Breaking new grounds in injury risk screening in soccer by deploying unsupervised learning with a special focus on sex and fatigue effects.

In injury prevention, a vertical drop jump (DJ) is often used for screening athletes at risk for injury; however, the large variation in individual movement patterns might mask potentially relevant strategies when analysed on a group-based level. Two movement strategies are commonly discussed as predisposing athletes to ACL injuries: a deficient leg axis and increased leg stiffness during landing. This study investigated the individual movement pattern of 39 female and male competitive soccer players performing DJs at rest and after being fatigued. The joint angles were used to train a Kohonen self-organising map. Out of 19,596 input vectors, the SOM identified 700 unique postures. Visualising the movement trajectories and adding the latent parameters contact time, medial knee displacement (MKD) and knee abduction moment allow identification of zones with presumably increased injury risk and whether the individual movement patterns pass these zones. This information can be used, e.g., for individual screening and for feedback purposes. Additionally, an athlete's reaction to fatigue can be explored by comparing the rested and fatigued movement trajectories. The results highlight the ability of unsupervised learning to visualise movement patterns and to give further insight into an individual athlete's status without the necessity of a priori assumptions.