research-article

Framework for planning the training sessions in triathlon

Authors:

Iztok Fister,

Janez Brest,

Andres Iglesias,

Iztok Fister, Jr.Authors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1829 - 1834

https://doi.org/10.1145/3205651.3208242

Published: 06 July 2018 Publication History

Get Access

Abstract

In recent years, planning sport training sessions with computational intelligence have been studied by many authors. Most of the algorithms were used for proposing basic and advanced training plans for athletes. In a nutshell, most of the solutions focused on the individual sports disciplines, such as, for example, cycling and running. To the knowledge of the authors, few efforts were invested into planning sports training sessions in triathlon. Triathlon is considered as a popular multi-disciplinary sport consisting of three different sport disciplines. Therefore, planning the triathlon training sessions is much harder than the planning in individual sport disciplines. In this paper, we propose an initial framework for planning triathlon training sessions using Particle Swarm Optimization. Preliminary results are also shown.

References

[1]

David L Carey, Justin Crow, Kok-Leong Ong, Peter Blanch, Meg E Morris, Ben J Dascombe, and Kay M Crossley. 2017. Optimising pre-season training loads in Australian football. International journal of sports physiology and performance (2017), 1--19.

Crossref

Google Scholar

[2]

Elnaz Davoodi and Ali Reza Khanteymoori. 2010. Horse racing prediction using artificial neural networks. Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing 2010 (2010), 155--160.

Digital Library

Google Scholar

[3]

R. Eberhart and J. Kennedy. 1995. A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS '95., Proceedings of the Sixth International Symposium on. 39--43.

Google Scholar

[4]

Iztok Fister, Samo Rauter, Xin-She Yang, Karin Ljubič, and Iztok Fister Jr. 2015. Planning the sports training sessions with the bat algorithm. Neurocomputing 149 (2015), 993--1002.

Digital Library

Google Scholar

[5]

Nattapon Kumyaito, Preecha Yupapin, and Kreangsak Tamee. 2018. Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological constraints. BMC research notes 11, 1 (2018), 9.

Google Scholar

[6]

Hristo Novatchkov and Arnold Baca. 2012. Machine learning methods for the automatic evaluation of exercises on sensor-equipped weight training machines. Procedia Engineering 34 (2012), 562--567.

Crossref

Google Scholar

[7]

Hristo Novatchkov and Arnold Baca. 2013. Fuzzy logic in sports: a review and an illustrative case study in the field of strength training. International Journal of Computer Applications 71, 6 (2013).

Crossref

Google Scholar

[8]

David Schaefer, Alexander Asteroth, and Melanie Ludwig. 2015. Training plan evolution based on training models. In Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on. IEEE, 1--8.

Crossref

Google Scholar

[9]

António José Silva, Aldo Manuel Costa, Paulo Moura Oliveira, Victor Machado Reis, José Saavedra, Jürgen Perl, Abel Rouboa, and Daniel Almeida Marinho. 2007. The use of neural network technology to model swimming performance. Journal of sports science & medicine 6, 1 (2007), 117.

Google Scholar

Cited By

View all

Ariyaratne MSilva R(2022)Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithmsInternational Journal of Computer Science in Sport10.2478/ijcss-2022-000321:1(49-92)Online publication date: 15-Jun-2022
https://doi.org/10.2478/ijcss-2022-0003
Fister I(2021)The Relevance of Nature-Inspired Metaheuristic Algorithms in Smart Sport TrainingInternational Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)10.1007/978-3-030-80216-5_1(1-8)Online publication date: 15-Jul-2021
https://doi.org/10.1007/978-3-030-80216-5_1
Rajšp AFister I(2020)A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport TrainingApplied Sciences10.3390/app1009301310:9(3013)Online publication date: 26-Apr-2020
https://doi.org/10.3390/app10093013
Show More Cited By

Index Terms

Framework for planning the training sessions in triathlon
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

Recommendations

Planning the sports training sessions with the bat algorithm

Planning proper sports training has always been a very challenging task for coaches. In line with this, they need to have almost two special abilities: firstly, to have a lot of earlier experiences with sports training and secondly, to know the ...
Muscle strength of the lumbar spine in different sports

BACKGROUND: The ability to stabilize the body center (core stability) against dynamic movements of the extremities and capability to absorb repetitive loading forces in the trunk play a crucial role in any professional sport specific ...
An improved cooperative quantum-behaved particle swarm optimization

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm ...

Comments

Information & Contributors

Information

Published In

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO '18

Sponsor:

SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
228
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)1

Reflects downloads up to 13 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ariyaratne MSilva R(2022)Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithmsInternational Journal of Computer Science in Sport10.2478/ijcss-2022-000321:1(49-92)Online publication date: 15-Jun-2022
https://doi.org/10.2478/ijcss-2022-0003
Fister I(2021)The Relevance of Nature-Inspired Metaheuristic Algorithms in Smart Sport TrainingInternational Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)10.1007/978-3-030-80216-5_1(1-8)Online publication date: 15-Jul-2021
https://doi.org/10.1007/978-3-030-80216-5_1
Rajšp AFister I(2020)A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport TrainingApplied Sciences10.3390/app1009301310:9(3013)Online publication date: 26-Apr-2020
https://doi.org/10.3390/app10093013
Connor MFagan DO’Neill M(2019)Optimising Team Sport Training Plans With Grammatical Evolution2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790369(2474-2481)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1109/CEC.2019.8790369

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Planning the sports training sessions with the bat algorithm

Muscle strength of the lumbar spine in different sports

An improved cooperative quantum-behaved particle swarm optimization

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Planning the sports training sessions with the bat algorithm

Muscle strength of the lumbar spine in different sports

An improved cooperative quantum-behaved particle swarm optimization

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations