Electronics Department, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.
Advanced Studies Center, Universidad de Playa Ancha, Valparaíso 2360072, Chile.
Sensors (Basel). 2020 Mar 19;20(6):1709. doi: 10.3390/s20061709.
In collective sports, reactive agility training methodologies allow to evaluate and improve the player performance, being able to consider a mixture of technical, tactical, physical, and psychological abilities, similarly to real game-play situations. In this article, we present a new methodology for reactive agility training (neural training), the technological setup for the methodology, and a new footstep tracking algorithm, as the key element for automating the speed data gathering process, necessary for obtaining the relevant variables of the neural training approach. This new methodology is oriented to accurately measure two of the most relevant variables for reactive agility training: total response time (sprint time) and response correctness, related to a stimuli sequence presented to a player. The stimuli were designed to properly represent realistic competitive conditions for player training, contextualized to soccer. In order to automate the gathering process, a new computer vision based automatic footstep detection algorithm has been integrated to the system. The algorithm combines Kalman Filters, segmentation techniques, and perspective geometry, for obtaining highly precise detections of the moment a relevant footstep occurs in real-time, reaching a precision higher than 97%. Plus, the algorithm does not require any special marker, invasive sensor, or clothing constraint on the player.
在集体运动中，反应敏捷性训练方法可以评估和提高运动员的表现，能够综合考虑技术、战术、身体和心理能力，类似于真实的比赛情况。在本文中，我们提出了一种新的反应敏捷性训练方法（神经训练），介绍了该方法的技术设置，以及一种新的脚步跟踪算法，作为自动化速度数据收集过程的关键要素，这是获得神经训练方法相关变量所必需的。这种新的方法旨在准确测量反应敏捷性训练中两个最相关的变量：总反应时间（冲刺时间）和反应正确性，这与向运动员呈现的刺激序列有关。这些刺激旨在为球员训练提供适当的现实竞争条件，与足球相关。为了实现自动化收集过程，我们已经将一种新的基于计算机视觉的自动脚步检测算法集成到系统中。该算法结合了卡尔曼滤波器、分割技术和透视几何，实时获得了高度精确的相关脚步的检测，精度高于 97%。此外，该算法不需要对运动员使用任何特殊标记、侵入式传感器或服装限制。
