Jocelyn Zhang ¹ and Andrew Park ² , ¹ USA, ²California State Polytechnic University, USA

Figure skating is a physically demanding and expensive sport, and beginners often lack enough accessible support to practice safely and improve efficiently. Skate Sensor addresses this problem through a wearable sensing system paired with a mobile application that helps users analyze skating techniques and access educational resources [1]. The project combines two shin-mounted sensor pods, a Flutter mobile app, and machine learning models developed with Python and PyTorch [2]. Together, these components collect motion data, synchronize left and right leg activity, classify techniques, and evaluate whether an attempt matches successful motion patterns. Several implementation challenges had to be considered, including sensor synchronization, limited training data, and memory use within the app’s media features. These issues were addressed through timeline alignment, expanded data collection strategies, and more efficient loading of visual content. Experimental design focused on model accuracy and the impact of informational screens. Overall, Skate Sensor demonstrates strong potential as a practical tool for safer, more informed, and more independent beginner training.

Figure Skating, Coaching, Injuries, Skating techniques, Artificial Intelligence