# Intelligent Strategy of Internet of Things Computing in Badminton Sports Activities

Authors: Shengyao Xiong; Xinwei Li
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

One of the most popular sports in Asia is badminton. Asia has the most talented players in badminton. The players in the conventional badminton teaching are totally reliant on the coach and the fitness instructor. But in today’s technologically advanced world, the educational system has caught up with technology in intelligence. By using the smart technology, the gamers may train on their own. These technologies range from mobile scheduling apps to fitness apps. In this study, a unique intelligent K-Nearest Neighbor algorithm is used, and the outcomes are assessed. Additionally, all conversation is recorded and made available in the database using the Deep K-Nearest Neighbor (DKNN) method. This DKNN model will perform the analysis of neighbor points about the badminton sports activities for training the players with the utilization of Internet of Things (IoT). Results indicate that the suggested DKNN algorithm performed 89% accurately, which is 6.5 percent better than the current sport motion segmentation technique. People may be able to accomplish enough while introducing this method as a real-time application to prepare for subsequent sports and its computing techniques.


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## Body

## 1. Introduction

After being introduced to China in 1920, badminton has grown in popularity [1]. Despite the fact that many colleges include badminton in their physical education curriculum, it has an effective technological foundation [2]. Badminton foundations are commonly forgotten and erroneously learned if they are taught from simple to complex, from part to whole; second, students become easily sidetracked and lose interest in studying when tough skills are discussed [3]. Typical college students are encountered when teaching badminton in universities and colleges. The roots of badminton technology are either extremely thin or nonexistent, which is problematic when considering the increasingly harder badminton methods and the typical transition from easy to complex instruction [4]. Students frequently forget and improperly master technological principles as a result of this procedure. Second, when presented with a great deal of tiresome and perplexing detail, students are quickly distracted and lack motivation to learn [5]. There are very limited badminton teaching hours at many colleges and universities, and the majority of these institutions only offer one semester of badminton instruction [6]. Teaching badminton, which is complex and varied, requires time. Students with low levels of receptivity are unable to keep up with the rapid pace of schooling. Therefore, it is crucial to create an intelligent badminton education system based on neural networks.Both domestic and international experts have done much research on the subject of intelligent badminton instruction. The United States, European nations, Japan, Canada, and other countries have the most active research programmes on intelligent teaching abroad. Universities in the US have created some intelligent prototype systems, including Stanford, MIT, Memphis, Carnegie Mellon, and California [7]. For instance, the US National Science Foundation has allocated $22.5 million to the study and production of studying and intelligent systems for human creation. Computers can use the tutor system, which Memphis University developed over a 25-year period, to provide students with prompts and hints [8]. The researcher can then makes decisions based on typing and verbal responses to the question without multiple choices and provide corresponding justifications for any potentially grammatical or semantically incorrect language [9]. It has also become increasingly crucial to support the overall physical and psychological growth of students. Additionally, college students’ physical condition has been deteriorating year over year. With badminton’s gradual rise in popularity, collegiate badminton instruction also seems to be a crucial component. Teachers should advance their own technical proficiency and develop new teaching techniques in addition to raising the caliber of instruction [10]. The author noted that badminton is an adversarial sport across the net, particularly in doubles, which necessitates the tacit cooperation of two players. In order to effectively teach badminton in colleges and universities, it should be used [11]. More experiments with novel teaching strategies and teaching practise are needed to foster students’ sense of community, cooperation, and learning interest. Following an experimental comparison study, students’ cooperative awareness and their curiosity, initiative, and excitement for learning were examined. These studies have served as some starting points for the investigation in this study, but since there were no samples used in the earlier experiments, it is difficult to duplicate and use the results [12].Badminton is a popular athletic competition. Despite the unstandardized nature of the movements, virtually, anyone can play. After teaching badminton foundations in a college setting, there are generally insufficient class hours for systematic learning and training [13]. Some of the students’ uncomfortable movements were not addressed through direct badminton sparring practise, nor was there sufficient time to assist and carefully complete the movement learning. Students find classroom instruction tiresome and uninteresting when courses cannot be connected. The student will not get another opportunity to practise after the exam since the instructor must complete test and exam preparation in order to complete the instructional progress [14]. Although teaching badminton is a sport with both theoretical and technical components, technical teaching still maintains a disproportionate amount of power in college badminton training. Before beginning the technical instruction technique, the necessary facilities and equipment must be in place [15]. Despite the fact that badminton has fewer venue and equipment needs than football, basketball, and volleyball, many schools and institutions continue to use their football, basketball, and volleyball stadiums as the majority of their sports grounds [16]. Typically, badminton facilities are far smaller than those for the other three major sports. Numerous institutions must limit the number of badminton players in order to retain the same number of classes. The availability of courts and equipment for badminton is another big impediment.The badminton learning objectives of students are more definite, detailed, and deliberate. The learning purpose is to acquire core badminton knowledge in addition to its technology, techniques, and refereeing basics [17]. The goal of this ability is to enable the user to master fundamental badminton methods, training methodologies, and physical preparation for classroom-based badminton training. College students should have a mentality that combines the features of badminton with healthy workout habits and long-term sports principles. Encourage youngsters to be physically active and to have a strong work ethic and the tenacity to endure through adversity [18]. There are numerous new uses for Internet of Things (IoT)-based smart wearable technologies that recognize sports activities [19]. Comprehensive performance evaluations of players’ shooting and passing abilities are vital for coaches and players during practise and competition [20]. Typically, professional soccer players are trained with the assistance of coaches or trainers who provide subjective advice based on their own experiences [21]. However, different coaches may have different interpretations depending on their own experiences, and there are numerous quick and subtle movements that cannot be noticed during a soccer play. A digital objective method is recommended to be better than a manual, subjective one [22]. Despite this, videography is frequently confined by its environment and technology, making it difficult to provide coaching staff or soccer players with real-time feedback due to storage and computing requirements [23]. MEMS and BTLE technologies are quickly implementing real-time data transport and continuous motion data gathering. Using inexpensive on-body inertial sensors, motion recognition research is expanding. Using wrist-worn inertial sensors, the authors determine the type of table tennis stroke, for instance. To classify diverse volleyball and badminton actions, wrist-worn sensing devices (WSDs) are utilised [24]. As a result of the nature of soccer, however, the ankle movements of players are complex and suggestive of multiple actions. In addition, soccer motions are exceedingly difficult to categorise due to the diversity of training and the distinct execution of each activity. The classification of the most fundamental soccer movements, such as shooting and passing, provides a specific issue. The tactics currently utilised in other sports virtually always apply [25].A WSD-based IoT system is given as a means of supporting soccer coaches and players in their talent development. It will provide them with objective feedback following or during a training session. The recommended IoT system consists of wearable devices, a mobile device (such as a phone or tablet), and a cloud-based data processing platform [26]. The raw data of soccer players is obtained using a MEMS WSD. Using BTLE technology, the data is transported to a cloud-based data processing platform, where it is assessed and given to a mobile device in real time. A support vector machine (SVM) model classification technique employing an ankle-based attitude angle model is advised for identifying diverse motions and evaluating various skill levels. This strategy is perfect for developing young soccer players. Passes, crosses, and shoots are regularly practised in soccer practise [27]. In a sports club or training facility, tens or hundreds of male adolescents practise shooting or passing, for instance. Clearly, a minority of soccer coaches find it tough to examine the individual performance of each player at each session. Instead, our technology will display on a coach’s mobile device the number of completed passes/shots and their execution quality. With the advancement of this work, soccer players will be able to track their training efforts [28]. Managers and trainers could use a summary of prior days, weeks, or months’ workouts to strategically schedule future training sessions [29]. People aim to apply the technology in a realistic training setting, with a focus on detecting fundamental soccer moves. Badminton has drawn both large audiences and young students in recent years.In the realm of teaching and training, broad concern has been raised by the growth and popularization of badminton [30]. In their research and practise, the predecessors also discovered a variety of cutting-edge and useful training techniques. The majority of the present badminton teaching research, however, focuses on teaching badminton in college classrooms and professional sports teams’ training sessions. Research on instruction and instruction methods for the general public and youth is lacking [31]. Young people can mobilize all of their body’s cells by playing badminton, but there has not been any systematic badminton training and there are no any decent hardware facilities to record athletes practising badminton, so it is not very good [32]. To close this gap, it is necessary to do research and identify efficient youth education strategies. This is a challenging task as well. The participants in this study are split into two groups: the experimental group uses the core strength training approach, while the control group uses the conventional strength training method. Prior to and after the training, the training material is organized by several groups [33]. Following a 12-week training period, both the experimental groups and the control group assessed the badminton students’ sporting prowess, as well as their physical strength and unique speed, and then used a fuzzy algorithm model to analyze the findings. This study focused on evaluating the intelligent strategy of IoT computing in badminton sports activities.


## 2. Motivation of the Study

The objective of this study is to create a portable system for classifying sports activities and to conduct associated learning on sports in the classroom through interaction and algorithmic classification. A Deep K-Nearest Neighbors (DKNN) was used to extract the inherent features of the wireless devices to collect sports’ two portable inertial sensing components from the spectrum analyzer of the Internet of Things (IoT) sport movement signals. These components were accessorized on the wrists and ankles of the badminton player. To record the electrical motion activity caused by athletic events, each spectator wore one of the two portable acceleration detecting modules on their wrist and their legs. Then, in order to identify various badminton sport activity types, people created a sophisticated learning-based Deep K-Nearest Neighbor algorithm that includes sports movement signal collection, signal preprocessing, sport movement segmentation, signal normalization, spectrum analyzer generation, image merging or resizing, and finally working on with the classification methods.


## 3. Materials and Methods

The pricey sport of badminton is more well-liked in Asia than everywhere else. In conventional badminton instruction, players must travel to the practise facilities and work out under the direction of the designated coach. After the training session, the coaches will have a difficult time keeping track of the athletes. The Internet of Things (IoT) provides a variety of application support to get around the difficulties. In this modern environment, coaches have also adapted to mobile applications and their uses. Through an interactive system, the coach will briefly go through dependable fitness software, wearable trackers, management analysis, and nonwearable trackers with the players (refer Figure1). The fitness app contains features for tracking your diet, exercise schedules, and other activities. Smaller-sized sensors included in wearable trackers can monitor a player’s precise activity while keeping time in mind. Smart watches, wristbands, sleep trackers, and other wearable sensors are examples. The interactive systems are crucial in keeping track of the player’s activity in the nonwearable trackers. The nonwearable sensors include scheduling the work, a digital coach, and intelligent gym recommendations. The sort of body motions and activities that the players take are examined and trained in the final movement tracking. The database is regularly updated with all these developments. As a result, both the athlete and the coach can monitor activity history and enhance performance. Intelligent technology may be used to build this interactive system and warn both the coach and the players. In this study, a DKNN algorithm is used in conjunction with artificial intelligence and machine learning to provide players and coaches with a system of automated alerts.Figure 1
Players data collected according to the movements in their body.The deformation accelerometer and gyroscope measuring procedures are initially modified to reduce sensitivity and compensate errors from the inertial sensors’ raw outputs. For human calibration, the deformation accelerometer must first be set up on a flat surface (refer Figure2). The deformation accelerometer’s axes are then alternatively positioned above and below to correlate with the gravity effect, which will be observed by a gyroscope whenever the portable inertial identifies the features is stable.Figure 2
Data collection from the coach and passed to the interaction systems.


## 4. Implementation

The spectrum analyzer of the Internet of Things (IoT) sport movement signals was utilised to extract the intrinsic properties of the wireless devices to collect the two portable inertial sensing components for sports. The badminton player wore these accessories on his wrists and ankles. Each spectator wore one of the two portable acceleration detecting modules on their wrists and their legs in order to record the electrical motion activity brought on by athletic events. Then, a sophisticated learning-based Deep K-Nearest Neighbor algorithm was developed to identify different types of badminton sport activity. This algorithm includes sports movement signal collection, signal preprocessing, sport movement segmentation, signal normalization, spectrum analyzer generation, image merging or resizing, and finally working with the classification methods.Figure3 represents the proposed model, which displays actual working model. Badminton matches using all of the accelerometer’s axes are collected and used to standardize the raw accelerometer recorded data as represented in Equation (1). The sports perception ratings on the datasheet for each axis can be used as the measuring scalars in navigational systems. The average rotational speeds are obtained by utilizing a portable inertial important channel stationary at the beginning later to calculate the offsets with entire rotational axes. Humans will eventually be able to estimate offsets and the outcomes of a new gyroscope measurement accurately utilizing the component at a time, as follows:
(1)Ra=KDp000KDq000KDr×Rv+QpQqQr.Figure 3
Proposed model that shows the actual working model.From Equation (1), Ra highlights and scaled excitations pa=bapbaqbarE or angular type of acceleration ρa=ρaqρarρapE. Rv denotes only the clear type of amplitudes cm=bmqbmpbmrE or else with the rotational motion ρa=ρaqρapρarE. The building is included in the n, m, and p axes of the deformation accelerometer or gyroscope, which are denoted by the symbols KDp,KDq,andKDr. The letters Qp,Qq are used in place of Qr to signify the p, q, and r axes offsets in the deformation accelerometer and otherwise gyroscope.After the match, the surface area and magnitude of the badminton motion signals are collected from a range of untrustworthy users. The vibration interval is divided into segments during the segmentation procedure of the badminton motion. In order to lessen the influence of human distinction, each badminton motion signal during the sports motion halt was inserted into the size of the signal with the most significant duration. Humans then normalize the sport evidence by employing methods to lessen the discrepancies between the two users within a single amplitude.Equation (2) is the a−score type standardization.
(2)CPxr=∑nm∏CPr−∏CPmean∏CPstd,where CPrandCPxr are signals inside the badminton motion interval that are both the original and normalized sport motions. Within a sport motion interval, there are time steps. In fact, CPmean and CPstd represent the variation of the sport motion signals inside the badminton motion period.When all the information from the spectrogram is available, divide it into small samples of equal length using a linear transformation, and then quantify the Fourier transform individually for each group.CT is the normalization of motion signal in badminton is determined as follows:
(3)CTnxy,ρ=Ny,ρ=∑x=−φφnxρx−ye−jρx.The DKNN is represented by theCT, where nx stands for the normalized badminton motion signals, pixel value is signified by [φ], and y for the window stored procedure centre. Using a varying point transform and a modulated signal that connections with a 60 percent accuracy, the CT is calculated. The normalized badminton signals’ square of the CT is divided by the amplitude of the retrieved N features, which is defined by the following:
(4)spectromnxy,ρ=Ny,ρ2.The output data of a convolution operation is established using the characteristics determined more by the convolutionalm, and the picture resolution is the same as that of the convolutional layers’ input data. The signals from the convolution layers’ output are first known as “extracted features” and are shown as in the following:
(5)pijI,p=∫ipI+∑y=1XUy,xI,pni+y−1j+x−1I−1,p.The runtime environment’s size and shape are still represented byU and n in the equation, but l refers to the surface index, hpI refers to most of the type of prejudiced for such pth feature in according to the space of lth layer, Uy,xI,p do has the relation between the information ni+y−1j+x−1I−1,p as according to the pth feature type of space of a Ith layer, as same ∫n is indeed the Equation (6), where it is with the type of separated function.
(6)∫a=∑a=1amax0,a.Equation (6) can be formed in the case of GijI,p. The convolution operation utilised in this paper is max0,n max-pooling, which can be computed using the following Equation (7). It also provides the highest value among adjacent extracted features.
(7)GijI,p=maxo∈Osi×M+o,j×M+oI,p,where O=2∗2 and N=2 stand for the accumulation size and running style, respectively. Additionally, the data for such a comprehensive layer, QcI fundamental characteristics are taken from the completely linked layers that are closely packed as ubPI−1 a one-dimensional classification model is created by removing the convolution layer, as illustrated in the following:
(8)QbI=∫∑PubPI−1+hbI.The last essential frame performance, soft max, assesses an over predicted posterior distribution and several sports classes.(9)GsQ=argmaxs∈SexpQbd∑y=1XSexpQydTheCT algorithm is used to update the network parameters to minimize a classification cross-entropy gradient descent. In Equation (9), S represents the sport exercise class, XS denotes the total number of sport activities in the classroom, and last surface index is represented using the variable d during the training phase. Equation (10) is used to evaluate the effectiveness of the indicated accuracy taught in the classroom badminton activity CT classification method.
(10)accuracy%=UQ+UMUQ+UM+YQ+YM.The acronymsUQ stands for “true positive,” UM for “true negative,” YQ for “false positive,” and YM for “false negative”, respectively. The learnt classroom’s accuracy classification using
(11)Pg%=UQUQ+YQ×100.K, the collection of classes is utilised to evaluate the classifier’s overall performance as shown in
(12)Pe%=UQUQ+YN×100.Additionally, the effectiveness of the classifier of a sport activity learnt in the classroom is evaluated as in the following:(13)CV%=∑i=1STPiUQ+UN+YQ+YN×100.In order to properly categorise badminton activities, a portable classification system and IoT application learning, the CT classification method usesCV cross-validation approaches.


## 5. Results and Discussion

In this research, we have utilised the data which is collected earlier with the provided configurations and the analysis part is performed with the proposed model. Male players partaking in exercise or training for badminton is shown in Figure4. The players must put in their absolute best effort and focus during this badminton training, thus the programme must meet those requirements. All the players will benefit from training because scheduling becomes more necessary as the number of players rises. The players were only able to devote a maximum of five hours to training, as represented in Figure 4.Figure 4
Training by number of players (male).Additionally, there were as many male players as possible available for five hours. Approximately 7,000 players were trained over the course of four hours. However, hundreds of athletes were able to receive training that lasted for longer than five hours. This study assumes that the athlete is always training because it does not pay attention to how long they relax. Additionally, it is anticipated that the information was gathered via wearable technology and fitness apps that adhere to the Internet of Things (IoT) principle.The same as the male players, training is extremely difficult for the female players. Through IoT devices and wireless networking, the training may be carried out at the coach’s locality or in a remote place. In this training mechanism, the coach and the players make use of modern information technology ideas like IoT with the assistance of mobile applications for interactive sessions or real-time observation of the players’ activities. According to the figure, only a small percentage of female athletes have been able to train for more than six hours, and their combined performance is less than five hours long. The players in China’s badminton training programmes for both sexes were regarded as being registered in two separate gyms. The total number of players from both the gym’s male and female teams is considered for analysis in Figures4 and 5.Figure 5
Number of female players in training.The total male and female players signed up for training in their gym is shown in Figures6 and 7. The finest instructor being available at the first gym, being close to where they live, or having flexible training times, among other factors, may be the motivation for going there. This does not imply that the alternative gym is less significant for training. There are hundreds of players that have signed up for badminton training.Figure 6
Male players interested in gym.Figure 7
Female players interested in gym.The number of players active in training for badminton is divided into five divisions, including the overall number of players, the number of men, the number of females, and residents and nonresidents of China. A matrix was used to determine how accurate the proposed HMM was in recognizing objects. The discriminant function of the experimental data shown in this paper is shown in Figure7. When training for badminton training methods that comprise five different ball-hitting movements, serving the ball, rubbing groundstrokes, and backhands, the DKNN approach achieved correct recognition performance of 95% for the male, female, resident, and nonresident. Data from wearable and nonwearable categories is collected using IoT devices to accomplish these classifications. The training is carried out under the direction of the coach, and depending on the difficult scenario, it may be scheduled to take place online or offline.Every day, more and more applicants are engaging in different sports. Due to the advantages of less restriction on that area and ease of learning, badminton has become more and more popular among them. This study introduced a categorization system for sporting activities used in health monitoring that successfully classifies badminton play. The badminton action is monitored by a separate speed sensor that is fastened to a component of the badminton tennis racquet. The extraction of features its goal-scoring signal is extracted using the DKNN technique. Regarding male and female athletes, it compares the current training methods for badminton sports activity as 67 percent and 21 percent, respectively. Male and female data categorization is seen to take 0.75 and 0.19 seconds, respectively. Based on the current approach, the categorization accuracy for the five required groups for men is 83% and for women it is 30%. However, the suggested DKNN algorithm demonstrates the optimum badminton training activity in men (65%) and females (43%) based on time for males (0.79%) and females (0.23%) based on the total accuracy of males (89%) and females (40%). When compared to the current approach, the new algorithm’s overall accuracy is 7% higher. As a result, using Internet of Things devices and the coach’s guidance, the suggested algorithm may be used to teach candidates for both genders in badminton. With the use of wearable, nonwearable, and certain mobile applications with internet capabilities, this training can be improved. Most coaches advise using these tools and strategies to help with player activity tracking (refer Table1).Table 1
Comparison analysis of intelligence in badminton training methods.
Classification (or) algorithmBadminton training sports activities (%)Time in (s)Accuracy in (%)DKNN algorithmMale650.7989Female230.2440Existing method: sport under motion segmentation optionsMale610.8989Female430.2033


## 6. Conclusions

Badminton is one of the most popular sports in Asia. Asia produces the best badminton players. During traditional badminton coaching, the players’ only sources of support are the coach and the fitness instructor. However, in the contemporary, technologically advanced world, the educational system has exceeded technology in terms of intellect. Through the use of intelligent technologies, gamers may train independently. These technologies range from mobile scheduling apps to wellness apps. This study uses a novel, clever K-Nearest Neighbor approach, and the outcomes are assessed. Additionally, every conversation is recorded and made available in the database using the Deep K-Nearest Neighbor (DKNN) technique. The suggested DKNN algorithm performed 89 percent accurately, outperforming the current sport motion segmentation approach by 6.5 percent, according to the results. By using this concept as a real-time application, people might be able to achieve enough to prepare for upcoming sports and their computer strategies. For future research, it is highly recommended to implement sensor techniques for analyzing the performance of badminton sports.



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