Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation.

Justin Carrard,Boris Gojanovic,Petr Kloucek

doi:10.3390/sports8010008

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.

Highlights

Training monitoring is widely understood to be a crucial part of modern athletes’ follow-up as it helps to assess individual response to training [1]
The study was conducted in accordance with the Declaration of Helsinki and the protocol was approved on the 3rd of September 2014 by the Human Research Ethics Committee of the “Canton de Vaud” (Lausanne, Switzerland; study protocol no 321/13)
The suggested model permitted the systematic tracking of athletes during the course of the season and allowed for a determination of whether a swimmer is located in the adaptation or maladaptation area, based on the learning process of Artificial Neural Network (ANN) geometric optimisation

Summary

Introduction

Training monitoring is widely understood to be a crucial part of modern athletes’ follow-up as it helps to assess individual response to training [1]. Training monitoring could reduce the risk of overtraining syndrome, injury, illness, and eventually benefit athletic performance [1,2]. Several methods have been developed to quantify training load and assess fatigue and recovery [1,2,3]. The choice of adequate methods should depend on the sport-specific context, the goal of the monitoring program, as well as on available means and resources [1,4]. Data need to be analysed to provide coaches and athletes with actionable information [1,4,5]. Several mathematical techniques to model training effects on performance have been proposed [6]

Objectives

Methods

Results

Discussion

Conclusion