# Optimization of the Intelligent Sports Health Management System Based on Big Data Technology and Internet of Things

Authors: Jian Dang
Journal: Journal of Function Spaces
 (2022)

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## Abstract

People often suffer from unpredictable injuries during physical exercise. One of the important reasons is the absence of a scientific sports health management system. Therefore, the construction of such a scientific and effective system has gradually attracted the attention of scholars, which is of great significance to realizing people’s scientific and personalized physical fitness. An intelligent sports health management system based on big data analysis and the Internet of things (IoT) is constructed to solve this problem. The system consists of the user, IoT, cloud, system analysis, evaluation, and data layers. Firstly, a new multilabel feature selection algorithm is proposed in the system analysis layer. The suggested multilabel feature selection algorithm maps the sample space to the label space through theL21 norm. Then, the consistency of various topologies is guaranteed by combining with feature popularity so that the factors affecting user health can be better selected. Secondly, the experiment is compared with SCLS, SSFS, and six other multilabel feature selection algorithms in 6 classic medical multilabel datasets. Experimental results under five indexes show the effectiveness and superiority of the proposed feature selection algorithm. Finally, the feasibility of the proposed intelligent sports management system is analyzed.


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## Body

## 1. Introduction

Nowadays, people will pay more attention to their health and beauty and more people will choose to experience a happy life, which requires the support of a fitness system tailored for themselves. Scientific and reasonable fitness behavior can meet people’s needs for a better life and make people have a beautiful appearance and happy mood. Moreover, the unscientific fitness mode often brings all sorts of misfortune to people, for example, causing muscle injury, joint deformation, heart function injury, iron deficiency anemia, sports sex hematuria proteinuria, and other diseases. At present, people’s cognition of how to reasonable fitness is still relatively narrow and most of them follow the trainer’s years of experience to give a more rational way of wellness. However, despite this, there will still be many nonstandard, accessible to damage practices of physical fitness and damage user health. Combining the excellent experience of coaches with people’s situations and building a tailor-made sports health management system for people has become an urgent problem to be solved. In life, we should nip it in the bud. User health should be based on prevention rather than treatment. Scientific and reasonable health management and fitness are the main ways to maintain health.In the era of rapid development of science and technology, user health is no longer limited to running, Tai Chi, long jump, and other ways. There are a variety of exercises, even for one part of the body, such as wrist kicks, which have become popular in recent years. It has become a headache for people to choose fitness methods for themselves. After all, the correct choice will get twice the result with half the effort. The wrong choice is likely to be ineffective or even counterproductive. This paper proposes an intelligent sports health management system based on big data analysis and the IoT to solve the abovementioned problems. The main contributions of this paper are as follows:(1)
In order to make the intelligent sports health management system more targeted, combined with big data analysis technology, a new multilabel feature selection algorithm is suggested to ensure that the main factors affecting user health can be extracted effectively by this method(2)
Combining the proposed multilabel feature selection algorithm with the IoT technology, the basic framework of the intelligent sports management system is constructed(3)
A series of comparative experiments were carried out on six classical medical multilabel datasets, and the experimental results proved the effectiveness of the suggested algorithm. The feasibility of the suggested intelligent sports health management system is illustrated through the feasibility analysisThe structure of the paper is as follows: in the second section, the symbol description and related work are given. In the third section, the theoretical support, optimization solution technique, algorithm design, and convergence proof of the proposed feature selection model are given and the design principle of the proposed system is introduced. In the fourth section, the experimental settings are compared with SCLS and SSFS and the experimental results are analyzed. Finally, the summary and prospects of this paper are given in the fifth section.


## 2. Literature Review

In this section, we not only make a brief overview of feature selection in big data analysis but also make a brief overview of the health management system as follows:There has been much research on feature selection algorithms in recent years. In many works, feature selection models are divided into three types: filter [1–3], wrapper [4, 5], and embedded [6, 7]. This paper mainly uses embedded feature selection. Literature [8] combines logistic regression, manifold learning, and sparse regularization to construct a feature selection algorithm. In the literature [9], a possible structure sharing (LSS) term is designed and the construction of the feature selection algorithm is completed in combination with spiritual learning.Both [10, 11] used dynamic graphs to learn the basic manifold structure of samples or labels and then combined them with linear regression to build feature selection models. Reference [10] strengthens the local connection between samples and labels by combining with subspace to better special features. Reference [11] strengthened the correlation between the weight matrix and sample space and between the weight matrix and label space by comprehensively restricting the weight matrix, making the weight matrix more representative of the weight of features and more accessible to distinguish features.Furthermore, several multilabel feature selection algorithms include a variety of factors. For instance, many scholars use a multilabel feature selection strategy characterized by mutual information:Among them, modeling the feature selection process as a multicriteria decision process was proposed for the first time in [12]. This approach applies to multilabel data, using the TOPSIS (order preference technology similar to ideal solution) approach as the well-known MCDM algorithm to evaluate features based on relationships with multiple labels as different criteria.The SCLS algorithm was proposed in [13], which is an effective feature selection algorithm. Still, the feature selection algorithm is easily affected due to the excessive combination of labels and features. Multilabel feature selection algorithms also use mutual information to evaluate features. They feature sorting algorithms to achieve feature selection, such as MDMR [14], PMU [15], and FIMF [16].And there are a lot of studies on health management systems. The comprehensive health management service platform for the public is proposed in [17], which promotes the development of sports health management services through intelligent sensors and intelligent health system detection equipment. Aiming at the problems of a long cycle and high cost of existing intelligent sports health management systems, an improved particle swarm optimization algorithm is proposed in [18] to optimize the intelligent sports health management system. A new type of intelligent sports management system is proposed in [19], which is constructed by using information technology and human-computer interaction technology under artificial intelligence and combining it with deep learning technology. There is an organized review of healthcare management systems in [20] and enhancement of healthcare management systems through many of the latest IoT-oriented healthcare applications.Firstly, most of the abovementioned multilabel feature selection algorithms are based on linear regression and mutual information. Among them, the multilabel feature selection algorithm based on linear regression has poor robustness due to the loss function (least square) of linear regression. Moreover, the multilabel feature selection algorithm based on mutual information has high algorithm complexity and is not suitable for the analysis of high-dimensional data. Therefore, in order to better analyze user data features, a multilabel feature selection algorithm based onL21 norm regression is constructed to promote the analysis ability of the system. In addition, the existing intelligent sports health management system seldom uses expert experience data to guide data analysis. Finally, a new type of intelligent sports health management system is constructed by adding expert experience data into system analysis and combining the proposed multitag feature selection algorithm.


## 3. Method

To address the problem identified in Section2, in this section, the design principle of an intelligent sports health management system based on big data analysis and IoT is introduced in detail from three aspects: feature selection in big data analysis technology, the IoT technology, and intelligent sports health management system.


### 3.1. IoT Technology

The IoT technology refers to the use of the contractual network protocol, and the use of IoT information induction of all kinds of equipment (infrared image sensor, the world’s first GPS satellite positioning system, radio and laser image scanner, etc.) will be connected to the Internet at the same time with any kind of objects. The IoT network can simultaneously achieve a variety of voice and image control, track objects’ location, and make image monitoring and information management to facilitate the IoT information network exchange and data communication. Thus, the IoT has unique advantages in fitness data collection and information output.About the IoT and its application in intelligent sports health management systems, the first aspect is data collection: a large number of actual and reliable data are the basis for the construction of the suggested model, including not only users’ physical health status data but also medical index data, as well as the experience summary data of well-known coaches, etc. With these data, data mining technology can be used to analyze people’s physical conditions and give a reasonable way of fitness. At the same time, the IoT technology can collect information through various intelligent fitness equipment, such as brilliant fitness bracelets, and innovative tracks.Among them, wearable devices have been widely used in sports activities. The most common are smart bracelets, mobile phones, etc. Such devices can collect a user’s daily energy intake and output and data such as the user’s heart rate and exercise.In terms of data output, the IoT technology can push the evaluation results of big data analysis and suggested fitness methods to users through various intelligent devices, such as wearable devices, mobile phones, computers, and VR. These intelligent devices typically have screens that allow users to read information pushed by the IoT.Among them, VR equipment can help users learn standard movements. Users can achieve the maximum fitness effect in the shortest time. In addition, due to the novelty of VR, it can also enhance users’ interest in fitness, develop more users, and lead the wave of reasonable fitness for all.


### 3.2. Symbol Description

This subsection will briefly introduce the symbols and meanings used in this article. The specific content is shown in Table1. For any matrix M:Table 1
Notations of this paper.
SymbolMeaningMijTheith row and jth column of MMi∗Theith row vector of MM∗jThejth column vector of MMTM transposetrMTrace ofMIn addition,X∈Rn×d represents the sample matrix, Y∈Rn×m represents the real label matrix, and W∈Rd×m is the matrix of coefficients.


### 3.3. Big Data Analysis Technology (Feature Selection)

Big data analysis technology is an essential component of knowledge discovery, using computer algorithms to analyze data. In many databases, the required data should be obtained and the data should be properly converted, mined, and utilized to obtain valuable information. Generally speaking, the objects of big data analysis are basically structured, semistructured, or other structured data.Feature selection is a kind of big data analysis technology that selectsd most representative features from n features of the sample. Feature selection is used for data dimensionality reduction. The selected features are also the most representative, so we can analyze the main factors affecting user health through feature selection to provide more targeted scientific and reasonable fitness methods. Due to the complexity of data from users and their lives, multilabel feature selection is more of the system, for analyzing the main factors affecting user health. Therefore, we propose a new multilabel feature selection method as follows:Given multilabel datasetX∈Rn×d, its corresponding label set Y∈Rn×m. According to the assumption of the problem, the loss function LW of the model is constructed and the optimal solution is sought by minimizing LW. The formula is as follows, where W∈Rd×m is the feature weight matrix, which can reflect the importance of features.
(1)Φ=minLW.In addition, the penalty functionRW is often imposed on the weight matrix W and the property of constraint W is that the model is further idealized:
(2)Φ=minLW+αRW,where α is the penalty factor.Due to the least square loss function of linear regression, although the calculation is simple, it is not as robust as theL21 norm. Therefore, L21 norm regression is used to describe the relationship between the sample set and the label set:
(3)LW=XW−Y2,1.The basic framework of the suggested algorithm is constructed:(4)Φ=minXW−Y2,1+αRW.In the suggested algorithm, we constrain the learning of feature weight matrix in the following ways:LetX•i represent the ith-column vector of dataset X and Wi• represent the ith-row vector of the weight matrix W; if X•i and X•j are similar, then, Wi• and Wj• are similar. Thus, the similarity between features can be used to guide the learning of the weight matrix and build the feature manifold learning model:
(5)12∑i=1d∑j=1dWi•−Wj•22SXTij=12∑i=1d∑j=1dWi•−Wj•Wi•−Wj•TSXTij=TrWTPXT−SXTW=TrWTLXTW,where LXT∈Rd×d is the Laplacian matrix of the Eigen similar matrix; PXT∈Rd×d is the diagonal matrix, PXTii=∑j=1dSXTij is the ith diagonal element of PXT, SXT is the feature similarity matrix, and SXTij represents the similarity between feature X•i and X•j. In this study, SXT is calculated by Pearson’s correlation coefficient with square constraint and its formula is as follows:
(6)SXTij=X•i−X•j22σ,ifX•i∈NKX•jorX•j∈NKX•i0,others,,where NKX•i is the K neighborhood of X•i.Thus, a multilabel feature selection model is constructed:(7)Φ=minXW−Y2,1+αtrWTLXTW.Meanwhile, in order to enable the suggested algorithm to perform feature selection better and faster,L21 norm sparse constraint is applied to W in equation (7):
(8)Φ=minXW−Y2,1+αtrWTLXTW+βW2,1.In terms of the optimization solution of the abovementioned formula, due to the nonsmoothness of theL21 norm, it can be transformed into the following:
(9)Φ=mintrXW−YTHXW−Y+αtrWTLXTW+βtrWTDW,where H=1/XW−Yi•2, D=1/Wi•2.For the solution of equation (9), the derivative function with respect to W is required:
(10)∂Φ∂W=XTHXW−XTHY+αLXTW+βDW.Set the derivative to 0:(11)XTHXW−XTHY+αLXTW+βDW=0.Thus, it can be concluded that the updating formula forW is as follows:
(12)W=D−1XTHX+αD−1LXT+β−1D−1XTHY.Through solving the abovementioned problems, the suggested multilabel feature selection algorithm is shown in Algorithm1:Algorithm 1: The suggested algorithm.
Input: data matrixX∈Rn×d, label matrix Y∈Rn×m, regularization parameters α and β, select the number of features K.Output: feature selection resultI.a) CalculateLXT according to Eq (5) and (6).b) The initialization matricesH and D are the identity matrices.Repeat:UpdateW: W=D−1XTHX+αD−1LXT+β−1D−1XTHYUpdateD: D=1/Wi•2UpdateH: H=1/XW−Yi•2Until it converges.c) ComputeWi•2;i=1,2,⋯,d and sort it to find the first k largest assigned to IThe big data analysis technology can conduct cluster analysis and collation of the massive sports fitness and health data of residents collected to evaluate the user health status and sports injury status. Applying big data processing technology in sports injury assessment can improve the accuracy and effectiveness of sports injury assessment and enhance the efficiency of sports injury data processing. Specifically, the predictive model predicts possible sports injuries and chronic diseases caused by excessive exercise for users; data mining technology is used to analyze people’s fitness behaviors to guide sports training. The recommendation model is used to recommend personalized scientific fitness methods for users.


### 3.4. The Structure and Function of the Suggested System

The intelligent sports health management system based on big data analysis and the IoT comprises five layers (Figure1): user, IoT, cloud, system analysis, evaluation, and data, respectively.Figure 1
Structure diagram of smart sports health system.


#### 3.4.1. First, Basic Information Collection and Management Functions

This function is used to manage the basic health information of users. The specific role is to input and view primary personal exercise data (including resident number, name, region, age, gender, weight, item, duration of continuous exercise, time of each exercise, sports injury history, and past medical history). Data entry and viewing of the personal physical activity status (including basic personal information, maximum heart rate during exercise, exercise time, amount of exercise, calories consumed, and lactic acid accumulation). Introduction of personal fitness and health information, including: residents will automatically import WeChat sports information, wearable smart device information, innovative fitness equipment information into the system, etc; Fitness and health information statistics, including fitness statistics chart, health status statistics chart, etc.


#### 3.4.2. Secondly, Data Analysis, and Health Status Assessment Function

The feature can assess a user health at the right time based on the user input, information collected by wearable devices and intelligent fitness equipment, and big data analysis technology. This function uses a predetermined health assessment model to assess the user health at rest and during exercise according to the real-time collection of basic exercise information and exercise status information. These predetermined fitness assessment models are derived from fitness assessment models in sports medicine.


#### 3.4.3. Thirdly, Exercise Reminder and Disease Prediction Function

Based on big data analysis technology and the sports medicine model, this function predicts sports injuries and diseases that may occur during users’ fitness. Fuzzy clustering technology was used to cluster users into multiple user types according to the five indicators of age, weight, gender, exercise program, duration of continuous exercise, and medical history. Collaborative filtering technology and a fuzzy time series prediction model were used to predict possible sports injuries and diseases.


#### 3.4.4. Finally, a Scientific and Reasonable Fitness Program Recommended Functions

The feature is based on collaborative filtering of content, data mining techniques such as association rules, and medical models of motion. This function can recommend scientific exercise and fitness methods for residents according to their age, gender, weight, medical history, exercise environment and other information, so as to correct the wrong actions of residents in the exercise, avoid sports injury, physical disease and other adverse conditions, and improve the health promotion effect of physical fitness.


## 3.1. IoT Technology

The IoT technology refers to the use of the contractual network protocol, and the use of IoT information induction of all kinds of equipment (infrared image sensor, the world’s first GPS satellite positioning system, radio and laser image scanner, etc.) will be connected to the Internet at the same time with any kind of objects. The IoT network can simultaneously achieve a variety of voice and image control, track objects’ location, and make image monitoring and information management to facilitate the IoT information network exchange and data communication. Thus, the IoT has unique advantages in fitness data collection and information output.About the IoT and its application in intelligent sports health management systems, the first aspect is data collection: a large number of actual and reliable data are the basis for the construction of the suggested model, including not only users’ physical health status data but also medical index data, as well as the experience summary data of well-known coaches, etc. With these data, data mining technology can be used to analyze people’s physical conditions and give a reasonable way of fitness. At the same time, the IoT technology can collect information through various intelligent fitness equipment, such as brilliant fitness bracelets, and innovative tracks.Among them, wearable devices have been widely used in sports activities. The most common are smart bracelets, mobile phones, etc. Such devices can collect a user’s daily energy intake and output and data such as the user’s heart rate and exercise.In terms of data output, the IoT technology can push the evaluation results of big data analysis and suggested fitness methods to users through various intelligent devices, such as wearable devices, mobile phones, computers, and VR. These intelligent devices typically have screens that allow users to read information pushed by the IoT.Among them, VR equipment can help users learn standard movements. Users can achieve the maximum fitness effect in the shortest time. In addition, due to the novelty of VR, it can also enhance users’ interest in fitness, develop more users, and lead the wave of reasonable fitness for all.


## 3.2. Symbol Description

This subsection will briefly introduce the symbols and meanings used in this article. The specific content is shown in Table1. For any matrix M:Table 1
Notations of this paper.
SymbolMeaningMijTheith row and jth column of MMi∗Theith row vector of MM∗jThejth column vector of MMTM transposetrMTrace ofMIn addition,X∈Rn×d represents the sample matrix, Y∈Rn×m represents the real label matrix, and W∈Rd×m is the matrix of coefficients.


## 3.3. Big Data Analysis Technology (Feature Selection)

Big data analysis technology is an essential component of knowledge discovery, using computer algorithms to analyze data. In many databases, the required data should be obtained and the data should be properly converted, mined, and utilized to obtain valuable information. Generally speaking, the objects of big data analysis are basically structured, semistructured, or other structured data.Feature selection is a kind of big data analysis technology that selectsd most representative features from n features of the sample. Feature selection is used for data dimensionality reduction. The selected features are also the most representative, so we can analyze the main factors affecting user health through feature selection to provide more targeted scientific and reasonable fitness methods. Due to the complexity of data from users and their lives, multilabel feature selection is more of the system, for analyzing the main factors affecting user health. Therefore, we propose a new multilabel feature selection method as follows:Given multilabel datasetX∈Rn×d, its corresponding label set Y∈Rn×m. According to the assumption of the problem, the loss function LW of the model is constructed and the optimal solution is sought by minimizing LW. The formula is as follows, where W∈Rd×m is the feature weight matrix, which can reflect the importance of features.
(1)Φ=minLW.In addition, the penalty functionRW is often imposed on the weight matrix W and the property of constraint W is that the model is further idealized:
(2)Φ=minLW+αRW,where α is the penalty factor.Due to the least square loss function of linear regression, although the calculation is simple, it is not as robust as theL21 norm. Therefore, L21 norm regression is used to describe the relationship between the sample set and the label set:
(3)LW=XW−Y2,1.The basic framework of the suggested algorithm is constructed:(4)Φ=minXW−Y2,1+αRW.In the suggested algorithm, we constrain the learning of feature weight matrix in the following ways:LetX•i represent the ith-column vector of dataset X and Wi• represent the ith-row vector of the weight matrix W; if X•i and X•j are similar, then, Wi• and Wj• are similar. Thus, the similarity between features can be used to guide the learning of the weight matrix and build the feature manifold learning model:
(5)12∑i=1d∑j=1dWi•−Wj•22SXTij=12∑i=1d∑j=1dWi•−Wj•Wi•−Wj•TSXTij=TrWTPXT−SXTW=TrWTLXTW,where LXT∈Rd×d is the Laplacian matrix of the Eigen similar matrix; PXT∈Rd×d is the diagonal matrix, PXTii=∑j=1dSXTij is the ith diagonal element of PXT, SXT is the feature similarity matrix, and SXTij represents the similarity between feature X•i and X•j. In this study, SXT is calculated by Pearson’s correlation coefficient with square constraint and its formula is as follows:
(6)SXTij=X•i−X•j22σ,ifX•i∈NKX•jorX•j∈NKX•i0,others,,where NKX•i is the K neighborhood of X•i.Thus, a multilabel feature selection model is constructed:(7)Φ=minXW−Y2,1+αtrWTLXTW.Meanwhile, in order to enable the suggested algorithm to perform feature selection better and faster,L21 norm sparse constraint is applied to W in equation (7):
(8)Φ=minXW−Y2,1+αtrWTLXTW+βW2,1.In terms of the optimization solution of the abovementioned formula, due to the nonsmoothness of theL21 norm, it can be transformed into the following:
(9)Φ=mintrXW−YTHXW−Y+αtrWTLXTW+βtrWTDW,where H=1/XW−Yi•2, D=1/Wi•2.For the solution of equation (9), the derivative function with respect to W is required:
(10)∂Φ∂W=XTHXW−XTHY+αLXTW+βDW.Set the derivative to 0:(11)XTHXW−XTHY+αLXTW+βDW=0.Thus, it can be concluded that the updating formula forW is as follows:
(12)W=D−1XTHX+αD−1LXT+β−1D−1XTHY.Through solving the abovementioned problems, the suggested multilabel feature selection algorithm is shown in Algorithm1:Algorithm 1: The suggested algorithm.
Input: data matrixX∈Rn×d, label matrix Y∈Rn×m, regularization parameters α and β, select the number of features K.Output: feature selection resultI.a) CalculateLXT according to Eq (5) and (6).b) The initialization matricesH and D are the identity matrices.Repeat:UpdateW: W=D−1XTHX+αD−1LXT+β−1D−1XTHYUpdateD: D=1/Wi•2UpdateH: H=1/XW−Yi•2Until it converges.c) ComputeWi•2;i=1,2,⋯,d and sort it to find the first k largest assigned to IThe big data analysis technology can conduct cluster analysis and collation of the massive sports fitness and health data of residents collected to evaluate the user health status and sports injury status. Applying big data processing technology in sports injury assessment can improve the accuracy and effectiveness of sports injury assessment and enhance the efficiency of sports injury data processing. Specifically, the predictive model predicts possible sports injuries and chronic diseases caused by excessive exercise for users; data mining technology is used to analyze people’s fitness behaviors to guide sports training. The recommendation model is used to recommend personalized scientific fitness methods for users.


## 3.4. The Structure and Function of the Suggested System

The intelligent sports health management system based on big data analysis and the IoT comprises five layers (Figure1): user, IoT, cloud, system analysis, evaluation, and data, respectively.Figure 1
Structure diagram of smart sports health system.


### 3.4.1. First, Basic Information Collection and Management Functions

This function is used to manage the basic health information of users. The specific role is to input and view primary personal exercise data (including resident number, name, region, age, gender, weight, item, duration of continuous exercise, time of each exercise, sports injury history, and past medical history). Data entry and viewing of the personal physical activity status (including basic personal information, maximum heart rate during exercise, exercise time, amount of exercise, calories consumed, and lactic acid accumulation). Introduction of personal fitness and health information, including: residents will automatically import WeChat sports information, wearable smart device information, innovative fitness equipment information into the system, etc; Fitness and health information statistics, including fitness statistics chart, health status statistics chart, etc.


### 3.4.2. Secondly, Data Analysis, and Health Status Assessment Function

The feature can assess a user health at the right time based on the user input, information collected by wearable devices and intelligent fitness equipment, and big data analysis technology. This function uses a predetermined health assessment model to assess the user health at rest and during exercise according to the real-time collection of basic exercise information and exercise status information. These predetermined fitness assessment models are derived from fitness assessment models in sports medicine.


### 3.4.3. Thirdly, Exercise Reminder and Disease Prediction Function

Based on big data analysis technology and the sports medicine model, this function predicts sports injuries and diseases that may occur during users’ fitness. Fuzzy clustering technology was used to cluster users into multiple user types according to the five indicators of age, weight, gender, exercise program, duration of continuous exercise, and medical history. Collaborative filtering technology and a fuzzy time series prediction model were used to predict possible sports injuries and diseases.


### 3.4.4. Finally, a Scientific and Reasonable Fitness Program Recommended Functions

The feature is based on collaborative filtering of content, data mining techniques such as association rules, and medical models of motion. This function can recommend scientific exercise and fitness methods for residents according to their age, gender, weight, medical history, exercise environment and other information, so as to correct the wrong actions of residents in the exercise, avoid sports injury, physical disease and other adverse conditions, and improve the health promotion effect of physical fitness.


## 3.4.1. First, Basic Information Collection and Management Functions

This function is used to manage the basic health information of users. The specific role is to input and view primary personal exercise data (including resident number, name, region, age, gender, weight, item, duration of continuous exercise, time of each exercise, sports injury history, and past medical history). Data entry and viewing of the personal physical activity status (including basic personal information, maximum heart rate during exercise, exercise time, amount of exercise, calories consumed, and lactic acid accumulation). Introduction of personal fitness and health information, including: residents will automatically import WeChat sports information, wearable smart device information, innovative fitness equipment information into the system, etc; Fitness and health information statistics, including fitness statistics chart, health status statistics chart, etc.


## 3.4.2. Secondly, Data Analysis, and Health Status Assessment Function

The feature can assess a user health at the right time based on the user input, information collected by wearable devices and intelligent fitness equipment, and big data analysis technology. This function uses a predetermined health assessment model to assess the user health at rest and during exercise according to the real-time collection of basic exercise information and exercise status information. These predetermined fitness assessment models are derived from fitness assessment models in sports medicine.


## 3.4.3. Thirdly, Exercise Reminder and Disease Prediction Function

Based on big data analysis technology and the sports medicine model, this function predicts sports injuries and diseases that may occur during users’ fitness. Fuzzy clustering technology was used to cluster users into multiple user types according to the five indicators of age, weight, gender, exercise program, duration of continuous exercise, and medical history. Collaborative filtering technology and a fuzzy time series prediction model were used to predict possible sports injuries and diseases.


## 3.4.4. Finally, a Scientific and Reasonable Fitness Program Recommended Functions

The feature is based on collaborative filtering of content, data mining techniques such as association rules, and medical models of motion. This function can recommend scientific exercise and fitness methods for residents according to their age, gender, weight, medical history, exercise environment and other information, so as to correct the wrong actions of residents in the exercise, avoid sports injury, physical disease and other adverse conditions, and improve the health promotion effect of physical fitness.


## 4. Results and Discussion

To verify the effectiveness of the proposed feature selection algorithm and the feasibility of the proposed intelligent sports fitness system, six classical medical datasets were used to test the effectiveness of the suggested multilabel feature selection algorithm and the quality of the FSML algorithm was compared with SCLS [13], MDMR [14], PMU [15], FIMF [16], SSFS [9], and MFS_MCDM [12]. In addition, ML-KNN [21] is used for classification and evaluation.


### 4.1. Experimental Setup

Six classic datasets from the medical field were used: Abide, Oasis, Ddsm, Mias, Mura, and Luna16. The specific parameters of each dataset are shown in Table2:Table 2
Dataset parameters.
No.DatasetInstancesFeaturesLabelsCardinalityTrainingTest1Abide2417103144.23715009172Oasis5937261.8693912023Ddsm645260191.0143223234Mias240729461.047121111965Mura60029451.2364002006Luna166621185271.252463199In terms of experimental settings, first, the experimental data are discretized [22]. Second, set the parameters in algorithms MDMR, ML-KNN, and FIMF to default values. Finally, because some parameters need to be randomly initialized in the MFS_MCDM algorithm and SSFS algorithm, the experimental results given by the MFS_MCDM algorithm and SSFS algorithm are the mean of 10 times running and the experimental results provided by all algorithms are the optimal results under the optimal parameters.In terms of evaluation indicators, five evaluation indexes: hamming loss, one-error, coverage, average precision, and ranking loss, were selected to evaluate and compare the quality of each experimental algorithm comprehensively. The specific meanings of each indicator are as follows:LetD∈Rn×d be the sample data of the training set and Y∈Rn×m be the corresponding label set data. hDi• represents the binary label vector, and ranki•q represents the rank predicted by label Yq•.
(1)
Hamming loss indicates the percentage of misclassified labels(13)HL=1n∑i=1n1mhDi•ΔYi•1,where Δ is the symbol of symmetry difference
(2)
Ranking loss is the ratio of labels to the reverse order(14)RL=1n∑i=1n11mTYi•1mTYi•~∑q:Yi•q=1∑q′:Yi•q′=0P,where P=δranki•q≥ranki•q′, δz is indicator functions and Yi•~ is the complement of Yi• on Y(3)
One-error indicates the sample proportion of the “most relevant predicted label” that does not exist in the “real label.”(15)OE=1n∑i=1nδYi•l=0,where l=argminq∈1,mranki•q(4)
Coverage is the average number of moves that the “sorted labels” need to make to cover the real label correlation set(16)CV=1n∑i=1nargmaxq:Yi•q=1ranki•q−1(5)
Average precision refers to the label whose correlation is higher than that of a particular label(17)AP=1n∑i=1n11mTYi•∑q:Yi•q=1∑q′:Yi•q′=1Pranki•qAmong the five evaluation indexes, except coverage, the range of the other indexes is0,1. Among these five evaluation indexes, the larger the value of the average precision index is, the better the algorithm quality is, while the smaller the value of other indexes is, the better the algorithm quality is.


### 4.2. Analysis of Experimental Results

Tables3–7 show the experimental comparison results of the suggested algorithm with six advanced multilabel feature selection algorithms, and the optimal results are shown in bold. The experiment was carried out under five commonly used multilabel feature selection evaluation indexes. Tables 3 and 5 show that the hamming loss and one-error index values of the suggested algorithm on each experimental dataset are optimal. In Tables 4, 6, and 7, although the index values of ranking loss, coverage, and average precision of the suggested algorithm on Ddsm datasets are slightly inferior to those of the MFS_MCDM algorithm, the index values on the other five experimental datasets are still optimal. Therefore, the overall quality of the suggested algorithm is better than that of the comparison algorithms.Table 3
Display of optimal experimental results under index hamming loss.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.20140.21370.20060.19990.20060.20210.1947Oasis0.23020.24180.25000.24090.26730.22520.2162Ddsm0.04710.04950.04990.05050.05040.05200.0468Mias0.10660.12900.10730.13480.11370.15870.1024Mura0.20500.21640.21100.22400.22700.23400.2000Luna160.03420.04280.03260.00240.00560.00300.0020Table 4
Display of optimal experimental results under index ranking loss.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.17420.19250.17450.17100.17230.17470.1673Oasis0.18640.20100.20560.19940.25700.20120.1700Ddsm0.19440.21380.26550.25910.25850.25860.2055Mias0.12010.14630.11290.14440.12900.19940.1027Mura0.22580.24770.21670.25500.24830.26620.1954Luna160.04800.08690.06530.00660.00800.00780.0062Table 5
Display of optimal experimental results under index one-error.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.23560.25080.22680.23660.23660.23660.2236Oasis0.31190.34550.36140.35640.36140.35150.2673Ddsm0.54650.58720.64540.67440.63950.70350.5291Mias0.31690.36610.29770.39050.39040.49830.2617Mura0.42000.43000.40000.44500.47000.50000.3650Luna160.42210.54770.44720000Table 6
Display of optimal experimental results under index coverage.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide6.45696.67726.44826.36426.37086.37406.2814Oasis1.98512.08422.11392.08912.36142.05451.9158Ddsm2.27552.55423.20123.04953.05263.05262.3746Mias0.70320.83290.66810.82530.74921.09530.6154Mura1.17501.25201.16501.32001.29001.35501.0650Luna161.61812.72362.18090.59300.60800.59300.5729Table 7
Display of optimal experimental results under index average precision.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.75510.73120.75630.75790.75620.75520.7655Oasis0.78150.75840.74960.75510.71430.75100.8071Ddsm0.53020.51430.44350.41580.44350.40740.5162Mias0.80580.77270.81630.76330.80340.69060.8365Mura0.72880.72080.74370.70580.70020.67910.7655Luna160.70710.60440.68820.99190.99070.99150.9939In addition, in Tables3–7, we can find that the quality of the suggested feature selection algorithm has been improved to some extent compared with that of the comparison algorithm under different data and indicators.It can be calculated in Table3 that, on Mias and Luna16 datasets, the quality of the feature selection algorithm suggested in this paper under the hamming loss index improves by a maximum of 35.48% and 95.33%, respectively, compared with the comparison algorithm. As can be seen in Table 4, on Mias and Mura datasets, the quality of the feature selection algorithm suggested in this paper under the ranking loss index is 48.5%–90.3% and 26.6%–98.3% higher than that of the comparison algorithm, respectively.In addition, it can be calculated in Table5 that on the Mura dataset, the quality of the suggested algorithm under the one-error index is improved by 27%–87.5% compared with that of the comparison algorithm. In Table 6, the quality of the suggested algorithm under index coverage improves 43.8%–78.9% on dataset Mias. It can be calculated in Table 7 that, on Ddsm and Mura datasets, the quality of the feature selection algorithm suggested in this paper under the ranking loss index is 2.6%–26.7% and 2%–64.4% higher than that of the comparison algorithm, respectively.In addition, to display the comparison of various algorithms in a more specific and intuitive way, the number of selected features is taken as the horizontal axis. The quality value under the corresponding index is taken as the vertical axis to show the change of the corresponding index value of the selected features in each algorithm within the range of10,15,20,25,30,35,40,45,50.Specifically, in Figures2–6, we can intuitively observe the change of the optimal result of the comparison algorithm under the optimal parameter as the number of selected features increases and the quality comparison of each algorithm under the same dataset and the same indicator when the number of selected features is 10,15,20,25,30,35,40,45,50. As can be seen in Figure 2, under the hamming loss index, the curves of the suggested algorithm on all datasets are below the curves of the comparison algorithm, thus indicating the superiority of the suggested algorithm in the hamming loss index. It can be seen in Figures 3–5 that the curve of the suggested algorithm in Abide, Oasis, Mias, and Mura datasets is obviously below the angle of the suggested algorithm. It shows the superiority of the suggested algorithm in ranking loss, one-error, and coverage index. It can be seen in Figure 6 that the curve of the suggested algorithm in Abide, Oasis, Mias, and Mura datasets is obviously above the angle of the suggested algorithm, thus demonstrating the superiority of the suggested algorithm in the average precision index.Figure 2
Hamming loss: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 3
Ranking loss: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 4
One-error: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 5
Coverage: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 6
Average precision: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16The multilabel feature selection algorithm suggested in this paper effectively deals with feature selection problems and has certain advantages over the comparison algorithm.In addition, ranking and significant differences among experimental algorithms are shown in Figure7 [23]. In Figure 7, the horizontal axis represents rankings, from left to right, ranking higher and higher, and a horizontal line connects algorithms with no significant difference. Specifically, it can be observed in Figure 7 that the ranking of the suggested algorithm under each index always remains the first, which indicates that the overall quality of the suggested algorithm is better than that of the comparison algorithm. Although the suggested algorithm has no significant difference with SCLS and MFS_MCDM algorithms under each index, it significantly differs from other comparison algorithms.Figure 7
The form of the mean rank graph of the results of Bonferroni-Dunn test.
(a)
Hamming Loss(b)
Ranking Loss(c)
One-error(d)
Coverage(e)
Average precision


### 4.3. Performance Analysis of the Proposed Algorithm

To prove the feasibility of feature selection, a convergence experiment of the proposed algorithm was designed and carried out. In the experiment, regular term parametersα=1 and β=1 were set and the algorithm iteration times were 50. The experimental results are shown in Figure 8:Figure 8
Convergence of the suggested algorithm on experimental data.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16In addition, we also analyze the time complexity of the suggested algorithm. As can be seen in the pseudocode in Algorithm1, if the number of iterations is T, the time complexity of updating W of the suggested algorithm is Otd2n; the time complexity of update D is Otdm; and the time complexity of updating H is Otdn. Therefore, the total time complexity of the suggested algorithm is Otd2n+tdn+tdm. As can be seen in Figure 8, the value of t is generally less than 10, so the time complexity of the suggested algorithm is greatly affected by the sample number n and feature number d.Finally, we compared the running time of experimental algorithms on six experimental datasets and the results are shown in Table8:Table 8
Running time (sec) of the multilabel feature selection methods.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.00814.09960.504034.7042131.98684.26890.0076Oasis0.00210.28290.08202.95529.16050.24070.1820Ddsm0.02120.69200.362235.502467.25895.64930.0020Mias0.04137.67260.984340.1615161.04451.84380.1431Mura0.02920.98560.306912.473249.87730.51060.0193Luna160.00100.02900.02010.20260.80250.04430.0204As shown in Table8, although the running time of the proposed algorithm in Oasis, Mias, and Luna16 data is not as good as the MFS_MCDM algorithm, it is better than other algorithms. In addition, the running time of the proposed algorithm is also optimal on the whole.


### 4.4. Feasibility Analysis of the Suggested System

#### 4.4.1. Technical Feasibility Analysis

First, under the rapid development of computer technology and science and technology, the number of computer operation talents is increasing, which can play a role of talent support for big data analysis techniques in the design of sports health management systems. Secondly, with the rapid development of computer technology in recent years, the continuous improvement of relevant hardware and software technology can lay an excellent technical guarantee for the data analysis link in the design of sports and health management systems, continuously improving the application value of unstructured data, and expand the path for users to obtain data information. Third, the rapid development of cloud storage, computing technology, data storage technology, and the IoT provide intelligent and diversified support for constructing a sports health management system.They have targeted feasibility analysis. Sports health management systems’ construction, innovation, and development are closely related to data analysis, feedback, and application. In the era of big data, data mining can play the role of economic evaluation. The application of big data analysis technology to analyze user health data and design fitness programs can comprehensively analyze the physical health indicators of each user and collect and master the fitness situation of each user, as well as the fitness experience of relevant experts, to provide users with scientific and reasonable fitness programs and optimize the application of various fitness methods. Data mining technology can integrate user health information, expert experience, medical index information, and the IoT information, play the role of data retrieval, application, and interaction, pay full attention to users’ physical health, and provide targeted efficient scientific and reasonable fitness programs.


## 4.1. Experimental Setup

Six classic datasets from the medical field were used: Abide, Oasis, Ddsm, Mias, Mura, and Luna16. The specific parameters of each dataset are shown in Table2:Table 2
Dataset parameters.
No.DatasetInstancesFeaturesLabelsCardinalityTrainingTest1Abide2417103144.23715009172Oasis5937261.8693912023Ddsm645260191.0143223234Mias240729461.047121111965Mura60029451.2364002006Luna166621185271.252463199In terms of experimental settings, first, the experimental data are discretized [22]. Second, set the parameters in algorithms MDMR, ML-KNN, and FIMF to default values. Finally, because some parameters need to be randomly initialized in the MFS_MCDM algorithm and SSFS algorithm, the experimental results given by the MFS_MCDM algorithm and SSFS algorithm are the mean of 10 times running and the experimental results provided by all algorithms are the optimal results under the optimal parameters.In terms of evaluation indicators, five evaluation indexes: hamming loss, one-error, coverage, average precision, and ranking loss, were selected to evaluate and compare the quality of each experimental algorithm comprehensively. The specific meanings of each indicator are as follows:LetD∈Rn×d be the sample data of the training set and Y∈Rn×m be the corresponding label set data. hDi• represents the binary label vector, and ranki•q represents the rank predicted by label Yq•.
(1)
Hamming loss indicates the percentage of misclassified labels(13)HL=1n∑i=1n1mhDi•ΔYi•1,where Δ is the symbol of symmetry difference
(2)
Ranking loss is the ratio of labels to the reverse order(14)RL=1n∑i=1n11mTYi•1mTYi•~∑q:Yi•q=1∑q′:Yi•q′=0P,where P=δranki•q≥ranki•q′, δz is indicator functions and Yi•~ is the complement of Yi• on Y(3)
One-error indicates the sample proportion of the “most relevant predicted label” that does not exist in the “real label.”(15)OE=1n∑i=1nδYi•l=0,where l=argminq∈1,mranki•q(4)
Coverage is the average number of moves that the “sorted labels” need to make to cover the real label correlation set(16)CV=1n∑i=1nargmaxq:Yi•q=1ranki•q−1(5)
Average precision refers to the label whose correlation is higher than that of a particular label(17)AP=1n∑i=1n11mTYi•∑q:Yi•q=1∑q′:Yi•q′=1Pranki•qAmong the five evaluation indexes, except coverage, the range of the other indexes is0,1. Among these five evaluation indexes, the larger the value of the average precision index is, the better the algorithm quality is, while the smaller the value of other indexes is, the better the algorithm quality is.


## 4.2. Analysis of Experimental Results

Tables3–7 show the experimental comparison results of the suggested algorithm with six advanced multilabel feature selection algorithms, and the optimal results are shown in bold. The experiment was carried out under five commonly used multilabel feature selection evaluation indexes. Tables 3 and 5 show that the hamming loss and one-error index values of the suggested algorithm on each experimental dataset are optimal. In Tables 4, 6, and 7, although the index values of ranking loss, coverage, and average precision of the suggested algorithm on Ddsm datasets are slightly inferior to those of the MFS_MCDM algorithm, the index values on the other five experimental datasets are still optimal. Therefore, the overall quality of the suggested algorithm is better than that of the comparison algorithms.Table 3
Display of optimal experimental results under index hamming loss.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.20140.21370.20060.19990.20060.20210.1947Oasis0.23020.24180.25000.24090.26730.22520.2162Ddsm0.04710.04950.04990.05050.05040.05200.0468Mias0.10660.12900.10730.13480.11370.15870.1024Mura0.20500.21640.21100.22400.22700.23400.2000Luna160.03420.04280.03260.00240.00560.00300.0020Table 4
Display of optimal experimental results under index ranking loss.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.17420.19250.17450.17100.17230.17470.1673Oasis0.18640.20100.20560.19940.25700.20120.1700Ddsm0.19440.21380.26550.25910.25850.25860.2055Mias0.12010.14630.11290.14440.12900.19940.1027Mura0.22580.24770.21670.25500.24830.26620.1954Luna160.04800.08690.06530.00660.00800.00780.0062Table 5
Display of optimal experimental results under index one-error.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.23560.25080.22680.23660.23660.23660.2236Oasis0.31190.34550.36140.35640.36140.35150.2673Ddsm0.54650.58720.64540.67440.63950.70350.5291Mias0.31690.36610.29770.39050.39040.49830.2617Mura0.42000.43000.40000.44500.47000.50000.3650Luna160.42210.54770.44720000Table 6
Display of optimal experimental results under index coverage.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide6.45696.67726.44826.36426.37086.37406.2814Oasis1.98512.08422.11392.08912.36142.05451.9158Ddsm2.27552.55423.20123.04953.05263.05262.3746Mias0.70320.83290.66810.82530.74921.09530.6154Mura1.17501.25201.16501.32001.29001.35501.0650Luna161.61812.72362.18090.59300.60800.59300.5729Table 7
Display of optimal experimental results under index average precision.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.75510.73120.75630.75790.75620.75520.7655Oasis0.78150.75840.74960.75510.71430.75100.8071Ddsm0.53020.51430.44350.41580.44350.40740.5162Mias0.80580.77270.81630.76330.80340.69060.8365Mura0.72880.72080.74370.70580.70020.67910.7655Luna160.70710.60440.68820.99190.99070.99150.9939In addition, in Tables3–7, we can find that the quality of the suggested feature selection algorithm has been improved to some extent compared with that of the comparison algorithm under different data and indicators.It can be calculated in Table3 that, on Mias and Luna16 datasets, the quality of the feature selection algorithm suggested in this paper under the hamming loss index improves by a maximum of 35.48% and 95.33%, respectively, compared with the comparison algorithm. As can be seen in Table 4, on Mias and Mura datasets, the quality of the feature selection algorithm suggested in this paper under the ranking loss index is 48.5%–90.3% and 26.6%–98.3% higher than that of the comparison algorithm, respectively.In addition, it can be calculated in Table5 that on the Mura dataset, the quality of the suggested algorithm under the one-error index is improved by 27%–87.5% compared with that of the comparison algorithm. In Table 6, the quality of the suggested algorithm under index coverage improves 43.8%–78.9% on dataset Mias. It can be calculated in Table 7 that, on Ddsm and Mura datasets, the quality of the feature selection algorithm suggested in this paper under the ranking loss index is 2.6%–26.7% and 2%–64.4% higher than that of the comparison algorithm, respectively.In addition, to display the comparison of various algorithms in a more specific and intuitive way, the number of selected features is taken as the horizontal axis. The quality value under the corresponding index is taken as the vertical axis to show the change of the corresponding index value of the selected features in each algorithm within the range of10,15,20,25,30,35,40,45,50.Specifically, in Figures2–6, we can intuitively observe the change of the optimal result of the comparison algorithm under the optimal parameter as the number of selected features increases and the quality comparison of each algorithm under the same dataset and the same indicator when the number of selected features is 10,15,20,25,30,35,40,45,50. As can be seen in Figure 2, under the hamming loss index, the curves of the suggested algorithm on all datasets are below the curves of the comparison algorithm, thus indicating the superiority of the suggested algorithm in the hamming loss index. It can be seen in Figures 3–5 that the curve of the suggested algorithm in Abide, Oasis, Mias, and Mura datasets is obviously below the angle of the suggested algorithm. It shows the superiority of the suggested algorithm in ranking loss, one-error, and coverage index. It can be seen in Figure 6 that the curve of the suggested algorithm in Abide, Oasis, Mias, and Mura datasets is obviously above the angle of the suggested algorithm, thus demonstrating the superiority of the suggested algorithm in the average precision index.Figure 2
Hamming loss: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 3
Ranking loss: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 4
One-error: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 5
Coverage: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16Figure 6
Average precision: the quality of each algorithm varies with the selected feature quantity.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16The multilabel feature selection algorithm suggested in this paper effectively deals with feature selection problems and has certain advantages over the comparison algorithm.In addition, ranking and significant differences among experimental algorithms are shown in Figure7 [23]. In Figure 7, the horizontal axis represents rankings, from left to right, ranking higher and higher, and a horizontal line connects algorithms with no significant difference. Specifically, it can be observed in Figure 7 that the ranking of the suggested algorithm under each index always remains the first, which indicates that the overall quality of the suggested algorithm is better than that of the comparison algorithm. Although the suggested algorithm has no significant difference with SCLS and MFS_MCDM algorithms under each index, it significantly differs from other comparison algorithms.Figure 7
The form of the mean rank graph of the results of Bonferroni-Dunn test.
(a)
Hamming Loss(b)
Ranking Loss(c)
One-error(d)
Coverage(e)
Average precision


## 4.3. Performance Analysis of the Proposed Algorithm

To prove the feasibility of feature selection, a convergence experiment of the proposed algorithm was designed and carried out. In the experiment, regular term parametersα=1 and β=1 were set and the algorithm iteration times were 50. The experimental results are shown in Figure 8:Figure 8
Convergence of the suggested algorithm on experimental data.
(a)
Abide(b)
Oasis(c)
Ddsm(d)
Mias(e)
Mura(f)
Luna16In addition, we also analyze the time complexity of the suggested algorithm. As can be seen in the pseudocode in Algorithm1, if the number of iterations is T, the time complexity of updating W of the suggested algorithm is Otd2n; the time complexity of update D is Otdm; and the time complexity of updating H is Otdn. Therefore, the total time complexity of the suggested algorithm is Otd2n+tdn+tdm. As can be seen in Figure 8, the value of t is generally less than 10, so the time complexity of the suggested algorithm is greatly affected by the sample number n and feature number d.Finally, we compared the running time of experimental algorithms on six experimental datasets and the results are shown in Table8:Table 8
Running time (sec) of the multilabel feature selection methods.
AlgorithmMFS_MCDMSSFSSCLSMDMRPMUFIMFOursAbide0.00814.09960.504034.7042131.98684.26890.0076Oasis0.00210.28290.08202.95529.16050.24070.1820Ddsm0.02120.69200.362235.502467.25895.64930.0020Mias0.04137.67260.984340.1615161.04451.84380.1431Mura0.02920.98560.306912.473249.87730.51060.0193Luna160.00100.02900.02010.20260.80250.04430.0204As shown in Table8, although the running time of the proposed algorithm in Oasis, Mias, and Luna16 data is not as good as the MFS_MCDM algorithm, it is better than other algorithms. In addition, the running time of the proposed algorithm is also optimal on the whole.


## 4.4. Feasibility Analysis of the Suggested System

### 4.4.1. Technical Feasibility Analysis

First, under the rapid development of computer technology and science and technology, the number of computer operation talents is increasing, which can play a role of talent support for big data analysis techniques in the design of sports health management systems. Secondly, with the rapid development of computer technology in recent years, the continuous improvement of relevant hardware and software technology can lay an excellent technical guarantee for the data analysis link in the design of sports and health management systems, continuously improving the application value of unstructured data, and expand the path for users to obtain data information. Third, the rapid development of cloud storage, computing technology, data storage technology, and the IoT provide intelligent and diversified support for constructing a sports health management system.They have targeted feasibility analysis. Sports health management systems’ construction, innovation, and development are closely related to data analysis, feedback, and application. In the era of big data, data mining can play the role of economic evaluation. The application of big data analysis technology to analyze user health data and design fitness programs can comprehensively analyze the physical health indicators of each user and collect and master the fitness situation of each user, as well as the fitness experience of relevant experts, to provide users with scientific and reasonable fitness programs and optimize the application of various fitness methods. Data mining technology can integrate user health information, expert experience, medical index information, and the IoT information, play the role of data retrieval, application, and interaction, pay full attention to users’ physical health, and provide targeted efficient scientific and reasonable fitness programs.


## 4.4.1. Technical Feasibility Analysis

First, under the rapid development of computer technology and science and technology, the number of computer operation talents is increasing, which can play a role of talent support for big data analysis techniques in the design of sports health management systems. Secondly, with the rapid development of computer technology in recent years, the continuous improvement of relevant hardware and software technology can lay an excellent technical guarantee for the data analysis link in the design of sports and health management systems, continuously improving the application value of unstructured data, and expand the path for users to obtain data information. Third, the rapid development of cloud storage, computing technology, data storage technology, and the IoT provide intelligent and diversified support for constructing a sports health management system.They have targeted feasibility analysis. Sports health management systems’ construction, innovation, and development are closely related to data analysis, feedback, and application. In the era of big data, data mining can play the role of economic evaluation. The application of big data analysis technology to analyze user health data and design fitness programs can comprehensively analyze the physical health indicators of each user and collect and master the fitness situation of each user, as well as the fitness experience of relevant experts, to provide users with scientific and reasonable fitness programs and optimize the application of various fitness methods. Data mining technology can integrate user health information, expert experience, medical index information, and the IoT information, play the role of data retrieval, application, and interaction, pay full attention to users’ physical health, and provide targeted efficient scientific and reasonable fitness programs.


## 5. Conclusion

Through a lot of research and experiments, a new type of intelligent sports management system is proposed in this paper. The system was built by combining big data analysis with IoT technology. In the system, the relevant information is collected and pushed through the IoT technology, ensuring the reliability of collected information and the timeliness of pushed information. Information analysis is achieved through the multilabel feature selection algorithm proposed in this paper, which can better analyze and extract the main factors affecting user health and provide a solid basis for designing targeted fitness programs. In addition, we not only compared the proposed feature selection algorithm with SSFS, SCLS, and other algorithms but also carried out the feasibility analysis of the proposed intelligent sports health management system. The experimental results and system feasibility analysis show that the proposed intelligent sports health management system is feasible and superior. We will combine more big data analysis techniques to make the suggested system more targeted and effective in future studies.



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*Source: 5053150-2022-07-11.xml*