scholarly journals Effects of face mask use on exercise during the covid-19 pandemic: the principal component analysis (PCA)

2021 ◽  
Author(s):  
Paloma Priscila Porreca ◽  
Mayara Jeronymo Uébe Mansur ◽  
Victor Paes Dias Gonçalves ◽  
Bárbara Vieira Bolckau Miranda ◽  
Marlana Ribeiro Monteiro
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Face Mask ◽  
Component Analysis ◽  
Cycle Ergometer ◽  
World Health ◽  
Vastus Lateralis Muscle ◽  
Total Body Mass ◽  
Maximum Time ◽  
The Individual

The COVID-19 pandemic also raised questions about the practice of physical activity using a face mask and how this would affect breathing and performance. The aim of this study was to investigate the effects of using a tissue face mask recommended by the World Health Organization (WHO) on the variation of heart rate (HR), minute volume (VE), and muscle O 2 saturation (SO 2 m) parameters during performing the incremental load exercise and verifying the maximum time obtained at the end of the exercise. A 21-year-old male, 85 kg of total body mass and 1.68 m of height were selected. The individual performed an incremental load test to maximum exhaustion on the XT cycle ergometer (TRG Fitness ®️ ) in two moments: No mask -Control (C); Cloth Mask (CM). The individual was instructed to maintain a cadence of 61-65 rpm and every 2 minutes a load of 30.8 watts was added until maximum exhaustion. A ventilometer VO2 Pro (Cefise ®️ ) and a near infrared spectroscopy sensor (Moxy ®️ ) were used, placed in the vastus lateralis muscle of the right leg.Data were analyzed every 20% of the total time (20%, 40%, 60%, 80% and 100%) under conditions C and CM. The parameters of HR, VE and SO 2 m were monitored throughout the test and the data were statistically processed by a software (Origin Pro ®️ 3.226) using a multivariate analysis technique (Principal Component Analysis -PCA) to analyze interrelationships between the variables. In test condition C, an eigenvalue of 2.979 was observed with two variables (VE and SO 2 m) associated with greater variation (PC1). The maximum time obtained at the end of the test was 1535 seconds. In the CM condition, na eigenvalue of 2.881 was observed with two variables (HR and SO 2 m) associated with greater variation (PC1). The maximum time obtained at the end of the test was1330 seconds. It is concluded that the use of CM reduces VE variation, which may be associated with greater airflow resistance. The greatest variation observed in HR was due to the use of CM, impacting the delay in the appearance of the plateau. In addition, the use of a tissue mask recommended by the WHO reduces the maximum exercise time performed on the cycle ergometer in a healthy individual. These findings are useful for evaluating the effects of using CM in high-performance sports. Additional studies in the elderly and people with lung or heart disease are needed.

Muscle Fatigue Assessment During Cycle Ergometer Exercise Using Principal Component Analysis of Electromyogram Power Spectra

2016 ◽  
Vol 32 (6) ◽  
pp. 593-598 ◽  
Author(s):  
Igor Ramathur Telles Jesus ◽  
Roger Gomes Tavares Mello ◽  
Jurandir Nadal
Keyword(s):  
Principal Component Analysis ◽  
Muscle Fatigue ◽  
Vastus Lateralis ◽  
Power Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Fatigue Analysis ◽  
Cycle Ergometer ◽  
Vastus Lateralis Muscle ◽  
Ergometer Exercise

During muscle fatigue analysis some standard indexes are calculated from the surface electromyogram (EMG) as root mean square value (RMS), mean (Fmean), and median power frequency (Fmedian). However, these parameters present limitations and principal component analysis (PCA) appears to be an adequate alternative. In this context, we propose two indexes based on PCA to enhance the quantitative muscle fatigue analysis during cyclical contractions. Signals of vastus lateralis muscle were collected during a maximal exercise test. Twenty-four subjects performed the test starting at 12.5 W power output with increments of 12.5 W⋅min–1, maintaining cadence of 50 rpm until voluntary exhaustion. The epochs of myoelectric activation were identified and used to estimate the power spectra. PCA was then applied to the power spectra of each subject. The standard (ST) and Euclidean (ED) distances were employed to estimate the alteration occurred due to fatigue. For comparison, the standard indexes were calculated. ST, ED, and RMS value were adequate for muscle fatigue analysis. Among these parameters, ST was more sensitive with higher effect size. Moreover, the Fmean and Fmedian were not sensitive to fatigue. The proposed method based on PCA of EMG in frequency domain allowed producing fatigue indexes suitable for cyclical contractions.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

Development of a New Tool for Managing Performance Nutrition: The Athlete Food Choice Questionnaire

2019 ◽  
Vol 29 (6) ◽  
pp. 620-627 ◽  
Author(s):  
Rachael L. Thurecht ◽  
Fiona E. Pelly
Keyword(s):  
Principal Component Analysis ◽  
Nutrition Education ◽  
Food Choice ◽  
Weight Control ◽  
Principal Component ◽  
Component Analysis ◽  
Key Factors ◽  
Dietary Interventions ◽  
Frequency Scale ◽  
The Individual

This study aimed to develop and refine an Athlete Food Choice Questionnaire (AFCQ) to determine the key factors influencing food choice in an international cohort of athletes. A questionnaire that contained 84 items on a 5-point frequency scale was developed for this study. Athletes at the 2017 Universiade, in Taiwan, were invited to participate. Principal component analysis was utilized to identify key factors and to refine the questionnaire. Completed questionnaires were received from 156 athletes from 31 countries and 17 sports. The principal component analysis extracted 36 items organized into nine factors explaining 68.0% of variation. The nine factors were as follows: nutritional attributes of the food, emotional influences, food and health awareness, influence of others, usual eating practices, weight control, food values and beliefs, sensory appeal, and performance. The overall Kaiser–Meyer–Olkin measure was 0.75, the Bartlett test of sphericity was statistically significant, χ2(666) =2,536.50, p < .001, and all of the communalities remained >0.5. Intercorrelations were detected between performance and both nutritional attributes of the food and weight control. The price of food, convenience, and situational influences did not form part of the factorial structure. This research resulted in an AFCQ that includes factors specific to athletic performance and the sporting environment. The AFCQ will enable researchers and sports dietitians to better tailor nutrition education and dietary interventions to suit the individual or team. The next phase will test the accuracy and reliability of the AFCQ both during and outside of competition. The AFCQ is a useful tool to assist with management of performance nutrition for athletes.

A strategy for selecting diverse accessions using Principal Component Analysis from a large germplasm collection of soybean

2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principle Component Analysis ◽  
Quantitative Traits ◽  
Germplasm Collection ◽  
Principal Component ◽  
Target Population ◽  
Component Analysis ◽  
Principle Component ◽  
The Individual

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.

scholarly journals MEMONITOR KUALITAS PEMBELAJARAN DI FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNIVERSITAS UDAYANA

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
RAHMAD RAHMAD WIDODO ◽  
I PUTU EKA NILA KENCANA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Principal Component Analysis ◽  
Control Chart ◽  
Principal Component ◽  
Component Analysis ◽  
Natural Sciences ◽  
Quality Of Learning ◽  
Study Programs ◽  
The Individual ◽  
New Variables

Controlling the quality of learning is very important and influences the accreditation of study programs at the Faculty of Mathematics and Natural Sciences Udayana University, as a guarantor of the quality of graduates. Apply pricipal component analysis to reduce the number of determinant attributes of learning quality, with the aim of looking at the data structure with fewer variables. The control chart is a multivariate control chart that is used to view the potrait of the quality of learning in the Mathematics and Natural Sciences Faculty, using new variables obtained from principal component analysis. The results obtained from principal component analysis show that the contribution of the learning quality indicators is univen. The potrait of the quality of learning at the Faculty of Mathematics and Natural Sciences obtained from the individual-moving range (I-MR) and the control chart shows the need for corrective actions and monitor regularly to improve the quality of learning.

scholarly journals Sign Language Recognition using Principal Component Analysis and Support Vector Machine

2021 ◽  
Vol 4 (01) ◽  
pp. 49
Author(s):  
Astri Novianty ◽  
Fairuz Azmi
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sign Language ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
World Health ◽  
Support Vector ◽  
Language Recognition ◽  
Sign Language Recognition

The World Health Organization (WHO) estimates that over five percent of the world's population are hearing-impaired. One of the communication problems that often arise between deaf or speech impaired with normal people is the low level of knowledge and understanding of the deaf or speech impaired's normal sign language in their daily communication. To overcome this problem, we build a sign language recognition system, especially for the Indonesian language. The sign language system for Bahasa Indonesia, called Bisindo, is unique from the others. Our work utilizes two image processing algorithms for the pre-processing, namely the grayscale conversion and the histogram equalization. Subsequently, the principal component analysis (PCA) is employed for dimensional reduction and feature extraction. Finally, the support vector machine (SVM) is applied as the classifier. Results indicate that the use of the histogram equalization significantly enhances the accuracy of the recognition. Comprehensive experiments by applying different random seeds for testing data confirm that our method achieves 76.8% accuracy. Accordingly, a more robust method is still open to enhance the accuracy in sign language recognition.

scholarly journals Dissecting individual differences in responses to naturalistic stimuli in functional MRI: effects of development and gender

2020 ◽  
Author(s):  
Xin Di ◽  
Bharat B. Biswal
Keyword(s):  
Principal Component Analysis ◽  
Individual Differences ◽  
Functional Mri ◽  
Principal Component ◽  
Component Analysis ◽  
Gender Effects ◽  
Delayed Response ◽  
List Type ◽  
And Gender ◽  
The Individual

AbstractFunctional MRI (fMRI) study of naturalistic conditions, e.g. movie watching, usually focuses on shared responses across subjects. However, individual differences in the responses have been attracting increasing attention in search of group differences or associations with behavioral outcomes. The individual differences have been studied by directly modeling the cross-subject correlation matrix or projecting the relations into a 1-D space. We contend that it is critical to examine whether there are single or multiple consistent components of responses underlying the whole population, because multiple components may undermine the individual relations using the previous methods. We use principal component analysis (PCA) to examine the heterogeneity of brain responses across subjects in terms of the eigenvalues of the covariance matrix, and utilize this approach to study developmental trajectories and gender effects in a movie watching dataset. We identified several brain networks in the parietal cortex that showed a significant second principal component (PC) of regional responses, which were mainly represented the younger children. The second PCs in some networks, i.e. the supramarginal network, resembled a delayed version of the first PCs for 4 seconds (2 TR), indicating delayed responses in the younger children than the older children and adults. However, no apparent gender effects were found in the first and second PCs. The analyses highlight the importance of identifying multiple consistent responses underlying individual differences in responses to naturalistic stimuli. And the PCA-based approach could be complementary to the commonly used intersubject correlation analysis.HighlightsThere may be multiple consistent responses among subjects during movie watchingPrincipal component analysis can be used to identify the multiple consistent responsesMany brain regions showed two principal components that were separated by ageYounger children showed delayed response in the supramarginal gyrus and precuneus

Quantification of Upper-Body Synergies: A Case Comparison for Stroke and Non-Stroke Victims

2016 ◽  
Author(s):  
Omid Heidari ◽  
John O. Roylance ◽  
Alba Perez-Gracia ◽  
Eydie Kendall
Keyword(s):  
Principal Component Analysis ◽  
Degrees Of Freedom ◽  
Principal Component ◽  
Component Analysis ◽  
Joint Space ◽  
Upper Body ◽  
Dimensional Synthesis ◽  
Revolute Joints ◽  
Case Comparison ◽  
The Individual

Motion synergies are principal components of the movement, obtained as combinations of joint degrees of freedom, that account for common postures of the human body. These synergies are usually obtained by capturing the motion of the human joints and reducing the dimensionality of the joint space with techniques such as principal component analysis. In this work, an experimental procedure to investigate the synergies of the upper body is developed and the results of the pilot study are shown. The upper-limb kinematics includes the joint complexes of the hand, wrist, forearm, elbow, and shoulder. The different kinematic models in the literature have been reviewed, and a serial chain is considered from the upper arm. A three degree of freedom (3-DOF) linkage containing two revolute joints and one prismatic joint has been chosen to simulate the shoulder motion. A spherical joint represents the Glenohumeral (GH) joint; the elbow and ulna-radius rotations are represented by two revolute joints and the wrist is modeled with two revolute joints. The hand has a tree structure and branches into the individual phalanges, with a 2-dof MCP joint and single R joints for the rest of the phalangeal joints. The data are collected using motion capture and the joint angles are calculated using a combination of dimensional synthesis and inverse kinematics. Principal component analysis can be used to extract the synergies for a set of previously-selected motions. The motions are performed by healthy subjects and subjects who have suffered stroke, in order to see the changes in the motion primitives. It is expected that this study will help quantify and classify some of the loss of motion due to stroke.

Compliant Assembly Variation Analysis Using Components Geometric Covariance

Manufacturing ◽  
2002 ◽  
Author(s):  
Jaime A. Camelio ◽  
S. Jack Hu
Keyword(s):  
Principal Component Analysis ◽  
Finite Element ◽  
Sheet Metal ◽  
Finite Element Methods ◽  
Principal Component ◽  
Deformation Mode ◽  
Component Analysis ◽  
Variation Analysis ◽  
The Individual ◽  
Sheet Metal Assembly

Dimensional variation is one of the most critical issues in the design of assembled products. This is especially important for the assembly of compliant, non-rigid parts since clamping and joining during assembly may introduce additional variation due to part deformation and springback. This paper presents a new methodology to predict sheet metal assembly variation using the components geometric covariance. The approach combines the use of principal component analysis and finite element methods to estimate the effect of components variation on assembly variation. Principal component analysis is applied to extract deformation patterns from production data, decomposing the component covariance in the individual contribution of these deformation “modes”. Finite element methods are used to determine the effect of each deformation “mode” over the assembly variation. The proposed methodology allows significant reduction in the computation effort required for variation analysis in sheet metal assembly. A case study is presented to illustrate the methodology.

scholarly journals Discrimination of flax cultivars based on visible diffusion reflectance spectra and colour parameters of whole seeds

2019 ◽  
Vol 37 (No. 3) ◽  
pp. 199-204
Author(s):  
Yana Troshchynska ◽  
Roman Bleha ◽  
Lenka Kumbarová ◽  
Marcela Sluková ◽  
Andrej Sinica ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Linear Correlation ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Component Analysis ◽  
Reflectance Spectra ◽  
Strong Linear Correlation ◽  
Colour Parameters ◽  
The Individual

Discrimination of yellow and brown seeded flax cultivars was made based on visible (Vis) diffusion reflectance spectra of whole seeds. Hierarchy cluster analysis (HCA) and principal component analysis (PCA) were used for the discrimination. Multivariate analyses of Vis spectra led to satisfactory discrimination of all flax cultivars of this study. The CIE L*a*b* colour parameters were calculated from the diffusion reflectance Vis spectra. The values of L* were in the range of 48.8–53.6 and 62.6–66.0% for brown and yellow seeded cultivars, respectively. Chromatic parameters a* and b* were in the range of 2.8–4.9 and 7.9–16.4%, respectively. A strong linear correlation (R<sub>2</sub> = 0.9712) was found between a* and b* parameters for all the flaxseed samples. The L* and a* parameters were sufficient for HCA clustering of the individual flax cultivars.

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