Abstract

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.