then find the optimal human pose parameters. Generally, image features such as color, contour, and edge are used in the observation process. The prediction stage is to reduce the feasible and searching space of attitude. Low-dimensional subspace is used to represent the constraints in the process of human motion, and various dimensionality reduction methods are used to reduce the dimension of attitude vector.The accuracy of human pose estimation based on the model is high, and it can also solve the problems of occlusion and self-occlusion. However, the human pose parameters need to be searched and matched in high-dimensional pose space, so the optimization is slow and time-consuming. Moreover, with the increase of search time, the noise and error will increase, which may lead to the failure of attitude estimation.


#### 2.2.2. Model-Free Human Pose Estimation

Model-free human pose estimation generally uses the mapping relationship between human features in image observation space and human pose space to transform pose estimation problem into pattern recognition problem [14]. There are two types of estimation methods: learning based and sample based.Learning-based human pose estimation method needs to use a large number of training samples to test the linear regression model, which is based on the learning of big data samples. It has the advantages of no specific initialization operation, low storage cost, and high execution efficiency. However, the results are easily affected by the size of training samples, resulting in bias [15]. It is difficult to build a multidimensional feature model, and it takes a long time to adjust the parameters.The human pose estimation method based on matching firstly extracts the corresponding features in the image and then compares them with the features in the sample template library. Therefore, to use this method, we must first obtain a large number of known human posture training samples to establish the sample template library. It is difficult to build sample template library, and this method is only suitable for the pose in sample set. If it is a relatively complex motion, it is easy to produce deviation.


#### 2.2.3. Human Pose Estimation Based on Contour Edge Features

Canny edge detection algorithm is generally used to extract human contour edge [16]. Firstly, the Gaussian filter is used to smooth the image and suppress the noise. Gaussian filtering can be realized by one-dimensional Gaussian kernel function and two-dimensional Gaussian kernel function, as shown in formulas (12) and (13), respectively:(12)K=12πσnx∗x/2σ∗σ,(13)K=12πσ∗σnx∗x+y∗y/2σ∗σ.The matrix of partial derivatives of image inxandy directions is calculated by first-order partial derivative difference. The expression of gray value gradient of image is shown in(14)J1=−1−111,J2=1−11−1,(15)ϕ1a,b=fa,b∗J1x,y,(16)ϕ2a,b=fa,b∗J2x,y,(17)ϕa,b=ϕ12a,b+ϕ22a,b,(18)θϕ=tan−1ϕ2a,bϕ1a,b,where J1andJ2 represent convolution operator and ϕandθ represent amplitude and direction of edge gradient, respectively.According to the global gradient alone cannot accurately locate the edge position, so it is necessary to suppress the gradient amplitude to highlight the edge. At the same time, the double threshold method can be used to reduce the number of false edges. Double threshold needs to select high threshold and low threshold to get different threshold images [17]. After obtaining the contour edge, it is further processed to obtain the corresponding human joint data. Joint points of the head and foot are extracted by horizontal line scanning algorithm, joint points of the knee, hip, neck, and chest are extracted by length proportion constraint, and joint points of the hand, elbow, and shoulder are extracted by vertical scanning algorithm [18, 19].


### 2.3. Digital-Assisted Physical Training

#### 2.3.1. Content System of Physical Training

Physical training is a systematic project with certain structural characteristics and process. A complete physical training content structure system needs to integrate isolated elements and follow a reasonable training process. The internal structure of physical training includes body shape, body function, sports quality, and health level [20].The process of physical training generally consists of five steps: test, evaluation, target determination, plan making, and plan implementation [21]. Generally speaking, the goal of physical training is to improve competitive ability and obtain excellent results. In the guidance stage, we need to divide the training time and the specific tasks of each section to ensure that the athletes present the best competitive state in the competition. Therefore, it is necessary to arrange the periodic training reasonably in the guidance stage. In the implementation stage, we should determine the specific training methods and consider the athletes’ load and fatigue recovery degree. Training methods should be combined with sports characteristics, personal ability, and external conditions. The load is generally measured by the percentage of the maximum load of an individual. Fatigue recovery plays an important role in the prevention of injury and physical maintenance. The control stage is an accurate and comprehensive evaluation of the whole training process, including goals and implementation, which is conducive to timely understanding of athletes.


#### 2.3.2. Scientific Physical Training System

Functional training theory, periodic training theory, systematic training theory, and plate structure theory are the theoretical basis of scientific physical training. Functional training theory emphasizes the frequency and load intensity of training and thinks that physical training can enhance athletes’ anaerobic and aerobic ability and make muscles more developed, but ignores the cultivation of flexibility [22]. The purpose of functional training theory is to eliminate the gap between the physical training and the physical ability required by the competition. The training strategy has the characteristics of specialization and individuation, and the performance in the competition reflects the training effect.Periodic training theory divides the training process of athletes into preparation period, competition period, and transition period. Different periods have different training tasks, objectives, and load arrangements. According to the change law of athletes’ competitive ability and the periodic characteristics of competition, the periodic training is arranged. It is necessary to consider the changes of training intensity and time when athletes appear in the best competitive state during training and combine practical training according to different cycles [23]. And, the adjacent cycles should be gradual and moderate convergence.According to the theory of systematic training, if we want to achieve the ideal training effect, we need continuous and systematic training because the competitive ability is unstable. If there is no continuous training, the competitive ability will gradually degenerate [24]. According to the theory of plate structure, more concentrated short-term training can bring greater load stimulation, meet the intensity requirements of athletes, and achieve better training effect. Under the theory of plate structure, the competition can be arranged in each training stage.


#### 2.3.3. The Demand of Intelligent Physical Assistant Training

With the development of artificial intelligence technology, physical training also began to use scientific and technological means to assist. At present, there are some problems in physical training, such as unscientific teaching methods, unclear selection criteria, frequent sports injuries, and lack of digital sports resources [25].Physical training is relatively boring, and it is difficult to master the main points of movements in high-tech training. Traditional physical training teaching has some problems in theory explanation and action demonstration, so it is difficult for learners to have a deep understanding of it. Intelligent training and teaching can help learners understand better from multiple sensory aspects. The inaccuracy of the selection criteria is reflected in the fact that the selection of athletes only depends on the performance of the competition. Some athletes with strong plasticity may find it difficult to perform well because of their irregular technical movements.Unreasonable training methods and load will lead to sports injury. Although sports injury cannot be completely avoided, it can be prevented through the physical assistant training system to reduce the possibility of injury. Physical training and modern teaching technology have not been closely combined, leading to the lack of digital sports resources. Intelligent physical assistant training can quantify the training situation of athletes as parameters, analyze the advantages and disadvantages, and make personalized training programs.


## 2.1. Motion Recognition Algorithm

### 2.1.1. Bone Data Filtering

The basic data of motion recognition is the joint data provided by the bone tracking system, but most of the joint data returned by the bone tracking system will have some noise. So, it is necessary to denoise and filter the data. Bone data belongs to time series data, there will be a certain time interval between each frame data, and the joint position data will have a jump change [7]. There will be noise and peak tip in time series, so the filtering method is needed to smooth the data.The data filter of joint position can eliminate unnecessary noise and jitter, obtain smooth and accurate joint position data, and avoid the phenomenon of data delay. If the difference between the input data and the output data of the filter is less than the threshold, the filtered output is equivalent to the input [8]. Otherwise, the output change will be limited. The jitter filter variant of exponential filter can be used to suppress the input change, as shown in(1)X^n=Xn,Xn−X^n−1<z,ϖXn+1−ϖX^n−1,Xn−X^n−1≥z,where z is the threshold. In addition, the median filter can eliminate the peak, and the median can limit the change of input, as shown in(2)X^n=Xn,Xn−Xm<z,Xm,Xn−Xm<z,where Xm is the median value of the last n input data. The data filtered by jitter clearing can be processed by using double exponential smoothing filter. The double exponential smoothing filter, as shown in (3) and (4), respectively, corresponds to the trend of input data and the filtered smoothing output:(3)Q:an=λX^n−X^n−1+1−λan−1,(4)S:X^n=ηXn+1−ηX^n−1+an−1,where λandη are parameters that control the weight of input data. Bone data will become smoother after filtering peak tip and noise, and the data will be more accurate in describing bone features.


### 2.1.2. Finite State Machine Based on Bone Spatial Features

In the past, the traditional analytic geometry method was always used to calculate the angle of three-dimensional space, but this method needs to consider the boundary conditions, so the calculation is very complex. The space vector rule does not need to consider the boundary conditions, first maps the space coordinate system to the conventional mathematical coordinate system, and uses the vector method [9]. The coordinates of PQ two points are x,y,zandx′,y′,z′ respectively, so the transformation of vector is shown in(5)PQ⟶=x′−x,y′−y,z′−z.The vectors between the joints of the human body can be transformed in the above way. By calculating the angle between the two vectors, the angle between the joints can be obtained. Using the space vector method, we can extract the spatial features of bones and use them as the state description of creating the finite state machine, which can achieve more accurate and complete motion recognition [10].The function of FSM is to describe the state sequence of the target in the period and the influence of external events on the state sequence. The main characteristics of FSM are the discreteness and finiteness of states, which are widely used in modeling. The components of FSM include state, transition, detector, and event [11]. When the transition condition is satisfied, the state will change, and the detector will detect the state transition condition in real time.


### 2.1.3. Dynamic Time Warping

Based on the idea of dynamic programming, dynamic time warping (DTW) measures the similarity of two discrete time series to find the minimum matching path [12]. The reference templates of two time series are given as follows:(6)X=X1,X2...Xi...XI,(7)Y=Y1,Y2...Yj...YJ,where ij represents the timing label of the reference template X and the timing label of the test template Y, respectively. The distance between the corresponding points of two sequences can be calculated by Euclidean distance, as shown in(8)da,b=Xa−Yb2.Dynamic programming is based on the local optimal path to achieve the global optimal path. Thenth element of regular path is defined as follows:(9)Wn=i,jn.Regular paths need to satisfy the constraints of boundary conditions, monotonicity, and continuity. When the constraint conditions are satisfied, the path with the least cost of regularization is selected, and the calculation is shown in(10)DTW=X,Y=min∑r=1RWrR.According to the setR value, from the matching similarity of the current node and the maximum similarity of the direct cumulative similarity, the formula of the regular path is as follows:(11)Dai,bj=dXi,Yj+minDai,bj+1,Dai+1,bj,Dai+1,bj+1.


## 2.1.1. Bone Data Filtering

The basic data of motion recognition is the joint data provided by the bone tracking system, but most of the joint data returned by the bone tracking system will have some noise. So, it is necessary to denoise and filter the data. Bone data belongs to time series data, there will be a certain time interval between each frame data, and the joint position data will have a jump change [7]. There will be noise and peak tip in time series, so the filtering method is needed to smooth the data.The data filter of joint position can eliminate unnecessary noise and jitter, obtain smooth and accurate joint position data, and avoid the phenomenon of data delay. If the difference between the input data and the output data of the filter is less than the threshold, the filtered output is equivalent to the input [8]. Otherwise, the output change will be limited. The jitter filter variant of exponential filter can be used to suppress the input change, as shown in(1)X^n=Xn,Xn−X^n−1<z,ϖXn+1−ϖX^n−1,Xn−X^n−1≥z,where z is the threshold. In addition, the median filter can eliminate the peak, and the median can limit the change of input, as shown in(2)X^n=Xn,Xn−Xm<z,Xm,Xn−Xm<z,where Xm is the median value of the last n input data. The data filtered by jitter clearing can be processed by using double exponential smoothing filter. The double exponential smoothing filter, as shown in (3) and (4), respectively, corresponds to the trend of input data and the filtered smoothing output:(3)Q:an=λX^n−X^n−1+1−λan−1,(4)S:X^n=ηXn+1−ηX^n−1+an−1,where λandη are parameters that control the weight of input data. Bone data will become smoother after filtering peak tip and noise, and the data will be more accurate in describing bone features.


## 2.1.2. Finite State Machine Based on Bone Spatial Features

In the past, the traditional analytic geometry method was always used to calculate the angle of three-dimensional space, but this method needs to consider the boundary conditions, so the calculation is very complex. The space vector rule does not need to consider the boundary conditions, first maps the space coordinate system to the conventional mathematical coordinate system, and uses the vector method [9]. The coordinates of PQ two points are x,y,zandx′,y′,z′ respectively, so the transformation of vector is shown in(5)PQ⟶=x′−x,y′−y,z′−z.The vectors between the joints of the human body can be transformed in the above way. By calculating the angle between the two vectors, the angle between the joints can be obtained. Using the space vector method, we can extract the spatial features of bones and use them as the state description of creating the finite state machine, which can achieve more accurate and complete motion recognition [10].The function of FSM is to describe the state sequence of the target in the period and the influence of external events on the state sequence. The main characteristics of FSM are the discreteness and finiteness of states, which are widely used in modeling. The components of FSM include state, transition, detector, and event [11]. When the transition condition is satisfied, the state will change, and the detector will detect the state transition condition in real time.


## 2.1.3. Dynamic Time Warping

Based on the idea of dynamic programming, dynamic time warping (DTW) measures the similarity of two discrete time series to find the minimum matching path [12]. The reference templates of two time series are given as follows:(6)X=X1,X2...Xi...XI,(7)Y=Y1,Y2...Yj...YJ,where ij represents the timing label of the reference template X and the timing label of the test template Y, respectively. The distance between the corresponding points of two sequences can be calculated by Euclidean distance, as shown in(8)da,b=Xa−Yb2.Dynamic programming is based on the local optimal path to achieve the global optimal path. Thenth element of regular path is defined as follows:(9)Wn=i,jn.Regular paths need to satisfy the constraints of boundary conditions, monotonicity, and continuity. When the constraint conditions are satisfied, the path with the least cost of regularization is selected, and the calculation is shown in(10)DTW=X,Y=min∑r=1RWrR.According to the setR value, from the matching similarity of the current node and the maximum similarity of the direct cumulative similarity, the formula of the regular path is as follows:(11)Dai,bj=dXi,Yj+minDai,bj+1,Dai+1,bj,Dai+1,bj+1.


## 2.2. Human Pose Estimation Algorithm

### 2.2.1. Human Pose Estimation Based on Model

In the model-based human pose estimation method, the joint tree or other complex model of the human object is established first, and then, the parameters of human activity are obtained to realize the estimation of human pose. The preparation stage of model-based human pose estimation is to build a human model based on prior knowledge, so as to effectively constrain the feature change space, improve the computational efficiency, and further narrow the search range of matching [13]. The skeleton model is the simplest one in the human body structure model. The joints are represented by points and the bones are represented by line segments, as shown in Figure 1.Figure 1
Skeleton model diagram.In the observation phase of model-based human pose estimation, it is necessary to project the three-dimensional human model to the image plane and then find the optimal human pose parameters. Generally, image features such as color, contour, and edge are used in the observation process. The prediction stage is to reduce the feasible and searching space of attitude. Low-dimensional subspace is used to represent the constraints in the process of human motion, and various dimensionality reduction methods are used to reduce the dimension of attitude vector.The accuracy of human pose estimation based on the model is high, and it can also solve the problems of occlusion and self-occlusion. However, the human pose parameters need to be searched and matched in high-dimensional pose space, so the optimization is slow and time-consuming. Moreover, with the increase of search time, the noise and error will increase, which may lead to the failure of attitude estimation.


### 2.2.2. Model-Free Human Pose Estimation

Model-free human pose estimation generally uses the mapping relationship between human features in image observation space and human pose space to transform pose estimation problem into pattern recognition problem [14]. There are two types of estimation methods: learning based and sample based.Learning-based human pose estimation method needs to use a large number of training samples to test the linear regression model, which is based on the learning of big data samples. It has the advantages of no specific initialization operation, low storage cost, and high execution efficiency. However, the results are easily affected by the size of training samples, resulting in bias [15]. It is difficult to build a multidimensional feature model, and it takes a long time to adjust the parameters.The human pose estimation method based on matching firstly extracts the corresponding features in the image and then compares them with the features in the sample template library. Therefore, to use this method, we must first obtain a large number of known human posture training samples to establish the sample template library. It is difficult to build sample template library, and this method is only suitable for the pose in sample set. If it is a relatively complex motion, it is easy to produce deviation.


### 2.2.3. Human Pose Estimation Based on Contour Edge Features

Canny edge detection algorithm is generally used to extract human contour edge [16]. Firstly, the Gaussian filter is used to smooth the image and suppress the noise. Gaussian filtering can be realized by one-dimensional Gaussian kernel function and two-dimensional Gaussian kernel function, as shown in formulas (12) and (13), respectively:(12)K=12πσnx∗x/2σ∗σ,(13)K=12πσ∗σnx∗x+y∗y/2σ∗σ.The matrix of partial derivatives of image inxandy directions is calculated by first-order partial derivative difference. The expression of gray value gradient of image is shown in(14)J1=−1−111,J2=1−11−1,(15)ϕ1a,b=fa,b∗J1x,y,(16)ϕ2a,b=fa,b∗J2x,y,(17)ϕa,b=ϕ12a,b+ϕ22a,b,(18)θϕ=tan−1ϕ2a,bϕ1a,b,where J1andJ2 represent convolution operator and ϕandθ represent amplitude and direction of edge gradient, respectively.According to the global gradient alone cannot accurately locate the edge position, so it is necessary to suppress the gradient amplitude to highlight the edge. At the same time, the double threshold method can be used to reduce the number of false edges. Double threshold needs to select high threshold and low threshold to get different threshold images [17]. After obtaining the contour edge, it is further processed to obtain the corresponding human joint data. Joint points of the head and foot are extracted by horizontal line scanning algorithm, joint points of the knee, hip, neck, and chest are extracted by length proportion constraint, and joint points of the hand, elbow, and shoulder are extracted by vertical scanning algorithm [18, 19].


## 2.2.1. Human Pose Estimation Based on Model

In the model-based human pose estimation method, the joint tree or other complex model of the human object is established first, and then, the parameters of human activity are obtained to realize the estimation of human pose. The preparation stage of model-based human pose estimation is to build a human model based on prior knowledge, so as to effectively constrain the feature change space, improve the computational efficiency, and further narrow the search range of matching [13]. The skeleton model is the simplest one in the human body structure model. The joints are represented by points and the bones are represented by line segments, as shown in Figure 1.Figure 1
Skeleton model diagram.In the observation phase of model-based human pose estimation, it is necessary to project the three-dimensional human model to the image plane and then find the optimal human pose parameters. Generally, image features such as color, contour, and edge are used in the observation process. The prediction stage is to reduce the feasible and searching space of attitude. Low-dimensional subspace is used to represent the constraints in the process of human motion, and various dimensionality reduction methods are used to reduce the dimension of attitude vector.The accuracy of human pose estimation based on the model is high, and it can also solve the problems of occlusion and self-occlusion. However, the human pose parameters need to be searched and matched in high-dimensional pose space, so the optimization is slow and time-consuming. Moreover, with the increase of search time, the noise and error will increase, which may lead to the failure of attitude estimation.


## 2.2.2. Model-Free Human Pose Estimation

Model-free human pose estimation generally uses the mapping relationship between human features in image observation space and human pose space to transform pose estimation problem into pattern recognition problem [14]. There are two types of estimation methods: learning based and sample based.Learning-based human pose estimation method needs to use a large number of training samples to test the linear regression model, which is based on the learning of big data samples. It has the advantages of no specific initialization operation, low storage cost, and high execution efficiency. However, the results are easily affected by the size of training samples, resulting in bias [15]. It is difficult to build a multidimensional feature model, and it takes a long time to adjust the parameters.The human pose estimation method based on matching firstly extracts the corresponding features in the image and then compares them with the features in the sample template library. Therefore, to use this method, we must first obtain a large number of known human posture training samples to establish the sample template library. It is difficult to build sample template library, and this method is only suitable for the pose in sample set. If it is a relatively complex motion, it is easy to produce deviation.


## 2.2.3. Human Pose Estimation Based on Contour Edge Features

Canny edge detection algorithm is generally used to extract human contour edge [16]. Firstly, the Gaussian filter is used to smooth the image and suppress the noise. Gaussian filtering can be realized by one-dimensional Gaussian kernel function and two-dimensional Gaussian kernel function, as shown in formulas (12) and (13), respectively:(12)K=12πσnx∗x/2σ∗σ,(13)K=12πσ∗σnx∗x+y∗y/2σ∗σ.The matrix of partial derivatives of image inxandy directions is calculated by first-order partial derivative difference. The expression of gray value gradient of image is shown in(14)J1=−1−111,J2=1−11−1,(15)ϕ1a,b=fa,b∗J1x,y,(16)ϕ2a,b=fa,b∗J2x,y,(17)ϕa,b=ϕ12a,b+ϕ22a,b,(18)θϕ=tan−1ϕ2a,bϕ1a,b,where J1andJ2 represent convolution operator and ϕandθ represent amplitude and direction of edge gradient, respectively.According to the global gradient alone cannot accurately locate the edge position, so it is necessary to suppress the gradient amplitude to highlight the edge. At the same time, the double threshold method can be used to reduce the number of false edges. Double threshold needs to select high threshold and low threshold to get different threshold images [17]. After obtaining the contour edge, it is further processed to obtain the corresponding human joint data. Joint points of the head and foot are extracted by horizontal line scanning algorithm, joint points of the knee, hip, neck, and chest are extracted by length proportion constraint, and joint points of the hand, elbow, and shoulder are extracted by vertical scanning algorithm [18, 19].


## 2.3. Digital-Assisted Physical Training

### 2.3.1. Content System of Physical Training

Physical training is a systematic project with certain structural characteristics and process. A complete physical training content structure system needs to integrate isolated elements and follow a reasonable training process. The internal structure of physical training includes body shape, body function, sports quality, and health level [20].The process of physical training generally consists of five steps: test, evaluation, target determination, plan making, and plan implementation [21]. Generally speaking, the goal of physical training is to improve competitive ability and obtain excellent results. In the guidance stage, we need to divide the training time and the specific tasks of each section to ensure that the athletes present the best competitive state in the competition. Therefore, it is necessary to arrange the periodic training reasonably in the guidance stage. In the implementation stage, we should determine the specific training methods and consider the athletes’ load and fatigue recovery degree. Training methods should be combined with sports characteristics, personal ability, and external conditions. The load is generally measured by the percentage of the maximum load of an individual. Fatigue recovery plays an important role in the prevention of injury and physical maintenance. The control stage is an accurate and comprehensive evaluation of the whole training process, including goals and implementation, which is conducive to timely understanding of athletes.


### 2.3.2. Scientific Physical Training System

Functional training theory, periodic training theory, systematic training theory, and plate structure theory are the theoretical basis of scientific physical training. Functional training theory emphasizes the frequency and load intensity of training and thinks that physical training can enhance athletes’ anaerobic and aerobic ability and make muscles more developed, but ignores the cultivation of flexibility [22]. The purpose of functional training theory is to eliminate the gap between the physical training and the physical ability required by the competition. The training strategy has the characteristics of specialization and individuation, and the performance in the competition reflects the training effect.Periodic training theory divides the training process of athletes into preparation period, competition period, and transition period. Different periods have different training tasks, objectives, and load arrangements. According to the change law of athletes’ competitive ability and the periodic characteristics of competition, the periodic training is arranged. It is necessary to consider the changes of training intensity and time when athletes appear in the best competitive state during training and combine practical training according to different cycles [23]. And, the adjacent cycles should be gradual and moderate convergence.According to the theory of systematic training, if we want to achieve the ideal training effect, we need continuous and systematic training because the competitive ability is unstable. If there is no continuous training, the competitive ability will gradually degenerate [24]. According to the theory of plate structure, more concentrated short-term training can bring greater load stimulation, meet the intensity requirements of athletes, and achieve better training effect. Under the theory of plate structure, the competition can be arranged in each training stage.


### 2.3.3. The Demand of Intelligent Physical Assistant Training

With the development of artificial intelligence technology, physical training also began to use scientific and technological means to assist. At present, there are some problems in physical training, such as unscientific teaching methods, unclear selection criteria, frequent sports injuries, and lack of digital sports resources [25].Physical training is relatively boring, and it is difficult to master the main points of movements in high-tech training. Traditional physical training teaching has some problems in theory explanation and action demonstration, so it is difficult for learners to have a deep understanding of it. Intelligent training and teaching can help learners understand better from multiple sensory aspects. The inaccuracy of the selection criteria is reflected in the fact that the selection of athletes only depends on the performance of the competition. Some athletes with strong plasticity may find it difficult to perform well because of their irregular technical movements.Unreasonable training methods and load will lead to sports injury. Although sports injury cannot be completely avoided, it can be prevented through the physical assistant training system to reduce the possibility of injury. Physical training and modern teaching technology have not been closely combined, leading to the lack of digital sports resources. Intelligent physical assistant training can quantify the training situation of athletes as parameters, analyze the advantages and disadvantages, and make personalized training programs.


## 2.3.1. Content System of Physical Training

Physical training is a systematic project with certain structural characteristics and process. A complete physical training content structure system needs to integrate isolated elements and follow a reasonable training process. The internal structure of physical training includes body shape, body function, sports quality, and health level [20].The process of physical training generally consists of five steps: test, evaluation, target determination, plan making, and plan implementation [21]. Generally speaking, the goal of physical training is to improve competitive ability and obtain excellent results. In the guidance stage, we need to divide the training time and the specific tasks of each section to ensure that the athletes present the best competitive state in the competition. Therefore, it is necessary to arrange the periodic training reasonably in the guidance stage. In the implementation stage, we should determine the specific training methods and consider the athletes’ load and fatigue recovery degree. Training methods should be combined with sports characteristics, personal ability, and external conditions. The load is generally measured by the percentage of the maximum load of an individual. Fatigue recovery plays an important role in the prevention of injury and physical maintenance. The control stage is an accurate and comprehensive evaluation of the whole training process, including goals and implementation, which is conducive to timely understanding of athletes.


## 2.3.2. Scientific Physical Training System

Functional training theory, periodic training theory, systematic training theory, and plate structure theory are the theoretical basis of scientific physical training. Functional training theory emphasizes the frequency and load intensity of training and thinks that physical training can enhance athletes’ anaerobic and aerobic ability and make muscles more developed, but ignores the cultivation of flexibility [22]. The purpose of functional training theory is to eliminate the gap between the physical training and the physical ability required by the competition. The training strategy has the characteristics of specialization and individuation, and the performance in the competition reflects the training effect.Periodic training theory divides the training process of athletes into preparation period, competition period, and transition period. Different periods have different training tasks, objectives, and load arrangements. According to the change law of athletes’ competitive ability and the periodic characteristics of competition, the periodic training is arranged. It is necessary to consider the changes of training intensity and time when athletes appear in the best competitive state during training and combine practical training according to different cycles [23]. And, the adjacent cycles should be gradual and moderate convergence.According to the theory of systematic training, if we want to achieve the ideal training effect, we need continuous and systematic training because the competitive ability is unstable. If there is no continuous training, the competitive ability will gradually degenerate [24]. According to the theory of plate structure, more concentrated short-term training can bring greater load stimulation, meet the intensity requirements of athletes, and achieve better training effect. Under the theory of plate structure, the competition can be arranged in each training stage.


## 2.3.3. The Demand of Intelligent Physical Assistant Training

With the development of artificial intelligence technology, physical training also began to use scientific and technological means to assist. At present, there are some problems in physical training, such as unscientific teaching methods, unclear selection criteria, frequent sports injuries, and lack of digital sports resources [25].Physical training is relatively boring, and it is difficult to master the main points of movements in high-tech training. Traditional physical training teaching has some problems in theory explanation and action demonstration, so it is difficult for learners to have a deep understanding of it. Intelligent training and teaching can help learners understand better from multiple sensory aspects. The inaccuracy of the selection criteria is reflected in the fact that the selection of athletes only depends on the performance of the competition. Some athletes with strong plasticity may find it difficult to perform well because of their irregular technical movements.Unreasonable training methods and load will lead to sports injury. Although sports injury cannot be completely avoided, it can be prevented through the physical assistant training system to reduce the possibility of injury. Physical training and modern teaching technology have not been closely combined, leading to the lack of digital sports resources. Intelligent physical assistant training can quantify the training situation of athletes as parameters, analyze the advantages and disadvantages, and make personalized training programs.


## 3. Experiment on the Construction of the Intelligence System of Teenagers’ Physical Ability Assistant Training

### 3.1. System Experiment Platform

In this paper, the intelligent system of physical assistant training is designed for young athletes and coaches to improve the efficiency of young physical training. The structure of the specific experimental platform is shown in Figure2. Sensor nodes are connected to the test host through serial port, and data is collected and transmitted to the experimental platform.Figure 2
Structure of experimental platform.


### 3.2. System Components

#### 3.2.1. Target Detection Module

The first step of the system is to extract human targets and their features effectively, which is also the basic step of the auxiliary training system. The background modeling method based on the vibe model is adopted. Firstly, the pixel background model is initialized, then the target segmentation is performed on the subsequent image sequence, and finally the background model is updated according to the neighborhood update method.


#### 3.2.2. Data Analysis Module

Canny edge detection algorithm is used to process the data detected by the target detection module, including bone filtering and smoothing, contour extraction, and edge thinning. Then, the processed data are analyzed, and the parameters of athletes in the training process are calculated, which are displayed in the system interface in the form of a chart. Athletes can log in to the mobile client to view their training data. The client login page is shown in Figure3.Figure 3
Client login page.


#### 3.2.3. Human Pose Estimation Module

In this paper, the most important part of the intelligent system of physical training is to estimate the human body posture, obtain the human body posture information through the data analysis module, and establish the posture auxiliary index. This link can help athletes correct the nonstandard posture in the process of training, predict the next action of athletes, and give corresponding instructions.


### 3.3. System Application Test

This paper makes a systematic application test on a youth sports team. Fourteen boys in a youth sports team were randomly divided into two groups. The experimental group adopted the AI-based physical training system for physical training, while the control group adopted the traditional physical training method. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again.


## 3.1. System Experiment Platform

In this paper, the intelligent system of physical assistant training is designed for young athletes and coaches to improve the efficiency of young physical training. The structure of the specific experimental platform is shown in Figure2. Sensor nodes are connected to the test host through serial port, and data is collected and transmitted to the experimental platform.Figure 2
Structure of experimental platform.


## 3.2. System Components

### 3.2.1. Target Detection Module

The first step of the system is to extract human targets and their features effectively, which is also the basic step of the auxiliary training system. The background modeling method based on the vibe model is adopted. Firstly, the pixel background model is initialized, then the target segmentation is performed on the subsequent image sequence, and finally the background model is updated according to the neighborhood update method.


### 3.2.2. Data Analysis Module

Canny edge detection algorithm is used to process the data detected by the target detection module, including bone filtering and smoothing, contour extraction, and edge thinning. Then, the processed data are analyzed, and the parameters of athletes in the training process are calculated, which are displayed in the system interface in the form of a chart. Athletes can log in to the mobile client to view their training data. The client login page is shown in Figure3.Figure 3
Client login page.


### 3.2.3. Human Pose Estimation Module

In this paper, the most important part of the intelligent system of physical training is to estimate the human body posture, obtain the human body posture information through the data analysis module, and establish the posture auxiliary index. This link can help athletes correct the nonstandard posture in the process of training, predict the next action of athletes, and give corresponding instructions.


## 3.2.1. Target Detection Module

The first step of the system is to extract human targets and their features effectively, which is also the basic step of the auxiliary training system. The background modeling method based on the vibe model is adopted. Firstly, the pixel background model is initialized, then the target segmentation is performed on the subsequent image sequence, and finally the background model is updated according to the neighborhood update method.


## 3.2.2. Data Analysis Module

Canny edge detection algorithm is used to process the data detected by the target detection module, including bone filtering and smoothing, contour extraction, and edge thinning. Then, the processed data are analyzed, and the parameters of athletes in the training process are calculated, which are displayed in the system interface in the form of a chart. Athletes can log in to the mobile client to view their training data. The client login page is shown in Figure3.Figure 3
Client login page.


## 3.2.3. Human Pose Estimation Module

In this paper, the most important part of the intelligent system of physical training is to estimate the human body posture, obtain the human body posture information through the data analysis module, and establish the posture auxiliary index. This link can help athletes correct the nonstandard posture in the process of training, predict the next action of athletes, and give corresponding instructions.


## 3.3. System Application Test

This paper makes a systematic application test on a youth sports team. Fourteen boys in a youth sports team were randomly divided into two groups. The experimental group adopted the AI-based physical training system for physical training, while the control group adopted the traditional physical training method. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again.


## 4. Discussion on the Performance and Effect of the System

### 4.1. System Performance

Firstly, an athlete in the youth sports team is randomly selected as the object of the system monitoring, and the physiological load monitoring performance of the physical fitness assistant training system based on artificial intelligence is tested. The results are as follows.As shown in Figure4, when the athlete carries out a complete single training, the intelligent assistant system created in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. The OA stage is the mobilization stage, the AB stage is the stable stage, the BD stage is the fatigue stage, and the DF stage is recovery stage.Figure 4
Physiological load response curve.


### 4.2. Changes of Physical Fitness

#### 4.2.1. Pretest Results

Before the beginning of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table1, by comparing the results of the physical fitness test conducted between the two groups before the experiment, it can be seen that there was no significant difference in the physical fitness indexes between the experimental group and the control group before the experiment began. The results of standing long jump and squat 1RM of the experimental group were slightly better than those of the control group. The results of 50 m sprint, 30 s rope skipping, and pull-up of the control group were slightly better than those of the experimental group.Table 1
Physical fitness test results of two groups of athletes before the experiment.
ProjectExperience groupControl groupStanding long jump (cm)172.67 ± 10.15169.94 ± 11.7250 meters dash (s)8.35 ± 1.148.24 ± 1.2130 s single swing rope skipping53.58 ± 5.7456.24 ± 6.15Pull up6.84 ± 1.036.91 ± 1.09Squat 1RM105.5 ± 8.34104.8 ± 7.85As shown in Figure5, there is no significant difference between the experimental group and the control group in the test results of standing long jump, 50m sprint, 30s single swing rope skipping, pull-up, and squat 1RM. The smallest difference of all was in the pull-up test. There were 6.84 in the experimental group and 6.91 in the control group, a difference of 0.07 between the two groups.Figure 5
Results of pretest.


#### 4.2.2. The Measured Results in the Experiment

Three weeks after the beginning of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table2, after three weeks of training, the experimental group performed slightly better than the control group in the standing long jump, the 50-meters dash, the single-swing rope jump for 30s, the pull-up, and the squat 1RM.Table 2
Physical fitness test results of two groups of athletes after three weeks of training.
ProjectExperience groupControl groupStanding long jump (cm)178.23 ± 11.05174.34 ± 11.7850 meters dash (s)8.21 ± 1.088.22 ± 1.1730s single swing rope skipping58.84 ± 6.0158.25 ± 6.17Pull up7.36 ± 1.137.21 ± 1.13Squat 1RM107.67 ± 8.21106.35 ± 7.64As shown in Figure6, the biggest difference between the experimental group and the control group is the test results of standing long jump, the experimental group is 178.23 ± 11.05 cm, the control group is 174.34 ± 11.78 cm, the difference between the two groups is 3.89 cm. The second is the test result of squat 1RM, the experimental group is 107.67 ± 8.21, the control group is 106.35 ± 7.64, and the difference between the two groups is 1.32.Figure 6
Intermediate test results.


#### 4.2.3. Post-Test Results

Six weeks after the beginning of the experiment, that is, at the end of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table3, after six weeks of training, the test results of the experimental group and the control group were significantly different. The standing long jump scores of the two groups were 193.64 ± 9.38 cm and 179.39 ± 12.35 cm, respectively. The results of 50 meters dash were 7.14 ± 1.03 s and 7.59 ± 1.07 s, respectively. The results of rope skipping in 30 s were 67.34 ± 5.24 and 61.25 ± 6.13, respectively. The results of the chin up were 8.25 + 0.84 and 7.42 + 1.27, respectively. The results of 1 RM squat were 120.66 ± 5.31 and 111.39 ± 7.35, respectively.Table 3
Physical fitness test results of two groups of athletes after six weeks of training.
ProjectExperience groupControl groupStanding long jump (cm)193.64 ± 9.38179.39 ± 12.3550 meters dash (s)7.14 ± 1.037.59 ± 1.0730s single swing rope skipping67.34 ± 5.2461.25 ± 6.13Pull up8.25 ± 0.847.42 ± 1.27Squat 1RM120.66 ± 5.31111.39 ± 7.35As shown in Figure7, the biggest difference between the experimental group and the control group is the test results of standing long jump and the difference between the two groups is 14.25 cm. The second is the test result of squat 1RM; the difference between the two groups is 9.27. 30 s single jump rope skipping results in two groups of 6.15; the difference between the chin up performance is 0.83, and 50 m sprint results’ difference is 0.45 s.Figure 7
Post-test results.


#### 4.2.4. Longitudinal Comparison of Two Groups of Test Results

The longitudinal comparison results of each item in the experimental group and the control group are shown in Table4.As shown in Table4, the scores of the experimental group and the control group were improved in the three tests. However, the experimental group’s performance of various indicators improved significantly faster than the control group. In the first test, the scores of the two groups were almost the same, and the scores of the control group were even better than the experimental group in some items. In the second test, the results of the experimental group have begun to be slightly better than the control group. In the third test, the results of the experimental group have been significantly better than that of the control group. The results of the three tests in the experimental group are shown in Figure 8.Table 4
Longitudinal comparison of test results between the two groups.
TimesStanding long jump50 meters dash30 s single swing rope skippingPull upSquat 1RME1172.678.3553.586.84105.5E2178.238.2158.847.36107.67E3193.647.1467.348.25120.66C1169.948.2456.246.91104.8C2174.348.2258.257.21106.35C3179.397.5961.257.42111.39Figure 8
Changes of test results of the experimental group for three times.As shown in Figure8, after six weeks of training of the physical assistance training intelligent system, the experimental group’s standing long jump improved 20.97 cm, 50 m sprint accelerated 1.21 s, 30 s single shake rope jump increased 13.76, pull-up increased 1.41, and squat 1RM increased 15.16.The results of the three tests in the control group are shown in Figure9.Figure 9
Results of three tests in the control group.As shown in Figure9, after six weeks of training with the intelligent system of physical assistant training, the standing long jump of the control group has been improved by 9.45 cm, the 50 meters dash has been accelerated by 0.65 s, the 30 second single swing rope jumping has increased by 5.01, the pull-up has increased by 0.51, and the squat 1RM has increased by 6.59.


## 4.1. System Performance

Firstly, an athlete in the youth sports team is randomly selected as the object of the system monitoring, and the physiological load monitoring performance of the physical fitness assistant training system based on artificial intelligence is tested. The results are as follows.As shown in Figure4, when the athlete carries out a complete single training, the intelligent assistant system created in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. The OA stage is the mobilization stage, the AB stage is the stable stage, the BD stage is the fatigue stage, and the DF stage is recovery stage.Figure 4
Physiological load response curve.


## 4.2. Changes of Physical Fitness

### 4.2.1. Pretest Results

Before the beginning of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table1, by comparing the results of the physical fitness test conducted between the two groups before the experiment, it can be seen that there was no significant difference in the physical fitness indexes between the experimental group and the control group before the experiment began. The results of standing long jump and squat 1RM of the experimental group were slightly better than those of the control group. The results of 50 m sprint, 30 s rope skipping, and pull-up of the control group were slightly better than those of the experimental group.Table 1
Physical fitness test results of two groups of athletes before the experiment.
ProjectExperience groupControl groupStanding long jump (cm)172.67 ± 10.15169.94 ± 11.7250 meters dash (s)8.35 ± 1.148.24 ± 1.2130 s single swing rope skipping53.58 ± 5.7456.24 ± 6.15Pull up6.84 ± 1.036.91 ± 1.09Squat 1RM105.5 ± 8.34104.8 ± 7.85As shown in Figure5, there is no significant difference between the experimental group and the control group in the test results of standing long jump, 50m sprint, 30s single swing rope skipping, pull-up, and squat 1RM. The smallest difference of all was in the pull-up test. There were 6.84 in the experimental group and 6.91 in the control group, a difference of 0.07 between the two groups.Figure 5
Results of pretest.


### 4.2.2. The Measured Results in the Experiment

Three weeks after the beginning of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table2, after three weeks of training, the experimental group performed slightly better than the control group in the standing long jump, the 50-meters dash, the single-swing rope jump for 30s, the pull-up, and the squat 1RM.Table 2
Physical fitness test results of two groups of athletes after three weeks of training.
ProjectExperience groupControl groupStanding long jump (cm)178.23 ± 11.05174.34 ± 11.7850 meters dash (s)8.21 ± 1.088.22 ± 1.1730s single swing rope skipping58.84 ± 6.0158.25 ± 6.17Pull up7.36 ± 1.137.21 ± 1.13Squat 1RM107.67 ± 8.21106.35 ± 7.64As shown in Figure6, the biggest difference between the experimental group and the control group is the test results of standing long jump, the experimental group is 178.23 ± 11.05 cm, the control group is 174.34 ± 11.78 cm, the difference between the two groups is 3.89 cm. The second is the test result of squat 1RM, the experimental group is 107.67 ± 8.21, the control group is 106.35 ± 7.64, and the difference between the two groups is 1.32.Figure 6
Intermediate test results.


### 4.2.3. Post-Test Results

Six weeks after the beginning of the experiment, that is, at the end of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table3, after six weeks of training, the test results of the experimental group and the control group were significantly different. The standing long jump scores of the two groups were 193.64 ± 9.38 cm and 179.39 ± 12.35 cm, respectively. The results of 50 meters dash were 7.14 ± 1.03 s and 7.59 ± 1.07 s, respectively. The results of rope skipping in 30 s were 67.34 ± 5.24 and 61.25 ± 6.13, respectively. The results of the chin up were 8.25 + 0.84 and 7.42 + 1.27, respectively. The results of 1 RM squat were 120.66 ± 5.31 and 111.39 ± 7.35, respectively.Table 3
Physical fitness test results of two groups of athletes after six weeks of training.
ProjectExperience groupControl groupStanding long jump (cm)193.64 ± 9.38179.39 ± 12.3550 meters dash (s)7.14 ± 1.037.59 ± 1.0730s single swing rope skipping67.34 ± 5.2461.25 ± 6.13Pull up8.25 ± 0.847.42 ± 1.27Squat 1RM120.66 ± 5.31111.39 ± 7.35As shown in Figure7, the biggest difference between the experimental group and the control group is the test results of standing long jump and the difference between the two groups is 14.25 cm. The second is the test result of squat 1RM; the difference between the two groups is 9.27. 30 s single jump rope skipping results in two groups of 6.15; the difference between the chin up performance is 0.83, and 50 m sprint results’ difference is 0.45 s.Figure 7
Post-test results.


### 4.2.4. Longitudinal Comparison of Two Groups of Test Results

The longitudinal comparison results of each item in the experimental group and the control group are shown in Table4.As shown in Table4, the scores of the experimental group and the control group were improved in the three tests. However, the experimental group’s performance of various indicators improved significantly faster than the control group. In the first test, the scores of the two groups were almost the same, and the scores of the control group were even better than the experimental group in some items. In the second test, the results of the experimental group have begun to be slightly better than the control group. In the third test, the results of the experimental group have been significantly better than that of the control group. The results of the three tests in the experimental group are shown in Figure 8.Table 4
Longitudinal comparison of test results between the two groups.
TimesStanding long jump50 meters dash30 s single swing rope skippingPull upSquat 1RME1172.678.3553.586.84105.5E2178.238.2158.847.36107.67E3193.647.1467.348.25120.66C1169.948.2456.246.91104.8C2174.348.2258.257.21106.35C3179.397.5961.257.42111.39Figure 8
Changes of test results of the experimental group for three times.As shown in Figure8, after six weeks of training of the physical assistance training intelligent system, the experimental group’s standing long jump improved 20.97 cm, 50 m sprint accelerated 1.21 s, 30 s single shake rope jump increased 13.76, pull-up increased 1.41, and squat 1RM increased 15.16.The results of the three tests in the control group are shown in Figure9.Figure 9
Results of three tests in the control group.As shown in Figure9, after six weeks of training with the intelligent system of physical assistant training, the standing long jump of the control group has been improved by 9.45 cm, the 50 meters dash has been accelerated by 0.65 s, the 30 second single swing rope jumping has increased by 5.01, the pull-up has increased by 0.51, and the squat 1RM has increased by 6.59.


## 4.2.1. Pretest Results

Before the beginning of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table1, by comparing the results of the physical fitness test conducted between the two groups before the experiment, it can be seen that there was no significant difference in the physical fitness indexes between the experimental group and the control group before the experiment began. The results of standing long jump and squat 1RM of the experimental group were slightly better than those of the control group. The results of 50 m sprint, 30 s rope skipping, and pull-up of the control group were slightly better than those of the experimental group.Table 1
Physical fitness test results of two groups of athletes before the experiment.
ProjectExperience groupControl groupStanding long jump (cm)172.67 ± 10.15169.94 ± 11.7250 meters dash (s)8.35 ± 1.148.24 ± 1.2130 s single swing rope skipping53.58 ± 5.7456.24 ± 6.15Pull up6.84 ± 1.036.91 ± 1.09Squat 1RM105.5 ± 8.34104.8 ± 7.85As shown in Figure5, there is no significant difference between the experimental group and the control group in the test results of standing long jump, 50m sprint, 30s single swing rope skipping, pull-up, and squat 1RM. The smallest difference of all was in the pull-up test. There were 6.84 in the experimental group and 6.91 in the control group, a difference of 0.07 between the two groups.Figure 5
Results of pretest.


## 4.2.2. The Measured Results in the Experiment

Three weeks after the beginning of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table2, after three weeks of training, the experimental group performed slightly better than the control group in the standing long jump, the 50-meters dash, the single-swing rope jump for 30s, the pull-up, and the squat 1RM.Table 2
Physical fitness test results of two groups of athletes after three weeks of training.
ProjectExperience groupControl groupStanding long jump (cm)178.23 ± 11.05174.34 ± 11.7850 meters dash (s)8.21 ± 1.088.22 ± 1.1730s single swing rope skipping58.84 ± 6.0158.25 ± 6.17Pull up7.36 ± 1.137.21 ± 1.13Squat 1RM107.67 ± 8.21106.35 ± 7.64As shown in Figure6, the biggest difference between the experimental group and the control group is the test results of standing long jump, the experimental group is 178.23 ± 11.05 cm, the control group is 174.34 ± 11.78 cm, the difference between the two groups is 3.89 cm. The second is the test result of squat 1RM, the experimental group is 107.67 ± 8.21, the control group is 106.35 ± 7.64, and the difference between the two groups is 1.32.Figure 6
Intermediate test results.


## 4.2.3. Post-Test Results

Six weeks after the beginning of the experiment, that is, at the end of the experiment, the physical fitness of the experimental group and the control group were tested.As shown in Table3, after six weeks of training, the test results of the experimental group and the control group were significantly different. The standing long jump scores of the two groups were 193.64 ± 9.38 cm and 179.39 ± 12.35 cm, respectively. The results of 50 meters dash were 7.14 ± 1.03 s and 7.59 ± 1.07 s, respectively. The results of rope skipping in 30 s were 67.34 ± 5.24 and 61.25 ± 6.13, respectively. The results of the chin up were 8.25 + 0.84 and 7.42 + 1.27, respectively. The results of 1 RM squat were 120.66 ± 5.31 and 111.39 ± 7.35, respectively.Table 3
Physical fitness test results of two groups of athletes after six weeks of training.
ProjectExperience groupControl groupStanding long jump (cm)193.64 ± 9.38179.39 ± 12.3550 meters dash (s)7.14 ± 1.037.59 ± 1.0730s single swing rope skipping67.34 ± 5.2461.25 ± 6.13Pull up8.25 ± 0.847.42 ± 1.27Squat 1RM120.66 ± 5.31111.39 ± 7.35As shown in Figure7, the biggest difference between the experimental group and the control group is the test results of standing long jump and the difference between the two groups is 14.25 cm. The second is the test result of squat 1RM; the difference between the two groups is 9.27. 30 s single jump rope skipping results in two groups of 6.15; the difference between the chin up performance is 0.83, and 50 m sprint results’ difference is 0.45 s.Figure 7
Post-test results.


## 4.2.4. Longitudinal Comparison of Two Groups of Test Results

The longitudinal comparison results of each item in the experimental group and the control group are shown in Table4.As shown in Table4, the scores of the experimental group and the control group were improved in the three tests. However, the experimental group’s performance of various indicators improved significantly faster than the control group. In the first test, the scores of the two groups were almost the same, and the scores of the control group were even better than the experimental group in some items. In the second test, the results of the experimental group have begun to be slightly better than the control group. In the third test, the results of the experimental group have been significantly better than that of the control group. The results of the three tests in the experimental group are shown in Figure 8.Table 4
Longitudinal comparison of test results between the two groups.
TimesStanding long jump50 meters dash30 s single swing rope skippingPull upSquat 1RME1172.678.3553.586.84105.5E2178.238.2158.847.36107.67E3193.647.1467.348.25120.66C1169.948.2456.246.91104.8C2174.348.2258.257.21106.35C3179.397.5961.257.42111.39Figure 8
Changes of test results of the experimental group for three times.As shown in Figure8, after six weeks of training of the physical assistance training intelligent system, the experimental group’s standing long jump improved 20.97 cm, 50 m sprint accelerated 1.21 s, 30 s single shake rope jump increased 13.76, pull-up increased 1.41, and squat 1RM increased 15.16.The results of the three tests in the control group are shown in Figure9.Figure 9
Results of three tests in the control group.As shown in Figure9, after six weeks of training with the intelligent system of physical assistant training, the standing long jump of the control group has been improved by 9.45 cm, the 50 meters dash has been accelerated by 0.65 s, the 30 second single swing rope jumping has increased by 5.01, the pull-up has increased by 0.51, and the squat 1RM has increased by 6.59.


## 5. Conclusions

Physical training is a systematic project with certain structural characteristics and process. A complete physical training content structure system needs to integrate isolated elements and follow a reasonable training process. At present, there are some problems in physical training, such as unscientific teaching methods, unclear selection criteria, frequent sports injuries, and lack of digital sports resources. Therefore, with the development of artificial intelligence technology, physical training also began to use scientific and technological means to assist.First of all, it is necessary to extract the human body target and its features effectively, which is also the basic step of the auxiliary training system. Canny edge detection algorithm is used to process the data detected by the target detection module, including bone filtering and smoothing, contour extraction, and edge thinning. Then, the processed data is analyzed, and the module obtains the human posture information and establishes the posture auxiliary index.Through the comparison of the three stages of physical fitness test data, the artificial intelligence-based adolescent physical training system can help adolescent athletes improve their physical quality and enhance their mastery of sports skills. However, due to the limited time and knowledge, this paper did not investigate the feedback of the system in the end. In the future research work, we should communicate with athletes and coaches, get feedback information, and optimize and improve the system.



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*Source: 5526509-2021-03-13.xml*