Seenovate, Montpellier, 34000, France.
EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, 34090, France.
Sci Rep. 2022 Sep 8;12(1):15229. doi: 10.1038/s41598-022-19484-y.
This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019-2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.
本研究旨在从全球导航卫星系统（GNSS）测量值中预测个体加速度-速度曲线（A-V），这些测量值是在真实情况下收集的。数据来自超级联赛的职业球员，收集时间为一个半赛季（2019-2021 年）。通过时间序列预测方法提供了基线建模性能，并与使用脊正则化和长短期记忆神经网络的两种多元建模方法进行了比较。多元模型考虑了商业特征和从 GNSS 原始数据中提取的新特征作为预测变量。一种控制条件是根据同一会话的预测变量来预测 A-V 曲线，以说明 A-V 曲线的可预测性。多元模型是针对每个球员还是针对整个球员群体进行拟合的。预测变量根据平均值或指数加权函数进行汇总。不出所料，与其他模型相比，控制条件的平均误差率较低（p = 0.001）。参考模型和多元模型的误差率没有显著差异（p = 0.124），这与预测变量的性质（商业特征或从信号处理方法中提取的特征）或使用的汇总方法无关。此外，基于更大人群建立的模型并没有提供更准确的预测。总之，GNSS 特征似乎对预测个体 A-V 曲线的相关性有限。然而，新的信号处理特征为运动表现或损伤发生建模开辟了新的视角，特别是如果考虑更高采样率跟踪系统的话。
