# Auxiliary Teaching of Badminton Basic Movements Based on Wireless Network Communication and Kinect

Authors: Fusheng Liang
Journal: Journal of Sensors
 (2022)

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## Abstract

Badminton is a sport with relatively delicate and complicated technical movements, which is widely welcomed in China. In order to study the specific impact of the sports game teaching method and traditional teaching method on the three aspects of the badminton learning technology mastery level, interest level, and subjective experience, with the reference of the wireless network to the students and Kinect on the students, in a three-month group teaching of students without foundation in a medical school in this city, the badminton action is broken down and the doubling of the badminton action is detected by WiFi and Kinect, to compare the traditional pedagogy and the method with this paper before and the lesson and to draw out conclusions. The results of the study found that badminton-assisted teaching based on wireless network communication and Kinect can achieve good results. The learning efficiency of students is significantly higher than that of traditional teaching methods, and badminton teaching based on wireless network communication and Kinect can effectively improve students’ interest in learning, 24% higher than the traditional method. This shows that wireless network communication and Kinect technology can play an important role in badminton teaching.


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## Body

## 1. Introduction

With the continuous in-depth development of the teaching reform in my country’s universities, the continuous improvement of various hardware and software conditions, and the development of networked teaching, there are more and more ways for students to acquire knowledge [1, 2]. It is also multichannel. The difficulties of the current teaching reforms are also becoming more and more common in order to improve the quality of physical education. In recent years, my country’s badminton industry has developed rapidly and universities have successively opened badminton courses but the teaching effect is not very satisfactory. Badminton is a seemingly simple sport. There are many of his technical movements, and they are complex and subtle. They were very difficult to master quickly in classes for which the students had no foundation. There was also a crowded practice space for the most usual classes, a large number of a class’ students, and only one class to teach per week. A shortage of teachers also contributes significantly to its teaching due to these limitations of teaching time and space. There is therefore an urgent need to improve the instruction of badminton in colleges and institutes.Based on wireless network communication, Kinect can play an important role in badminton teaching [3, 4]. Kinect is a 3D sensor camera, which relies on the camera to record the player’s movement in 3D space. Users can interact with objects in the virtual environment in a nearly natural way, so that participants can interact with the virtual environment. In recent years, wireless communication and Kinect technology have changed from a hot spot in the information field to competition among multiple disciplines. Kinect-related topics and terminology have always been the focus of attention in many fields. Kinect technology is very important for the progress and progress of educational theory. The biggest feature of Kinect is the presence of participants. Compared with traditional multimedia teaching, students are no longer just passive recipients but the two aspects of Kinect participants and the virtual environment interact as a whole, allowing students to change from passive to active, encouraging students to explore independently, and changing the traditional learning mode. “Teaching to promote learning” means that students learn knowledge through interaction with the virtual environment.For badminton teaching, many domestic and foreign experts have studied it. Mehmood et al. proposes an energy-saving fault-tolerant scheme to improve the reliability of WBAN. The proposed scheme adopts cooperative communication and network coding strategies to minimize channel damage and volume fading effects, thereby reducing the subsequent failure, bit error rate, and energy consumption. It can be seen from the obtained results that the proposed scheme reduces energy consumption, delay, and bit error rate, thereby improving the throughput and reliability of WBAN [5]. Mehmood et al. proposed a trust-based communication scheme to ensure the reliability and privacy of WBAN. In order to ensure reliability, a cooperative communication method is adopted, and in order to protect privacy, a cryptographic mechanism is adopted. The performance of the proposed scheme is evaluated using the MATLAB simulator. The output results show that the proposed scheme improves the service delivery rate, reliability, and trust, while reducing the average delay [6]. Kang et al. analyzed the application of multimedia simulation platform in badminton teaching. The results show that multimedia technology is only an auxiliary method and cannot replace traditional teaching methods. In order to obtain a good teaching effect, multimedia technology should be combined with traditional teaching methods [7]. For teaching methods, different researchers have different methods. Yang obtained 40 people through purposeful sampling techniques. The samples were processed 8 times, 3 times a week. The data collection technology of the badminton technical test used the data analysis technology of the t-test. The results of the research have a significant effect on the implementation of a distributed practice learning model using audiovisual media to improve the basic technical skills of badminton students [8]. In order to study the best badminton teaching, on the basis of previous research, Kamaruddin et al. introduced neural networks, connected the intelligent learning of the network, designed experimental applications, and then conducted data analysis. The results of the study show that using the smart phone mobile learning teaching method, the experimental group students’ technical movements, theoretical knowledge, learning interest, and learning enthusiasm are about 20% higher than those of the control group [9]. Wang introduced the teaching experience of badminton teaching based on the critical teaching method, aiming to overcome the limitations of physical education based only on technical improvement and physical fitness in the school environment. The practice of badminton enhances the critical reflection of local sports culture, allowing students to place themselves at the center of the educational process through dialogue and realize that the problematic social background is more possible [10]. In order to identify the influence of the democratic coaching style on badminton players’ game strategies, Araujo et al. evaluated the effectiveness of democratic coaching on their game strategies. The results show that the democratic coaching style based on popularity has a positive impact on players’ competitive strategies in the game. Finally, it is found that the democratic coaching style has a greater impact on players’ game strategies. These data will increase the settings between universities and departmental badminton projects and ultimately more successful chasing capabilities [11]. These studies have a certain guiding role for this article, but there are also certain problems in the study, such as insufficient experimental samples and time, resulting in unconvincing results.A new point of innovation in this paper is that we have built a badminton sports action database from the wireless network communication and Kinect. With the wireless network communication and Kinect, we trap the students’ badminton sports action and compare the badminton action with the standard action, with the Kinect device being noninvasive for the hard mass-oriented movement which pointed out the shortcomings of the traditional action comparison method based on direct comparison and proposed an action comparison method based on wireless network communication and Kinect [12].


## 2. Auxiliary Teaching Methods of Basic Badminton Movements

### 2.1. Wireless Network Communication

With the rapid development of network and communication technology, people’s requirements for wireless communication are getting higher and higher [13, 14]. In the meantime, the longest used short-range wireless communicate technologies are Bluetooth, wireless area networks, and infrared data transmission technologies. And there are also some low-bandwidth wireless technology standards with exploit capability, such as Zigbee, high bandwidth, GPS/short-range wireless communication, and dedicated wireless and systems. Both have their own basic set of features or are in accordance with specific thrusts on transmission speed, a distance, and power consumption or focus on functional scalability, meeting or introducing differentiation of specific requirements of in some individual applications to be used in competing services [15]. The badminton movement is captured by the sensor and Kinect to ensure the movement standard. The result is shown in Figure 1:Figure 1
Badminton action to capture.In the transmission process of the signal from the transmitter to the receiver, it has to go through various complicated transmission paths. Due to the different terrain and the existence of obstacles, phenomena such as reflection, diffraction, and diffraction will occur. Due to the multipath effect [16], the signal received by the receiver is not a single signal but a composite signal composed of incident waves from different paths [17, 18]. In addition, the signal is also accompanied by attenuation in the propagation process. Since the arrival time of the incoming waves of different paths is different [19], the phase of the received signal is also different. Waveform amplitude of the signals follows the principle of in-phase and reverse-phase superimposed and attenuated. The appropriate modifications are made. The fading is generally of a frequency selective nature, so it will not only affect the signal amplitude but also cause frequency transconductance, thus limiting the background bandwidth of the waveform. With the change of relative position between the transmitter and acceptor, a Doppler effect is generated, which makes the signal received by the receiver different in value from the signal emitted by the transmitter. In this case, the difference exists between the receiving and receiving transmitters. The difference between the two is therefore called the Doppler shift [20].The wireless network designed in this article adopts the Zigbee technology, so the Zigbee technology is mainly introduced and other technologies are not introduced. Zigbee comes from a swarm of bees dancing in the shape of ZigZag to inform other bees of the location of pollen and other information to achieve the purpose of mutual communication, so it is called a new generation of wireless communication technology [21, 22]. For embedded devices, those with higher values and relatively complex protocol stacks are not suitable. Compared with the Bluetooth technology, a larger main network is the advantage of using the Zigbee technology. The Bluetooth technology can only have one slave unit in the piconet at most, while the Zigbee network includes at most one network node [23]. The different technical parameters are shown in Table 1.Table 1
Comparison of network communication indicators.
AttributesIEEE802.11bZigbeeBluetoothData rate11 Mbps868 MHz : 200 Kbps, 2.4 GHz : 250 KbpsAsymmetric connection: 721Kbps, symmetrical connection: 432.6 KbpsCommunication rangeIndoor≤100m, outdoor≤300mIndoor≤10m, outdoor≤75mIndoor≥10mNetwork node>2552557Frequency band2.4 GHz868 MHz/2.4 GHz2.4 GHz–2.48 GHzRelative priceHighMediumMediumThe Zigbee technology has the advantages of low power consumption, low cost, large network capacity, and flexible frequency bands. Although the communication rate is relatively slow, the communication data volume of badminton-assisted training is only one byte per second, which can fully meet the needs of the system. The Zigbee technology can support network operation well, but it occupies relatively few hardware resources [24, 25]. Through a comprehensive comparison with other short-range wireless communication technologies, in systems with limited resources but more sensitive to interference, such as badminton teaching, it is more appropriate to use this technology as a wireless communication method [26].For the Zigbee chip architecture, the chip is mainly used for packet processing and it is used for receiving and processing RF signals by PHY. As the Zigbee system architecture [27], due to its low transmission rate on the simple data processing, it only requires a simple bit handler on the host side in one of the system structure, making the chip unit for a single chip, where the future Zigbee processor chip will be integrated [28]. The Zigbee chip structure is shown in Figure 2.Figure 2
Zigbee system architecture diagram.


### 2.2. Kinect

Kinect is a 3D sports camera that can record sports. The current materials used to capture human motion have bid farewell to the usual traditional cameras, and a variety of home sports cameras have been developed [29, 30]. Although the current motion capture systems are diverse and use similar or completely different principles, they can all be divided into two categories: filtered motion recording systems and noninvasive motion capture systems [31].Medical motion capture devices began as a way to capture human movements. Usually this kind of equipment is light or medium weight, it is connected to a computer through a cable, and the collected electromagnetic wave, ultrasound or traffic data is downloaded for later data calculation [32]. Mediator devices are relatively in simple principle and easy to use. By their invasive nature, however, it is more restrictive for the perforators and these devices inappropriate for a recording of complex moves [33].The delayed appearance of nonintrusive motion capture devices is also the basic method of current motion capture. Generally speaking, performers only need to place some highly reflective signs or wear tights, so the impact on the performers is small [34]. Compared with invasive devices, noninvasive devices tend to be more expensive and inaccurate. With the development of high-precision cameras, the accuracy of noninvasive devices has been offset but such systems are relatively expensive [35].It was initially announced by Microsoft at E3 in June 2009 as the “NatalProject,” a 3D Somatic (body-aware) camera for Xbox 360 with instant motion capture, image recognition, microphone input, voice recognition, and social interactive capabilities. In addition, Natal is a 3D somatosensory camera which provides instant motion capture, image recognition, microphone input, voice awareness, and community interaction, providing computers of the ability to understand people’s body language [36]. At the same time, it makes “human-computer interaction” as natural, interesting, and rich as possible and brings out the concept of “human-computer interest” more successfully.Although the Kinect device was officially launched in November 2010, its WindowsKinectSDK for academic research and application development was only late in June 2011 but the appearance of Kinect aroused the attention of researchers and application developers [37]. In the past year, related research and development based on Kinect have sprung up like bamboo shoots after a rain. Some people even cracked Kinect’s communication method to obtain more internal data. Nevertheless, Kinect has not been allowed to enter the Chinese Mainland market. Kinect has three cameras in total: the middle of the front is the RGB color camera and the left and right sides are the infrared transmitter and the infrared CMOS camera, respectively. An array microphone system is integrated at the bottom of the Kinect fuselage for voice recognition. In addition, Kinect is also equipped with the focus tracking technology and the base motor will follow the movement of the focus object. The Kinect organization is shown in Figure 3.Figure 3
Kinect appearance and disassembly diagram.Kinect is able to have powerful in-depth image acquisition and motion data capture functions, mainly because of the built-in somatosensory detection device PrimeSensor developed by the Israeli PrimeSense company and sensor chip PS1080; these two hardware devices rely on light coding (LightCoding) technology to capture the depth information of the current scene. The so-called optical encoding technology refers to the use of continuous light (near infrared) to encode the measurement space, and then, the encoded light is read by the sensor and then decoded by the chip operation to generate an image with depth information.The key to optical coding technology is laser speckle. When the laser hits rough objects or passes through ground glass, it will form random reflection points, called light spots. The stigma has a high degree of randomness, and its pattern will change with the distance. The points at any two positions in the space will have different patterns, which is equivalent to adding a point to the entire scanning space, so any object can accurately record the position of the object when it enters the space and moves in the space [38, 39]. The so-called coding of the space to be measured refers to the use of lasers to create this position. Kinect uses infrared to emit a type of laser that is invisible to the human eye. Through the grid in front of the lens, also called the diffuser, the laser is evenly distributed and projected to the measurement area, and then, each spot in the area is recorded by the infrared camera, and finally, the image information with depth is calculated by the wafer.The platform integrates motion data collection and Kinect control functions, which are mainly implemented by the function interface provided by KinectSDK. A standard Kinect motion data collection process is shown in Figure4.Figure 4
Kinect motion data collection process.When Kinect is turned on, the team will detect the number of Kinect devices used on the current machine and then turn over all Kinect devices and find one that is at the ready state, and after the initialization, we will enter the data recovery cycle and the kinematic data acquisition and algorithm processing stage.


### 2.3. Action Data Collection and Image Processing

It is a key aspect of this work to construct an intermediate representation layer for human regions. This intermediate presentation layer turns the problem into a problem that can be easily solved using effective classifier algorithms. This regions are defined in a vein map that redirects to different skin when rendering different characters. Breadth images and those of the human body area are tagged data when learned from the classifier.In order to classify pixelx in image I, it is necessary to repeatedly calculate the expression from the root node and determine whether to take the left branch or the right branch by comparing the result with the threshold. The leaf node of each decision tree stores a learned distribution of the human body area PtcI,x(1)PtcI,x=1T∑t=1TPtcI,x.We define a density evaluation function for each body region:(2)fcx^∞∑i=1Nωicexp−x∧−x∧ibc2.x^ is the three-dimensional coordinates in the world coordinate system, N is the total number of pixels in the image, ωic is the weight of the pixels, and x^i is the relocation coordinate of image pixel x in the world coordinate system according to the given depth, but the width of each learning area. Pixel weight bc is based on the possibility of the human body area in the pixel and the world surface area where the pixel ωic is located
(3)ωic=PcI,xi∗dIxi2.In order to describe the movement of the limbs, it is necessary to record the relative posture of the limbs in each frame of movement data. Since the movement of the limbs is basically formed by the rotation of the joints, pure displacement movement is not common. Therefore, the posture changes during the movement of the limbs can be described by the angle-axis posture description method.Assuming that the direction vector of the right arm isv^2 and the z-axis direction vector of the upper body root coordinate system v^1 is v^2, it is stipulated that the initial time and direction are the same; then at any time, the posture of the right arm can be expressed as
(4)G=θ,ω1,ω2,ω3.Therefore,(5)θ=arccosv^1v^2v^1v^2,Aωx,ωy,ωz=v^1∗v^2.The general method of driving joint rotation by angular velocity is(6)AngVel=TarAng−CurAng∗kstep.Among them,TarAng is the target joint angle, CurAng is the current joint angle, k is the scale factor, step is the simulation cycle length, and AngVel is the calculated angular velocity.In order to obtain better edge calculation, the fitness function is determined according to the idea of the maximum between-class variance method and the formula is as follows:(7)ft=σt2=w1t∗w2t∗u1t−u1t2.Among them,t is the threshold, fx is the fitness function, w1t is the number of nodes less than the threshold, and w2t is the number of nodes greater than the threshold. Generate a random number in the interval, and select the individual corresponding to the area to which the random number belongs.WhereK is the set of possible values of x and when another y is known, conditional entropy is used to measure the residual uncertainty in the discrete random variable x. It is defined as follows:
(8)hxy=−∑x∈κ,y∈κpx,y∗logxy.The algorithm is generally as follows:(9)ts=exp−∫0sκtdt.From this, we can see(10)∂=1−ts=1−exp−∫0sκtdt.WhenΔs approaches zero, use the following differential equation to illustrate the change:
(11)dIds=Ts∗ρs∗A=Ts∗κs,Is=I0+∫0sgtdt.We generally adopt the following formula to proceed:(12)xk+1=Ixk+Jvk,k=1,2.The quadratic performance indicators are as follows(13)K=∑k=1∞xikJxk+rikcJ.If two or more standard samples are given and their characteristic values are all the same and their corresponding categories are also the same, it means that two or more samples are consistent; otherwise, it means that they are inconsistent.The customization of data is based on the premise of which distribution it conforms to, and then, training and analysis are carried out according to the hypothetical distribution model. Therefore, learning the distribution of feature data according to the energy model can solve all the above problems. Later,(14)Ev,hθ=−∑i=1naivi−∑j=1mbjhj.Among them,θ is the parameter model, ai is the bias of the visible layer unit, bj is the bias of the hidden layer unit, and Wij is the connection weight between the visible layer and the hidden layer. The joint probability distribution that can be obtained according to the energy function is as follows:
(15)Zθ=∑v,he−Ev,hθ,Where Zθ represents the normalization factor in the calculation of joint probability. The likelihood function solved through specific calculations can be expressed as
(16)pvθ=∑he−Ev,hθZθ.According to the state of the hidden layer unit, the formula for obtaining the visible layer unit in reverse is(17)Pvi=1h,θ=σai+∑jWijhj.The specific solutionPvθ algorithm of the function is to use the contrast divergence algorithm, and the specific solution process will be described as follows in conjunction with specific applications.


## 2.1. Wireless Network Communication

With the rapid development of network and communication technology, people’s requirements for wireless communication are getting higher and higher [13, 14]. In the meantime, the longest used short-range wireless communicate technologies are Bluetooth, wireless area networks, and infrared data transmission technologies. And there are also some low-bandwidth wireless technology standards with exploit capability, such as Zigbee, high bandwidth, GPS/short-range wireless communication, and dedicated wireless and systems. Both have their own basic set of features or are in accordance with specific thrusts on transmission speed, a distance, and power consumption or focus on functional scalability, meeting or introducing differentiation of specific requirements of in some individual applications to be used in competing services [15]. The badminton movement is captured by the sensor and Kinect to ensure the movement standard. The result is shown in Figure 1:Figure 1
Badminton action to capture.In the transmission process of the signal from the transmitter to the receiver, it has to go through various complicated transmission paths. Due to the different terrain and the existence of obstacles, phenomena such as reflection, diffraction, and diffraction will occur. Due to the multipath effect [16], the signal received by the receiver is not a single signal but a composite signal composed of incident waves from different paths [17, 18]. In addition, the signal is also accompanied by attenuation in the propagation process. Since the arrival time of the incoming waves of different paths is different [19], the phase of the received signal is also different. Waveform amplitude of the signals follows the principle of in-phase and reverse-phase superimposed and attenuated. The appropriate modifications are made. The fading is generally of a frequency selective nature, so it will not only affect the signal amplitude but also cause frequency transconductance, thus limiting the background bandwidth of the waveform. With the change of relative position between the transmitter and acceptor, a Doppler effect is generated, which makes the signal received by the receiver different in value from the signal emitted by the transmitter. In this case, the difference exists between the receiving and receiving transmitters. The difference between the two is therefore called the Doppler shift [20].The wireless network designed in this article adopts the Zigbee technology, so the Zigbee technology is mainly introduced and other technologies are not introduced. Zigbee comes from a swarm of bees dancing in the shape of ZigZag to inform other bees of the location of pollen and other information to achieve the purpose of mutual communication, so it is called a new generation of wireless communication technology [21, 22]. For embedded devices, those with higher values and relatively complex protocol stacks are not suitable. Compared with the Bluetooth technology, a larger main network is the advantage of using the Zigbee technology. The Bluetooth technology can only have one slave unit in the piconet at most, while the Zigbee network includes at most one network node [23]. The different technical parameters are shown in Table 1.Table 1
Comparison of network communication indicators.
AttributesIEEE802.11bZigbeeBluetoothData rate11 Mbps868 MHz : 200 Kbps, 2.4 GHz : 250 KbpsAsymmetric connection: 721Kbps, symmetrical connection: 432.6 KbpsCommunication rangeIndoor≤100m, outdoor≤300mIndoor≤10m, outdoor≤75mIndoor≥10mNetwork node>2552557Frequency band2.4 GHz868 MHz/2.4 GHz2.4 GHz–2.48 GHzRelative priceHighMediumMediumThe Zigbee technology has the advantages of low power consumption, low cost, large network capacity, and flexible frequency bands. Although the communication rate is relatively slow, the communication data volume of badminton-assisted training is only one byte per second, which can fully meet the needs of the system. The Zigbee technology can support network operation well, but it occupies relatively few hardware resources [24, 25]. Through a comprehensive comparison with other short-range wireless communication technologies, in systems with limited resources but more sensitive to interference, such as badminton teaching, it is more appropriate to use this technology as a wireless communication method [26].For the Zigbee chip architecture, the chip is mainly used for packet processing and it is used for receiving and processing RF signals by PHY. As the Zigbee system architecture [27], due to its low transmission rate on the simple data processing, it only requires a simple bit handler on the host side in one of the system structure, making the chip unit for a single chip, where the future Zigbee processor chip will be integrated [28]. The Zigbee chip structure is shown in Figure 2.Figure 2
Zigbee system architecture diagram.


## 2.2. Kinect

Kinect is a 3D sports camera that can record sports. The current materials used to capture human motion have bid farewell to the usual traditional cameras, and a variety of home sports cameras have been developed [29, 30]. Although the current motion capture systems are diverse and use similar or completely different principles, they can all be divided into two categories: filtered motion recording systems and noninvasive motion capture systems [31].Medical motion capture devices began as a way to capture human movements. Usually this kind of equipment is light or medium weight, it is connected to a computer through a cable, and the collected electromagnetic wave, ultrasound or traffic data is downloaded for later data calculation [32]. Mediator devices are relatively in simple principle and easy to use. By their invasive nature, however, it is more restrictive for the perforators and these devices inappropriate for a recording of complex moves [33].The delayed appearance of nonintrusive motion capture devices is also the basic method of current motion capture. Generally speaking, performers only need to place some highly reflective signs or wear tights, so the impact on the performers is small [34]. Compared with invasive devices, noninvasive devices tend to be more expensive and inaccurate. With the development of high-precision cameras, the accuracy of noninvasive devices has been offset but such systems are relatively expensive [35].It was initially announced by Microsoft at E3 in June 2009 as the “NatalProject,” a 3D Somatic (body-aware) camera for Xbox 360 with instant motion capture, image recognition, microphone input, voice recognition, and social interactive capabilities. In addition, Natal is a 3D somatosensory camera which provides instant motion capture, image recognition, microphone input, voice awareness, and community interaction, providing computers of the ability to understand people’s body language [36]. At the same time, it makes “human-computer interaction” as natural, interesting, and rich as possible and brings out the concept of “human-computer interest” more successfully.Although the Kinect device was officially launched in November 2010, its WindowsKinectSDK for academic research and application development was only late in June 2011 but the appearance of Kinect aroused the attention of researchers and application developers [37]. In the past year, related research and development based on Kinect have sprung up like bamboo shoots after a rain. Some people even cracked Kinect’s communication method to obtain more internal data. Nevertheless, Kinect has not been allowed to enter the Chinese Mainland market. Kinect has three cameras in total: the middle of the front is the RGB color camera and the left and right sides are the infrared transmitter and the infrared CMOS camera, respectively. An array microphone system is integrated at the bottom of the Kinect fuselage for voice recognition. In addition, Kinect is also equipped with the focus tracking technology and the base motor will follow the movement of the focus object. The Kinect organization is shown in Figure 3.Figure 3
Kinect appearance and disassembly diagram.Kinect is able to have powerful in-depth image acquisition and motion data capture functions, mainly because of the built-in somatosensory detection device PrimeSensor developed by the Israeli PrimeSense company and sensor chip PS1080; these two hardware devices rely on light coding (LightCoding) technology to capture the depth information of the current scene. The so-called optical encoding technology refers to the use of continuous light (near infrared) to encode the measurement space, and then, the encoded light is read by the sensor and then decoded by the chip operation to generate an image with depth information.The key to optical coding technology is laser speckle. When the laser hits rough objects or passes through ground glass, it will form random reflection points, called light spots. The stigma has a high degree of randomness, and its pattern will change with the distance. The points at any two positions in the space will have different patterns, which is equivalent to adding a point to the entire scanning space, so any object can accurately record the position of the object when it enters the space and moves in the space [38, 39]. The so-called coding of the space to be measured refers to the use of lasers to create this position. Kinect uses infrared to emit a type of laser that is invisible to the human eye. Through the grid in front of the lens, also called the diffuser, the laser is evenly distributed and projected to the measurement area, and then, each spot in the area is recorded by the infrared camera, and finally, the image information with depth is calculated by the wafer.The platform integrates motion data collection and Kinect control functions, which are mainly implemented by the function interface provided by KinectSDK. A standard Kinect motion data collection process is shown in Figure4.Figure 4
Kinect motion data collection process.When Kinect is turned on, the team will detect the number of Kinect devices used on the current machine and then turn over all Kinect devices and find one that is at the ready state, and after the initialization, we will enter the data recovery cycle and the kinematic data acquisition and algorithm processing stage.


## 2.3. Action Data Collection and Image Processing

It is a key aspect of this work to construct an intermediate representation layer for human regions. This intermediate presentation layer turns the problem into a problem that can be easily solved using effective classifier algorithms. This regions are defined in a vein map that redirects to different skin when rendering different characters. Breadth images and those of the human body area are tagged data when learned from the classifier.In order to classify pixelx in image I, it is necessary to repeatedly calculate the expression from the root node and determine whether to take the left branch or the right branch by comparing the result with the threshold. The leaf node of each decision tree stores a learned distribution of the human body area PtcI,x(1)PtcI,x=1T∑t=1TPtcI,x.We define a density evaluation function for each body region:(2)fcx^∞∑i=1Nωicexp−x∧−x∧ibc2.x^ is the three-dimensional coordinates in the world coordinate system, N is the total number of pixels in the image, ωic is the weight of the pixels, and x^i is the relocation coordinate of image pixel x in the world coordinate system according to the given depth, but the width of each learning area. Pixel weight bc is based on the possibility of the human body area in the pixel and the world surface area where the pixel ωic is located
(3)ωic=PcI,xi∗dIxi2.In order to describe the movement of the limbs, it is necessary to record the relative posture of the limbs in each frame of movement data. Since the movement of the limbs is basically formed by the rotation of the joints, pure displacement movement is not common. Therefore, the posture changes during the movement of the limbs can be described by the angle-axis posture description method.Assuming that the direction vector of the right arm isv^2 and the z-axis direction vector of the upper body root coordinate system v^1 is v^2, it is stipulated that the initial time and direction are the same; then at any time, the posture of the right arm can be expressed as
(4)G=θ,ω1,ω2,ω3.Therefore,(5)θ=arccosv^1v^2v^1v^2,Aωx,ωy,ωz=v^1∗v^2.The general method of driving joint rotation by angular velocity is(6)AngVel=TarAng−CurAng∗kstep.Among them,TarAng is the target joint angle, CurAng is the current joint angle, k is the scale factor, step is the simulation cycle length, and AngVel is the calculated angular velocity.In order to obtain better edge calculation, the fitness function is determined according to the idea of the maximum between-class variance method and the formula is as follows:(7)ft=σt2=w1t∗w2t∗u1t−u1t2.Among them,t is the threshold, fx is the fitness function, w1t is the number of nodes less than the threshold, and w2t is the number of nodes greater than the threshold. Generate a random number in the interval, and select the individual corresponding to the area to which the random number belongs.WhereK is the set of possible values of x and when another y is known, conditional entropy is used to measure the residual uncertainty in the discrete random variable x. It is defined as follows:
(8)hxy=−∑x∈κ,y∈κpx,y∗logxy.The algorithm is generally as follows:(9)ts=exp−∫0sκtdt.From this, we can see(10)∂=1−ts=1−exp−∫0sκtdt.WhenΔs approaches zero, use the following differential equation to illustrate the change:
(11)dIds=Ts∗ρs∗A=Ts∗κs,Is=I0+∫0sgtdt.We generally adopt the following formula to proceed:(12)xk+1=Ixk+Jvk,k=1,2.The quadratic performance indicators are as follows(13)K=∑k=1∞xikJxk+rikcJ.If two or more standard samples are given and their characteristic values are all the same and their corresponding categories are also the same, it means that two or more samples are consistent; otherwise, it means that they are inconsistent.The customization of data is based on the premise of which distribution it conforms to, and then, training and analysis are carried out according to the hypothetical distribution model. Therefore, learning the distribution of feature data according to the energy model can solve all the above problems. Later,(14)Ev,hθ=−∑i=1naivi−∑j=1mbjhj.Among them,θ is the parameter model, ai is the bias of the visible layer unit, bj is the bias of the hidden layer unit, and Wij is the connection weight between the visible layer and the hidden layer. The joint probability distribution that can be obtained according to the energy function is as follows:
(15)Zθ=∑v,he−Ev,hθ,Where Zθ represents the normalization factor in the calculation of joint probability. The likelihood function solved through specific calculations can be expressed as
(16)pvθ=∑he−Ev,hθZθ.According to the state of the hidden layer unit, the formula for obtaining the visible layer unit in reverse is(17)Pvi=1h,θ=σai+∑jWijhj.The specific solutionPvθ algorithm of the function is to use the contrast divergence algorithm, and the specific solution process will be described as follows in conjunction with specific applications.


## 3. Auxiliary Teaching Experiment and Results of Basic Badminton Movements

### 3.1. Student Information

Before the teaching experiment to test that the wireless network communication and Kinect-assisted effect of the method applied to the teaching of the optional general college badminton class, the forehand high stroke technique and round return technique were tested, and the effect of the independent samplet-test after is being analyzed by SPSS to indicate that the technical evaluation and conformance scores of the experimental class and the controlled group for the forehand high squad and the preselected backhand technical appreciation and conformance scores do not show significant differences. Teaching experiment (P>0.05). At this time, the experimental class and the control class are comparable in indicators, laying a foundation for the next experiment and ensuring the convenience of the experiment. The student indicators before the experiment are shown in Table 2:Table 2
Student index test before the experiment.
IndexControl groupTest grouptPHigh-flying skill evaluation7.14±0.7127.34±0.7620.792>0.05High ball reaches the target7.09±0.8366.95±1.1470.624>0.05Backhand technique review7.45±0.8537.14±0.821−0.741>0.05Backhand goal7.14±0.9817.45±1.0140.585>0.05A forehand is a ball near the finish line of the backcourt area, and the front handle is used to pass the ball back to the opponent’s frontcourt area (near the front serving line and between the net) near the corner of the sideline. The flight trajectory of the pod is suitable for the ball to fall quickly after crossing the net. Combining the high ball in the backcourt can effectively mobilize the opponent and is the main offensive technique in the backcourt.It can be seen in Table3 that after the experiment, the comparison between the control class and the experimental class forehand technical evaluation score P<0.01, indicating that there is a significant difference between the two. And because the average score of 7.94 in the rod evaluation of the experimental class is higher than the average score of 6.97 in the forehand leaf evaluation of the control class, it shows that teaching with the help of a video tape is helpful. Improve students’ forehand podium scores. Although the average score of 7.14 for the experimental class forehand is higher than the average score of 7.03 for the control class, the comparison of the experimental class and the control class forehand score P>0.05, so there is no significant difference between the two, indicating that wireless communication and Kinect teaching aids have no significant effect on the performance of badminton lob hand.Table 3
Comparison of forehand lob technical evaluation.
IndexControl groupTest grouptPForehand lob technical review6.97±0.9577.94±0.8771.757<0.01Forehand lob meets the target7.02±0.7747.14±1.5420.356>0.05A forehand shot is aimed at the opponent hitting the ball in the backcourt or midfield. Try to hit the ball as high as possible. Use the forehand to hold the ball from top to bottom on the opponent’s court. The master hits the ball. The smash technique has the greatest hitting force, fastest speed, and greatest power and is an important means of offensive scoring.It can be seen in Table4 that after the end of the experiment, the average score of forehand in the experimental class was 7.76 greater than the average score of forehand in the control class, 6.69, and the average score of forehand in the experimental class was 8.64 greater. The average forehand score of the control class is 7.27, and the comparison of the forehand technical evaluation scores of the two classes and the comparison of the standard scores are P<0.05, indicating the technical evaluation scores of the two classes of forehands and the forehand scores. There are significant differences in the achievement of the standard, so wireless network communication and Kinect-assisted teaching can help improve the performance of the badminton forehand technique evaluation and the achievement of the forehand goal.Table 4
Comparison of forehand statistics.
IndexControl groupTest grouptPForehand technique review6.69±1.0217.76±1.0532.486<0.05Forehand hit goal7.27±1.4338.64±1.3272.596<0.05Make statistics on the basic physical conditions of students, as shown in Figure5.Figure 5
Basic situation of students.It can be seen that the students before the experiment have similar physical functions, regardless if the indicators between height, weight, and athletic ability are at a similar level, which shows that good results have been achieved in selecting students.


### 3.2. Training Process

We train the students in the experimental group on wireless network communication and Kinect. First, they train the learning through wireless network communication and Kinect and analyze the students’ movements, as shown in Figure6.Figure 6
Wireless network communication and Kinect training for students.The training content of this experiment is mainly divided into three parts: introduction of badminton, physical fitness training of badminton, and basic technical training of badminton. Among them, wireless network communication and Kinect auxiliary training are mainly used in basic technical training of badminton and the other two training content experiments. There is no significant difference between the group and the control group.Before the training begins, students and parents are consulted through classroom inquiry or questionnaire surveys to understand the badminton learning experience of the experimental subjects, and further observation and screening of students with learning experience are carried out to determine their existing learning level. The experience will not interfere with the experimental results, and the survey results are shown in Table5.Table 5
Student learning experience.
NMore than 3 monthsWithin 3 monthsNever touchedNumber of peoplePercentageNumber of peoplePercentageNumber of peoplePercentageTest group00%211.5%2488.5%Control group11.66%36.54%2691.6%Total11.61%57.49%5090.1%It can be seen that most of the students have no badminton learning experience, which also shows that in learning groups, the two groups of learning are in line with the positioning of badminton learners and have homogeneity. Investigate students’ interest in badminton, and the statistical results are shown in Figure7.Figure 7
Preexperiment interest survey results.It can be seen in Figure7 that both the experimental group and the control group have higher enthusiasm for badminton training, and there is no forced training. There is no significant difference in badminton training interest between the experimental group and the control group.


### 3.3. Comparison of Experimental Results

We taught different groups of students for 3 months and then compared the differences between the badminton parameters between the two groups. The changes in the technical control group of the experimental group are shown in Figure8.Figure 8
Training data comparison.It can be seen in Figure8 that regardless of the experimental group and the control group, the average value of the test data before and after training has been significantly improved, the variance is generally reduced, and the statistical analysis P value is less than 0.01, so there is a significant difference between the data before and after training. It shows that the physical fitness of the trainees before and after the training has been significantly improved; whether it is the traditional training method or the training method that combines wireless network communication and Kinect, the training effect has been achieved. We statistically analyze the physical fitness test data of the experimental group and the control group after the experiment, as shown in Figure 9.Figure 9
Physical fitness test results.After the experiment, the test data of the experimental group and the control group are slightly higher than those of the control group but the gap is smaller (less than 5%). In the independentt-test, the P values of the four test data are all greater than 0.05, indicating that there is no significant difference between the test data of the experimental group and the control group.We surveyed the two groups of experimental subjects about their interest in badminton after 3 months of teaching. The survey results are shown in Figure10.Figure 10
Preexperiment interest survey results.After three months of group teaching, it follows in Figure10 that in the experimental group applying the method of this paper, the learning of a lot of interest in badminton accounts for more than 90%, and in the traditional teaching method, the students taking a lot of interest in badminton represent only about 66%, more than the method of this paper by 24 percentage points, which also shows that the teaching of badminton in wireless network communication with Kinect than the method of traditional can cause students’ confidence more.


## 3.1. Student Information

Before the teaching experiment to test that the wireless network communication and Kinect-assisted effect of the method applied to the teaching of the optional general college badminton class, the forehand high stroke technique and round return technique were tested, and the effect of the independent samplet-test after is being analyzed by SPSS to indicate that the technical evaluation and conformance scores of the experimental class and the controlled group for the forehand high squad and the preselected backhand technical appreciation and conformance scores do not show significant differences. Teaching experiment (P>0.05). At this time, the experimental class and the control class are comparable in indicators, laying a foundation for the next experiment and ensuring the convenience of the experiment. The student indicators before the experiment are shown in Table 2:Table 2
Student index test before the experiment.
IndexControl groupTest grouptPHigh-flying skill evaluation7.14±0.7127.34±0.7620.792>0.05High ball reaches the target7.09±0.8366.95±1.1470.624>0.05Backhand technique review7.45±0.8537.14±0.821−0.741>0.05Backhand goal7.14±0.9817.45±1.0140.585>0.05A forehand is a ball near the finish line of the backcourt area, and the front handle is used to pass the ball back to the opponent’s frontcourt area (near the front serving line and between the net) near the corner of the sideline. The flight trajectory of the pod is suitable for the ball to fall quickly after crossing the net. Combining the high ball in the backcourt can effectively mobilize the opponent and is the main offensive technique in the backcourt.It can be seen in Table3 that after the experiment, the comparison between the control class and the experimental class forehand technical evaluation score P<0.01, indicating that there is a significant difference between the two. And because the average score of 7.94 in the rod evaluation of the experimental class is higher than the average score of 6.97 in the forehand leaf evaluation of the control class, it shows that teaching with the help of a video tape is helpful. Improve students’ forehand podium scores. Although the average score of 7.14 for the experimental class forehand is higher than the average score of 7.03 for the control class, the comparison of the experimental class and the control class forehand score P>0.05, so there is no significant difference between the two, indicating that wireless communication and Kinect teaching aids have no significant effect on the performance of badminton lob hand.Table 3
Comparison of forehand lob technical evaluation.
IndexControl groupTest grouptPForehand lob technical review6.97±0.9577.94±0.8771.757<0.01Forehand lob meets the target7.02±0.7747.14±1.5420.356>0.05A forehand shot is aimed at the opponent hitting the ball in the backcourt or midfield. Try to hit the ball as high as possible. Use the forehand to hold the ball from top to bottom on the opponent’s court. The master hits the ball. The smash technique has the greatest hitting force, fastest speed, and greatest power and is an important means of offensive scoring.It can be seen in Table4 that after the end of the experiment, the average score of forehand in the experimental class was 7.76 greater than the average score of forehand in the control class, 6.69, and the average score of forehand in the experimental class was 8.64 greater. The average forehand score of the control class is 7.27, and the comparison of the forehand technical evaluation scores of the two classes and the comparison of the standard scores are P<0.05, indicating the technical evaluation scores of the two classes of forehands and the forehand scores. There are significant differences in the achievement of the standard, so wireless network communication and Kinect-assisted teaching can help improve the performance of the badminton forehand technique evaluation and the achievement of the forehand goal.Table 4
Comparison of forehand statistics.
IndexControl groupTest grouptPForehand technique review6.69±1.0217.76±1.0532.486<0.05Forehand hit goal7.27±1.4338.64±1.3272.596<0.05Make statistics on the basic physical conditions of students, as shown in Figure5.Figure 5
Basic situation of students.It can be seen that the students before the experiment have similar physical functions, regardless if the indicators between height, weight, and athletic ability are at a similar level, which shows that good results have been achieved in selecting students.


## 3.2. Training Process

We train the students in the experimental group on wireless network communication and Kinect. First, they train the learning through wireless network communication and Kinect and analyze the students’ movements, as shown in Figure6.Figure 6
Wireless network communication and Kinect training for students.The training content of this experiment is mainly divided into three parts: introduction of badminton, physical fitness training of badminton, and basic technical training of badminton. Among them, wireless network communication and Kinect auxiliary training are mainly used in basic technical training of badminton and the other two training content experiments. There is no significant difference between the group and the control group.Before the training begins, students and parents are consulted through classroom inquiry or questionnaire surveys to understand the badminton learning experience of the experimental subjects, and further observation and screening of students with learning experience are carried out to determine their existing learning level. The experience will not interfere with the experimental results, and the survey results are shown in Table5.Table 5
Student learning experience.
NMore than 3 monthsWithin 3 monthsNever touchedNumber of peoplePercentageNumber of peoplePercentageNumber of peoplePercentageTest group00%211.5%2488.5%Control group11.66%36.54%2691.6%Total11.61%57.49%5090.1%It can be seen that most of the students have no badminton learning experience, which also shows that in learning groups, the two groups of learning are in line with the positioning of badminton learners and have homogeneity. Investigate students’ interest in badminton, and the statistical results are shown in Figure7.Figure 7
Preexperiment interest survey results.It can be seen in Figure7 that both the experimental group and the control group have higher enthusiasm for badminton training, and there is no forced training. There is no significant difference in badminton training interest between the experimental group and the control group.


## 3.3. Comparison of Experimental Results

We taught different groups of students for 3 months and then compared the differences between the badminton parameters between the two groups. The changes in the technical control group of the experimental group are shown in Figure8.Figure 8
Training data comparison.It can be seen in Figure8 that regardless of the experimental group and the control group, the average value of the test data before and after training has been significantly improved, the variance is generally reduced, and the statistical analysis P value is less than 0.01, so there is a significant difference between the data before and after training. It shows that the physical fitness of the trainees before and after the training has been significantly improved; whether it is the traditional training method or the training method that combines wireless network communication and Kinect, the training effect has been achieved. We statistically analyze the physical fitness test data of the experimental group and the control group after the experiment, as shown in Figure 9.Figure 9
Physical fitness test results.After the experiment, the test data of the experimental group and the control group are slightly higher than those of the control group but the gap is smaller (less than 5%). In the independentt-test, the P values of the four test data are all greater than 0.05, indicating that there is no significant difference between the test data of the experimental group and the control group.We surveyed the two groups of experimental subjects about their interest in badminton after 3 months of teaching. The survey results are shown in Figure10.Figure 10
Preexperiment interest survey results.After three months of group teaching, it follows in Figure10 that in the experimental group applying the method of this paper, the learning of a lot of interest in badminton accounts for more than 90%, and in the traditional teaching method, the students taking a lot of interest in badminton represent only about 66%, more than the method of this paper by 24 percentage points, which also shows that the teaching of badminton in wireless network communication with Kinect than the method of traditional can cause students’ confidence more.


## 4. Discuss

The survey found that most of the trainees are positive about their interest in badminton based on wireless network communication and Kinect, the cultivation of sports confidence, and the effect of badminton training. The trainees’ enthusiasm for badminton training has been greatly improved. In the sense of progress, look forward to the opportunity to train and learn in this environment in the future. Of course, the problems caused by wireless network communication and Kinect cannot be ignored. Some students cannot accept or adapt to this training method. Try to find solutions to make wireless network communication and Kinect acceptable to everyone.With the emergence of new technologies, there are indeed certain difficulties for those who are not very skilled or have little exposure to it. It is impossible to say that there is no difficulty at all. In the wireless network with the communication and Kinect learning platform, it is not only the loading that comes with the use of new technology. As a result of the wireless network communicating with the Kinect learning environment, teachers not only need to prepare the teaching content on the classroom, their teachers also need to continue to be involved in the students’ learning after the period, and they should be ready to solve the students’ questions after the class, while for the students, it provides them with seamless learning opportunities from nowhere.The emergence of a new technology is always full of controversy. The application of wireless network communication and Kinect’s mobile learning to the badminton teaching classroom has been compared and analyzed through experiments. Although the new teaching method is conducive to stimulating the enthusiasm and initiative of students in learning, it has changed the traditional model of “teaching” by teachers and “learning” by students. The interaction between teachers, students, and classmates has brought closer the relationship, but it requires the cooperation and support of more people.Although students have a certain degree of self-control ability and a certain ability to restrain themselves, sometimes, the nature to like to play is still exposed and the tasks assigned by the teacher may be forgotten. This requires the teacher to set and participate in the evaluation during the implementation process and form an evaluation to fully mobilize the participation of students.


## 5. Conclusions

This paper mainly studies the motion modeling method for the human motion data captured by Kinect and establishes a human skeleton model based on the Kinect motion data and uses the kinematics model and the captured motion data to drive human actions. Based on the motion model proposed in this paper, a motion evaluation method based on similarity comparison is proposed. The algorithm is introduced into the action evaluation, and the movement data of different lengths are aligned on the time axis, and then, the distance is calculated as the evaluation index of the similarity of the two groups of actions. It makes up for the shortcomings of the traditional Euclidean distance in measuring the similarity of nonequal length data and can evaluate actions more objectively. While the complexity and variability of human movements make it impractical to describe them with a common mathematical model, there are no guarantees that different people will perform the same movement data; however, the same kinds of movements will always have similar characteristics. Analyzing and categorizing the acquired human motion data and motion characteristics, and identifying the type of exercise performed is the next research focus of this article.



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*Source: 9511009-2022-01-07.xml*