# Construction and Simulation of Injury Early Warning Model for Retired Athletes Based on Improved Self-organizing Neural Network

Authors: Deli Li
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

With the progress of sci-tech, the interdisciplinary and comprehensive development, and various advanced sci-tech gradually integrated into the field of sports, it has become possible to study how to reasonably prevent sports injuries, minimize the risk of sports injuries, and maintain the best physical condition of retired athletes. Due to the long-term high-load exercise of retired athletes during their sports career, athletes’ physical functions have been damaged to varying degrees, resulting in more injuries. According to the characteristics that many factors need to be considered in the prediction of retired athletes’ injuries, this paper puts forward an improved self-organizing neural network (SOM) method to predict retired athletes’ injuries. In this paper, an early warning analysis model of retired athletes’ susceptibility to injury based on SOM is proposed, which screens the state of retired athletes’ physical function variables in each stage, considers athletes’ physical function data whose standard deviation is higher than the limit specification of susceptibility to injury as susceptible injury data, quickly judges all vulnerable injury data, and completes the high-speed early warning analysis of retired athletes’ susceptibility to injury.


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## Body

## 1. Introduction

Nowadays, the professionalization process of competitive sports has been accelerating, various competitions have become more and more frequent, the competition is becoming and more and more intense and tense, the difficulty requirements of technical movements have become higher, the training time has become longer, and the sports load on athletes has also been increasing [1]. Injuries also pose a great threat to the health of athletes. Some serious injuries will not only make athletes unable to participate in competition and training, but also cause physical injury and even disability [2]. In actual sports training, there are many factors causing athletes’ sports injury, including internal factors such as athletes’ age, physical quality, and health status and external environmental factors such as weather and equipment. There are many injuries in the process of athletes’ long-term sports [3]. In order to improve the recovery quality of retired athletes’ physical function, we should carry out early warning analysis on athletes’ vulnerable injuries according to the physical function data of retired athletes, and then carry out targeted treatment on athletes to enhance the health care quality of athletes’ physical function [4]. In order to effectively prevent retired athletes from injuries and reduce their occurrence probability, it is necessary to clarify the relationship between the above factors and athletes’ injuries by collecting and mining relevant data [5]. Many factors should be screened and important indicators should be determined, so as to realize the effective control and early warning of retired athletes’ injuries.In the process of sports training, it acts on the human body as a stimulus, and the human body will produce corresponding response or adaptation [6]. When athletes as organisms are stimulated, the relative stability of their bodies will be broken. When the load exceeds the athlete’s maximum bearing capacity, the athlete’s body will deteriorate. Severe cases will also lead to excessive fatigue [7], cause sports function decline, sports injury, etc. Therefore, it is very necessary to carry out real-time early warning for athletes’ training adaptation. The more indicators used in the early warning process, the wider the fields involved, the more comprehensive and reliable the evaluation will be, but the greater the workload and the more the scientific researchers needed [8]. The uneven quality and professional level of scientific researchers and the limited level and depth of analysis conclusions of test indicators have become difficult problems in the early warning of athletes’ training adaptation [9]. The traditional athlete’s injury analysis model usually analyzes each target feature separately and does not comprehensively analyze the correlation between the physical function characteristics of different athletes, resulting in a large deviation in the depth mining of injuries, which greatly reduces the accuracy of athlete’s vulnerable injury analysis and has certain limitations [10]. If we use neural network to integrate all the test results and use artificial intelligence to warn athletes’ training and competition status, we can effectively solve the above problems.It is the premise for coaches to implement the training plan to carry out real-time early warning of athletes’ training adaptation status and timely understanding athletes’ training adaptation status [11]. With the progress of sci-tech, when the interdisciplinary and comprehensive development of various disciplines and various advanced sci-tech are gradually integrated into the field of sports, it is possible to study how to reasonably prevent sports injury, minimize the risk of sports injury, and maintain the best physical state of retired athletes. In the judgment of athletes’ injuries, there are many index factors and different index factors have different effects on athletes’ injuries [12]. To find the main related indicators, we need to scientifically screen the indicators in the transportation data. If we can dig out the risk indicators that can directly or indirectly cause sports injury from a large number of internal and external factors that can cause sports injury, as well as the potential relationship between each index and sports injury, and control and pay attention to it in future actual sports training, it will greatly reduce the occurrence of sports injury of athletes, so as to play a real preventive role in sports injury [13]. This paper presents an early warning analysis model of retired athletes’ vulnerable injuries based on self-organizing neural network (SOM). The SOM is used to screen the state of retired athletes’ physical function variables in each stage, and the athletes’ physical function data whose standard deviation is higher than the limit specification of vulnerable injuries are regarded as vulnerable injury data, quickly judging all the vulnerable injury data and completing the high-speed early warning analysis of retired athletes.The innovative contribution of this paper is to propose an improved self-organizing neural network (SOM) method to predict the injury of retired athletes. This paper puts forward the early warning analysis model of injury susceptibility of retired athletes based on SOM, selects the state of physical function variables of retired athletes in each stage, takes the athlete’s physical function data whose standard deviation is higher than the injury susceptibility limit specification as the susceptible injury data, quickly judges all the susceptible injury data, and completes the high-speed early warning analysis of injury susceptibility of the retired athletes.This paper is divided into five parts. The first and second parts describe the relevant research and development background. The third part includes materials and methods, expounds the self-organization characteristics of sports training adaptation and SOM, and the SOM model learning algorithm. The fourth part discusses the results and compares the SOM before and after the improvement. Compared with the improved algorithm, the standard SOM algorithm has more iterations, lower convergence accuracy, and higher improved algorithm. Finally, the full text is summarized. This method can efficiently and accurately extract the key indicators of injury factors of retired athletes and evaluate their early warning level, so as to effectively obtain the injury of athletes and reduce the probability of injury recurrence of retired athletes.


## 2. Related Work

At present, many scholars have studied sports training early warning from different angles. Wang et al. [14] gives early warning to sports training through special indicators such as blood lactic acid, blood urea, HRV, urinary protein composition, immunity, and psychology. In order to effectively prevent athletes’ injuries and reduce their probability, we must collect and mine relevant data to clarify the relationship between many factors and retired athletes’ injuries. This needs screening of many factors and determining the important indicators, so as to realize the effective control and early warning of retired athletes’ injuries. Ma et al. [15] established a dynamic etiological model of sports injury in order to vividly describe the relationship between internal risk factors, external risk factors, and stimulation events and sports injury in the process of sports training. Yan et al. [16] believes that the internal risk factors will not directly cause injury to athletes, but only have the tendency of injury. If combined with the role of external risk factors, it is likely to make athletes vulnerable to injury. If there is a stimulus or induced event at this time, it will cause injury to athletes. The research by Li et al. [17] shows that the injury of retired athletes is affected by many factors, and there are complex links between the influencing factors, which is difficult to be explained by the structural causal model, and this interdependence between data is the most important and useful characteristic of the research object.With the progress of sci-tech, when the interdisciplinary and comprehensive development of various disciplines and various advanced sci-tech are gradually integrated into the field of sports, it is possible to study how to improve sports performance, reasonably prevent sports injury, minimize the risk of sports injury, and maintain the best competitive state of athletes. Song et al. [18] established five subsystems of sports training early warning and constructed the theoretical system of sports training early warning. Peng et al. [19] have systematically monitored the implementation of athletes’ training process for a long time and diagnosed athletes’ physical function, technical characteristics, and psychological state. Ma et al. [20] takes retired athletes as the mining object and combines the association rule model to predict and analyze the injuries and injuries of retired athletes. Hassan et al. [21] uses the data in the injury management system for mining and gives some mining results and rules. Tian et al. [22], combined with relevant algorithms, proposed a potential identification method of athlete’s injury based on SOM, and the results show that the algorithm improves the accuracy of athlete’s injury prediction. Compared with the traditional statistical methods, the SOM method can better reflect the epidemic law from the transmission mechanism of the disease, so that people can understand some global characteristics in the epidemic process [23]. Sun et al. [24] shows that compared with the traditional statistical methods, the SOM method can better reflect the injury law of retired athletes from the injury mechanism. In the application of the SOM network, there are some shortcomings that the classification results are related to the sample input order and easy to fall into local optimization. Therefore, this study uses the attribute reduction algorithm to improve the classification accuracy and generalization performance of the SOM algorithm, so as to better reflect the injury occurrence law of retired athletes. The improved SOM makes the weight learning rate and neighborhood size of each neuron change with the affinity of neurons, so as to ensure that the network converges to the global optimization with great probability and overcome the deficiency that the classification effect of SOM is affected by the input order.


## 3. Materials and Methods

### 3.1. Self-Organizing Characteristics of Sports Training Adaptation and SOM

Sports training is a process in which the organism receives external stimuli and introduces negative entropy, and the tissues, organs, and systems in the organism are organized and ordered independently through cooperation and competition. The human body is an open, nonlinear, and highly complex giant system, which constantly exchanges material, energy, and information with the outside world. When the internal and external environment changes, its subsystems will fluctuate, and the microfluctuations near the critical point will be amplified into giant fluctuations by a nonlinear mechanism and suddenly change. Through the nonlinear interaction between the subsystems and the overall synergistic effect, the original relative balance state is broken and transformed into another orderly state in time, space, or function. SOM can find out the rules and relationships from the input data sample information, and the network automatically classifies the input patterns through its own training.


### 3.2. SOM Model Learning Algorithm

In the SOM, we imitate the thinking of human brain, pass through the transmission of neurons, and then pass through the weight function, and then get different output results [25–27]. Then, according to the error between the output results and the actual expected results, we push back the optimization parameters and then continue to learn and adjust to achieve the goal of optimal learning. SOM is a new double-layer network and each input node is connected by weight w, so as to realize nonlinear dimension reduction mapping of input signals [28–30]. Topological invariance is maintained in mapping, that is, similar inputs in topological sense are mapped to the nearest output node. The typical structure of SOM is shown in Figure 1.Figure 1
Typical structure of SOM.The SOM network is composed of input layer and competition layer, and the neurons of input layer and competition layer are completely connected. Model learning samples are composed of samples withn classification indexes. Assuming that these samples with the same category or some similar characteristics are relatively close in n-dimensional space, these samples form a class and form a cluster in the n-dimensional space. When the input samples belong to multiple classes, the n-dimensional space will show the characteristics of multiple cluster distribution. Each cluster represents a type, and the center of the cluster is the cluster center of the type. The distance between samples belonging to the same class and the cluster center of this class is small. This distance can be measured by the Euclidean distance as follows:(1)Dj=∑i=1Nxi−Wij2.In the formula,xi is the classification index, Wij is the cluster center of the j-th dynamic type, and Dj is the Euclidean distance.Through the analysis of the related algorithms of the SOM, and according to the specific environment of the application field, the algorithm is now modified to meet the requirements of the system. The algorithm steps are as follows:First give the thresholdβ. β is used to control the thickness of the classification. The larger the β, the thicker the classification and the fewer types. The smaller the β, the finer the classification and the greater the number of types. Therefore, the determination of the β value requires trial calculation. Let the initial number of neurons in the output layer be, i.e., j = 1, and a learning sample to give the connection weight Wij is chosen as the initial value. A new learning sample is entered and the Euclidean distance Dj is calculated between it and each dynamic type of cluster center Wij. The minimum Euclidean distance is calculated as(2)Dj∗=minDj.IfDj<β, it is considered that the current input sample belongs to the dynamic type represented by the output neuron, and the connection weight Wij is adjusted as follows:(3)ΔWij=xi−Wijhj.In the formula,ΔWij is the adjusted value of Wij and hj belongs to the current sample number of the j-th dynamic category. Then, go to step (3).IfDj>β, it means that although the output neuron wins the competition, the current input sample still cannot be regarded as belonging to the dynamic type represented by the output neuron, but should belong to a new type. Therefore, the input sample is used as the initial value of Wij+1. Then, turn to step (3). This cycle is repeated until all samples have been learned. Finally, the output neuron number of the network model is the type number of all samples, and the connection weight is the cluster center value of each dynamic type.


### 3.3. Attribute Reduction Algorithm Model

The attribute reduction algorithm has strong data classification ability. Without any prior knowledge and additional data information, it uses knowledge reduction to reveal the relevance and decision-making hidden among various data and can comprehensively analyze and identify the injuries and illnesses of retired athletes [31, 32]. When calculating the sports attribute reduction algorithm, it is necessary to build the mathematical model of attribute reduction algorithm and establish the early warning model of athletes’ injuries. The data model construction steps are shown in Figure 2.Figure 2
Data model construction.Set the data collection and attribute positioning that caused the athlete’s injury to C andcj, respectively, then cj∈C. Then, vj is expressed as the measured value of attribute cj, vc′ is expressed as the dimensionless value of attribute cj, vmaxj is defined as the maximum value of attribute cj, and vminj is defined as the minimum value of cj, and then the above definition is used for modeling.A mathematical model for quantifying quantitative attributes for the dimensionless valuevc′ of attribute cj can be constructed and expressed as(4)vj′=1,vj≤vminj,vmax−vjvmaxj−vminj,vminj<vj<vmaxj,0,vj≥vmaxj.Suppose the index evaluation set is represented by A,rj is represented by the membership degree vector, and the dataset is represented as(5)rj=rj1,rj2,rj3,rj4,rj5.In the formula,rj represents the membership degree vector corresponding to the index evaluation set A. Assumptions are as follows:(6)B=B1,B2,B3,B4,B5.Among them,Bi is used as the scale element corresponding to the i-th evaluation in the dataset B. Through the dataset B, the data membership vector representing the injury of the athlete can be effectively integrated into a scalar. The formula is expressed as(7)V=rj×B,where V is the quantitative value of the qualitative evaluation index of the athlete’s injury data under the given scale B.


### 3.4. Acquiring Injury Complexity Model Based on Improved SOM

The data basis of the SOM is the polynomial sequence of increasing complexity in the n-dimensional compact retired athlete’s body function set C can approximate any point in C with arbitrary precision, shaping the Kolmogorov–Gabor polynomial composed of variablex1,x2,…,xm as(8)fx1,x2,…,xm=∑i=1masxi+∑i=1m∑j=1maijxixj+….Among them, a indicates the qualified coefficient of the physical function of the retired athletes,i and j, respectively, describe different physical function variables, and m represents the change threshold of the physical function. Equation (8) shows that with the increasing number of independent variables and polynomial complexity, polynomial sequences can fit arbitrary data with high precision. The observed physical function sample data of retired athletes are divided into a training subset and a test subset. The intermediate candidate injury models are generated in the training subset through internal specifications, and the intermediate candidate injury models are collected in the test subset through external specifications. In the process of modeling, the SOM algorithm screens the input physical function variables of retired athletes at all levels by adopting relevant specifications, and then combines them to obtain the screening model of the next level, until the best injury complexity model is finally obtained.Divide the retired athlete’s physical function sample dataset W into training set A and test set B, thenW=A∪B. If the prediction model is modeled, the prediction subset C needs to be divided again to ensure that W=A∪B∪C.Shape the general functional relationship between the outputy of the injury model and the input x1,x2,…,xn. The Kolmogorov–Gabor polynomial is as follows:(9)y=fx1,x2=a0+a1x1+a2x2+a3x12+a4x22+a5x1x2.In addition, treating each of the monomials asm input models in the original structure of the modeling network,(10)v1=a0,v2=a1x1,v3=a2x2,…,v6=a5x1x2.Self-organizing process adaptively forms the first-level intermediate model,(11)zk=fkvi,vj,i=1,2,…,6.In addition, in the training set A, the parameter prediction method is used to predict the coefficient ofzk. In the test set B, the competition model zk is filtered through external specifications, and the middle candidate injury model wk=zk is collected and used as the input of the second layer of the network. Continuing the process (10) and (11), the second layer and the third layer can be formed successively until the minimum value of the external specification is obtained, and the best injury complexity model can be obtained. The mining accuracy of different methods is shown in Figure 3.Figure 3
Mining accuracy of different methods.With the continuous increase in the signal-to-noise ratio, the mining accuracy of traditional methods has a significant downward trend, while the mining efficiency of this method has a gentle downward trend. The mining efficiency of this method is much higher than that of traditional methods, which shows that this method has higher superiority and achieves satisfactory results.


### 3.5. Normalization Processing

Studies on the brain have shown that the brain is composed of a large number of neurons that work together. The neural network of brain is a very complex feedback system, which contains various feedback effects, including global feedback, local feedback, and chemical interaction. Clustering is an extremely important function in the process of brain processing information. The brain recognizes external signals through clustering process and produces self-organizing process. The data of the training adaptation state are given as input, including the brain information entropy value, ratio of the brain main sequence parameters, parameter competition changes of 8 Hz, 9 Hz, and 10 Hz, athletes’ sports skill level optimization state level, brain function state, central tension, central fatigue score, average score, best score in each test stage, etc. The data utilization function is normalized, so that the index data, which are not in the same order of magnitude are mapped between [−1, +1].


## 3.1. Self-Organizing Characteristics of Sports Training Adaptation and SOM

Sports training is a process in which the organism receives external stimuli and introduces negative entropy, and the tissues, organs, and systems in the organism are organized and ordered independently through cooperation and competition. The human body is an open, nonlinear, and highly complex giant system, which constantly exchanges material, energy, and information with the outside world. When the internal and external environment changes, its subsystems will fluctuate, and the microfluctuations near the critical point will be amplified into giant fluctuations by a nonlinear mechanism and suddenly change. Through the nonlinear interaction between the subsystems and the overall synergistic effect, the original relative balance state is broken and transformed into another orderly state in time, space, or function. SOM can find out the rules and relationships from the input data sample information, and the network automatically classifies the input patterns through its own training.


## 3.2. SOM Model Learning Algorithm

In the SOM, we imitate the thinking of human brain, pass through the transmission of neurons, and then pass through the weight function, and then get different output results [25–27]. Then, according to the error between the output results and the actual expected results, we push back the optimization parameters and then continue to learn and adjust to achieve the goal of optimal learning. SOM is a new double-layer network and each input node is connected by weight w, so as to realize nonlinear dimension reduction mapping of input signals [28–30]. Topological invariance is maintained in mapping, that is, similar inputs in topological sense are mapped to the nearest output node. The typical structure of SOM is shown in Figure 1.Figure 1
Typical structure of SOM.The SOM network is composed of input layer and competition layer, and the neurons of input layer and competition layer are completely connected. Model learning samples are composed of samples withn classification indexes. Assuming that these samples with the same category or some similar characteristics are relatively close in n-dimensional space, these samples form a class and form a cluster in the n-dimensional space. When the input samples belong to multiple classes, the n-dimensional space will show the characteristics of multiple cluster distribution. Each cluster represents a type, and the center of the cluster is the cluster center of the type. The distance between samples belonging to the same class and the cluster center of this class is small. This distance can be measured by the Euclidean distance as follows:(1)Dj=∑i=1Nxi−Wij2.In the formula,xi is the classification index, Wij is the cluster center of the j-th dynamic type, and Dj is the Euclidean distance.Through the analysis of the related algorithms of the SOM, and according to the specific environment of the application field, the algorithm is now modified to meet the requirements of the system. The algorithm steps are as follows:First give the thresholdβ. β is used to control the thickness of the classification. The larger the β, the thicker the classification and the fewer types. The smaller the β, the finer the classification and the greater the number of types. Therefore, the determination of the β value requires trial calculation. Let the initial number of neurons in the output layer be, i.e., j = 1, and a learning sample to give the connection weight Wij is chosen as the initial value. A new learning sample is entered and the Euclidean distance Dj is calculated between it and each dynamic type of cluster center Wij. The minimum Euclidean distance is calculated as(2)Dj∗=minDj.IfDj<β, it is considered that the current input sample belongs to the dynamic type represented by the output neuron, and the connection weight Wij is adjusted as follows:(3)ΔWij=xi−Wijhj.In the formula,ΔWij is the adjusted value of Wij and hj belongs to the current sample number of the j-th dynamic category. Then, go to step (3).IfDj>β, it means that although the output neuron wins the competition, the current input sample still cannot be regarded as belonging to the dynamic type represented by the output neuron, but should belong to a new type. Therefore, the input sample is used as the initial value of Wij+1. Then, turn to step (3). This cycle is repeated until all samples have been learned. Finally, the output neuron number of the network model is the type number of all samples, and the connection weight is the cluster center value of each dynamic type.


## 3.3. Attribute Reduction Algorithm Model

The attribute reduction algorithm has strong data classification ability. Without any prior knowledge and additional data information, it uses knowledge reduction to reveal the relevance and decision-making hidden among various data and can comprehensively analyze and identify the injuries and illnesses of retired athletes [31, 32]. When calculating the sports attribute reduction algorithm, it is necessary to build the mathematical model of attribute reduction algorithm and establish the early warning model of athletes’ injuries. The data model construction steps are shown in Figure 2.Figure 2
Data model construction.Set the data collection and attribute positioning that caused the athlete’s injury to C andcj, respectively, then cj∈C. Then, vj is expressed as the measured value of attribute cj, vc′ is expressed as the dimensionless value of attribute cj, vmaxj is defined as the maximum value of attribute cj, and vminj is defined as the minimum value of cj, and then the above definition is used for modeling.A mathematical model for quantifying quantitative attributes for the dimensionless valuevc′ of attribute cj can be constructed and expressed as(4)vj′=1,vj≤vminj,vmax−vjvmaxj−vminj,vminj<vj<vmaxj,0,vj≥vmaxj.Suppose the index evaluation set is represented by A,rj is represented by the membership degree vector, and the dataset is represented as(5)rj=rj1,rj2,rj3,rj4,rj5.In the formula,rj represents the membership degree vector corresponding to the index evaluation set A. Assumptions are as follows:(6)B=B1,B2,B3,B4,B5.Among them,Bi is used as the scale element corresponding to the i-th evaluation in the dataset B. Through the dataset B, the data membership vector representing the injury of the athlete can be effectively integrated into a scalar. The formula is expressed as(7)V=rj×B,where V is the quantitative value of the qualitative evaluation index of the athlete’s injury data under the given scale B.


## 3.4. Acquiring Injury Complexity Model Based on Improved SOM

The data basis of the SOM is the polynomial sequence of increasing complexity in the n-dimensional compact retired athlete’s body function set C can approximate any point in C with arbitrary precision, shaping the Kolmogorov–Gabor polynomial composed of variablex1,x2,…,xm as(8)fx1,x2,…,xm=∑i=1masxi+∑i=1m∑j=1maijxixj+….Among them, a indicates the qualified coefficient of the physical function of the retired athletes,i and j, respectively, describe different physical function variables, and m represents the change threshold of the physical function. Equation (8) shows that with the increasing number of independent variables and polynomial complexity, polynomial sequences can fit arbitrary data with high precision. The observed physical function sample data of retired athletes are divided into a training subset and a test subset. The intermediate candidate injury models are generated in the training subset through internal specifications, and the intermediate candidate injury models are collected in the test subset through external specifications. In the process of modeling, the SOM algorithm screens the input physical function variables of retired athletes at all levels by adopting relevant specifications, and then combines them to obtain the screening model of the next level, until the best injury complexity model is finally obtained.Divide the retired athlete’s physical function sample dataset W into training set A and test set B, thenW=A∪B. If the prediction model is modeled, the prediction subset C needs to be divided again to ensure that W=A∪B∪C.Shape the general functional relationship between the outputy of the injury model and the input x1,x2,…,xn. The Kolmogorov–Gabor polynomial is as follows:(9)y=fx1,x2=a0+a1x1+a2x2+a3x12+a4x22+a5x1x2.In addition, treating each of the monomials asm input models in the original structure of the modeling network,(10)v1=a0,v2=a1x1,v3=a2x2,…,v6=a5x1x2.Self-organizing process adaptively forms the first-level intermediate model,(11)zk=fkvi,vj,i=1,2,…,6.In addition, in the training set A, the parameter prediction method is used to predict the coefficient ofzk. In the test set B, the competition model zk is filtered through external specifications, and the middle candidate injury model wk=zk is collected and used as the input of the second layer of the network. Continuing the process (10) and (11), the second layer and the third layer can be formed successively until the minimum value of the external specification is obtained, and the best injury complexity model can be obtained. The mining accuracy of different methods is shown in Figure 3.Figure 3
Mining accuracy of different methods.With the continuous increase in the signal-to-noise ratio, the mining accuracy of traditional methods has a significant downward trend, while the mining efficiency of this method has a gentle downward trend. The mining efficiency of this method is much higher than that of traditional methods, which shows that this method has higher superiority and achieves satisfactory results.


## 3.5. Normalization Processing

Studies on the brain have shown that the brain is composed of a large number of neurons that work together. The neural network of brain is a very complex feedback system, which contains various feedback effects, including global feedback, local feedback, and chemical interaction. Clustering is an extremely important function in the process of brain processing information. The brain recognizes external signals through clustering process and produces self-organizing process. The data of the training adaptation state are given as input, including the brain information entropy value, ratio of the brain main sequence parameters, parameter competition changes of 8 Hz, 9 Hz, and 10 Hz, athletes’ sports skill level optimization state level, brain function state, central tension, central fatigue score, average score, best score in each test stage, etc. The data utilization function is normalized, so that the index data, which are not in the same order of magnitude are mapped between [−1, +1].


## 4. Result Analysis and Discussion

Comparing the SOMs before and after improvement, Figure4 shows the training error trend diagram of the standard SOM, and Figure 5 shows the training error trend diagram of the improved SOM. Comparing the curves of the two Figures, it is easy to draw that in the training process, in order to achieve the training target error, the standard SOM has not converged to the expected value after 3000 iterations, and the improved SOM has a fast convergence speed and meets the requirements after 12 iterations. Compared with the improved algorithm, the standard SOM algorithm has more iterations and lower convergence accuracy. This is because one of the important reasons for the slow convergence of the standard SOM algorithm is that it is difficult to choose the learning rate, which is too large to oscillate and too small to converge slowly. The learning rate of the improved SOM algorithm can be adjusted adaptively, which accelerates the convergence speed.Figure 4
Standard SOM neural network training error trend diagram.Figure 5
Improved SOM neural network training error trend graph.Figures6 and 7 are the training error curves of standard SOM and improved SOM, respectively. The error of standard SOM in training 140 sets of sample data is still very large. The error of the improved SOM is very small when training 100 groups of sample data, and the error hovers around 0 when the training samples are increased from 100 groups to 140 groups, so the training of the improved SOM is quite successful. Multilayer SOM can improve the recognition rate of failure modes because the clustering area built in the feature graph will converge with the increase in SOM neural network layers, but the number of SOM layers cannot be determined in advance. The fault pattern recognition can also be considered to divide the fault pattern clustering area in the SOM feature map in pairs. The SOM feature map is a discrete two-dimensional lattice composed of 0, 1 elements, which can be classified by the binary image segmentation method.Figure 6
Training error curve of standard SOM.Figure 7
The training error curve of the improved SOM.Figures8 and 9 are fitting curves of standard SOM and improved SOM training, respectively. It can be easily concluded from the two figures that the simulated output of the improved SOM after training is basically consistent with the actual output, which shows that its accuracy is high. There are many pattern points on the boundary of the region when the pattern area is divided in the discrete two-dimensional plan, and these boundary points cannot determine its pattern category. This is because the region boundary is determined by a discrete generation algorithm, and there is at least one boundary point in a row/column between any two pattern regions, and sometimes there are two boundary points. There are more points on the boundary in one row/column in the multimode region division. In order to reduce the number of points on the boundary of a pattern region, a pattern region division method using continuous variables as the judgment condition of boundary points is used. Let the boundary of the mode area be a curve in continuous space and only a few points are on the boundary in the discrete state.Figure 8
Fitting curve of standard SOM training.Figure 9
Improved fitting curve of SOM training.All the above graphs show that the improved SOM is more effective than the standard SOM in the early warning of retired athletes’ injuries. The application of the attribute reduction algorithm and neural network in early warning of retired athletes’ injuries can not only improve the performance of the neural network, reduce the complexity of network, and reduce the training time of network, but also prevent track and field sports injuries efficiently, conveniently, and in real time to a certain extent, which has certain practical value in the actual training process. For the injury warning of retired athletes, SOM does not reflect the results with one neuron, but with several neurons at the same time. The memory of learning mode is not completed once, but by repeated learning, which dissolves the statistical characteristics of input mode to each connection weight and has strong anti-interference ability. Once a neuron is damaged or completely failed for some reason, the remaining neurons can still guarantee that the corresponding memory information will not disappear. This redundancy will be reflected in the superior stability of equipment in practical applications. The global topology also avoids the problem that many neural network methods fall into local optimum.


## 5. Conclusions

In this paper, an early warning method of injury based on self-organization and restrictive norms is proposed. The self-organization neural network is used to screen the physical function variables of retired athletes at all levels by adopting relevant norms, and the best injury complexity model is obtained. The best injury complexity model is analyzed by mining restrictive norms. In this study, according to various factors of early warning of retired athletes’ injuries, the idea of using SOM to predict its epidemic law is put forward, and the attribute reduction algorithm is introduced into the adjustment of the neuron neighborhood range and the learning of weights in the SOM network, so as to overcome the shortcomings of the SOM network algorithm that its classification results are related to the sample input order and easily fall into local optimum. The learning algorithm of the self-organizing competitive neural network is improved, and the attribute reduction algorithm is used for data mining. The classification of the input learning samples is known, and they have been classified at the time of input. Using this sample to train the network model can shorten the learning time and improve the classification accuracy, compared with other algorithms, such as emperor butterfly optimization (MBO), earthworm optimization (EWA), elephant grazing optimization (EHO), moth search (MS), slime mold algorithm (SMA), and Harris Hawkes optimization (HHO). The mining efficiency and accuracy of this method are higher than those of traditional methods, and it has strong anti-interference ability and can achieve satisfactory results. After the network training, the prediction becomes very simple, and the evaluation results can be obtained quickly by inputting the monitoring data of the samples to be evaluated. The method can efficiently and accurately extract the key indicators of the injury factors of retired athletes and evaluate their early warning levels, so as to effectively obtain the injury situation of athletes and reduce the probability of recurrence of injuries of retired athletes.



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*Source: 6728678-2021-10-18.xml*