# A Study on the Application of a Multisubject Collaborative Model Based on Intelligent Sensor Networks in Sports Training

Authors: Bin Ding; Yanchun Jian; Xingliang Yuan
Journal: Journal of Sensors
 (2022)

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## Abstract

In today’s rapidly developing information technology, computers are becoming faster and more accurate with more and more sensors, and the birth of new technologies such as cloud computing, big data, the Internet of Things, and artificial intelligence has turned the whole world upside down. At the same time, there is a growing interest in more natural and harmonious ways of human-machine interaction, and many people are devoting themselves to research in this area. Its nodes can be distributed arbitrarily in the target environment and can obtain relevant information about the surrounding environment. In a multinode wireless sensor network, the nodes organize themselves with each other through special protocols to realize their respective functions. In this paper, the application of a multisubject collaboration model to sports training is analyzed, and its superiority is verified by means of examples.


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## Body

## 1. Introduction

Human society has fully entered the era of information and electronics, and the development of information and electronics cannot be achieved without the support of information technology [1]. Information technology is a comprehensive technology which involves the collection, identification, extraction, conversion, storage, transmission, processing, detection, inspection, analysis, and utilization of data. These actions can be divided into three areas: information collection, information processing, and information transmission. The development of these three aspects affects each other; they complement each other and cannot be achieved without one another.China’s economic development is the fastest in the world, with GDP already ranking second in the world, and people’s living standards have also made great strides [2]. The corresponding physical education syllabus has been developed in accordance with the basic requirements for students’ physical fitness in the national physical activity standards. Through the analysis of the four times of national physical fitness monitoring data from 2000 to 2014, it is found that the body shape of youths has continued to increase, endurance, speed, explosive power, and strength quality, all of which are decreasing [3]. In 2008, the “Sunshine Sports” program was introduced in schools, which has improved the endurance of students, but not the strength, explosive power, and balance.Smart sports is based on sports equipment and sports equipment, through human-computer interaction, the Internet of Things, cloud computing, big data, and other advanced technology intervention, so that a variety of equipment, equipment, digital, networked, intelligent, and entertainment. Unlike current sports activities, such as traditional fitness, it breaks through the limitations of time and space. By putting the fragmentation of time and space to use, people are able to carry out scientific physical exercise in a more intelligent and convenient manner. Smart sport brings a whole new opportunity for fitness for all [4]. With the continuous improvement of human perception technology, people’s health concerns are becoming more and more urgent, and with the country’s vigorous development, intelligent sports training systems based on human perception are receiving more and more attention. At the same time, the research of this system can also provide a useful reference for the application of sports training techniques beyond physical education.


## 2. Introduction to Relevant Technologies

### 2.1. Sensor Technology

From a product point of view, the sensor module is a sensitive device which can change the resistance of the thermistor according to the surface of the sensor and the surrounding environmental parameters [5]. For example, a change in temperature can change the resistance of the moisture sensitive resistor, and if a change in light affects the resistance of the sensor, an AD-analogue conversion module is required to change the subtle parameters in the environment with signal processing to take out the error information. Through the physical and chemical characteristics of the sensor, the information obtained is converted into an electronic signal, which is analyzed and calculated by the main controller to obtain the amount of simulated information. The solution enables the transformation of the moving signal through the characteristics of the sensor itself [6].


### 2.2. Data Fusion

There are energy limitations in the network. Energy can be effectively saved by reducing the amount of data sent, so data can be fused by the local computing and storage capabilities of the nodes, eliminating redundant information and saving energy. The nodes in a sensor network are highly susceptible to failure, so data fusion techniques must also be used in sensor networks to synthesize multiple data to improve their accuracy [7].In sensor networks, data fusion techniques can be combined with multilayer protocols. In the application layer, many routing protocols employ data fusion mechanisms to reduce data transfer; alternatively, a data fusion layer, independent of other protocols, has been proposed, which can reduce send conflicts and header overheads in the MAC layer, thus saving energy without introducing real-time performance and information integrity. Data fusion techniques are now widely used in applications such as target tracking and automatic target recognition. In the design of sensor networks, it is important to design a targeted approach to data fusion that takes full account of the needs of the actual application [8].With the premise of saving energy and improving information accuracy, the application of data fusion technology in other areas is difficult. Firstly, there is the cost of latency; finding easy data fusion paths, performing data fusion operations, and waiting for other data to arrive during data transmission all contribute to the average latency of the network. Secondly, there is the cost of robustness. Compared to traditional networks, sensing networks have a high rate of node failure and data loss, and data fusion can greatly reduce data redundancy, but the same data loss can lead to a large amount of information being lost, resulting in a less robust network [9].


### 2.3. Serial Interface Communication Technology

Serial interface refers to the sequential transmission of data one by one. It is characterized by a simple communication line, which can be used to achieve two-way communication with a pair of transmission lines (telephone lines can be used directly as transmission lines), thus greatly reducing the cost, and is particularly suitable for long-distance communication, but the transmission speed is slow. A communication method in which each bit of data of a message is transmitted bit by bit in sequence is called serial communication. Serial communication is characterized by the transmission of data bits, in bit order, with at least one transmission line being required to complete the process, at low cost but at a slow transmission speed [10].


## 2.1. Sensor Technology

From a product point of view, the sensor module is a sensitive device which can change the resistance of the thermistor according to the surface of the sensor and the surrounding environmental parameters [5]. For example, a change in temperature can change the resistance of the moisture sensitive resistor, and if a change in light affects the resistance of the sensor, an AD-analogue conversion module is required to change the subtle parameters in the environment with signal processing to take out the error information. Through the physical and chemical characteristics of the sensor, the information obtained is converted into an electronic signal, which is analyzed and calculated by the main controller to obtain the amount of simulated information. The solution enables the transformation of the moving signal through the characteristics of the sensor itself [6].


## 2.2. Data Fusion

There are energy limitations in the network. Energy can be effectively saved by reducing the amount of data sent, so data can be fused by the local computing and storage capabilities of the nodes, eliminating redundant information and saving energy. The nodes in a sensor network are highly susceptible to failure, so data fusion techniques must also be used in sensor networks to synthesize multiple data to improve their accuracy [7].In sensor networks, data fusion techniques can be combined with multilayer protocols. In the application layer, many routing protocols employ data fusion mechanisms to reduce data transfer; alternatively, a data fusion layer, independent of other protocols, has been proposed, which can reduce send conflicts and header overheads in the MAC layer, thus saving energy without introducing real-time performance and information integrity. Data fusion techniques are now widely used in applications such as target tracking and automatic target recognition. In the design of sensor networks, it is important to design a targeted approach to data fusion that takes full account of the needs of the actual application [8].With the premise of saving energy and improving information accuracy, the application of data fusion technology in other areas is difficult. Firstly, there is the cost of latency; finding easy data fusion paths, performing data fusion operations, and waiting for other data to arrive during data transmission all contribute to the average latency of the network. Secondly, there is the cost of robustness. Compared to traditional networks, sensing networks have a high rate of node failure and data loss, and data fusion can greatly reduce data redundancy, but the same data loss can lead to a large amount of information being lost, resulting in a less robust network [9].


## 2.3. Serial Interface Communication Technology

Serial interface refers to the sequential transmission of data one by one. It is characterized by a simple communication line, which can be used to achieve two-way communication with a pair of transmission lines (telephone lines can be used directly as transmission lines), thus greatly reducing the cost, and is particularly suitable for long-distance communication, but the transmission speed is slow. A communication method in which each bit of data of a message is transmitted bit by bit in sequence is called serial communication. Serial communication is characterized by the transmission of data bits, in bit order, with at least one transmission line being required to complete the process, at low cost but at a slow transmission speed [10].


## 3. Application Method Design

### 3.1. Intelligent Sensor Network Multisubject Collaboration Model

In sports training, the multisubject collaborative model based on intelligent sensor network can analyze and feedback the sports information of sports objects from multiple perspectives; through the analysis of physical education objectives, we can discover relevant information from the information feedback and improve it; the purpose of the multisubject collaborative sports training model is to improve the basic knowledge of sports, through the exchange, feedback and evaluation of multiple information, the basic skills, basic movement patterns, and the development of comprehensive qualities [11–13]. As the old saying goes, it is better to give you fish than to give you. In the multisubject collaborative movement training model, it is important to analyze it from multiple perspectives and to train it with all-round precision.This multisubject collaborative model is shown in Figure1. The main objects of physical exercise are social sports people, students, and schools. Different sports objects get different information through the intelligent sensory network and, because of the different roles they play, they play different roles in sports training [14].Figure 1
Multisubject collaboration model for intelligent sensor networks.Through the selection of sports teaching objects, data is collected from a wide range of information in sports teaching, thus forming an intelligent sensing network and analyzing the interrelationships between the members [15]. The main idea is that social sports workers and students are the data sources for the sensors, which are analyzed to discover their strengths and fed back to the different objects [16].


### 3.2. Sensor Data Acquisition

In this design, the body movement information during the movement is collected using the body sensor and the AD conversion module [17]. The working principle: the sliding resistor action at the set position of the body sensor is used to send out the skeletal change filter signal, and the felt filter signal is transmitted to the ADC0832 conversion chip by converting it into a voltage signal, which is converted into a digital signal by A/D conversion and transmitted to the microcontroller, which converts the current movement information according to the input The microcontroller converts the current collected motion information according to the input signal [18, 19].The ADC0832 is an 8-bit resolution A/D converter chip with a maximum resolution of 256 levels, which can be adapted to the general requirements of analogue conversion. The fast response time and the ability to quickly complete the analogue-to-digital conversion are also clear advantages compared to similar chips. It helps to reduce the cost of the whole device and is also useful in terms of improving the performance and reliability of the whole machine [20].The ADC0832 is connected to the microcontroller with only three pins, CS, CLK, DO and DI. Its CS input should be high when the ADC0832 is not operating, when the chip is disabled, and the levels of CLK and DO/DI can be arbitrary. When A/D conversion is to be carried out, the CS enable terminal must first be placed low and held low until the conversion is completely finished. At this point, the chip starts to convert, while the processor inputs a clock pulse to the chip clock input CLK, and the DO/DI side uses the DI side to input the data signal for the channel function selection [21]. CH0 of the ADC0832 is connected to the data output pin of the body sensor. The circuit diagram is shown in Figure 2.Figure 2
Data acquisition module circuit.


### 3.3. Motion Recognition Algorithm Flow

The motion recognition algorithm based on DTW template matching is proposed in this paper, which is described by using spatial features based on the angle between bone off points; this algorithm can well meet the demand of motion recognition time delay, can well achieve high accuracy matching between each frame, and can eliminate the motion recognition errors caused by individual differences. The basic flow of the algorithm is shown in Figure3.Figure 3
Motion recognition algorithm flow.Data collection: The data streams obtained from KinectV2, including depth data streams, skeletal streams, and color image streams, will be used for subsequent motion recognition, while other data will be used for client interaction and storage.Bone data processing: Firstly, the bones are filtered using a combination of jitter elimination and double exponential smoothing to obtain smooth bone data, which provides a good basis for the smooth output of bone features. Next, the space vector method is used to extract joint angle features from the bones.Motion recognition: According to the characteristics of motion data, corresponding motion models are established according to different motion targets, and motion recognition is carried out using the DTW template matching algorithm and real-time motion data.Application: The motion recognition described above is only for a cyclic action, continuous recognition and movement of the motion process are required, and the user needs to interact according to different states. Finally, the data is saved to an application related to sports training.


### 3.4. Skeletal Data Filtering

The method of skeletal filtering focuses on the processing of the collected data and its error correction. Measurement errors and noise are a by-product of all systems in which physical quantities are measured by sensors. The characteristics of such errors generally include the accuracy and precision of the system. The accuracy of the system can be easily understood as the relationship between the measurement result of the system and the true measurement result. Accuracy is the concentration of all systems measured repeatedly at a point. The position of the hand is repeatedly measured in four different ways to demonstrate precision and accuracy.The black X in Figure4 indicates the position of the hand in the real world, the red dots are the hands of several measuring instruments: (1, 2) a measurement system that is not precise enough, but not precise enough; (3) a measurement system which is both precise and accurate; and (4) a more precise measurement system. The first two are too inaccurate and error-prone to be applied, while the third method does not exist. The fourth method is good enough and does exist, this time using KinectV2, but by filtering, and the bias in the data can be reduced to achieve a measurement system closer to the real one (Figure 5).Figure 4
Diagram of the four measurement systems measuring the position of the hand.Figure 5
KinectV2 spatial coordinate system.


### 3.5. Skeletal Space Feature Extraction

Based on this, smoothed skeletal data was obtained by filtering the skeletal data to remove peaks and noise, thus enabling a more accurate characterization of the skeletal features in motion. The system selects different motion nodes in different dimensions according to the pull-up, deep squat, and long jump and gives the corresponding motion features.The calculation of three-dimensional spatial angles is usually done using analytical geometry. Analytical geometry has to take into account parallelism, overlap, perpendicularity, and intersection, so that the solution of these problems has to be targeted, which is not only a large amount of work but also complex. However, the space vector method was used to solve for the joints as the boundary conditions do not need to be considered.The space vector method requires a traditional mathematical coordinate system, but the coordinate system of KinectV2 is different from a normal spatial coordinate system. The zeros of theX and Y axes of the Kinect spatial coordinate system are the same as a normal spatial coordinate system, but the z-axis zeros are the infrared sensors of the Kinect, and the positive direction is the positive direction of the Kinect.In order to use the vector method, the first step is to map the coordinate system of KinectV2 to any two points in the KinectV2 coordinate system,A(x1,y1,z1)andB(x2,y2,z2), using the translatability and directionality of vectors after transformation can be transformed into the traditional spatial coordinate system, the composition of the vector AB, which can be considered as a vector leading from the origin of the coordinates, using the following transformation formula:
(1)AB=x2−x1,y2−y1,z1−z2.By using the above method, the vectors of the body at each off point can be combined, and then, the angle between the two vectors can be found and can be seen as the angle between the joints. As an example, take the left arm, the left elbow, and the left shoulder as the three off points, and take the left arm V-shape to simplify the body and off points (see Figure6).Figure 6
Simplified diagram of the off node.


## 3.1. Intelligent Sensor Network Multisubject Collaboration Model

In sports training, the multisubject collaborative model based on intelligent sensor network can analyze and feedback the sports information of sports objects from multiple perspectives; through the analysis of physical education objectives, we can discover relevant information from the information feedback and improve it; the purpose of the multisubject collaborative sports training model is to improve the basic knowledge of sports, through the exchange, feedback and evaluation of multiple information, the basic skills, basic movement patterns, and the development of comprehensive qualities [11–13]. As the old saying goes, it is better to give you fish than to give you. In the multisubject collaborative movement training model, it is important to analyze it from multiple perspectives and to train it with all-round precision.This multisubject collaborative model is shown in Figure1. The main objects of physical exercise are social sports people, students, and schools. Different sports objects get different information through the intelligent sensory network and, because of the different roles they play, they play different roles in sports training [14].Figure 1
Multisubject collaboration model for intelligent sensor networks.Through the selection of sports teaching objects, data is collected from a wide range of information in sports teaching, thus forming an intelligent sensing network and analyzing the interrelationships between the members [15]. The main idea is that social sports workers and students are the data sources for the sensors, which are analyzed to discover their strengths and fed back to the different objects [16].


## 3.2. Sensor Data Acquisition

In this design, the body movement information during the movement is collected using the body sensor and the AD conversion module [17]. The working principle: the sliding resistor action at the set position of the body sensor is used to send out the skeletal change filter signal, and the felt filter signal is transmitted to the ADC0832 conversion chip by converting it into a voltage signal, which is converted into a digital signal by A/D conversion and transmitted to the microcontroller, which converts the current movement information according to the input The microcontroller converts the current collected motion information according to the input signal [18, 19].The ADC0832 is an 8-bit resolution A/D converter chip with a maximum resolution of 256 levels, which can be adapted to the general requirements of analogue conversion. The fast response time and the ability to quickly complete the analogue-to-digital conversion are also clear advantages compared to similar chips. It helps to reduce the cost of the whole device and is also useful in terms of improving the performance and reliability of the whole machine [20].The ADC0832 is connected to the microcontroller with only three pins, CS, CLK, DO and DI. Its CS input should be high when the ADC0832 is not operating, when the chip is disabled, and the levels of CLK and DO/DI can be arbitrary. When A/D conversion is to be carried out, the CS enable terminal must first be placed low and held low until the conversion is completely finished. At this point, the chip starts to convert, while the processor inputs a clock pulse to the chip clock input CLK, and the DO/DI side uses the DI side to input the data signal for the channel function selection [21]. CH0 of the ADC0832 is connected to the data output pin of the body sensor. The circuit diagram is shown in Figure 2.Figure 2
Data acquisition module circuit.


## 3.3. Motion Recognition Algorithm Flow

The motion recognition algorithm based on DTW template matching is proposed in this paper, which is described by using spatial features based on the angle between bone off points; this algorithm can well meet the demand of motion recognition time delay, can well achieve high accuracy matching between each frame, and can eliminate the motion recognition errors caused by individual differences. The basic flow of the algorithm is shown in Figure3.Figure 3
Motion recognition algorithm flow.Data collection: The data streams obtained from KinectV2, including depth data streams, skeletal streams, and color image streams, will be used for subsequent motion recognition, while other data will be used for client interaction and storage.Bone data processing: Firstly, the bones are filtered using a combination of jitter elimination and double exponential smoothing to obtain smooth bone data, which provides a good basis for the smooth output of bone features. Next, the space vector method is used to extract joint angle features from the bones.Motion recognition: According to the characteristics of motion data, corresponding motion models are established according to different motion targets, and motion recognition is carried out using the DTW template matching algorithm and real-time motion data.Application: The motion recognition described above is only for a cyclic action, continuous recognition and movement of the motion process are required, and the user needs to interact according to different states. Finally, the data is saved to an application related to sports training.


## 3.4. Skeletal Data Filtering

The method of skeletal filtering focuses on the processing of the collected data and its error correction. Measurement errors and noise are a by-product of all systems in which physical quantities are measured by sensors. The characteristics of such errors generally include the accuracy and precision of the system. The accuracy of the system can be easily understood as the relationship between the measurement result of the system and the true measurement result. Accuracy is the concentration of all systems measured repeatedly at a point. The position of the hand is repeatedly measured in four different ways to demonstrate precision and accuracy.The black X in Figure4 indicates the position of the hand in the real world, the red dots are the hands of several measuring instruments: (1, 2) a measurement system that is not precise enough, but not precise enough; (3) a measurement system which is both precise and accurate; and (4) a more precise measurement system. The first two are too inaccurate and error-prone to be applied, while the third method does not exist. The fourth method is good enough and does exist, this time using KinectV2, but by filtering, and the bias in the data can be reduced to achieve a measurement system closer to the real one (Figure 5).Figure 4
Diagram of the four measurement systems measuring the position of the hand.Figure 5
KinectV2 spatial coordinate system.


## 3.5. Skeletal Space Feature Extraction

Based on this, smoothed skeletal data was obtained by filtering the skeletal data to remove peaks and noise, thus enabling a more accurate characterization of the skeletal features in motion. The system selects different motion nodes in different dimensions according to the pull-up, deep squat, and long jump and gives the corresponding motion features.The calculation of three-dimensional spatial angles is usually done using analytical geometry. Analytical geometry has to take into account parallelism, overlap, perpendicularity, and intersection, so that the solution of these problems has to be targeted, which is not only a large amount of work but also complex. However, the space vector method was used to solve for the joints as the boundary conditions do not need to be considered.The space vector method requires a traditional mathematical coordinate system, but the coordinate system of KinectV2 is different from a normal spatial coordinate system. The zeros of theX and Y axes of the Kinect spatial coordinate system are the same as a normal spatial coordinate system, but the z-axis zeros are the infrared sensors of the Kinect, and the positive direction is the positive direction of the Kinect.In order to use the vector method, the first step is to map the coordinate system of KinectV2 to any two points in the KinectV2 coordinate system,A(x1,y1,z1)andB(x2,y2,z2), using the translatability and directionality of vectors after transformation can be transformed into the traditional spatial coordinate system, the composition of the vector AB, which can be considered as a vector leading from the origin of the coordinates, using the following transformation formula:
(1)AB=x2−x1,y2−y1,z1−z2.By using the above method, the vectors of the body at each off point can be combined, and then, the angle between the two vectors can be found and can be seen as the angle between the joints. As an example, take the left arm, the left elbow, and the left shoulder as the three off points, and take the left arm V-shape to simplify the body and off points (see Figure6).Figure 6
Simplified diagram of the off node.


## 4. Application Implementation and Application Analysis

### 4.1. Application Prototype Realization

The application system designed in this paper with a multisubject collaborative model of intelligent sensor networks as its core is a representative embedded control system. The close integration of software and hardware is a feature of embedded devices; therefore, simulation and debugging circuits are required when designing the application. The model diagram is shown in Figure7.Figure 7
Diagram of the model without activation.


### 4.2. Pull-up Application

The pull-up is the use of upper body strength and back strength to lift one’s torso upwards, which fully exercises one’s upper body strength and is one of the National Student Fitness Standards and the weakest of all candidates, many of whom are unable to keep up with their weight and meet the requirements. Therefore, pull-ups are the most lacking and essential exercise. Its motion analysis diagram is shown in Figure8Figure 8
Pull-ups demonstrated in real life.In accordance with the national physical exercise standards, under the guidance of the teacher, such as 8 pull-ups like a real person, first propped up on the bar with two hands, both hands open, keeping both arms vertical and maintaining a hanging position, when the body no longer swayed, both arms exerted simultaneously, pulling the body up, when your chin exceeded the arms, you would return to the original state.The data from the above operation is extracted with features and using the user’s movement posture, and the posture decomposition of the pull-up is converted into the red dots as weighted bone nodes in Figure9.Figure 9
Movement posture transformation diagram for pull-ups.


### 4.3. Deep Squat Application

The identification of deep squat movements is also systematically carried out in accordance with the physical fitness requirements in the national physical fitness exercise standards. Unlike the pull-up, the deep squat is suitable for both men and women, does not require much of an athletic base, and can be performed with weights to increase the difficulty of the movement. However, the deep squat is very physically demanding, and if the technique is inappropriate, it will not only fail to achieve the training objectives but will also damage the knee joint and affect the health of the body. A demonstration is shown in Figure10.Figure 10
Demonstration of the deep squat.Movement: Step 1: Hold your hands flat, keep your back straight, and assume a ready stance; squat down with your hips lower than your knees and both hands straight. The deep squat is a great way to improve your physical exercise.With this step and operation in mind, data features were extracted for the above operations. Figure11 shows the postural decomposition and transformation diagram for the deep squat, where the red points are the weighted nodes of the skeleton.Figure 11
Deep squat movement posture transformation diagram.


### 4.4. Application Analysis

In order to provide better data analysis of this model, a field example was conducted, and prior to this example test, each person measured the details of this exercise based on the model and made adjustments based on this feedback. Figure12 shows the data for the quantities measured based on the model feedback.Figure 12
Presentation of improved sports data.Compared to traditional sports training models, the multisubject collaborative model with intelligent sensor networks breaks down the detailed limitations of the previous sports training process. In addition, the multisubject collaborative model with intelligent sensor networks was constructed in strict adherence to the requirements of each component of the physical education model. After using this application for data feedback, the user then corrects the details based on the feedback. It is observed in the above data that the user’s measurements have improved in comparison to the original class test results, indicating that the multisubject collaborative model with intelligent sensor networks can be useful for sports training in practice.


## 4.1. Application Prototype Realization

The application system designed in this paper with a multisubject collaborative model of intelligent sensor networks as its core is a representative embedded control system. The close integration of software and hardware is a feature of embedded devices; therefore, simulation and debugging circuits are required when designing the application. The model diagram is shown in Figure7.Figure 7
Diagram of the model without activation.


## 4.2. Pull-up Application

The pull-up is the use of upper body strength and back strength to lift one’s torso upwards, which fully exercises one’s upper body strength and is one of the National Student Fitness Standards and the weakest of all candidates, many of whom are unable to keep up with their weight and meet the requirements. Therefore, pull-ups are the most lacking and essential exercise. Its motion analysis diagram is shown in Figure8Figure 8
Pull-ups demonstrated in real life.In accordance with the national physical exercise standards, under the guidance of the teacher, such as 8 pull-ups like a real person, first propped up on the bar with two hands, both hands open, keeping both arms vertical and maintaining a hanging position, when the body no longer swayed, both arms exerted simultaneously, pulling the body up, when your chin exceeded the arms, you would return to the original state.The data from the above operation is extracted with features and using the user’s movement posture, and the posture decomposition of the pull-up is converted into the red dots as weighted bone nodes in Figure9.Figure 9
Movement posture transformation diagram for pull-ups.


## 4.3. Deep Squat Application

The identification of deep squat movements is also systematically carried out in accordance with the physical fitness requirements in the national physical fitness exercise standards. Unlike the pull-up, the deep squat is suitable for both men and women, does not require much of an athletic base, and can be performed with weights to increase the difficulty of the movement. However, the deep squat is very physically demanding, and if the technique is inappropriate, it will not only fail to achieve the training objectives but will also damage the knee joint and affect the health of the body. A demonstration is shown in Figure10.Figure 10
Demonstration of the deep squat.Movement: Step 1: Hold your hands flat, keep your back straight, and assume a ready stance; squat down with your hips lower than your knees and both hands straight. The deep squat is a great way to improve your physical exercise.With this step and operation in mind, data features were extracted for the above operations. Figure11 shows the postural decomposition and transformation diagram for the deep squat, where the red points are the weighted nodes of the skeleton.Figure 11
Deep squat movement posture transformation diagram.


## 4.4. Application Analysis

In order to provide better data analysis of this model, a field example was conducted, and prior to this example test, each person measured the details of this exercise based on the model and made adjustments based on this feedback. Figure12 shows the data for the quantities measured based on the model feedback.Figure 12
Presentation of improved sports data.Compared to traditional sports training models, the multisubject collaborative model with intelligent sensor networks breaks down the detailed limitations of the previous sports training process. In addition, the multisubject collaborative model with intelligent sensor networks was constructed in strict adherence to the requirements of each component of the physical education model. After using this application for data feedback, the user then corrects the details based on the feedback. It is observed in the above data that the user’s measurements have improved in comparison to the original class test results, indicating that the multisubject collaborative model with intelligent sensor networks can be useful for sports training in practice.


## 5. Conclusion

In recent years, with the increasing prominence of ideological and political education in China’s colleges and universities, the ideological and political work of colleges and universities in the new era has also been newly deployed and deployed, and higher requirements have been put forward for the quality of cultivated talents. At present, China’s ideological and political education is in a period of rapid development, and in the process of continuous exploration and development, it inevitably encounters some difficulties and problems. This paper analyzes the application of the multisubject collaboration model in sports training and verifies the superiority of the model through examples.



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*Source: 4352876-2022-08-26.xml*