Abstract

We explore the use of machine learning to detect under-rotation in figure skating jumps. Under- rotation in jumps is difficult for the skater to sense but learning to recognize under-rotation is an impor- tant part of learning proper jump technique. To address this difficulty, we present the Under-rotation Monitor, or UR Monitor, a system for detecting under-rotated figure skating jumps in real-time. UR Monitor uses a single inertial measurement unit (IMU) attached to the skater's waist that sends a stream of accelerometer and gyroscope data to a mobile phone via Bluetooth. The mobile phone creates and sends an input vector of each jump to a web-hosted API that returns a response from our trained classifier indicating whether it considered that jump as 'under-rotated', or 'completed rotation'. The classifier is trained and tested on a collection of 444 jumps, of which only 121 are under-rotated. We also present a process for addressing an imbalanced dataset on which the classifier trains. Our classifier achieves an F1-score of only 0.66, suggesting that noise and imbalance in the data set are significant issues.

Degree

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

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