Imbach Frank, Sutton-Charani Nicolas, Montmain Jacky, Candau Robin, Perrey Stéphane
Seenovate, Montpellier, France.
DMeM, INRAe, Univ Montpellier, Montpellier, France.
Sports Med Open. 2022 Mar 3;8(1):29. doi: 10.1186/s40798-022-00426-x.
The emergence of the first Fitness-Fatigue impulse responses models (FFMs) have allowed the sport science community to investigate relationships between the effects of training and performance. In the models, athletic performance is described by first order transfer functions which represent Fitness and Fatigue antagonistic responses to training. On this basis, the mathematical structure allows for a precise determination of optimal sequence of training doses that would enhance the greatest athletic performance, at a given time point. Despite several improvement of FFMs and still being widely used nowadays, their efficiency for describing as well as for predicting a sport performance remains mitigated. The main causes may be attributed to a simplification of physiological processes involved by exercise which the model relies on, as well as a univariate consideration of factors responsible for an athletic performance. In this context, machine-learning perspectives appear to be valuable for sport performance modelling purposes. Weaknesses of FFMs may be surpassed by embedding physiological representation of training effects into non-linear and multivariate learning algorithms. Thus, ensemble learning methods may benefit from a combination of individual responses based on physiological knowledge within supervised machine-learning algorithms for a better prediction of athletic performance.In conclusion, the machine-learning approach is not an alternative to FFMs, but rather a way to take advantage of models based on physiological assumptions within powerful machine-learning models.
首个体能 - 疲劳脉冲响应模型（FFMs）的出现，使运动科学界能够研究训练效果与运动表现之间的关系。在这些模型中，运动表现由一阶传递函数描述，该函数代表体能和疲劳对训练的拮抗反应。在此基础上，数学结构允许精确确定在给定时间点能提升最大运动表现的最佳训练剂量序列。尽管FFMs有了若干改进且至今仍被广泛使用，但其描述和预测运动表现的效率仍然有限。主要原因可能归因于该模型所依赖的运动所涉及的生理过程的简化，以及对运动表现相关因素的单变量考量。在这种背景下，机器学习视角对于运动表现建模而言似乎很有价值。通过将训练效果的生理表征嵌入非线性和多变量学习算法中，FFMs的弱点可能会被克服。因此，集成学习方法可能受益于在有监督的机器学习算法中基于生理知识对个体反应的组合，从而更好地预测运动表现。总之，机器学习方法并非FFMs的替代品，而是一种在强大的机器学习模型中利用基于生理假设的模型的方式。
