scholarly journals Exploring Physical Fitness Profile of Male and Female Semiprofessional Basketball Players through Principal Component Analysis—A Case Study

2021 ◽  
Vol 6 (3) ◽  
pp. 67
Author(s):  
Carlos D. Gómez-Carmona ◽  
David Mancha-Triguero ◽  
José Pino-Ortega ◽  
Sergio J. Ibáñez
Keyword(s):  
Principal Component Analysis ◽  
Physical Fitness ◽  
Principal Component ◽  
Component Analysis ◽  
Physical Conditioning ◽  
Basketball Players ◽  
Jump Performance ◽  
Male And Female ◽  
Practical Applications ◽  
Fitness Level

Basketball is a sport in continuous evolution, being one of these key aspects of the players’ physical fitness that has an impact on the game. Therefore, this study aimed to characterize and identify the physical fitness level and profiles of basketball players according to sex. Total of 26 semi-professional basketball players were assessed (13 male, 13 female) through inertial devices in different previously validated fitness tests. T-test for independent samples and principal component analysis were used to analyze sex-related differences and to identify physical fitness profiles. The results showed differences according to sex in all physical fitness indexes (p < 0.01; d > 1.04) with higher values in males, except in accelerometer load during small-sided games (p = 0.17; d < 0.20). Four principal components were identified in male and female basketball players, being two common ([PC1] aerobic capacity and in-game physical conditioning, [PC4 male, PC3 female] unipodal jump performance) and two different profiles (male: [PC2] bipodal jump capacity and acceleration, [PC3] curvilinear displacement; female: [PC2] bipodal jump capacity and curvilinear displacement, [PC4] deceleration). In conclusion, training design must be different and individualized according to different variables, including physical fitness profiles between them. For practical applications, these results will allow knowing the advantages and weaknesses of each athlete to adapt training tasks and game systems based on the skills and capabilities of the players in basketball.

scholarly journals Variability in size and shape in muscovy duck with age: Principal component analysis

2014 ◽  
Vol 30 (1) ◽  
pp. 125-136 ◽  
Author(s):  
D.M. Ogah ◽  
M. Kabir
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Dependence Structure ◽  
Muscovy Duck ◽  
Body Measurements ◽  
Male And Female ◽  
Size And Shape

Body weight and six linear body measurements, body length (BL), breast circumference (BCC), thigh length (TL), shank length (SL), total leg length (TLL) and wing length were recorded on 150 male and female muscovy ducklings and evaluated at 3, 5, 10, 15 and 20 weeks of age. Principal component analysis was used to study the dependence structure among the body measurements and to quantify sex differences in morphometric size and shape variations during growth. The first principal components at each of the five ages in both sexes accounted between 71.54 to 92.95% of the variation in the seven measurements and provided a linear function of size with nearly equal emphasis on all traits. The second principal components in all cases also accounted for between 6.7 to 16.17% of the variations in the dependence structure of the system in the variables as shape, the coefficient for the PCs at various ages were sex dependent with males showing higher variability because of spontaneous increase in size and shape than females. Contribution of the general size factor to the total variance increase with age in both male and female ducklings, while shape factor tend to be stable in males and inconsistent in females.

scholarly journals Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Hou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Physical Fitness ◽  
Physical Health ◽  
Convolutional Neural Network ◽  
Deep Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Standing Long Jump ◽  
Long Jump

The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short-Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull-up, 50 m sprint, and 1000 m long-distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.

Relation between Planning and Simultaneous-Successive Processing

1980 ◽  
Vol 51 (2) ◽  
pp. 371-382 ◽  
Author(s):  
Adrian F. Ashman ◽  
J. P. Das
Keyword(s):  
Principal Component Analysis ◽  
Information Processing ◽  
Test Scores ◽  
Principal Component ◽  
Component Analysis ◽  
Female Students ◽  
Information Processing Model ◽  
Male And Female ◽  
Planning Factor ◽  
Grade 8

The simultaneous-successive processing battery and five tests reputed to measure planning were administered to 104 Grade 8 male and female students. Test scores were submitted to principal component analysis and a planning factor was identified which was orthogonal to the two coding dimensions. The study clearly delineates independent coding and planning dimensions and provides support for and extends the simultaneous-successive information-processing model.

P6328Relationship between exercise capacity and muscle function in adult patients with heart failure

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
O Chiala ◽  
L Klompstra ◽  
E Vellone ◽  
A Stromberg ◽  
T Jaarsma
Keyword(s):  
Principal Component Analysis ◽  
Physical Fitness ◽  
Exercise Capacity ◽  
Muscle Function ◽  
Principal Component ◽  
Component Analysis ◽  
Function Tests ◽  
Body Strength ◽  
Shoulder Flexion ◽  
Heel Lift

Abstract Introduction Physical fitness is a multidimensional concept and is of great importance to assess in heart failure (HF) patients, due to that HF is associated with exercise intolerance, dyspnea and fatigue. Some measures of physical fitness have been associated to HF severity, rehospitalization and mortality risk. Purpose The aim of this study was to explore the relationship between two different measures of physical fitness in patients with HF: exercise capacity and muscle function. Methods This is a secondary analysis performed on the baseline data of a multi-centre RCT study aimed at exploring the effect of exergame access to improving exercise capacity in HF patients. Physical fitness was assessed by two different measures: exercise capacity assessed by the 6-minute walk test (6MWT), and muscle function assessed by 3 different isometric tests of upper and lower limbs. Descriptive statistics, Spearman correlation and principal component analysis were used to analyse the data. Results In total, 605 HF patients were included in this analysis (mean age 67±12, 29% women, 90% NYHA II/III). Exercise capacity (6MWT) was correlated with all of the muscle function tests, with correlation-coefficients ranging from r=0.23 to r=0.50 (p<0.001). Principal component analysis showed a bi-dimensional nature of physical fitness, with the first dimension including the lower body strength and the second dimension including the upper body strength and exercise capacity. These two dimensions explained 71% of the total variance of the measured variables. Correlation matrix (n=605) 6MWT Right Heel-Lift Left Heel-Lift Shoulder abduction Right Shoulder Flexion Left Shoulder Flexion 6MWT 1 Right Heel-Lift 0.23** 1 Left Heel-Lift 0.26** 0.86** 1 Shoulder abduction 0.40** 0.30** 0.34** 1 Right Shoulder Flexion 0.47** 0.51** 0.48** 0.52** 1 Left Shoulder Flexion 0.50** 0.49** 0.48** 0.52** 0.83** 1 **p<0.001. Principal Component Analysis Conclusions Although strong correlations between exercise capacity and muscle function tests, the measures loaded on two different factors. The results suggest that upper body strength and exercise capacity had a stronger relationship than with lower body strength. This may imply that muscle function tests for upper and lower limbs and exercise capacity represent different aspects of physical fitness and to attain a more complete assessment of physical fitness all three tests are important. Acknowledgement/Funding Swedish National Science Council, The Swedish Heart and Lung Association, The Swedish Heart and Lung Association, Swedish Heart-Lung Foundation

scholarly journals Feasibility and validity of the PERF-FIT  test battery  to  assess movement skills, agility  and  power  among  children  in  low- resource  settings.

2020 ◽  
Author(s):  
Bouwien Smits-Engelsman ◽  
Emmanuel Bonney ◽  
Jorge Lopes Cavalcante Neto ◽  
Dorothee L Jelsma
Keyword(s):  
Principal Component Analysis ◽  
Physical Fitness ◽  
Content Validity ◽  
Principal Component ◽  
Component Analysis ◽  
Test Battery ◽  
Structural Validity ◽  
Low Resource Settings ◽  
Low Resource ◽  
Movement Skills

Abstract Background: Numerous movement skills and physical fitness tests have been developed for children in high-income countries. However, adaptation of these tests to low-resource settings has been slow and norms are still unavailable for children living in low-income communities. The aim of this paper was to describe the development and validation of the Performance and Fitness (PERF-FIT) test battery, a new test to assess motor skill-related physical fitness in children in low-resource settings. Method: The PERF-FIT test was developed in a stepwise manner. This involved defining the relevant domains of the construct of interest and selecting and evaluating test items. The Content Validity Index (CVI) was used to estimate content validity. Following development of the PERF-FIT test, a preliminary study was performed to validate items and to examine the feasibility of implementing the test in a low-resource community. Structural validity was also determined based on data from eighty (n=80) children (aged 7-12 years) using principal component analysis.Results: The CVI for the throw and catch item was 0.86 and 1.00 for the other nine items, leading to a total CVI score of 0.99. The hierarchical sequence of the item series was demonstrated by highly significant (p<0.001) linear trends, confirming the increase in difficulty of subsequent items. Principal component analysis revealed three factors; the first component is represented by locomotor skills that require static and dynamic balance, the second component by throwing and catching items and the third component by agility and power items. These findings suggest that it is feasible to implement the PERF-FIT in low-resource settings. Conclusion: The PERF-FIT test battery is easy to administer and may be suitable for measuring skill-related physical fitness in in low-resource settings. It has excellent content validity and good structural validity. After minor adaptions, further studies should be conducted to establish normative values, evaluate reliability, and document criterion and cross-cultural validity of this test.

scholarly journals Analysis of the Competitive Activity Structure of Skilled Female Basketball Players

2020 ◽  
Vol 20 (4) ◽  
pp. 219-227
Author(s):  
Eduard Doroshenko ◽  
Ruslana Sushko ◽  
Valerij Shamardin ◽  
Volodymyr Prykhodko ◽  
Iryna Shapovalova ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Basketball Players ◽  
Factor Loadings ◽  
Special Analysis ◽  
Activity Structure ◽  
Competitive Activity ◽  
Study Participants ◽  
Selection Of

The study purpose was to examine, analyze, and generalize the competitive activity structure based on the hierarchy of technical and tactical indicators of skilled female basketball players in won and lost games using principal component analysis. Materials and methods. The study participants were 96 professional female basketball players, members of national teams of Spain, France, Belgium, Greece, Turkey, Latvia, Italy, and Slovakia, which took the 1st-8th places in the final tournament of the European Basketball Championship 2017. The study analyzed 16 main technical and tactical indicators of skilled female basketball players in 52 official games to examine and interpret the obtained results using principal component analysis. The total number of observations is 52. Results. The experimental indicators obtained during the study made it possible to examine and analyze the grouping of elements of the competitive activity structure of skilled female basketball players, to interpret the obtained results in order to define informative criteria for optimizing training and improving the competitive activity effectiveness. The study revealed considerable differences in the competitive activity structure in won and lost games: in accordance with the most significant indicators of factor loadings, the percentage of a sample of elements that correlate with one another is: for won games – 67.40%, for lost games – 69.52%. Conclusions. Principal component analysis is quite effective and informative for studying the competitive activity structure of skilled female basketball players. It was demonstrated that the greatest difficulties in studying the competitive activity structure in basketball using principal component analysis are the selection of indicators that do not duplicate one another and are not calculated, expert interpretation of the obtained results, and algorithmization of special analysis of technical and tactical indicators.

Dimensionality Reduction for Countermovement Jump Metrics

2020 ◽  
pp. 1-4
Author(s):  
Lachlan P. James ◽  
Haresh Suppiah ◽  
Michael R. McGuigan ◽  
David L. Carey
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
High Performance ◽  
Performance Testing ◽  
Principal Component ◽  
Component Analysis ◽  
Countermovement Jump ◽  
Data Set ◽  
Jump Performance

Purpose: Dozens of variables can be derived from the countermovement jump (CMJ). However, this does not guarantee an increase in useful information because many of the variables are highly correlated. Furthermore, practitioners should seek to find the simplest solution to performance testing and reporting challenges. The purpose of this investigation was to show how to apply dimensionality reduction to CMJ data with a view to offer practitioners solutions to aid applications in high-performance settings. Methods: The data were collected from 3 cohorts using 3 different devices. Dimensionality reduction was undertaken on the extracted variables by way of principal component analysis and maximum likelihood factor analysis. Results: Over 90% of the variance in each CMJ data set could be explained in 3 or 4 principal components. Similarly, 2 to 3 factors could successfully explain the CMJ. Conclusions: The application of dimensional reduction through principal component analysis and factor analysis allowed for the identification of key variables that strongly contributed to distinct aspects of jump performance. Practitioners and scientists can consider the information derived from these procedures in several ways to streamline the transfer of CMJ test information.

scholarly journals Caracterización fenotípica de accesiones de especies de Rubus L. de los municipios de Pamplona y Chitagá, región Nororiental de Colombia

2011 ◽  
Vol 16 (3) ◽  
pp. 219 ◽  
Author(s):  
Giovani Orlando Cancino-Escalante ◽  
Luis Roberto Sánchez-Montaño ◽  
Enrique Quevedo-García ◽  
Claudia Díaz-Carvajal
Keyword(s):  
Principal Component Analysis ◽  
Key Words ◽  
Wild Species ◽  
Morphological Differentiation ◽  
Principal Component ◽  
Component Analysis ◽  
Male And Female ◽  
Commercial Farms ◽  
Roadside Plants

<br /><p><strong></strong><strong>Objective. </strong>To identify the cultivated and wild species of <em>Rubus</em> in 53 farms with commercial plantations of <em>Rubus glaucus </em>Benth, owned by four blackberry grower associations in the towns of Pamplona and Chitagá, (Norte de Santander, Colombia).<strong> Materials and methods. </strong>Three to five specimens were collected from each farm and along the roadside. Plants aged 9 to 12 months since their plantation in the commercial farms and wild materials with characteristics of <em>Rubus</em> were selected. Twenty two descriptors (fourteen quantitative and eight qualitative) were assessed. We considered the seventh and eighth branch buds both male and female (with five repetitions) and fruits and flowers of each material. Principal component analysis was done with the fourteen quantitative variables, to identify the descriptors that most contribute to the morphological differentiation of accessions. A conglomerate analysis was used for grouping accessions according to their similarity and dissimilarity. <strong>Results. </strong>Among the 147 accessions analyzed from the different farms, our study determined the presence of 6 different taxa: <em>R. glaucus</em> Benth (with and without spines), <em>R. alpinus </em>Macfad<em>, R. adenotrichos </em>Schltdl<em>, R. rosifolius </em>Sm.<em>,</em> <em>R. bogotensis </em>Kunth<em> </em>and<em> R. floribundus </em>Kunth <strong>Conclusions. </strong>The descriptors that differentiated the species and discriminated them by groups by providing 77% of the information with the use of principal component analysis, were:  length and width of central and lateral leaflets, length of flower and leaf structures, apex shape and number of secondary veins.</p> <p><strong>Key words</strong>: <em>Rubus glaucus </em>Benth, identification, <em>taxa,</em> descriptors, principal component analysis, conglomerate analysis</p> <p> </p><br />

scholarly journals A face recognition software framework based on principal component analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254965
Author(s):  
Peng Peng ◽  
Ivens Portugal ◽  
Paulo Alencar ◽  
Donald Cowan
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Software Framework ◽  
Software Developers ◽  
Practical Applications

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

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