Carrard Justin, Kloucek Petr, Gojanovic Boris
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.
Sports (Basel). 2020 Jan 16;8(1):8. doi: 10.3390/sports8010008.
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.
本研究旨在使用人工神经网络（ANN）几何优化对训练适应性进行建模。在26周的时间里，38名游泳运动员在一个网络平台上记录了他们的训练和恢复数据。基于这些数据，使用ANN几何优化对适应性与（训练）适应不良进行建模并以图形方式区分。引入了几何活动表现指数（GAPI），定义为适应区域与适应不良区域的比率。使用抖动和集成建模技术来减少模型的过拟合。在GAPI与表现指标之间进行相关性（斯皮尔曼等级）和独立性（布洛姆奎斯特β）测试，以检查所收集参数的相关性。38名游泳运动员中有13名符合分析的前提条件并被纳入建模。基于外部负荷（距离）和内部负荷（训练感知用力评分）的GAPI与表现指标显示出最强的相关性。ANN几何优化似乎是一种很有前景的训练适应性建模技术，并且GAPI可能是一个在赛季中值得追踪的有趣数值替代指标。
