Coimbra Polytechnic, Instituto Superior de Engenharia de Coimbra, 3030-199 Coimbra, Portugal.
Faculdade de Ciência e Tecnologia, New University of Lisbon, 2829-516 Lisbon, Portugal.
Sensors (Basel). 2020 May 27;20(11):3040. doi: 10.3390/s20113040.
Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.
优化运动员的表现是教练工作中最重要和最具挑战性的方面之一。生理和位置数据，通常使用可穿戴设备获取，对于识别模式很有用，从而更好地了解比赛，并因此有机会提高运动员的表现。尽管在模式识别方面有很多研究，但在非控制环境下，例如在运动训练和比赛中，仍存在差距。本研究论文结合使用生理和位置数据作为不同人工智能方法在真实比赛环境下动作识别的顺序特征，采用五人制足球作为案例研究。将传统的人工神经网络 (ANN) 与深度学习方法——长短期记忆网络 (LSTM) 进行比较，还与动态贝叶斯混合模型 (DBMM) 进行比较，后者是一种集成分类方法。这些方法被用于处理所有数据序列，这使得我们能够根据精度和召回率之间的平衡来确定，动态贝叶斯混合模型的性能更优，F1 得分为 80.54%，而长短期记忆网络为 33.31%，人工神经网络为 14.74%。
