Development of a skateboarding trick classifier using accelerometry and machine learning
Abstract 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.
Auteurs principaux: | Corrêa,Nicholas Kluge, Lima,Júlio César Marques de, Russomano,Thais, Santos,Marlise Araujo dos |
---|---|
Format: | Digital revista |
Langue: | English |
Publié: |
Sociedade Brasileira de Engenharia Biomédica
2017
|
Accès en ligne: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400362 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Learning to Classify Text Using Support Vector Machines [electronic resource] /
par: Joachims, Thorsten. author., et autres
Publié: (2002) -
Learning to Classify Text Using Support Vector Machines [electronic resource] /
par: Joachims, Thorsten. author., et autres
Publié: (2002) -
Asociación Femenina de Skateboarding ASOFESB: Avisos
par: Costa Rica. Registro Nacional. Registro de Personas Jurídicas. Asociaciones Civiles Autoría -
Asociación Femenina de Skateboarding ASOFESB: Avisos
par: Costa Rica. Registro Nacional. Registro de Personas Jurídicas. Asociaciones Civiles Autoría 33024
Publié: ((8 s) -
Use of accelerometry to measure physical activity in adults and the elderly
par: Bento,Teresa, et autres
Publié: (2012)