# Simulation Analysis of Physical Fitness Training via Deep Learning Algorithm

Authors: Qingdong Ling
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Physical fitness is defined as a person’s capacity to perform at a high level in terms of their strength, speed, endurance, coordination, flexibility, agility, and other athletic attributes. The morphological and functional aspects of the human body are intimately linked when it comes to determining one’s level of physical fitness. It is possible to categorize physical fitness into two types: healthy physical fitness and competitive physical fitness, depending on the performance and role it plays in various groups of individuals. Competitive physical fitness is built on the foundation of healthy physical fitness, which is the ability of the organ systems to work properly for a particular set of people. Healthful physical fitness is a prerequisite for the future growth of competitive competition. The simulation study of physical fitness training is an important topic, regardless of whether the goal is to achieve healthy physical fitness or competitive physical fitness. This work combines it with deep learning algorithms to propose a strategy MPRN-ATT-LSTM for physical training simulation analysis. First, this work proposes the idea of a hybrid model, which uses a residual network structure (MPRN) with a pooling layer to learn features from time series to reduce the dimension. Then, the extracted feature vector is sent to the LSTM model for further feature extraction. Considering that the LSTM model has high requirements on sequence of input sequence, when input sequence is changed or unreasonable, it may lead to inaccurate feature extraction and affect the classification results. This work solves this problem by adding a self-attention mechanism, which can better focus on information important for classification and give higher weights. Finally, a large number of experiments are carried out in this work to verify the superiority of this method for simulation analysis of physical training.


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## Body

## 1. Introduction

With rapid development for modern society, competition between elite athletes and strong teams has become increasingly fierce. This puts higher and higher demands on athletes in terms of psychology, technique, tactics, and physical fitness, especially for physical fitness. Physical fitness is not omnipotent, but without physical fitness, it is absolutely impossible. Without physical fitness skills and tactics, it becomes a castle in the air and water without a source. Without physical fitness and mental ability, there is no way to depend. Coaches and athletes throughout the world place a high emphasis on physical fitness training as an integral aspect of athletic preparation. Physical fitness training for athletes is becoming increasingly scientific as current information technology and network big data expand at a breakneck pace, amplifying their influence and penetration throughout society. There have been numerous theoretical and empirical studies on the current state of physical fitness training research, difficulties that exist, and potential directions for growth by domestic scholars in recent years. Studies on physical training theory and practice in the United States and overseas tend to focus on the microcosmic level of understanding and quantitative analysis, instead of the macrocosmic level [1–5].Physical fitness is a complex comprehensive ability expressed through effective training on the basis of congenital heredity. Physical fitness includes physical fitness related to health and physical fitness related to athletic ability, that is, physical fitness, body shape, and physical function. Physical fitness is a combination of bodily structure and function, as well as physical fitness, which is the basic aspect of physical fitness and the external manifestation of physical fitness. Training in physical education can be categorized as either general or specific. In the context of physical training, basic refers to physical fitness and physical function in the context of basic physical training. Sport-specific physical training refers to a style of training that is tailored to the specific needs of the sport as well as the specific needs of each athlete. The goal of physical training is to help athletes improve their physical fitness as well as their overall health and well-being, while also enhancing their ability to compete at their best on the field of play. It is the foundation of talent and tactic in competitive sports. It plays an important role in mastering special techniques and tactics, undertaking heavy-duty training and intense competitions, promoting the improvement of athletes’ physical fitness, preventing sports injuries, and prolonging sports life. Physical training is the training of the human body’s ability to work continuously. The most important thing in physical training is the overload adaptation training of various organs and functions of the human body. Athletes of different sports have specific and personalized requirements for the functions of different systems. In the process of physical training, a variety of scientific, reasonable, and efficient training methods and means should be used to achieve the purpose of transforming the physical form of athletes and improving their functional level, sports quality, and sports performance. Physical training plays an important role in improving athletes’ competitive ability. It carries out the purposeful transformation of the athlete’s body structure and function, to achieve training process of improving the athlete’s physical fitness as well as athletic ability, improving the body shape, and improving the physical function and sports performance [6–10].Physical training can be split into general physical training and specific physical training based on practice methods. General physical training refers to the use of nonspecialized physical exercises to transform the physical shape of athletes, improve health, improve physical function, and comprehensively develop sports quality. Special physical training refers to the process of developing and improving athletes’ special sports qualities and the body shape and physical functions required by special projects according to the actual needs of special projects, so as to improve sports performance. Physical training can be separated into broad physical training and restricted physical training according to the theoretical category of physical training. Physical training in a broad sense includes purposeful and planned training activities to improve physical fitness related to body health, or physical fitness related to competitive ability, transform body shape, and improve physical function. Physical fitness training refers to the purposeful and planned improvement of physical fitness related to competitive ability, various indicators of physical fitness improvement, and training activities of physical shape and physical function required by competitive sports [11–15]. Various studies have been conducted involving deep learning technique in analyzing sports fitness. As an example, the study in [16] analyzed the importance of physical fitness in case of adolescent individuals and based on the results proposed to improve the physical fitness training for service system. The study in [17, 18] used image segmentation on deep learning models to design experiments emphasizing on body building and fitness. The recurrent neural network and gated recurrent neural network were used to compare, analyze, and determine the stability of data processing when subjected to different activation functions. The results reflected the combination of aerobic and anaerobic exercises to be most beneficial.


## 2. Related Work

Literature [19] pointed out that physical fitness can not only promote physical health but also enable the comprehensive development of sports quality, while ensuring that the body adapts to the training needs of heavy load. This is conducive to mastering complex and advanced technology, thereby achieving excellent results and prolonging exercise life. Reference [20] pointed out that physical fitness plays a very important role in modern sports. In modern sports, physical fitness training is the basis of athlete’s technical and tactical training, which can effectively improve athlete’s performance. In international competitions, there is no fundamental difference between domestic technical and tactical levels compared with foreign ones. The difference is mainly reflected in two aspects: physical fitness and confrontation. Literature [21] pointed out that physical training of athletes can help prevent injury and prolong sports life. In modern competitive competitions, the basis for athletes to achieve ideal performance is a highly developed athletic quality, the improvement of body shape, and the high development of physical function. The higher level for physical training of athlete, the greater change to their body shape, the better the physical function, and the longer the athlete’s lifespan. At the same time, a good level of strength can ensure that athletes gain an advantage in confrontation. This ensures the duration of the athlete’s competitive level, while the sports life of the athlete is effectively extended. Reference [22] proposed that physical fitness is the foundation and technology is the key. In competitions, technology and physical fitness are inseparable, and in competitive competitions, technical tactics and physical fitness are closely integrated. The development and improvement of physical fitness must be consistent with the technical and tactical aspects. Only when the technical and tactical techniques are synchronized to a certain extent can the physical fitness of the athletes be reflected in the competition and achieve the desired effect. Reference [23, 24] pointed out that in modern competitions, physical fitness plays an increasingly important role and is a key influencing factor in competitions. During the development of modern sports, relevant experts in training and scientific research have conducted detailed research on technology and found that it is difficult for athletes to rely solely on technology to win in competitions. The technical characteristics of a sports event will not change. Coaches and athletes can only rely on scientific means to improve the physical fitness level of athletes, thereby improving sports performance. Literature [25] proposes that extensive attention to current physical fitness is an inevitable reflection of objective laws in the development of things. At this stage of sports development, the development of technology has basically taken shape, and the role of physical fitness is particularly important. At present, the technical and tactical levels of each team in the world are not much different, but the physical fitness is far apart. In key games, you can rely on physical fitness to win.Reference [26] pointed out that endurance refers to the physical energy stored on the basis of innate and acquired attributes, including three aspects of physical shape, physical function, and athletic quality. Moreover, the athlete’s physical fitness program affects all three aspects. Athlete’s physical fitness refers to the power required for high-level competitive sports and its related overall quality. The content of physical fitness training includes physical training, sports training, energy metabolism, and rehabilitation training. Literature [27] proposed that the multicombination training method has a promoting effect on improving the physical fitness of basketball players. Moreover, the content of the training method is flexible, the athletes are not easily fatigued, the training enthusiasm is relatively high, and there is a certain interest, which can prevent the fatigue of nerves and muscles during exercise and can reduce the injury of the athletes. Literature [28] proposed that the dynamic stability of the core of athletes is different from the traditional core strength training. However, in the actual exercise process, the body posture is constantly changing, and it is difficult to achieve efficient training in a stable state, and training in a stable state can only train the large muscle groups in the superficial core area. Literature [29] proposed that the suspension method of physical training can improve the balance ability and core stability of net athletes, and the suspension training should be combined with flexibility, special quality, and other aspects of training to achieve better results. Literature [30] proposed that the specific endurance of athletes is affected by the level of aerobic endurance, and aerobic endurance, strength endurance, speed, and explosive power should be the focus of physical training for athletes. To improve the level of sports, physical training should run through the whole process of athletes’ growth, and special characteristics and individualization should be highlighted in physical training. Literature [31] proposed that the improved physical training method is superior to the traditional physical training method, mainly in the improvement of aerobic endurance, speed endurance, and strength. Literature [32] proposed that balance and stability play an important role in physical fitness, so balance and stability training should be strengthened during physical training. It can use Swiss ball, balance ball, balance board, etc., to train balance ability under unbalanced conditions and develop the stability of athletes through strength training. Reference [18] explored the study, extraction, lack of training, and accuracy of complex algorithms for classifying sports medical data. An optimized CNN-based deep learning model was implemented to accurately detect and assess the risks associated with sports medicine diseases. To fulfil this objective, a self-adjustment resizing algorithm was also used to evaluate sports medicine in multidimensional aspect.


## 3. Method

This work combines physical training analysis with deep learning algorithms to propose a strategy MPRN-ATT-LSTM for physical training simulation analysis. First, this work proposes the idea of a hybrid model, which uses a residual network structure with a pooling layer to extract features from time series data and reduce the dimension. Then, the extracted feature vector is sent to the LSTM model for further feature extraction. Considering that the LSTM model has high requirements on the sequence of the input sequence, when the sequence of the input sequence is changed or unreasonable, it may lead to inaccurate feature extraction and affect the classification results. This work solves this problem by adding a self-attention mechanism, which can better focus on information important for classification and give higher weights.


### 3.1. CNN Algorithm

After the concept of deep research in representation learning was further proposed, the representation and learning technology capabilities of convolutional neural networks have received extensive attention and have developed rapidly with the improvement of computers’ ability to process complex numerical values and sequences. Each layer of data input to CNN must be a set of data that can be used to process multiple dimensions at the same time.The convolutional hidden layer in the convolutional model neural network is also widely known as the convolutional inner layer and is the most important part of all the convolutional model neural networks. The acquisition and processing functions of the convolutional hidden layer are mainly used to analyze and extract the accuracy characteristics of the accuracy data and information model output by the layer. Its interior is composed of multiple convolutional layer dual-kernel precision units, and each precision element of the convolutional layer single-kernel must have the same or corresponding precision weight and measure the coefficient and precision deviation. The basic parameters of the convolutional inner layer can mainly include three, which directly point out the area size of the convolutional layer image size, the step size, and the step short fill. The larger the convolutional layer kernel, the more complex the network input information features that can be directly extracted. The step size defined by the step size filling setting method is the time distance of the position where the convolutional layer kernel automatically scans a network feature pixel map every two times. The step filling method is a scanning method that increases the size of the image in the user network and offsets the effect of the shrinking speed on the image size in the user network during network computation. In the convolutional layer structure, there is an activation function to assist the complex features that are difficult to express to be better understood by the machine.It is a common practice to design the excitation function after the convolution kernel, with the result that the excitation function kernel is placed before the convolution kernel during implementation. You may think of it as transforming a high-quality image into an image that is lower in resolution. To speed up training and avoid overfitting, the pooling layer can lower the number of nodes in the fully connected layer and, as a result, the network’s parameters. Once the convolutional and pooling layers have processed a large quantity of data, it is possible to tell whether the information was abstracted and transformed into a feature with high information content. To put it another way, convolutional neural networks have a hidden layer structure like an ordinary feedforward neural network’s hidden layer structure. Convolutional neural networks have a hidden layer structure with this layer as the final one, and it merely connects the several fully linked layers. The input picture is enlarged as a vector rather than being in the spatial topology of the fully linked layer. The fully connected layer itself does not have the ability of feature combination, and its function is to obtain the input nonlinearly through the proposed feature combination of the convolution layer and the pooling layer.


### 3.2. LSTM Algorithm

The data collected in this work belongs to time series data, although RNN can effectively extract the features of time series and use the information of past time steps to predict the information of the next time step. But the main problem with gradient descent to optimize RNNs when using backpropagation through time is that gradients can quickly vanish as they propagate along the sequence. This leads to the problem of vanishing gradients and exploding gradients, failing to learn long-term regularities. LSTM can solve the long-term dependent time series data gradient disappearance problem, so that the recurrent neural network has better memory performance and can perform better in longer time changes. LSTM is appropriate in classification, processing, and prediction of time series wherein there is a time lag of unknown duration. It also has a relative insensitivity on gap length which acts as an added advantage. The structure of LSTM has similarities with hidden Markov model. The need of fine adjustments is also eliminated in case of LSTM as it provides users to work with a large range of parameters, namely, the learning rates and input and output biases.The LSTM long-distance memory problem depends on three main internal stages: forgetting stage, selection memory stage, and output stage. These three stages are used to increase or decrease the information in the cell state. LSTM cell unit is demonstrated in Figure1.Figure 1
LSTM cell unit structure.The forgetting stage mainly operates on the information passed from the hidden layer of the cell unit at the previous moment, and the information input in the current stage adds a bias and finally maps the value to the interval through the sigmoid function. Then, multiplication is performed with the state of the previous cell unit, and the incoming input can be selectively forgotten through the operation of the forget gate to decide what information to discard from the cell state.(1)ff=σWfht−1,xt+bf.If the result calculated by the formula is 0, it means that the LSTM should delete the corresponding information. If the result is 1, it means that the LSTM should keep this information.The selection memory stage mainly selects the input information, determines what kind of information is to be memorized, and stores the new information in the cell state. Then, update the cell unit state, and finally update the network state.(2)fu=σWuht−1,xt+bu,(3)c~t=tanhWuht−1,xt+bc,(4)ct=fuct−1+it−1c~t.The new cell state incorporates cell state information from the past time, hidden information inside the old cell, and new input data.The output stage determines the output of the current state, which will be based on the cell state but is also a filtered version.(5)fo=σWoht−1,xt+bo,(6)hi=fo∗tanhct.


### 3.3. MPRN Pipeline

Residual network has a deep level, and deep neural network shows high accuracy in image speech recognition and other fields; the basic structure of the residual network is demonstrated in Figure2.Figure 2
The basic structure of the residual network.The residual block has a skip structure, and the input vector is directly transmitted to the output layer through the identity mapping connection as the initial input of the next hidden layer. Instead of learning the output of the entire model, the residual network learns the local target, which is the residual value. This kind of jumping structure is no longer that the output of the upper layer can only be used as the input of the next layer but can directly cross several hidden layers in the middle and jump directly to the back as the input. Finally, the training result of the residual is close to 0, and the classification accuracy will not decrease regardless of whether the network level is deepened or not.Residual networks simplify the learning process and enhance gradient propagation. Before the residual network was widely used, the problem of gradient disappearance and gradient explosion in deep networks was solved by adding BN and proposing the ReLU activation function, but it still could not solve the fundamental problem. Compared with learning the original signal, the residual network learns the difference of the signal, and the residual network can still effectively backpropagate because the derivative contains the identity term. Second, the residual network addresses the symmetry of the network. In the network structure, there are weight coefficients between layers, and the weight coefficients between layers constitute a weight matrix, and the weight matrix is high dimensional. However, the part with information only accounts for a small part, so the expressive ability of the network is not so powerful. This situation arises mainly from the symmetry of the network, and the residual network breaks the symmetry of the network. There is a jump structure in the residual network, which can restore the expressive ability of the network through the skip layer connection, breaking the symmetry and linear dependence. Finally, the residual network enhances the generalization ability of the network. Residual network has a deep hierarchical structure, which can be regarded as a combination of multiple shallow neural networks. When a deep network is trained and the test set is used for testing, a network layer is randomly removed, and the performance of the residual network will not be weakened.Different from the traditional residual network, the residual network used in this paper is an improvement on the typical residual network. It combines the advantages of the pooling layer in the convolutional neural network and uses a three-layer residual network for feature extraction. There are three convolutional layers in each residual block, followed by a pooling layer, which reduces the size of the model feature through the pooling layer. This can not only speed up the training but also enhance the robustness of the extracted features. The improved model MPRN structure is demonstrated in Figure3.Figure 3
MPRN pipeline.It can be seen from the structure diagram of the improved residual network that each residual network contains three layers of convolution layers and one layer of pooling layers and finally outputs feature vectors through the fully connected layer. There is a skip layer connection between each hidden layer, which can directly transfer the input vector of the previous layer to the next hidden layer to achieve the purpose of learning local features. In order to compare the effect of adding a pooling layer and not adding a pooling layer to the residual network, the model structure is used to conduct experiments on the physical training time series, and the resulting data is used for analysis.


### 3.4. MPRN-ATT-LSTM Network

Since the attention mechanism was proposed, it has been widely used in various fields. Humans have visual attention, observe a large amount of information visually, and then use brain intuition to screen out valuable information from a large amount of information and give it a higher degree of attention. The human attention mechanism is a unique human brain signal processing mechanism that can focus attention on a certain area. Then, pay more attention to the target area, get more useful information about the target you need to focus on, and ignore unimportant information. The attention mechanism in deep learning is similar to that of human beings. It acquires a lot of information and selects the information that is more useful to the current target.The attention mechanism mode is an improvement over the traditional encoder-decoder architecture, where the traditional encoder-decoder implementation process is that the encoder must compress all the information into a fixed-length vector. This is then passed to the decoder, which compresses the input sequence with a lot of detail into a fixed-length vector, potentially causing loss of information. Second, the alignment between input and output sequences cannot be modeled. The attention mechanism uses the decoder to access the entire encoding of the input sequence and then assigns different attention weights to the input sequence. Positions with higher weights are given priority to get the next output. The special point of the self-attention mechanism in the QKV model is that the three are the same. In this sense, the original input sequence is the same. The specific internal schematic diagram of self-attention is demonstrated in Figure4.Figure 4
Dot-multiplied attention.The self-attention mechanism focuses on the connections between local time series and can capture the dependencies between time series. And it is easier to capture feature information for long-distance time series, which is very widely used in long-term series.LSTM model can perform feature extraction on long time series, but when the length of the time series is too long, the time cost is very high, and when the sequence of the input training data set is not suitable, it will also affect the feature extraction. Therefore, the proposal of the mixed model is mainly devoted to solving the problem of too long time series and achieving the effect of reducing the time cost. When the data set span is large, the time span of the training data of the long short-term memory network is also large, and the network is very deep; the calculation amount will be large and time-consuming. In order to reduce the time overhead, feature extraction can be performed on the data set first, so that the data can be processed for dimensionality reduction, and the processed data is sent to the self-attention model for feature extraction, which can reduce the consumption of training time.In this hybrid model, the MPRN model used includes an input layer, three hidden layers, and an output layer. Each hidden layer contains three convolutional layers and one pooling layer. The filter sizes in the three convolutional layers in each layer are 512, 256, and 128, and the pooling kernel sizes of the pooling layers are 6, 4, and 2, respectively. The residual network used in this paper is improved from the typical residual network, and a pooling layer is added between each residual layer to compress data and reduce fitting. Feature extraction is performed on the original data set through the multilayer ResNet model; the dimension of the data set is reduced. The ResNet model can be used to extract features from data sets which are excessively big to fit into the memory. The model is used to extract feature, and then, incremental learning may be used to train the classifier on top of the extracted features. It is then sent to the self-attention LSTM feature extractor, the training data is obtained, and the classification result is obtained through softmax layer. MPRN-ATT-LSTM is demonstrated in Figure5.Figure 5
MPRN-ATT-LSTM pipeline.


## 3.1. CNN Algorithm

After the concept of deep research in representation learning was further proposed, the representation and learning technology capabilities of convolutional neural networks have received extensive attention and have developed rapidly with the improvement of computers’ ability to process complex numerical values and sequences. Each layer of data input to CNN must be a set of data that can be used to process multiple dimensions at the same time.The convolutional hidden layer in the convolutional model neural network is also widely known as the convolutional inner layer and is the most important part of all the convolutional model neural networks. The acquisition and processing functions of the convolutional hidden layer are mainly used to analyze and extract the accuracy characteristics of the accuracy data and information model output by the layer. Its interior is composed of multiple convolutional layer dual-kernel precision units, and each precision element of the convolutional layer single-kernel must have the same or corresponding precision weight and measure the coefficient and precision deviation. The basic parameters of the convolutional inner layer can mainly include three, which directly point out the area size of the convolutional layer image size, the step size, and the step short fill. The larger the convolutional layer kernel, the more complex the network input information features that can be directly extracted. The step size defined by the step size filling setting method is the time distance of the position where the convolutional layer kernel automatically scans a network feature pixel map every two times. The step filling method is a scanning method that increases the size of the image in the user network and offsets the effect of the shrinking speed on the image size in the user network during network computation. In the convolutional layer structure, there is an activation function to assist the complex features that are difficult to express to be better understood by the machine.It is a common practice to design the excitation function after the convolution kernel, with the result that the excitation function kernel is placed before the convolution kernel during implementation. You may think of it as transforming a high-quality image into an image that is lower in resolution. To speed up training and avoid overfitting, the pooling layer can lower the number of nodes in the fully connected layer and, as a result, the network’s parameters. Once the convolutional and pooling layers have processed a large quantity of data, it is possible to tell whether the information was abstracted and transformed into a feature with high information content. To put it another way, convolutional neural networks have a hidden layer structure like an ordinary feedforward neural network’s hidden layer structure. Convolutional neural networks have a hidden layer structure with this layer as the final one, and it merely connects the several fully linked layers. The input picture is enlarged as a vector rather than being in the spatial topology of the fully linked layer. The fully connected layer itself does not have the ability of feature combination, and its function is to obtain the input nonlinearly through the proposed feature combination of the convolution layer and the pooling layer.


## 3.2. LSTM Algorithm

The data collected in this work belongs to time series data, although RNN can effectively extract the features of time series and use the information of past time steps to predict the information of the next time step. But the main problem with gradient descent to optimize RNNs when using backpropagation through time is that gradients can quickly vanish as they propagate along the sequence. This leads to the problem of vanishing gradients and exploding gradients, failing to learn long-term regularities. LSTM can solve the long-term dependent time series data gradient disappearance problem, so that the recurrent neural network has better memory performance and can perform better in longer time changes. LSTM is appropriate in classification, processing, and prediction of time series wherein there is a time lag of unknown duration. It also has a relative insensitivity on gap length which acts as an added advantage. The structure of LSTM has similarities with hidden Markov model. The need of fine adjustments is also eliminated in case of LSTM as it provides users to work with a large range of parameters, namely, the learning rates and input and output biases.The LSTM long-distance memory problem depends on three main internal stages: forgetting stage, selection memory stage, and output stage. These three stages are used to increase or decrease the information in the cell state. LSTM cell unit is demonstrated in Figure1.Figure 1
LSTM cell unit structure.The forgetting stage mainly operates on the information passed from the hidden layer of the cell unit at the previous moment, and the information input in the current stage adds a bias and finally maps the value to the interval through the sigmoid function. Then, multiplication is performed with the state of the previous cell unit, and the incoming input can be selectively forgotten through the operation of the forget gate to decide what information to discard from the cell state.(1)ff=σWfht−1,xt+bf.If the result calculated by the formula is 0, it means that the LSTM should delete the corresponding information. If the result is 1, it means that the LSTM should keep this information.The selection memory stage mainly selects the input information, determines what kind of information is to be memorized, and stores the new information in the cell state. Then, update the cell unit state, and finally update the network state.(2)fu=σWuht−1,xt+bu,(3)c~t=tanhWuht−1,xt+bc,(4)ct=fuct−1+it−1c~t.The new cell state incorporates cell state information from the past time, hidden information inside the old cell, and new input data.The output stage determines the output of the current state, which will be based on the cell state but is also a filtered version.(5)fo=σWoht−1,xt+bo,(6)hi=fo∗tanhct.


## 3.3. MPRN Pipeline

Residual network has a deep level, and deep neural network shows high accuracy in image speech recognition and other fields; the basic structure of the residual network is demonstrated in Figure2.Figure 2
The basic structure of the residual network.The residual block has a skip structure, and the input vector is directly transmitted to the output layer through the identity mapping connection as the initial input of the next hidden layer. Instead of learning the output of the entire model, the residual network learns the local target, which is the residual value. This kind of jumping structure is no longer that the output of the upper layer can only be used as the input of the next layer but can directly cross several hidden layers in the middle and jump directly to the back as the input. Finally, the training result of the residual is close to 0, and the classification accuracy will not decrease regardless of whether the network level is deepened or not.Residual networks simplify the learning process and enhance gradient propagation. Before the residual network was widely used, the problem of gradient disappearance and gradient explosion in deep networks was solved by adding BN and proposing the ReLU activation function, but it still could not solve the fundamental problem. Compared with learning the original signal, the residual network learns the difference of the signal, and the residual network can still effectively backpropagate because the derivative contains the identity term. Second, the residual network addresses the symmetry of the network. In the network structure, there are weight coefficients between layers, and the weight coefficients between layers constitute a weight matrix, and the weight matrix is high dimensional. However, the part with information only accounts for a small part, so the expressive ability of the network is not so powerful. This situation arises mainly from the symmetry of the network, and the residual network breaks the symmetry of the network. There is a jump structure in the residual network, which can restore the expressive ability of the network through the skip layer connection, breaking the symmetry and linear dependence. Finally, the residual network enhances the generalization ability of the network. Residual network has a deep hierarchical structure, which can be regarded as a combination of multiple shallow neural networks. When a deep network is trained and the test set is used for testing, a network layer is randomly removed, and the performance of the residual network will not be weakened.Different from the traditional residual network, the residual network used in this paper is an improvement on the typical residual network. It combines the advantages of the pooling layer in the convolutional neural network and uses a three-layer residual network for feature extraction. There are three convolutional layers in each residual block, followed by a pooling layer, which reduces the size of the model feature through the pooling layer. This can not only speed up the training but also enhance the robustness of the extracted features. The improved model MPRN structure is demonstrated in Figure3.Figure 3
MPRN pipeline.It can be seen from the structure diagram of the improved residual network that each residual network contains three layers of convolution layers and one layer of pooling layers and finally outputs feature vectors through the fully connected layer. There is a skip layer connection between each hidden layer, which can directly transfer the input vector of the previous layer to the next hidden layer to achieve the purpose of learning local features. In order to compare the effect of adding a pooling layer and not adding a pooling layer to the residual network, the model structure is used to conduct experiments on the physical training time series, and the resulting data is used for analysis.


## 3.4. MPRN-ATT-LSTM Network

Since the attention mechanism was proposed, it has been widely used in various fields. Humans have visual attention, observe a large amount of information visually, and then use brain intuition to screen out valuable information from a large amount of information and give it a higher degree of attention. The human attention mechanism is a unique human brain signal processing mechanism that can focus attention on a certain area. Then, pay more attention to the target area, get more useful information about the target you need to focus on, and ignore unimportant information. The attention mechanism in deep learning is similar to that of human beings. It acquires a lot of information and selects the information that is more useful to the current target.The attention mechanism mode is an improvement over the traditional encoder-decoder architecture, where the traditional encoder-decoder implementation process is that the encoder must compress all the information into a fixed-length vector. This is then passed to the decoder, which compresses the input sequence with a lot of detail into a fixed-length vector, potentially causing loss of information. Second, the alignment between input and output sequences cannot be modeled. The attention mechanism uses the decoder to access the entire encoding of the input sequence and then assigns different attention weights to the input sequence. Positions with higher weights are given priority to get the next output. The special point of the self-attention mechanism in the QKV model is that the three are the same. In this sense, the original input sequence is the same. The specific internal schematic diagram of self-attention is demonstrated in Figure4.Figure 4
Dot-multiplied attention.The self-attention mechanism focuses on the connections between local time series and can capture the dependencies between time series. And it is easier to capture feature information for long-distance time series, which is very widely used in long-term series.LSTM model can perform feature extraction on long time series, but when the length of the time series is too long, the time cost is very high, and when the sequence of the input training data set is not suitable, it will also affect the feature extraction. Therefore, the proposal of the mixed model is mainly devoted to solving the problem of too long time series and achieving the effect of reducing the time cost. When the data set span is large, the time span of the training data of the long short-term memory network is also large, and the network is very deep; the calculation amount will be large and time-consuming. In order to reduce the time overhead, feature extraction can be performed on the data set first, so that the data can be processed for dimensionality reduction, and the processed data is sent to the self-attention model for feature extraction, which can reduce the consumption of training time.In this hybrid model, the MPRN model used includes an input layer, three hidden layers, and an output layer. Each hidden layer contains three convolutional layers and one pooling layer. The filter sizes in the three convolutional layers in each layer are 512, 256, and 128, and the pooling kernel sizes of the pooling layers are 6, 4, and 2, respectively. The residual network used in this paper is improved from the typical residual network, and a pooling layer is added between each residual layer to compress data and reduce fitting. Feature extraction is performed on the original data set through the multilayer ResNet model; the dimension of the data set is reduced. The ResNet model can be used to extract features from data sets which are excessively big to fit into the memory. The model is used to extract feature, and then, incremental learning may be used to train the classifier on top of the extracted features. It is then sent to the self-attention LSTM feature extractor, the training data is obtained, and the classification result is obtained through softmax layer. MPRN-ATT-LSTM is demonstrated in Figure5.Figure 5
MPRN-ATT-LSTM pipeline.


## 4. Experiment

### 4.1. Evaluation for MPRN-ATT-LSTM Training

This work first evaluates the training process of the MPRN-ATT-LSTM network. The main analysis indicators are the training loss and training accuracy, as demonstrated in Figures6 and 7.Figure 6
MPRN-ATT-LSTM training loss.Figure 7
MPRN-ATT-LSTM training accuracy.From the data comparison between the two figures, it can be seen that with the deepening of training, the network training loss gradually decreases, while the training accuracy gradually increases. When the training reaches a certain level, both of them will no longer change significantly, and the convergence will be obtained.


### 4.2. Evaluation for MPRN-ATT-LSTM Effectiveness

To verify the superiority of the MPRN-ATT-LSTM method, this work compares it with other methods. The model is compared with CNN, LSTM, and BiLSTM. The BiLSTM is also known as bidirectional LSTM, which is a sequence processing model that includes two LSTMs–one for feeding the input in forward direction and one in the backward direction. The compared indicators include precision and recall, as demonstrated in Table1.Table 1
Comparison of MPRN-ATT-LSTM with traditional methods.
MethodAccuracyRecallCNN88.385.5LSTM90.588.6BiLSTM92.190.5MPRN-ATT-LSTM94.391.9As shown in the comparison data in the table, the MPRN-ATT-LSTM method designed in this work can achieve the highest precision and recall. Compared with other methods in the table, it can achieve different degrees of improvement.


### 4.3. Evaluation for Combining MPRN and Self-Attention LSTM

The method proposed in this work for physical training simulation analysis combines MPRN and self-attention LSTM. To verify the feasibility of this combined measure, the performance of individual networks is compared separately, as demonstrated in Figure8.Figure 8
Evaluation results of the combination of MPRN and self-attention LSTM.As shown in the comparison data in the table, compared with the MPRN-ATT-LSTM method, neither a single MPRN nor self-attention LSTM can obtain the best precision and recall. This corroborates the superiority of combining the two in this work.


### 4.4. Evaluation for ResNet Improvement

This work improves the traditional ResNet to build MPRN. To verify the feasibility of this improvement, this work compares the performance when using ResNet and when using MPRN, respectively, as demonstrated in Figure9.Figure 9
Evaluation results reflecting the improvement of ResNet.Compared with ResNet network, after improving it, combining it with self-attention and LSTM can achieve the best precision and recall; improvements are 1.4% and 1.0%.


### 4.5. Evaluation for Training Batch

In MPRN-ATT-LSTM network training, the training batch is a variable parameter. In order to verify the impact of different batches on network performance, this work compares the precision and recall rates of different batch sizes, as demonstrated in Table2.Table 2
Evaluation of the training batch.
Batch size8163264128Accuracy92.193.294.393.691.7Recall90.591.291.991.490.2When the training batch changes, the accuracy and recall rate corresponding to the network are also constantly changing, and the overall trend is to increase first and then decrease. When the value of this parameter is 64, the best precision and recall can be obtained.


## 4.1. Evaluation for MPRN-ATT-LSTM Training

This work first evaluates the training process of the MPRN-ATT-LSTM network. The main analysis indicators are the training loss and training accuracy, as demonstrated in Figures6 and 7.Figure 6
MPRN-ATT-LSTM training loss.Figure 7
MPRN-ATT-LSTM training accuracy.From the data comparison between the two figures, it can be seen that with the deepening of training, the network training loss gradually decreases, while the training accuracy gradually increases. When the training reaches a certain level, both of them will no longer change significantly, and the convergence will be obtained.


## 4.2. Evaluation for MPRN-ATT-LSTM Effectiveness

To verify the superiority of the MPRN-ATT-LSTM method, this work compares it with other methods. The model is compared with CNN, LSTM, and BiLSTM. The BiLSTM is also known as bidirectional LSTM, which is a sequence processing model that includes two LSTMs–one for feeding the input in forward direction and one in the backward direction. The compared indicators include precision and recall, as demonstrated in Table1.Table 1
Comparison of MPRN-ATT-LSTM with traditional methods.
MethodAccuracyRecallCNN88.385.5LSTM90.588.6BiLSTM92.190.5MPRN-ATT-LSTM94.391.9As shown in the comparison data in the table, the MPRN-ATT-LSTM method designed in this work can achieve the highest precision and recall. Compared with other methods in the table, it can achieve different degrees of improvement.


## 4.3. Evaluation for Combining MPRN and Self-Attention LSTM

The method proposed in this work for physical training simulation analysis combines MPRN and self-attention LSTM. To verify the feasibility of this combined measure, the performance of individual networks is compared separately, as demonstrated in Figure8.Figure 8
Evaluation results of the combination of MPRN and self-attention LSTM.As shown in the comparison data in the table, compared with the MPRN-ATT-LSTM method, neither a single MPRN nor self-attention LSTM can obtain the best precision and recall. This corroborates the superiority of combining the two in this work.


## 4.4. Evaluation for ResNet Improvement

This work improves the traditional ResNet to build MPRN. To verify the feasibility of this improvement, this work compares the performance when using ResNet and when using MPRN, respectively, as demonstrated in Figure9.Figure 9
Evaluation results reflecting the improvement of ResNet.Compared with ResNet network, after improving it, combining it with self-attention and LSTM can achieve the best precision and recall; improvements are 1.4% and 1.0%.


## 4.5. Evaluation for Training Batch

In MPRN-ATT-LSTM network training, the training batch is a variable parameter. In order to verify the impact of different batches on network performance, this work compares the precision and recall rates of different batch sizes, as demonstrated in Table2.Table 2
Evaluation of the training batch.
Batch size8163264128Accuracy92.193.294.393.691.7Recall90.591.291.991.490.2When the training batch changes, the accuracy and recall rate corresponding to the network are also constantly changing, and the overall trend is to increase first and then decrease. When the value of this parameter is 64, the best precision and recall can be obtained.


## 5. Conclusion

Basic physical training is the starting point and cornerstone of each sports event, and its training focus is to develop the comprehensiveness of athletes’ athletic ability, so as to improve athletic quality, improve body shape, and master technical skills of nonspecialized sports. Physical fitness plays an increasingly important role in modern competitions, and coaches and athletes pay more and more attention to physical training. With the continuous progress of science and the development of sports training, physical fitness plays an increasingly important role in modern training and competition. This is determined by the internal and external factors of training sports. In modern sports, the role of physical fitness has become more and more important. This work combines it with deep learning algorithms to propose a strategy MPRN-ATT-LSTM for physical training simulation analysis. First, this work proposes the idea of a hybrid model, which uses a residual network structure with a pooling layer to extract features from time series data and reduce the dimension. Then, the extracted feature vector is sent to the LSTM model for further feature extraction. Considering that the LSTM model has high requirements on the sequence of the input sequence, when the sequence of the input sequence is changed or unreasonable, it may lead to inaccurate feature extraction and affect the classification results. This work solves this problem by adding a self-attention mechanism, which can better focus on information important for classification and give higher weights. Finally, a large number of experiments are carried out in this work to verify the superiority of this method for simulation analysis of physical training. Although the model yields promising results, it is evaluated based on two metrics alone, namely, accuracy and recall. The justification of the superiority of the model could be further strengthened in the future by the inclusion of other metric, namely, precision and sensitivity.



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