# Application of Long-Term and Short-Term Memory Neural Network in Technical Evaluation of Hurdle Track and Field

Authors: Zhenming Liu
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to study the application of long-term and short-term memory neural networks in the technical evaluation of hurdle track and field, firstly, the related contents of long-term and short-term memory neural networks and hurdle track and field sports are analyzed, the variation characteristics of kinematics and surface electromyography of swinging leg in the landing link of lower hurdle are analyzed, and the relationship between surface electromyography and kinematics parameters is analyzed. Then, the kinematics and surface EMG data of three male hurdlers in the hurdle team and two men in the College of Physical Education were collected when completing the technical link of pressing down and landing. The subject data were processed and analyzed by using the Simi motion video analysis system and DASY lab10.0 EMG analysis software. Finally, SPSS13.0 and origin are used to analyze the average, standard deviation, and correlation of the data results and draw a graph. Experiments show that neural network theory and finite element method, as the new forces of sports biomechanics, have been used to diagnose track and field technology. With the popularization of this method, it will play a greater role in the research of track and field technology.


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## Body

## 1. Introduction

Hurdle has always been a popular research project in China. Since Liu Xiang won the world championship, people have exerted the pursuit of hurdle research to the extreme. However, due to the limitation of experimental conditions and experimental environment, previous research can only stop at motion and dynamics. However, there is relatively little research on the power source of the human body—muscles, especially the mutual coordinated effort between muscles and the influence of myoelectricity on muscle effort is even rarer [1].The performance of the 110-meter hurdles consists of four technical links: starting with the first hurdle, hurdle step, inter-hurdle run, and the lower hurdle to the end of the tenth hurdle. Among the four components, the technical link of the hurdle step is the most important one. The hurdle step is a big step to connect the hurdle running technology. The quality of hurdle step technology directly affects the rhythm and speed of hurdle running technology after the next hurdle and determines whether we can achieve ideal results in this project. Therefore, the key to improving the performance of modern hurdles is to improve the hurdle step technology. The hurdle step technology is more complex, which can be divided into swing leg technology and starting leg technology. The main task of the kick-off leg in the hurdle step technology is to complete the kick-off. The quality of the kick-off leg will affect the kick-off angle and speed and will have a certain impact on the quality of the kick-off technology of the hurdle step [2]. The swinging leg runs through the whole hurdle step technology, which will not only affect the size of the starting angle, the starting speed, and the height of the center of gravity but also affect the flying time of the hurdle step, the distance between the swinging leg and the landing place, and the support angle of the swinging leg when landing to affect the horizontal movement speed of the center of gravity when landing and the connection between hurdle step technology and hurdle running technology. Therefore, the key to improving the technical level of the hurdle step is to improve the technology of swinging legs. The artificial neural network has good robustness (Figure 1), self-organization, self-adaptability, parallel processing ability, distributed storage, and excellent fault tolerance and nonlinear approximation ability. At present, artificial neural network algorithm has been widely used in hardware fault detection, medical diagnosis, medical image processing, and other complex tasks of the complex system, classification, and prediction, and achieved good results.Figure 1
Long- and short-term memory neural network.The technology of swinging leg can be divided into four stages: jumping stage, attacking hurdle stage, falling hurdle stage, and landing stage. In these four stages, the quality of the hurdle technique is directly related to the time of the hurdle and the distance of the swinging leg landing place, which will determine the length of the hurdle step flying time and the quality of the combination of stride and run and ultimately affect the performance of athletes. Therefore, we can see that improving the quality of swing leg pressing technology will be the key to improving athletes’ performance [3].Based on the above analysis, this paper establishes the learning sample of the neural network according to the quality training level and special sports performance of China’s excellent male hurdlers and puts forward the men’s Hurdle Training Model Based on the neural network. The training model does not need to determine the mathematical expression of the training model in advance, more objectively reflects the mapping relationship between the special sports performance and various subordinate training indicators in men’s hurdles, and shows high prediction accuracy.


## 2. Literature Review

Wen and others believe that hurdle running is a relatively common event in sports. It is the coordinated action of people on themselves, upper and lower limbs, and trunk muscles. It is required to complete the action quickly and keep the body in the air for a short time, but it also needs to create the necessary conditions for running between hurdles in the process; it is mainly composed of three parts: jumping over the hurdle, flying over the hurdle, and pressing the hurdle to the ground. The following three parts are emphatically analyzed in order to provide help for technological progress [4]. Illa and others believe that the position of athletes’ take-off has an important impact on their performance. At present, according to the survey, there are still some problems with China’s athletes in terms of take-off distance and take-off angle. On the one hand, the take-off point is too far, resulting in too long take-off distance and too small take-off angle, which will reduce the strength of athletes’ legs and seriously restrict the effect of hurdles. On the other hand, the starting point is too close, resulting in a too close starting distance and too large starting angle, which reduces the speed in the horizontal direction and reduces the speed of hurdles [5]. Zuo and others believe that when athletes cross the hurdles, they have strict requirements for jumping legs. During the whole movement process, athletes should keep their shoulders and hips upright, and pedal back quickly after crossing the hurdles and landing, to make the athletes’ body center of gravity move forward quickly along the straight track. After the pedal action is completed, they should attack the hurdles quickly; this is a movement action completed without certain support of the body [6]. Chen et al. believe that artificial neural network has good robustness, self-organization, adaptability, parallel processing, distributed storage, and other characteristics, as well as excellent fault tolerance and nonlinear approximation [7]. At present, Du and others believe that artificial neural network algorithms are widely used in complex tasks such as hardware damage detection, medical diagnosis, classification, and prediction of complex clinical image processing systems and have achieved good results [8]. Jane and others developed a dynamic chain model to explain the interactions between damage risk factors and their contributions to damage [9]. Yan et al. summarized the early warning research on sports injury risk. The predecessors conducted a lot of research on sports injury risk factors and developed a qualitative model, but there was no quantitative evaluation research. Due to its good performance, early warning of sports injury risk is very important [10]. Chen et al. argue that artificial neural network algorithms are better at solving such problems, but using neural network algorithms requires a database of samples to train them. The quality of the sample database directly affects the accuracy of the neural network algorithm. Demand data from a sample database provides information on all factors related to early warning of sports injury risk in athletes and their corresponding warning levels [11].


## 3. Method

An artificial neural network suffers from its huge number of parameters and is limited by the depth of the network and does not consider the information relationship between pixels. In order to solve the defects of the artificial neural network, CNN abandoned the full connection mode of the artificial neural network, adopted the mode of the image matrix, and introduced the ideas of “local perception, weight sharing, and downsampling,” which greatly improved its performance and application scenarios. CNN is mainly composed of a convolution layer, pooling layer, and full connection layer. If the input layer and output layer are added, a simple and complete CNN model can be constructed.


### 3.1. Convolution Layer

The main purpose of convolution operation in a convolution neural network is to extract some features by using a convolution kernel. The convolution kernel is equivalent to a filter. The feature map can be obtained by calculating the weighted average between the weight of the convolution kernel and the pixels in each small window region of the image. The convolution layer has a very strong feature learning ability. With the increase of network depth, the extracted features often gradually change from low-level features to high-level features. In addition, unlike the fully connected network, the convolution neural network is connected between each neuron between layers. The convolution kernel in the convolution neural network is only connected with some local areas in the input data, and the same convolution kernel in the same layer shares the weight parameters, which greatly reduces the number of network parameters. Figure2 shows the calculation process of convolution operation [12].Figure 2
Operation diagram of convolution calculation.In Figure2, the size of the sliding window is the same as that of the convolution kernel. Multiply and sum the elements in the convolution kernel and the corresponding position elements in the sliding window. When performing a convolution operation, it is necessary to pay attention to the two parameters of convolution step size and peripheral filling. Convolution step size refers to the one-time sliding distance of the sliding window. With the continuous convolution operation, its output size will become smaller, and some data at the edge of input data are not used, resulting in the loss of some features. In order to solve the problem that the output size becomes smaller, a peripheral padding operation is proposed. The main purpose of this operation is to prevent the decline of data output size and the loss of features. Its specific operation is to carry out zero padding around the input data and control the size of output data by controlling the number of layers of data peripheral padding. The output calculation formula of data after convolution operation is(1)Mconv=n+2p−fs+1∗n+2p−fs+1,where n represents the size of the input data matrix, f represents the size of the convolution kernel, s represents the convolution step size, and p represents the number of layers filled around the data matrix.


### 3.2. Pooling Layer

The feature map extracted by the convolution layer will have redundant data information. The purpose of pooling is to remove these redundant data and leave the most important data information, to reduce the size of feature data and achieve the effect of dimension reduction at the same time. The pooling layer plays a certain role in preventing overfitting. The common forms of pooling layer include maximum pooling and average pooling. Generally, the effect of maximum pooling operation is better. The calculation operation of the maximum pooling layer and average pooling layer is shown in Figure3, in which the size of the pooling layer core is 2 ∗ 2 and the step size is 20.Figure 3
Calculation diagram of maximum pooling and average pooling.


### 3.3. Full Connection Layer

Each neuron in the fully connected (FC) layer is connected to all neurons in the anterior layer. The full connection layer in a convolutional neural network plays the role of “Classifier.” For example, for an image classification task with three categories, the feature map obtained after feature extraction through the input image needs multiple full connection layers to integrate it and reconnect it to form a new three-dimensional vector, in which each dimension represents a category; that is, the purpose of constructing a “Classifier” is achieved. However, not all convolution neural networks must have a full connection layer. The convolution layer can be used to replace the full connection layer. This convolution neural network is called a full convolution neural network [13].The most common structure of the convolution neural network model is to connect the pooling layer behind the convolution layer. Its purpose is to reduce the dimension of the input data after convolution operation, reduce the redundant data information, and retain the important information. Then, it is repeated several times to extract the high-dimensional features of the data and finally output through the feature integration of the whole connection layer. The most common and simplest convolutional neural network model structure is shown in Figure4.Figure 4
Common convolutional neural network structure.


#### 3.3.1. Structure Composition of Long-Term and Short-Term Memory Neural Network

LSTM (Long Short-Term Memory Neural Network) is a new type of RNN-based deep machine learning neural network. In this model, the LSTM cell contains a memory cell that attempts to store information for a long time. The entrance to this memory unit is controlled by some special doors. Controlled functions include save, write, and read. These gates are logic units responsible for weighting the edges that connect the rest of the neural network to memory cells. The memory unit itself is a linear neuron with internal connections. Specifically, each of the three doors has three entrances. Forget about exit doors and gates. Correction parameters are used to select and memorize the gradient decreasing error function. When you open the gate of forgetting, the connection weight itself is 1, and the memory part writes the content to itself. When the output of the forget gate is 0, the memory cell will delete the previous content. The output gate allows the rest of the neural network to write to the memory cell when the output value is 1, while the input gate allows the memory cell to read the memory cell to the rest of the neural network when the output value is 1 [14]. The structure of the model is shown in Figure 5.Figure 5
Model structure.Clell is the memory of the state of nerve cells. There is a parameter called the state to record a state. Forget Gate: select and fix the state of the last neuron. For each memory cell, three sets of weights are prepared from the input, including the fully hidden state of the previous time step. Insert one in the input node at the bottom of the image above. I put one on the forgotten door and showed it on the right. Another entry exit door is visible from the top left. Each black node is associated with an activation function; a typical activation function is a sigmoid function. The most important node of the cell is the internal state, where the first dimension is weighted over a period and then returns to itself. The self-adhesive edge of the internal state is called the Fixed Error Conveyor or CEC.For example, in the past, entry gates determined when to activate activation states in memory cell slots, while output gates determined when to activate outgoing memory cells, which was determined by training. Finally, forget gates are used to learn to forget the final state of a neuron completely or partially or completely. The same is true after delivery. The output gate learns when the error flows to the storage unit, while the input gate learns when the error flows out of the storage unit and transmits it to the rest of the neural network. The same goes for forgetting doors. The formulas for each part are given in the order of calculation-general algorithm:(2)alt=∑i=1IWilXit+∑h=1HWhlbht−1+∑c=1cWclSct−1,bIt=falt.


#### 3.3.2. Operation Process of Long-Term and Short-Term Memory Neural Network

The forward propagation calculation process of LSTM is as follows.(1)
Update the output of the forgetting gate. The formula is(3)ft=σWfht−1+Ufxt+bf.(2)
Update the two-output status information of the input gate. The formula is(4)it=σWiht−1+Uixt+bi.(3)
Update the cell status. The formula is(5)Ct=Ct−1ft+itat.(4)
Update the output gate. The formula is(6)σt=σWoht−1+Uoxt+bo.The backpropagation calculation process of LSTM is as follows.In LSTM, there are two hidden states,ht and ct, respectively. Therefore, two δt are defined, and the formula is(7)ht=ottanhCt.The backpropagation calculation process of LSTM is as follows.① In LSTM, there are two hidden states,ht and ct, respectively. Therefore, two RTs are defined, and the formula is(8)δht=∂L∂ht,δct=∂L∂Ct.② The loss functionLt adopts the cross line loss function, and the formula is(9)Lt=lt+Lt+1ift<τltift=τ.③δhτ and δcτ of the last index position τ of the sequence obtained by combining ① and ② are given as follows:(10)δhτ=∂Lτ∂Oτ∂Oτ∂hτ=VTy^τ−yτ,δcτ=∂Lτ∂hτ∂hτ∂cτ=δhτoτ1−tanh2Cτ.④δht and δCt are inversely derived from δht+1 and δCt+1, and the gradient of each parameter is calculated. The derivation formula of δht and δCt is(11)WSδht=Vty^t−yt+δht+1∂ht+1∂ht.⑤ Calculate the gradient of the network parameterWf, and the formula is(12)∂L∂Wf=∑t=1τδctCt−1Ft1−ftht−1T.Repeat the above steps until the error of the network is lower than the set threshold.Once the neural network model of the first data series is built, a series of predicted values for the first data series can be obtained. These predicted values may be slightly different from the original data, and these bias ratios can be combined in a neural network model: take the predicted value as a neural network input sample and the actual value as a neural network output sample. Approve a specific structure; then train, the neural network to obtain several weights and thresholds corresponding to the corresponding nodes [15].


## 3.1. Convolution Layer

The main purpose of convolution operation in a convolution neural network is to extract some features by using a convolution kernel. The convolution kernel is equivalent to a filter. The feature map can be obtained by calculating the weighted average between the weight of the convolution kernel and the pixels in each small window region of the image. The convolution layer has a very strong feature learning ability. With the increase of network depth, the extracted features often gradually change from low-level features to high-level features. In addition, unlike the fully connected network, the convolution neural network is connected between each neuron between layers. The convolution kernel in the convolution neural network is only connected with some local areas in the input data, and the same convolution kernel in the same layer shares the weight parameters, which greatly reduces the number of network parameters. Figure2 shows the calculation process of convolution operation [12].Figure 2
Operation diagram of convolution calculation.In Figure2, the size of the sliding window is the same as that of the convolution kernel. Multiply and sum the elements in the convolution kernel and the corresponding position elements in the sliding window. When performing a convolution operation, it is necessary to pay attention to the two parameters of convolution step size and peripheral filling. Convolution step size refers to the one-time sliding distance of the sliding window. With the continuous convolution operation, its output size will become smaller, and some data at the edge of input data are not used, resulting in the loss of some features. In order to solve the problem that the output size becomes smaller, a peripheral padding operation is proposed. The main purpose of this operation is to prevent the decline of data output size and the loss of features. Its specific operation is to carry out zero padding around the input data and control the size of output data by controlling the number of layers of data peripheral padding. The output calculation formula of data after convolution operation is(1)Mconv=n+2p−fs+1∗n+2p−fs+1,where n represents the size of the input data matrix, f represents the size of the convolution kernel, s represents the convolution step size, and p represents the number of layers filled around the data matrix.


## 3.2. Pooling Layer

The feature map extracted by the convolution layer will have redundant data information. The purpose of pooling is to remove these redundant data and leave the most important data information, to reduce the size of feature data and achieve the effect of dimension reduction at the same time. The pooling layer plays a certain role in preventing overfitting. The common forms of pooling layer include maximum pooling and average pooling. Generally, the effect of maximum pooling operation is better. The calculation operation of the maximum pooling layer and average pooling layer is shown in Figure3, in which the size of the pooling layer core is 2 ∗ 2 and the step size is 20.Figure 3
Calculation diagram of maximum pooling and average pooling.


## 3.3. Full Connection Layer

Each neuron in the fully connected (FC) layer is connected to all neurons in the anterior layer. The full connection layer in a convolutional neural network plays the role of “Classifier.” For example, for an image classification task with three categories, the feature map obtained after feature extraction through the input image needs multiple full connection layers to integrate it and reconnect it to form a new three-dimensional vector, in which each dimension represents a category; that is, the purpose of constructing a “Classifier” is achieved. However, not all convolution neural networks must have a full connection layer. The convolution layer can be used to replace the full connection layer. This convolution neural network is called a full convolution neural network [13].The most common structure of the convolution neural network model is to connect the pooling layer behind the convolution layer. Its purpose is to reduce the dimension of the input data after convolution operation, reduce the redundant data information, and retain the important information. Then, it is repeated several times to extract the high-dimensional features of the data and finally output through the feature integration of the whole connection layer. The most common and simplest convolutional neural network model structure is shown in Figure4.Figure 4
Common convolutional neural network structure.


### 3.3.1. Structure Composition of Long-Term and Short-Term Memory Neural Network

LSTM (Long Short-Term Memory Neural Network) is a new type of RNN-based deep machine learning neural network. In this model, the LSTM cell contains a memory cell that attempts to store information for a long time. The entrance to this memory unit is controlled by some special doors. Controlled functions include save, write, and read. These gates are logic units responsible for weighting the edges that connect the rest of the neural network to memory cells. The memory unit itself is a linear neuron with internal connections. Specifically, each of the three doors has three entrances. Forget about exit doors and gates. Correction parameters are used to select and memorize the gradient decreasing error function. When you open the gate of forgetting, the connection weight itself is 1, and the memory part writes the content to itself. When the output of the forget gate is 0, the memory cell will delete the previous content. The output gate allows the rest of the neural network to write to the memory cell when the output value is 1, while the input gate allows the memory cell to read the memory cell to the rest of the neural network when the output value is 1 [14]. The structure of the model is shown in Figure 5.Figure 5
Model structure.Clell is the memory of the state of nerve cells. There is a parameter called the state to record a state. Forget Gate: select and fix the state of the last neuron. For each memory cell, three sets of weights are prepared from the input, including the fully hidden state of the previous time step. Insert one in the input node at the bottom of the image above. I put one on the forgotten door and showed it on the right. Another entry exit door is visible from the top left. Each black node is associated with an activation function; a typical activation function is a sigmoid function. The most important node of the cell is the internal state, where the first dimension is weighted over a period and then returns to itself. The self-adhesive edge of the internal state is called the Fixed Error Conveyor or CEC.For example, in the past, entry gates determined when to activate activation states in memory cell slots, while output gates determined when to activate outgoing memory cells, which was determined by training. Finally, forget gates are used to learn to forget the final state of a neuron completely or partially or completely. The same is true after delivery. The output gate learns when the error flows to the storage unit, while the input gate learns when the error flows out of the storage unit and transmits it to the rest of the neural network. The same goes for forgetting doors. The formulas for each part are given in the order of calculation-general algorithm:(2)alt=∑i=1IWilXit+∑h=1HWhlbht−1+∑c=1cWclSct−1,bIt=falt.


### 3.3.2. Operation Process of Long-Term and Short-Term Memory Neural Network

The forward propagation calculation process of LSTM is as follows.(1)
Update the output of the forgetting gate. The formula is(3)ft=σWfht−1+Ufxt+bf.(2)
Update the two-output status information of the input gate. The formula is(4)it=σWiht−1+Uixt+bi.(3)
Update the cell status. The formula is(5)Ct=Ct−1ft+itat.(4)
Update the output gate. The formula is(6)σt=σWoht−1+Uoxt+bo.The backpropagation calculation process of LSTM is as follows.In LSTM, there are two hidden states,ht and ct, respectively. Therefore, two δt are defined, and the formula is(7)ht=ottanhCt.The backpropagation calculation process of LSTM is as follows.① In LSTM, there are two hidden states,ht and ct, respectively. Therefore, two RTs are defined, and the formula is(8)δht=∂L∂ht,δct=∂L∂Ct.② The loss functionLt adopts the cross line loss function, and the formula is(9)Lt=lt+Lt+1ift<τltift=τ.③δhτ and δcτ of the last index position τ of the sequence obtained by combining ① and ② are given as follows:(10)δhτ=∂Lτ∂Oτ∂Oτ∂hτ=VTy^τ−yτ,δcτ=∂Lτ∂hτ∂hτ∂cτ=δhτoτ1−tanh2Cτ.④δht and δCt are inversely derived from δht+1 and δCt+1, and the gradient of each parameter is calculated. The derivation formula of δht and δCt is(11)WSδht=Vty^t−yt+δht+1∂ht+1∂ht.⑤ Calculate the gradient of the network parameterWf, and the formula is(12)∂L∂Wf=∑t=1τδctCt−1Ft1−ftht−1T.Repeat the above steps until the error of the network is lower than the set threshold.Once the neural network model of the first data series is built, a series of predicted values for the first data series can be obtained. These predicted values may be slightly different from the original data, and these bias ratios can be combined in a neural network model: take the predicted value as a neural network input sample and the actual value as a neural network output sample. Approve a specific structure; then train, the neural network to obtain several weights and thresholds corresponding to the corresponding nodes [15].


## 3.3.1. Structure Composition of Long-Term and Short-Term Memory Neural Network

LSTM (Long Short-Term Memory Neural Network) is a new type of RNN-based deep machine learning neural network. In this model, the LSTM cell contains a memory cell that attempts to store information for a long time. The entrance to this memory unit is controlled by some special doors. Controlled functions include save, write, and read. These gates are logic units responsible for weighting the edges that connect the rest of the neural network to memory cells. The memory unit itself is a linear neuron with internal connections. Specifically, each of the three doors has three entrances. Forget about exit doors and gates. Correction parameters are used to select and memorize the gradient decreasing error function. When you open the gate of forgetting, the connection weight itself is 1, and the memory part writes the content to itself. When the output of the forget gate is 0, the memory cell will delete the previous content. The output gate allows the rest of the neural network to write to the memory cell when the output value is 1, while the input gate allows the memory cell to read the memory cell to the rest of the neural network when the output value is 1 [14]. The structure of the model is shown in Figure 5.Figure 5
Model structure.Clell is the memory of the state of nerve cells. There is a parameter called the state to record a state. Forget Gate: select and fix the state of the last neuron. For each memory cell, three sets of weights are prepared from the input, including the fully hidden state of the previous time step. Insert one in the input node at the bottom of the image above. I put one on the forgotten door and showed it on the right. Another entry exit door is visible from the top left. Each black node is associated with an activation function; a typical activation function is a sigmoid function. The most important node of the cell is the internal state, where the first dimension is weighted over a period and then returns to itself. The self-adhesive edge of the internal state is called the Fixed Error Conveyor or CEC.For example, in the past, entry gates determined when to activate activation states in memory cell slots, while output gates determined when to activate outgoing memory cells, which was determined by training. Finally, forget gates are used to learn to forget the final state of a neuron completely or partially or completely. The same is true after delivery. The output gate learns when the error flows to the storage unit, while the input gate learns when the error flows out of the storage unit and transmits it to the rest of the neural network. The same goes for forgetting doors. The formulas for each part are given in the order of calculation-general algorithm:(2)alt=∑i=1IWilXit+∑h=1HWhlbht−1+∑c=1cWclSct−1,bIt=falt.


## 3.3.2. Operation Process of Long-Term and Short-Term Memory Neural Network

The forward propagation calculation process of LSTM is as follows.(1)
Update the output of the forgetting gate. The formula is(3)ft=σWfht−1+Ufxt+bf.(2)
Update the two-output status information of the input gate. The formula is(4)it=σWiht−1+Uixt+bi.(3)
Update the cell status. The formula is(5)Ct=Ct−1ft+itat.(4)
Update the output gate. The formula is(6)σt=σWoht−1+Uoxt+bo.The backpropagation calculation process of LSTM is as follows.In LSTM, there are two hidden states,ht and ct, respectively. Therefore, two δt are defined, and the formula is(7)ht=ottanhCt.The backpropagation calculation process of LSTM is as follows.① In LSTM, there are two hidden states,ht and ct, respectively. Therefore, two RTs are defined, and the formula is(8)δht=∂L∂ht,δct=∂L∂Ct.② The loss functionLt adopts the cross line loss function, and the formula is(9)Lt=lt+Lt+1ift<τltift=τ.③δhτ and δcτ of the last index position τ of the sequence obtained by combining ① and ② are given as follows:(10)δhτ=∂Lτ∂Oτ∂Oτ∂hτ=VTy^τ−yτ,δcτ=∂Lτ∂hτ∂hτ∂cτ=δhτoτ1−tanh2Cτ.④δht and δCt are inversely derived from δht+1 and δCt+1, and the gradient of each parameter is calculated. The derivation formula of δht and δCt is(11)WSδht=Vty^t−yt+δht+1∂ht+1∂ht.⑤ Calculate the gradient of the network parameterWf, and the formula is(12)∂L∂Wf=∑t=1τδctCt−1Ft1−ftht−1T.Repeat the above steps until the error of the network is lower than the set threshold.Once the neural network model of the first data series is built, a series of predicted values for the first data series can be obtained. These predicted values may be slightly different from the original data, and these bias ratios can be combined in a neural network model: take the predicted value as a neural network input sample and the actual value as a neural network output sample. Approve a specific structure; then train, the neural network to obtain several weights and thresholds corresponding to the corresponding nodes [15].


## 4. Experimental Analyses

There are 3 male hurdlers on the hurdle team and 2 male hurdlers in the Institute of physical education. The basic information about the athletes is shown in Table1. Athletes are in good health, without muscle injury and other diseases, and are in a normal training state.Table 1
Basic information of subjects.
AthletesAge (y)Height (cm)Weight (kg)Years of exercise (y)Sport levelZhu XX20186.4788MasterSong XX20189.2707Class aHu XX2018673.57Class aZhang XX19185756Class aSouth XX20187.978.38Class a


### 4.1. Experimental Method

The EMG measuring instrument used in the experiment is a 16 lead biovision multifunctional exercise bioelectricity measurement and analysis system imported from Germany, and the software version is dasylab10.0. In this experiment, the EMG of surface muscle is collected by EMG measuring instrument. In the experiment, the sampling frequency of the EMG measuring instrument is 1000 Hz and the magnification is 10000. The obtained data are processed by DASYLab 10.0 EMG analysis software [16]. The EMG electrode used in the experiment is a disposable ECG electrode (silver/silver chloride) produced by Renhe Medical Equipment Co., Ltd. The diameter of the electrode adhesion area is 30 mm, and the diameter of the conductive area is 10 mm.


#### 4.1.1. Treatment Method of Sticking Position

Before the sEMG test, firstly scrape the surface skin with a razor, then Polish the skin of relevant muscles with fine gauze, clean the skin surface with a 75% alcohol cotton ball, and remove the oil on the skin surface. After the alcohol is evaporated, the illumination electrode and detection electrode are placed on the muscle abdomen of the muscle, and the placement direction is consistent with the long axis of the muscle fiber. The diameter of the electrode is 0.5 cm and the electrode spacing is 2 cm.


#### 4.1.2. Data Processing and Statistical Methods

The experimental data were processed and analyzed by the Simi motion video analysis system and DASYLab 10.0 EMG analysis software. Finally, SPSS13.0, origin 7.5, and Excel12003 are used to analyze the average value, standard deviation, and correlation of the data results and draw the graph.


### 4.2. Experimental Steps and Processes

#### 4.2.1. Before Experiment

The staff shall register the height, weight, history of injury, and other basic information of the subjects and inform the subjects of the whole experimental process so that the subjects can be familiar with the whole process of the experiment. It is beneficial for the subjects to cooperate with the experiment.


#### 4.2.2. Commissioning of Experimental Instruments and Equipment

Set the sampling frequency of MotionPro to 100 Hz and debug the acquisition range. Connect, debug, and check the EMG equipment and check whether the equipment operates normally [17].


#### 4.2.3. Preparation of Subjects before Test

Warm-Up. The subject carries out a warm-up exercise to open the muscles, especially the muscles on the exerting side, to prevent the subject from being injured during the experiment. At the same time, carry on the simulation exercise with the data acquisition instrument to get familiar with the action process and make the experimental data closer to the real value.Dress. Subjects are required to wear track and field clothes. Reduce the influence of external factors.Marking. Mark each joint (shoulder, elbow, wrist, skeleton, knee, and step) and head, hand, and foot with marking points.Sticking Electrode. Scrub the measured muscle skin oil with alcohol and scrape off its hair. Stick the electrode to the muscle belly of the measured muscle along the direction of the muscle fiber, and connect the acquisition line. Then, the elastic foam bandage is used to fix it, to avoid the shaking of electrodes and amplifiers in the test process and affect the true value of the collection.


#### 4.2.4. Action Requirements of Subjects during Test

The subjects were asked to start after hearing the command and cross four standard height hurdles while maintaining high speed. The staff collected the relevant data in the third column 3 times. If the collected data is checked to be accurate, the subjects will be replaced and the steps will be carried out. After the experimental data of all subjects are collected and archived, the data will be analyzed.


#### 4.2.5. Experimental Data Processing

In this study, the surface electromyography instrument was used to collect, process, and analyze the electromyographic activity signals of the muscles related to the technical link of the lower hurdle landing of the hurdle swing leg (biceps femoris long head, Hemi key muscle, rectus femoris, gluteus maximus, and tensor fascia lata), to confirm the activity characteristics of the muscles in the technical link of the lower hurdle landing of the hurdle swing leg. The signal collected by the electromyograph is the original electromyography, which must be processed in the following steps before quantitative analysis [18].The original EMG is filtered first: high pass 20 Hz and low pass 400 Hz to eliminate the noise. After full-wave rectification, the negative wave is transformed into a positive wave. Finally, integrate EMG (iEMG), take out the iEMG of the landing link of the lower hurdle, calculate the muscle contribution through iEMG, and take the envelope as the analysis index. The score is the sum of the electromyographic contribution of a muscle in a certain time. It can reflect the degree of muscle participation in completing a certain action and the importance of the muscle in the action.


### 4.3. Kinematic Data Processing Results

As a speed project, speed is the decisive factor affecting the performance of a short-span project. The coordination between the angular velocity of the skeleton joint and the angular velocity of the knee joint will reflect the movement of the swinging leg. At the same time, it also directly affects the quality of hurdle step technology.It can be seen from Figure6 that in the hurdle step technology, the angular velocity of the athlete’s skeleton joint and the angular velocity of the knee joint cooperate with the angular velocity change. The results are as follows: in the landing stage of the starting leg, the swinging leg bends the knee and retracts the lower leg. Relevant studies believe that it is best to keep the folding leg folded before the starting leg leaves the ground as much as possible. From Figure 7, we can see that the peak value of the angular velocity of knee flexion appears before the valley value of the angular velocity of bone flexion, and the time difference between the two values is 4.5070. This phenomenon shows that the contraction angular velocity of the lower leg is greater than that of the thigh. The full folding of the lower leg will be conducive to the swing of the thigh. Because according to I=∑Δmr2 (I: moment of inertia, M: mass of swinging leg, R: radius of rotation of swinging leg), the shorter the radius of rotation of swinging leg, the smaller the moment of inertia of swinging leg. Greater rotational speed will be obtained with the same contraction force. Or the same rotational speed will require less contraction force.Figure 6
Change of knee joint angular velocity.Figure 7
Original electromyography.From Table2, we can also see that there is a good linear relationship between the angle of the knee joint and the angular velocity of the skeleton joint in the take-off stage of the swing leg, showing a significant negative correlation. This result also proves that the folding of the knee joint will affect the rotation speed of the skeleton joint. It conforms to the law of moment of inertia.Table 2
Relationship between knee joint angle and skeletal joint angular velocity in the take-off stage of swing leg hurdle step technology.
Zhu XXHu XXSouth XXZhang XXSong XXAverage valueP valueCorrelation coefficientKnee angle (rad)1.08 ± 0.171.00 ± 0.231.8 ± 0.341.34 ± 0.181.15 ± 0.25Hip angle0.00−0.911Speed (rad/s)12.04 ± 1.5613.05 ± 3.8510.32 ± 1.1712.32 ± 0.1713.32 ± 2.56However, Figure7 shows that the angular velocity curve of the knee joint of the swinging leg of the tested athlete is steep at this stage, indicating that the movement direction is changed immediately after the folding of the knee joint is completed, the knee joint is extended, and there is no sign of prolonging the folding of the swinging leg. Premature extension of the knee joint will not be conducive to the swing of the knee joint. Premature expansion of the knee joint will increase the swing inertia of the swing leg and increase the control difficulty of the swing leg. Excessive flexion of the swing leg joint will delay the pressing time [19].In this paper, through the relationship between the distance of the center of gravity of the foot tip, the descent distance of the center of gravity in the depression stage, the rate of speed loss in the depression stage, the time of hurdle step, and the angular velocity of the skeleton joint, the results are as follows.Through statistics, it is found that there is a negative correlation between the time of hurdle step, the rate of speed loss in the pressing stage of swinging leg, and the angular velocity of the skeleton joint. No correlation was found between other indicators.In the past, relevant scholars have also obtained relevant results in the theoretical research of hurdle step technology. However, the relationship between relevant parameters is rarely proved by statistics. The main reason may be the limitation of experimental conditions and the accuracy of measured parameters is not high enough. Therefore, it is impossible to demonstrate the reasoning of hurdle step technology in the form of numerical value.With the deepening of research, people gradually realize that the motion amplitude and speed of the skeleton joint are more important than those of the knee joint. In the process of pressing down the swinging leg, the movement of the swinging leg is driven by the skeleton joint. Therefore, the movement of the skeleton joint will determine the downward pressure of the swinging leg. From Tables3 and 4, we can see that in the downward pressing stage of swinging leg, the greater the angular velocity of the skeleton joint, the less time required for the hurdle step and the smaller the rate of center of gravity speed loss. The relationship between skeletal joint movement and angular velocity proves that the change in the relative position of each limb can change the athlete’s flying time in the flying stage. In hurdle events, the shortening of flying time is an important way to improve performance.Table 3
Relationship between the time of swinging leg hurdle step and the mean value of joint angular velocity in the next hurdle stage.
Zhu XXHu XXSouth XXZhang XXSong XXAverage valueP valueCorrelation coefficientHurdle step time (S)0.62 ± 0.010.66 ± 0.010.67 ± 0.010.63 ± 0.010.65 ± 0.02Hip angle0.034−0.567Speed (rad/s)12.08 ± 0.6710.12 ± 0.3510.81 ± 0.5710.57 ± 1.1310.99 ± 0.99Table 4
Relationship between velocity loss rate and mean angular velocity of skeleton joint in the pressing stage of swinging leg.
Zhu XXHu XXSouth XXZhang XXSong XXAverage valueP valueCorrelation coefficientSpeed loss rate in pressing stage (%)4.29 ± 0.866.78 ± 2.44.58 ± 0.545.87 ± 0.010.65 ± 0.02Hip angle0.034−0.567Speed (rad/s)12.08 ± 0.6710.12 ± 0.3510.81 ± 0.5710.57 ± 1.1310.99 ± 0.99


#### 4.3.1. Surface Electromyography Processing Results

SEMG is the result of bioelectricity generated by nerve and muscle excitation. It is the source of electrical signal generating muscle force. It has different degrees of correlation with muscle activity and functional state, so it can reflect the activity of nerves and muscles to a certain extent. SEMG signal can be used to analyze the characteristics of muscle exertion, muscle activation sequence, exertion degree, and action coordination ability in sports. When the human body is moving, it is used to analyze the important joint angle, the relationship between torque and angle, and EMG. Therefore, it is widely used in the field of sports research. The measured images and data processing are as follows.In order to observe the muscle action or phase change over a period, the rectified high-frequency signal existing in the EMG signal is usually removed, and the obtained curve is called an envelope. By observing the change of envelope, we can know the phase of muscle activity and understand the control and coordination of neuromuscular.From Figures8–11, we can see that in the hurdle step technology of five athletes, the overall trend of EMG activity of swinging legs is relatively similar, while there are differences in EMG amplitude between athletes. The main reason for this phenomenon is that in the process of completing a specific technical action, the relevant muscles need to participate in the work according to their own functions to complete a specific joint movement. The difference in EMG amplitude between athletes is mainly due to the difference in cortical thickness of athletes’ own skin. Cortical thickness is an important factor affecting surface electromyography acquisition. For example, in Zhu XX (the highest sports level among the five), in the whole hurdle step technology, the EMG amplitude of the swinging leg is smaller and more stable than that of other athletes. Through the observation of athletes, in addition to the influence of sebum, there should be other factors affecting Zhu XX’s EMG amplitude which is small and stable. According to the neural control law, Zhu XX’s EMG may be caused by Zhu XX’s own training level [20]. EMG amplitude is small because athletes have a high degree of training. Smaller EMG activities can produce greater muscle strength, which is particularly obvious for beginners. As the EMG is relatively stable, the muscles measured by the swinging leg produce obvious EMG activity only in the take-off stage and support landing stage. It is due to the intermittent mechanism between muscles. Although the swinging leg has been moving throughout the hurdle step technology, no muscle in the measured muscles is involved in the whole process, but intermittently. This intermittent activity will be more conducive to the recovery of fast muscle fiber ability and the next stage of exertion. The stability of Zhu XX’s EMG amplitude shows that the relaxation ability of the measured muscles is better than that of other subjects in the high-speed movement of skeleton joints. This phenomenon is closely related to the training level of athletes.Figure 8
Time sequence of Hu XX muscle activity.Figure 9
Sequence of muscle activity in South XX.Figure 10
Timing of muscle activity.Figure 11
Timing of muscle activity.There are two main reasons for the obvious increase of muscle EMG amplitude measured by the swinging leg before landing. There is a delay in the generation of EMG between joint movements. There is a delay between EMG activity and the movement of lower limb joints. The delay between EMG and muscle strength generation is 10 milliseconds, and the delay between muscle strength generation and joint movement is 30 milliseconds. There is a phenomenon of preactivation in skeletal muscle. By observing the EMG activities of the long head and half key muscle of the biceps femoris, it is found that the EMG activities of the long head and half key muscle of the biceps femoris are similar. From the perspective of action anatomy, the biceps femoris starts from the upper part of the posterior part of the sciatic tubercle, ends at the fat bone, and partially ends at the collateral ligament on the outer side of the knee and the external stepping of the tibia. Its function is to bend the knee joint, rotate the tibia outward, and the function of the divine skeleton joint. The half key muscle starts from the inner upper part of the posterior part of the sciatic tubercle and ends above the inner side of the tibial shaft. Its function is to extend the thigh, bend the knee joint, and rotate the tibia internally. From the perspective of anatomy, the functions of the biceps femoris and Hemi key muscle are similar. They are cross joint muscles, which have the functions of both bone flexion and knee flexion. Therefore, in the hurdle step technology, the EMG activities of the half key muscle of the swinging leg and the biceps femoris are similar and reasonable.Through Figure12, it can be found that there are three fluctuations in the EMG amplitude of the biceps femoris and biceps femoris in the hurdle step technology: the first appears in the support stage, in which the biceps femoris and biceps femoris bend their knees to retract the lower leg, reduce the swing radius of the swing leg, and increase the swing speed of the swing leg when the swing leg is swinging. The fluctuation of the second EMG amplitude occurred in the middle and late stages of the swing leg attacking the hurdle. Considering the disguised movement of the swing leg and the EMG action delay, it can be judged that the main function of this EMG activity is to extend the skeleton. However, we found that the amplitude of EMG in this stage is the smallest of the three fluctuations. Therefore, to some extent, it can be inferred that the hemidesmus and biceps femoris are not the main muscles of bone extension at this stage, and other muscles should undertake the main task of bone extension. In the third wave, the EMG amplitude began to increase significantly from the depression stage, reached the peak at the landing support stage, and remained at a high level throughout the support stage. The main reason for this is that the knee joint starts to bend the knee before landing, to prepare for the formation of strong ground picking action. As shown in Figure 13, actively bending the knee, and retracting the lower leg will play an important role in obtaining a favorable landing position when the swinging leg lands on the ground and completing the cross-run connection better. Because the active recovery of the lower leg before landing will help to form a “pick the ground” posture, reduce the force in the negative direction caused by the ground reaction, reduce the loss of speed, and help swing the leg, at the same time, it can avoid the injury of the rear muscle group caused by the load overload of the rear muscle group due to the unreasonable landing location. In the landing support stage, the EMG amplitude continued to increase until it reached the peak and began to decline steadily. At this stage, the biceps femoris and hemidesqualis are one of the main power sources of the horizontal backward direction of the center of gravity. In the first half of the landing support, it is also the time when the rear muscle group carries the maximum load, so athletes are often prone to injury at this stage. This is especially true when the landing place of the swinging leg is far from the center of gravity. Therefore, it can be concluded that accelerating the downward pressure of the swing leg is not only more conducive to the force of the swing leg when landing but also can effectively avoid the strain of the posterior femoral muscle group.Figure 12
Envelope characteristics of swinging leg of hurdle step of tested athletes.Figure 13
Changes in knee joint angular velocity.The technical muscle contribution of men’s 110-meter hurdler’s swinging leg hurdle step in Figure14 is as follows.Figure 14
Average value and standard deviation of technical muscle contribution of swinging leg hurdle step (%).The discrete degree of muscle contribution can explain the stability of a certain technique of the same athlete to a certain extent. The higher the dispersion of muscle contribution is, the more unstable the technology is. On the contrary, the lower the dispersion of muscle contribution is, the more stable the technology is. Through the observation of muscle contribution, we find that the dispersion of muscle contribution between the three tests of the same athlete is relatively small, indicating that the swing leg technology of the tested athletes is relatively stable in the hurdle step. However, the standard deviation of muscle contribution between different athletes is much larger than that between different tests of the same athlete. There are many reasons for this phenomenon, but the main reason may be the different habits of exerting a force between different athletes. Although the contribution degree of different athletes is relatively different, at the same time, we can also see that the contribution degree of individual muscles in the technical link of the hurdle step is much higher than that of other muscles, for example, tensor fascia lata. As a single joint muscle, tensor fascia lata mainly participates in the thigh retraction in the ground take-off stage and the upper leg lifting in the hurdle attack stage, and the strength and amplitude are relatively large. Therefore, excluding other factors, the contribution of tensor fascia lata in the measured muscles is large, which is also normal. From Figures13 and 14, we can see that in the hurdle step technology, the measured muscle contribution of the swinging leg is as follows: the tensor fascia lata and biceps femoris participate in the activity obviously, followed by the half bond muscle, rectus femoris, and gluteus maximus.


## 4.1. Experimental Method

The EMG measuring instrument used in the experiment is a 16 lead biovision multifunctional exercise bioelectricity measurement and analysis system imported from Germany, and the software version is dasylab10.0. In this experiment, the EMG of surface muscle is collected by EMG measuring instrument. In the experiment, the sampling frequency of the EMG measuring instrument is 1000 Hz and the magnification is 10000. The obtained data are processed by DASYLab 10.0 EMG analysis software [16]. The EMG electrode used in the experiment is a disposable ECG electrode (silver/silver chloride) produced by Renhe Medical Equipment Co., Ltd. The diameter of the electrode adhesion area is 30 mm, and the diameter of the conductive area is 10 mm.


### 4.1.1. Treatment Method of Sticking Position

Before the sEMG test, firstly scrape the surface skin with a razor, then Polish the skin of relevant muscles with fine gauze, clean the skin surface with a 75% alcohol cotton ball, and remove the oil on the skin surface. After the alcohol is evaporated, the illumination electrode and detection electrode are placed on the muscle abdomen of the muscle, and the placement direction is consistent with the long axis of the muscle fiber. The diameter of the electrode is 0.5 cm and the electrode spacing is 2 cm.


### 4.1.2. Data Processing and Statistical Methods

The experimental data were processed and analyzed by the Simi motion video analysis system and DASYLab 10.0 EMG analysis software. Finally, SPSS13.0, origin 7.5, and Excel12003 are used to analyze the average value, standard deviation, and correlation of the data results and draw the graph.


## 4.1.1. Treatment Method of Sticking Position

Before the sEMG test, firstly scrape the surface skin with a razor, then Polish the skin of relevant muscles with fine gauze, clean the skin surface with a 75% alcohol cotton ball, and remove the oil on the skin surface. After the alcohol is evaporated, the illumination electrode and detection electrode are placed on the muscle abdomen of the muscle, and the placement direction is consistent with the long axis of the muscle fiber. The diameter of the electrode is 0.5 cm and the electrode spacing is 2 cm.


## 4.1.2. Data Processing and Statistical Methods

The experimental data were processed and analyzed by the Simi motion video analysis system and DASYLab 10.0 EMG analysis software. Finally, SPSS13.0, origin 7.5, and Excel12003 are used to analyze the average value, standard deviation, and correlation of the data results and draw the graph.


## 4.2. Experimental Steps and Processes

### 4.2.1. Before Experiment

The staff shall register the height, weight, history of injury, and other basic information of the subjects and inform the subjects of the whole experimental process so that the subjects can be familiar with the whole process of the experiment. It is beneficial for the subjects to cooperate with the experiment.


### 4.2.2. Commissioning of Experimental Instruments and Equipment

Set the sampling frequency of MotionPro to 100 Hz and debug the acquisition range. Connect, debug, and check the EMG equipment and check whether the equipment operates normally [17].


### 4.2.3. Preparation of Subjects before Test

Warm-Up. The subject carries out a warm-up exercise to open the muscles, especially the muscles on the exerting side, to prevent the subject from being injured during the experiment. At the same time, carry on the simulation exercise with the data acquisition instrument to get familiar with the action process and make the experimental data closer to the real value.Dress. Subjects are required to wear track and field clothes. Reduce the influence of external factors.Marking. Mark each joint (shoulder, elbow, wrist, skeleton, knee, and step) and head, hand, and foot with marking points.Sticking Electrode. Scrub the measured muscle skin oil with alcohol and scrape off its hair. Stick the electrode to the muscle belly of the measured muscle along the direction of the muscle fiber, and connect the acquisition line. Then, the elastic foam bandage is used to fix it, to avoid the shaking of electrodes and amplifiers in the test process and affect the true value of the collection.


### 4.2.4. Action Requirements of Subjects during Test

The subjects were asked to start after hearing the command and cross four standard height hurdles while maintaining high speed. The staff collected the relevant data in the third column 3 times. If the collected data is checked to be accurate, the subjects will be replaced and the steps will be carried out. After the experimental data of all subjects are collected and archived, the data will be analyzed.


### 4.2.5. Experimental Data Processing

In this study, the surface electromyography instrument was used to collect, process, and analyze the electromyographic activity signals of the muscles related to the technical link of the lower hurdle landing of the hurdle swing leg (biceps femoris long head, Hemi key muscle, rectus femoris, gluteus maximus, and tensor fascia lata), to confirm the activity characteristics of the muscles in the technical link of the lower hurdle landing of the hurdle swing leg. The signal collected by the electromyograph is the original electromyography, which must be processed in the following steps before quantitative analysis [18].The original EMG is filtered first: high pass 20 Hz and low pass 400 Hz to eliminate the noise. After full-wave rectification, the negative wave is transformed into a positive wave. Finally, integrate EMG (iEMG), take out the iEMG of the landing link of the lower hurdle, calculate the muscle contribution through iEMG, and take the envelope as the analysis index. The score is the sum of the electromyographic contribution of a muscle in a certain time. It can reflect the degree of muscle participation in completing a certain action and the importance of the muscle in the action.


## 4.2.1. Before Experiment

The staff shall register the height, weight, history of injury, and other basic information of the subjects and inform the subjects of the whole experimental process so that the subjects can be familiar with the whole process of the experiment. It is beneficial for the subjects to cooperate with the experiment.


## 4.2.2. Commissioning of Experimental Instruments and Equipment

Set the sampling frequency of MotionPro to 100 Hz and debug the acquisition range. Connect, debug, and check the EMG equipment and check whether the equipment operates normally [17].


## 4.2.3. Preparation of Subjects before Test

Warm-Up. The subject carries out a warm-up exercise to open the muscles, especially the muscles on the exerting side, to prevent the subject from being injured during the experiment. At the same time, carry on the simulation exercise with the data acquisition instrument to get familiar with the action process and make the experimental data closer to the real value.Dress. Subjects are required to wear track and field clothes. Reduce the influence of external factors.Marking. Mark each joint (shoulder, elbow, wrist, skeleton, knee, and step) and head, hand, and foot with marking points.Sticking Electrode. Scrub the measured muscle skin oil with alcohol and scrape off its hair. Stick the electrode to the muscle belly of the measured muscle along the direction of the muscle fiber, and connect the acquisition line. Then, the elastic foam bandage is used to fix it, to avoid the shaking of electrodes and amplifiers in the test process and affect the true value of the collection.


## 4.2.4. Action Requirements of Subjects during Test

The subjects were asked to start after hearing the command and cross four standard height hurdles while maintaining high speed. The staff collected the relevant data in the third column 3 times. If the collected data is checked to be accurate, the subjects will be replaced and the steps will be carried out. After the experimental data of all subjects are collected and archived, the data will be analyzed.


## 4.2.5. Experimental Data Processing

In this study, the surface electromyography instrument was used to collect, process, and analyze the electromyographic activity signals of the muscles related to the technical link of the lower hurdle landing of the hurdle swing leg (biceps femoris long head, Hemi key muscle, rectus femoris, gluteus maximus, and tensor fascia lata), to confirm the activity characteristics of the muscles in the technical link of the lower hurdle landing of the hurdle swing leg. The signal collected by the electromyograph is the original electromyography, which must be processed in the following steps before quantitative analysis [18].The original EMG is filtered first: high pass 20 Hz and low pass 400 Hz to eliminate the noise. After full-wave rectification, the negative wave is transformed into a positive wave. Finally, integrate EMG (iEMG), take out the iEMG of the landing link of the lower hurdle, calculate the muscle contribution through iEMG, and take the envelope as the analysis index. The score is the sum of the electromyographic contribution of a muscle in a certain time. It can reflect the degree of muscle participation in completing a certain action and the importance of the muscle in the action.


## 4.3. Kinematic Data Processing Results

As a speed project, speed is the decisive factor affecting the performance of a short-span project. The coordination between the angular velocity of the skeleton joint and the angular velocity of the knee joint will reflect the movement of the swinging leg. At the same time, it also directly affects the quality of hurdle step technology.It can be seen from Figure6 that in the hurdle step technology, the angular velocity of the athlete’s skeleton joint and the angular velocity of the knee joint cooperate with the angular velocity change. The results are as follows: in the landing stage of the starting leg, the swinging leg bends the knee and retracts the lower leg. Relevant studies believe that it is best to keep the folding leg folded before the starting leg leaves the ground as much as possible. From Figure 7, we can see that the peak value of the angular velocity of knee flexion appears before the valley value of the angular velocity of bone flexion, and the time difference between the two values is 4.5070. This phenomenon shows that the contraction angular velocity of the lower leg is greater than that of the thigh. The full folding of the lower leg will be conducive to the swing of the thigh. Because according to I=∑Δmr2 (I: moment of inertia, M: mass of swinging leg, R: radius of rotation of swinging leg), the shorter the radius of rotation of swinging leg, the smaller the moment of inertia of swinging leg. Greater rotational speed will be obtained with the same contraction force. Or the same rotational speed will require less contraction force.Figure 6
Change of knee joint angular velocity.Figure 7
Original electromyography.From Table2, we can also see that there is a good linear relationship between the angle of the knee joint and the angular velocity of the skeleton joint in the take-off stage of the swing leg, showing a significant negative correlation. This result also proves that the folding of the knee joint will affect the rotation speed of the skeleton joint. It conforms to the law of moment of inertia.Table 2
Relationship between knee joint angle and skeletal joint angular velocity in the take-off stage of swing leg hurdle step technology.
Zhu XXHu XXSouth XXZhang XXSong XXAverage valueP valueCorrelation coefficientKnee angle (rad)1.08 ± 0.171.00 ± 0.231.8 ± 0.341.34 ± 0.181.15 ± 0.25Hip angle0.00−0.911Speed (rad/s)12.04 ± 1.5613.05 ± 3.8510.32 ± 1.1712.32 ± 0.1713.32 ± 2.56However, Figure7 shows that the angular velocity curve of the knee joint of the swinging leg of the tested athlete is steep at this stage, indicating that the movement direction is changed immediately after the folding of the knee joint is completed, the knee joint is extended, and there is no sign of prolonging the folding of the swinging leg. Premature extension of the knee joint will not be conducive to the swing of the knee joint. Premature expansion of the knee joint will increase the swing inertia of the swing leg and increase the control difficulty of the swing leg. Excessive flexion of the swing leg joint will delay the pressing time [19].In this paper, through the relationship between the distance of the center of gravity of the foot tip, the descent distance of the center of gravity in the depression stage, the rate of speed loss in the depression stage, the time of hurdle step, and the angular velocity of the skeleton joint, the results are as follows.Through statistics, it is found that there is a negative correlation between the time of hurdle step, the rate of speed loss in the pressing stage of swinging leg, and the angular velocity of the skeleton joint. No correlation was found between other indicators.In the past, relevant scholars have also obtained relevant results in the theoretical research of hurdle step technology. However, the relationship between relevant parameters is rarely proved by statistics. The main reason may be the limitation of experimental conditions and the accuracy of measured parameters is not high enough. Therefore, it is impossible to demonstrate the reasoning of hurdle step technology in the form of numerical value.With the deepening of research, people gradually realize that the motion amplitude and speed of the skeleton joint are more important than those of the knee joint. In the process of pressing down the swinging leg, the movement of the swinging leg is driven by the skeleton joint. Therefore, the movement of the skeleton joint will determine the downward pressure of the swinging leg. From Tables3 and 4, we can see that in the downward pressing stage of swinging leg, the greater the angular velocity of the skeleton joint, the less time required for the hurdle step and the smaller the rate of center of gravity speed loss. The relationship between skeletal joint movement and angular velocity proves that the change in the relative position of each limb can change the athlete’s flying time in the flying stage. In hurdle events, the shortening of flying time is an important way to improve performance.Table 3
Relationship between the time of swinging leg hurdle step and the mean value of joint angular velocity in the next hurdle stage.
Zhu XXHu XXSouth XXZhang XXSong XXAverage valueP valueCorrelation coefficientHurdle step time (S)0.62 ± 0.010.66 ± 0.010.67 ± 0.010.63 ± 0.010.65 ± 0.02Hip angle0.034−0.567Speed (rad/s)12.08 ± 0.6710.12 ± 0.3510.81 ± 0.5710.57 ± 1.1310.99 ± 0.99Table 4
Relationship between velocity loss rate and mean angular velocity of skeleton joint in the pressing stage of swinging leg.
Zhu XXHu XXSouth XXZhang XXSong XXAverage valueP valueCorrelation coefficientSpeed loss rate in pressing stage (%)4.29 ± 0.866.78 ± 2.44.58 ± 0.545.87 ± 0.010.65 ± 0.02Hip angle0.034−0.567Speed (rad/s)12.08 ± 0.6710.12 ± 0.3510.81 ± 0.5710.57 ± 1.1310.99 ± 0.99


### 4.3.1. Surface Electromyography Processing Results

SEMG is the result of bioelectricity generated by nerve and muscle excitation. It is the source of electrical signal generating muscle force. It has different degrees of correlation with muscle activity and functional state, so it can reflect the activity of nerves and muscles to a certain extent. SEMG signal can be used to analyze the characteristics of muscle exertion, muscle activation sequence, exertion degree, and action coordination ability in sports. When the human body is moving, it is used to analyze the important joint angle, the relationship between torque and angle, and EMG. Therefore, it is widely used in the field of sports research. The measured images and data processing are as follows.In order to observe the muscle action or phase change over a period, the rectified high-frequency signal existing in the EMG signal is usually removed, and the obtained curve is called an envelope. By observing the change of envelope, we can know the phase of muscle activity and understand the control and coordination of neuromuscular.From Figures8–11, we can see that in the hurdle step technology of five athletes, the overall trend of EMG activity of swinging legs is relatively similar, while there are differences in EMG amplitude between athletes. The main reason for this phenomenon is that in the process of completing a specific technical action, the relevant muscles need to participate in the work according to their own functions to complete a specific joint movement. The difference in EMG amplitude between athletes is mainly due to the difference in cortical thickness of athletes’ own skin. Cortical thickness is an important factor affecting surface electromyography acquisition. For example, in Zhu XX (the highest sports level among the five), in the whole hurdle step technology, the EMG amplitude of the swinging leg is smaller and more stable than that of other athletes. Through the observation of athletes, in addition to the influence of sebum, there should be other factors affecting Zhu XX’s EMG amplitude which is small and stable. According to the neural control law, Zhu XX’s EMG may be caused by Zhu XX’s own training level [20]. EMG amplitude is small because athletes have a high degree of training. Smaller EMG activities can produce greater muscle strength, which is particularly obvious for beginners. As the EMG is relatively stable, the muscles measured by the swinging leg produce obvious EMG activity only in the take-off stage and support landing stage. It is due to the intermittent mechanism between muscles. Although the swinging leg has been moving throughout the hurdle step technology, no muscle in the measured muscles is involved in the whole process, but intermittently. This intermittent activity will be more conducive to the recovery of fast muscle fiber ability and the next stage of exertion. The stability of Zhu XX’s EMG amplitude shows that the relaxation ability of the measured muscles is better than that of other subjects in the high-speed movement of skeleton joints. This phenomenon is closely related to the training level of athletes.Figure 8
Time sequence of Hu XX muscle activity.Figure 9
Sequence of muscle activity in South XX.Figure 10
Timing of muscle activity.Figure 11
Timing of muscle activity.There are two main reasons for the obvious increase of muscle EMG amplitude measured by the swinging leg before landing. There is a delay in the generation of EMG between joint movements. There is a delay between EMG activity and the movement of lower limb joints. The delay between EMG and muscle strength generation is 10 milliseconds, and the delay between muscle strength generation and joint movement is 30 milliseconds. There is a phenomenon of preactivation in skeletal muscle. By observing the EMG activities of the long head and half key muscle of the biceps femoris, it is found that the EMG activities of the long head and half key muscle of the biceps femoris are similar. From the perspective of action anatomy, the biceps femoris starts from the upper part of the posterior part of the sciatic tubercle, ends at the fat bone, and partially ends at the collateral ligament on the outer side of the knee and the external stepping of the tibia. Its function is to bend the knee joint, rotate the tibia outward, and the function of the divine skeleton joint. The half key muscle starts from the inner upper part of the posterior part of the sciatic tubercle and ends above the inner side of the tibial shaft. Its function is to extend the thigh, bend the knee joint, and rotate the tibia internally. From the perspective of anatomy, the functions of the biceps femoris and Hemi key muscle are similar. They are cross joint muscles, which have the functions of both bone flexion and knee flexion. Therefore, in the hurdle step technology, the EMG activities of the half key muscle of the swinging leg and the biceps femoris are similar and reasonable.Through Figure12, it can be found that there are three fluctuations in the EMG amplitude of the biceps femoris and biceps femoris in the hurdle step technology: the first appears in the support stage, in which the biceps femoris and biceps femoris bend their knees to retract the lower leg, reduce the swing radius of the swing leg, and increase the swing speed of the swing leg when the swing leg is swinging. The fluctuation of the second EMG amplitude occurred in the middle and late stages of the swing leg attacking the hurdle. Considering the disguised movement of the swing leg and the EMG action delay, it can be judged that the main function of this EMG activity is to extend the skeleton. However, we found that the amplitude of EMG in this stage is the smallest of the three fluctuations. Therefore, to some extent, it can be inferred that the hemidesmus and biceps femoris are not the main muscles of bone extension at this stage, and other muscles should undertake the main task of bone extension. In the third wave, the EMG amplitude began to increase significantly from the depression stage, reached the peak at the landing support stage, and remained at a high level throughout the support stage. The main reason for this is that the knee joint starts to bend the knee before landing, to prepare for the formation of strong ground picking action. As shown in Figure 13, actively bending the knee, and retracting the lower leg will play an important role in obtaining a favorable landing position when the swinging leg lands on the ground and completing the cross-run connection better. Because the active recovery of the lower leg before landing will help to form a “pick the ground” posture, reduce the force in the negative direction caused by the ground reaction, reduce the loss of speed, and help swing the leg, at the same time, it can avoid the injury of the rear muscle group caused by the load overload of the rear muscle group due to the unreasonable landing location. In the landing support stage, the EMG amplitude continued to increase until it reached the peak and began to decline steadily. At this stage, the biceps femoris and hemidesqualis are one of the main power sources of the horizontal backward direction of the center of gravity. In the first half of the landing support, it is also the time when the rear muscle group carries the maximum load, so athletes are often prone to injury at this stage. This is especially true when the landing place of the swinging leg is far from the center of gravity. Therefore, it can be concluded that accelerating the downward pressure of the swing leg is not only more conducive to the force of the swing leg when landing but also can effectively avoid the strain of the posterior femoral muscle group.Figure 12
Envelope characteristics of swinging leg of hurdle step of tested athletes.Figure 13
Changes in knee joint angular velocity.The technical muscle contribution of men’s 110-meter hurdler’s swinging leg hurdle step in Figure14 is as follows.Figure 14
Average value and standard deviation of technical muscle contribution of swinging leg hurdle step (%).The discrete degree of muscle contribution can explain the stability of a certain technique of the same athlete to a certain extent. The higher the dispersion of muscle contribution is, the more unstable the technology is. On the contrary, the lower the dispersion of muscle contribution is, the more stable the technology is. Through the observation of muscle contribution, we find that the dispersion of muscle contribution between the three tests of the same athlete is relatively small, indicating that the swing leg technology of the tested athletes is relatively stable in the hurdle step. However, the standard deviation of muscle contribution between different athletes is much larger than that between different tests of the same athlete. There are many reasons for this phenomenon, but the main reason may be the different habits of exerting a force between different athletes. Although the contribution degree of different athletes is relatively different, at the same time, we can also see that the contribution degree of individual muscles in the technical link of the hurdle step is much higher than that of other muscles, for example, tensor fascia lata. As a single joint muscle, tensor fascia lata mainly participates in the thigh retraction in the ground take-off stage and the upper leg lifting in the hurdle attack stage, and the strength and amplitude are relatively large. Therefore, excluding other factors, the contribution of tensor fascia lata in the measured muscles is large, which is also normal. From Figures13 and 14, we can see that in the hurdle step technology, the measured muscle contribution of the swinging leg is as follows: the tensor fascia lata and biceps femoris participate in the activity obviously, followed by the half bond muscle, rectus femoris, and gluteus maximus.


## 4.3.1. Surface Electromyography Processing Results

SEMG is the result of bioelectricity generated by nerve and muscle excitation. It is the source of electrical signal generating muscle force. It has different degrees of correlation with muscle activity and functional state, so it can reflect the activity of nerves and muscles to a certain extent. SEMG signal can be used to analyze the characteristics of muscle exertion, muscle activation sequence, exertion degree, and action coordination ability in sports. When the human body is moving, it is used to analyze the important joint angle, the relationship between torque and angle, and EMG. Therefore, it is widely used in the field of sports research. The measured images and data processing are as follows.In order to observe the muscle action or phase change over a period, the rectified high-frequency signal existing in the EMG signal is usually removed, and the obtained curve is called an envelope. By observing the change of envelope, we can know the phase of muscle activity and understand the control and coordination of neuromuscular.From Figures8–11, we can see that in the hurdle step technology of five athletes, the overall trend of EMG activity of swinging legs is relatively similar, while there are differences in EMG amplitude between athletes. The main reason for this phenomenon is that in the process of completing a specific technical action, the relevant muscles need to participate in the work according to their own functions to complete a specific joint movement. The difference in EMG amplitude between athletes is mainly due to the difference in cortical thickness of athletes’ own skin. Cortical thickness is an important factor affecting surface electromyography acquisition. For example, in Zhu XX (the highest sports level among the five), in the whole hurdle step technology, the EMG amplitude of the swinging leg is smaller and more stable than that of other athletes. Through the observation of athletes, in addition to the influence of sebum, there should be other factors affecting Zhu XX’s EMG amplitude which is small and stable. According to the neural control law, Zhu XX’s EMG may be caused by Zhu XX’s own training level [20]. EMG amplitude is small because athletes have a high degree of training. Smaller EMG activities can produce greater muscle strength, which is particularly obvious for beginners. As the EMG is relatively stable, the muscles measured by the swinging leg produce obvious EMG activity only in the take-off stage and support landing stage. It is due to the intermittent mechanism between muscles. Although the swinging leg has been moving throughout the hurdle step technology, no muscle in the measured muscles is involved in the whole process, but intermittently. This intermittent activity will be more conducive to the recovery of fast muscle fiber ability and the next stage of exertion. The stability of Zhu XX’s EMG amplitude shows that the relaxation ability of the measured muscles is better than that of other subjects in the high-speed movement of skeleton joints. This phenomenon is closely related to the training level of athletes.Figure 8
Time sequence of Hu XX muscle activity.Figure 9
Sequence of muscle activity in South XX.Figure 10
Timing of muscle activity.Figure 11
Timing of muscle activity.There are two main reasons for the obvious increase of muscle EMG amplitude measured by the swinging leg before landing. There is a delay in the generation of EMG between joint movements. There is a delay between EMG activity and the movement of lower limb joints. The delay between EMG and muscle strength generation is 10 milliseconds, and the delay between muscle strength generation and joint movement is 30 milliseconds. There is a phenomenon of preactivation in skeletal muscle. By observing the EMG activities of the long head and half key muscle of the biceps femoris, it is found that the EMG activities of the long head and half key muscle of the biceps femoris are similar. From the perspective of action anatomy, the biceps femoris starts from the upper part of the posterior part of the sciatic tubercle, ends at the fat bone, and partially ends at the collateral ligament on the outer side of the knee and the external stepping of the tibia. Its function is to bend the knee joint, rotate the tibia outward, and the function of the divine skeleton joint. The half key muscle starts from the inner upper part of the posterior part of the sciatic tubercle and ends above the inner side of the tibial shaft. Its function is to extend the thigh, bend the knee joint, and rotate the tibia internally. From the perspective of anatomy, the functions of the biceps femoris and Hemi key muscle are similar. They are cross joint muscles, which have the functions of both bone flexion and knee flexion. Therefore, in the hurdle step technology, the EMG activities of the half key muscle of the swinging leg and the biceps femoris are similar and reasonable.Through Figure12, it can be found that there are three fluctuations in the EMG amplitude of the biceps femoris and biceps femoris in the hurdle step technology: the first appears in the support stage, in which the biceps femoris and biceps femoris bend their knees to retract the lower leg, reduce the swing radius of the swing leg, and increase the swing speed of the swing leg when the swing leg is swinging. The fluctuation of the second EMG amplitude occurred in the middle and late stages of the swing leg attacking the hurdle. Considering the disguised movement of the swing leg and the EMG action delay, it can be judged that the main function of this EMG activity is to extend the skeleton. However, we found that the amplitude of EMG in this stage is the smallest of the three fluctuations. Therefore, to some extent, it can be inferred that the hemidesmus and biceps femoris are not the main muscles of bone extension at this stage, and other muscles should undertake the main task of bone extension. In the third wave, the EMG amplitude began to increase significantly from the depression stage, reached the peak at the landing support stage, and remained at a high level throughout the support stage. The main reason for this is that the knee joint starts to bend the knee before landing, to prepare for the formation of strong ground picking action. As shown in Figure 13, actively bending the knee, and retracting the lower leg will play an important role in obtaining a favorable landing position when the swinging leg lands on the ground and completing the cross-run connection better. Because the active recovery of the lower leg before landing will help to form a “pick the ground” posture, reduce the force in the negative direction caused by the ground reaction, reduce the loss of speed, and help swing the leg, at the same time, it can avoid the injury of the rear muscle group caused by the load overload of the rear muscle group due to the unreasonable landing location. In the landing support stage, the EMG amplitude continued to increase until it reached the peak and began to decline steadily. At this stage, the biceps femoris and hemidesqualis are one of the main power sources of the horizontal backward direction of the center of gravity. In the first half of the landing support, it is also the time when the rear muscle group carries the maximum load, so athletes are often prone to injury at this stage. This is especially true when the landing place of the swinging leg is far from the center of gravity. Therefore, it can be concluded that accelerating the downward pressure of the swing leg is not only more conducive to the force of the swing leg when landing but also can effectively avoid the strain of the posterior femoral muscle group.Figure 12
Envelope characteristics of swinging leg of hurdle step of tested athletes.Figure 13
Changes in knee joint angular velocity.The technical muscle contribution of men’s 110-meter hurdler’s swinging leg hurdle step in Figure14 is as follows.Figure 14
Average value and standard deviation of technical muscle contribution of swinging leg hurdle step (%).The discrete degree of muscle contribution can explain the stability of a certain technique of the same athlete to a certain extent. The higher the dispersion of muscle contribution is, the more unstable the technology is. On the contrary, the lower the dispersion of muscle contribution is, the more stable the technology is. Through the observation of muscle contribution, we find that the dispersion of muscle contribution between the three tests of the same athlete is relatively small, indicating that the swing leg technology of the tested athletes is relatively stable in the hurdle step. However, the standard deviation of muscle contribution between different athletes is much larger than that between different tests of the same athlete. There are many reasons for this phenomenon, but the main reason may be the different habits of exerting a force between different athletes. Although the contribution degree of different athletes is relatively different, at the same time, we can also see that the contribution degree of individual muscles in the technical link of the hurdle step is much higher than that of other muscles, for example, tensor fascia lata. As a single joint muscle, tensor fascia lata mainly participates in the thigh retraction in the ground take-off stage and the upper leg lifting in the hurdle attack stage, and the strength and amplitude are relatively large. Therefore, excluding other factors, the contribution of tensor fascia lata in the measured muscles is large, which is also normal. From Figures13 and 14, we can see that in the hurdle step technology, the measured muscle contribution of the swinging leg is as follows: the tensor fascia lata and biceps femoris participate in the activity obviously, followed by the half bond muscle, rectus femoris, and gluteus maximus.


## 5. Conclusion

Photogrammetry, camera measurement, isokinetic force measurement, 3D dynamic force measurement, and computer numerical simulation are the main test methods of track and field technical sports biomechanics. The kinematic analysis of track and field technology generally adopts high-speed photography or video recording. After analysis, the action details of fast movement can be obtained accurately, to make a technical diagnosis. The kinematic analysis of track and field technology generally adopts cy-bex and safms-t. through the analysis and statistics of the test results, investigate the size and direction of the muscle force of each joint of athletes, and reflect the external work efficiency of athletes. These dynamic indicators can guide strength training and special technology evaluation. The application of computer numerical simulation in the research of track and field technology has promoted the development of track and field technology. Through it, the dynamic model of different track and field events is established to help coaches and athletes make a technical diagnosis and improve movement technology. As the new force of sports biomechanics, neural network theory and finite element method have been used to diagnose track and field technology. With the popularization of this method, it will play a greater role in the research of track and field technology.



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*Source: 3095032-2022-05-06.xml*