Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis

The integration of wearable sensors with artificial intelligence forms the base foranalyzing physical activities through digital signal processing, numerical methods, and machine learning.Computational intelligence and communication technologies enable personalized monitoring, training, andrehabilitation, with applications in sports, neurology, and biomedicine. This paper focuses on motionanalysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate skimovement patterns. The proposed methodology employs functional transforms to estimate motion patternsand utilizes artificial intelligence for signal segmentation and feature classification related to lower limbmovement. Machine learning results indicate differences in energy distribution before and after ski turnsand demonstrate the feasibility of classifying associated motion patterns with accuracies of 98.1% and90.7%, respectively, using a two-layer neural network. The interdisciplinary application of computationalintelligence in this domain enhances motion analysis, injury prevention, and performance optimization. Thisstudy highlights the unifying role of digital signal processing, which uses similar mathematical tools acrossvarious applications.