Development of a skateboarding trick classifier using accelerometry and machine learning

Paper | Data | Models

This repository contains accelerometry signals from a skateboard mounted with an accelerometer/recorder. The accelerometer was used to record several skateboarding maneuvers from 5 different classes. To solve the classification task we trained a neural network with our dataset. We trained both a flat-dense and a recurrent network (LSTM). Ensemble models for the 'flat-dense' and 'rnn' architectures were also trained. The dataset can be found in the data folder and models in the skateboarding_models folder. You can also follow the procedure with our Skateboarding_Trick_Classifier notebook.

Cite as 🤗

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@article{correa2017development,
  title={Development of a skateboarding trick classifier using accelerometry and machine learning},
  author={Corr{\^e}a, Nicholas Kluge and Lima, J{\'u}lio C{\'e}sar Marques de and Russomano, Thais and Santos, Marlise Araujo dos},
  journal={Research on Biomedical Engineering},
  volume={33},
  pages={362--369},
  year={2017},
  publisher={SciELO Brasil}
}

License

Contents of this repository are licensed under the Apache License, Version 2.0. See the LICENSE file for more details.