  Introduction: Skateboarding is one of the most popular cultures in Brazil,
with more than 8.5 million skateboarders. Nowadays, the discipline of street
skating has gained recognition among other more classical sports and awaits its
debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the
state-of-the-art for inertial measurement unit (IMU) use in skateboarding trick
detection, and to develop new classification methods using supervised machine
learning and artificial neural networks (ANN). Methods: State-of-the-art
knowledge regarding motion detection in skateboarding was used to generate 543
artificial acceleration signals through signal modeling, corresponding to 181
flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV,
OLLIE). The classifier consisted of a multilayer feed-forward neural network
created with three layers and a supervised learning algorithm
(backpropagation). Results: The use of ANNs trained specifically for each
measured axis of acceleration resulted in error percentages inferior to 0.05%,
with a computational efficiency that makes real-time application possible.
Conclusion: Machine learning can be a useful technique for classifying
skateboarding flat ground tricks, assuming that the classifiers are properly
constructed and trained, and the acceleration signals are preprocessed
correctly.
