A supervised machine learning approach for technique identification in cross-country skiing using pole-embedded IMU sensors - Viscioni, G.; Meneghetti, M.; Dimo, E.; Feola, E.; Zoppirolli, C.; Pellegrini, B.; Calanca, A.; Bortolan, L. (10th International Congress on Science and Skiing, January 28 - February 1, 2025, Val di Fiemme, Italy, 2025)
INTRODUCTION: In both classical and skating styles, choosing the right sub-techniques is key to reducing energy use and enhancing performance. Tracking these choices during training and competition provides insights into race strategies and highlights areas for physical improvement. While previous studies have proposed automatic detection systems for sub-technique identification, none have utilized commercially available acquisition systems. This study aims to develop a machine learning algorithm capable of accurately identifying techniques and sub-techniques in XC skiing, leveraging existing technology.