Next Article in Journal
The Weather Impact on Physical Activity of 6–12 Year Old Children: A Clustered Study of the Health Oriented Pedagogical Project (HOPP)
Previous Article in Journal
Skeletal Muscle Myofibrillar Protein Abundance Is Higher in Resistance-Trained Men, and Aging in the Absence of Training May Have an Opposite Effect
Open AccessArticle

Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation

Doctoral School, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, 4052 Basel, Switzerland
CAMPsyN, Hôpital de Cery, Lausanne University Hospital, 1008 Prilly, Switzerland
Sports Medicine, Swiss Olympic Medical Centre, Hôpital de La Tour, 1217 Meyrin, Switzerland
Sports Medicine, Swiss Olympic Medical Centre, Lausanne University Hospital, 1011 Lausanne, Switzerland
Author to whom correspondence should be addressed.
Received: 10 December 2019 / Revised: 12 January 2020 / Accepted: 14 January 2020 / Published: 16 January 2020
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. View Full-Text
Keywords: training monitoring; online tool; machine learning training monitoring; online tool; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Carrard, J.; Kloucek, P.; Gojanovic, B. Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation. Sports 2020, 8, 8.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop