IMU-based trick classification in skateboarding - Groh, B. H.; Kautz, T.; Schuldhaus, D.; Eskofier, B. M. (KDD Workshop 9: Workshop on Large-Scale Sports Analytics, 2015)
The popularity of skateboarding continuously grows for athletes performing the sport and for spectators following competitions. The presentation and the assessment of the athletes' performance can be supported by state-of-the-art motion analysis and pattern recognition methods. In this paper, we present a trick classi cation analysis based on motion data of inertial measurement units. Six tricks were performed by seven skateboarders. A trick event detection algorithm and four di erent classi cation methods were applied to the collected data. A sensitivity of the event detection of 94.2% was achieved. The classi cation of correctly detected trick events provides an accuracy of 97.8% for the best performing classi ers. The proposed algorithm holds the potential to be extended to a real-time application that could be used to make competitions fairer, to better present the assessment to spectators and to support the training of athletes.