Researchers from HSE University in Perm Teach AI to Analyse Figure Skating
Researchers from HSE University in Perm have developed NeuroSkate, a neural network that identifies the movements of skaters on video and determines the correctness of the elements performed. The algorithm has already demonstrated success with the basic elements, and further development of the model will improve its accuracy in identifying complex jumps.
Figure skating is a technically challenging sport in which not only speed and strength are important, but so is precision of movement. Coaches rely on their eyes and experience, but objective data can provide more information on which elements work better, which require improvement, and how technique changes over time. This is especially valuable in children's sports, where groups can have as many as 15 people and it is difficult for coaches to monitor each athlete at the same time.
The team of the NeuroSkate project, which includes Anna Provorova, Daria Semenova, Lyudmila Gergert, Sofya Kulikova, and Irina Polyakova, decided to test how artificial intelligence would cope with analysing the movements of figure skaters. The researchers selected six movements that are easy to identify by an athlete's posture: Biellmann, spin, boat, and several basic single jumps—flip, Rittberger, and Lutz. The main idea was that the neural network could automatically find the skater in the video, track their movements, and mark the performed element.
‘It sounds simple, but in practice it’s not an easy task. Large databases are required to train algorithms, and very few of them are open source. Research analyses adult athletes, but no one has collected videos of young figure skaters with marked movements so far,’ says Anna Provorova, Junior Research Fellow at the HSE Centre for Cognitive Neuroscience in Perm.
The project was implemented in collaboration with the Orlyonok Sports School of the Olympic Reserve in Perm, which provided data for training the neural network.
‘While the skaters were on their summer break, we used competition recordings and publicly available videos. Later, we managed to take our own training footage, but only on a phone, without professional equipment, which affected the image quality and marking accuracy,’ says Sofya Kulikova, Director of the HSE Centre for Cognitive Neuroscience in Perm.
Motion recognition includes several stages. First, the video is divided into frames, each of which highlights key points on the skater's body. Then a sequence of 60 frames with marked dots is transmitted to a neural network that analyses the athlete's movements.
First, the model was tested in binary classification: it was offered pairs of movements to distinguish, such as a Rittberger and a boat, without adding other elements. In this format, the system worked stably, showing an accuracy of 72%. But as soon as the task became more complicated and more elements were added, the system started to make mistakes. The researchers continued to mark up new videos and further train the model, making it easier to recognise athletes in the video.
The developers have also created a web application that allows users to upload training videos and analyse the statistics of a particular athlete. This could become a tool that helps coaches monitor student progress without having to review hours of recordings.
‘We hope to keep working on the project, as we have ideas on how to improve the algorithm: the most important thing is to collect a large and high-quality database of athletes' videos. There is also an understanding of how to improve the motion recognition process, for example, using graph neural networks. This is a very promising area. One of these models (HD-GCN) showed impressive results, which were presented at the International Conference on Computer Vision in 2023. However, it has not yet been possible to launch it on real project data,’ says Anna Provorova.
The study was implemented as part of the Priority 2030 programme.
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