# Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

Authors: Qi Han; Shenglu Huo; Rui Li
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

As a traditional Chinese sport, competitive martial arts routines have a long history. The competition rules are the unified norms and standards formulated for sports competitions. They are a yardstick for referees to judge the technical level and competitive ability of athletes and an essential basis for coaches during training. In particular, the new rules increase the difficulty of martial arts routines training and score, improve the balance movement of various groups, highlight the action specifications, increase the proportion of the score, and strengthen the scoring measures for the performance level. Subsequently, this puts higher requirements for the exceptional technical level of routine athletes. Therefore, it is vital to formulate scientific martial arts systematic training methods. This paper considers the above problem and current popular artificial intelligence technology and constructs a neural network algorithm to solve it. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers extensive lactate measurement data to construct martial arts systematic training methods. Through simulations, our experimental verification and the obtained results demonstrate the effectiveness of the proposed algorithm.


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## Body

## 1. Introduction

As a traditional Chinese sports event [1, 2], competitive martial arts routines [3] have a long history and a symbol of Eastern culture. It has a history of nearly a hundred years with the spread and development of modern sports. The competition rules have carried out bold innovations and reforms based on the original rules to meet the needs of form and action. The introduction of the new regulations is conducive to improving the game’s fairness, viewing, and operability. It leads the development of competitive martial arts in the direction of high, complex, beautiful, and novel. It is more consistent with the competition rules of the Olympic skills, challenging, and beautiful. It is in line with the Olympics and is a higher, faster, and more vigorous competitive sport than the other closest rivals, thus, a good trend and beginning of new direction development.The new rules have undergone drastic and leap-forward changes in the scoring methods and scoring content, especially the drastic reforms in the scoring techniques and scoring range of the self-selected routines. It can break the unique Chinese culture that can only be expected but not acceptable anyway. The connotative judging method of rhetoric and rumor is closer to the judging method of western sports. The most prominent is that the optional items increase the score of complex actions. Difficulty movements, including movement difficulty and connection difficulty, and the difficulty of throwing equipment, have changed the value orientation of Wushu routines. These characteristics make these martial arts routines more thrilling, more compact, and more beautiful.The evolution of the new rules has led to the innovation of martial arts routines. In particular, the new rules have increased the coefficients and scores of the complex movements of martial arts routines, added various balance movements in different groups, highlighted the movements’ specifications, increased the proportion of points, and enhanced the scoring measures for the level of practice. The exceptional technical level of routine athletes puts forward higher requirements. From this, we are facing a significant change in the training methods of competitive martial arts [4–6]. Undoubtedly, the past training methods and methods cannot fully meet the current development of competitive martial arts. It is urgent to establish and improve training methods that are compatible with the current rules. To adapt to the new regulations’ requirements, in the future training work, how to arrange our sports training in a targeted and scientific manner and improve the level of sports technology has become an essential topic for our coaches and athletes. The practice of martial arts routines is based on segmented and complete sets of exercises, combined with physical fitness and special quality training, to improve the final set of movements’ quality. A large amount of literature data show that most of the studies use various physiological and biochemical indicators to understand the characteristics of martial arts athletes’ energy metabolism [7] and monitor training and the physical function status of athletes before and after competitions. However, there are few studies using indicators to evaluate specific sports training processes. Most coaches often use their own experience to master the exercise load intensity to arrange training. They have no objective evaluation of the unique training methods and methods, and they cannot reflect the problems existing in sports training. Therefore, according to some classic training methods in martial arts routine practice, the blood lactic acid is used to evaluate the training intensity. It is used to recover athletes with specific application value in assessing the sports training and its effect on martial arts athletes. It also provides suggestions for improving the drill level of complete sets of exercises and challenging exercises’ success rate. This paper considers the current popular artificial intelligence technology and constructs a neural network [8–12] algorithm based on the above observations. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers the extensive lactate measurement data used to construct standard training methods.Following are the main contributions points of this paper:(1)
This paper considers the current popular artificial intelligence technology and constructs a multilayer deep neural network algorithm for martial arts routine prediction.(2)
This paper proposes to use the lactic acid determination method to construct martial arts routine training method because lactic acid is a good monitoring index of martial arts regular exercise training load intensity and effect.(3)
We also constructed a data set and conducted experiments. Experimental results show that this method has achieved excellent results and can formulate a scientific martial arts training plan.The rest of the paper is organized as follows. In section2, background study and literature review are elaborated. The methodology is discussed in section 3, followed by experimental setup and results in section 4. Finally, section 5 concludes the paper.


## 2. Background

With the development of modern competitive sports [13], people have to pay more and improve sports performance. Preparing athletes through usual scientific training, showing the best sports performance in the game without losing the opportunity, has become contemporary sports training. This critical issue needs to be resolved urgently. Coaches must not only master the existing training methods but also understand their characteristics and functions, learn to make the right choices according to specific conditions, use them flexibly, and solve significant problems. Moreover, it is necessary to continuously summarize the practical experience of applying sports training methods and create new and more effective training methods to achieve multiplier efficiency. Since the implementation of the new rules, higher requirements have been put forward for routine athletes’ unique skills. In this regard, coaches should fully understand the characteristics and technical development of martial arts routines and adopt practical and effective training methods. These methods adhere to specialization, intensity, and science principles and comprehensively improve the physical fitness training. Physical fitness directly promotes the mastery and exertion of techniques, thereby enhancing martial arts routines’ skill levels.Many martial arts routines are generally composed of more than a dozen or dozens of single-action movements. There are also many fluctuations and changes in the direction of the route, with different requirements. For example, Changquan exercises [14] require a series of helpful stretching, fast and powerful, precise rhythm, jumping, ups, downs, etc., to be completed cleanly. An action contains many factors. Externally, it requires the coordination and cooperation of hands, eyes, body, and steps, while internally, it has different requirements for spirit, breathing, will, strength, etc. The martial art routine competition is carried out through a whole set of drills, so it is necessary to carry out the subsection, complete set and superset of routine techniques. Through periodic technical training, regular exercise skills will be improved and the unique qualities and functional capabilities required for regular exercises will be further developed. Suppose the technical level of the segmented exercises in the routine exercises is the highest level of the athletes’ skills as the standard. in that case, many athletes often practice the third; the four movements’ strength and speed have decreased, which affects the movement specifications and movement rhythm. The technique cannot be fully utilized. Therefore, it is necessary to practice in segments, sets, and supersets.During exercise, the human body’s energy consumption, especially the skeletal muscle, is significantly increased. The decomposition of adenosine triphosphate provides the energy used by forces, called the direct energy supply material for muscle contraction. Sugar, fat, and protein will gradually release the chemical energy stored in the molecule through corresponding catabolism and transfer and store it into the molecule to ensure energy supply continuity. During exercise, the energy supply in the human body is a complete system, which can be divided into two types of metabolism: aerobic metabolism and anaerobic metabolism. These two types of metabolism include an energy supply system, namely, phosphate system, glycolysis system, and aerobic oxidation system of sugar, fat, and protein. Different sports items and different exercise intensities have different proportions of energy supply systems. Understanding the characteristics and laws of energy supply for other projects and their training methods is the cornerstone of scientific training. This is very important for developing the energy supply capacity of the material energy metabolism system required for particular sports, thus effectively preventing rapid and effective athletic ability [15].The energy metabolism of routine exercises mainly relies on the anaerobic metabolism [16] of lactic acid energy. The average intensity is between meters and meters. As the athletes’’ training level increases, the glycolytic ability also increases. Through the determination of lactic acid, analysis, and comparison of the lactic acid values between men and women athletes of different levels, different unique routines and routine exercises verified that the energy supply of Wushu routines is mainly anaerobic glycolysis. By synthesizing the results of previous studies on the energy metabolism characteristics of martial arts routines, it is concluded that the energy metabolism of competitive martial arts routines is mainly provided by anaerobic glycolysis, accounting for 75%–80%, and aerobic metabolism is used as the supplement of about 20%–25%.


## 3. Methodology

The key to martial arts routine training methods lies in the prediction of martial arts routines. This section proposes an improved multilayer recurrent neural network [17–22]. In addition, the big data of lactate determination will assist the neural network in predicting.


### 3.1. Improved Multilayer Recurrent Neural Network

The improved network structure has two main modules: the expanded convolutional pyramid module and the multilayer convolutional long and short-term memory module. It is used to realize the functions of processing martial arts action features and action recognition, respectively.


#### 3.1.1. Expanded Convolutional Pyramid Module

In the traditional target recognition network, Hongmei Song et al. [23] proposed a new pyramid expanded convolution module that uses multiple parallel two-dimensional expanded convolutions to achieve multiscale while retaining the original static features images. Feature extraction avoids the use of downsampling operations and reduces the parameters in the network. We combine three-dimensional convolution and expansion convolution. The model can read continuous martial arts action image sequences and obtains the ability to extract multi-scale features of martial arts routine actions. We call this advanced module the expanded convolutional pyramid module.The use of dilated convolution to achieve multiscale feature extraction is to make full use of dilated convolution characteristics. We can briefly understand dilated convolution as a convolution operation combined with a downsampling operation. Take a two-dimensional convolution kernel with a size of 3 × 3 as an example. When the degree of expansion is 1, the convolution kernel can only act on the range of 3 × 3 on the image. At this time, the number of pixels participating in the convolution operation on the image is 3 × 3. If the degree of expansion is two, then the convolution kernel acts on the image. In this case, the distance between adjacent elements of the convolution kernel is expanded to two and the range of the convolution kernel is expanded to 5 × 5. However, the number of pixels involved in the convolution operation is still 3 × 3. Figure1 graphically shows the above example. It can be seen that, under the premise of not changing the original image, the process of downsampling and convolution can be realized by using dilated convolution.Figure 1
Diagram of dilated convolution network. 3∗3 convolution kernel with (a) expansion degree 1 and (b) expansion degree 2.
(a)(b)In the dilated convolution pyramid module, we use four parallel three-dimensional dilated convolutions with expansion degrees of 2, 4, 8, and 16, respectively. Based on extracting the characteristic information of a single martial art routine action image, 3D convolution can obtain related information among multiple martial arts images. This allows 3D convolution to read continuous martial arts action images while extracting martial arts action sequences’’ spatial information. Combined with dilated convolution, 3D dilated convolution has the ability of 3D convolution to read continuous martial arts action images and extract spatial data. It has the essential characteristics of dilated convolution, which can achieve feature extraction of different scales by adjusting the expansion degree.The performance of three-dimensional dilated convolution on a single image is similar to that of two-dimensional dilated convolution. The distance between adjacent elements changes with the expansion degree change, but when extracting the related information between multiple images, the expansion degree is not affected. The changes change. Figure2 shows a schematic diagram of a three-dimensional dilated convolution with a dilation degree of 2.Figure 2
Schematic diagram of 3D dilated convolution network.


#### 3.1.2. Multilayer Convolutional Long- and Short-Term Memory Module

In our model, because the three-dimensional convolution cannot obtain the more comprehensive spatial information in the martial arts action sequence, it is necessary to use the cyclic neural network to get more comprehensive spatial information in the martial arts action sequence so that the model can recognize and understand the image sequence of the martial arts action. In the work of Hongmei Song et al., the use of bidirectional recurrent neural networks also deepens the entire network. The bidirectional cyclic neural network can obtain the associated information of a specific image in the sequence from the two directions of the martial arts action image sequence. Using this method can allow the network to learn better and understand the martial arts action image sequence. In the multilayer convolutional long- and short-term memory modules, we use two parallel double-layer convolutional long- and short-term memory networks with expansion degrees of 1 and 2, respectively. It is used to extract the action information martial arts action sequence. Compared with the bidirectional cyclic neural network, the double-layer convolutional long- and short-term memory can obtain the deep semantic information hidden in the video sequence and, at the same time, eliminate the information that has nothing to do with the human body in the martial arts sequence, such as the color of clothes and the intensity of light and other information.Figure3 is the structure of LSTM. There are three operations called “gates” in LSTM: forget gate, input gate, and output gate. The function of these three doors simulates forgetting, acquisition, and memory in the process of human memory. At the same time, long-term memory Ct and short-term memory ht are designed in LSTM. They are used to record the long-term dependence in the sequence and the connection between the inputs. These unique designs help LSTM continuously obtain the connections between adjacent inputs while ensuring that long-span long-term dependency information is not lost. Each node of LSTM has three input values: the long-term memory Ct−1 of the previous node, the short-term memory ht−1, and the read input Xt of the current node, and two output values: the long-term memory of the current node Ct and short-term memory ht. When three input values are input to LSTM, ht−1 and Xt through forgetting gate, input gate, and output gate in turn, the process of forgetting invalid information in short-term memory and acquiring and memorizing useful information is realized. This process can be expressed as(1)ft=σWf×ht−1,Xt+bf,it=σWi×ht−1,Xt+bi,ot=σWo×ht−1,Xt+bo,where W represents the weight matrix, b represents the offset, σ represents the activation function, ft represents the output of the forget gate, it represents the output of the input gate, and represents the output of the output gate.Figure 3
LSTM network cell.At the same time, according toht−1 and Xt, the candidate information Ct that needs to be recorded C˜t can be expressed as(2)C˜t=tanhWC×ht−1,Xt+bC.Finally, combineCt−1, ft, it, ot, and C˜t to get Ct and ht of the current node:(3)Ct=ft∗Ct−1+it∗C˜t,ht=ot∗tanhCt.The convolutional long- and short-term memory networks replace the matrix multiplication in the forget gate, input gate, output gate, and candidate informationC˜t in LSTM with convolution operations:(4)ft=σWf∘ht−1,Xt+bf,it=σWi∘ht−1,Xt+bi,ot=σWo∘ht−1,Xt+bo,where ∘ represents the convolution operation.


#### 3.1.3. Proposed Model

In the practice of the existing dual-flow method models, it is found that the codes of these models cannot realize the reading and processing of continuous martial arts action images. However, only a single image of martial arts activities can be read, making the model unable to establish the connection between martial arts action sequence and martial arts action tag. As a result, we improved the model so that it can read continuous martial arts action images.The improved model has two branches with the same structure, one of which input is the color martial arts action image and the other is the optical flow information image. We refer to the standard RESNET-50 module and modify the 2-D convolution into 3-D convolution to make it 3-D RESNET-50. ResNet has achieved excellent results in the field of image recognition. After that, according to their implementation code and model description, they launched a standard RESNET module for others to quickly call and realize image recognition, including the standard RESNET-50 module. The transformation of the standard RESNET-50 is straightforward. We only replace the two-dimensional convolution layer with the three-dimensional convolution layer and then remove the last pooling layer and classifier in the standard RESNET-50 to get the three-dimensional RESNET-50 feature extractor. Then, we use the expanded convolutional pyramid module for multiscale feature extraction. After that, the multiscale features and ResNet-50 features are fused and input into the multilayer convolutional long- and short-term memory modules. In the multilayer convolutional network, long- and short-term memory modules fuse the extracted information. The extraction takes place through two parallel double-layer convolutional long and short-term memory modules with expansion degrees of 1 and 2, respectively, for multiscale semantic feature extraction. Finally, the classification vector is obtained through the average pooling layer, the fully connected layer, and the softmax classifier.By replacing the two-dimensional convolution operation in the ResNet-50 and PDC modules with three-dimensional convolution, the improved model-pyramid convolution long- and short-term memory network can read continuous material arts action images but also obtain the constant input. This shows the information characteristic of the links between images. Besides, when modifying two-dimensional convolution to three-dimensional convolution, parameters such as step size and normalization also need to be changed. Figure4 shows the pyramid convolutional long and short-term memory network’’s specific structure and the connections between the modules.Figure 4
Pyramid convolutional long- and short-term memory network structure diagram.


### 3.2. Application of Blood Lactic Acid Index in Wushu Routine Sports

Routine athletes of different levels in the same event have different maximum blood lactic acid concentrations after regular exercises. The higher the exercise level, the greater the total value of blood lactate after exercise. Compared with the change in blood lactate after training, the higher the exercise level, the more significant the change in blood lactate value. After the same load, the higher the exercise level, the greater the maximum blood lactate. Before the blood lactate reaches its maximum value during exercise, athletes with high levels of training have a higher increase in blood lactate than ordinary athletes in a certain period. Still, the ultimate value is higher than that of common athletes. During the recovery period after exercise, athletes with high exercise levels have a more significant decrease in blood lactate than ordinary athletes. This shows that high-level athletes have their anaerobic glycolysis ability.The ability of blood lactic acid is higher than that of ordinary athletes. The change of blood lactic acid concentration before and after exercise is the balance performance between the production speed of lactic acid in the body tissue and the elimination speed of lactic acid in the blood. It is related to the energy used. This further shows that high-level athletes are more capable of using an anaerobic glycolysis system than ordinary athletes. The stronger the athlete’s anaerobic glycolysis ability, the higher the blood lactic acid concentration after the routine exercise, the higher the exercise level, and the greater the maximum blood lactic acid after the regular exercise. Therefore, the level of blood lactic acid concentration can indirectly reflect the athlete’s athletic ability and competitive ability. After training, the higher the blood lactic acid concentration, the stronger the athlete’s competitive ability. Blood lactic acid concentration reflects the changes in athletes’ competitive ability. Athletes measure their blood lactic acid concentration before training or before training. After a period of training, their blood lactic acid concentration is then measured. The previous improvement will improve the competitive ability and vice versa. Therefore, lactic acid can assist martial arts routine training.


## 3.1. Improved Multilayer Recurrent Neural Network

The improved network structure has two main modules: the expanded convolutional pyramid module and the multilayer convolutional long and short-term memory module. It is used to realize the functions of processing martial arts action features and action recognition, respectively.


### 3.1.1. Expanded Convolutional Pyramid Module

In the traditional target recognition network, Hongmei Song et al. [23] proposed a new pyramid expanded convolution module that uses multiple parallel two-dimensional expanded convolutions to achieve multiscale while retaining the original static features images. Feature extraction avoids the use of downsampling operations and reduces the parameters in the network. We combine three-dimensional convolution and expansion convolution. The model can read continuous martial arts action image sequences and obtains the ability to extract multi-scale features of martial arts routine actions. We call this advanced module the expanded convolutional pyramid module.The use of dilated convolution to achieve multiscale feature extraction is to make full use of dilated convolution characteristics. We can briefly understand dilated convolution as a convolution operation combined with a downsampling operation. Take a two-dimensional convolution kernel with a size of 3 × 3 as an example. When the degree of expansion is 1, the convolution kernel can only act on the range of 3 × 3 on the image. At this time, the number of pixels participating in the convolution operation on the image is 3 × 3. If the degree of expansion is two, then the convolution kernel acts on the image. In this case, the distance between adjacent elements of the convolution kernel is expanded to two and the range of the convolution kernel is expanded to 5 × 5. However, the number of pixels involved in the convolution operation is still 3 × 3. Figure1 graphically shows the above example. It can be seen that, under the premise of not changing the original image, the process of downsampling and convolution can be realized by using dilated convolution.Figure 1
Diagram of dilated convolution network. 3∗3 convolution kernel with (a) expansion degree 1 and (b) expansion degree 2.
(a)(b)In the dilated convolution pyramid module, we use four parallel three-dimensional dilated convolutions with expansion degrees of 2, 4, 8, and 16, respectively. Based on extracting the characteristic information of a single martial art routine action image, 3D convolution can obtain related information among multiple martial arts images. This allows 3D convolution to read continuous martial arts action images while extracting martial arts action sequences’’ spatial information. Combined with dilated convolution, 3D dilated convolution has the ability of 3D convolution to read continuous martial arts action images and extract spatial data. It has the essential characteristics of dilated convolution, which can achieve feature extraction of different scales by adjusting the expansion degree.The performance of three-dimensional dilated convolution on a single image is similar to that of two-dimensional dilated convolution. The distance between adjacent elements changes with the expansion degree change, but when extracting the related information between multiple images, the expansion degree is not affected. The changes change. Figure2 shows a schematic diagram of a three-dimensional dilated convolution with a dilation degree of 2.Figure 2
Schematic diagram of 3D dilated convolution network.


### 3.1.2. Multilayer Convolutional Long- and Short-Term Memory Module

In our model, because the three-dimensional convolution cannot obtain the more comprehensive spatial information in the martial arts action sequence, it is necessary to use the cyclic neural network to get more comprehensive spatial information in the martial arts action sequence so that the model can recognize and understand the image sequence of the martial arts action. In the work of Hongmei Song et al., the use of bidirectional recurrent neural networks also deepens the entire network. The bidirectional cyclic neural network can obtain the associated information of a specific image in the sequence from the two directions of the martial arts action image sequence. Using this method can allow the network to learn better and understand the martial arts action image sequence. In the multilayer convolutional long- and short-term memory modules, we use two parallel double-layer convolutional long- and short-term memory networks with expansion degrees of 1 and 2, respectively. It is used to extract the action information martial arts action sequence. Compared with the bidirectional cyclic neural network, the double-layer convolutional long- and short-term memory can obtain the deep semantic information hidden in the video sequence and, at the same time, eliminate the information that has nothing to do with the human body in the martial arts sequence, such as the color of clothes and the intensity of light and other information.Figure3 is the structure of LSTM. There are three operations called “gates” in LSTM: forget gate, input gate, and output gate. The function of these three doors simulates forgetting, acquisition, and memory in the process of human memory. At the same time, long-term memory Ct and short-term memory ht are designed in LSTM. They are used to record the long-term dependence in the sequence and the connection between the inputs. These unique designs help LSTM continuously obtain the connections between adjacent inputs while ensuring that long-span long-term dependency information is not lost. Each node of LSTM has three input values: the long-term memory Ct−1 of the previous node, the short-term memory ht−1, and the read input Xt of the current node, and two output values: the long-term memory of the current node Ct and short-term memory ht. When three input values are input to LSTM, ht−1 and Xt through forgetting gate, input gate, and output gate in turn, the process of forgetting invalid information in short-term memory and acquiring and memorizing useful information is realized. This process can be expressed as(1)ft=σWf×ht−1,Xt+bf,it=σWi×ht−1,Xt+bi,ot=σWo×ht−1,Xt+bo,where W represents the weight matrix, b represents the offset, σ represents the activation function, ft represents the output of the forget gate, it represents the output of the input gate, and represents the output of the output gate.Figure 3
LSTM network cell.At the same time, according toht−1 and Xt, the candidate information Ct that needs to be recorded C˜t can be expressed as(2)C˜t=tanhWC×ht−1,Xt+bC.Finally, combineCt−1, ft, it, ot, and C˜t to get Ct and ht of the current node:(3)Ct=ft∗Ct−1+it∗C˜t,ht=ot∗tanhCt.The convolutional long- and short-term memory networks replace the matrix multiplication in the forget gate, input gate, output gate, and candidate informationC˜t in LSTM with convolution operations:(4)ft=σWf∘ht−1,Xt+bf,it=σWi∘ht−1,Xt+bi,ot=σWo∘ht−1,Xt+bo,where ∘ represents the convolution operation.


### 3.1.3. Proposed Model

In the practice of the existing dual-flow method models, it is found that the codes of these models cannot realize the reading and processing of continuous martial arts action images. However, only a single image of martial arts activities can be read, making the model unable to establish the connection between martial arts action sequence and martial arts action tag. As a result, we improved the model so that it can read continuous martial arts action images.The improved model has two branches with the same structure, one of which input is the color martial arts action image and the other is the optical flow information image. We refer to the standard RESNET-50 module and modify the 2-D convolution into 3-D convolution to make it 3-D RESNET-50. ResNet has achieved excellent results in the field of image recognition. After that, according to their implementation code and model description, they launched a standard RESNET module for others to quickly call and realize image recognition, including the standard RESNET-50 module. The transformation of the standard RESNET-50 is straightforward. We only replace the two-dimensional convolution layer with the three-dimensional convolution layer and then remove the last pooling layer and classifier in the standard RESNET-50 to get the three-dimensional RESNET-50 feature extractor. Then, we use the expanded convolutional pyramid module for multiscale feature extraction. After that, the multiscale features and ResNet-50 features are fused and input into the multilayer convolutional long- and short-term memory modules. In the multilayer convolutional network, long- and short-term memory modules fuse the extracted information. The extraction takes place through two parallel double-layer convolutional long and short-term memory modules with expansion degrees of 1 and 2, respectively, for multiscale semantic feature extraction. Finally, the classification vector is obtained through the average pooling layer, the fully connected layer, and the softmax classifier.By replacing the two-dimensional convolution operation in the ResNet-50 and PDC modules with three-dimensional convolution, the improved model-pyramid convolution long- and short-term memory network can read continuous material arts action images but also obtain the constant input. This shows the information characteristic of the links between images. Besides, when modifying two-dimensional convolution to three-dimensional convolution, parameters such as step size and normalization also need to be changed. Figure4 shows the pyramid convolutional long and short-term memory network’’s specific structure and the connections between the modules.Figure 4
Pyramid convolutional long- and short-term memory network structure diagram.


## 3.1.1. Expanded Convolutional Pyramid Module

In the traditional target recognition network, Hongmei Song et al. [23] proposed a new pyramid expanded convolution module that uses multiple parallel two-dimensional expanded convolutions to achieve multiscale while retaining the original static features images. Feature extraction avoids the use of downsampling operations and reduces the parameters in the network. We combine three-dimensional convolution and expansion convolution. The model can read continuous martial arts action image sequences and obtains the ability to extract multi-scale features of martial arts routine actions. We call this advanced module the expanded convolutional pyramid module.The use of dilated convolution to achieve multiscale feature extraction is to make full use of dilated convolution characteristics. We can briefly understand dilated convolution as a convolution operation combined with a downsampling operation. Take a two-dimensional convolution kernel with a size of 3 × 3 as an example. When the degree of expansion is 1, the convolution kernel can only act on the range of 3 × 3 on the image. At this time, the number of pixels participating in the convolution operation on the image is 3 × 3. If the degree of expansion is two, then the convolution kernel acts on the image. In this case, the distance between adjacent elements of the convolution kernel is expanded to two and the range of the convolution kernel is expanded to 5 × 5. However, the number of pixels involved in the convolution operation is still 3 × 3. Figure1 graphically shows the above example. It can be seen that, under the premise of not changing the original image, the process of downsampling and convolution can be realized by using dilated convolution.Figure 1
Diagram of dilated convolution network. 3∗3 convolution kernel with (a) expansion degree 1 and (b) expansion degree 2.
(a)(b)In the dilated convolution pyramid module, we use four parallel three-dimensional dilated convolutions with expansion degrees of 2, 4, 8, and 16, respectively. Based on extracting the characteristic information of a single martial art routine action image, 3D convolution can obtain related information among multiple martial arts images. This allows 3D convolution to read continuous martial arts action images while extracting martial arts action sequences’’ spatial information. Combined with dilated convolution, 3D dilated convolution has the ability of 3D convolution to read continuous martial arts action images and extract spatial data. It has the essential characteristics of dilated convolution, which can achieve feature extraction of different scales by adjusting the expansion degree.The performance of three-dimensional dilated convolution on a single image is similar to that of two-dimensional dilated convolution. The distance between adjacent elements changes with the expansion degree change, but when extracting the related information between multiple images, the expansion degree is not affected. The changes change. Figure2 shows a schematic diagram of a three-dimensional dilated convolution with a dilation degree of 2.Figure 2
Schematic diagram of 3D dilated convolution network.


## 3.1.2. Multilayer Convolutional Long- and Short-Term Memory Module

In our model, because the three-dimensional convolution cannot obtain the more comprehensive spatial information in the martial arts action sequence, it is necessary to use the cyclic neural network to get more comprehensive spatial information in the martial arts action sequence so that the model can recognize and understand the image sequence of the martial arts action. In the work of Hongmei Song et al., the use of bidirectional recurrent neural networks also deepens the entire network. The bidirectional cyclic neural network can obtain the associated information of a specific image in the sequence from the two directions of the martial arts action image sequence. Using this method can allow the network to learn better and understand the martial arts action image sequence. In the multilayer convolutional long- and short-term memory modules, we use two parallel double-layer convolutional long- and short-term memory networks with expansion degrees of 1 and 2, respectively. It is used to extract the action information martial arts action sequence. Compared with the bidirectional cyclic neural network, the double-layer convolutional long- and short-term memory can obtain the deep semantic information hidden in the video sequence and, at the same time, eliminate the information that has nothing to do with the human body in the martial arts sequence, such as the color of clothes and the intensity of light and other information.Figure3 is the structure of LSTM. There are three operations called “gates” in LSTM: forget gate, input gate, and output gate. The function of these three doors simulates forgetting, acquisition, and memory in the process of human memory. At the same time, long-term memory Ct and short-term memory ht are designed in LSTM. They are used to record the long-term dependence in the sequence and the connection between the inputs. These unique designs help LSTM continuously obtain the connections between adjacent inputs while ensuring that long-span long-term dependency information is not lost. Each node of LSTM has three input values: the long-term memory Ct−1 of the previous node, the short-term memory ht−1, and the read input Xt of the current node, and two output values: the long-term memory of the current node Ct and short-term memory ht. When three input values are input to LSTM, ht−1 and Xt through forgetting gate, input gate, and output gate in turn, the process of forgetting invalid information in short-term memory and acquiring and memorizing useful information is realized. This process can be expressed as(1)ft=σWf×ht−1,Xt+bf,it=σWi×ht−1,Xt+bi,ot=σWo×ht−1,Xt+bo,where W represents the weight matrix, b represents the offset, σ represents the activation function, ft represents the output of the forget gate, it represents the output of the input gate, and represents the output of the output gate.Figure 3
LSTM network cell.At the same time, according toht−1 and Xt, the candidate information Ct that needs to be recorded C˜t can be expressed as(2)C˜t=tanhWC×ht−1,Xt+bC.Finally, combineCt−1, ft, it, ot, and C˜t to get Ct and ht of the current node:(3)Ct=ft∗Ct−1+it∗C˜t,ht=ot∗tanhCt.The convolutional long- and short-term memory networks replace the matrix multiplication in the forget gate, input gate, output gate, and candidate informationC˜t in LSTM with convolution operations:(4)ft=σWf∘ht−1,Xt+bf,it=σWi∘ht−1,Xt+bi,ot=σWo∘ht−1,Xt+bo,where ∘ represents the convolution operation.


## 3.1.3. Proposed Model

In the practice of the existing dual-flow method models, it is found that the codes of these models cannot realize the reading and processing of continuous martial arts action images. However, only a single image of martial arts activities can be read, making the model unable to establish the connection between martial arts action sequence and martial arts action tag. As a result, we improved the model so that it can read continuous martial arts action images.The improved model has two branches with the same structure, one of which input is the color martial arts action image and the other is the optical flow information image. We refer to the standard RESNET-50 module and modify the 2-D convolution into 3-D convolution to make it 3-D RESNET-50. ResNet has achieved excellent results in the field of image recognition. After that, according to their implementation code and model description, they launched a standard RESNET module for others to quickly call and realize image recognition, including the standard RESNET-50 module. The transformation of the standard RESNET-50 is straightforward. We only replace the two-dimensional convolution layer with the three-dimensional convolution layer and then remove the last pooling layer and classifier in the standard RESNET-50 to get the three-dimensional RESNET-50 feature extractor. Then, we use the expanded convolutional pyramid module for multiscale feature extraction. After that, the multiscale features and ResNet-50 features are fused and input into the multilayer convolutional long- and short-term memory modules. In the multilayer convolutional network, long- and short-term memory modules fuse the extracted information. The extraction takes place through two parallel double-layer convolutional long and short-term memory modules with expansion degrees of 1 and 2, respectively, for multiscale semantic feature extraction. Finally, the classification vector is obtained through the average pooling layer, the fully connected layer, and the softmax classifier.By replacing the two-dimensional convolution operation in the ResNet-50 and PDC modules with three-dimensional convolution, the improved model-pyramid convolution long- and short-term memory network can read continuous material arts action images but also obtain the constant input. This shows the information characteristic of the links between images. Besides, when modifying two-dimensional convolution to three-dimensional convolution, parameters such as step size and normalization also need to be changed. Figure4 shows the pyramid convolutional long and short-term memory network’’s specific structure and the connections between the modules.Figure 4
Pyramid convolutional long- and short-term memory network structure diagram.


## 3.2. Application of Blood Lactic Acid Index in Wushu Routine Sports

Routine athletes of different levels in the same event have different maximum blood lactic acid concentrations after regular exercises. The higher the exercise level, the greater the total value of blood lactate after exercise. Compared with the change in blood lactate after training, the higher the exercise level, the more significant the change in blood lactate value. After the same load, the higher the exercise level, the greater the maximum blood lactate. Before the blood lactate reaches its maximum value during exercise, athletes with high levels of training have a higher increase in blood lactate than ordinary athletes in a certain period. Still, the ultimate value is higher than that of common athletes. During the recovery period after exercise, athletes with high exercise levels have a more significant decrease in blood lactate than ordinary athletes. This shows that high-level athletes have their anaerobic glycolysis ability.The ability of blood lactic acid is higher than that of ordinary athletes. The change of blood lactic acid concentration before and after exercise is the balance performance between the production speed of lactic acid in the body tissue and the elimination speed of lactic acid in the blood. It is related to the energy used. This further shows that high-level athletes are more capable of using an anaerobic glycolysis system than ordinary athletes. The stronger the athlete’s anaerobic glycolysis ability, the higher the blood lactic acid concentration after the routine exercise, the higher the exercise level, and the greater the maximum blood lactic acid after the regular exercise. Therefore, the level of blood lactic acid concentration can indirectly reflect the athlete’s athletic ability and competitive ability. After training, the higher the blood lactic acid concentration, the stronger the athlete’s competitive ability. Blood lactic acid concentration reflects the changes in athletes’ competitive ability. Athletes measure their blood lactic acid concentration before training or before training. After a period of training, their blood lactic acid concentration is then measured. The previous improvement will improve the competitive ability and vice versa. Therefore, lactic acid can assist martial arts routine training.


## 4. Experiments

In section4.1, we discuss the experimental environment. In section 4.2, a sketch of the simulation setup and parameters is given. The methods used to evaluate the performance of the proposed work are described in section 4.3. Finally, results are elaborated in section 4.4.


### 4.1. Experimental Environment

We use GTX 1060 to accelerate the entire training process, the running platform is winning 10, and the running memory is 8.OOGB. The whole model is built using the Pytorch framework. In the data processing stage, we extract 10% of the data from each category as the test set, 63% as the training set, and 27% as the validation set. The data in the test set is stored separately. During the training and verification process, the model cannot touch the test set data. Only in the testing phase, the test put information is read.


### 4.2. Experimental Setup

We read two martial arts routine training videos each time during training. Each video is ten frames in length, and the read martial arts routine training videos are randomly intercepted fragments from the original video. The initial learning rate is 0.001. For every fifty rounds of training, the learning rate is multiplied by 0.1. In the training process, the model’s loss value is recorded every 100 times of training activity; after each round of training, a verification operation is performed, and the accuracy of the model is recorded. We observed the changes in the loss value and accuracy rate after the 50th round during the training process. However, the changes in the loss value and accuracy rate were significantly slower. When the training entered the 60th round, the loss value tended to be more stable and the accuracy rate remains unchanged. After retrieving the model data saved in the 50th round, the training and validation set data are adjusted by exchanging part of the training data with the guarantee information. Now, adjust the learning rate to the initial value and perform training again, but this time, the loss value and accuracy rate have not changed. At this time, we judge that the model training has reached its limit.


### 4.3. Evaluation Method

To further analyze the network’s performance, we calculated the model’s confusion matrix and used the confusion matrix to investigate further and evaluate the proposed model. We can intuitively understand how much data in a certain action in the test set, such as a fall, are correctly classified and how much is incorrectly classified through the confusion matrix. The evaluation equations are calculated as follows:(5)TPrate=TPTP+FN,ACC=TP+TNTP+TN+FP+FN.


### 4.4. Experimental Results

The performance of the long instrument model, double instrument, soft instrument, and other actions is relatively satisfactory. However, the account in optional boxing and other activities needs to be improved. It can be seen from Tables1 and 2 that, in the probability vector given by the model, the probability of the accurate label is at a high level, but other options affect the judgment. We judged that there are two main reasons for the above situation: one is that after the data are preprocessed, the image resolution is reduced, the movement of the character is highly blurred, and the range of action is reduced; the second is that the model can only receive ten consecutive frames of video images, part of the action. There is a significant overlap between them, and the received video is too short to perceive the complete action.Table 1
TP and ACC of different types of martial arts (%).
TypeTPACCOptional boxing0.890.89Prescribed boxing0.880.91Traditional boxing0.870.92Short instrument0.910.87Long instrument0.920.93Double instrument0.900.91Soft instrument0.910.92Table 2
The prediction time of our method for different martial art(s).
TypeTimeOptional boxing5.3Prescribed boxing6.6Traditional boxing5.2Short instrument4.9Long instrument5.9Double instrument6.9Soft instrument3.7Before preprocessing, although the characters’ actions are only concentrated in a small area in the video image, the image resolution is high and the characters’ actions can be seen. After preprocessing, the image is compressed and the solution is significantly reduced. Human body movements are concentrated in a small area, and some minor signs are difficult to capture and distinguish. On the other hand, the video sequence that our model can receive is too short of capturing the complete action. For example, if only the first few frames of the action are obtained for a prescribed boxing action, people cannot determine the category of actions. In general, the method in this paper has achieved excellent performance in terms of prediction accuracy and model calculation time. It can provide a specific reference for the martial arts competition team to formulate scientific training programs.


## 4.1. Experimental Environment

We use GTX 1060 to accelerate the entire training process, the running platform is winning 10, and the running memory is 8.OOGB. The whole model is built using the Pytorch framework. In the data processing stage, we extract 10% of the data from each category as the test set, 63% as the training set, and 27% as the validation set. The data in the test set is stored separately. During the training and verification process, the model cannot touch the test set data. Only in the testing phase, the test put information is read.


## 4.2. Experimental Setup

We read two martial arts routine training videos each time during training. Each video is ten frames in length, and the read martial arts routine training videos are randomly intercepted fragments from the original video. The initial learning rate is 0.001. For every fifty rounds of training, the learning rate is multiplied by 0.1. In the training process, the model’s loss value is recorded every 100 times of training activity; after each round of training, a verification operation is performed, and the accuracy of the model is recorded. We observed the changes in the loss value and accuracy rate after the 50th round during the training process. However, the changes in the loss value and accuracy rate were significantly slower. When the training entered the 60th round, the loss value tended to be more stable and the accuracy rate remains unchanged. After retrieving the model data saved in the 50th round, the training and validation set data are adjusted by exchanging part of the training data with the guarantee information. Now, adjust the learning rate to the initial value and perform training again, but this time, the loss value and accuracy rate have not changed. At this time, we judge that the model training has reached its limit.


## 4.3. Evaluation Method

To further analyze the network’s performance, we calculated the model’s confusion matrix and used the confusion matrix to investigate further and evaluate the proposed model. We can intuitively understand how much data in a certain action in the test set, such as a fall, are correctly classified and how much is incorrectly classified through the confusion matrix. The evaluation equations are calculated as follows:(5)TPrate=TPTP+FN,ACC=TP+TNTP+TN+FP+FN.


## 4.4. Experimental Results

The performance of the long instrument model, double instrument, soft instrument, and other actions is relatively satisfactory. However, the account in optional boxing and other activities needs to be improved. It can be seen from Tables1 and 2 that, in the probability vector given by the model, the probability of the accurate label is at a high level, but other options affect the judgment. We judged that there are two main reasons for the above situation: one is that after the data are preprocessed, the image resolution is reduced, the movement of the character is highly blurred, and the range of action is reduced; the second is that the model can only receive ten consecutive frames of video images, part of the action. There is a significant overlap between them, and the received video is too short to perceive the complete action.Table 1
TP and ACC of different types of martial arts (%).
TypeTPACCOptional boxing0.890.89Prescribed boxing0.880.91Traditional boxing0.870.92Short instrument0.910.87Long instrument0.920.93Double instrument0.900.91Soft instrument0.910.92Table 2
The prediction time of our method for different martial art(s).
TypeTimeOptional boxing5.3Prescribed boxing6.6Traditional boxing5.2Short instrument4.9Long instrument5.9Double instrument6.9Soft instrument3.7Before preprocessing, although the characters’ actions are only concentrated in a small area in the video image, the image resolution is high and the characters’ actions can be seen. After preprocessing, the image is compressed and the solution is significantly reduced. Human body movements are concentrated in a small area, and some minor signs are difficult to capture and distinguish. On the other hand, the video sequence that our model can receive is too short of capturing the complete action. For example, if only the first few frames of the action are obtained for a prescribed boxing action, people cannot determine the category of actions. In general, the method in this paper has achieved excellent performance in terms of prediction accuracy and model calculation time. It can provide a specific reference for the martial arts competition team to formulate scientific training programs.


## 5. Conclusion

The new rules increase the coefficients and scores of the complex movements of martial art routines, improve the balance movements of various groups, highlight the movements’ specifications, increase the proportion of scores, and enhance the scoring measures for the level of practice. The unique technical level puts forward higher requirements. Therefore, it is essential to formulate scientific martial art routine training methods. This article considers the current popular artificial intelligence technology and constructs a neural network algorithm. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers the significant lactate measurement data used to construct martial arts regular training methods. This paper also carried out experimental verification, and the results proved the effectiveness of the proposed algorithm.



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*Source: 5522899-2021-05-12.xml*