Advancing Artistic Swimming Officiating and Performance Assessment: A Computer Vision Study Using MediaPipe

Abstract

Artistic Swimming (AS) requires complete execution and synchronization of movements for performance evaluation. The interest in objective and subjective performance analysis worldwide in sports via valid and reliable Artificial Intelligence (AI) tools is spreading depending on the required analysis parameters to design a novel system. This study investigated a novel application of the MediaPipe-based computer vision tool validation by examining biomechanical aspects and the objective performance impact in ballet leg and barracuda AS techniques. Twenty experienced AS athletes participated and executed these techniques under controlled conditions. Thirty-six recorded video trials were captured and analyzed via computer vision using MediaPipe, Kinovea, and AutoCAD (gold standard), with correlations calculated to assess the reliability of measurements and tools. The results indicated a non-significant difference (p<0.05) among the software tools, supported by one-way ANOVA and Bland-Altman tests. Notably, in ballet leg technique, maintaining alignment between the upper body trunk and knee in a line had a small correlation with other leg deviations; however, this aspect had a moderate negative correlation in scoring. Overall, this study suggests MediaPipe efficiency in computer vision for AS officiating and performance analysis, offering a reliable, real-time alternative to traditional methods and providing perceptions of AS techniques.