Arctic HARE: A Machine Learning-Based System for Performance Analysis of Cross-Country Skiers

Abstract

Advances in sensor technology and big data processing enable new and improved performance analysis of sport athletes. With the increase in data variety and volume, both from on-body sensors and cameras, it has become possible to quantify the specific movement patterns that make a good athlete. This paper describes Arctic Human Activity Recognition on the Edge (Arctic HARE): a skiing-technique training system that captures movement of skiers to match those against optimal patterns in well-known cross-country techniques. Arctic HARE uses on-body sensors in combination with stationary cameras to capture movement of the skier, and provides classification of the perceived technique. We explore and compare two approaches for classifying data, and determine optimal representations that embody the movement of the skier. We achieve higher than 96% accuracy for real-time classification of cross-country techniques.