Influence of artificial turf on football technique using motion capture and 3D modelling

While markerless motion capture provided acceptable accuracy, no clear patterns emerged regarding the individual effects of surface properties on technique. This is most likely due to limitations such as sample size, lack of standardizing data set (players) across facilities, and limited control over player behavior. However, analyzing one individual's motion capture data across surfaces showed potential for distinguishing turning styles based on facility parameters. The method in this thesis demonstrates the potential of markerless motion capture for injury prevention research in football. Despite inconclusive results on the individual facility parameter effects, the ability to distinguish player styles across surfaces suggests valuable future directions for investigating personalized risk factors and optimizing playing surfaces. Further research with larger, more diverse samples and a broader set of biomechanical and facility features could provide deeper insight into injury prevention strategies.