Development of a skateboarding trick classifier using accelerometry and machine learning
AUTOR(ES)
Corrêa, Nicholas Kluge, Lima, Júlio César Marques de, Russomano, Thais, Santos, Marlise Araujo dos
FONTE
Res. Biomed. Eng.
DATA DE PUBLICAÇÃO
2017-10
RESUMO
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.
Documentos Relacionados
- Digital mapping of soil attributes using machine learning
- Lung disease detection using feature extraction and extreme learning machine
- Identifying olive oil fraud and adulteration using machine learning algorithms
- CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS
- Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area