Analyzing Exercise Repetitions: YOLOv8-Enhanced Dynamic Time Warping Approach on InfiniteRep Dataset

Michal Slupczynski, Aleksandra Nekhviadovich, Nghia Duong-Trung, Stefan Decker

Abstract: This paper presents a novel approach to exercise repetition analysis using the YOLOv8-pose model and Dynamic Time Warping (DTW) techniques applied to the InfiniteRep dataset. Our research addresses the challenges of accurate pose estimation and tracking in dynamic camera environments and with varying occlusions in synthetic datasets. By integrating YOLOv8’s pose detection capabilities with the temporal analysis strength of DTW, we propose a method that significantly improves the detection and classification of exercise repetitions across diverse conditions. We demonstrate the effectiveness of this approach through rigorous experiments that test various scenarios, including changes in camera angles and exercise complexity. Our results indicate notable improvements in the accuracy and robustness of exercise recognition, suggesting promising applications in sports science and personal fitness coaching.