# Evaluation of the Physical Education Teaching and Training Efficiency by the Integration of Ideological and Political Courses with Lightweight Deep Learning

Authors: Shuaiqi Zhang
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

The purpose is to improve the training effect of physical education (PE) based on the teaching concept of ideological and political courses. The research is supported by the lightweight deep learning (DL) model of the Internet of things (IoT). Through intelligent recognition and classification of human action and images, it discusses the PE and training scheme based on the lightweight DL model. In addition, by the optimization of the accelerated compression algorithm and the evaluation of the PE and training effect of the Openpose algorithm, an optimization model of the PE and training effect has been successfully established. The research data results indicate that after 120 iterations of the model, the system recognition accuracy of the convolutional neural network (CNN) algorithm can only be improved to about 75%, while the recognition accuracy of the Openpose algorithm can reach about 85%. Compared with the CNN algorithm under the same number of iterations, the recognition accuracy can be improved by 9.8%. In addition, when the number of nodes in the network layer is 60, the system delay time of the proposed Openpose algorithm is smaller. At this time, the system delay of the algorithm is only 10.8s. Compared with the CNN algorithm under the same conditions, the proposed algorithm can save at least 1.2s in system delay time. The advantage of the algorithm is that it can improve the efficiency of physical training and teaching, and this research has important reference significance for the digital and intelligent development of the teaching mode of PE.


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## Body

## 1. Introduction

In the process of physical education (PE), the traditional PE method is limited by the professional and technical level of teachers, and the teaching quality and teaching effect are often difficult to guarantee [1–3]. Meanwhile, traditional PE requires a certain space and venue and has strict requirements for venue and training time. Because there are certain teaching specialties and teaching preferences among the teachers of PE training, this will also affect the improvement in teaching and training efficiency to a certain extent. Furthermore, during the process of PE training, some movements are too technically difficult. For technical movements such as flying and high-speed flips in the process of training, the too high difficulty will affect students’ enthusiasm for learning. It is difficult to establish a complete demonstration action, which will affect the process and efficiency of classroom teaching [4–6].With the rapid development of Internet of things (IoT) technology, ideological and political courses have broken through the content of traditional ideological and political teaching. Combining lightweight deep learning (DL) models can improve the strategies of PE teaching. The process of evaluating PE courses by integrating ideological and political courses into lightweight DL models can further improve the training efficiency of PE [7]. On the basis of computer vision technology and human pose estimation algorithm, through the extraction of video and image features, the human skeleton structure map in the three-dimensional (3D) space is restored, and then the physical training actions of the human are identified. The expected value of the research is to formulate corresponding evaluation algorithms in combination with professional sports evaluation indicators, to guide and optimize the training actions of athletes in the images, and to further improve the learning effect of training [8–10]. Some scholars have conducted related research on the combination of ideological and political courses in colleges and universities and lightweight DL models. For example, Yi et al. (2020) [11] reformed and realized the ideological and political teaching of college English based on cultural confidence. By summarizing and reflecting on the current situation of ideological and political education in China, the practical significance of cultivating students’ cultural self-confidence through curriculum setting is introduced, and the correlation between college English courses and ideological and political education is analyzed. Perrotta et al. (2020) [12] conducted research on the relationship understanding of artificial intelligence (AI) in education, trying to add AI elements to a few online learning environments. The research has practical application value for methods such as neural networks to perform tasks without human intervention.Based on the concept of ideological and political courses integration, the research improves the training effect of PE through a lightweight DL model and expects to find the optimal solution to improve the training efficiency of PE. Through the optimization of the lightweight DL model, the innovation lies in putting forward a teaching improvement scheme for ideological and political courses that can effectively solve the problem of low training efficiency of traditional PE teaching. This research is divided into five parts, in the first part, through the introduction of the background of PE and the DL model of the IoT, the purpose and the theme of the research are introduced. In the second part, through recent relevant literature, the research status of ideological and political courses and the lightweight DL model is discussed, and the relationship between the DL model and PE training is obtained. In the third part, through the systematic analysis and overview of the DL target detection algorithm, by the optimization of the accelerated compression algorithm of the model, a lightweight DL training model based on the Openpose algorithm is proposed. In the fourth part, through the comparison of the result parameters of the algorithm model, the experimental result data are obtained. Finally, the experimental conclusion is drawn in the fifth part. The research has practical reference value for the application of lightweight DL models in the field of PE and training.


## 2. Recent Related Works

### 2.1. Related Research on Ideological and Political Courses and Lightweight DL

Scholars have conducted many explorations and discussions on the teaching concept of ideological and political courses in colleges and universities. Li and Fu [13] conducted related research on college English teaching based on the concept of ideological and political education. The research results demonstrate that college English, as a prominent feature of instrumentalism and humanism, is an important platform to realize the teaching of ideological and political theory courses. Liu et al. [14] conducted related research on the structure and practice of ideological and political education in Chinese universities, drawing on the thematic analysis of data files in Chinese universities, and provided empirical evidence on the party’s leadership structure and various activities in ideological and political education in Chinese universities. Zhang and Li [15] studied ideology in college English teaching under the background of ideological and political courses and made investments and analyses on the ideological data of college English teaching in the current context from the aspects of cognitive ability, environment, characteristics, and measures. The research proposed helpful measures to consolidate ideological security. Hu and Zhou [16] analyzed the ideological and political theory courses on the cultivation of college students’ personal morality and studied related research on the teaching of ideological and political theory courses from the three dimensions of textbooks, teachers, and teaching methods. The results refer that some college students have the problem of a mismatch of personal morality and professional knowledge, and it is necessary to cultivate personal morality through ideological and political theory education.Besides, regarding the application of IoT technology in lightweight DL models, Zhu et al. [17] adopted a lightweight DL model in mobile edge computing to identify and study radar-based human actions. The research proposed an extremely efficient convolutional neural network (CNN) architecture called Mobile-RadarNet. The research results illustrate that the use of one-dimensional deep convolution and point-by-point convolution to build a lightweight DL architecture can improve the precision and accuracy of model recognition. Xu et al. [18] studied a lightweight DL detector for ship detection and proposed a lightweight shipborne SAR ship detector named Lite-YOLOv5, which reduces the size of the model and improves the efficiency of floating-point arithmetic. The research results denote that Lite-YOLOv5 can achieve a lighter architecture with a model’s size of only 2.38 M (14.18% of the size of the YOLOv5 model), and the model’s computational cost is only 26.59% of the original YOLOv5 model. To sum up, the main advantage of current ideological and political courses and lightweight DL research is that with the technical support of lightweight DL models, the teaching efficiency of ideological and political courses can be greatly improved. Moreover, the extraction of network features and the sharing of weights further reduce the network complexity of the model. However, ideological and political courses combined with lightweight DL research have certain shortcomings. The main disadvantage is that the system memory is limited, and network deployment resources cannot be reasonably allocated.


### 2.2. Research on Lightweight DL Model and PE Training

Regarding the relationship between DL models and classroom teaching in colleges and universities, An et al. (2021) [19] studied the related teaching of flipped classrooms in colleges and universities based on the hierarchical learning model of DL. The research results show that the improved teaching mode significantly improves the ability of non-computer majors to use computational thinking to solve practical problems encountered by their majors through data analysis of evaluation results and students’ survey feedback. Chen et al. (2021) [20] conducted an application and case study on the network security of smart cities based on DL. Through a comprehensive overview of the existing network security of DL technology, six DL models were classified into network security applications. The research summarizes the knowledge and interpretation of the smart city, network security, and concepts of DL and discusses existing work related to IoT security in smart cities, which has practical reference value for the future development trend of network security in smart cities. Li et al. (2021) [21] conducted related research on medical image fusion methods by using DL, overcoming the difficulty of manual design by automatically extracting the most effective features from the data. By building an image training database to obtain a new model in the successful fusion results, it is suitable for image fusion to improve the efficiency and accuracy of image processing. Experimental results indicate that the proposed method achieves state-of-the-art performance in both visual quality and quantitative evaluation indicators, and the algorithm stability and time efficiency are greatly improved.Furthermore, for the research progress of the training process of PE, Dekun et al. (2021) [22] designed and researched the mobile intelligent evaluation algorithm in PE and designed a new mobile intelligent evaluation algorithm and applied it to the mobile intelligent evaluation of PE. The experimental results demonstrate that the proposed algorithm has a higher evaluation accuracy than the traditional mobile intelligent evaluation algorithm, and the evaluation accuracy can be maintained above 96%. Lei et al. [23] analyzed sports image detection and action recognition based on particle swarm optimization (PSO). By locating image detection technology, video, and video files, the results show that the proposed method is always practical and can provide a theoretical basis for future research. Wang et al. [24] conducted research on sports motion recognition systems driven by IoT. Cost-effective heterogeneous devices were connected with mobile applications. The experimental results indicate that the proposed solution can create a good environment for PE teaching. In conclusion, regarding the many drawbacks of traditional sports, scholars have used new mobile intelligence technologies and lightweight DL models to evaluate and experience teaching effects. Combined with the lightweight DL target detection algorithm, the innovation is that the teaching effect of physical training can be further optimized by identifying and detecting human action images in the process of physical training.


## 2.1. Related Research on Ideological and Political Courses and Lightweight DL

Scholars have conducted many explorations and discussions on the teaching concept of ideological and political courses in colleges and universities. Li and Fu [13] conducted related research on college English teaching based on the concept of ideological and political education. The research results demonstrate that college English, as a prominent feature of instrumentalism and humanism, is an important platform to realize the teaching of ideological and political theory courses. Liu et al. [14] conducted related research on the structure and practice of ideological and political education in Chinese universities, drawing on the thematic analysis of data files in Chinese universities, and provided empirical evidence on the party’s leadership structure and various activities in ideological and political education in Chinese universities. Zhang and Li [15] studied ideology in college English teaching under the background of ideological and political courses and made investments and analyses on the ideological data of college English teaching in the current context from the aspects of cognitive ability, environment, characteristics, and measures. The research proposed helpful measures to consolidate ideological security. Hu and Zhou [16] analyzed the ideological and political theory courses on the cultivation of college students’ personal morality and studied related research on the teaching of ideological and political theory courses from the three dimensions of textbooks, teachers, and teaching methods. The results refer that some college students have the problem of a mismatch of personal morality and professional knowledge, and it is necessary to cultivate personal morality through ideological and political theory education.Besides, regarding the application of IoT technology in lightweight DL models, Zhu et al. [17] adopted a lightweight DL model in mobile edge computing to identify and study radar-based human actions. The research proposed an extremely efficient convolutional neural network (CNN) architecture called Mobile-RadarNet. The research results illustrate that the use of one-dimensional deep convolution and point-by-point convolution to build a lightweight DL architecture can improve the precision and accuracy of model recognition. Xu et al. [18] studied a lightweight DL detector for ship detection and proposed a lightweight shipborne SAR ship detector named Lite-YOLOv5, which reduces the size of the model and improves the efficiency of floating-point arithmetic. The research results denote that Lite-YOLOv5 can achieve a lighter architecture with a model’s size of only 2.38 M (14.18% of the size of the YOLOv5 model), and the model’s computational cost is only 26.59% of the original YOLOv5 model. To sum up, the main advantage of current ideological and political courses and lightweight DL research is that with the technical support of lightweight DL models, the teaching efficiency of ideological and political courses can be greatly improved. Moreover, the extraction of network features and the sharing of weights further reduce the network complexity of the model. However, ideological and political courses combined with lightweight DL research have certain shortcomings. The main disadvantage is that the system memory is limited, and network deployment resources cannot be reasonably allocated.


## 2.2. Research on Lightweight DL Model and PE Training

Regarding the relationship between DL models and classroom teaching in colleges and universities, An et al. (2021) [19] studied the related teaching of flipped classrooms in colleges and universities based on the hierarchical learning model of DL. The research results show that the improved teaching mode significantly improves the ability of non-computer majors to use computational thinking to solve practical problems encountered by their majors through data analysis of evaluation results and students’ survey feedback. Chen et al. (2021) [20] conducted an application and case study on the network security of smart cities based on DL. Through a comprehensive overview of the existing network security of DL technology, six DL models were classified into network security applications. The research summarizes the knowledge and interpretation of the smart city, network security, and concepts of DL and discusses existing work related to IoT security in smart cities, which has practical reference value for the future development trend of network security in smart cities. Li et al. (2021) [21] conducted related research on medical image fusion methods by using DL, overcoming the difficulty of manual design by automatically extracting the most effective features from the data. By building an image training database to obtain a new model in the successful fusion results, it is suitable for image fusion to improve the efficiency and accuracy of image processing. Experimental results indicate that the proposed method achieves state-of-the-art performance in both visual quality and quantitative evaluation indicators, and the algorithm stability and time efficiency are greatly improved.Furthermore, for the research progress of the training process of PE, Dekun et al. (2021) [22] designed and researched the mobile intelligent evaluation algorithm in PE and designed a new mobile intelligent evaluation algorithm and applied it to the mobile intelligent evaluation of PE. The experimental results demonstrate that the proposed algorithm has a higher evaluation accuracy than the traditional mobile intelligent evaluation algorithm, and the evaluation accuracy can be maintained above 96%. Lei et al. [23] analyzed sports image detection and action recognition based on particle swarm optimization (PSO). By locating image detection technology, video, and video files, the results show that the proposed method is always practical and can provide a theoretical basis for future research. Wang et al. [24] conducted research on sports motion recognition systems driven by IoT. Cost-effective heterogeneous devices were connected with mobile applications. The experimental results indicate that the proposed solution can create a good environment for PE teaching. In conclusion, regarding the many drawbacks of traditional sports, scholars have used new mobile intelligence technologies and lightweight DL models to evaluate and experience teaching effects. Combined with the lightweight DL target detection algorithm, the innovation is that the teaching effect of physical training can be further optimized by identifying and detecting human action images in the process of physical training.


## 3. PE Training and Optimization Scheme by Lightweight DL Model

### 3.1. System Analysis and Overview of the Lightweight DL Target Detection Algorithm

In the lightweight DL target detection system, the collection of research data sets is extracted by video sensors, and the extracted images are the action images in the process of PE training [25–27]. After the image collection is completed, it is necessary to extract the position and action features of the target person in the image, classify and organize the action images of the same category, and directly convert the marked image information data into the corresponding xml file for saving. In the system, the training and optimization of the model algorithm is the key to determining the effect of the model detection. By building a network structure model, the research selects the network integrated with MobileNet as the training structure of the target detection algorithm. The network structure first estimates the error between the real value of the target image and the predicted value of the network through forward propagation calculation and minimizes the error of network parameters through continuous back propagation, which further improves the effect of model detection. The model structure and overall frame of the DL target detection algorithm are shown in Figure 1.Figure 1
The model structure of the DL target detection algorithm.


### 3.2. Optimization of Accelerated Compression Algorithms for Lightweight Models

In the compression optimization algorithm of the lightweight model, the matrix factorization (MF) is a frequently used algorithm. This algorithm extracts the parameter elements of each layer of matrix by using an approximate estimation method and improves the operation speed through model compression [28–30]. In the process of model compression, the convolution operation will affect the calculation amount of the model, and the optimization of the fully connected layer of the network will directly affect the size of the data stored in the network model. The feature tensor value of the two-dimensional matrix can be extended to the three-dimensional convolution kernel after being optimized by the singular value decomposition (SVD). For the vital eigenvalue vectors in the diagonal matrix, the weight coefficient matrix is used to split the fully connected layer, and a linear activation function is used between the non-fully connected layers for optimization. Beyond that, the problem of loss of precision after the matrix data are compressed requires low-rank factorization of the redundant data in the convolution kernel, and local approximate solutions are obtained through the number of input and output feature maps. Meanwhile, for the hyperparameters in the network model filter, the matrix elements are compressed and factorized on the original network structure through the minimized objective function. The grouping operation is carried out in the form of deep separable convolution, which further reduces the number of parameters of the network, realizes the optimization and replacement of the average pooling layer of the network, and further ensures the model fitting ability of the neural network. For the model fitting ability and the training process of the accelerated compression algorithm, the results are shown in Figure 2.Figure 2
The model training process of the accelerated compression algorithm.


### 3.3. Optimization of Lightweight Deep Teaching Training Model Based on Openpose Algorithm

The traditional DL model has many defects in the detection of the target field. The biggest disadvantage is that it needs to manually extract the color, action, and other edge features of the images, which puts forward high requirements for the comprehensive quality of researchers [31, 32]. In addition, due to the complexity of the application scene background, the shape of the recognition target often changes greatly. In the case of insufficient lighting conditions, the recognition and detection effect of the system for the target image is very poor. To solve these problems, it is necessary for the user to provide the coordinates of the skeletal point data of the detector. By matching and judging the data of human action and posture, taking the elbow joint of the human body as the base point, the angle between the upper arm and the forearm of the human body is measured, and the human skeleton space is divided into 6 major limbs. The division and processing of human skeleton point data is carried out through the characteristics of different angles of the limbs.In the process of constructing a lightweight deep teaching training target detection algorithm, the image size and shape of target detection are uniformly processed through the region proposal network. The sliding window of feature extraction generates a reference rectangular frame with three angles of 1282, 2562, and 5122, respectively, to identify the target waiting to be detected. Through the correction of the position of the feature point and the position of the rectangular frame, the target detection score output from the fully connected layer to the result layer is uniformly corrected, and then converted into the confidence parameter of the corresponding category after normalization. The activation function between the connection layers adopts the Softmax function, and the output of the feature images of the same size through the pooling layer, to realize the construction of the system identification network of the target detection algorithm. Before the system identification network is constructed, users extract data through different servers and build the main identification network with the help of edge service equipment. The image of the lightweight DL and training model constructed based on the Openpose algorithm is shown in Figure3.Figure 3
Schematic diagram of the lightweight DL and training model.


### 3.4. Parameter Settings of Experiment

Ideological and political courses are used to integrate lightweight DL models to identify and match PE actions. Image recognition and feature extraction of human pose features are carried out through the system. PE actions are graded on a standard level. After the extracted data eliminate the different lengths of video frame sequence arrangement, the action types are identified by matching similar actions. In the action of PE, the eight-beat data of stretching exercise are extracted to construct the test data set, and 2 representative actions are selected for each beat; therefore, a total of 16 representative actions are selected. Each action is an action video clip with a duration of about 3s. Five groups of different motion data of 15 motion collectors are extracted, and a total of 1350 valid motion samples are collected. The data acquisition uses an image sensor, the sensor is about 3m away from the action collector, the sensor model is Imx400, the effective pixel is 5520∗3840, the unit pixel size is 1.22 microns, and the built-in Dram chip performs dynamic random access. For model training, the CPU used is GeForce GTX 1080, the system memory size is 16 GB, the development environment is Windows 10, and the development language is Java. The computer hardware facilities and overall network structure are shown in Figure 4.Figure 4
The overall network structure of experiment.Moreover, to compare the optimization and improvement effects of the proposed lightweight DL model for the PE training scheme, the proposed Openpose-based sports action recognition algorithm and the optimization results of the convolutional neural network (CNN) algorithm are compared, respectively, in terms of accuracy, precision, recall, and F1 value. Meanwhile, to compare the efficiency and optimization degree of the proposed system model, the running time of the model and the delay time of the system are also compared, and the experimental data results are counted and discussed. Among them, the recognition accuracy refers to the number of samples that the system predicts correctly, the precision means the probability that all predicted positive samples are actually positive samples, the recall manifests the probability that the actual positive samples are predicted to be positive samples, and the F1 value refers to a balanced value that considers both precision and recall. The calculation of accuracy, precision, recall, and F1 value is shown in equations (1) to (4), respectively.(1)Accuracy=TP+TNTP+TN+FP+FN,(2)Precision=TPTP+FP,(3)Recall=TPTP+FN,(4)F1=2∗Precision∗RecallPrecision+Recall.“Accuracy,” “Precision”, and “Recall” represent the accuracy rate, precision rate, and recall rate, respectively. TP expresses the number of samples whose actual and predicted values are true. FP indicates the number of samples that are actually false and predicted to be true. FN is the number of samples that are actually true and predicted to be false, andTN denotes the number of samples for which both actual and predicted are false.


## 3.1. System Analysis and Overview of the Lightweight DL Target Detection Algorithm

In the lightweight DL target detection system, the collection of research data sets is extracted by video sensors, and the extracted images are the action images in the process of PE training [25–27]. After the image collection is completed, it is necessary to extract the position and action features of the target person in the image, classify and organize the action images of the same category, and directly convert the marked image information data into the corresponding xml file for saving. In the system, the training and optimization of the model algorithm is the key to determining the effect of the model detection. By building a network structure model, the research selects the network integrated with MobileNet as the training structure of the target detection algorithm. The network structure first estimates the error between the real value of the target image and the predicted value of the network through forward propagation calculation and minimizes the error of network parameters through continuous back propagation, which further improves the effect of model detection. The model structure and overall frame of the DL target detection algorithm are shown in Figure 1.Figure 1
The model structure of the DL target detection algorithm.


## 3.2. Optimization of Accelerated Compression Algorithms for Lightweight Models

In the compression optimization algorithm of the lightweight model, the matrix factorization (MF) is a frequently used algorithm. This algorithm extracts the parameter elements of each layer of matrix by using an approximate estimation method and improves the operation speed through model compression [28–30]. In the process of model compression, the convolution operation will affect the calculation amount of the model, and the optimization of the fully connected layer of the network will directly affect the size of the data stored in the network model. The feature tensor value of the two-dimensional matrix can be extended to the three-dimensional convolution kernel after being optimized by the singular value decomposition (SVD). For the vital eigenvalue vectors in the diagonal matrix, the weight coefficient matrix is used to split the fully connected layer, and a linear activation function is used between the non-fully connected layers for optimization. Beyond that, the problem of loss of precision after the matrix data are compressed requires low-rank factorization of the redundant data in the convolution kernel, and local approximate solutions are obtained through the number of input and output feature maps. Meanwhile, for the hyperparameters in the network model filter, the matrix elements are compressed and factorized on the original network structure through the minimized objective function. The grouping operation is carried out in the form of deep separable convolution, which further reduces the number of parameters of the network, realizes the optimization and replacement of the average pooling layer of the network, and further ensures the model fitting ability of the neural network. For the model fitting ability and the training process of the accelerated compression algorithm, the results are shown in Figure 2.Figure 2
The model training process of the accelerated compression algorithm.


## 3.3. Optimization of Lightweight Deep Teaching Training Model Based on Openpose Algorithm

The traditional DL model has many defects in the detection of the target field. The biggest disadvantage is that it needs to manually extract the color, action, and other edge features of the images, which puts forward high requirements for the comprehensive quality of researchers [31, 32]. In addition, due to the complexity of the application scene background, the shape of the recognition target often changes greatly. In the case of insufficient lighting conditions, the recognition and detection effect of the system for the target image is very poor. To solve these problems, it is necessary for the user to provide the coordinates of the skeletal point data of the detector. By matching and judging the data of human action and posture, taking the elbow joint of the human body as the base point, the angle between the upper arm and the forearm of the human body is measured, and the human skeleton space is divided into 6 major limbs. The division and processing of human skeleton point data is carried out through the characteristics of different angles of the limbs.In the process of constructing a lightweight deep teaching training target detection algorithm, the image size and shape of target detection are uniformly processed through the region proposal network. The sliding window of feature extraction generates a reference rectangular frame with three angles of 1282, 2562, and 5122, respectively, to identify the target waiting to be detected. Through the correction of the position of the feature point and the position of the rectangular frame, the target detection score output from the fully connected layer to the result layer is uniformly corrected, and then converted into the confidence parameter of the corresponding category after normalization. The activation function between the connection layers adopts the Softmax function, and the output of the feature images of the same size through the pooling layer, to realize the construction of the system identification network of the target detection algorithm. Before the system identification network is constructed, users extract data through different servers and build the main identification network with the help of edge service equipment. The image of the lightweight DL and training model constructed based on the Openpose algorithm is shown in Figure3.Figure 3
Schematic diagram of the lightweight DL and training model.


## 3.4. Parameter Settings of Experiment

Ideological and political courses are used to integrate lightweight DL models to identify and match PE actions. Image recognition and feature extraction of human pose features are carried out through the system. PE actions are graded on a standard level. After the extracted data eliminate the different lengths of video frame sequence arrangement, the action types are identified by matching similar actions. In the action of PE, the eight-beat data of stretching exercise are extracted to construct the test data set, and 2 representative actions are selected for each beat; therefore, a total of 16 representative actions are selected. Each action is an action video clip with a duration of about 3s. Five groups of different motion data of 15 motion collectors are extracted, and a total of 1350 valid motion samples are collected. The data acquisition uses an image sensor, the sensor is about 3m away from the action collector, the sensor model is Imx400, the effective pixel is 5520∗3840, the unit pixel size is 1.22 microns, and the built-in Dram chip performs dynamic random access. For model training, the CPU used is GeForce GTX 1080, the system memory size is 16 GB, the development environment is Windows 10, and the development language is Java. The computer hardware facilities and overall network structure are shown in Figure 4.Figure 4
The overall network structure of experiment.Moreover, to compare the optimization and improvement effects of the proposed lightweight DL model for the PE training scheme, the proposed Openpose-based sports action recognition algorithm and the optimization results of the convolutional neural network (CNN) algorithm are compared, respectively, in terms of accuracy, precision, recall, and F1 value. Meanwhile, to compare the efficiency and optimization degree of the proposed system model, the running time of the model and the delay time of the system are also compared, and the experimental data results are counted and discussed. Among them, the recognition accuracy refers to the number of samples that the system predicts correctly, the precision means the probability that all predicted positive samples are actually positive samples, the recall manifests the probability that the actual positive samples are predicted to be positive samples, and the F1 value refers to a balanced value that considers both precision and recall. The calculation of accuracy, precision, recall, and F1 value is shown in equations (1) to (4), respectively.(1)Accuracy=TP+TNTP+TN+FP+FN,(2)Precision=TPTP+FP,(3)Recall=TPTP+FN,(4)F1=2∗Precision∗RecallPrecision+Recall.“Accuracy,” “Precision”, and “Recall” represent the accuracy rate, precision rate, and recall rate, respectively. TP expresses the number of samples whose actual and predicted values are true. FP indicates the number of samples that are actually false and predicted to be true. FN is the number of samples that are actually true and predicted to be false, andTN denotes the number of samples for which both actual and predicted are false.


## 4. Results and Discussion

### 4.1. Comparison of Accuracy and Precision of the System Model

To study the performance of the Openpose-based human action recognition algorithm of PE, the accuracy and precision of the human action recognition of the constructed Openpose algorithm are compared. The results are shown in Figures5 and 6.Figure 5
Comparison of the accuracy results of different algorithms for human action recognition.Figure 6
Comparison of the precision results of different algorithms for human action recognition.In Figure5, it denotes from the comparison results of the accuracy rates of different algorithms for human action recognition that with the increase of the number of model iterations, the system recognition accuracy of the two algorithms is gradually increasing. In the 20 iterations of the model, the system recognition accuracy of the CNN algorithm can only reach 55%, while the recognition accuracy of the Openpose algorithm can reach more than 70% at this time, which is at least 15% higher than that of the CNN algorithm. As the number of model iterations increases, after 120 iterations of the model, the system recognition accuracy of the CNN algorithm can only increase to about 75%, while the recognition accuracy of the proposed Openpose algorithm can reach about 85%. Compared with the CNN algorithm under the same number of iterations, the recognition accuracy can be improved by 9.8%. Therefore, the research data results demonstrate the advantages of the proposed Openpose algorithm in terms of recognition accuracy of the model.In Figure6, it indicates from the comparison of the precision results of different algorithms for human action recognition that with the increase of the number of model iterations, the action recognition accuracy of the two algorithms is significantly improved, which shows that through the training of the model iteration, the DL model can grasp the characteristics of human action and improve recognition precision of the image. Besides, when the model has only experienced 20 iterations, the recognition precision of the CNN algorithm is only 40%, while the recognition precision of the proposed Openpose algorithm can reach about 60%. As the number of model iterations increases, after 120 iterations of the model, the image recognition precision of the CNN algorithm can reach more than 70%. At this time, the recognition precision of the Openpose algorithm is 79.6%, and the difference between the recognition accuracy of the two algorithms can be reduced to less than 10%. In summary, the recognition precision of the Openpose algorithm is always better than that of the CNN algorithm, which can demonstrate the superiority of the performance of the proposed algorithm.


### 4.2. Comparison of Recall and F1 Value of System Model

For the comparison of the performance of the model system, in addition to considering the accuracy and precision of the algorithm for action image recognition, it is also necessary to compare the recall of the algorithm with the F1 value. The recall of the algorithm refers to the probability that the model predicts a positive sample in the actual positive sample data, and the F1 value of the model refers to an evaluation index based on the precision and recall of the algorithm model, which is comprehensively compared and analyzed to achieve a balance between the two. The recall and F1 value data of the Openpose algorithm and CNN algorithm are counted, and the results are shown in Figure7 and Figure 8.Figure 7
Comparison of the recall results of different algorithms for human action recognition.Figure 8
Comparison of the F1 value results of different algorithms for human action recognition.From the comparison of the recall results of different algorithms for human action recognition in Figure7, it means that the system recall of the two algorithms is gradually increasing with the increase of the number of model iterations, and the CNN algorithm has a larger growth rate. In the 20 iterations of the model, the recognition recall of the CNN algorithm is only about 30%. With the increase of the number of model iterations, in the 120 iterations of the system model, the recognition recall of the CNN algorithm can reach more than 70%, and the overall recall of the algorithm increased by more than 40%. However, under the same number of model iterations, the recognition recall of the Openpose algorithm is always better than that of the CNN algorithm. For example, when the number of system iterations is 100, the recognition recall of the Openpose algorithm can reach 72.3%. At this time, the recognition recall of the CNN algorithm is only 60%. Therefore, the performance results of the algorithm system using Openpose are better.From the comparison of the F1 value results of different algorithms for human action recognition in Figure8, it expresses that for the test results of the system F1 value, the test volatility of the CNN algorithm is relatively large. When the number of model iterations is 20 to 100 times, the average result of the F1 value of the CNN algorithm is 52.5%. However, at 120 iterations of the model, the F1 value of the CNN algorithm suddenly increased to about 80%. The excessive growth rate shows that the stability of the system is difficult to guarantee. Compared with the volatility of the CNN algorithm, the average F1 value of the Openpose algorithm is about 65%, which makes it easier to guarantee system stability and security. Therefore, the Openpose algorithm outperforms the CNN algorithm.


### 4.3. Evaluation of the Effect of Improving the Efficiency of PE

To compare the effect of improving the training efficiency of the proposed Openpose algorithm on PE, it is necessary to compare the system running time and system delay time of the Openpose algorithm and the CNN algorithm. When comparing the system running time, to reduce the experimental error, a set of experiments are carried out in the system of the CNN algorithm, and the data are shown in model A1 in Figure9. Among them, three sets of different abscissas represent different models A1, A2, and A3, and their running times all correspond to the left abscissa, but the range of running times is different. Meanwhile, two sets of experiments are carried out in the same system of the Openpose algorithm, and the results of the running time of the two sets of systems are shown as models A2 and A3 in Figure 9. The system running time and delay time of the model are shown in Figures 9 and 10, respectively.Figure 9
Comparison of system running time of different algorithm models.Figure 10
Comparison of system delay time of different algorithm models.In Figure9, model A1 refers to a set of experimental data in the system of the CNN algorithm, while models A2 and A3 represent two different sets of experimental data in the Openpose algorithm, respectively. Comparing the system running times of different algorithm models, it denotes that the system running time of CNN algorithm is obviously too long, the average running time is 20∼50s, and a lot of time of the system are wasted. Compared with the CNN algorithm, the two sets of experimental data of the proposed Openpose algorithm show that the average running time of the system is about 20s. It can be seen from the comparison results of the research data that the Openpose algorithm can greatly improve the operating efficiency and the operating speed of the system, which have important practical significance for the improvement in the efficiency of PE.From the comparison of the system delay time of different algorithm models in Figure10, it means that the number of network nodes is inversely proportional to the system delay time. With the increase of the number of system network nodes, the system delay time of the two algorithms gradually decreases. However, the system delay of the CNN algorithm decreases more greatly. When there are 10 nodes, the system delay time is 17s. When the number of nodes increases to 60, the system delay time of the CNN algorithm is reduced to 12s, which is larger than that of the Openpose algorithm. However, when the number of nodes is 60, the system delay time of the proposed Openpose algorithm is smaller. At this time, the system delay of the algorithm is only 10.8s. Compared with the CNN algorithm under the same conditions, the proposed algorithm can save at least 1.2s in system delay time. Therefore, the system delay time performance of the proposed algorithm is better than that of the CNN algorithm.


### 4.4. Discussion

To sum up, based on the lightweight DL model, the system analysis and design of the target detection algorithm are studied, and the model is optimized by the accelerated compression algorithm. The image recognition and feature extraction of the human posture features is carried out through the system under the training and optimization of the algorithm. The results of the algorithm performance analysis of the system model indicate that the accuracy of human action recognition of the constructed Openpose algorithm is about 85%. Comparing the system running time of different algorithm models shows that the system running time of the CNN algorithm is obviously too long. The average running time is 20-50s, which wastes a lot of time in the system. Compared with the CNN algorithm, the two sets of experimental data of the Openpose algorithm demonstrate that the average running time of the system is about 20s. The results of the performance evaluation refer that the Openpose algorithm can greatly improve the operating efficiency and speed of the system and has practical reference value for the application of lightweight DL models in the fields of PE and training.


## 4.1. Comparison of Accuracy and Precision of the System Model

To study the performance of the Openpose-based human action recognition algorithm of PE, the accuracy and precision of the human action recognition of the constructed Openpose algorithm are compared. The results are shown in Figures5 and 6.Figure 5
Comparison of the accuracy results of different algorithms for human action recognition.Figure 6
Comparison of the precision results of different algorithms for human action recognition.In Figure5, it denotes from the comparison results of the accuracy rates of different algorithms for human action recognition that with the increase of the number of model iterations, the system recognition accuracy of the two algorithms is gradually increasing. In the 20 iterations of the model, the system recognition accuracy of the CNN algorithm can only reach 55%, while the recognition accuracy of the Openpose algorithm can reach more than 70% at this time, which is at least 15% higher than that of the CNN algorithm. As the number of model iterations increases, after 120 iterations of the model, the system recognition accuracy of the CNN algorithm can only increase to about 75%, while the recognition accuracy of the proposed Openpose algorithm can reach about 85%. Compared with the CNN algorithm under the same number of iterations, the recognition accuracy can be improved by 9.8%. Therefore, the research data results demonstrate the advantages of the proposed Openpose algorithm in terms of recognition accuracy of the model.In Figure6, it indicates from the comparison of the precision results of different algorithms for human action recognition that with the increase of the number of model iterations, the action recognition accuracy of the two algorithms is significantly improved, which shows that through the training of the model iteration, the DL model can grasp the characteristics of human action and improve recognition precision of the image. Besides, when the model has only experienced 20 iterations, the recognition precision of the CNN algorithm is only 40%, while the recognition precision of the proposed Openpose algorithm can reach about 60%. As the number of model iterations increases, after 120 iterations of the model, the image recognition precision of the CNN algorithm can reach more than 70%. At this time, the recognition precision of the Openpose algorithm is 79.6%, and the difference between the recognition accuracy of the two algorithms can be reduced to less than 10%. In summary, the recognition precision of the Openpose algorithm is always better than that of the CNN algorithm, which can demonstrate the superiority of the performance of the proposed algorithm.


## 4.2. Comparison of Recall and F1 Value of System Model

For the comparison of the performance of the model system, in addition to considering the accuracy and precision of the algorithm for action image recognition, it is also necessary to compare the recall of the algorithm with the F1 value. The recall of the algorithm refers to the probability that the model predicts a positive sample in the actual positive sample data, and the F1 value of the model refers to an evaluation index based on the precision and recall of the algorithm model, which is comprehensively compared and analyzed to achieve a balance between the two. The recall and F1 value data of the Openpose algorithm and CNN algorithm are counted, and the results are shown in Figure7 and Figure 8.Figure 7
Comparison of the recall results of different algorithms for human action recognition.Figure 8
Comparison of the F1 value results of different algorithms for human action recognition.From the comparison of the recall results of different algorithms for human action recognition in Figure7, it means that the system recall of the two algorithms is gradually increasing with the increase of the number of model iterations, and the CNN algorithm has a larger growth rate. In the 20 iterations of the model, the recognition recall of the CNN algorithm is only about 30%. With the increase of the number of model iterations, in the 120 iterations of the system model, the recognition recall of the CNN algorithm can reach more than 70%, and the overall recall of the algorithm increased by more than 40%. However, under the same number of model iterations, the recognition recall of the Openpose algorithm is always better than that of the CNN algorithm. For example, when the number of system iterations is 100, the recognition recall of the Openpose algorithm can reach 72.3%. At this time, the recognition recall of the CNN algorithm is only 60%. Therefore, the performance results of the algorithm system using Openpose are better.From the comparison of the F1 value results of different algorithms for human action recognition in Figure8, it expresses that for the test results of the system F1 value, the test volatility of the CNN algorithm is relatively large. When the number of model iterations is 20 to 100 times, the average result of the F1 value of the CNN algorithm is 52.5%. However, at 120 iterations of the model, the F1 value of the CNN algorithm suddenly increased to about 80%. The excessive growth rate shows that the stability of the system is difficult to guarantee. Compared with the volatility of the CNN algorithm, the average F1 value of the Openpose algorithm is about 65%, which makes it easier to guarantee system stability and security. Therefore, the Openpose algorithm outperforms the CNN algorithm.


## 4.3. Evaluation of the Effect of Improving the Efficiency of PE

To compare the effect of improving the training efficiency of the proposed Openpose algorithm on PE, it is necessary to compare the system running time and system delay time of the Openpose algorithm and the CNN algorithm. When comparing the system running time, to reduce the experimental error, a set of experiments are carried out in the system of the CNN algorithm, and the data are shown in model A1 in Figure9. Among them, three sets of different abscissas represent different models A1, A2, and A3, and their running times all correspond to the left abscissa, but the range of running times is different. Meanwhile, two sets of experiments are carried out in the same system of the Openpose algorithm, and the results of the running time of the two sets of systems are shown as models A2 and A3 in Figure 9. The system running time and delay time of the model are shown in Figures 9 and 10, respectively.Figure 9
Comparison of system running time of different algorithm models.Figure 10
Comparison of system delay time of different algorithm models.In Figure9, model A1 refers to a set of experimental data in the system of the CNN algorithm, while models A2 and A3 represent two different sets of experimental data in the Openpose algorithm, respectively. Comparing the system running times of different algorithm models, it denotes that the system running time of CNN algorithm is obviously too long, the average running time is 20∼50s, and a lot of time of the system are wasted. Compared with the CNN algorithm, the two sets of experimental data of the proposed Openpose algorithm show that the average running time of the system is about 20s. It can be seen from the comparison results of the research data that the Openpose algorithm can greatly improve the operating efficiency and the operating speed of the system, which have important practical significance for the improvement in the efficiency of PE.From the comparison of the system delay time of different algorithm models in Figure10, it means that the number of network nodes is inversely proportional to the system delay time. With the increase of the number of system network nodes, the system delay time of the two algorithms gradually decreases. However, the system delay of the CNN algorithm decreases more greatly. When there are 10 nodes, the system delay time is 17s. When the number of nodes increases to 60, the system delay time of the CNN algorithm is reduced to 12s, which is larger than that of the Openpose algorithm. However, when the number of nodes is 60, the system delay time of the proposed Openpose algorithm is smaller. At this time, the system delay of the algorithm is only 10.8s. Compared with the CNN algorithm under the same conditions, the proposed algorithm can save at least 1.2s in system delay time. Therefore, the system delay time performance of the proposed algorithm is better than that of the CNN algorithm.


## 4.4. Discussion

To sum up, based on the lightweight DL model, the system analysis and design of the target detection algorithm are studied, and the model is optimized by the accelerated compression algorithm. The image recognition and feature extraction of the human posture features is carried out through the system under the training and optimization of the algorithm. The results of the algorithm performance analysis of the system model indicate that the accuracy of human action recognition of the constructed Openpose algorithm is about 85%. Comparing the system running time of different algorithm models shows that the system running time of the CNN algorithm is obviously too long. The average running time is 20-50s, which wastes a lot of time in the system. Compared with the CNN algorithm, the two sets of experimental data of the Openpose algorithm demonstrate that the average running time of the system is about 20s. The results of the performance evaluation refer that the Openpose algorithm can greatly improve the operating efficiency and speed of the system and has practical reference value for the application of lightweight DL models in the fields of PE and training.


## 5. Conclusion

In recent years, with the development of Internet of things technology, lightweight DL optimization models have begun to be widely used in the field of visual images. Based on the PE concept of ideological and political courses, the optimization model of lightweight DL is improved. To improve the training efficiency of PE, the research proposes an action recognition model for human sports training based on the Openpose algorithm. By comparing the performance results with the traditional CNN algorithm, the research data results denote that after 120 iterations of the model, the recognition accuracy of the proposed Openpose algorithm can reach about 85%, and the recognition precision is 79.6%. Moreover, the average running time of the Openpose algorithm is about 20s, and the system delay of the algorithm is only 10.8s. Compared with the CNN algorithm under the same conditions, the proposed algorithm can save at least 1.2s in system delay time. The research has practical reference value for improving the training efficiency of PE teaching. However, some shortcomings are unavoidable. In the process of model training, although there is a certain improvement in the accuracy and operation speed of the target detection network, the overall compression and acceleration of the network have certain limitations. In future research, it is necessary to further improve the computational efficiency of the network model by combining the lightweight network with the target detection algorithm.



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*Source: 4670523-2022-06-11.xml*