A Study of Sub-Technique Identification Algorithm for Cross-Country Skiing Skating Technique using Kinematic GNSS

Abstract

Cross-country skiing skating techniques combines several types of sub-techniques such as V1, V2, V2a, Turn. If we can clarify what sub-techniques are used throughout the course to enhance the performance of the athletes, we can indicate the direction of training for each individual athlete. Using a kinematic GNSS (sampling frequency 100 Hz), we aimed to improve the discrimination accuracy of the sub-techniques of and to derive an algorithm for automatic discrimination. One adult male athlete and one middle school male athlete were analyzed during a skate-style 4.2 km time trial recorded with a GNSS attached to the skier's head. A video camera was mounted on a snowmobile and followed the athlete to detect the type and number of cycles of each technique used throughout the time trial. Based on the GNSS trajectories, different patterns of head displacement (vertical head movement and direction of movement) for each skating technique were defined. The accuracy for each technique, discriminated from the waveform of the vertical motion and the pattern of the appearance of peaks and troughs obtained from the GNSS data of the head, was 96.6.% for V1, 98.2% for V2, 97.5% for V2a, and 95.7% for Turn, and 16.7% for the others. The total accuracy rate was 97.1%. In this study, the Turn was discriminated with a 95.7% accuracy rate based on data collected from a high-precision GNSS instrument. It was found that the timing of the appearance of peaks and troughs of the waveform can be patterned by each sub-technique.