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Step-Counting Function of Adolescent Physical Training APP Based on Artificial Intelligence

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With the rapid development of the information age, Internet and other technologies have been making progress, people’s fitness awareness has been gradually enhanced, and sports fitness app has emerged as the times require. This paper mainly studies the step-counting function of physical training app for teenagers based on artificial intelligence. This paper uses the modular development method to achieve the functional requirements of the system as the goal, respectively, for parameter management, website configuration, system log, interface security settings, SMS configuration, WeChat template message and several functional modules to achieve system configuration. In this paper, three types of sensors are used to analyze the data changes in the process of walking through three types of data, and different weights are given as the results of step-counting. When the peak value of sensor data is measured, only the peak value of the primary axial data of each sensor is analyzed, which should be determined according to the actual axial value of the sensor. In this paper, the users’ evaluation indexes of sports fitness app are divided into two groups: importance and satisfaction, so the obtained data are directly divided into two groups: importance and satisfaction of user experience indexes of sports fitness app, and the two groups of data are matched with the sample t test to ensure the scientific conclusion. Finally, the advantages and disadvantages of the user experience of college students’ sports fitness app are analyzed through IPA analysis. Heuristic evaluation is carried out on the step app to score the second-level usability index of the app. The first-level usability index score and the total usability score of the step app are obtained by calculation. There is not much difference between male and female students who use sports apps. Among them, 288 are male students, accounting for 58.2% of the total and 16.4% are female students. The results show that the use of artificial intelligence technology can reduce the overall energy consumption of step-counting algorithm, so as to achieve an energy-saving step-counting algorithm. 1. Introduction With the increase of the strength of the youth physical confrontation, in order to have a place in the world basketball, it is necessary to make the overall ability of the team outstanding, and the basis of each ability is the good physical quality of the players. Therefore, for teenagers, scientific fitness and reasonable avoidance of competitive risk events are particularly important for the participation, development, and breakthrough of competitive sports. The virtual technology used by cloud computing technology isolates system resources, allowing users to perform artificial intelligence model training operations in their own unique virtualized systems, so that they can be adjusted for virtual environments with low resource utilization. It can avoid the unavailability of the system environment due to human factors. Artificial intelligence technology can improve resource utilization. Din et al. believe that, due to the existence of various pollutants produced by human, agricultural, and industrial activities, the quality of surface water has decreased. Therefore, plot the concentration of different surface water quality parameters. He tried to develop an artificial intelligence modeling method for drawing concentration maps of optical and nonoptical SWQP. For the first time, he developed a remote sensing framework based on a back-propagation neural network to quantify the concentration of different SWQP in Landsat8 satellite images. Compared with other methods (such as support vector machine), the developed Landsat8-based BPNN model is used to obtain an important measurement coefficient between Landsat8 surface reflectivity and SWQP concentration. Although his research is innovative, it lacks certain experimental data [1]. Kulkarni and Padmanabham used the extended waterfall and agile models to model the entire process of software (SW) development. They integrate AI activities such as intelligent decision making, ML, Turing test, search, and optimization into the agile model. They evaluated two indicators in five independent software projects, such as the usability target achievement indicator and the integration index. Once the SW project is developed using these models, feedback queries will be formally collected, and the collected data will be extensively analyzed to identify the various characteristics of the product, thereby determining the product’s related behavior in terms of models and indicators. Although their research is relatively comprehensive, the test content is not accurate enough [2]. Goyache et al. developed a method to use artificial intelligence to improve the design and implementation of linear morphological systems for beef cattle. The process they proposed involves an iterative mechanism, in which knowledge engineering methods are used to continuously define and calculate type features, scored by a group of well-trained human experts, and finally performed by four famous machine learning algorithms’ analysis. The results obtained in this way can be used as feedback for the next iteration to improve the accuracy and effectiveness of the proposed evaluation system. Although his research sample is relatively complete, it is not innovative enough [3]. In this paper, user demands were obtained through user interviews and analysis of competing products. Then, questionnaire survey was adopted to determine the importance of teenagers’ demands for mobile health applications. Then, the weight of demands was calculated through data analysis. In view of the difference in use motivation caused by gender, the users are classified by gender and age from the beginning of registration, and different user groups are pushed with different content of exercise knowledge. Combined with APP, this paper carries out professional evaluation on the exercise ability of users before exercise, quantifies and grades the evaluation results, gives scientific and reasonable exercise suggestions, promotes the formation of exercise habits, and provides a reference for sports and fitness enthusiasts to reasonably choose their own exercise projects. 2. Youth Physical Training 2.1. Artificial Intelligence Technology The classic sigmoid-based ESN state update equation is composed of N storage pool units, K input layer units, and L output layer units. Among them, is an N-dimensional reserve pool [4]. The output result obtained from the extended system can be expressed as Among them, is the activation function of an output layer. The expression of the hidden layer is as follows: Normally, the form of the GARCH model is as follows: When the actual output of the network model is inconsistent with the expected output, an output error E will be generated. The expression is as follows: Expand the error to the hidden layer; there are When the weight and threshold iterations corresponding to the neurons in each layer are over, the learning and training phase of the neural network enters the forward propagation link again [5, 6]. As a basic platform, in order to overcome the occurrence of the above situation, it must have basic isolation to ensure the independence of the service execution environment and hardware resources of each user. Containerized virtualization technology can provide system isolation for the platform, from the operating system to the software services, which are all defined by users, so as to provide users with a more flexible service execution environment [7]. Since the nodes in the cluster sometimes stop for various reasons, the cluster management tool usually automatically migrates all the containers running on this node to other nodes in the cluster. However, if some containers use local data volumes, data loss will occur when the containers are migrated. Using network storage disks or distributed storage disks will be a viable choice [8]. The calculation formula of the autocorrelation coefficient of the current period and the previous period data is as follows: The FFT calculation formula is as follows: Based on the above analysis, it can be seen that compared with the step-counting algorithm in the frequency domain and the time domain, the calculation cost of the former is obviously higher than that of the latter. Although the step-counting algorithm in the frequency domain has a higher computational cost, compared with most step-counting algorithms in the time domain, the step-counting algorithm in the frequency domain usually achieves higher step-counting accuracy [9, 10]. 2.2. Physical Training Between functional physical training and traditional physical training, they are interrelated and complement each other. Specialization and integrity are the most prominent features of the former. But in the traditional physical training, it can not achieve these two points. In the physical training system, the traditional physical training is the most important foundation. At the beginning, the traditional physical training can be carried out first, and then the functional training can lay a good foundation for the body, so as to prevent the defects of strength training from causing unnecessary sports injury [11]. Functional physical training is not unitary. It needs to integrate and improve the advantages of traditional physical training. We can not ignore the traditional physical training, nor can we just carry out a kind of functional physical training. The two complement each other and complement each other, so as to make a special targeted and integrated training arrangement, so as to improve the athletes’ special technical level and ability [12]. Physical fitness itself is an organic whole, not the mechanical or simple addition of various parts. We should understand physical fitness with the help of system theory. Systematic method has become an important method for people to understand and analyze things in modern science. The core idea of systematic view is the overall concept of system [13]. In general, in order to improve the basic shape of sports, improve the system initiative of athletes’ organs, and give full play to the best mode of sports mechanism and effect, the physical fitness index system is taken as an important reference standard in the process of training. It belongs to the basic index of technical training and tactical training and has a positive impact on the technology, tactics, load training, physical condition, and sports life of special sports. The establishment of a reasonable physical fitness index system can be used as a powerful carrier for the selection mechanism of athletes in reality [14]. Physical fitness is the foundation of young athletes and provides strong support for their technical level. Ordinary teenagers are mostly in the system of compulsory education or secondary and higher education, and their training purposes and means are different from those of young athletes. As far as the means of physical training are concerned, athletes will be better than ordinary teenagers in terms of selection, training, competition, and other aspects, but from the perspective of physiological development characteristics, they are in the second peak of development. The stimulation of training means will have a more obvious effect on athletes’ training, which can provide training support for ordinary teenagers [15]. This will make the competition time longer and test the physical fitness of athletes. If one side’s physical condition is not strong, there will be calf muscle cramps, or even acute sports injury. On the court, long-term muscle contraction and ball extension, such as fast movement, kicking, swinging, and wrist strength, are different from the periodic endurance of other sports. Athletes must have special endurance quality, special strength quality, special speed quality, etc. that change with the change of competition intensity [16]. 2.3. Pedometer APP The primary task of the pedometer algorithm is to obtain the original three-axis acceleration data based on the sensor module and then perform data analysis and algorithm design based on the entire waveform. The actual test shows that there are many interference clutters in the acceleration signal generated by the human body when counting steps in various scenes. Therefore, it is very important to preprocess the original data before formally analyzing the motion waveform [17]. The data collection function of the pedometer is realized by the main controller reading data from the sensor, and its core is the acceleration sensor. The use of analog signal sensors requires additional analog-to-digital converters, which will increase the complexity of the circuit and the space utilization rate; the use of digital signal sensors avoids this problem while using high-precision sensors to ensure the reliability of data. In addition, it is necessary to ensure a higher speed data interface, a certain processing capacity, and lower power consumption in the selection of the main controller and the sensor [18]. The overall architecture of pedometer is shown in Figure 1. According to the function requirement analysis, the acceleration and angular velocity data selected collection of six-axis accelerometer and gyroscope inertial sensor MPU6050, master controller selects 16 ultra-low power consumption microprocessor MSP430G2553. Data transmission can use serial port transmission or wireless module transmission, and the programming of the main controller can be realized through online programmable function [19].
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Research Article
Step-Counting Function of Adolescent Physical Training APP
Based on Artificial Intelligence
Cong Du
College of Physical Education, Xuchang University, Xuchang 461000, Henan, China
Correspondence should be addressed to Cong Du; 12018001@xcu.edu.cn
Received 12 January 2021; Revised 2 February 2021; Accepted 3 March 2021; Published 16 March 2021
Academic Editor: Sang-Bing Tsai
Copyright ©2021 Cong Du. 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.
With the rapid development of the information age, Internet and other technologies have been making progress, people’s fitness
awareness has been gradually enhanced, and sports fitness app has emerged as the times require. is paper mainly studies the
step-counting function of physical training app for teenagers based on artificial intelligence. is paper uses the modular de-
velopment method to achieve the functional requirements of the system as the goal, respectively, for parameter management,
website configuration, system log, interface security settings, SMS configuration, WeChat template message and several functional
modules to achieve system configuration. In this paper, three types of sensors are used to analyze the data changes in the process of
walking through three types of data, and different weights are given as the results of step-counting. When the peak value of sensor
data is measured, only the peak value of the primary axial data of each sensor is analyzed, which should be determined according
to the actual axial value of the sensor. In this paper, the users’ evaluation indexes of sports fitness app are divided into two groups:
importance and satisfaction, so the obtained data are directly divided into two groups: importance and satisfaction of user
experience indexes of sports fitness app, and the two groups of data are matched with the sample ttest to ensure the scientific
conclusion. Finally, the advantages and disadvantages of the user experience of college students’ sports fitness app are analyzed
through IPA analysis. Heuristic evaluation is carried out on the step app to score the second-level usability index of the app. e
first-level usability index score and the total usability score of the step app are obtained by calculation. ere is not much difference
between male and female students who use sports apps. Among them, 288 are male students, accounting for 58.2% of the total and
16.4% are female students. e results show that the use of artificial intelligence technology can reduce the overall energy
consumption of step-counting algorithm, so as to achieve an energy-saving step-counting algorithm.
1. Introduction
With the increase of the strength of the youth physical
confrontation, in order to have a place in the world bas-
ketball, it is necessary to make the overall ability of the team
outstanding, and the basis of each ability is the good physical
quality of the players. erefore, for teenagers, scientific
fitness and reasonable avoidance of competitive risk events
are particularly important for the participation, develop-
ment, and breakthrough of competitive sports.
e virtual technology used by cloud computing tech-
nology isolates system resources, allowing users to perform
artificial intelligence model training operations in their own
unique virtualized systems, so that they can be adjusted for
virtual environments with low resource utilization. It can
avoid the unavailability of the system environment due to
human factors.
Artificial intelligence technology can improve resource
utilization. Din et al. believe that, due to the existence of
various pollutants produced by human, agricultural, and
industrial activities, the quality of surface water has de-
creased. erefore, plot the concentration of different sur-
face water quality parameters. He tried to develop an
artificial intelligence modeling method for drawing con-
centration maps of optical and nonoptical SWQP. For the
first time, he developed a remote sensing framework based
on a back-propagation neural network to quantify the
concentration of different SWQP in Landsat8 satellite im-
ages. Compared with other methods (such as support vector
machine), the developed Landsat8-based BPNN model is
Hindawi
Mathematical Problems in Engineering
Volume 2021, Article ID 5582598, 11 pages
https://doi.org/10.1155/2021/5582598
used to obtain an important measurement coefficient be-
tween Landsat8 surface reflectivity and SWQP concentra-
tion. Although his research is innovative, it lacks certain
experimental data [1]. Kulkarni and Padmanabham used the
extended waterfall and agile models to model the entire
process of software (SW) development. ey integrate AI
activities such as intelligent decision making, ML, Turing
test, search, and optimization into the agile model. ey
evaluated two indicators in five independent software
projects, such as the usability target achievement indicator
and the integration index. Once the SW project is developed
using these models, feedback queries will be formally col-
lected, and the collected data will be extensively analyzed to
identify the various characteristics of the product, thereby
determining the product’s related behavior in terms of
models and indicators. Although their research is relatively
comprehensive, the test content is not accurate enough [2].
Goyache et al. developed a method to use artificial intelli-
gence to improve the design and implementation of linear
morphological systems for beef cattle. e process they
proposed involves an iterative mechanism, in which
knowledge engineering methods are used to continuously
define and calculate type features, scored by a group of well-
trained human experts, and finally performed by four fa-
mous machine learning algorithms’ analysis. e results
obtained in this way can be used as feedback for the next
iteration to improve the accuracy and effectiveness of the
proposed evaluation system. Although his research sample is
relatively complete, it is not innovative enough [3].
In this paper, user demands were obtained through user
interviews and analysis of competing products. en,
questionnaire survey was adopted to determine the im-
portance of teenagers’ demands for mobile health applica-
tions. en, the weight of demands was calculated through
data analysis. In view of the difference in use motivation
caused by gender, the users are classified by gender and age
from the beginning of registration, and different user groups
are pushed with different content of exercise knowledge.
Combined with APP, this paper carries out professional
evaluation on the exercise ability of users before exercise,
quantifies and grades the evaluation results, gives scientific
and reasonable exercise suggestions, promotes the formation
of exercise habits, and provides a reference for sports and
fitness enthusiasts to reasonably choose their own exercise
projects.
2. Youth Physical Training
2.1. Artificial Intelligence Technology. e classic sigmoid-
based ESN state update equation is composed of Nstorage pool
units, Kinput layer units, and Loutput layer units.
x(n+1) � f Wx(n) + Winu(n+1) + Wfb y(n)
􏼐 􏼑.(1)
Among them, x(n)is an N-dimensional reserve pool [4].
e output result obtained from the extended system can
be expressed as
y(n) � g Woutz(n)
􏼐 􏼑.(2)
Among them, gis the activation function of an output
layer.
e expression of the hidden layer is as follows:
vi(t) � ur(t), i A,
xc(t), i B,
􏼨
ωi(t) � ω2, i A,
ω3, i B,
netn(t+1) � 􏽘
iAB
ωi(t)vi(t),
xn(t+1) � fnetn(t+1)
 􏼁.
(3)
Normally, the form of the GARCH model is as follows:
rtϕ0+􏽘
R
i1
ϕirti+􏽘
M
i1
φiεti+εt,
εtut
ht
􏽱,
htk+􏽘
p
i1
Aiε2
ti+􏽘
q
i1
Gihti.
(4)
When the actual output of the network model is in-
consistent with the expected output, an output error Ewill
be generated. e expression is as follows:
E1
2(dO)21
2􏽘
l
k1
dkok
 􏼁2.(5)
Expand the error to the hidden layer; there are
E1
2􏽘
l
k1
dkfnetk
 􏼁􏼂 􏼃21
2􏽘
l
k1
dkf􏽘
m
j1
wjkyj
2
.
(6)
When the weight and threshold iterations corresponding
to the neurons in each layer are over, the learning and
training phase of the neural network enters the forward
propagation link again [5, 6].
P(T(X(t)) � X(t+1)) � P X(t+1) � X|X(t) � X
􏼈 􏼉
􏽙
n
i1
P Xi(t+1) � xi(t+1)|Xi(t) � xi(t)
􏼈 􏼉.
(7)
As a basic platform, in order to overcome the occurrence
of the above situation, it must have basic isolation to ensure
the independence of the service execution environment and
hardware resources of each user. Containerized virtualiza-
tion technology can provide system isolation for the plat-
form, from the operating system to the software services,
which are all defined by users, so as to provide users with a
more flexible service execution environment [7]. Since the
nodes in the cluster sometimes stop for various reasons, the
cluster management tool usually automatically migrates all
the containers running on this node to other nodes in the
cluster. However, if some containers use local data volumes,
2Mathematical Problems in Engineering
data loss will occur when the containers are migrated. Using
network storage disks or distributed storage disks will be a
viable choice [8].
e calculation formula of the autocorrelation coeffi-
cient of the current period and the previous period data is as
follows:
pk􏽘
n
i1
1
n􏽥
ak
i􏽥
uk
􏽥
σk
􏼠 􏼡 􏽥
ak+1
i􏽥
uk+1
􏽥
σk+1
􏼠 􏼡.(8)
e FFT calculation formula is as follows:
X(k) � 􏽘
N1
n0
ω(n)WN
 􏼁nk,
WNej(2π/N).
(9)
Based on the above analysis, it can be seen that
compared with the step-counting algorithm in the fre-
quency domain and the time domain, the calculation cost
of the former is obviously higher than that of the latter.
Although the step-counting algorithm in the frequency
domain has a higher computational cost, compared with
most step-counting algorithms in the time domain, the
step-counting algorithm in the frequency domain usually
achieves higher step-counting accuracy [9, 10].
2.2. Physical Training. Between functional physical training
and traditional physical training, they are interrelated and
complement each other. Specialization and integrity are the
most prominent features of the former. But in the traditional
physical training, it can not achieve these two points. In the
physical training system, the traditional physical training is
the most important foundation. At the beginning, the tra-
ditional physical training can be carried out first, and then
the functional training can lay a good foundation for the
body, so as to prevent the defects of strength training from
causing unnecessary sports injury [11]. Functional physical
training is not unitary. It needs to integrate and improve the
advantages of traditional physical training. We can not ig-
nore the traditional physical training, nor can we just carry
out a kind of functional physical training. e two com-
plement each other and complement each other, so as to
make a special targeted and integrated training arrangement,
so as to improve the athletes’ special technical level and
ability [12].
Physical fitness itself is an organic whole, not the me-
chanical or simple addition of various parts. We should
understand physical fitness with the help of system theory.
Systematic method has become an important method for
people to understand and analyze things in modern science.
e core idea of systematic view is the overall concept of
system [13]. In general, in order to improve the basic shape
of sports, improve the system initiative of athletes’ organs,
and give full play to the best mode of sports mechanism and
effect, the physical fitness index system is taken as an im-
portant reference standard in the process of training. It
belongs to the basic index of technical training and tactical
training and has a positive impact on the technology, tactics,
load training, physical condition, and sports life of special
sports. e establishment of a reasonable physical fitness
index system can be used as a powerful carrier for the se-
lection mechanism of athletes in reality [14].
Physical fitness is the foundation of young athletes and
provides strong support for their technical level. Ordinary
teenagers are mostly in the system of compulsory education
or secondary and higher education, and their training
purposes and means are different from those of young
athletes. As far as the means of physical training are con-
cerned, athletes will be better than ordinary teenagers in
terms of selection, training, competition, and other aspects,
but from the perspective of physiological development
characteristics, they are in the second peak of development.
e stimulation of training means will have a more obvious
effect on athletes’ training, which can provide training
support for ordinary teenagers [15]. is will make the
competition time longer and test the physical fitness of
athletes. If one side’s physical condition is not strong, there
will be calf muscle cramps, or even acute sports injury. On
the court, long-term muscle contraction and ball extension,
such as fast movement, kicking, swinging, and wrist
strength, are different from the periodic endurance of other
sports. Athletes must have special endurance quality, special
strength quality, special speed quality, etc. that change with
the change of competition intensity [16].
2.3. Pedometer APP. e primary task of the pedometer
algorithm is to obtain the original three-axis acceleration
data based on the sensor module and then perform data
analysis and algorithm design based on the entire waveform.
e actual test shows that there are many interference
clutters in the acceleration signal generated by the human
body when counting steps in various scenes. erefore, it is
very important to preprocess the original data before for-
mally analyzing the motion waveform [17].
e data collection function of the pedometer is realized
by the main controller reading data from the sensor, and its
core is the acceleration sensor. e use of analog signal
sensors requires additional analog-to-digital converters,
which will increase the complexity of the circuit and the
space utilization rate; the use of digital signal sensors avoids
this problem while using high-precision sensors to ensure
the reliability of data. In addition, it is necessary to ensure a
higher speed data interface, a certain processing capacity,
and lower power consumption in the selection of the main
controller and the sensor [18].
e overall architecture of pedometer is shown in Fig-
ure 1. According to the function requirement analysis, the
acceleration and angular velocity data selected collection of
six-axis accelerometer and gyroscope inertial sensor
MPU6050, master controller selects 16 ultra-low power
consumption microprocessor MSP430G2553. Data trans-
mission can use serial port transmission or wireless module
transmission, and the programming of the main controller
can be realized through online programmable function [19].
e energy-saving pedometer mainly processes the ac-
celeration sensor data collected by the smart phone using the
Mathematical Problems in Engineering 3
energy-saving pedometer algorithm to realize the pedometer
function, which is also the core function of the pedometer. In
addition, on the premise of meeting the step-counting
function, it is also necessary to update the pedestrian walking
steps in real time. In addition, in order to reduce the energy
consumption of the pedometer, it is also necessary to per-
form the screen-out operation during pedestrian walking, so
at this time, it is necessary to introduce services to realize
that the pedometer program can run in the background, so
as to ensure that even if the user does not interact with the
front end of the pedometer for a long time or the program is
switched to the background, it can still run successfully [20].
3. Physical Fitness Training APP Step-Counting
Function Experiment
3.1. Operating Environment Configuration. is article
adopts a modular development method to achieve the
functional requirements of the system as the goal, based on
the principle of science and practicality, and implements the
system with several functional modules including parameter
management, website configuration, system logs, interface
security settings, SMS configuration, and WeChat template
messages. Configuration: this system uses PHP dynamic
development language, Php5.0–7.0 to build the system
framework, MYSQL database management, LINUX,
WINDOWS and other mainstream platform operating
systems, HTML, CSS, JS, JQUERY, and other technologies to
build front-end pages [21]. e experimental equipment
parameters are shown in Table 1.
3.2. Establishment of the Human Motion Model. When the
human body is walking normally, the arm swing can be
regarded as a simple pendulum movement. According to
the characteristics of pendulum, the acceleration changes
sinusoidally. Although there are differences in the swing
of human walking, the characteristic of sinusoidal
variation of acceleration is not affected [22]. A periodic
sinusoidal waveform corresponds to a pair of peaks and
troughs, and the number of sine waves detected is
equivalent to the number of peaks detected. erefore, the
actual step-counting algorithm detects the number of
steps through the wave peak of acceleration signal, and a
wave peak represents a further advance. Because the
human body has a certain rhythm when walking, that is,
complete a stepping action as a cycle for circular motion.
By calculating the peak or trough formed by the accel-
eration of gravity, the number of human steps can be
detected and the step-counting function of mobile phone
can be realized [23].
3.3. Step-Counting Rules. is article uses three types of
sensors to analyze the data changes during walking through
three types of data and assigns different weights as the result
of step-counting. ere is a maximum and minimum ac-
celeration and a minimum and maximum angular velocity
in a step cycle. e acceleration is set to 0.2 g2 g, and the
angular velocity is set to 20°/s–200°/s. When the data ex-
ceeds, it is considered as an invalid step. When measuring
the peak value of sensor data, only the primary axial data of
each sensor is analyzed for peak value, which needs to be
determined according to the axial direction of the actual
wearing sensor [24].
3.4. Model Evaluation Indicators. First of all, the BPNN
model does not need to make any assumptions about the
functional relationship between lagged returns and future
returns. Secondly, by orthogonalizing the input space, the
possible multicollinearity is eliminated and the uniqueness
of hidden nodes is guaranteed. Again, the step-by-step se-
lection process selects the most streamlined model to ensure
that the training data will not be overfitted. Finally, it reduces
the computational cost required to find the best model
structure. Among the methods to achieve this goal, the
Crystal oscillator
circuit
Reset circuit
Download and
debug
Indicating circuit
Power circuit
I/O
ISP
RST
XTAL
UAR T
I2C
PWR
RF module
MPU6050
HMC5883
Figure 1: e overall architecture of the pedometer.
4Mathematical Problems in Engineering
cross-validation method can avoid the occurrence of
overfitting and ensure the stability of model performance by
controlling the variance of model performance [25].
3.5. Pedometer APP Interface Test. e test of APP system is
to correct the interface, check whether the interface is
complete enough, whether there is content omission,
whether the text in the interface is accurate, whether the
format is beautiful, whether the interface style is consistent
with the requirements, and whether the pictures and in-
structions are confused in the most intuitive way. In this
paper, the users’ evaluation indexes of sports fitness app are
divided into two groups: importance and satisfaction, so the
obtained data are directly divided into two groups: im-
portance and satisfaction of user experience indexes of
sports fitness app, and the two groups of data are matched
with sample ttest to ensure the scientific conclusion. Finally,
the advantages and disadvantages of the user experience of
college students’ sports fitness app in the emerging stage are
analyzed through IPA analysis method [26].
3.6. Pedometer APP Usability Evaluation. First, the APP user
demand expansion table is listed on the left wall of the house
of quality, and then the APP usability index expansion table
is included on the ceiling of the house of quality, and the
APP usability evaluation quality house is established. e
second-level index weight is calculated by the percentage
within the second-level index and the first-level index
weight. Carry out heuristic evaluation of step-counting APP,
score the second-level index of APP usability, and obtain the
first-level index score of step-counting APP usability and the
total usability score through calculation [27].
4. Experimental Results of Step-
Counting Function
4.1. Physical Training Results. With the growth of age, de-
generative changes of body function and aging appear.
Women’s aging rate is faster than men’s; participating in
sports can improve the degenerative changes caused by age,
improve immunity, and delay aging. Women put forward
higher requirements for their own health. Sports app has
relatively perfect guidance on sports content and sports
mode, which meets the needs of women to participate in
sports. e results of reliability analysis are shown in Table 2.
e reliability of each dimension of the questionnaire is
greater than 0.70, so the internal consistency of the data
measured in the questionnaire is high and the reliability is
high.
Figure 2 shows the degree of college students’ under-
standing of the pedometer APP. As can be seen from the
figure, those who know very well account for 13.37% of the
total; those who know basics account for 57.90% of the total;
those who are unclear account for 12.09% of the total; those
who do not know well account for 9.67% of the total; those
who do not understand at all account for 6.97% of the total.
According to the sample data, less than 10% of college
students still do not understand sports fitness APP at all. It
can be seen that, on the one hand, the current sports fitness
APP has a high degree of recognition among college students
and has a certain social influence. Sports fitness APP has not
only received the attention of the public, but also received
extensive attention from college students. On the other
hand, it can be seen that the publicity of sports and fitness
apps for college students is still lacking. In a group that
accepts emerging things so quickly, there are still many
college students who do not understand sports and fitness
apps at all. is should cause sports and fitness apps. e
attention of managers and publicity still need to be
strengthened.
e gender differences in the use of step app by ad-
olescents are shown in Figure 3. According to the data,
there is no significant difference in the proportion of male
and female college students using sports app, of which 288
are male students, accounting for 58.2% of the total
number, and 16.4% are more than female students. ere
are three possible reasons for this situation: first, boys’
interest in sports far exceeds girls’ and their curiosity
about sports app far exceeds girls’ level; second, boys are
better than girls in sports talent and physiological function
due to differences in body structure at both the student
stage and the adult stage; third, boys should be more
determined to exercise.
e number of strength trainings for teenagers is shown
in Table 3. In the process of strength training, 26 people can
train according to the coach’s arrangement, accounting for
27.08% of the total number of people; in the process of
strength training, 31 people can adjust the training content
and intensity according to their own actual situation, ac-
counting for 32.29%; in the process of strength training, 21
people can adjust the training content and intensity
according to their own wishes. Accounting for 21.88% of the
total number of people, 18 people did not know how to carry
out strength training, accounting for 18.75% of the total
number. Combined with the data in the table, the results of
the male and female subjects in the control group before and
after the experiment were tested, and the Pvalues were 0.003
and 0.002, respectively, both <0.01, showing a very signif-
icant difference. In the mode of upper limb strength as the
basis of basic quality training, dynamic training can ensure
the athletes to complete the required actions within the
specified time and limit the time to a certain range. It can
effectively cultivate the rapid contraction and relaxation
ability of athletes’ body muscles, adapt to the basic re-
quirements of competitive sports, and assist with flexibility
Table 1: Experimental equipment parameters.
Phone name CPU model CPU frequency (GHz) RAM capacity (GB) Battery capacity (mAh)
Google Nexus 5 Qualcomm Snapdragon 800 2.3 2 2300
Google Nexus 6 Qualcomm Snapdragon 805 2.7 3 3220
Mathematical Problems in Engineering 5
relaxation training. is can improve the elasticity and
extensibility of individual energy parts of the body.
With the continuous improvement of the competitive level
of athletes in specialized training, due to the competitive
characteristics of competitive sports that explore the limits of the
human body, the imbalance of the body caused by specialized
training has become more serious. Managers from sports
training should establish the concept of rehabilitation physical
training and consider the stages of athletes’ training for many
years when designing strategic goals. e comparison of pe-
dometer’s step accuracy is shown in Figure 4. e accuracies of
energy-saving pedometer and autocorrelation pedometer are
very similar, and they are better than that of crest detection
pedometer. However, under the latter two walking modes, the
step-counting accuracy of the three pedometers changed sig-
nificantly, which may be caused by the obvious changes in the
Table 2: Reliability analysis results.
Cronbach’s alpha coefficient Number of items
Overall data 0.940 23
Health anxiety 0.899 6
Leisure and entertainment 0.830 6
Social needs 0.867 7
Emotional catharsis 0.807 4
0
5
10
15
20
25
Frequency
Amount of users
Know very well
Can not tell
Do not know much
Basic understanding
Do not understand
12345678910
Figure 2: Undergraduates’ understanding of step-counting APP.
Number
Sample 4
Sample 5
Sample 3
Sample 1
Sample 2
0.00
20.00
40.00
60.00
80.00
100.00
Rate (%)
Figure 3: Gender differences between men and women of teenagers using pedometer apps.
Table 3: Number of strength trainings performed by adolescents.
Frequency Coach Athlete
Number of people Proportion Number of people Proportion (%)
1 time 2 6.67 13 13.54
2 times 24 80 75 78.13
3 times 2 6.67 3 3.13
4 times and above 2 6.67 5 5.21
6Mathematical Problems in Engineering
walking amplitude of the experimenters during the upstairs
process. e experimental results show that the average ac-
curacies of the crest detection step-counting algorithm, the
autocorrelation coefficient step-counting algorithm, and the
energy-saving step-counting algorithm are 92.4%, 94.4%, and
93.9%, respectively, and the standard deviations of the overall
step-counting accuracy of the three step-counting algorithms
are 0.045, 0.053, and 0.058, respectively. rough the above
experimental results, the average step-accuracy of the three step-
counting algorithms can be obtained as 92.7%, 94.7%, and
94.1%, and the average standard deviation of the overall step
accuracy is 0.04, 0.045, and 0.055. is result is sufficient to
show that the step-counting performance of the energy-saving
step-counting algorithm proposed in this paper is approxi-
mately the same as the autocorrelation coefficient step-counting
algorithm and is better than the step-counting performance of
the crest detection step-counting algorithm.
4.2. Analysis of Physical Characteristics. For athletes, in most
cases, physical training is to place people in a more difficult
environment for high-intensity, long-term, heavy-load
continuous training. e survey results of coaches’ aware-
ness of youth physical training are shown in Table 4. 96.7%
of the coaches believe that young athletes should be phys-
ically trained by age, and they have also considered age
characteristics and energy expenditure characteristics.
Figure 5 shows the comparison of the sitting position
before and after bending. e data well supports the theory
of the sensitive period for the development of flexibility.
6–13 years of age is the best period for children and ado-
lescents to develop flexibility. e high starting point of the
pretest data illustrates this point, but there is room for
improvement between the experimental group and the
control group. e comparison shows that scientific and
continuous physical training can accelerate the development
of flexibility, but a thorough warm-up should be carried out
before the development of flexibility. e experimental
group t5.11, P0<0.001, the difference between the test
variables is extremely significant. In the control group,
t0.36, P0.72 >0.05, there was no significant difference
between the test variables. Analyze the data of the
experimental group and the control group. Although the
experimental group has a significant increase in the mea-
sured data before and after, the improvement is not large,
and the control group data has not improved at all. Not
much, but physical training also has a direct impact on the
improvement of strength quality.
e score of the first-level index of youth special physical
training is shown in Figure 6. e K-S values of each sports
quality index are 0.781, 0.344, 0.391, 0.912, 0.135, 0.935,
0.169, 0.914, 0.517, 0.352, 0.807, and 0.694, which are all
greater than 0.05. e index data obey the normal distri-
bution, and the standard percentage method can be used to
establish a single score table. rough the first-level index
score and comprehensive score, we can easily determine the
score of any one of the 30 young male badminton players at
all levels. However, this has not yet achieved the purpose of
comprehensive evaluation. If we do not establish the first-
Figure 4: Comparison of step-counting accuracy of pedometer.
Table 4: Survey results.
Problem Yes Percentage (%) No Percentage (%)
1 118 96.7 4 3.3
2 118 96.7 4 3.3
3 4 3.3 118 96.7
4 108 88.5 14 11.5
5 31 25.4 91 74.6
12345678910
0
2
4
6
8
10
12
14
16
Mean
Var i abl e
Experimental group posttest
Experimental group pretest
Control group pretest
Control group posttest
Figure 5: Comparison of sitting position before and after bending.
Mathematical Problems in Engineering 7
level index and comprehensive quality evaluation standard
of athletes, we can not scientifically judge the level and level
of athletes in the first-level index and comprehensive quality.
e comparison of FMS test results is shown in Figure 7.
is is because the two-point movement is easy to complete,
and it can reach the standard in the initial test. e three-point
movement is difficult, and only one guard can complete it in
the posttest. e three-point movement requires the athletes to
complete the balance movement under the condition of the
same knee support, which is very difficult for the basketball
players with high average height. e average score of the third
test is 2.86 higher than that of the first test, and the total score of
the three tests shows a very significant difference (P<0.01),
which indicates that the formulation and implementation of
the plan for the athletes’ action mode and prerehabilitation
training are successful.
4.3. Impact of Artificial Intelligence Technology. e results of
the core group endurance test for adolescents are shown in
Figure 8. e average duration of the whole team’s plate
support is 103 s, and the duration of the back bridge is
108.14 s. Compared with the two events, the duration of the
back bridge is shorter. is is because the force environment
of the back bridge is obviously better than that of the plate
support no matter from the force arm or the strength of the
active muscle. eoretically, there should be a large gap
between the maximum duration of the two movements.
rough training, the gluteal muscle activation was higher,
and the ability of back bridge was also greatly improved.
ere was a very significant difference between the results of
the first back bridge test and that of the first back bridge test
(P<0.01). In basketball, the completion of technical action
is mostly in the form of explosive force, so the explosive force
directly affects the technical effect of athletes. Especially for
young athletes, under the premise of lack of muscle and
absolute strength, giving priority to the development of
explosive force is the key to this stage of training.
e growth and development of adolescents are char-
acterized by persistence and stages. Body shape and motor
function are the two dimensions of physical fitness. e
characteristics of adolescents aged 13 to 15 are the basis for
the construction of physical training content. e physical
activity characteristics of children, adolescents, and adults
are also reflected in these two aspects. For example, the
height and weight of training equipment should be reduced
for teenagers before their height suddenly increases, so as to
adapt to the adaptability of their training. e sensitivity of
different types of strength quality of adolescents is shown in
Figure 9. rough the results, we can see that, in the process
of describing each strength, its weight and proportion are
also different. In this way, in the process of evaluation, we
can classify and analyze different strength elements and
qualities according to different types, which is also helpful
for us to grasp the core index connotation in the training
process, and avoid blindness and ineffectiveness in training.
e experimental results of the pedometer method are
shown in Table 5. It can be seen from the table that even though
the proposed method does not always perform the best for each
walking activity considered, its accuracy is close to the optimal,
with an average accuracy of 95.74%, which is at least 3.81%
higher than the commonly used algorithm, which is at least
3.39% higher than the comparative step-counting software.
From the maximum and minimum values, it can be known that
the accuracy of this method is in the range of 77.97%100%.
Compared with other methods and step-counting software in
the table, the difference between the maximum and minimum
value of this method is the smallest, which indicates that the
performance of FG method is relatively stable. is method is
less affected by personal factors, and there is no case that the
step-counting method has a high accuracy rate for one vol-
unteer, but the other is very poor. rough observation, it can
be found that, for RBF neural network, the more the number of
hidden layer nodes, the higher the prediction accuracy. For the
four models with hard ridge penalty, when the number of
hidden layer nodes is reduced from 50 to 16, they also have the
highest prediction accuracy; that is, the optimal hidden layer
node is 16.
5. Conclusions
is article first selects NFS files and CEPH files as the object
of the system performance test, selects the distributed file
system as the basic storage, selects the CEPH file system as
Sample
1
2
3
4
5
6
7
8
9
10
0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00
Score
A1
A2
A3
A4
Figure 6: e first-level index scores of youth special physical training.
8Mathematical Problems in Engineering
0
2
4
6
8
10
12
14
Score
e third time
e rst time
e fourth time e second time
e h time
e sixth time
Numb er
12345678910
Figure 7: Comparison of FMS test results.
0
2
4
6
8
10
12
14
16
18
Test resu lt
Position
Plank
Le bridge
Right bridge
Back bridge
12345678910
Figure 8: Endurance test results of adolescent core muscles.
0
0.2
0.4
0.6
0.8
1
Proportion
Number of people
Body posture
Flexible
Power
Speed
Endurance
Sensitive
12345678 9 10
Figure 9: e sensitivity of adolescents to different types of strength qualities.
Mathematical Problems in Engineering 9
the storage medium, and selects files based on the network
layer. e traditional analysis method based on correlation
coefficient can no longer deal with the relationship between
dependent variables and multiple groups of independent
variables, but the data mining algorithm can find the in-
dependent variables with strong correlation with the de-
pendent variables from the massive data, so as to take them
as the input set of the model.
Pedometers can not only play a certain medical role in
aging and obesity patients, but also are increasingly used in
people’s daily exercise and fitness. It is accepted by the public
and pays attention to the health of today’s fast social life. It
will be beneficial to the improvement of the overall health of
the society and the development of pedometer design. e
step-counting method uses peak detection method to obtain
the fusion result of step-counting by taking different weights
for different and quality data.
rough actual measurement and evaluation, the data
shows that the pedometer designed in this paper has a
step accuracy of 95%, stable performance, and strong
anti-interference ability. Different from other mobile
phone step-counting algorithms that only use accelera-
tion sensor data, this paper combines acceleration and
distance sensor data to realize step-counting, using ac-
celeration sensor data for gait feature analysis, using
distance sensor data to determine the location of the
mobile phone, and improving the accuracy of human
hand-held mobile phone step-counting, especially when
walking without swinging arms.
Data Availability
No data were used to support this study.
Conflicts of Interest
e author declares that there are no conflicts of interest
regarding the publication of this paper.
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FG
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FA
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AC
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Mathematical Problems in Engineering 11
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