Yeongje Park

In speed skating, the number of strokes in the first 100 m section serves as an important metric of final performance. However, the conventional method, relying on human vision, has limitations in terms of real-time counting and accuracy. This study presents a solution for counting strokes in the first 100 m of a speed skating race, aiming to overcome the limitations of human vision. The method uses image recognition technology, specifically MediaPipe, to track key body joint coordinates during the skater’s motion. These coordinates are calculated into important body angles, including those from the shoulder to the knee and from the pelvis to the ankle. To quantify the skater’s motion, the study introduces generalized labeling logic (GLL), a key index derived from angle data. The GLL signal is refined using Gaussian filtering to remove noise, and the number of inflection points in the filtered GLL signal is used to determine the number of strokes. The method was designed with a focus on frontal videos and achieved an excellent accuracy of 99.91% when measuring stroke counts relative to actual counts. This technology has great potential for enhancing training and evaluation in speed skating.