scholarly journals Matching Golfers’ Movement Patterns during a Golf Swing

2018 ◽  
Vol 8 (12) ◽  
pp. 2452 ◽  
Author(s):  
Aimée Mears ◽  
Jonathan Roberts ◽  
Stephanie Forrester
Keyword(s):  
Data Analysis ◽  
Multiple Correspondence Analysis ◽  
Movement Patterns ◽  
Principal Component ◽  
Golf Swing ◽  
Fuzzy Coding ◽  
Linear Relationships ◽  
Ball Velocity ◽  
Impact Parameters ◽  
Dimension One

The golf swing is a multidimensional movement requiring alternative data analysis methods to interpret non-linear relationships in biomechanics data related to golf shot outcomes. The purpose of this study was to use a combined principal component analysis (PCA), fuzzy coding, and multiple correspondence analysis (MCA) data analysis approach to visualise associations within key biomechanics movement patterns and impact parameters in a group of low handicap golfers. Biomechanics data was captured and analysed for 22 golfers when hitting shots with their own driver. Relationships between biomechanics variables were firstly achieved by quantifying principal components, followed by fuzzy coding and finally MCA. Clubhead velocity and ball velocity were included as supplementary data in MCA. A total of 35.9% of inertia was explained by the first factor plane of MCA. Dimension one and two, and subsequent visualisation of MCA results, showed a separation of golfers’ biomechanics (i.e., swing techniques). The MCA plot can be used to simply and quickly identify movement patterns of a group of similar handicap golfers if supported with appropriate descriptive interpretation of the data. This technique also has the potential to highlight mismatched golfer biomechanics variables which could be contributing to weaker impact parameters.

Multiple Correspondence Analysis and Geometric Data Analysis

2021 ◽  
pp. 177-184
Author(s):  
Amal Tawfik ◽  
Stephan Davidshofer
Keyword(s):  
Data Analysis ◽  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Categorical Variables ◽  
Large Set ◽  
Geometric Data ◽  
Jean Paul ◽  
Geometric Data Analysis ◽  
Or Groups

This chapter focuses on multiple correspondence analysis (MCA), which is a factor analysis statistical method used to analyse relations between a large set of categorical variables. Developed by Jean-Paul Benzécri in the early 1970s, MCA is one of the principal methods of geometric data analysis (GDA). Three different statistical methods can be identified as GDA: correspondence analysis (CA), which enables the cross-tabulation of two categorical variables; MCA for the analysis of a matrix of individuals and categorical variables; and principal component analysis (PCA), which uses numerical variables. In GDA, data is represented as a cloud of points to allow statistical interpretations. Although MCA is a relational method, it differs from social network analysis (SNA) as it focuses on the objective relations that characterize actors or groups, rather than the effective relations.

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

Influence of psychoanalytical training on professional performance in a sample of French speaking psychotherapists

1994 ◽  
Vol 9 (4) ◽  
pp. 211-220 ◽  
Author(s):  
A Dazord ◽  
P Gerin ◽  
JD Davis ◽  
ML Davis ◽  
N Aapro ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Common Core ◽  
Multivariate Analyses ◽  
Multiple Correspondence Analysis ◽  
International Study ◽  
Principal Component ◽  
Questionnaire Data ◽  
Professional Performance ◽  
French Speaking ◽  
Perceived Effects

SummaryThis study is part of a more extensive research conducted by a group of scientists from different countries, who have joined forces to conduct an international study on the development of the therapist, and to develop a novel instrument for therapists, the “Development of Psychotherapists’ Common Core Questionnaire” (CCQ). We report here the results based on the answers of a French-speaking sample to this questionnaire. Data were analyzed using univariate (non parametric tests) and multivariate analyses (Principal Component Analysis and Multiple Correspondence Analysis). The perceived effects of psychoanalytical training were examined. The therapists’ own perception on their current skills and the type of difficulties they experienced were very similar, whether or not they had received psychoanalytical training. However, striking and significant differences in coping strategies were observed between the two groups.

scholarly journals Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Michael Li ◽  
Santoso Wibowo ◽  
Wei Li ◽  
Lily D. Li
Keyword(s):  
Data Analysis ◽  
Conceptual Framework ◽  
Spectral Data ◽  
Learning Algorithm ◽  
Structural Parameters ◽  
Inverse Function ◽  
Principal Component ◽  
Learning Task ◽  
Classification Problems ◽  
Spectral Data Analysis

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

scholarly journals Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

2018 ◽  
Vol 8 (10) ◽  
pp. 1766 ◽  
Author(s):  
Arthur Leroy ◽  
Andy MARC ◽  
Olivier DUPAS ◽  
Jean Lionel REY ◽  
Servane Gey
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Clustering Algorithms ◽  
Principal Component ◽  
Continuous Functions ◽  
Functional Principal Component Analysis ◽  
Data Set ◽  
Functional Clustering ◽  
Sport Science

Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.

Seasonal distribution and movement of juvenile Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the lower Saint John River Basin, New Brunswick, Canada

2018 ◽  
Vol 75 (12) ◽  
pp. 2354-2363 ◽  
Author(s):  
Margaret M. Whitmore ◽  
Matthew K. Litvak
Keyword(s):  
River Basin ◽  
Movement Patterns ◽  
Principal Component ◽  
Wide Distribution ◽  
Atlantic Sturgeon ◽  
Coarse Scale ◽  
Brackish Waters ◽  
Single Location ◽  
Saint John ◽  
Acipenser Oxyrinchus

Juvenile Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) movement and distribution varies seasonally within the lower Saint John River Basin. We use acoustic telemetry to track coarse-scale movement and a network-based approach to develop metrics describing distribution and movement patterns of juvenile Atlantic sturgeon tagged in two rivers, the Saint John and Kennebecasis rivers. We use principal component analysis to develop indices of movement and residency and test for differences among seasons and between fish from each river. Juvenile Atlantic sturgeon exhibit higher residency during summer months compared with winter and spring. Juveniles are primarily concentrated in the brackish waters of the lower river reaches but make movements as far upriver as rkm 88. There was high variation in distribution and movement patterns exhibited by fish tagged in the Kennebecasis River, ranging from single-location occupancy to wide distribution. Three fish left the system during the study, indicating juveniles embark on brief marine excursions. Identifying spatiotemporal distributions of juvenile Atlantic sturgeon in their natal river systems is a crucial step towards identifying critical habitat and informing management decisions for this species.

Sign in / Sign up

Export Citation Format

Share Document