Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation

Justin Carrard; Petr Kloucek; Boris Gojanovic

doi:10.3390/sports8010008

Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation

Sports (Basel). 2020 Jan 16;8(1):8. doi: 10.3390/sports8010008.

Authors

Justin Carrard^{1

2}, Petr Kloucek³, Boris Gojanovic^{4

5}

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.
⁴ Sports Medicine, Swiss Olympic Medical Centre, Hôpital de La Tour, 1217 Meyrin, Switzerland.
⁵ Sports Medicine, Swiss Olympic Medical Centre, Lausanne University Hospital, 1011 Lausanne, Switzerland.

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.

Keywords: machine learning; online tool; training monitoring.