Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation

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Abstract

This study aims to model training adaptation using Artificial Neural Network (ANN) geometric optimisation. Over 26 weeks, 38 swimmers recorded their training and recovery data on a web platform. Based on these data, ANN geometric optimisation was used to model and graphically separate adaptation from maladaptation (to training). Geometric Activity Performance Index (GAPI), defined as the ratio of the adaptation to the maladaptation area, was introduced. The techniques of jittering and ensemble modelling were used to reduce overfitting of the model. Correlation (Spearman rank) and independence (Blomqvist β) tests were run between GAPI and performance measures to check the relevance of the collected parameters. Thirteen out of 38 swimmers met the prerequisites for the analysis and were included in the modelling. The GAPI based on external load (distance) and internal load (session-Rating of Perceived Exertion) showed the strongest correlation with performance measures. ANN geometric optimisation seems to be a promising technique to model training adaptation and GAPI could be an interesting numerical surrogate to track during a season.

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Monitoring Training Load to Understand Fatigue in Athletes

Shona L. Halson (2014)

Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature. Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity. The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized. Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.

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Monitoring Athlete Training Loads: Consensus Statement.

Pitre Bourdon, Marco Cardinale, Andrew Murray … (2017)

Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.

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New horizons for the methodology and physiology of training periodization.

Vladimir B. Issurin (2010)

The theory of training was established about five decades ago when knowledge of athletes' preparation was far from complete and the biological background was based on a relatively small amount of objective research findings. At that time, traditional 'training periodization', a division of the entire seasonal programme into smaller periods and training units, was proposed and elucidated. Since then, international sport and sport science have experienced tremendous changes, while the traditional training periodization has remained at more or less the same level as the published studies of the initial publications. As one of the most practically oriented components of theory, training periodization is intended to offer coaches basic guidelines for structuring and planning training. However, during recent decades contradictions between the traditional model of periodization and the demands of high-performance sport practice have inevitably developed. The main limitations of traditional periodization stemmed from: (i) conflicting physiological responses produced by 'mixed' training directed at many athletic abilities; (ii) excessive fatigue elicited by prolonged periods of multi-targeted training; (iii) insufficient training stimulation induced by workloads of medium and low concentration typical of 'mixed' training; and (iv) the inability to provide multi-peak performances over the season. The attempts to overcome these limitations led to development of alternative periodization concepts. The recently developed block periodization model offers an alternative revamped approach for planning the training of high-performance athletes. Its general idea proposes the sequencing of specialized training cycles, i.e. blocks, which contain highly concentrated workloads directed to a minimal number of targeted abilities. Unlike the traditional model, in which the simultaneous development of many athletic abilities predominates, block-periodized training presupposes the consecutive development of reasonably selected target abilities. The content of block-periodized training is set down in its general principles, a taxonomy of mesocycle blocks, and guidelines for compiling an annual plan.

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Author and article information

Journal

Journal ID (nlm-ta): Sports (Basel)

Journal ID (iso-abbrev): Sports (Basel)

Journal ID (publisher-id): sports

Title: Sports

Publisher: MDPI

ISSN (Electronic): 2075-4663

Publication date (Electronic): 16 January 2020

Publication date Collection: January 2020

Volume: 8

Issue: 1

Electronic Location Identifier: 8

Affiliations

[1 ]Doctoral School, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland

[2 ]Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, 4052 Basel, Switzerland

[3 ]CAMPsyN, Hôpital de Cery, Lausanne University Hospital, 1008 Prilly, Switzerland; kloucek@ 123456me.com

[4 ]Sports Medicine, Swiss Olympic Medical Centre, Hôpital de La Tour, 1217 Meyrin, Switzerland; boris.gojanovic@ 123456latour.ch

[5 ]Sports Medicine, Swiss Olympic Medical Centre, Lausanne University Hospital, 1011 Lausanne, Switzerland

Author notes

[* ]Correspondence: justin.carrard@ 123456unibas.ch ; Tel.: +41-6120-747-41

Author information

Justin Carrard https://orcid.org/0000-0002-2380-105X

Boris Gojanovic https://orcid.org/0000-0001-5075-9371

Article

Publisher ID: sports-08-00008

DOI: 10.3390/sports8010008

PMC ID: 7022998

PubMed ID: 31963218

SO-VID: 2f8f7448-5f9d-4728-a58b-9c4cdade17ea

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation

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Abstract

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Artificial Intelligence in Medicine

Most cited references 32

Monitoring Training Load to Understand Fatigue in Athletes

Monitoring Athlete Training Loads: Consensus Statement.

New horizons for the methodology and physiology of training periodization.

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