Abstract
The objective of this work was to implement a data-driven biomechanical approach that can assess the biomechanical determinants of cross-country skiing performance. To achieve this, full-body kinematic data were obtained and analyzed during over-ground cross-country skiing trials of varied efforts to quantify propulsion strategies, spatiotemporal coordination, drag, and joint power outputs. Eight athletes of varied skill levels were analyzed, encompassing a total of 5,568 movement cycles (i.e., propulsion strategies). To assess the many interacting modes of variation potentially associated with the skilled performance in cross-country skiing two complementary analyses were implemented. First, an automated objective classifier was trained on a subset of data to detect varied propulsion strategies associated with different athlete skill levels. Second, a principal component analysis was utilized to provide animated reconstructions of representative movement styles and relevant indicators of variance related to skill level. Results suggest that several factors were associated with skill-level including: (1) dominant propulsion strategy, (2) smaller frontal area, (3) reduced ski external rotation, (4) increased upper and lower body joint power. The data driven approaches implemented here can identify key features associated with cross-country skiing performance and have the capacity to be used in a sport-field setting to communicate efficient strategies to athletes.