Physical education (PE) is related to students’ sports ability and physical health. In recent years, with the gradual attention paid by the state, PE has returned to the right track from the status of ‘ignored subjects’ in the past, and sports ability has become a basic accomplishment that all students must master, which puts forward new requirements for the design of a PE teaching system. Artificial intelligence technology can process massive data efficiently and accurately. Based on this, the PE system in colleges and universities can optimise the teaching scheme, make a detailed analysis of teaching results, reconstruct the evaluation mechanism of PE teaching and rationally optimise the teaching curriculum, which is reflected in the port design of the system. It gives students and teachers better visiting experience and more practical learning and teaching assistance [1–4].
The design of PE teaching system is generally adopted in colleges and universities. However, at present, PE institutes or PE majors in ordinary colleges have not established their own system of PE teaching. Colleges usually only have one teaching system, and the teaching systems of each major are affiliated to it without forming an independent portal system which affects the scientific and effective development of PE teaching. In addition, the system of these colleges mostly depends on the school-level teaching system, and the capacity of database and design of systematic framework that can be occupied are limited, and thus cannot satisfy the demand of independent implementation according to the basic requirements and characteristics of PE teaching [5–7]. Therefore, the design of PE teaching system is still in the traditional and primary stage.
In fact, even in sport schools, the design of the PE teaching system still follows the traditional design ideas, and the hardware system, software system and design technology are quite backward, but a ‘stand-alone’ electronic portal website is simply constructed, with a low degree of system intelligence, and a low capacity of carrying and processing data . It can only serve as a simple centre of data storage, and its port design is not humanised and intelligent enough, which affects the daily development of PE. In addition, it has no independent intelligent analysis ability. Therefore, it is necessary to design an intelligent teaching system of basic movements in PE courses for solving problems arising from the difficulties involved in obtaining teachers’ guidance when their workload is heavy; such a system helps the evaluation of teaching on basic sports movements.
In order to obtain the coordinate information of key points in the human body, a Gaussian modelling method  is used to obtain the confidence diagram of key points, where the value represents the probability of being a certain key point. The confidence diagram of the key point position can be expressed as:
OpenPose is the first open source library for estimation of real-time multi-person pose based on deep learning. It can accurately estimate the posture of everyone in the image in real time, and realise the extrmovement of face, trunk, limbs and hand bone points, and this representation is both real-time and accurate with strong robustness [10, 11]. The core of this method is a bottom-up human pose estimation algorithm based on partial affinity domain, that is, detecting key points before obtaining the skeleton, which avoids the problem of long calculation in multi-person scenarios.
In the two branches of OpenPose, one is used to predict the confidence (
The total loss is the sum of the losses in each stage:
In order to further improve the generalisation ability and accuracy of the pose estimation algorithm, two weights and one penalty term are introduced into the total loss function:
Figure 1 shows the network structure of multi-level prediction designed by OpenPose. The framework is based on VGG19 network model, and the input image is transformed into image features
In the field of movement analysis and recognition based on computer vision, the single tag classification method aims to solve the problem that an example belongs to only one category where different tags are completely independent and unrelated to each other. However, in practical application, a person’s limb movements may need to be analysed in many aspects, and there are often multiple tags in one frame of image, such as simultaneous analysis of upper limb and lower limb in one movement. In the problem of multi-label classification, labels are not completely independent, and there is a certain dependence or mutual exclusion . In addition, the number of labels is relatively large in the multi-label classification task, which makes the relationship between categories complicated.
The methods of multi-label classification can be divided into two categories: problem conversion and algorithm adaptation [17, 18]. The method based on problem conversion usually transforms multi-label classification problems into other learning scenarios, such as transforming multi-label classification problems into multiple binary classification problems. The label sorting method is the most commonly used, and transforms the problem of multi-label classification into the problem of sorting multi-labels. If there are
Basic sports movements are the basic learning content of PE curriculum. As the foothold of basic sports movement teaching and improvement of students’ physical quality, it is an important content of developing sports activities in PE class. A large number of PE movements are designed through the combination of various basic movements and the arrangement of changing rhythm, whose movement route and sequence usually have their own specific requirements, and so beginners may have many problems in learning coherent movements , which are mainly the following:
Inconsistency of upper and lower limbs: For example, when students make basic movements such as standing still with their hands and feet, they often have the same hands with feet. Incorrect movement route: There are many movements of lateral raise in broadcast gymnastics; for example, under the straight arm movement of the first beat in chest expansion movement, the correct route of arm lifting should be that the arms are lifted in a straight arm state, but there often will be errors, such as curved arms being lifted initially and then straightened. Low completion degree: Take the basic movement of side lift as an example. The standard of movement completion is that the arms are straight and parallel to the ground, but many students’ horizontal movements are not standard; in addition, when doing lateral movements, some students have difficulty in controlling the height. Therefore, many students cannot feel whether their movements are standard or not in the process of learning basic movements.
Inconsistency of upper and lower limbs: For example, when students make basic movements such as standing still with their hands and feet, they often have the same hands with feet.
Incorrect movement route: There are many movements of lateral raise in broadcast gymnastics; for example, under the straight arm movement of the first beat in chest expansion movement, the correct route of arm lifting should be that the arms are lifted in a straight arm state, but there often will be errors, such as curved arms being lifted initially and then straightened.
Low completion degree: Take the basic movement of side lift as an example. The standard of movement completion is that the arms are straight and parallel to the ground, but many students’ horizontal movements are not standard; in addition, when doing lateral movements, some students have difficulty in controlling the height. Therefore, many students cannot feel whether their movements are standard or not in the process of learning basic movements.
In order to evaluate the standard degree of movement through gesture matching in basic movement teaching of PE, a PE teaching system is designed with the basic process as follows. First, students learn from the template movement videos that are uploaded by teachers at the client, constantly practice the basic movements, imitate the standard movements of teachers in the videos as required and strive to achieve the specified movements. Then, students will upload and submit the video of movement learning results with their mobile phones, and wait for the systematic evaluation. After getting the video uploaded by the students, the system further processes the video.
The server is implemented by SSM framework and MySQL database [20, 21]. The SSM framework is a combination of three frameworks, namely Spring framework, SpringMVC and MyBatis; it has been widely used in the development of Web sites, and has become more and more popular in the development of commercial software. The APP client of this system adopts the current popular basic model of MVP that consists of three major components: View, Model and Presenter. View is responsible for displaying pages, Model is responsible for providing data support for business processing and Presenter is responsible for processing business logic. The architecture of the system is shown in Figure 4. Users transmit data through cameras in mobile phone, MySql database is used to store data where the server accepts users’ data requests and the processed result is provided as feedback to the user.
In the design of each functional module of the system, the relationship between each module and the calling mode is mainly designed. In order to meet the principle of high cohesion and low coupling in the process of software design , each module should be reasonably divided and each function should be described in detail. Through this process, not only should the basic requirements be met but also the maintainability and scalability of the software should be considered. The module of the PE teaching system is shown in Figure 5.
It can be seen from the figure that the system is divided into seven modules: system login module (administrator login, common user login), model upload module, user management module, message release module, data upload module and analysis report viewing module.
In the teaching research of basic movements oriented to PE, the correctness of the students’ movements is evaluated with reference to teachers’ movements, and the similarity between their movements is calculated with the DTW algorithm based on the differences in pose characteristics mentioned above. The smaller the regular path distance between the two movements data sequences, the higher the similarity.
Motion pose feature sequence to be matched is expressed as
In the ideal standard case, when the distance is 0, it means that the two features are exactly the same, and it can be judged that the two movements are exactly the same. Attitude feature information is standardised arc data, and the distance is less than 2π in extreme nonstandard cases. Regarding Euclidean distance as a similarity for DTW algorithm, matching can be a good way to search the best regular path, but it is difficult to quantitatively score different people who make the same movements. Therefore, the search condition of the maximum matching principle is redefined such that the Euclidean distance is scored and converted with the similarity converted to [0, 1], which means that the smaller the distance, the greater the similarity. The specific calculation is shown in Eq. (9):
In Eq. (9), sim(
Replacing the original Euclidean distance in the matching process as the basis of similarity judgment with the movement score score( Construct a matrix S with a size of The shortest path from The sum of the score of the shortest path from Obtain
Construct a matrix S with a size of
The shortest path from
The sum of the score of the shortest path from
Based on the above algorithm, the movement that to be matched is marked as the movement with the highest matching score, that is, the most similar movement is used to identify its category, and the effectiveness of the teaching system is verified by the correct matching rate .
Taking broadcast gymnastics as the evaluation object, each movement of the official video is manually cut into a single segment as a movement template library, in which three movement segments are captured in every eight beats, the first four eight beats are used in each session, 108 standard movements are obtained in nine sessions as a template library and 150 samples to be matched for each movement are used for matching. The movement with the highest score is obtained as the matching result and the matching success rate is calculated. A high matching rate indicates a better matching effect. The results are shown in Table 1.
Systematic matching results
|Number||Movement name||Number of movement samples||Number of correct matches||Matching success rate (%)|
|6||Body rotation motion||150||137||91.33|
From the results, it can be seen that the successful matching rate is excellent for movements with less self-occlusion, which indicates that this algorithm can well eliminate the problem that the length of the movement frame sequence to be matched is inconsistent with that of the template movement frame sequence. The matching rate of simple slow movements in the plane, such as stretching movement, body movement and finishing movement, is high. However, when there are many areas of self-occlusion of limbs in kicking exercise and abdominal back exercise, the included angle of limbs, the vector of gravity of human body and the vector of gravity of limb block are lost, and the success rate of matching is perceptibly reduced.
With the rapid development of computer technology and its continuous application in the teaching field, adopting digital technology to achieve efficient and convenient teaching has become an inevitable trend. Based on extraction and classification of basic movement teaching features, this paper designs the basic movement teaching system of PE, including the division of basic module and introduction of function, and takes the broadcast exercises as an example to evaluate the system. The results show that the system has a good matching rate for movements with less self-occlusion, and its maximum value is 95.33%. By simulating the teaching process of basic movements, students can learn them conveniently, and correct the wrong movements that are difficult to self-check in time, which helps to liberate the teaching of basic movements in PE class and improve the diversity and accuracy of PE teaching content.
Systematic matching results
|Number||Movement name||Number of movement samples||Number of correct matches||Matching success rate (%)|
|6||Body rotation motion||150||137||91.33|
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Equation Study of agricultural finance policy information extraction based on ELECTRA-BiLSTM-CRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network