scholarly journals Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1321
Author(s):  
Wenjing Quan ◽  
Huiyu Zhou ◽  
Datao Xu ◽  
Shudong Li ◽  
Julien S. Baker ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Sagittal Plane ◽  
Three Dimensional ◽  
Frontal Plane ◽  
Principal Component ◽  
Component Analysis ◽  
Ankle Inversion ◽  
Motion System ◽  
Recreational Runners

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

scholarly journals A sparse principal component analysis of Class III malocclusions

2019 ◽  
Vol 89 (5) ◽  
pp. 768-774 ◽  
Author(s):  
Tae-Joo Kang ◽  
Soo-Heang Eo ◽  
HyungJun Cho ◽  
Richard E. Donatelli ◽  
Shin-Jae Lee
Keyword(s):  
Principal Component Analysis ◽  
Soft Tissue ◽  
Principal Components ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Ct Images ◽  
Class Iii ◽  
Hard Tissue ◽  
Input Variables

ABSTRACT Objectives: To identify the most characteristic variables out of a large number of anatomic landmark variables on three-dimensional computed tomography (CT) images. A modified principal component analysis (PCA) was used to identify which anatomic structures would demonstrate the major variabilities that would most characterize the patient. Materials and Methods: Data were collected from 217 patients with severe skeletal Class III malocclusions who had undergone orthognathic surgery. The input variables were composed of a total of 740 variables consisting of three-dimensional Cartesian coordinates and their Euclidean distances of 104 soft tissue and 81 hard tissue landmarks identified on the CT images. A statistical method, a modified PCA based on the penalized matrix decomposition, was performed to extract the principal components. Results: The first 10 (8 soft tissue, 2 hard tissue) principal components from the 740 input variables explained 63% of the total variance. The most conspicuous principal components indicated that groups of soft tissue variables on the nose, lips, and eyes explained more variability than skeletal variables did. In other words, these soft tissue components were most representative of the differences among the Class III patients. Conclusions: On three-dimensional images, soft tissues had more variability than the skeletal anatomic structures. In the assessment of three-dimensional facial variability, a limited number of anatomic landmarks being used today did not seem sufficient. Nevertheless, this modified PCA may be used to analyze orthodontic three-dimensional images in the future, but it may not fully express the variability of the patients.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Principal Component Analysis of Munich Functional Developmental Diagnosis

2021 ◽  
Vol 13 (2) ◽  
pp. 227-233
Author(s):  
Grażyna Pazera ◽  
Marta Młodawska ◽  
Jakub Młodawski ◽  
Kamila Klimowska
Keyword(s):  
Principal Component Analysis ◽  
Social Skills ◽  
Motor Development ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Three Dimensions ◽  
Matrix Analysis ◽  
Psychomotor Development ◽  
First Year Of Life

Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life. The tool is based on standardized tables of physical development and is used to detect developmental deficits. It consists of eight axes on which the following skills are assessed: crawling, sitting, walking, grasping, perception, speaking, speech understanding, social skills. Methods: The study included 110 children in the first year of life examined with the MFDD by the same physician. The score obtained on a given axis was coded as a negative value (defined in months) below the child’s age-specific developmental level. Next, we examined the dimensionality of the scale and the intercorrelation of its axes using polychoric correlation and principal component analysis. Results: Correlation matrix analysis showed high correlation of MFDD axes 1–4, and MFDD 6–8. The PCA identified three principal components consisting of children’s development in the areas of large and small motor skills (axis 1–4), perception (axis 5), active speech, passive speech and social skills (axis 6–8). The three dimensions obtained together account for 80.27% of the total variance. Conclusions: MFDD is a three-dimensional scale that includes motor development, perception, and social skills and speech. There is potential space for reduction in the number of variables in the scale.

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

scholarly journals Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira
Keyword(s):  
Principal Component Analysis ◽  
Disease Severity ◽  
Principal Components ◽  
Leaf Spot ◽  
Oil Content ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Gge Biplot ◽  
Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

scholarly journals Principal component analysis for evaluating a ranking method used in the performance testing in sheep of Morada Nova breed

2015 ◽  
Vol 36 (6) ◽  
pp. 3909
Author(s):  
Michelle Santos da Silva ◽  
Luciana Shiotsuki ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Correlation Coefficients ◽  
Performance Testing ◽  
Principal Component ◽  
Component Analysis ◽  
Ranking Method ◽  
Daily Weight Gain ◽  
Meat Production ◽  
Body Depth

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

Sign in / Sign up

Export Citation Format

Share Document