Applications of neural networks in training science

Autoren

Mark Pfeiffer
Andreas Hohmann

Autoren-URL

https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000305304300008&DestLinkType=FullRecord&DestApp=WOS_CPL

DOI

10.1016/j.humov.2010.11.004

eISSN

1872-7646

Externe Identifier

Clarivate Analytics Document Solution ID: 959HU
PubMed Identifier: 21315468

ISSN

0167-9457

Ausgabe der Veröffentlichung

2

Zeitschrift

HUMAN MOVEMENT SCIENCE

Schlüsselwörter

Neural network
Sports athletes

Paginierung

344 - 359

Datum der Veröffentlichung

2012

Status

Published

Titel

Applications of neural networks in training science

Sub types

Article

Ausgabe der Zeitschrift

31

Datenquelle: Web of Science (Lite)

Autoren

Mark Pfeiffer
Andreas Hohmann

DOI

10.1016/j.humov.2010.11.004

ISSN

0167-9457

Ausgabe der Veröffentlichung

2

Zeitschrift

Human Movement Science

Sprache

en

Paginierung

344 - 359

Datum der Veröffentlichung

2012

Status

Published

Herausgeber

Elsevier BV

Herausgeber URL

http://dx.doi.org/10.1016/j.humov.2010.11.004

Datum der Datenerfassung

2019

Titel

Applications of neural networks in training science

Ausgabe der Zeitschrift

31

Datenquelle: Crossref

Abstract

Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming.

Addresses

Department of Training and Movement Science, Institute of Sport Science, University of Bayreuth, Bayreuth, Germany. mark.pfeiffer@uni-bayreuth.de

Autoren

Mark Pfeiffer
Andreas Hohmann

DOI

10.1016/j.humov.2010.11.004

eISSN

1872-7646

Externe Identifier

PubMed Identifier: 21315468

Open access

false

ISSN

0167-9457

Ausgabe der Veröffentlichung

2

Zeitschrift

Human movement science

Schlüsselwörter

Humans
Linear Models
Longitudinal Studies
Competitive Behavior
Aptitude
Motor Skills
Swimming
Algorithms
Nonlinear Dynamics
Science
Research
Physical Education and Training
Sports
Pattern Recognition, Automated
Adolescent
Child
Germany
Female
Male
Statistics as Topic
Athletic Performance
Neural Networks, Computer

Sprache

eng

Medium

Print-Electronic

Online publication date

2011

Paginierung

344 - 359

Datum der Veröffentlichung

2012

Status

Published

Datum der Datenerfassung

2011

Titel

Applications of neural networks in training science.

Sub types

Comparative Study
Journal Article

Ausgabe der Zeitschrift

31

Datenquelle: Europe PubMed Central

Abstract

Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming.

Date of acceptance

2010

Autoren

Mark Pfeiffer
Andreas Hohmann

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/21315468

DOI

10.1016/j.humov.2010.11.004

eISSN

1872-7646

Ausgabe der Veröffentlichung

2

Zeitschrift

Hum Mov Sci

Schlüsselwörter

Adolescent
Algorithms
Aptitude
Athletic Performance
Child
Competitive Behavior
Female
Germany
Humans
Linear Models
Longitudinal Studies
Male
Motor Skills
Neural Networks, Computer
Nonlinear Dynamics
Pattern Recognition, Automated
Physical Education and Training
Research
Science
Sports
Statistics as Topic
Swimming

Sprache

eng

Country

Netherlands

Paginierung

344 - 359

PII

S0167-9457(10)00185-5

Datum der Veröffentlichung

2012

Status

Published

Datum, an dem der Datensatz öffentlich gemacht wurde

2012

Titel

Applications of neural networks in training science.

Sub types

Comparative Study
Journal Article

Ausgabe der Zeitschrift

31

Datenquelle: PubMed

Applications of neural networks in training science

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