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Quantitative Evaluation Method of Physical Fitness Factor Indicators in Youth Endurance Running Events
Quantitative Evaluation Method of Physical Fitness Factor Indicators in Youth Endurance Running...
Guo, Bailing;Zhou, Changlei
Hindawi International Transactions on Electrical Energy Systems Volume 2022, Article ID 1994263, 10 pages https://doi.org/10.1155/2022/1994263 Research Article Quantitative Evaluation Method of Physical Fitness Factor Indicators in Youth Endurance Running Events 1 1,2 Bailing Guo and Changlei Zhou Department of Leisure Services and Sports, Pai Chai University, Daejeon Metropolitan City 302735, Republic of Korea College of Sports and Health, Linyi University, Linyi 276000, Shandong, China Correspondence should be addressed to Changlei Zhou; firstname.lastname@example.org Received 28 June 2022; Revised 3 August 2022; Accepted 11 August 2022; Published 30 August 2022 Academic Editor: Raghavan Dhanasekaran Copyright © 2022 Bailing Guo and Changlei Zhou. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Adolescents are in a critical period of physical and intellectual development, and their growth represents the future of a country. However, with the rapid development of social economy and science and technology, sports and health-related education has not been fully developed, and due to some deviations in the current school curriculum, the physical quality of young people generally declines. Endurance running is a comprehensive index to measure a person’s physical ﬁtness. It reﬂects the basic motor function of the matrix. It is a must-test item in the physical ﬁtness test of young people. However, the level of endurance running has shown a downward trend in recent years. In the current endurance running training, there are many disadvantages such as extensive training methods, low eﬃciency, and human errors during detection. In order to improve the performance of endurance running, this paper establishes the index system of endurance running elements by introducing the concept of healthy physical ﬁtness. Based on the elements of endurance running, the article made a detection system and compared it with the standard test method. )e data showed that P< 0.001, indicating that the test results of the two were consistent. )e detection system in this paper is suitable for the detection of physical ﬁtness index elements. )en, the endurance running performance of the selected 124 adolescents was combined with the physical ﬁtness index elements, and the correlation was analyzed, indicating that the en- durance running level is closely related to the human body shape, cardiopulmonary function, muscle strength, and endurance level. Systematic testing and quantitative results showed that body mass index was signiﬁcantly correlated with endurance running performance in adolescents (P< 0.01). Also, the number of vertical jumps in place was signiﬁcantly correlated with the number of sit-ups completed (r� 0.55, P< 0.01). )is strongly suggests that it is important to quantitatively evaluate the ﬁtness factor indicators of endurance running in adolescents. physical ﬁtness testing system lacks detailed indicators that 1. Introduction reﬂect students’ endurance running level, resulting in In recent years, the physical quality of Chinese adolescent untargeted training in endurance running and slow per- students has generally declined. Some scholars believe that formance improvement. In order to solve the problem of low this may be due to the lack of research on related theories of training eﬃciency at present, this paper introduces the physical health education and countermeasures, resulting in concept of healthy physical ﬁtness, and establishes the index the current situation of adolescent physical decline. En- of physical ﬁtness elements of endurance running, which can durance running, as a compulsory item in the physical help students evaluate the level of endurance running and education examination in China, comprehensively considers improve the eﬀect of training. the cardiorespiratory function of students. However, en- Physical ﬁtness is a new concept in sports health that has durance running has a high risk of exercise, the training had a major impact on the ﬁeld of sports and health. From an method is single, and young people have certain resistance to exercise perspective, physical ﬁtness is now considered a endurance running. At the same time, the current youth comprehensive measure of health. Fitness is the eﬀective 2 International Transactions on Electrical Energy Systems performance of the human body in performing its functions health level, and the guidance for the article is relatively eﬀectively and eﬃciently. In a word, physical ﬁtness is a test general. For the research on the relevant physical ﬁtness index index of physical health from the perspective of human function and skills, and it is closely related to the ability to elements of adolescents, relevant explorations have been deal with emergencies. carried out in various ﬁelds. )e primary objective of Man X Based on the above thinking, this paper evaluates the was to examine associations between adolescent health-re- physical ﬁtness factor indicators of adolescents’ endurance lated PF, skills-related PF, depression, and academic running, hoping to obtain the best test indicators of the achievement. Findings have suggested that people who are physical ﬁtness of adolescents’ endurance running on the physically ﬁt and exhibit positive mental functioning may basis of experimental investigation. In addition, in order to achieve better academic achievement in adolescence . avoid the inﬂuence of human error, a detection model is also Gontarev S aimed to analyze the relationship between established in this paper. By comparing with the data results cardiorespiratory ﬁtness and obesity, blood pressure, and of conventional detection methods, it is found that the hypertension in adolescents. In conclusion, these results should be considered when developing strategies and rec- system in this paper is suitable for physical ﬁtness detection, and then, it is applied to the detection of endurance running ommendations to improve adolescents’ lifestyle and health events. )e correlation analysis was carried out in combi- . )e purpose of Ucok K was to compare maximal aerobic nation with the endurance running performance of 124 capacity (VO2 max), muscle strength, trunk ﬂexibility, total adolescents, showing that BMI, VO2maxde, and endurance energy expenditure, daily physical activity, resting metabolic running performance were signiﬁcantly correlated, with rate (RMR), and body composition and body fat distribution P< 0.01. )e eﬀective number of vertical jumps in place and in diabetic patients and healthy controls . Tan S explored the number of completed sit-ups belong to the compre- the eﬀects of exercise training on body composition, car- hensive reﬂection of muscle strength and endurance. Its diovascular function, and physique in obese and lean 5-year- old children. Well-trained obese children improved per- correlation coeﬃcient is around 0.5, and P< 0.01, indicating that it has a signiﬁcant correlation with endurance running formance in the long jump, the 10-meter 4 shuttle run, and the 3-meter balance beam walk, while well-trained lean performance, and has a strong linear correlation. children improved more physical activity . )e above- mentioned related researches on the elements of physical 2. Related Work ﬁtness indicators are mostly from the perspective of disease As an important concept in the ﬁeld of sports theory, and health, and their relevance to the article is low. physical ﬁtness has always been a hot spot tracked by rel- evant researchers. Firstly, Huang H took the lead in 3. Exploration Methods Related to establishing the development process of physical ﬁtness Endurance Running assessment for Chinese children and adolescents. Secondly, according to the speciﬁc program design, the children and 3.1. Physical Fitness Required for Endurance Running. adolescents’ grade indicators are used and optimized to Endurance running, also known as middle- and long-dis- verify the children and adolescents’ physical condition grade tance running, is an eﬀective method to evaluate the car- model . Due to the poor physical ﬁtness of current diorespiratory function and endurance level of students children, Kozakevych V K’s experiment aimed to examine [9, 10]. Additionally, running is associated with physical the physical health of school-age children and to identify risk ﬂexibility, coordination, balance, and other qualities. When factors for their interference. It was found that more than a running motion as shown in Figure 1 occurs, the move- 60% of teens now have low and below-average levels of ment and coordination of human muscles, bones, and joints physical ﬁtness. According to the multivariate model, the are required . level of physical ﬁtness was positively aﬀected by the level of From the perspective of related research, endurance material wealth (�+0.251), mother’s education level running is a complex exercise that integrates the human (�+0.295), nutritional balance (�+0.204), and residence time movement system, respiratory energy supply system, ner- in fresh air (�+0.106), and negatively aﬀected by parental vous system, and endocrine system, and these factors are harmful habits (� −0.167) . Youm S has developed an closely related to the body [12, 13]. Exploring the rela- automated radiofrequency identiﬁcation (RFID)-based tionship between the physical ﬁtness and long-distance scoring system for the Progressive Aerobic Cardiovascular running, and constructing a physical ﬁtness index system for Endurance Run (PACER) and 6-minute walk tests. )e long-distance running, is extremely important for im- proposed system is able to accurately test many students or proving the level of long-distance running, and has a certain candidates on a large scale and can signiﬁcantly reduce the value for cultivating students . At the same time, the burden on test administrators . Yassine designed to ex- study of the ﬁtness factor in endurance running can also amine the eﬀects of plyometric training on the physical contribute to the promotion of sports and generate a na- performance of prepubertal soccer players on stable (SPT) tional sporting boom. versus unstable (UPT) surfaces. If the goal is to further Physical ﬁtness is deﬁned as an individual’s ability to enhance the static balance, UPT has advantages over SPT perform adequate daily tasks, enjoy leisure time, and adapt . )e above-mentioned research on physical ﬁtness test to emergencies and stress [15, 16]. When classiﬁed by type, has a limited entry point, and most of them are based on the the physique can be divided into healthy physique and sports International Transactions on Electrical Energy Systems 3 Figure 1: Running action breakdown. physique. As the name suggests, physical ﬁtness is the )e software of the lower computer is based on the Keil physical ﬁtness related to the body’s sensitivity, regulation, MDK integrated development environment, and is devel- balance, and other physical capabilities [17, 18]. Figure 2 oped using the C language. Combined with the hardware shows how each element of physical ﬁtness works. circuit, it realizes the acquisition and processing of sensor )rough optimal ﬁtness training, students gain insight data, and the communication with the upper computer. into how to acquire healthy ﬁtness and healthy ﬁtness ac- )e main functions of the upper computer software quisition skills, as well as ways to apply ﬁtness principles into include the following: sending pressure information col- practice . In addition, from the above operating prin- lection instructions to the upper computer, receiving the ciples we can also see that good physical performance cannot pressure signal obtained by the lower computer, and cal- be achieved without the close cooperation of all body parts. culating the center position of the sole pressure according to the pressure value of the pressure sensor. 3.2. Preliminary Construction of the Physical Fitness Factor Index System. Cardiorespiratory endurance, strength and 4.2. Construction of the System Software Part. )e detection body composition, and physical ﬂexibility are four com- station in this paper uses the Kinect sensor, and the depth monly used test methods for healthy physique in the United image obtained by the Kinect can extract the human skeleton States . Maintaining a good state in these areas means model in real time. )is system uses the Kinect for Windows that a person’s physical level is good. In other words, you SDK2.0 as the development tool for driving the Kinect and have the ability to exercise safely . In recent years, the related data acquisition. During use, the application must government has determined diﬀerent inspection items for detect and discover the Kinect sensors linked to the device, citizens of diﬀerent ages to fully understand people’s health and before these sensors can be used, they must be initialized status. At present, the physical ﬁtness-level test items of and only then can data be generated. It should be pointed out Chinese adolescents are shown in Table 1. that the origin positions of the image coordinate system and the actual space coordinate system are not uniform, and the 4. Quantitative Detection Experiment of spatial positions of the depth camera and the color camera Physical Fitness Factors are not completely coincident, so coordinate conversion is required during use. However, the Kinect sensor script In order to better detect the indicators of physical ﬁtness provides a conversion method for the depth image coor- factors required for endurance running, this paper builds a dinate system, the color image coordinate system, and the measurement system from the detection of physical ﬁtness bone space coordinate system. )e conversion relationship factors that aﬀect endurance running performance. )e is shown in Figure 4, and it can also be converted according Kinect sensor and the force measuring platform based on the to the knowledge of space geometry. pressure sensor are used to build an information collection When we stand behind the Kinect, facing away from it, module, and an intelligent youth physical ﬁtness factor index the right side is positive on the x-axis, the top is positive on test platform is constructed. the y-axis, and the z-axis is pointing towards us, which is the same as the deﬁnition of a normal coordinate system. )e depth image obtained by the Kinect contains a lot of jitter 4.1. Construction of the Hardware Part of the System. )e system detection platform built in this paper adopts the noise; that is, there is random noise in the depth value of the image pixel position, which is called the ﬂicker eﬀect. )is JHBM-7-V-type load cell. Its working principle is based on the piezoresistive principle. With the increase in the force on phenomenon causes certain errors in the measurement the sensor, the resistance value basically decreases linearly. using depth information, so the depth map needs to be )e detection platform is mainly composed of signal ac- ﬁltered in real time. quisition and its ampliﬁcation module, A/D conversion )e extracted joints have jitter in a certain range; es- module, communication module, main control chip, and pecially, the jitter of the joints is large. In order to obtain host computer. It has the function of collecting the signal of more stable bone data, this paper ﬁrstly performs smooth ﬁltering on the joint position, which is the premise of using the weighing sensor and uploading it to the host computer. )e overall block diagram is shown in Figure 3. bone data. )e smoothing algorithm for skeletal data is 4 International Transactions on Electrical Energy Systems Contains frequency, intensity, time, type FITT Guidelines Physical ﬁtness component Health Fitness Ingredients Including Contains muscle agility, strength, muscular physical ﬁtness coordination, endurance, body balance, composition, and more explosiveness, etc. basic training principles Figure 2: Principle of operation between the various elements of physical ﬁtness. Table 1: Physical ﬁtness test items and indicators. Physical ﬁtness elements Test indicators Physical ﬁtness elements Test indicators Upper body muscle strength and Body shape BMI Pull-up endurance 1000 meters for men; 800 meters for Lower body muscle strength and Standing long Cardiopulmonary capacity women endurance jump Sitting forward Explosive force 50 m dash Flexibility bend Abdominal muscle strength and 1-minute sit-ups Body function Lung capacity endurance Weighing and main control A/D converter Wifi module On the plane amplifying circuit module Figure 3: Hardware block diagram of the system. Map depth to Skeleton point described in detail below. Considering the smoothing eﬀect and ﬁltering real-time requirements, this paper uses the Kalman ﬁltering algorithm to ﬁlter the bone data. Its idea is to update the state variable information iteratively and re- cursively when new data are obtained, which is an optimal Depth image coordinates estimation method. Bone space coordinates )e Kalman ﬁlter mainly contains the equation of state transition. � � M � DM + H . (1) X X−1 X−1 Among them, M represents the estimated value of the X−1 bone data at time x-1, M is the estimated value of the bone Color image coordinates Screen coordinate system data at time x, and D is the transition matrix of the state, which is also the basis for the algorithm to predict the state Figure 4: Kinect transformation diagram of each coordinate system. variables. H is the estimated error value. X−1 International Transactions on Electrical Energy Systems 5 )e calculation expression of the observed value is as � M � K , K , K , C , C , C , i , i , i . (10) X ax bX nX aX bX nX aX bX nX follows: )e observations are as follows: G � FM + U . (2) z X X G � K , K , K . (11) z aX bX nX Among them, G represents the observed value of the skeleton data at time X, and U represents the measurement So, the transition matrix D is expressed as error. F is the observation matrix. 1 0 0 1 Iterative process: according to the state prediction at ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ D � ⎢ 0 1 0 0 ⎥. (12) ⎢ ⎥ time X-1, the state at time X is expressed as follows: ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ � � 0 0 1 0 M � DM + Ce . (3) X X−1 X−1 )e measurement matrix F is expressed as follows: M represents the prior state estimate of the skeleton data at time x-1, and M represents the posterior state estimate of 1 0 0 0 0 0 ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ the skeleton data at time x. e represents the input quantity ⎢ ⎥ ⎢ ⎥ X−1 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ F � ⎢ 0 1 0 0 0 0 ⎥. (13) ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ that can be selected and controlled, and C represents the gain. However, in practical applications, there is generally no 0 0 1 0 0 0 control input, so these two items can be ignored. )e other parameters are as follows: Mean squared error prediction is given by the following equation: Q � 0.01, P � 0.001, r � 0.01. (14) � � (4) Q � DQ D + P. X−1 )e Kalman ﬁltering allows the optimal estimation of the system state from the system input and output observations. In the prediction equation, Q is the a priori estimated X−1 Taking hand joints as experimental samples, the eﬀect of the covariance of the data at time X, Q is the a posteriori Kalman ﬁltering is shown in Figure 5. )is enables further estimated covariance of the data at time X, and P is the smoothing of the hand joints. covariance of the excitation noise in the process, that is, the It can be seen from Figure 5 that although there are error between the transition matrix and the actual process. certain ﬂuctuations in the data curve between the eﬀect of Filter gain expression is given by the following equation: the Kalman ﬁltering and the observed value, the data dif- s s � � ference is small. On the whole, the ﬁltering eﬀect of the R � Q F FQ F + N. (5) X X Kalman algorithm is consistent with the eﬀect of the ob- N represents the covariance when measuring noise. served value. It shows that the Kalman ﬁlter algorithm ﬁlters Filter estimation expression is given by the following out the hand joint jitter, smoothes the joint position in- equation: formation, and provides a guarantee for the accuracy of the subsequent index measurement. � � � M � M + R G − FM . (6) X X X Z X )e mean squared error follows the mean: 4.3. Determination of Physical Fitness and Body Mass Index. � � Q � K − R F Q . (7) Determination of height and weight: generally speaking, the X X X measurement of body mass index is mainly carried out )e ﬁrst step is to conﬁrm the transition state matrix D, through the detection platform constructed in this paper. which is obtained according to the formula: )e height measurement method can be obtained using the 2 Kinect. K(s) � K(s − 1) + C(s − 1)Δs + 0.5i(s − 1)Δs , Determination of waist, abdomen, and lower limb muscle ﬁtness indicators: muscle ﬁtness is a very important (8) C(s) � C(s − 1) + i(s − 1)Δs, physical ﬁtness in endurance running, of which waist, ab- i(s) � i(s − 1). domen, and lower limb muscle ﬁtness play an important role. In this paper, the number of sit-ups completed is used Among them, s is the representation value of displace- to measure the strength and endurance of the waist and ment, c is velocity, and i is acceleration. abdominal muscles, while the muscle strength of the lower Assuming a value Δs is 1, the matrix expression for the body is measured by the maximum height of jumping in above equation is as follows: place, and the muscle endurance of the lower body is measured by the number of jumps in place. K(s) 1 1 0.5 K(s − 1) ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ Determination of ﬂexibility index: sitting and standing ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ C(s) ⎥ � ⎢ 0 1 1 ⎥∗ ⎢ C(s − 1) ⎥. (9) body forward ﬂexion are the international common methods ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎦ ⎣ ⎦ ⎣ ⎦ ⎣ for evaluating ﬂexibility, which mainly reﬂect the extension i(s) 0 0 1 i(s − 1) of the hamstrings, tendons, muscles, and joints of the trunk and the back of the thigh. Flexibility is not only an important )en, the state estimator of the system in this paper is part of healthy physical ﬁtness, but also promotes the given by the following equation: 6 International Transactions on Electrical Energy Systems explosion of strength and speed, which plays an important 0.8 role in improving athletic ability and preventing sports 0.6 injuries. Using the Kinect sensor, one end of the sensor is 0.4 ﬁxed to the ground and the other end is set at the start of forward bending in the station stereo. 0.2 Balance ability index determination: balance ability in- cludes static and dynamic balance ability. Static balance 0 100 200 250 -0.2 refers to the ability of a limb to maintain a ﬁxed posture, and dynamic balance refers to the ability to return to its own -0.4 balance under external disturbances. )e quality of balance -0.6 ability reﬂects the functional level of receptors and nervous Kalman filter effect system on the one hand, and the development level of ex- ecutive organs such as skeletal muscles on the other hand. Observations In this paper, two sensors are used to detect the same Kalman filter effect vertical jumping action. If both of them detect valid results, the average of the two test results is taken as the tester’s score 0.6 for this jump. If the Kinect has a false detection or missed detection, the result obtained by the force tester will be used 0.4 as the tester’s jump result. 0.2 5. Physical Fitness Test Results 250 300 400 500 -0.2 5.1. Data Sources and Basic Information. In this paper, 124 -0.4 adolescents were selected as the measurement objects for the indicators of physical ﬁtness elements required for endur- -0.6 ance running, and they were divided into two groups. )e -0.8 experimental group adopted the testing platform established Kalman filter effect in this paper. )e platform is equipped with two kinds of sensors, and the control group uses conventional sports Observations testing equipment, such as height scales, sitting body ﬂexion Kalman filter effect tester, vertical jump height test device, and stopwatch, and Figure 5: Kalman ﬁlter eﬀect. other equipment takes turns to measure. All test subjects were tested in the experimental group and the control group in the same place. In order to ensure the physical recovery of 1.67± 0.06 m and the female was 1.58± 0.04 m (P< 0.001). the experimental subjects, the interval between each test is In terms of body weight, the 12-year-old male was 52± 6.9 kg about 30 minutes. All the scores of all subjects in the two and the female was 50± 6.7 kg (P< 0.05); the 13-year-old tests were recorded, and the basic conditions of the subjects male was 60± 8.1 kg and the female was 52± 11 kg (P< 0.05); are shown in Table 2. and the 14-year-old male was 61± 13 kg and the female was Due to the diﬀerences in the age distribution and gender 51± 5.9 kg (P< 0.01). )e BMI of males in the same age of the subjects, more accurate results can be obtained for group was slightly higher than that of females, but there was subsequent experiments. )e subjects’ height, weight, and no signiﬁcant diﬀerence between genders (P> 0.05). BMI were statistically analyzed this time, and the results are shown in Figure 6. In Figure 6, the average age of the subjects is 12.95± 1.96 5.2. Muscle Strength and Endurance Indicators of Waist, years, the height is 1.6± 0.09 m, the weight is 53.5± 10.2 kg, Abdomen, and Lower Limbs. In this paper, the physical and the BMI is 20± 3.0 kg/m . )ere were signiﬁcant dif- ﬁtness index of waist, abdomen, and lower limbs is mea- ferences in height and weight among diﬀerent age groups of sured, and the number of completed sit-ups is used to the same gender, height (P< 0.001 for males, P< 0.001 for measure the strength and endurance of waist and abdomen muscles. )e muscle strength of the lower body is measured females), and weight (P< 0.001 for males, P< 0.01 for fe- males). BMI increased slightly with age (P> 0.05 for males, by the maximum height of jumping in place, and the muscle endurance of the lower body is measured by the number of P> 0.05 for females). From the analysis of diﬀerent genders in the same age group, there was no statistical diﬀerence in jumps in place. )e test results of waist, abdomen, and lower height and weight between men and women at the age of 11 limb muscle strength and endurance index are shown in (P> 0.05). However, from the age of 12, the two indicators of Table 3. males were signiﬁcantly higher than those of females. In )e data in Figure 7 show that there is a signiﬁcant terms of height, the 12-year-old male was 1.63± 0.06 m and diﬀerence between the number of sit-ups and the strength the female was 1.57± 0.04 m (P< 0.05); then 13-year-old and endurance of the lumbar and abdominal muscles male was 1.71± 0.05 m and the female was (r� 0.96, P< 0.01), which indicates that sit-ups can increase 1.61± 0.04 m (P< 0.001); and the 14-year-old male was the endurance index of adolescents to some extent. Also, by Position Position International Transactions on Electrical Energy Systems 7 Table 2: Basic information of subjects. Table 5 shows the test results of the balance ability index, where P< 0.001, indicating that there is no signiﬁcant dif- Classiﬁcation Feature Test group Control group ference in the test data of the two groups of subjects, which Grouping — 62 62 indicates that the consistency of the two groups of data is Male 36 38 very good. Gender Female 26 24 11 years 6 3 12 years 11 18 5.5. Simulation Case. )e endurance running perfor- Age 13 years 27 22 mance of 124 adolescents is taken as a sample to explore 14 years 18 19 the relationship between physical ﬁtness and endurance BMI — 20.28± 2.53 20.25± 2.43 running. Among them, the system equipment collects the index data of body composition, lower limb muscle strength and endurance, waist and abdominal muscle 1.8 80 endurance, ﬂexibility, and balance ability. )e endurance 1.7 60 running performance of all experimental subjects was graded according to relevant indicators and divided into 1.6 40 four grades: excellent, good, passing, and failing, cor- 1.5 20 responding to the numbers 4, 3, 2, and 1, respectively. )e obtained endurance running data are graded, and the 1.4 0 test graded data are shown in Figure 9. age 11 12 years old 13 years old 14 years old )e Pearson correlation coeﬃcient is calculated for Age all test results and endurance running results, and the correlation analysis results are shown in Table 6. tall female tall man weight female weight male It can be seen from Table 6 that BMI and VO2max are signiﬁcantly correlated with endurance running perfor- mance, and the height and number of jumps in place, sit-ups, and endurance running performance are signiﬁcantly cor- related. )erefore, it can be concluded that if the BMI of the tester is in the range of thin to overweight, BMI and en- durance running performance are positively correlated, indicating that the impact of body shape on endurance running performance is signiﬁcant, but the two do not age 11 12 years old 13 years old 14 years old completely belong to the same category. )ere was a sig- Age niﬁcant negative correlation between VO2max and endur- ance running performance, which was consistent with the male female ﬁndings of the literature. Due to diﬀerences in personal physique, psychological state, skills, and the characteristics Figure 6: Comparison of height, weight, and BMI between age of the race schedule, the relationship between the two is not groups. signiﬁcantly linear. )e maximum height of vertical jump in situ measures the explosive power of human muscles, and it comparing other training programs, we found signiﬁcant has a nonlinear negative correlation with endurance running diﬀerences between lumbar, abdominal, and lower limb performance. Explosive power plays a lesser role in long- muscle strength and the endurance index of the adolescents. distance running. It can be seen that the eﬀective number of vertical jumps in situ and the number of completed sit-ups belong to the comprehensive reﬂection of muscle strength 5.3. Determination of the Flexibility Index. )e ﬂexibility and endurance, and have a signiﬁcant correlation with index in this paper is measured by sitting and standing endurance running performance, and a strong linear forward ﬂexion scores. )e test results of the two groups of correlation. members are shown in Table 4. )e bivariate correlation analysis of the inﬂuencing )e data in Figure 8 show that by examining sitting factors of endurance running found that the number of forward and standing forward bending, we found an ex- vertical jumps in place and the number of completed sit- tremely statistically signiﬁcant diﬀerence between the ﬂex- ups were signiﬁcantly correlated (r 0.55, P< 0.01), and ibility index and the endurance index of the adolescents the two were not statistically independent. From a (r 0.98, P< 0.01; r 0.93, P< 0.01). physiological point of view, the muscles of the waist and abdomen and the muscles of the lower limbs work in coordination in many movements. However, the mea- 5.4. Balance Ability Index. In this paper, the measurement of surement actions and indicators selected in this paper balance ability, one of the physical ﬁtness indicators, is cannot accurately reﬂect the diﬀerence between lower measured by the standing time with one foot and eyes closed. limb muscle ﬁtness and core muscle ﬁtness. )erefore, )e test results are shown in Table 5. BMI Kg 8 International Transactions on Electrical Energy Systems Table 3: Waist, abdomen, and lower extremity test results. Test items Group Index Number of samples Experiment 35.14± 9.88 31 Number of sit-ups Control 35.9± 10 31 Experiment 33.7± 7.67 31 Maximum height of vertical jump in place Control 34.1± 7.51 31 Experiment 15± 2.69 31 Number of jumps in place Control 15.03± 2.9 31 1 0.001 0.98 0.0008 0.96 0.0006 0.94 0.0004 0.92 0.0002 0.9 Sit-ups Verticle number of Sit-ups Verticle number of Jump height jumps Jump height jumps Test items Test items r value P value Figure 7: Data analysis results. Table 4: Flexibility index test results. Test items Group Index Number of samples Experiment 22.87± 2.9 31 Sitting forward bend Control 23.2± 2.3 31 Experiment 14.1± 6.8 31 Standing forward bend Control 13.55± 6.7 31 0.001 1.05 0.0008 0.0006 0.95 0.0004 0.9 0.0002 0.85 sit station sit station Test items Test items r value P value Figure 8: Flexibility index analysis results. this paper will keep one of the two, and choose the Table 5: Test results of balance ability index. number of sit-ups that is more related to endurance Classiﬁcation Test results running as the test index of muscle strength and en- Test group 20.84± 11.8 durance. To sum up, for healthy middle school students, Control group 21.5± 11.5 the index system constructed in this paper includes the r value 0.996 following: body mass index (BMI), maximum oxygen P value 0.0007 uptake (VO ), jump height in place, and number of 2max r value r value P value P value International Transactions on Electrical Energy Systems 9 123 4 Grade boy girl total Figure 9: Endurance running performance grading results. Table 6: Relationship between physical ﬁtness indicators and Data Availability endurance running performance. )e data that support the ﬁndings of this study are available Pearson’s Endurance running test from the corresponding author upon reasonable request. correlation P value indicators coeﬃcient VO2max −0.61 <0.01 Conflicts of Interest BMI 0.51 <0.01 Jump height −0.42 <0.01 )e authors declare that they have no conﬂicts of interest. 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International Transactions on Electrical Energy Systems
Hindawi Publishing Corporation
Quantitative Evaluation Method of Physical Fitness Factor Indicators in Youth Endurance Running Events
International Transactions on Electrical Energy Systems
, Volume 2022 –
Aug 30, 2022
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