Machine learning-based classification of push techniques in speed skating

Speed skating is a prestigious sport that requires technical skills and optimal physical condition. During training, computer vision and Machine Learning (ML) techniques can help improve skating performance and biomechanical analysis. However, limited research has addressed the automated classification of speed skating push techniques using pose estimation methods. In this regard, the OpenPose model is used to extract data on the skater’s joints and key points for movement analysis and push classification.The classification methodology implied exploring two main approaches. The first approach uses image classification based on Skeleton Gait Energy Images (SGEI) and a Convolutional Neural Network (CNN) with the VGG19 architecture and transfer learning, achieving an accuracy of 90.72%. The second approach uses biomechanical feature vectors through a Support Vector Machine (SVM) system and a Random Forest (RF) algorithm, achieving accuracies of 94% and 92%, respectively. The classification task considered three skating push techniques: classic push, double push, and pendulum push, which are biomechanically relevant due to their influence on propulsion efficiency, balance, and skating performance. Key findings indicate that feature-based models (SVM/RF) achievedhigher precision and faster execution, while the CNN approach provided greater flexibility through data augmentation and automated parameter tuning. Furthermore, the “double push” technique was the most accurately classified movement across all evaluated models. The proposed framework contributes to the limited research on automated classification of skating push using pose estimation and ML techniques for biomechanical and sports performance analysis.