Sliding Performance Evaluation with Machine Learning-Based Trajectory Analysis for Skeleton

Skeleton is an extreme sliding sport in the Winter Olympics, where formulating targeted sliding strategies, based on training videos to navigate complex tracks, is particularly important. To make in-depth use of training video records, this study proposes an analytical method based on Mixture of Gaussians (MoG) and K-means clustering to extract and analyze trajectories from recorded videos for sliding performance evaluation and strategy development. A case study was conducted using data from the Chinese national skeleton team at the Yanqing Sliding Center, obtaining 741, 834, and 726 sliding trajectories from three representative curves. These trajectories were divided into groups based on sliding completion time (fast, medium, and slow groups). The consistency of trajectories within each group was calculated to evaluate sliding stability, while trajectory patterns in the fast group were clustered and described based on the average values of multiple features (starting position, ending position, and apex orthogonal offset). The results showed that more skilled athletes exhibited greater sliding stability (lower ρC-values), and on each curve, there were sliding patterns that performed significantly better than others. This research quantifies the characteristics of athletes’ sliding trajectories on curves, facilitating the visual tracking of training effects and the development of personalized strategies. It provides coaches and athletes with scientific decision-making support and clear directions for improvement, ultimately enabling precise enhancements in training efficiency and competitive performance, while also laying a technical foundation for the future development of intelligent training systems.